Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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\n'}],latestNews:[{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"},{slug:"intechopen-identified-as-one-of-the-most-significant-contributor-to-oa-book-growth-in-doab-20210809",title:"IntechOpen Identified as One of the Most Significant Contributors to OA Book Growth in DOAB"}]},book:{item:{type:"book",id:"5597",leadTitle:null,fullTitle:"Field - Programmable Gate Array",title:"Field",subtitle:"Programmable Gate Array",reviewType:"peer-reviewed",abstract:'This edited volume "Field-Programmable Gate Array" is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of semiconductors. The book comprises single chapters authored by various researchers and edited by an expert active in the aerospace engineering systems research area. All chapters are complete within themselves but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors and open new possible research paths for further novel developments.',isbn:"978-953-51-3208-0",printIsbn:"978-953-51-3207-3",pdfIsbn:"978-953-51-4819-7",doi:"10.5772/63664",price:119,priceEur:129,priceUsd:155,slug:"field-programmable-gate-array",numberOfPages:278,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"ee9b6139297123dec4d906c950913c0d",bookSignature:"George Dekoulis",publishedDate:"May 31st 2017",coverURL:"https://cdn.intechopen.com/books/images_new/5597.jpg",numberOfDownloads:21927,numberOfWosCitations:11,numberOfCrossrefCitations:8,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:9,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:28,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 24th 2016",dateEndSecondStepPublish:"June 14th 2016",dateEndThirdStepPublish:"September 10th 2016",dateEndFourthStepPublish:"December 9th 2016",dateEndFifthStepPublish:"February 7th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"9833",title:"Prof.",name:"George",middleName:null,surname:"Dekoulis",slug:"george-dekoulis",fullName:"George Dekoulis",profilePictureURL:"https://mts.intechopen.com/storage/users/9833/images/system/9833.jfif",biography:"Prof. George Dekoulis received his PhD in Space Engineering and Communications from Lancaster University, UK, in 2007. He was awarded a First Class BEng (Hons) degree in Communications Engineering from De Montfort University, UK, in 2001. He has received several awards from the Science and Technology Facilities Council (STFC), the Engineering and Physical Sciences Research Council (EPSRC), and the Hudswell International Research Scholarship (IET). He is currently the Dean of the Faculty of Sciences and Technology at the American University of Cyprus (AUCY), Cyprus. He was previously a professor at the Aerospace Engineering Institute (AEI), Cyprus. Founder of the IEEE Aerospace and Electronic Systems Society (AESS), Cyprus. General chair of IEEE Aerospace Engineering Innovations 2019 (IEEE AEI 2019), Limassol, Cyprus. He has worked as a professor in Space Computing & Engineering at various departments, such as Space & Planetary Physics, Aeronautical and Space Engineering, Professional Flight, Robotics/Mechatronics & Mechanical Engineering, Computer Science & Engineering, and Electrical and Electronics Engineering. His research focuses on the design of reconfigurable Space Computing and Engineering Systems.",institutionString:"Aerospace Engineering Institute",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"9",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"957",title:"Electronic Circuits",slug:"semiconductor-electronic-circuits"}],chapters:[{id:"52952",title:"Efficient Hardware Architecture for Correlation-Based Spike Detection and Unsupervised Clustering",doi:"10.5772/66105",slug:"efficient-hardware-architecture-for-correlation-based-spike-detection-and-unsupervised-clustering",totalDownloads:2360,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter presents a novel hardware architecture for correlation-based spike detection and unsupervised clustering. The architecture is able to utilize the information extracted from the results of spike clustering for efficient spike detection. The architecture supports the fast computation for the normalized correlation and OSORT operations. The normalized correlation is used for template matching for accurate spike detection. The OSORT algorithm is adopted for unsupervised classification of the detected spikes. The mean of spikes of each cluster produced by the OSORT algorithm is used as the templates for subsequent detection. The architecture adopts postnormalization technique for reducing the area costs. Modified OSORT operations are also proposed for facilitating unsupervised clustering by hardware. The proposed architecture is implemented by field programmable gate array (FPGA) for performance evaluation. In addition to attaining high detection and classification accuracy for spike sorting, experimental results reveal that the proposed architecture is an efficient design providing low area cost and high throughput for real-time offline spike sorting applications.",signatures:"Chien-Min Ou and Wen-Jyi Hwang",downloadPdfUrl:"/chapter/pdf-download/52952",previewPdfUrl:"/chapter/pdf-preview/52952",authors:[{id:"108614",title:"Prof.",name:"Wen-Jyi",surname:"Hwang",slug:"wen-jyi-hwang",fullName:"Wen-Jyi Hwang"},{id:"195159",title:"Prof.",name:"Chien-Min",surname:"Ou",slug:"chien-min-ou",fullName:"Chien-Min Ou"}],corrections:null},{id:"53710",title:"Efficient FPGA Implementation of a CTC Turbo Decoder for WiMAX/LTE Mobile Systems",doi:"10.5772/67017",slug:"efficient-fpga-implementation-of-a-ctc-turbo-decoder-for-wimax-lte-mobile-systems",totalDownloads:1607,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter describes the implementation on field programmable gate array (FPGA) of a turbo decoder for 3GPP long-term evolution (LTE) standard, respectively, for IEEE 802.16-based WiMAX systems. We initially present the serial decoding architectures for the two systems. The same approach is used; although for WiMAX the scheme implements a duo-binary code, while for LTE a binary code is included. The proposed LTE serial decoding scheme is adapted for parallel transformation. Then, considering the LTE high throughput requirements, a parallel decoding solution is proposed. Considering a parallelization with N = 2p levels, the parallel approach reduces the decoding latency N times versus the serial decoding one. For parallel approach the decoding performance suffers a small degradation, but we propose a solution that almost eliminates this degradation, by performing an overlapped data block split. Moreover, considering the native properties of the LTE quadratic permutation polynomial (QPP) interleaver, we propose a simplified parallel decoder architecture. The novelty of this scheme is that only one interleaver module is used, no matter the value of N, by introducing an even-odd merge sorting network. We propose for it a recursive approach that uses only comparators and subtractors.",signatures:"Cristian Anghel, Cristian Stanciu and Constantin Paleologu",downloadPdfUrl:"/chapter/pdf-download/53710",previewPdfUrl:"/chapter/pdf-preview/53710",authors:[{id:"76429",title:"Dr.",name:"Cristian",surname:"Anghel",slug:"cristian-anghel",fullName:"Cristian Anghel"},{id:"196342",title:"Dr.",name:"Cristian",surname:"Stanciu",slug:"cristian-stanciu",fullName:"Cristian Stanciu"},{id:"196343",title:"Prof.",name:"Constantin",surname:"Paleologu",slug:"constantin-paleologu",fullName:"Constantin Paleologu"}],corrections:null},{id:"54304",title:"Motion Control with FPGA",doi:"10.5772/67200",slug:"motion-control-with-fpga",totalDownloads:1985,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The aim of this chapter is to provide an introduction to the field programmable gate array (FPGA)‐based digital control system design for motion control. It is intended as a reference for the undergraduate students in science and engineering, professionals, and enthusiastic people who have a basic knowledge in discrete control theory and digital systems using reconfigurable logic. The scope of this chapter includes the analysis, simulation, and implementation of classic control algorithms. The presented topics serve as a foundation for the implementation of more complex systems. An experimental section is provided, which validates the proposed digital design.",signatures:"Miguel Angel Martínez Prado, Juvenal Rodríguez Reséndiz, Diana\nCarolina Toledo Pérez, Carlos Miguel Torres Hernández and\nGilberto Herrera Ruiz",downloadPdfUrl:"/chapter/pdf-download/54304",previewPdfUrl:"/chapter/pdf-preview/54304",authors:[{id:"92562",title:"Dr.",name:"Gilberto",surname:"Herrera",slug:"gilberto-herrera",fullName:"Gilberto Herrera"},{id:"192430",title:"Dr.",name:"Miguel Angel",surname:"Martínez Prado",slug:"miguel-angel-martinez-prado",fullName:"Miguel Angel Martínez Prado"},{id:"192788",title:"Dr.",name:"Juvenal",surname:"Rodríguez",slug:"juvenal-rodriguez",fullName:"Juvenal Rodríguez"},{id:"193392",title:"MSc.",name:"Diana Carolina",surname:"Toledo Pérez",slug:"diana-carolina-toledo-perez",fullName:"Diana Carolina Toledo Pérez"},{id:"195606",title:"MSc.",name:"Carlos Miguel",surname:"Torres Hernández",slug:"carlos-miguel-torres-hernandez",fullName:"Carlos Miguel Torres Hernández"}],corrections:null},{id:"53537",title:"FPGA-Based Software-Defined Radio and Its Real-Time Implementation Using NI-USRP",doi:"10.5772/66272",slug:"fpga-based-software-defined-radio-and-its-real-time-implementation-using-ni-usrp",totalDownloads:2776,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, we propose a novel design of scalable and real-time data acquisition software architecture for software-defined radio (SDR) using universal software radio peripheral (USRP). The software has been designed and tested in multi-thread model, using LabVIEW, which guarantees real-time performance and efficiency. With the help of this design, we have been able to improve the stability of the system besides providing a reconfigurable and flexible architecture. Wireless transfer of sensitive data using communication is not a very safe option. In this chapter, we aim to provide a safe and private wireless transmission between two terminals using the SDR approach and verifying the results in real-world environment with the use of USRP. The novel design being presented here can be used to transfer (random data, text or an image) encoded with different forward error correction (FEC) codes, which is then verified at the receiving terminal and then decoded accordingly to produce the desired result.",signatures:"Nikhil Marriwala, Om. Prakash. Sahu and Anil Vohra",downloadPdfUrl:"/chapter/pdf-download/53537",previewPdfUrl:"/chapter/pdf-preview/53537",authors:[{id:"192912",title:"Associate Prof.",name:"Nikhil",surname:"Marriwala",slug:"nikhil-marriwala",fullName:"Nikhil Marriwala"},{id:"198652",title:"Prof.",name:"O.P",surname:"Sahu",slug:"o.p-sahu",fullName:"O.P Sahu"},{id:"198654",title:"Prof.",name:"Anil",surname:"Vohra",slug:"anil-vohra",fullName:"Anil Vohra"}],corrections:null},{id:"53000",title:"Design Trade‐Offs for FPGA Implementation of LDPC Decoders",doi:"10.5772/66085",slug:"design-trade-offs-for-fpga-implementation-of-ldpc-decoders",totalDownloads:1714,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Low density parity check (LDPC) decoders represent important throughput bottlenecks, as well as major cost and power-consuming components in today's digital circuits for wireless communication and storage. They present a wide range of architectural choices, with different throughput, cost, and error correction capability trade-offs. In this book chapter, we will present an overview of the main design options in the architecture and implementation of these circuits on field programmable gate array (FPGA) devices. We will present the mapping of the main units within the LDPC decoders on the specific embedded components of FPGA device. We will review architectural trade-offs for both flooded and layered scheduling strategies in their FPGA implementation.",signatures:"Alexandru Amaricai and Oana Boncalo",downloadPdfUrl:"/chapter/pdf-download/53000",previewPdfUrl:"/chapter/pdf-preview/53000",authors:[{id:"3478",title:"Dr.",name:"Oana",surname:"Boncalo",slug:"oana-boncalo",fullName:"Oana Boncalo"},{id:"192798",title:"Dr.",name:"Alexandru",surname:"Amaricai",slug:"alexandru-amaricai",fullName:"Alexandru Amaricai"}],corrections:null},{id:"53952",title:"Design of Digital Advanced Systems Based on Programmable System on Chip",doi:"10.5772/66579",slug:"design-of-digital-advanced-systems-based-on-programmable-system-on-chip",totalDownloads:1677,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:1,abstract:"This chapter fills up an advanced analysis of the state-of-the-art design in programmable SoC systems, giving a critical overall vision for every designer to implement real time operating systems and concurrent processing. The content of the chapter is divided in the next four main sections.\nFirst the evolution timeline of FPGA based systems is covered from its beginning until the last AP SoC chips. They are complex devices and it is necessary to have a well-known understanding to utilise them in the more efficient form possible.\n\nThe more important advance digital systems structures and architectures are described. The embedded AP SoCs are analysed and main design methodologies are covered, focusing in hardware and co-design strategies.\n\nIn this section is described the development of a real open source application that covers the fundamental parts in the design of a SoC system, ranging from the hardware development until the software design involving the embedded operating system and the user interface application.\n\nFinally, the system described in the last section is tested in a real scientific experiment and the results are evaluated.",signatures:"Nordin Aranzabal, Adrián Suárez, José Torres, Raimundo García‐\nOlcina, Julio Martos, Jesús Soret, Abraham Menéndez and Pedro A.\nMartínez",downloadPdfUrl:"/chapter/pdf-download/53952",previewPdfUrl:"/chapter/pdf-preview/53952",authors:[{id:"2803",title:"Dr.",name:"Julio",surname:"Martos",slug:"julio-martos",fullName:"Julio Martos"},{id:"66221",title:"Dr.",name:"Jose",surname:"Torres",slug:"jose-torres",fullName:"Jose Torres"},{id:"77545",title:"Dr.",name:"Raimundo",surname:"Garcia",slug:"raimundo-garcia",fullName:"Raimundo Garcia"},{id:"125471",title:"Dr.",name:"Jesus",surname:"Soret",slug:"jesus-soret",fullName:"Jesus Soret"},{id:"193684",title:"MSc.",name:"Nordin",surname:"Aranzabal",slug:"nordin-aranzabal",fullName:"Nordin Aranzabal"},{id:"193685",title:"MSc.",name:"Abraham",surname:"Menendez",slug:"abraham-menendez",fullName:"Abraham Menendez"},{id:"193686",title:"MSc.",name:"Pedro A.",surname:"Martinez",slug:"pedro-a.-martinez",fullName:"Pedro A. Martinez"},{id:"193687",title:"Prof.",name:"Adrian",surname:"Suarez",slug:"adrian-suarez",fullName:"Adrian Suarez"}],corrections:null},{id:"53616",title:"The Use of FPGA in Drift Chambers for High Energy Physics Experiments",doi:"10.5772/66853",slug:"the-use-of-fpga-in-drift-chambers-for-high-energy-physics-experiments",totalDownloads:1533,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:0,abstract:"In this chapter, we describe the design of a field programmable gate array (FPGA) board capable of acquiring the information coming from a fast digitization of the signals generated in a drift chambers. The digitized signals are analyzed using an ad hoc real‐time algorithm implemented in the FPGA in order to reduce the data throughput coming from the particle detector.",signatures:"Gianluigi Chiarello, Claudio Chiri, Giuseppe Cocciolo, Alessandro\nCorvaglia, Francesco Grancagnolo, Marco Panareo, Aurora Pepino\nand Giovanni Francesco Tassielli",downloadPdfUrl:"/chapter/pdf-download/53616",previewPdfUrl:"/chapter/pdf-preview/53616",authors:[{id:"192935",title:"Ph.D.",name:"Francesco",surname:"Grancagnolo",slug:"francesco-grancagnolo",fullName:"Francesco Grancagnolo"},{id:"194022",title:"Dr.",name:"Gianluigi",surname:"CHIARELLO",slug:"gianluigi-chiarello",fullName:"Gianluigi CHIARELLO"},{id:"194023",title:"Dr.",name:"Claudio",surname:"CHIRI",slug:"claudio-chiri",fullName:"Claudio CHIRI"},{id:"194024",title:"Dr.",name:"Giuseppe",surname:"COCCIOLO",slug:"giuseppe-cocciolo",fullName:"Giuseppe COCCIOLO"},{id:"194025",title:"Dr.",name:"Alessandro",surname:"CORVAGLIA",slug:"alessandro-corvaglia",fullName:"Alessandro CORVAGLIA"},{id:"194028",title:"Dr.",name:"Marco",surname:"PANAREO",slug:"marco-panareo",fullName:"Marco PANAREO"},{id:"194029",title:"Dr.",name:"Aurora",surname:"PEPINO",slug:"aurora-pepino",fullName:"Aurora PEPINO"},{id:"194030",title:"Dr.",name:"Giovanni",surname:"TASSIELLI",slug:"giovanni-tassielli",fullName:"Giovanni TASSIELLI"}],corrections:null},{id:"53123",title:"Real‐Time Adaptive Optic System Using FPGAs",doi:"10.5772/66184",slug:"real-time-adaptive-optic-system-using-fpgas",totalDownloads:1458,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"For “adaptive optics” (AO) that are used in a control loop, sensing of the wavefront is essential for achieving a good performance. One facet in this context is the delay introduced by the wavefront evaluation. This delay should be kept to a minimum. Since the problem can be split into multiple subproblems, field-programmable gate arrays (FPGAs) may beneficially be employed in view of the FPGAs’ power to compute many tasks in parallel. The evaluation of, e.g., a Shack-Hartmann wavefront sensor (SHWFS) may simply be seen as the evaluation of an image. Therefore, in general, image processing methods may be split into multiple assignments such as connected component labeling (CCL). In this chapter, a new method for real-time evaluation of an SHWFS is introduced. The method is presented in combination with a rapid-control prototyping (RCP) system that is based on real-time Linux operating system.",signatures:"Steffen Mauch and Johann Reger",downloadPdfUrl:"/chapter/pdf-download/53123",previewPdfUrl:"/chapter/pdf-preview/53123",authors:[{id:"20929",title:"Dr.",name:"Johann",surname:"Reger",slug:"johann-reger",fullName:"Johann Reger"},{id:"193299",title:"Dr.",name:"Steffen",surname:"Mauch",slug:"steffen-mauch",fullName:"Steffen Mauch"}],corrections:null},{id:"53004",title:"FPGA‐SRAM Soft Error Radiation Hardening",doi:"10.5772/66195",slug:"fpga-sram-soft-error-radiation-hardening",totalDownloads:3605,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Due to integrated circuit technology scaling, a type of radiation effects called single event upsets (SEUs) has become a major concern for static random access memories (SRAMs) and thus for SRAM‐based field programmable gate arrays (FPGAs). These radiation effects are characterized by altering data stored in SRAM cells without permanently damaging them. However, SEUs can lead to unpredictable behavior in SRAM‐based FPGAs. A new hardening technique compatible with the current FPGA design workflows is presented. The technique works at the cell design level, and it is based on the modulation of cell transistor channel width. Experimental results show that to properly harden an SRAM cell, only some transistors have to be increased in size, while others need to be minimum sized. So, with this technique, area can be used in the most efficient way to harden SRAMs against radiation. Experimental results on a 65‐nm complementary metal‐oxide‐semiconductor (CMOS) SRAM demonstrate that the number of SEU events can be roughly reduced to 50% with adequate transitory sizing, while area is kept constant or slightly increased.",signatures:"Gabriel Torrens",downloadPdfUrl:"/chapter/pdf-download/53004",previewPdfUrl:"/chapter/pdf-preview/53004",authors:[{id:"193438",title:"Dr.",name:"Gabriel",surname:"Torrens",slug:"gabriel-torrens",fullName:"Gabriel Torrens"}],corrections:null},{id:"53882",title:"Power Efficient Data-Aware SRAM Cell for SRAM-Based FPGA Architecture",doi:"10.5772/67257",slug:"power-efficient-data-aware-sram-cell-for-sram-based-fpga-architecture",totalDownloads:1524,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"The design of low-power SRAM cell becomes a necessity in today's FPGAs, because SRAM is a critical component in FPGA design and consumes a large fraction of the total power. The present chapter provides an overview of various factors responsible for power consumption in FPGA and discusses the design techniques of low-power SRAM-based FPGA at system level, device level, and architecture levels. Finally, the chapter proposes a data-aware dynamic SRAM cell to control the power consumption in the cell. Stack effect has been adopted in the design to reduce the leakage current. The various peripheral circuits like address decoder circuit, write/read enable circuits, and sense amplifier have been modified to implement a power-efficient SRAM-based FPGA.",signatures:"Ajay Kumar Singh",downloadPdfUrl:"/chapter/pdf-download/53882",previewPdfUrl:"/chapter/pdf-preview/53882",authors:[{id:"192404",title:"Dr.",name:"Ajay",surname:"Singh",slug:"ajay-singh",fullName:"Ajay Singh"}],corrections:null},{id:"53730",title:"High‐Speed Deterministic‐Latency Serial IO",doi:"10.5772/67012",slug:"high-speed-deterministic-latency-serial-io",totalDownloads:1688,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In digital systems, serial IO at speeds in the range from 1 to 20 Gbps is realized by means of dedicated transceivers, named serializer-deserializers (SerDeses). In general, due to their internal architecture, the data transfer delay, or the latency, may vary after a reset of the device. On the other hand, some applications, such as high-speed transfer protocols for analog-to-digital and digital-to-analog converters, trigger and data acquisition systems, clock distribution, synchronization and control of radio equipment need this delay to be constant at each reset. In this chapter, we focus on a serial IO architecture based on configurable transceivers embedded in field-programmable gate arrays (FPGAs). We will show how it is possible to achieve deterministic-latency operation in a line-code-independent way. As a case study, we will consider a synchronous 2.5-Gbps serial link based on an 8b10b line code.",signatures:"Raffaele Giordano, Vincenzo Izzo and Alberto Aloisio",downloadPdfUrl:"/chapter/pdf-download/53730",previewPdfUrl:"/chapter/pdf-preview/53730",authors:[{id:"193125",title:"Prof.",name:"Raffaele",surname:"Giordano",slug:"raffaele-giordano",fullName:"Raffaele Giordano"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6003",title:"Robotics",subtitle:"Legal, Ethical and Socioeconomic Impacts",isOpenForSubmission:!1,hash:"15ab11f5bb5aac89956dd8b42f261011",slug:"robotics-legal-ethical-and-socioeconomic-impacts",bookSignature:"George Dekoulis",coverURL:"https://cdn.intechopen.com/books/images_new/6003.jpg",editedByType:"Edited by",editors:[{id:"9833",title:"Prof.",name:"George",surname:"Dekoulis",slug:"george-dekoulis",fullName:"George 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1. Introduction
The quality of a cosmetic product, in the same way as to other kind of products, is initially defined by the manufacturer that chooses the features that a product should present. On the other hand, the quality control of a product aims to verify if all of these defined features are in accordance with the standard definitions and if it will be maintained during the shelf life of the product (Shewhart, 1980).
The quality control of cosmetics is important to ensure the efficacy and safety of products and its raw-materials. Due to the rapid growth that cosmetic industries have exhibit all over the world, efficient, low cost and rapid methods to assay cosmetics’ quality control are a priority. Some current techniques used by the cosmetic industry can be applied to the evaluation of cosmetics’ quality control in an efficient manner, such as: rheology, sensory analysis and small angle X-ray scattering (SAXS).
Sensory analysis is a powerful tool, since there is no equipment able to measure the human feelings. It applies experimental design and statistical analysis to obtain information about a product in relation to what people feel when use or consume a product, in other words, it is used to indicate consumer acceptance of a particular product. It can be understood as the discipline that interprets, assess and measures characteristics of a product, after stimulating people in relation to their vital senses, as vision, touch, smell and taste (Stone et al., 1992). It is widely used in food industry and recently, it has also been applied in the cosmetic industry (Almeida et al., 2008; Aust et al., 1987; Backe et al., 1999; Lee et al., 2005; Parente et al., 2005; Wortel et al., 2000).
The sensory analysis can be applied in the research and development of a new cosmetic (Isaac et al., 2012a), in controlling the manufacturing process to evaluate raw-materials quality and, even, to make possible the substitution of a raw-material of a product that is traditional in the market without changes in the product’s features (Meilgaard et al., 1991; Muñoz et al., 1993).
The application of sensory analysis could be related to the product control, referring to the storage, packaging and maintenance of sensory quality in relation to time and temperature (Muñoz et al., 1993), since these factors can change a sensory attribute that the product present originally (Zague, 2008) and people who participates of the sensorial panel could realize the changes in the sensorial attributes. Another function of this important tool is to performance comparative tests between competing products.
Another tool that could be applied to evaluate cosmetics’ quality control is the rheology, which studies the flow and deformation of fluids. It has been used in research laboratories and industries as a tool for characterizing ingredients and products, and to predict the performance of products and consumer acceptance.
Rheology has been widely used because, by means of this tool, the researcher can determine physicochemical properties of a product. Constructing a rheogram, it is possible to check the flow curve, evaluate if there is a yield stress and a hysteresis area, which appears to be related to the release of drugs and actives. It is also possible to construct a creep and recovery curve obtaining information about viscoelasticity of each system.
Specifically, in relation to the quality control of cosmetics, specifically, rheology can be applied to help in determining the stability of products by means of the apparent viscosity measured periodically in a determined period exposing the samples to stress conditions (high and low temperatures, solar irradiation), and to monitor the flow characteristics during the shelf life or in the stability assay of a product.
The SAXS technique have being used for the analysis of cosmetics, in order to evaluate the presence of liquid crystalline structures, called liquid-crystals, which are known to increase the stability of formulations becoming, therefore, desirable in cosmetics (Makai et al., 2003).
Combining these three tools, it is possible to test the quality of cosmetics with a rich range of data, and obtain a deep characterization of the system. The results contribute to determining product use, or even, they provide indication of what need to be done to develop a product with predetermined characteristics.
2. Sensory analysis
Sensory analysis is defined by Piana et al. (2004) as the examination of a product through the evaluation of the attributes perceptible by the five sense organs (organoleptic attributes), such as color, odor, taste, touch, texture and noise, allowing the establishment of the organoleptic profile of diverse products, including cosmetics.
The sensory analysis was first applied to the food industry, but the high advance in other areas, such as the cosmetic and pharmaceutical industries, and the important data obtained with the sensory analysis, demanded this useful technique to describe what the consumers fell.
An important advantage of the use of sensory analysis in the quality control of a cosmetic product is that it yields a complex analysis in relation to all sensorial attributes that a product could present, it means that, the volunteer who participates of the sensorial panel is able to give information about the fragrance, the sensation, the appearance, the consistence, and other features that this person experience when use such product. The description of these characteristics by means of equipment would be an arduous work and would provide not sufficient or not valuable data when compared to the data provided by the human senses. Beyond that, the acquisition of this equipment could be of high cost when compared to the sensory analyses’ costs (Ross, 2009).
The association of data obtained from sensory analysis and instrumental analysis (especially physicochemical analysis) provides great information and a more complete profile of the product (Ross, 2009).
Nowadays, there are companies specialized in perform sensory analysis of cosmetic products, and thus, they could be contracted to perform this study for cosmetic industries that don’t have a sector trained to do it.
The sensorial features of a formulation are mainly related to the raw-materials and package (Dooley et al., 2009). The raw-materials influence directly in what the consumer feels when applies the cosmetic. The emollients, for example, are raw-materials of marked influence in the tactile sense (Parente et al., 2008; Gorcea and Laura, 2010). Other raw-materials are available at the market and are commercialized to be used in formulations as sensorial modifiers. The main representatives of this kind of product are the silicones and Polymethyl Methacrylate (Ozkan et al., 2012).
The package influences in the first impression of the consumer about a product, since the first sense used to choose a cosmetic in the market is the vision. After, the smell is used too. The tact is not involved in the first purchase attitude, but it will define if a consumer will become a loyal consumer.
In this context, it is possible to verify that the sensorial features of a cosmetic are of great importance in the success of it in the market.
Thus, the sensorial analysis could help a company to define the attributes that a product should or not present beyond the characteristics and intensity of these attributes.
Another point is that these desired sensorial characteristics should be maintained during the cosmetic shelf life. To obtain that, the raw-materials used should be of good quality, the manufacture practices should be appropriate, the preservatives used need to be efficient and the formulation should be stable.
In conclusion, the sensorial analysis is an indispensable technique to help the formulator to evaluate the quality of its new product, in relation to its sensorial characteristics and to its stability, testing if the product will keep the nice sensorial feelings that transmit to the consumer during the time of use. This tool is helpful to the research and development area of a company which aims to obtain good quality products of high acceptance by the consumers. The suitable application of sensory evaluation could avoid the outlay of a company with the launching of a product in the market that was rejected by the volunteers of the preliminary study.
Currently, the sensorial analysis have gained more scientific rigor due to the need to offer to the consumers products that meet their expectations and due to the high competition between the major industries of this sector.
To perform the sensorial analysis with rigor and organization, the laboratory destined to it must have the following areas:
A room destined to the analyst who leads the team (Figure 1a)
An area to the analyses with the volunteers (Figure 1d)
The laboratory should be located in an easy access place.
Figure 1.
An example of layout of a sensorial analysis laboratory (Isaac et al., 2012a).
The area where will be performed the analyses should be divided in individual cabins (Figure 2) with a window, where the analyst must offer the samples to the volunteer, sink and faucet, to the volunteer use when necessary (Isaac et al., 2012).
Figure 2.
An example of layout of sensorial analysis cabine (Isaac et al., 2012a).
The cabins must be ventilated and odor free, to avoid interferences in the analyses. The temperature and humidity should be controled around 22 ºC and at 45% of humidity (Isaac et al., 2012).
It is recommended that the walls and furniture of the rooms are colored with neutral and light colors to not disturb the attention of the volunteers and to not interfere in the attributes analyzed by the vision, such as color and appearance of the product.
The volunteers should not smoke, should be healthy, with ease of memorization and communication.
In the study, the volunteers judges could be an experienced judge or not, depending on the kind of evaluation and the answers that the professional team needs to obtain. In the case of utilization of sensorial analysis in the quality control of a cosmetic, usually the volunteers are regular users of the product in analysis, since they need to be familiarized with the characteristics of the product and have sensibility to perceive slight modification on it. When the aim of the sensorial analysis is to evaluate the acceptance of a product that should be launched in the market, it is recommended that the volunteers are potencial users of this new product, orienting the formulator to make changes in the formulation and guiding the company to evaluate if the costs of the product launch are recommended or not.
There are four different methods to perform the sensorial analysis that are most used, they are: affective, discriminative, descriptive (Aust et al., 1987) and methods to evaluate the effective of the product.
Independent of the method of sensory analysis suitable for each evaluation, the professional team should use printed questionnaires to obtain the answers from each volunteer. The use of printed questionnaires avoids the contact between the professional and the volunteers preventing that the professional is biased in his responses, beyond that, it facilitates the data collection.
In the elaboration of these questionnaires the professional team should use suitable lexicons for each class of product, for example, the lexicons used to the evaluation of lip products are different from that used for corporal lotions (Dooley et al., 2009). Some researches had developed suitable lexicons for different classes of cosmetic products (Civille and Dus, 1991; Wortel and Wiechers, 2000; Dooley et al., 2009). The manner as the volunteer is questioned is fundamental to obtain the information required from them. An inadequate formulary could invalidate a sensory evaluation. It is interesting also, that a description of all descriptors attributed to the formulation being provided to the volunteer, for example: “Thickness: Viscosity of the cream when picking up from the container”, “Ease of spreading: Ease of rubbing the sample over the skin”, “Absorption: Ease of absorption of the product through the skin”, “Residue: Amount of product left on the skin after application” (Parente et al., 2010).
The affective methods represent the consumer opinion and evaluate how much consumers like or dislike a product. It is a quantitative method that is performed in order to know the consumers preferences (Aust et al., 1987). This technique could be applied in the development of new products and when it is necessary to replace a constituent of a formulation without loss of the product quality. It could be performed in two different ways: offering two different samples to the volunteer asking him about what sample he prefers between them or using a hedonic scale for the volunteer attributes grades of intensity of its acceptation in relation to the sample.
The hedonic scale either can be presented to the panel of evaluators in different manners, as shown in Figure 3.
Figure 3.
Examples of presentation of hedonic scale (Olshan et al., 2000; Barkat et al., 2003).
The affective methods provide quantitative data and allow more than one attribute in each sample being evaluated at the same time.
The discriminative test is better represented by the Triangular test. It allows differentiating one between three different samples and is very useful in shelf life studies and in the quality control of cosmetics. The ideal is to perform this evaluation with twelve to forty volunteers, who will receive the three samples and should indicate the different one between them (Zenebon et al., 2008).
The descriptive tests provide a broad sensory description about the product that is being evaluated (Almeida et al., 2008), helping to predict the consumer acceptance and what consumers think about such product (Almeida et al., 2006; Aust et al., 1987).
The tests to evaluate the effective of the products should be performed in true conditions of use and the volunteer may use only the product that is being assessed. These tests could be conduct by the evaluation of dermatologists, by the evaluation of volunteers, and even, by the measurement of one parameter by un equipment, such as the equipment that measures hydration, sebum and transepidermal water loss, to define if a product is really effective. Based on these clinical evaluations, a company could create an efficacy claim to the product (Wortel and Wiechers, 2000).
The sensory analysis could be also applied when a cosmetic industry needs to replace a raw-material of a commercialized product without changes in the performance of it. This replacement could be originated by many factors, such as the reduction of costs, problems with the firm who provides this raw-material problems with same raw material which causes irritation, comedogenicity or other problems that affect the consumer. In this field, the sensorial analysis helps the formulator, who proposes different raw materials as substitute, to evaluate if the consumer will notice the adaptation in the cosmetic product.
The statistical analysis is indispensable in the sensory studies. The sensory analysis data should be evaluated transforming them in scores which allows the application of statistical analysis to calculate the mean and standard deviation of the results, and the determination if the difference between the scores obtained is statistically significant. Graphics, tables and preference maps could be elaborated with the results obtained to facilitate the analysis of the data by the professional team.
The sensory analysis is especially indispensable in the industries of fragrances and perfumes, and because of that, high-resolution instrumental methods for evaluation of flavor and aroma have been developed and between them are the breath analysis via mass spectrometry (Dijksterhuis and Piggott, 2001; Ross, 2009). Instrumental measurements are thought to be objective, representing an independent fact or truth, however, the human smell sense is irreplaceable, being considered by Ross (2009) not necessarily valid because instrumental methods cannot account for the complexity of human perception.
Nevertheless, rheological studies have been applied to objectify the sensations when cosmetic emulsions are applied to the skin (Brummer and Godersky, 1999).
3. Rheology
Rheology is a tool widely applied in the food, petrochemical and pharmaceutical industries, but to the cosmetic industry it is incipient yet. Until now, the majority of cosmetic industries use viscometers to guarantee that the viscosity of different batches of a product is maintained.
This chapter was elaborated in order to show that many other rheological characteristics could be used to evaluate and to predict the stability of cosmetic products and could be applied to compare competing products in the market and to assay if a change in the composition will cause alterations that could be perceived by the consumer.
First, it is necessary to define the three parameters of most importance in rheology: shear stress, shear rate and viscosity. Shear stress can be defined as a force applied in an area. Shear rate is the ratio of the velocity of material to its distance from a stationary object (Naé, 1993). The shear rate can be calculated by the ratio between the velocity and the layer or film thickness. In a lipstick application, for example, with a velocity estimated in 5 cm/s and a layer thickness of 0.1 mm, the ratio (shear rate) is 5.102 s-1. Finally, the viscosity can be defined as the resistance to flow. Thus, a viscous product presents smaller flow than others.
Concluding, rheology is the study of deformation and flow of materials under external forces. Some equations and the units of these parameters are (Naé, 1993):
σ=F/AE1
Where:
σ = shear stress (Pa = kg.m-1.s-2)
F = force (N or kg.m.s-2)
A = area (m2)
The viscosity can be defined as the ratio between shear stress and shear rate:
η=σ/γ˙E2
Where:
= viscosity
= shear stress (Pa)
γ˙= shear rate (s-1)
Since the unit of shear stress is Pa and the unit of deformation is s-1, the unit of viscosity is Pa.s. These parameters are involved in scientific measurements of rotational assays.
Using controlled shear rate and measuring shear stress is possible to carry out rotational assays, and determine flow curves and describe the models: Newtonian or non-Newtonian and, among the last one, plastic, pseudoplastic, dilatant, tixotropic and reopetic fluids. Newtonian fluids are materials that present constant viscosity, independent of time and temperature. These materials present flow curves with proportionality between shear stress and shear rate. The Figure 4 represents the flow curve of a Newtonian material.
Figure 4.
Flow curve of a Newtonian material.
In the case of non-Newtonians materials, this proportionality between shear stress and shear rate does not happen.
If in the beginning of the flow curve there is an increasing in the shear stress but the shear rate is equal to zero, and after to it is verified a Newtonian flow, this material is called plastic. This initial shear stress with shear rate equal to zero is called yield value and it represents the shear stress necessary for the material flow. The Figure 5 represents a plastic material. The yield value is related to the energy required to deform the material sufficiently so that they can flow. The value of the yield stress can be determined by measuring the deformation of the material as a function of the applied stress (Abdel-Rahem et al., 2005).
For non-Newtonian materials time-dependents, if the viscosity decreases with the shear rate, the material is called pseudoplastic and if the viscosity increases, the material is called dilatant. On the other hand, if the material is time-independent, it will be called tixotropic if the viscosity decreases with the shear rate or reopetic if the viscosity increases with the shear rate (Naé, 1993). When the ascending and the descending curves of the flow curve do not overlap it shows thixotropy which is a desirable feature for cosmetics and semisolid drug carriers for topical application (Lippacher et al., 2004). The Figures 6, 7, 8 and 9 represent the flow curves of non-Newtonian materials (Naé, 1993).
Figure 5.
Flow curve of a plastic material.
Figure 6.
Flow curve of a pseudoplastic material.
For screening purposes and in the initial phases of the formulation development, the rheological tests proved to be very useful for the study of stability.
In a stability assay to determine the shelf life of a recently developed product, the formulation should be exposed to stress conditions, such as storage at -5 ºC, 45 ºC, and cycles of -5 ºC during 24 hours followed by exposure to 45 ºC during more 24 hours. This procedure is done in order to induce the appearance of instability signals in the formulations, where can be cited the darkening of the formulation, the precipitation of a constituent, the phase separation in the case of emulsions, and other signals. These stressing conditions are kept for a period around 2 or 3 months.
Figure 7.
Flow curve of a dilatant material.
Figure 8.
Flow curve of a thixotropic material.
Figure 9.
Flow curve of a reopetic material.
It is usually measured the viscosity of the stressed formulations periodically during the stability assay. It could be done by means of a viscometer or by using a rheometer.
With a viscometer, it is possible to carry out rotational assays or measurements by steady-state flow. On the other hand, the rheometer allows the development of oscillatory assays or dynamic measurements (Biradar, 2009).
When using an oscillatory rheometer it is necessary to carry out a flow curve assay and determine the apparent viscosity of the formulation in a defined shear rate. It is recommended to use the higher shear rate in the ascendant curve of the flow curve, since in this point the sample is in a suitable condition, it means that the formulation is not starting to flow and is not excessively sheared (Figure 10).
Figure 10.
Example of a flow curve indicating the condition to the measurement of apparent viscosity.
In some papers, the flow curves have been plotted as viscosity as a function of shear stress instead of the traditional approach of plotting it versus shear rate because has been previously found that such curves are more discriminating and give better results for evaluation (Roberts, 2001; Samavati, 2011).
After obtaining, periodically, the minimum apparent viscosity of the samples exposed to stress conditions during a period, they should be compared with the initial value, and also compared the viscosity values of the control with the samples exposed to stress conditions, which allows the verification of the increase, decrease or maintenance of this attribute of the formulations.
Further exploiting the same assay, it is possible to calculate the hysteresis area of the formulation in each flow curve performed during the stability assay. The hysteresis loop areas can be obtained through a three-step experiment: upward curve, plateau, downward (Benchabane and Bekkour, 2008) and represents a way to measure, indirectly, the spreadability of the formulation, so it is possible to define if the formulation losses or gains easiness on spreadability during the shelf life. How much bigger is the hysteresis area, higher is the spreadability.
Figure 11.
Flow curve with hysteresis area.
Using the flow curve is possible to compare two samples in relation to its hysteresis area and viscosity. A simple way to verify what formulation have a higher viscosity is by simple observation of the rheogram, since the curve that forms a bigger inclination in relation to the x axis of the graphic is the one with higher viscosity. On Figure 12 is showed an example of it, where sample 2 is more viscous than sample 1. It happens because the tangent of the angle formed is correspondent to the viscosity of the formulation in each shear rate.
Figure 12.
Comparison between flow curves of different samples (a thixotropic and a plastic fluid).
Beyond the different sensorial features caused by the differences in viscosity is known that the viscosity of emulsioned systems is one of the factors that retards or avoids the phase separation processes. The coalescence of dispersed phase can be due to the emulsifier agent and can be related to an instability because of low viscosity of dispersed phase (Corrêa & Isaac, 2012). This low viscosity can occur because of high shear stress (Samavati et al., 2011).
In general, for emulsioned systems, the continuos phase is shear thinning, which means that its viscosity decreases with the increasing on shear rate and viscoelastic, which means that it has viscous and elastic components (Tadros, 2004).
An example of the verification of differences in viscosity and thixotropy between two samples is shown on Figure 13.
Figure 13.
Comparison between flow curves of different samples.
Sample 1 is less viscous but more thixotropic than formulation 2. This simple verification gives to the analyst wide information, depending on what he needs.
Lescanne et al. (2004) studied organogels and aging properties of them. Organogels can be obtained by precipitation processes. These authors verified that, when aggregates are formed by the cooling rate, can be observed a elastic behavior, however, these aggregates can be aligned in the direction of the flow without lost the structure and when the flow is stopped, the aggregates are quickly rearranged and it inducing an thixotropic behavior. When the hot solution is introduced between the flat and the conical plates of the rheometer cell it is cooled to 5 ºC with a cooling rate of 20ºC/min, during the first hour of the gel life, it was measured the elastic properties of a gel as a function of time just after the cooling. Five minutes after its formation, the gel was submitted to a periodic stress (0.5 Pa) at a constant frequency (f = 1Hz). The authors showed that the shear moduli are constants and the aging phenomenon did not modify the elastic properties at least in a period of 1 h. However, when more than a week of aging is waited the samples lost most of its elastic properties.
The flow curve is a rotational assay, but using a rheometer it is possible to perform oscillatory assays too. Among the oscillatory assays are stress sweep and the frequency sweep assays.
The elastic (storage) modulus G’ and the viscous (loss) modulus G’’ are determined as a function of frequency or stress. The elastic modulus is a measure of energy stored and recovered per cycle of deformation and represents the solid-like component of a viscoelastic material. If a sample is elastic or highly structured then the elastic modulus will be high. The viscous modulus is a measure of the energy lost per cycle and represents the liquid-like component. If a sample is viscous the viscous modulus will be high.
In the stress sweep analyses, the structure of the sample is progressively destroyed by applying oscillations with an increasing stress amplitude at a fixed frequency (Callens et al., 2003). The linear viscoelasticity region occurs over that region of strain where the complex modulus is independent of the strain (Hemar, 2000). The linear viscoelastic region is determined by the maximum stress which can be applied without affecting G’ and G’’. Furthermore, the relative magnitude of the moduli is a qualitative indication for the structure in the sample. Two different situations can occur: G’ > G’’ for a network consisting of secondary bonds and G’≤ G’’ for a physically entangled polymer solution (Callens et al., 2003).
Frequency sweep tests are performed in the linear viscoelastic region of each sample, keeping the structure of the system intact during the measurement. By performing such small stress amplitude oscillations at a whole range of frequencies, the type of network structure can be revealed. The main difference between a network of secondary bonds and one of physical entanglements is located in the low frequency range: in an entangled network the polymers can disentangle if the available time is long enough (low frequency). In a network with secondary bonds the bonds are fixed irrespective of the time scale. This results for an entangled solution in a limiting slope of 2 for G’ and 1 for G’’ at low frequency in a log-log plot of moduli versus frequency, while at intermediate frequency a plateau develops. For a network of secondary bonds an almost constant value of G’ and G’’ is observed over the whole frequency range, with the value of G’ exceeding that of G‘’(Callens et al., 2003; Madsen et al., 1998).
The stress sweep is important to evaluate the linear viscoelastic region of a sample that is a range of shear stress in which the formulation does not suffer profound alterations on it structure, being not disrupted. When a shear stress of the linear viscoelastic region is applied in an oscillatory assay, only the intermolecular and interparticle forces are being evaluated (Martin, 1993). To determine the linear viscoelastic region, the oscillating stress sweeps are carried out for the most extreme values. These measurements are used to determine where the reological properties are independent of the applied stress and the identify the critical rheological properties (Tuarez, 2011).
Knowing the values of shear stress that do not cause the disrupt in the formulation by means of the stress sweep, the analyst could perform a frequency sweep of the formulation. The frequency sweep is carried out in a constant shear stress found in the linear viscoelastic region. With this assay it is possible to evaluate the elastic or storage modulus (G’) and the viscous or loss modulus (G’’). The cosmetic excipients most used, emulsions and gels, are often viscoelastic samples. The viscoelastic samples when evaluated by means of the frequency sweep present G’ and G’’ values. When the G’ value is higher than G’’ it is an indicative that the formulation is more elastic than viscous. It is a characteristic of gels.
Emulsions which exhibits G’ values higher than G’’ (Figure 14) are described as more stable than formulations with G’’ values higher than G’ (Figure 15), since they tends to recovery its initial structure faster and more efficiently than the others, and are less susceptible to the gravitational forces which retards or avoids the coalescence process and the phase separation of emulsions (Alam and Aramaki, 2009). So, the G’ values higher than G’’ in emulsions is a desirable feature, being an indicative of stability of the cosmetic system.
Figure 14.
A frequency sweep example (G’>G’’).
Figure 15.
A frequency sweep example (G’’>G’).
Another assay that could be conducted using an oscillatory rheometer is the creep and recovery assay. It is done by submitting the samples to a constant shear stress during a period, and after, removing this shear stress and monitoring the formulation in relation to the deformation (measured by the compliance - J) during the same period. The compliance parameter is the resulting strain divided by the applied stress (Koop, 2009; Toro-Vazquez et al., 2010). If the compliance parameter is the relationship between strain and the applied stress, the strain is dimensionless and stress is measured in Pa, then, the compliance can be measured in 1/Pa.
In the example showed on the Figure 16 the samples were submitted to a shear stress during 300 seconds, and after removing this shear stress it was monitored during more 300 seconds.
Analyzing the result obtained in the first 300 seconds is verified that sample 1 exhibited lower compliance values than sample 2, which represents a higher difficult on being deformed than sample 1. The difficult on being deformed is always linked to higher viscosity values.
In the second part of the assay, where the shear stress imposed to the sample is removed, represented in the graphic by the time 301 to 600 seconds, is verified the viscoelastic properties of the samples. Formulations that are able to recovery its initial structure or part of it exhibit a gradually decrease in the compliance values. On Figure 17 there is an example of a formulation that is not a viscoelastic sample, it means that it do not exhibits storage modulus, and is not able to recovery its structure when the shear stress is ceased.
Figure 16.
A creep and recovery example of viscoelastic samples.
Figure 17.
A creep and recovery example of a non-viscoelastic sample.
In addition, the rheology can be used to evaluate the stability over the time by dynamic and oscillatory rheological measurements (Pénzes et al., 2004; Vasiljevic et al., 2006) and the release of active principles. According to Martinez et al. (2007), the transdermal absorption of topically administered drugs depends on the rate of release and the permeability of them into the skin and also of the viscosity of the formulation (Martinez et al., 2007).
Thus, it is possible to say that different categories of products should present peculiar rheological properties inherent to its application (Gregolin et al., 2010).
In this way, the rheology can influence the diffusion coefficient, altering the release and permeation of cosmetics active substances (Welin-Berger et al., 2001; A-sadutjarit et al., 2005; Vasiljevic et al., 2006). Some authors have related the influence of rheological characteristics on the release profiles and consequently in the permeation of active substances in the skin; thus, the addition of thickening agents or attainment of a weak-gel because of physical entanglement of polymer chains must be considered in the choice of cosmetics bases (Spiclin, et al., 2003). Thus, rheology can help in the assay of release and permeation in the skin. Some studies have been published about it.
So, in a short way, the rheology is a valuable tool that helps in the quality control of cosmetics, being used in the stability tests, in the comparison between competing samples, in the comparison between an original product and a product with an alteration in a constituent, and in the development of new products, aiming to develop cosmetic with rheological characteristics which indicate stability.
4. Small Angle X-ray Scattering (SAXS)
The use of this technique in determining the quality control of a cosmetic is closely related to the stability of the product, which could be improved with the presence of liquid crystals.
Liquid crystals are described as a state of matter between solids and liquids, it means that, they are fluid like liquids but are organized like solids, being called mesophases (Marsh, 1973; Kelker and Hatz, 1980; Müller-Goymann, 2004). These organization contributes to the highly stability of systems.
The formation of liquid crystals in emulsions could be induced by some components present in this system, such as surfactants (Müller-Goymann, 2004). So, what happens is that it is possible to find a peculiar system that is not a simple emulsion and not a genuine liquid crystal, but an emulsioned system that contains liquid crystals, commonly lamellar structures, that are formed around of the inner phase of the emulsion (Oka et al., 2008), making difficult the coalescence, flocculation and the separation of the oily and water phases, what makes the system formed more stable than a simple emulsion (Figures 18 and 19). Flocculation is defined as the formation of aggregates of droplets of an emulsion under the influence of interparticle colloidal forces which are net attractive (Dickinson, 1992) and the formation of lamellar structures avoid or prevent the occurrence of this phenomenon. The formation of lamellar structures is essential to obtain emulsified oil/water systems finely dispersed, with balanced hydrophilic-lipophilic properties, resulting in minimal interfacial tension between aqueous and oily phases, thus contributing to the stability of the system (Engels et al., 1995). Previous studies have also shown that it is possible to make correlation between SAXS and rheological analysis, since were verified that the thicker the interlamellar water layers, the higher the viscosity of the cream (Eccleston et al., 2000). Thus, liquid crystals could be responsible by the emulsion stabilization and by the increasing in the viscosity (Klein, 2002), being the presence of this structures desirable in cosmetic emulsions which could be an indicative of quality of them.
Figure 18.
Scheme of a cosmetic emulsion containing liquid crystals.
Figure 19.
Schemes of the microscopic visualization of lamellar gel networks surrounding emulsion droplets proposed by Klein (2002).
This kind of structure is more commonly found in cosmetics due to the high diversity of components used in it in order to obtain a moisturizer, emollient, humectant, good sensory and, above all, stable cosmetic. In other pharmaceutical forms, usually are used a less diversified composition, which gives a system easier to understand, described as emulsion or liquid crystal, or even, a gel, a suspension, etc. The quantity of these lamellar structures, found in cosmetic emulsions, probably is dependent of three main factors: the raw-materials, the amount of it used and the process of preparation, where should be cited, the temperature and the speed of agitation.
In cosmetics, other kinds of systems could be used, such as genuine liquid crystals aiming to explore its characteristics of controlled delivery systems.
There are different kinds of liquid crystals and different classifications, but this chapter has not the function of describe them, since it have been done by many authors (Bechtold, 2005; Formariz et al., 2005; Atkins and Jones, 2006), the aim was to demonstrate the importance of these structures in the maintenance of the cosmetics’ quality. Nevertheless, according to the literature data (Klein, 2002) and to our experience in this subject, it is possible to say that the lamellar arrangement is the most commonly found in cosmetic emulsions.
An initial analysis of the presence of liquid crystals in a cosmetic emulsion could be done using a polarized light microscope, but it should be confirmed and better analyzed by means of Small Angle X-Ray Scattering. When a microscope slide containing a sample of the system is studied and it presents structures that reflect the incident light, it is an evidence of the presence of liquid crystals (Figure 20). So, they should be submitted to SAXS analysis to confirm this expectation (Savic et al., 2011).
Figure 20.
Photomicrographs of liquid-crystal present in emulsions evaluated by polarized light microscope.
The SAXS method requires a synchrotron light source that is formed by means of a particle accelerator, and using a monochromatic beam, that is used to irradiate the sample. After that, the scattering of the rays in small angle should be analyzed (Glatter and Kratky, 1982; Urban, 2004; Koch, 2010). Liquid crystals can be analyzed by SAXS since they are able to disperse the X-rays focused on it. In the SAXS line is used an X-rays detector and an multichannel analyzer to capture the intense of the SAXS measures (I(q)) in function of the modulus of the scattering vector (q) (Glatter and Kratky, 1982; Molina et al., 2006; Koch, 2010).
Analyzing the data obtained (Figure 19), the d value obtained represents the distance between the particles able to scatter the X-rays. It is calculated by the equation: d = 2π / q max, where q max, is the maximum intensity of scattering (Craievich, 2002). The relation between the d values obtained indicates the type of arrangement found in the system (Glatter and Kratky, 1982; Craievich, 2002; Alexandridis et al., 1998).
Figure 21.
Hypothetical SAXS curve.
In the case of the hypothetical curve showed in Figure 19, d1 / d2 would result in 2, which describe lamellar structures (Alexandridis et al., 1998).
Beyond the advantages already mentioned, in a research conducted by Moaddel and Friberg (1995), the authors showed that the presence of lamellar liquid crystals in an emulsion avoids the water evaporation rate in this system, thus contributing in another way to the stability and maintenance of the cosmetic quality.
According to the advantages obtained with the presence of liquid crystals, these mesophases can be of great importance to the Cosmetic Industry in the development of very stable cosmetics and, the SAXS technique, an efficient tool to confirm the presence of these desirable structures that helps in the maintenance of cosmetics’ quality control.
Camerel et al. (2003) pointed the importance in correlate the microstructure of a colloidal suspension with its rheological behavior to define its better use in industry and in life, beyond that, according to these authors there are few reports correlating these analyses.
Our research group has invested in researches to assess the stability of cosmetics (Isaac et al., 2008); evaluating of the influence of the addition of thickening agents in creams using rheological measurements (Isaac et al., 2012a); evaluating the thickeners\' influence on the rheological properties of a cosmetic (Isaac et al., 2012b,c); proposing alternative methods to assay the efficacy and safety of them (Chiari et al., 2012a; Chiari et al., 2012b) and using of the sensory analysis in the cosmetics development (Isaac et al., 2012a) which, in different points of view of what was demonstrated in this chapter, also influence in the product quality.
5. Conclusion
This chapter aimed to show the facility that some simple or advanced techniques already used, sometimes to other finalities, could offer to the quality control of cosmetic products. The sensory analysis, rheology and SAXS technique have earned attention due to the important contribution that they can offer to the cosmetic area.
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Introduction",level:"1"},{id:"sec_2",title:"2. Sensory analysis",level:"1"},{id:"sec_3",title:"3. Rheology",level:"1"},{id:"sec_4",title:"4. Small Angle X-ray Scattering (SAXS)",level:"1"},{id:"sec_5",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Abdel-Rahem, R., Gradzielski, M., & Hoffmann, H. (2005). A novel viscoelastic system from a cationic surfactant and a hydrophobic counterion. Journal of Colloid and Interface Science, 288, 570–582.'},{id:"B2",body:'Alam, M.M., & Aramaki, K. (2009). Glycerol effects on the formation and rheology of hexagonal phase and related gel emulsion, J. Colloid Interface Sci., 336, 820-826.'},{id:"B3",body:'Alexandridis, P., Olsson, U., & Lindman, B. (1998). A record nine different phases (four cubic, two hexagonal, and one lamellar lyotropic liquid crystalline and two micellar solutions) in a ternary isothermal system of an amphiphilic block copolymer and selective solvents (water and oil). 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In: Laba, D. Rheological propertie of cosmetics and toiletries. New York: Marcel Dekker, 426 p. '},{id:"B55",body:'Oka, T., Miyahara, R., Teshigawara, T., & Watanabe, K. (2008). Development of novel cosmetic base using sterol surfactante. I. Preparation of novel emulsified particles with sterol surfactant. Journal of Oleo Science, 57(10), 567-575. '},{id:"B56",body:'Olshan, A.A., Kohut, B.E., Vincent, J.W., Borden, L.C., Delgado, N., Qaqish, J., Sharma, N.C., & Mcguire, J.A. (2000). Clinical effectiveness of essential oil-containing dentifrices in controlling oral malodor. American Journal of Dentistry, 13, 18C-22C.'},{id:"B57",body:'Ozkan, S., Gillece, T.W., Senak, L., & Moore, D.J. (2012). Characterization of yield stress and slip behaviour of skin/hair care gels using steady flow and LAOS measurements and their correlation with sensorial attributes. International Journal of Cosmetic Science, 34, 193–201.'},{id:"B58",body:'Parente, M.E., Ares, G., & Manzoni, A.V. (2010). 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(1980). Economic control of quality of manufactured product, American Society for Quality Control.'},{id:"B69",body:'Spiclin, P, Homar, M, Valant, A.Z., & Gasperlin, M. (2003). Sodium ascorbyl phosphate in topical microemulsions. International Journal of Pharmaceutics, 256, 65-73.'},{id:"B70",body:'Stone, H.S., & Sidel, J.L. (1992). Sensory evaluation practices. San Diego, CA: Academic Press.'},{id:"B71",body:'Tadros, T. (2004). Application of rheology for assessment and prediction of the long-term physical stability of emulsions. Advances in Colloid and Interface Science, 108 – 109, 227–258.'},{id:"B72",body:'Toro-Vazquez, J.F., Morales-Rueda, J., Ajay Mallia, V., & Weiss, R.G. (2010). Relationship between molecular structure and thermo-mechanical properties of candelilla wax and amides derived from (R)-12-hydroxystearic acid as gelators of safflower oil. Food Biophysics, 5, 193-202.'},{id:"B73",body:'Tuarez, E.P., Sadtler, V., Marchal, P, Choplin, L., & Salager, J.L. (2011). Making use of formulation-composition map to prepare highly concentrated emulsions with particular rheological properties. Ind. Eng. Chem. Res., 50, 2380-87.'},{id:"B74",body:'Urban, M. C. C. (2004). Desenvolvimento de sistemas de liberação micro e nanoestruturados para administração cutânea do acetato de dexametasona. 2004. Dissertação (Mestrado em Ciências Farmacêuticas) - Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista Júlio de Mesquita Filho, Araraquara.'},{id:"B75",body:'Vasiljevic, D., Parojcic, J., Primorac, M., & Vuleta, G. (2006). An investigation into the characteristics and drug release properties of multiple W/O/W emulsion systems containing low concentration of lipophilic polymeric emulsifier. International journal of Pharmaceutics, 309, 171-177.'},{id:"B76",body:'Welin-Berger, K., Neelissen, J.A.M., & Bergenstahl, B. (2001). The effect of rheological behavior of a topical anaesthetic formulation on the release and permeation rates of the active compound. European Journal of pharmaceutical Sciences, 13, 309-18.'},{id:"B77",body:'Wortel, V.A.L., & Wiechers, J.W. (2000). Skin sensory performance of individual personal care ingredients and marketed personal care products. Food Qual. Pref., 11(1-2), 121-127.'},{id:"B78",body:'Zague, V., Nishikawa, D.O., Silva, D.A., Baby, A.R., Behrens, J.H., Kaneko, T.M., & Velasco, M.V.R. (2008). Influence of storage temperature on cooling intensity of topical emulsions containing encapsulated menthol. J. Sens. Stud., 23(1), 26-34.'},{id:"B79",body:'Zenebon, O., Pascuet, N.S., & Tiglea, P. (2008). Métodos físico-químicos para análise de alimentos. Instituto Adolfo Lutz (São Paulo). On line version. Available at: http://www.crq4.org.br/sms/files/file/analisedealimentosial_2008.pdf'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Bruna Galdorfini Chiari",address:null,affiliation:'
Faculdade de Ciências Farmacêuticas, UNESP - Univ Estadual Paulista, Departamento de Fármacos e Medicamentos, Laboratório de Cosmetologia – LaCos, Araraquara, Laboratório de Cosmetologia, São Paulo, Brazil
'},{corresp:null,contributorFullName:"Maria Gabriela José de Almeida",address:null,affiliation:'
Faculdade de Ciências Farmacêuticas, UNESP - Univ Estadual Paulista, Departamento de Fármacos e Medicamentos, Laboratório de Cosmetologia – LaCos, Araraquara, Laboratório de Cosmetologia, São Paulo, Brazil
'},{corresp:null,contributorFullName:"Marcos Antonio Corrêa",address:null,affiliation:'
Faculdade de Ciências Farmacêuticas, UNESP - Univ Estadual Paulista, Departamento de Fármacos e Medicamentos, Laboratório de Cosmetologia – LaCos, Araraquara, Laboratório de Cosmetologia, São Paulo, Brazil
Faculdade de Ciências Farmacêuticas, UNESP - Univ Estadual Paulista, Departamento de Fármacos e Medicamentos, Laboratório de Cosmetologia – LaCos, Araraquara, Laboratório de Cosmetologia, São Paulo, Brazil
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Zwitser",slug:"eline-w.-zwitser"}]},{id:"23740",title:"Future Applications of Electronic-Nose Technologies in Healthcare and Biomedicine",slug:"future-applications-of-electronic-nose-technologies-in-healthcare-and-biomedicine",signatures:"Alphus Dan Wilson",authors:[{id:"40680",title:"Dr",name:"Alphus",middleName:"Dan",surname:"Wilson",fullName:"Alphus Wilson",slug:"alphus-wilson"}]},{id:"23741",title:"Quality Assurance and Quality Control of Equipment in Diagnostic Radiology Practice-The Ghanaian Experience",slug:"quality-assurance-and-quality-control-of-equipment-in-diagnostic-radiology-practice-the-ghanaian-exp",signatures:"Stephen Inkoom, Cyril Schandorf, Geoffrey Emi-Reynolds and John Justice Fletcher",authors:[{id:"48333",title:"Mr",name:"Stephen",middleName:null,surname:"Inkoom",fullName:"Stephen Inkoom",slug:"stephen-inkoom"},{id:"55912",title:"Prof.",name:"Cyril",middleName:null,surname:"Schandorf",fullName:"Cyril Schandorf",slug:"cyril-schandorf"},{id:"55913",title:"Prof.",name:"Geoffrey",middleName:null,surname:"Emi-Reynolds",fullName:"Geoffrey Emi-Reynolds",slug:"geoffrey-emi-reynolds"},{id:"55914",title:"Prof.",name:"John",middleName:null,surname:"Fletcher",fullName:"John Fletcher",slug:"john-fletcher"}]},{id:"23742",title:"Pressure-Sensitive Adhesives for Medical Applications",slug:"pressure-sensitive-adhesives-for-medical-applications",signatures:"Zbigniew Czech and Agnieszka Kowalczyk",authors:[{id:"53922",title:"Prof.",name:"Zbigniew",middleName:null,surname:"Czech",fullName:"Zbigniew Czech",slug:"zbigniew-czech"},{id:"94549",title:"Dr.",name:"Agnieszka",middleName:null,surname:"Kowalczyk",fullName:"Agnieszka Kowalczyk",slug:"agnieszka-kowalczyk"},{id:"94550",title:"Dr.",name:"Jolanta",middleName:null,surname:"Swiderska",fullName:"Jolanta Swiderska",slug:"jolanta-swiderska"}]},{id:"23743",title:"The Role of Empirical Rheology in Flour Quality Control",slug:"the-role-of-empirical-rheology-in-flour-quality-control",signatures:"Tamara Dapčević Hadnađev, Milica Pojić, Miroslav Hadnađev and Aleksandra Torbica",authors:[{id:"55755",title:"MSc",name:"Tamara",middleName:null,surname:"Dapčević Hadnađev",fullName:"Tamara Dapčević Hadnađev",slug:"tamara-dapcevic-hadnadjev"},{id:"55801",title:"Dr.",name:"Milica",middleName:null,surname:"Pojić",fullName:"Milica Pojić",slug:"milica-pojic"},{id:"55802",title:"MSc.",name:"Miroslav",middleName:null,surname:"Hadnađev",fullName:"Miroslav Hadnađev",slug:"miroslav-hadnadjev"},{id:"55803",title:"Dr.",name:"Aleksandra",middleName:null,surname:"Torbica",fullName:"Aleksandra Torbica",slug:"aleksandra-torbica"}]},{id:"23744",title:"Sensory Analysis in Quality Control: The Gin as an Example",slug:"sensory-analysis-in-quality-control-the-gin-as-an-example",signatures:"Montserrat Riu Aumatell",authors:[{id:"43906",title:"Dr.",name:"Montserrat",middleName:null,surname:"Riu-Aumatell",fullName:"Montserrat Riu-Aumatell",slug:"montserrat-riu-aumatell"}]},{id:"23745",title:"Spectral Imaging as a Tool in Food Research and Quality Monitoring of Food Production",slug:"spectral-imaging-as-a-tool-in-food-research-and-quality-monitoring-of-food-production",signatures:"Stina Frosch, Bjørn Skovlund Dissing, Jens Adler-Nissen and Michael Engelbrecht Nielsen",authors:[{id:"55298",title:"Dr.",name:"Stina",middleName:null,surname:"Frosch",fullName:"Stina Frosch",slug:"stina-frosch"},{id:"55301",title:"Dr.",name:"Michael",middleName:null,surname:"Engelbrecth Nielsen",fullName:"Michael Engelbrecth Nielsen",slug:"michael-engelbrecth-nielsen"},{id:"55302",title:"Prof.",name:"Jens",middleName:null,surname:"Adler-Nissen",fullName:"Jens Adler-Nissen",slug:"jens-adler-nissen"},{id:"118120",title:"MSc.",name:"Bjørn Skovlund",middleName:null,surname:"Dissing",fullName:"Bjørn Skovlund Dissing",slug:"bjorn-skovlund-dissing"}]},{id:"23746",title:"Mass Rearing and Quality Control Parameters for Tephritid Fruit Flies of Economic Importance in Africa",slug:"mass-rearing-and-quality-control-parameters-for-tephritid-fruit-flies-of-economic-importance-in-afri",signatures:"Sunday Ekesi and Samira A. Mohamed",authors:[{id:"42713",title:"Dr.",name:"Sunday",middleName:null,surname:"Ekesi",fullName:"Sunday Ekesi",slug:"sunday-ekesi"},{id:"53233",title:"Dr.",name:"Samira A.",middleName:null,surname:"Mohamed",fullName:"Samira A. Mohamed",slug:"samira-a.-mohamed"}]},{id:"23747",title:"Quality Control of Baculoviral Bioinsecticide Production",slug:"quality-control-of-baculoviral-bioinsecticide-production",signatures:"Solange Ana Belén Miele, Mariano Nicolás Belaich and Pablo Daniel Ghiringhelli",authors:[{id:"52118",title:"Dr.",name:"Pablo Daniel",middleName:null,surname:"Ghiringhelli",fullName:"Pablo Daniel Ghiringhelli",slug:"pablo-daniel-ghiringhelli"},{id:"54727",title:"Mrs.",name:"Solange Ana Belen",middleName:null,surname:"Miele",fullName:"Solange Ana Belen Miele",slug:"solange-ana-belen-miele"},{id:"54728",title:"Dr.",name:"Mariano Nicolas",middleName:null,surname:"Belaich",fullName:"Mariano Nicolas Belaich",slug:"mariano-nicolas-belaich"}]},{id:"23748",title:"Quality Control and Characterization of Scintillating Crystals for High Energy Physics and Medical Applications",slug:"quality-control-and-characterization-of-scintillating-crystals-for-high-energy-physics-and-medical-a",signatures:"Daniele Rinaldi, Michel Lebeau, Nicola Paone, Lorenzo Scalise and Paolo Pietroni",authors:[{id:"49866",title:"Ph.D.",name:"Lorenzo",middleName:null,surname:"Scalise",fullName:"Lorenzo Scalise",slug:"lorenzo-scalise"},{id:"54920",title:"Prof.",name:"Daniele",middleName:null,surname:"Rinaldi",fullName:"Daniele Rinaldi",slug:"daniele-rinaldi"},{id:"59026",title:"Dr.",name:"Michel",middleName:null,surname:"Lebeau",fullName:"Michel Lebeau",slug:"michel-lebeau"},{id:"59027",title:"Prof.",name:"Nicola",middleName:null,surname:"Paone",fullName:"Nicola Paone",slug:"nicola-paone"},{id:"78731",title:"Dr.",name:"Paolo",middleName:null,surname:"Pietroni",fullName:"Paolo Pietroni",slug:"paolo-pietroni"}]},{id:"23749",title:"Effect of Last Generation Additives on the Concrete Durability",slug:"effect-of-last-generation-additives-on-the-concrete-durability",signatures:"Ana M. Carvajal, M. Soledad Gómez, Pablo Maturana and Raul Molina",authors:[{id:"42072",title:"Prof.",name:"Ana",middleName:"MarÃa",surname:"Carvajal",fullName:"Ana Carvajal",slug:"ana-carvajal"},{id:"42100",title:"Prof.",name:"Pablo",middleName:null,surname:"Maturana",fullName:"Pablo Maturana",slug:"pablo-maturana"},{id:"84065",title:"Prof.",name:"Soledad",middleName:null,surname:"Gomez",fullName:"Soledad Gomez",slug:"soledad-gomez"},{id:"84067",title:"Mr.",name:"Raul",middleName:null,surname:"Molina",fullName:"Raul Molina",slug:"raul-molina"}]},{id:"23750",title:"A Convenient and Inexpensive Quality Control Method for Examining the Accuracy of Conjugate Cam Profiles",slug:"a-convenient-and-inexpensive-quality-control-method-for-examining-the-accuracy-of-conjugate-cam-prof",signatures:"Wen-Tung Chang and Long-Iong Wu",authors:[{id:"43114",title:"Prof.",name:"Wen-Tung",middleName:null,surname:"Chang",fullName:"Wen-Tung Chang",slug:"wen-tung-chang"},{id:"51808",title:"Prof.",name:"Long-Iong",middleName:null,surname:"Wu",fullName:"Long-Iong Wu",slug:"long-iong-wu"}]},{id:"23751",title:"Material Characterization and Failure Analysis for Microelectronics Assembly Processes",slug:"material-characterization-and-failure-analysis-for-microelectronics-assembly-processes",signatures:"Chien-Yi Huang, Ming-Shu Li, Shan-Yu Huang, Cheng-I Chang and Min-Hui Huang",authors:[{id:"52412",title:"Dr.",name:"Ming-Shu",middleName:null,surname:"Li",fullName:"Ming-Shu Li",slug:"ming-shu-li"},{id:"52426",title:"Prof.",name:"Chien-Yi",middleName:"Jay",surname:"Huang",fullName:"Chien-Yi Huang",slug:"chien-yi-huang"},{id:"98841",title:"MSc.",name:"Cheng-I",middleName:null,surname:"Chang",fullName:"Cheng-I Chang",slug:"cheng-i-chang"},{id:"98978",title:"BSc.",name:"Min Hui",middleName:null,surname:"Huang",fullName:"Min Hui Huang",slug:"min-hui-huang"},{id:"99353",title:"MSc.",name:"Shan-Yu",middleName:null,surname:"Huang",fullName:"Shan-Yu Huang",slug:"shan-yu-huang"}]}]}],publishedBooks:[{type:"book",id:"1924",title:"Quality Management and Practices",subtitle:null,isOpenForSubmission:!1,hash:"5cbd92bd3b7fbecef4c7ec04d7ef0b0b",slug:"quality-management-and-practices",bookSignature:"Kim-Soon Ng",coverURL:"https://cdn.intechopen.com/books/images_new/1924.jpg",editedByType:"Edited by",editors:[{id:"109304",title:"Prof.",name:"Kim-Soon",surname:"Ng",slug:"kim-soon-ng",fullName:"Kim-Soon Ng"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3276",title:"Latest Research into Quality Control",subtitle:null,isOpenForSubmission:!1,hash:"49d9b76f5f77b0ad5568b7f6b0feceb1",slug:"latest-research-into-quality-control",bookSignature:"Isin Akyar",coverURL:"https://cdn.intechopen.com/books/images_new/3276.jpg",editedByType:"Edited by",editors:[{id:"36323",title:"Dr.",name:"Isin",surname:"Akyar",slug:"isin-akyar",fullName:"Isin Akyar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],publishedBooksByAuthor:[{type:"book",id:"3276",title:"Latest Research into Quality Control",subtitle:null,isOpenForSubmission:!1,hash:"49d9b76f5f77b0ad5568b7f6b0feceb1",slug:"latest-research-into-quality-control",bookSignature:"Isin Akyar",coverURL:"https://cdn.intechopen.com/books/images_new/3276.jpg",editedByType:"Edited by",editors:[{id:"36323",title:"Dr.",name:"Isin",surname:"Akyar",slug:"isin-akyar",fullName:"Isin Akyar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},onlineFirst:{chapter:{type:"chapter",id:"65993",title:"Automatic Speech Emotion Recognition Using Machine Learning",doi:"10.5772/intechopen.84856",slug:"automatic-speech-emotion-recognition-using-machine-learning",body:'\n
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1. Introduction
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Emotion plays a significant role in daily interpersonal human interactions. This is essential to our rational as well as intelligent decisions. It helps us to match and understand the feelings of others by conveying our feelings and giving feedback to others. Research has revealed the powerful role that emotion play in shaping human social interaction. Emotional displays convey considerable information about the mental state of an individual. This has opened up a new research field called automatic emotion recognition, having basic goals to understand and retrieve desired emotions. In prior studies, several modalities have been explored to recognize the emotional states such as facial expressions [1], speech [2], physiological signals [3], etc. Several inherent advantages make speech signals a good source for affective computing. For example, compared to many other biological signals (e.g., electrocardiogram), speech signals usually can be acquired more readily and economically. This is why the majority of researchers are interested in speech emotion recognition (SER). SER aims to recognize the underlying emotional state of a speaker from her voice. The area has received increasing research interest all through current years. There are many applications of detecting the emotion of the persons like in the interface with robots, audio surveillance, web-based E-learning, commercial applications, clinical studies, entertainment, banking, call centers, cardboard systems, computer games, etc. For classroom orchestration or E-learning, information about the emotional state of students can provide focus on the enhancement of teaching quality. For example, a teacher can use SER to decide what subjects can be taught and must be able to develop strategies for managing emotions within the learning environment. That is why learner’s emotional state should be considered in the classroom.
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Three key issues need to be addressed for successful SER system, namely, (1) choice of a good emotional speech database, (2) extracting effective features, and (3) designing reliable classifiers using machine learning algorithms. In fact, the emotional feature extraction is a main issue in the SER system. Many researchers [4] have proposed important speech features which contain emotion information, such as energy, pitch, formant frequency, Linear Prediction Cepstrum Coefficients (LPCC), Mel-frequency cepstrum coefficients (MFCC), and modulation spectral features (MSFs) [5]. Thus, most researchers prefer to use combining feature set that is composed of many kinds of features containing more emotional information [6]. However, using a combining feature set may give rise to high dimension and redundancy of speech features; thereby, it makes the learning process complicated for most machine learning algorithms and increases the likelihood of overfitting. Therefore, feature selection is indispensable to reduce the dimensions redundancy of features. A review for feature selection models and techniques is presented in [7]. Both feature extraction and feature selection are capable of improving learning performance, lowering computational complexity, building better generalizable models, and decreasing required storage. The last step of speech emotion recognition is classification. It involves classifying the raw data in the form of utterance or frame of the utterance into a particular class of emotion on the basis of features extracted from the data. In recent years in speech emotion recognition, researchers proposed many classification algorithms, such as Gaussian mixture model (GMM) [8], hidden Markov model (HMM) [9], support vector machine (SVM) [10, 11, 12, 13, 14], neural networks (NN) [15], and recurrent neural networks (RNN) [16, 17, 18]. Some other types of classifiers are also proposed by some researchers such as a modified brain emotional learning model (BEL) [19] in which the adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) are merged for speech emotion recognition. Another proposed strategy is a multiple kernel Gaussian process (GP) classification [17], in which two similar notions in the learning algorithm are presented by combining the linear kernel and radial basis function (RBF) kernel. The Voiced Segment Selection (VSS) algorithm also proposed in [20] deals with the voiced signal segment as the texture image processing feature which is different from the traditional method. It uses the Log-Gabor filters to extract the voiced and unvoiced features from spectrogram to make the classification.
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In previous work [21], we present a system for the recognition of «seven acted emotional states (anger, disgust, fear, joy, sadness, and surprise)». To do that, we extracted the MFCC and MS features and used them to train three different machine learning paradigms (MLR, SVM, and RNN). We demonstrated that the combination of both features has a high accuracy above 94% on the Spanish database. All previously published works generally use the Berlin database. To our knowledge, the Spanish emotional database has never been used before. For this reason, we have chosen to compare them. In this chapter, we concentrate to improve accuracy; more experiments have been performed. This chapter mainly makes the following contributions:
The effect of speaker normalization (SN) is also studied, which removes the mean of features and normalizes them to unit variance. Experiments are performed under a speaker-independent condition.
Additionally, a feature selection technique is assessed to obtain good features from the set of features extracted in [21].
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The rest of the chapter is organized as follows. In the next section, we start by introducing the nature of speech emotions. Section 3 describes features we extracted from a speech signal. A feature selection method and machine learning algorithms used for SER are presented. Section 4 reports on the databases we used and presents the simulation results obtained using different features and different machine learning (ML) paradigms. Section 5 closes this chapter by analyses and conclusion.
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2. Emotion and classification
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This section is concerned with defining the term emotion, presenting its different models. Also for recognizing emotions, there are several techniques and inputs that can be used. A brief description of all of the techniques is presented here.
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2.1 Definition
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A definition is both important and difficult because the everyday word “emotion” is a notoriously fluid term in meaning. Emotion is one of the most difficult concepts to define in psychology. In fact, there are different definitions of emotions in the scientific literature. In everyday speech, emotion is any relatively brief conscious experience characterized by intense mental activity and a high degree of pleasure or displeasure [22, 23]. Scientific discourse has drifted to other meanings and there is no consensus on a definition. Emotion is often entwined with temperament, mood, personality, motivation, and disposition. In psychology, emotion is frequently defined as a complex state of feeling that results in physical and psychological changes. These changes influence thought and behavior. According to other theories, emotions are not causal forces but simply syndromes of components such as motivation, feeling, behavior, and physiological changes [24]. In 1884, in What is an emotion? [25], American psychologist and philosopher William James proposed a theory of emotion whose influence was considerable. According to his thesis, the feeling of intense emotion corresponds to the perception of specific bodily changes. This approach is found in many current theories: the bodily reaction is the cause and not the consequence of the emotion. The scope of this theory is measured by the many debates it provokes. This illustrates the difficulty of agreeing on a definition of this dynamic and complex phenomenon that we call emotion. “Emotion” refers to a wide range of affective processes such as moods, feelings, affects, and well-being [26]. The term “emotion” in [6] has been also referred to an extremely complex state associated with a wide variety of mental, physiological, and physical events.
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2.2 Categorization of emotions
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The categorization of emotions has long been a hot subject of debate in different fields of psychology, affective science, and emotion research. It is mainly based on two popular approaches: categorical (termed discrete) and dimensional (termed continuous). In the first approach, emotions are described with a discrete number of classes. Many theorists have conducted studies to determine which emotions are basic [27]. A most popular example is Ekman [28] who proposed a list of six basic emotions, which are anger, disgust, fear, happiness, sadness, and surprise. He explains that each emotion acts as a discrete category rather than an individual emotional state. In the second approach, emotions are a combination of several psychological dimensions and identified by axes. Other researchers define emotions according to one or more dimensions. Wilhelm Max Wundt proposed in 1897 that emotions can be described by three dimensions: (1) strain versus relaxation, (2) pleasurable versus unpleasurable, and (3) arousing versus subduing [29]. PAD emotional state model is another three-dimensional approach by Albert Mehrabian and James Russell where PAD stands for pleasure, arousal, and dominance. Another popular dimensional model was proposed by James Russell in 1977. Unlike the earlier three-dimensional models, Russell’s model features only two dimensions which include (1) arousal (or activation) and (2) valence (or evaluation) [29].
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The categorical approach is commonly used in SER [30]. It characterizes emotions used in everyday emotion words such as joy and anger. In this work, a set of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) plus neutral, corresponding to the six emotions of Ekman’s model, were used for the recognition of emotion from speech using the categorical approach.
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2.3 Sensory modalities for emotion expression
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There is vigorous debate about what exactly individual can express nonverbally. Humans can express their emotions through many different types of nonverbal communication including facial expressions, quality of speech produced, and physiological signals of the human body. In this section, we discuss each of these categories.
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2.3.1 Facial expressions
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The human face is extremely expressive, able to express countless emotions without saying a word [31]. And unlike some forms of nonverbal communication, facial expressions are universal. The facial expressions for happiness, sadness, anger, surprise, fear, and disgust are the same across cultures.
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2.3.2 Speech
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In addition to faces, voices are an important modality for emotional expression. Speech is a relevant communicational channel enriched with emotions: the voice in speech not only conveys a semantic message but also the information about the emotional state of the speaker. Some important voice feature vectors that have been chosen for research such as fundamental frequency, mel-frequency cepstral coefficient (MFCC), prediction cepstral coefficient (LPCC), etc.
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2.3.3 Physiological signals
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The physiological signals related to autonomic nervous system allow to assess objectively emotions. These include electroencephalogram (EEG), heart rate (HR), electrocardiogram (ECG), respiration (RSP), blood pressure (BP), electromyogram (EMG), skin conductance (SC), blood volume pulse (BVP), and skin temperature (ST) [32]. Using physiological signals to recognize emotions is also helpful to those people who suffer from physical or mental illness thus exhibit problems with facial expressions or tone of voice.
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3. Speech emotion recognition (SER) system
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3.1 Block diagram
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Our SER system consists of four main steps. First is the voice sample collection. The second features vector that is formed by extracting the features. As the next step, we tried to determine which features are most relevant to differentiate each emotion. These features are introduced to machine learning classifier for recognition. This process is described in Figure 1.
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Figure 1.
Block diagram of the proposed system.
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3.2. Feature extraction
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The speech signal contains a large number of parameters that reflect the emotional characteristics. One of the sticking points in emotion recognition is what features should be used. In recent research, many common features are extracted, such as energy, pitch, formant, and some spectrum features such as linear prediction coefficients (LPC), mel-frequency cepstrum coefficients (MFCC), and modulation spectral features. In this work, we have selected modulation spectral features and MFCC, to extract the emotional features.
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Mel-frequency cepstrum coefficient (MFCC) is the most used representation of the spectral property of voice signals. These are the best for speech recognition as it takes human perception sensitivity with respect to frequencies into consideration. For each frame, the Fourier transform and the energy spectrum were estimated and mapped into the Mel-frequency scale. The discrete cosine transform (DCT) of the Mel log energies was estimated, and the first 12 DCT coefficients provided the MFCC values used in the classification process. Usually, the process of calculating MFCC is shown in Figure 2.
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Figure 2.
Schema of MFCC extraction [33].
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In our research, we extract the first 12 order of the MFCC coefficients where the speech signals are sampled at 16 KHz. For each order coefficients, we calculate the mean, variance, standard deviation, kurtosis, and skewness, and this is for the other all the frames of an utterance. Each MFCC feature vector is 60-dimensional.
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Modulation spectral features (MSFs) are extracted from an auditory-inspired long-term spectro-temporal representation. These features are obtained by emulating the spectro-temporal (ST) processing performed in the human auditory system and consider regular acoustic frequency jointly with modulation frequency. The steps for computing the ST representation are illustrated in Figure 3. In order to obtain the ST representation, the speech signal is first decomposed by an auditory filterbank (19 filters in total). The Hilbert envelopes of the critical-band outputs are computed to form the modulation signals. A modulation filterbank is further applied to the Hilbert envelopes to perform frequency analysis. The spectral contents of the modulation signals are referred to as modulation spectra, and the proposed features are thereby named modulation spectral features (MSFs) [5]. Lastly, the ST representation is formed by measuring the energy of the decomposed envelope signals, as a function of regular acoustic frequency and modulation frequency. The energy, taken over all frames in every spectral band, provides a feature. In our experiment, an auditory filterbank with \n\nN\n=\n19\n\n filters and a modulation filterbank with \n\nM\n=\n5\n\n filters are used. In total, 95 \n\n\n\n19\n×\n5\n\n\n\n MSFs are calculated in this work from the ST representation.
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Figure 3.
Process for computing the ST representation [5].
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3.3 Feature selection
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As reported by Aha and Bankert [34], the objective of feature selection in ML is to “reduce the number of features used to characterize a dataset so as to improve a learning algorithm’s performance on a given task.” The objective will be the maximization of the classification accuracy in a specific task for a certain learning algorithm; as a collateral effect, the number of features to induce the final classification model will be reduced. Feature selection (FS) aims to choose a subset of the relevant features from the original ones according to certain relevance evaluation criterion, which usually leads to higher recognition accuracy [35]. It can drastically reduce the running time of the learning algorithms. In this section, we present an effective feature selection method used in our work, named recursive feature elimination with linear regression (LR-RFE).
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Recursive feature elimination (RFE) uses a model (e.g., linear regression or SVM) to select either the best- or worst-performing feature and then excludes this feature. These estimators assign weights to features (e.g., the coefficients of a linear model), so the goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features, and the predictive power of each feature is measured [36]. Then, the least important features are removed from the current set of features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached. In this work, we implemented the recursive feature elimination method of feature ranking via the use of basic linear regression (LR-RFE) [37]. Other research also uses RFE with another linear model such as SVM-RFE that is an SVM-based feature selection algorithm created by [38]. Using SVM-RFE, Guyon et al. selected key and important feature sets. In addition to improving the classification accuracy rate, it can reduce classification computational time.
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3.4 Classification methods
\n
Many machine learning algorithms have been used for discrete emotion classification. The goal of these algorithms is to learn from the training samples and then use this learning to classify new observation. In fact, there is no definitive answer to the choice of the learning algorithm; every technique has its own advantages and limitations. For this reason, here we chose to compare the performance of three different classifiers.
\n
Multivariate linear regression classification (MLR) is a simple and efficient computation of machine learning algorithms, and it can be used for both regression and classification problems. We have slightly modified the LRC algorithm described as follow Algorithm 1 [39]. We calculated (in step 3) the absolute value of the difference between original and predicted response vectors (\n\n∣\ny\n−\n\ny\ni\n\n∣\n\n), instead of the Euclidean distance between them (\n\n‖\ny\n−\n\ny\ni\n\n‖\n\n).
\n
Support vector machines (SVM) are an optimal margin classifier in machine learning. It is also used extensively in many studies that related to audio emotion recognition which can be found in [10, 13, 14]. It can have a very good classification performance compared to other classifiers especially for limited training data [11]. SVM theoretical background can be found in [40]. A MATLAB toolbox implementing SVM is freely available in [41]. A polynomial kernel is investigated in this work.
\n
Algorithm 1. Linear Regression Classification (LRC)
\n
Inputs: Class models \n\n\nX\ni\n\n∈\n\nR\n\nq\n×\n\np\ni\n\n\n\n,\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n and a test speech vector \n\ny\n∈\n\nR\n\nq\n×\n1\n\n\n\n
\n
Output: Class of \n\ny\n\n
\n
1. \n\n\n\nβ\n̂\n\ni\n\n∈\n\nR\n\n\np\ni\n\n×\n1\n\n\n\n is evaluated against each class model, \n\n\n\nβ\n̂\n\ni\n\n=\n\n\n\n\nX\ni\nT\n\n\nX\ni\n\n\n\n\n\n−\n1\n\n\n\n\nX\ni\nT\n\ny\n,\n\n\n\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n
\n
2. \n\n\n\ny\n̂\n\ni\n\n\n is computed for each \n\n\n\nβ\n̂\n\ni\n\n,\n\n\ny\n̂\n\ni\n\n=\n\nX\ni\n\n\n\nβ\n̂\n\ni\n\n,\n\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n;
\n
3. Distance calculation between original and predicted response variables \n\n\nd\ni\n\n\ny\n\n=\n∣\ny\n−\n\ny\ni\n\n∣\n,\n\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n;
\n
4. Decision is made in favor of the class with the minimum distance \n\n\nd\ni\n\n\ny\n\n\n
\n
Recurrent neural networks (RNN) are suitable for learning time series data, and it has shown improved performance for classification task [42]. While RNN models are effective at learning temporal correlations, they suffer from the vanishing gradient problem which increases with the length of the training sequences. To resolve this problem, long short-term memory (LSTM) RNNs were proposed by Hochreiter et al. [43]; it uses memory cells to store information so that it can exploit long-range dependencies in the data [17].
\n
Figure 4 shows a basic concept of RNN implementation. Unlike traditional neural network that uses different parameters at each layer, the RNN shares the same parameters (U, V, and W are presented in Figure 4) across all steps. The hidden state formulas and variables are as follows:
\n
Figure 4.
A basic concept of RNN and unfolding in time of the computation involved in its forward computation [18].
where \n\n\nx\nt\n\n\n, \n\n\ns\nt\n\n\n, and \n\n\no\nt\n\n\n are respectively the input, the hidden state, and the output at time step t and \n\nU\n,\nV\n,\nW\n\n are parameters matrices.
\n
\n
\n
\n
4. Experimental results and analysis
\n
\n
4.1 Emotional speech databases
\n
The performance and robustness of the recognition systems will be easily affected if it is not well trained with a suitable database. Therefore, it is essential to have sufficient and suitable phrases in the database to train the emotion recognition system and subsequently evaluate its performance. There are three main types of databases: acted emotions, natural spontaneous emotions, and elicited emotions [27, 44]. In this work, we used an acted emotion databases because they contain strong emotional expressions. The literature on speech emotion recognition [45] shows that the majority of studies have been conducted with emotional acted speech. In this section, we detailed the two emotional speech databases used for classifying discrete emotions in our experiments: Berlin Database and Spanish Database.
\n
\n
\n
4.2 Berlin database
\n
The Berlin database [46] is widely used in emotional speech recognition. It contains 535 utterances spoken by 10 actors (5 female, 5 male) in 7 simulated emotions (anger, boredom, disgust, fear, joy, sadness, and neutral). This database was chosen for the following reasons: (i) the quality of its recording is very good, and (ii) it is public [47] and popular database of emotion recognition that is recommended in the literature [19].
\n
\n
\n
4.3 Spanish database
\n
The INTER1SP Spanish emotional database contains utterances from two professional actors (one female and one male speaker).The Spanish corpus that we have the right to access (free for academic and research use) [48] was recorded twice in the «six basic emotions plus neutral (anger, sadness, joy, fear, disgust, surprise and neutral/normal)». Four additional neutral variations (soft, loud, slow, and fast) were recorded once. This is preferred to other created database because it is available for researchers use and it contains more data (6041 utterances in total). This paper has focused on only seven main emotions from the Spanish database in order to achieve a higher and more accurate rate of recognition and to make the comparison with the Berlin database detailed above.
\n
\n
\n
4.4 Results and analysis
\n
In this section, experimentation results are presented and discussed. We report the recognition accuracy of using MLR, SVM, and RNN classifiers. Experimental evaluation is performed on the Berlin and Spanish databases. All classification results are obtained under tenfold cross-validation. Cross-validation is a common practice used in performance analysis that randomly partitions the data into N complementary subsets, with \n\nN\n−\n1\n\n of them used for training in each validation and the remaining one used for testing. The neural network structure used is a simple LSTM. It consists of two consecutive LSTM layers with hyperbolic tangent activation followed by two classification dense layers. Features from data are scaled to \n\n\n\n−\n1\n\n1\n\n\n before applying classifiers. Scaling features before recognition is important, because when a learning phase is fit on unscaled data, it is possible for large inputs to slow down the learning and convergence and in some cases prevent the used classifier from effectively learning for the classification problem. The effect of speaker normalization (SN) step prior to recognition is investigated, and there are three different SN schemes that are defined in [6]. SN is useful to compensate for the variations due to speaker diversity rather than the change of emotional state. We used in this section the SN scheme that has given the best results in [6]. The features of each speaker are normalized with a mean of \n\n0\n\n and a standard deviation of \n\n1\n\n. Tables 1, 2, 3 show the recognition rate for each combination of various features and classifiers based on Berlin and Spanish databases. These experiments use feature set without feature selection. As shown in Table 1, SVM classifier yields better results above 81%, with feature combination of MFCC and MS for Berlin database. Our results have improved compared to previous results in [21] because we changed the SVM parameters for each type of features to develop a good model.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
\n
\n
\n
Recognition rate (%)
\n
\n
\n
\n
Test
\n
Feature
\n
Method
\n
SN
\n
A
\n
E
\n
F
\n
L
\n
N
\n
T
\n
W
\n
AVG.
\n
(\n\nσ\n\n)
\n
\n\n\n
\n
#1
\n
MS
\n
MLR
\n
No
\n
45.90
\n
45.72
\n
48.78
\n
77.08
\n
59.43
\n
79.91
\n
75.94
\n
66.23
\n
(5.85)
\n
\n
\n
MFCC
\n
56.55
\n
62.28
\n
45.60
\n
54.97
\n
57.35
\n
74.36
\n
91.37
\n
64.70
\n
(3.20)
\n
\n
\n
MFCC+SM
\n
70.26
\n
73.04
\n
51.95
\n
82.44
\n
69.55
\n
82.49
\n
76.55
\n
73.00
\n
(3.23)
\n
\n
\n
#2
\n
MS
\n
SVM
\n
No
\n
56.61
\n
54.78
\n
51.17
\n
70.98
\n
67.32
\n
67.50
\n
73.13
\n
70.63
\n
(6.45)
\n
\n
\n
MFCC
\n
73.99
\n
64.14
\n
64.76
\n
55.30
\n
62.28
\n
84.13
\n
83.13
\n
71.70
\n
(4.24)
\n
\n
\n
MFCC+SM
\n
82.03
\n
68.70
\n
69.09
\n
79.16
\n
76.99
\n
80.89
\n
80.63
\n
81.10
\n
(2.73)
\n
\n
\n
#3
\n
MS
\n
MLR
\n
Yes
\n
48.98
\n
35.54
\n
32.66
\n
80.35
\n
55.54
\n
88.79
\n
85.77
\n
64.20
\n
(5.27)
\n
\n
\n
MFCC
\n
59.71
\n
59.72
\n
48.65
\n
67.10
\n
67.98
\n
91.73
\n
87.51
\n
71.00
\n
(4.19)
\n
\n
\n
MFCC+SM
\n
72.32
\n
68.82
\n
51.98
\n
82.60
\n
81.72
\n
91.96
\n
80.71
\n
75.25
\n
(2.49)
\n
\n
\n
#4
\n
MS
\n
SVM
\n
Yes
\n
62.72
\n
49.44
\n
37.29
\n
76.14
\n
71.30
\n
88.44
\n
80.15
\n
71.90
\n
(2.38)
\n
\n
\n
MFCC
\n
70.68
\n
56.55
\n
56.99
\n
59.88
\n
68.14
\n
91.88
\n
85.44
\n
77.60
\n
(4.35)
\n
\n
\n
MFCC+SM
\n
77.37
\n
69.67
\n
58.16
\n
79.87
\n
88.57
\n
98.75
\n
86.64
\n
81.00
\n
(2.45)
\n
\n\n
Table 1.
Recognition results with MS, MFCC features, and their combination on Berlin database; AVG. denotes average recognition rate; \n\nσ\n\n denotes standard deviation of the 10-cross-validation accuracies.
Berlin (a, fear; e, disgust; f, happiness; l, boredom; n, neutral; t, sadness; w, anger).
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
\n
\n
\n
Recognition rate (%)
\n
\n
\n
\n
\n
Test
\n
Feature
\n
Method
\n
SN
\n
A
\n
D
\n
F
\n
J
\n
N
\n
S
\n
T
\n
AVG.
\n
(\n\nσ\n\n)
\n
\n\n\n
\n
#1
\n
MS
\n
MLR
\n
No
\n
67.72
\n
44.04
\n
68.78
\n
46.95
\n
89.58
\n
63.10
\n
78.49
\n
69.22
\n
(1.37)
\n
\n
\n
MFCC
\n
67.85
\n
61.41
\n
75.97
\n
60.17
\n
95.79
\n
71.89
\n
84.94
\n
77.21
\n
(0.76)
\n
\n
\n
MFCC+SM
\n
78.75
\n
78.18
\n
80.68
\n
63.84
\n
96.80
\n
82.44
\n
89.01
\n
83.55
\n
(0.55)
\n
\n
\n
#2
\n
MS
\n
SVM
\n
No
\n
70.33
\n
69.38
\n
78.09
\n
60.97
\n
89.25
\n
69.38
\n
85.95
\n
80.98
\n
(1.09)
\n
\n
\n
MFCC
\n
79.93
\n
79.02
\n
81.81
\n
75.71
\n
93.77
\n
80.15
\n
92.01
\n
90.94
\n
(0.93)
\n
\n
\n
MFCC+SM
\n
84.90
\n
88.26
\n
89.44
\n
80.90
\n
96.58
\n
83.89
\n
95.63
\n
89.69
\n
(0.62)
\n
\n
\n
#3
\n
MS
\n
MLR
\n
Yes
\n
64.76
\n
49.02
\n
66.87
\n
44.52
\n
87.50
\n
58.26
\n
78.70
\n
67.84
\n
(1.27)
\n
\n
\n
MFCC
\n
66.54
\n
57.83
\n
74.56
\n
56.98
\n
94.02
\n
72.32
\n
89.63
\n
76.47
\n
(1.51)
\n
\n
\n
MFCC+SM
\n
77.01
\n
78.45
\n
80.50
\n
64.18
\n
94.42
\n
80.14
\n
91.29
\n
83.03
\n
(0.97)
\n
\n
\n
#4
\n
MS
\n
SVM
\n
Yes
\n
69.81
\n
70.35
\n
75.44
\n
52.60
\n
86.77
\n
66.94
\n
82.57
\n
78.40
\n
(1.64)
\n
\n
\n
MFCC
\n
77.45
\n
77.41
\n
80.99
\n
69.47
\n
91.89
\n
75.17
\n
93.50
\n
87.47
\n
(0.95)
\n
\n
\n
MFCC+SM
\n
85.28
\n
84.54
\n
84.49
\n
73.47
\n
93.43
\n
81.79
\n
94.04
\n
86.57
\n
(0.72)
\n
\n\n
Table 2.
Recognition results with MS, MFCC features, and their combination on Spanish database.
Recognition results using RNN classifier based on Berlin and Spanish databases.
\n
From Table 1, it can be concluded that applying SN improves recognition results for Berlin database. But this is not the case for the Spanish database, as demonstrated in Tables 2 and 3. Results are the same with the three different classifiers. This can be explained by the number of speakers in each database. The Berlin database contains 10 different speakers, compared to the Spanish database that contains only two speakers and probably the language impact. As regarding the RNN method, we found that combining both types of features has the worst recognition rate for the Berlin database, as shown in Table 3. That is because the RNN model has too many parameters (155 coefficients in total) and a poor training data. This is the phenomena of overfitting. This is confirmed by the fact that when we reduced the number of features from 155 to 59 features, the results show an increase of above 13%, as shown in Table 4. To investigate whether a smaller feature space leads to better recognition performance, we repeated all evaluations on the development set by applying a recursive feature elimination (LR-RFE) for each modality combination. The stability of RFE depends heavily on the type of model that is used for feature ranking at each iteration. In our case, we tested the RFE based on an SVM and regression models; we found that using linear regression provides more stable results. We observed from the previous results that the combination of the features gives the best results. So we applied LR-RFE feature selection only for this combination to improve accuracy. In this work, a total of 155 features were used; best features were chosen from feature selection. Fifty-nine features were selected by RFE feature selection method based on LR from the Berlin database and 110 features from the Spanish database. The corresponding results of LR-RFE can be seen in Table 4. For most setting using the Spanish database, LR-RFE does not significantly improve the average accuracy. However, for recognition based on Berlin database using the three classifiers, LR-RFE leads to a remarkable performance gain, as shown in Figure 5. This increases the average of MFCC combined with MS features from 63.67 to 78.11% for RNN classifier. These results are illustrated in Table 4. For the Spanish database, the feature combination of MFCC and MS after applying LR-RFE selection using RNN has the best recognition rate which is above 94.01%.
\n
\n
\n
\n
\n
\n
\n\n
\n
SN
\n
Classifier
\n
LR-RFE
\n
Berlin
\n
Spanish
\n
\n\n\n
\n
No
\n
MLR
\n
No
\n
73.00 (3.23)
\n
83.55 (0.55)
\n
\n
\n
Yes
\n
79.40 (3.09)
\n
84.19 (0.96)
\n
\n
\n
SVM
\n
No
\n
81.10 (2.73)
\n
89.69 (0.62)
\n
\n
\n
Yes
\n
80.90 (3.17)
\n
90.05 (0.80)
\n
\n
\n
RNN
\n
No
\n
63.67 (7.74)
\n
90.05 (1.64)
\n
\n
\n
Yes
\n
78.11 (3.53)
\n
94.01 (0.76)
\n
\n
\n
Yes
\n
MLR
\n
No
\n
75.25 (2.49)
\n
83.03 (0.97)
\n
\n
\n
Yes
\n
83.20 (3.25)
\n
82.27 (1.12)
\n
\n
\n
SVM
\n
No
\n
81.00 (2.45)
\n
86.57 (0.72)
\n
\n
\n
Yes
\n
83.90 (2.46)
\n
86.47 (1.34)
\n
\n
\n
RNN
\n
No
\n
76.98 (4.79)
\n
87.02 (0.36)
\n
\n
\n
Yes
\n
83.42 (0.70)
\n
85.00 (0.93)
\n
\n\n
Table 4.
Recognition results with combination of MFCC and MS features using ML paradigm before and after applying LR-RFE feature selection method (Berlin and Spanish databases).
\n
Figure 5.
Performance comparison of three machine learning paradigms (MLR, SVM, RNN) using speaker normalization (SN) and RFE feature selection (FS), for the Berlin database, is shown.
\n
The confusion matrix for the best recognition of emotions using MFCC and MS features with RNN based on Spanish database is shown in Table 5. The rate column lists per class recognition rates and precision for a class are the number of samples correctly classified divided by the total number of samples classified to the class. It can be seen that Neutral was the emotion that was least difficult to recognize from speech as opposed to Disgust which was the most difficult and it forms the most notable confusion pair with Fear.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Emotion
\n
Anger
\n
Disgust
\n
Fear
\n
Joy
\n
Neutral
\n
Surprise
\n
Sadness
\n
Rate (%)
\n
\n\n\n
\n
Anger
\n
79
\n
1
\n
0
\n
1
\n
2
\n
3
\n
0
\n
91.86
\n
\n
\n
Disgust
\n
0
\n
67
\n
3
\n
0
\n
1
\n
0
\n
1
\n
93.05
\n
\n
\n
Fear
\n
0
\n
3
\n
70
\n
0
\n
1
\n
0
\n
2
\n
93.33
\n
\n
\n
Joy
\n
3
\n
1
\n
1
\n
71
\n
0
\n
0
\n
0
\n
93.42
\n
\n
\n
Neutral
\n
2
\n
0
\n
1
\n
0
\n
156
\n
0
\n
1
\n
97.50
\n
\n
\n
surprise
\n
2
\n
1
\n
0
\n
3
\n
0
\n
60
\n
0
\n
92.30
\n
\n
\n
Sadness
\n
0
\n
0
\n
1
\n
0
\n
2
\n
0
\n
66
\n
95.65
\n
\n
\n
Precision (%)
\n
91.86
\n
91.78
\n
92.10
\n
94.66
\n
96.29
\n
95.23
\n
94.28
\n
\n
\n\n
Table 5.
Confusion matrix for feature combination after LR-RFE selection based on Spanish database.
\n
\n
\n
\n
5. Conclusion
\n
In this current study, we presented an automatic speech emotion recognition (SER) system using three machine learning algorithms (MLR, SVM, and RNN) to classify seven emotions. Thus, two types of features (MFCC and MS) were extracted from two different acted databases (Berlin and Spanish databases), and a combination of these features was presented. In fact, we study how classifiers and features impact recognition accuracy of emotions in speech. A subset of highly discriminant features is selected. Feature selection techniques show that more information is not always good in machine learning applications. The machine learning models were trained and evaluated to recognize emotional states from these features. SER reported the best recognition rate of 94% on the Spanish database using RNN classifier without speaker normalization (SN) and with feature selection (FS). For Berlin database, all of the classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection (FS) are applied to the features. From this result, we can see that RNN often perform better with more data and it suffers from the problem of very long training times. Therefore, we concluded that the SVM and MLR models have a good potential for practical usage for limited data in comparison with RNN .
\n
Enhancement of the robustness of emotion recognition system is still possible by combining databases and by fusion of classifiers. The effect of training multiple emotion detectors can be investigated by fusing these into a single detection system. We aim also to use other feature selection methods because the quality of the feature selection affects the emotion recognition rate: a good emotion feature selection method can select features reflecting emotion state quickly. The overall aim of our work is to develop a system that will be used in a pedagogical interaction in classrooms, in order to help the teacher to orchestrate his class. For achieving this goal, we aim to test the system proposed in this work.
\n
\n\n',keywords:"speech emotion recognition, feature extraction recurrent neural, network SVM, multivariate linear regression, MFCC, modulation spectral features, machine learning",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65993.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65993.xml",downloadPdfUrl:"/chapter/pdf-download/65993",previewPdfUrl:"/chapter/pdf-preview/65993",totalDownloads:4566,totalViews:0,totalCrossrefCites:21,dateSubmitted:"February 21st 2018",dateReviewed:"January 31st 2019",datePrePublished:"March 25th 2019",datePublished:"February 19th 2020",dateFinished:"March 6th 2019",readingETA:"0",abstract:"This chapter presents a comparative study of speech emotion recognition (SER) systems. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65993",risUrl:"/chapter/ris/65993",signatures:"Leila Kerkeni, Youssef Serrestou, Mohamed Mbarki, Kosai Raoof, Mohamed Ali Mahjoub and Catherine Cleder",book:{id:"8141",type:"book",title:"Social Media and Machine Learning",subtitle:null,fullTitle:"Social Media and Machine Learning",slug:"social-media-and-machine-learning",publishedDate:"February 19th 2020",bookSignature:"Alberto Cano",coverURL:"https://cdn.intechopen.com/books/images_new/8141.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78984-028-5",printIsbn:"978-1-78984-027-8",pdfIsbn:"978-1-83880-616-3",isAvailableForWebshopOrdering:!0,editors:[{id:"200724",title:"Dr.",name:"Alberto",middleName:null,surname:"Cano",slug:"alberto-cano",fullName:"Alberto Cano"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"247090",title:"Ph.D. Student",name:"Leila",middleName:null,surname:"Kerkeni",fullName:"Leila Kerkeni",slug:"leila-kerkeni",email:"kerkeni.leila@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Emotion and classification",level:"1"},{id:"sec_2_2",title:"2.1 Definition",level:"2"},{id:"sec_3_2",title:"2.2 Categorization of emotions",level:"2"},{id:"sec_4_2",title:"2.3 Sensory modalities for emotion expression",level:"2"},{id:"sec_4_3",title:"2.3.1 Facial expressions",level:"3"},{id:"sec_5_3",title:"2.3.2 Speech",level:"3"},{id:"sec_6_3",title:"2.3.3 Physiological signals",level:"3"},{id:"sec_9",title:"3. Speech emotion recognition (SER) system",level:"1"},{id:"sec_9_2",title:"3.1 Block diagram",level:"2"},{id:"sec_10_2",title:"3.2. Feature extraction",level:"2"},{id:"sec_11_2",title:"3.3 Feature selection",level:"2"},{id:"sec_12_2",title:"3.4 Classification methods",level:"2"},{id:"sec_14",title:"4. Experimental results and analysis",level:"1"},{id:"sec_14_2",title:"4.1 Emotional speech databases",level:"2"},{id:"sec_15_2",title:"4.2 Berlin database",level:"2"},{id:"sec_16_2",title:"4.3 Spanish database",level:"2"},{id:"sec_17_2",title:"4.4 Results and analysis",level:"2"},{id:"sec_19",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Ali H, Hariharan M, Yaacob S, Adom AH. Facial emotion recognition using empirical mode decomposition. Expert Systems with Applications. 2015;42(3):1261-1277\n'},{id:"B2",body:'Liu ZT, Wu M, Cao WH, Mao JW, Xu JP, Tan GZ. Speech emotion recognition based on feature selection and extreme learning machine decision tree. Neurocomputing. 2018;273:271-280\n'},{id:"B3",body:'Ragot M, Martin N, Em S, Pallamin N, Diverrez JM. Emotion recognition using physiological signals: Laboratory vs. wearable sensors. In: International Conference on Applied Human Factors and Ergonomics. Springer; 2017. pp. 15-22\n'},{id:"B4",body:'Surabhi V, Saurabh M. 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Cruz received a graduate degree in Electrical Engineering from the University of Porto, Portugal, in 1993, and an M.Sc. in Digital Systems Engineering from UMIST, U.K., in 1994. He is currently a senior researcher with the robotics and intelligent systems group at INESC Porto - Institute for Systems and Computer Engineering of Porto. During the last 15 years he has been involved the design, development and operation of small-size Autonomous Underwater Vehicles (AUVs) and AUV navigation systems based on acoustics, both in Portugal and abroad. He has designed the mechanical structure and the onboard electronics of the MARES AUV, a hovering type vehicle that is being routinely used in environmental missions since 2007. Recently, this modular design was the basis for the TriMARES, a larger 3-body hybrid AUV/ROV for the inspection of dams and monitoring of reservoirs. \nHis current research activities address the development of innovative guidance techniques, in particular adapting sampling strategies to increase the efficiency in the use of AUVs.",institutionString:null,institution:{name:"University of Porto",institutionURL:null,country:{name:"Portugal"}}},{id:"12282",title:"Dr.",name:"Aníbal",surname:"Matos",slug:"anibal-matos",fullName:"Aníbal Matos",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Porto",institutionURL:null,country:{name:"Portugal"}}},{id:"32843",title:"Prof.",name:"Sérgio",surname:"Silva",slug:"sergio-silva",fullName:"Sérgio Silva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"80908",title:"Dr.",name:"Marc",surname:"Acheroy",slug:"marc-acheroy",fullName:"Marc Acheroy",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Royal Military Academy",institutionURL:null,country:{name:"Belgium"}}},{id:"131099",title:"Prof.",name:"J.",surname:"Groen",slug:"j.-groen",fullName:"J. 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In the formation of future specialists, computer science education cannot ignore the reality of a society in which research and technological progress are based primarily on interdisciplinarity and transdisciplinarity. Throughout this chapter, we will analyze the way in which all these elements are evolving in a very closely interdependency one of each other: the evolution of computer science accelerates the development of classical sciences, and the development of classical sciences and computer science generates the emergence and progress of new border sciences and how the educational curricula in computer sciences have to be adapted to this trend. We will present and analyze the ways in which computer science education can be performed in an interdisciplinary and/or transdisciplinary manner at all educational levels. 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Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment"},{id:"5",title:"Parasitic Infectious Diseases",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Infectious Diseases",id:"6"},selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. This series is intended for doctors, engineers, and scientists involved in biomedical engineering or those wanting to start working in this field.",coverUrl:"https://cdn.intechopen.com/series/covers/7.jpg",latestPublicationDate:"May 7th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:3,numberOfPublishedChapters:96,numberOfPublishedBooks:12,editor:{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTYNQA4/Profile_Picture_1630478535317",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia, Poland, Institute of Computer Science, Department of Biomedical Computer Systems. For 20 years, he has studied the analysis and processing of biomedical images, emphasizing the full automation of measurement for a large inter-individual variability of patients. Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. Osma",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDv7QAG/Profile_Picture_1626602531691",institutionString:null,institution:{name:"Universidad de Los Andes",institutionURL:null,country:{name:"Colombia"}}},{id:"69697",title:"Dr.",name:"Mani T.",middleName:null,surname:"Valarmathi",fullName:"Mani T. Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/131109",hash:"",query:{},params:{id:"131109"},fullPath:"/profiles/131109",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()