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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis 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.
\n\nWe 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.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{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"}]},book:{item:{type:"book",id:"6960",leadTitle:null,fullTitle:"Flame Retardants",title:"Flame Retardants",subtitle:null,reviewType:"peer-reviewed",abstract:"Flame retardants reduce the risk of fire by decreasing the combustion rate and flame propagation in the presence of fire, leading to the prevention and control of fire. Flame Retardants is divided into four sections: section 1 consists of the introduction, section 2 discusses properties, Section 3 comprises nanocomposites, and section 4 includes computational analysis. The book will be useful for scientists and researchers interested in the field of fire control.",isbn:"978-1-78985-880-8",printIsbn:"978-1-78985-879-2",pdfIsbn:"978-1-83881-139-6",doi:"10.5772/intechopen.73636",price:100,priceEur:109,priceUsd:129,slug:"flame-retardants",numberOfPages:96,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"506ea55aeb09b1a47f9113cc66594291",bookSignature:"Fahmina Zafar and Eram Sharmin",publishedDate:"July 24th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/6960.jpg",numberOfDownloads:4604,numberOfWosCitations:6,numberOfCrossrefCitations:6,numberOfCrossrefCitationsByBook:2,numberOfDimensionsCitations:11,numberOfDimensionsCitationsByBook:3,hasAltmetrics:0,numberOfTotalCitations:23,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 19th 2018",dateEndSecondStepPublish:"August 28th 2018",dateEndThirdStepPublish:"October 27th 2018",dateEndFourthStepPublish:"January 15th 2019",dateEndFifthStepPublish:"March 16th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"89672",title:"Dr.",name:"Fahmina",middleName:null,surname:"Zafar",slug:"fahmina-zafar",fullName:"Fahmina Zafar",profilePictureURL:"https://mts.intechopen.com/storage/users/89672/images/system/89672.png",biography:"Dr. Fahmina Zafar is a senior researcher working at the Department of Chemistry, JMI, New Delhi, India. Dr. Zafar has received her PhD (2006) and MSc (1999) degree in Chemistry from JMI. She has worked as a PI (DST WOS A), postdoctoral fellow (UGC Kothari Postdoctoral Fellowship) along with Scientist Pool, Research Associate, and Senior Research Fellow (CSIR) at the same Department. She has > 90 publications in peer-reviewed journals and books, and has presented >50 research papers in national and international conferences. Her research work involves the development of bio-based polymers, metallopolymers, organic-inorganic hybrids, coordination polymers, and nanocomposites for green environments in different fields including adsorption, antimicrobial, and corrosion-protective applications.",institutionString:"Jamia Millia Islamia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"7",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"Jamia Millia Islamia",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"107375",title:"Dr.",name:"Eram",middleName:null,surname:"Sharmin",slug:"eram-sharmin",fullName:"Eram Sharmin",profilePictureURL:"https://mts.intechopen.com/storage/users/107375/images/system/107375.jpeg",biography:'Dr. Eram Sharmin is an Associate Professor at the Department of Pharmaceutical Chemistry, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia. She obtained her Ph.D. degree in Chemistry from Jamia Millia Islamia (JMI) - A Central University, New Delhi, India, in the year 2007. She received her MSc degree in Organic Chemistry, in the year 2000, and BSc degree in Chemistry, in the year 1998, from Aligarh Muslim University (AMU), Aligarh, Uttar Pradesh (UP), India. She has previously worked as Senior Research Associate [Under Scientists’ Pool Scheme, Council of Scientific and Industrial Research (CSIR), New Delhi, India], Research Associate (CSIR, New Delhi), and Senior Research Fellow (CSIR, New Delhi), at Materials Research Laboratory, Department of Chemistry, JMI. She has more than 50 publications in peer-reviewed journals and books and has presented more than 30 research papers in national and international conferences. Her research interests include the development of "green” materials with applications as antimicrobial and protective coatings, films, hydrogels, and packaging materials.',institutionString:"Umm al-Qura University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Umm al-Qura University",institutionURL:null,country:{name:"Saudi Arabia"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"208",title:"Material Science",slug:"nanotechnology-and-nanomaterials-material-science"}],chapters:[{id:"65125",title:"Introductory Chapter: Flame Retardants",doi:"10.5772/intechopen.82783",slug:"introductory-chapter-flame-retardants",totalDownloads:1338,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:null,signatures:"Eram Sharmin and Fahmina Zafar",downloadPdfUrl:"/chapter/pdf-download/65125",previewPdfUrl:"/chapter/pdf-preview/65125",authors:[{id:"89672",title:"Dr.",name:"Fahmina",surname:"Zafar",slug:"fahmina-zafar",fullName:"Fahmina Zafar"},{id:"107375",title:"Dr.",name:"Eram",surname:"Sharmin",slug:"eram-sharmin",fullName:"Eram Sharmin"}],corrections:null},{id:"62802",title:"Physiochemical Properties and Environmental Levels of Legacy and Novel Brominated Flame Retardants",doi:"10.5772/intechopen.79823",slug:"physiochemical-properties-and-environmental-levels-of-legacy-and-novel-brominated-flame-retardants",totalDownloads:1105,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Polybrominated diphenyl ethers (PBDEs) and ‘novel’ brominated flame retardants (NBFRs) are synthetic chemicals widely used in consumer products to enhance their ignition resistance. Since in most applications, these chemicals are used additively, they can transfer from such products into the environment. PBDEs have been classified as significant pollutants in the environment. Knowledge of PBDE and NBFR physicochemical properties provides information about their potential environmental fate and behaviour. This chapter highlights the most important physiochemical properties such as molecular weight, vapour pressure, octanol/air partitioning coefficient, octanol/water partition coefficient, water solubility and organic carbon/water partitioning coefficient that influence the distribution pattern of these contaminants in the environment. In addition, this chapter provides an evaluation of the concentrations of these chemicals in various environmental media such as indoor and outdoor air, indoor dust, soil and sediment, sewage sludge, biota and food, and human tissues.",signatures:"Layla Salih Al-Omran",downloadPdfUrl:"/chapter/pdf-download/62802",previewPdfUrl:"/chapter/pdf-preview/62802",authors:[{id:"247995",title:"Dr.",name:"Layla",surname:"Al-Omran",slug:"layla-al-omran",fullName:"Layla Al-Omran"}],corrections:null},{id:"64525",title:"Flame Retardant Polymer Nanocomposites and Interfaces",doi:"10.5772/intechopen.79548",slug:"flame-retardant-polymer-nanocomposites-and-interfaces",totalDownloads:1327,totalCrossrefCites:3,totalDimensionsCites:7,hasAltmetrics:0,abstract:"The flame retardant efficiency of polymer nanocomposites is highly dependent on the dispersion of the nano-fillers within the polymer matrix. In order to control the filler dispersion, it is very essential to explore the interfacial compatibility between fillers and matrices, which provides a guide for the flame retardant nanocomposites compounding. In this short review, we mainly focus on the thermoplastic polymers and their interactions with the surfaces of the flame retardant fillers. Other physical properties of those nanocomposites such as mechanical properties, gas permeability, rheological performance and thermal conductivity are also briefly reviewed along with the flame retardancy, since they are all dispersion related.",signatures:"Yuan Xue, Yichen Guo and Miriam H. Rafailovich",downloadPdfUrl:"/chapter/pdf-download/64525",previewPdfUrl:"/chapter/pdf-preview/64525",authors:[{id:"176796",title:"Prof.",name:"Miriam",surname:"Rafailovich",slug:"miriam-rafailovich",fullName:"Miriam Rafailovich"},{id:"243777",title:"Dr.",name:"Yichen",surname:"Guo",slug:"yichen-guo",fullName:"Yichen Guo"},{id:"256679",title:"Ms.",name:"Yuan",surname:"Xue",slug:"yuan-xue",fullName:"Yuan Xue"}],corrections:null},{id:"65050",title:"Stochastic Finite Element Modelling of Char Forming Filler Addition and Alignment – Effects on Heat Conduction into Polymer Condensed Phase",doi:"10.5772/intechopen.82878",slug:"stochastic-finite-element-modelling-of-char-forming-filler-addition-and-alignment-effects-on-heat-co",totalDownloads:837,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Micro- and nano-filler particles have been considered as char-forming flame retardants for polymers. It has been shown that suitable particles may operate in the condensed phase to prevent or delay the escape of fuel into the gas phase. Good flame retardancy performance may be achieved in composites with comparatively low filler loadings. However, many candidate filler materials, such as rod-like and plate-like carbon allotrope fillers with high aspect ratio, will effectively enhance the composite’s thermal conductivity, and hence, may greatly increase heat input into the condensed phase. Moreover, anisotropy in terms of thermal conductivity must be considered when rod-like and plate-like particles are aligned, for example as a result of manufacturing processes. The presented study investigates these effects, i.e., thermal conductivity enhancement due to filler addition and alignment, using a modeling framework based on Monte Carlo simulation that was developed for predicting effective composite properties considering filler-matrix and particle-to-particle interfacial effects. A stochastic finite element analysis was performed to model rod-shaped carbon particles embedded in a polymer matrix. The chosen analysis is demonstrated to be an effective means for elucidating the effect of filler addition and alignment on the heat conduction into polymer materials containing fillers as char-forming flame retardants.",signatures:"Hamidreza Ahmadi Moghaddam and Pierre Mertiny",downloadPdfUrl:"/chapter/pdf-download/65050",previewPdfUrl:"/chapter/pdf-preview/65050",authors:[{id:"201419",title:"Dr.",name:"Pierre",surname:"Mertiny",slug:"pierre-mertiny",fullName:"Pierre Mertiny"},{id:"280514",title:"Mr.",name:"Hamidreza",surname:"Ahmadi Moghaddam",slug:"hamidreza-ahmadi-moghaddam",fullName:"Hamidreza Ahmadi Moghaddam"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"2003",title:"Polyurethane",subtitle:null,isOpenForSubmission:!1,hash:"7391b5a0085d7c0aa0a5c75ee6f275b2",slug:"polyurethane",bookSignature:"Fahmina Zafar and Eram Sharmin",coverURL:"https://cdn.intechopen.com/books/images_new/2003.jpg",editedByType:"Edited by",editors:[{id:"89672",title:"Dr.",name:"Fahmina",surname:"Zafar",slug:"fahmina-zafar",fullName:"Fahmina Zafar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5291",title:"Metal-Organic Frameworks",subtitle:null,isOpenForSubmission:!1,hash:"11a4acb20c880870e43c6f9dcf71e31e",slug:"metal-organic-frameworks",bookSignature:"Fahmina Zafar and Eram Sharmin",coverURL:"https://cdn.intechopen.com/books/images_new/5291.jpg",editedByType:"Edited by",editors:[{id:"89672",title:"Dr.",name:"Fahmina",surname:"Zafar",slug:"fahmina-zafar",fullName:"Fahmina Zafar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10032",title:"Fire Safety and Management Awareness",subtitle:null,isOpenForSubmission:!1,hash:"ba924ac3ec282316ae8ba97882cc4592",slug:"fire-safety-and-management-awareness",bookSignature:"Fahmina Zafar and Anujit Ghosal",coverURL:"https://cdn.intechopen.com/books/images_new/10032.jpg",editedByType:"Edited by",editors:[{id:"89672",title:"Dr.",name:"Fahmina",surname:"Zafar",slug:"fahmina-zafar",fullName:"Fahmina Zafar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6408",title:"Novel Nanomaterials",subtitle:"Synthesis and Applications",isOpenForSubmission:!1,hash:"f3585d338d78e4d31c200d9991b03692",slug:"novel-nanomaterials-synthesis-and-applications",bookSignature:"George Z. 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This book provides information on many aspects of this plant, such as its botanical information, nutritional values, bioactive compounds, pharmacology, cultivation, its use in treating diseases, and its applications in the food and pharmaceutical industries.",isbn:"978-1-83880-942-3",printIsbn:"978-1-83880-941-6",pdfIsbn:"978-1-83880-943-0",doi:"10.5772/intechopen.87455",price:119,priceEur:129,priceUsd:155,slug:"capsicum",numberOfPages:200,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"094b79463f9dadc5794fc8213727ac72",bookSignature:"Aman Dekebo",publishedDate:"October 14th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/9652.jpg",keywords:null,numberOfDownloads:6516,numberOfWosCitations:3,numberOfCrossrefCitations:9,numberOfDimensionsCitations:16,numberOfTotalCitations:28,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 15th 2019",dateEndSecondStepPublish:"March 18th 2020",dateEndThirdStepPublish:"May 17th 2020",dateEndFourthStepPublish:"August 5th 2020",dateEndFifthStepPublish:"October 4th 2020",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 years",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"191684",title:"Dr.",name:"Aman",middleName:null,surname:"Dekebo",slug:"aman-dekebo",fullName:"Aman Dekebo",profilePictureURL:"https://mts.intechopen.com/storage/users/191684/images/system/191684.jpeg",biography:"Aman Dekebo is currently a professor in the Department of Applied Chemistry, Adama Science and Technology University, Ethiopia. He obtained his first and second PhDs in Natural Products Chemistry from Addis Ababa University, Ethiopia in 2002 and Bio-organic Chemistry from Ehime University, Japan in 2007, respectively. He worked as a researcher during his sabbatical leave at Andong National University, South Korea (2017–2019). His research interests include isolation and synthesis of bioactive compounds, chemical ecology, and apiculture. He has authored more than fifty peer-reviewed scientific papers and served as editor of the book Plant Extracts (2019). He has also participated and presented his works in several conferences in many countries. He is co-author of Phytochemistry of Turmeric Rhizomes (Curcuma longa): Isolation and Characterization of Compounds from Foots of Turmeric Rhizomes (Curcuma longa) (2012). He serves as a reviewer for many reputable journals. He is recipient of best researcher awards and certificates from School of Applied Natural Sciences, Adama Science and Technology University, Korean Society of Applied Entomology, and Korean Apiculture Association.",institutionString:null,position:null,outsideEditionCount:null,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Adama Science and Technology University",institutionURL:null,country:{name:"Ethiopia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"41",title:"Plant Biology",slug:"agricultural-and-biological-sciences-plant-biology"}],chapters:[{id:"72985",title:"Diversity and Potency of Capsicum spp. Grown in Indonesia",slug:"diversity-and-potency-of-em-capsicum-em-spp-grown-in-indonesia",totalDownloads:437,totalCrossrefCites:0,authors:[null]},{id:"73319",title:"Anthracnose of Chilli: Status, Diagnosis, and Management",slug:"anthracnose-of-chilli-status-diagnosis-and-management",totalDownloads:877,totalCrossrefCites:0,authors:[null]},{id:"71304",title:"Hot Water Seed Treatment: A Review",slug:"hot-water-seed-treatment-a-review",totalDownloads:739,totalCrossrefCites:0,authors:[null]},{id:"73108",title:"Biological Control in Capsicum with Microbial Agents",slug:"biological-control-in-em-capsicum-em-with-microbial-agents",totalDownloads:502,totalCrossrefCites:0,authors:[null]},{id:"72053",title:"Management of Viruses and Viral Diseases of Pepper (Capsicum spp.) in Africa",slug:"management-of-viruses-and-viral-diseases-of-pepper-em-capsicum-em-spp-in-africa",totalDownloads:752,totalCrossrefCites:3,authors:[null]},{id:"71870",title:"Hot Pepper (Capsicum annuum L.): An Alternative Food to Reduce Micronutrient Deficiencies in Human",slug:"hot-pepper-em-capsicum-annuum-em-l-an-alternative-food-to-reduce-micronutrient-deficiencies-in-human",totalDownloads:444,totalCrossrefCites:1,authors:[null]},{id:"72322",title:"Capsicum: Chemistry and Medicinal Properties of Indigenous Indian Varieties",slug:"-em-capsicum-em-chemistry-and-medicinal-properties-of-indigenous-indian-varieties",totalDownloads:375,totalCrossrefCites:2,authors:[{id:"314279",title:"Dr.",name:"Mithun",surname:"Rudrapal",slug:"mithun-rudrapal",fullName:"Mithun Rudrapal"}]},{id:"71560",title:"Anticancer Effect of Capsaicin and Its Analogues",slug:"anticancer-effect-of-capsaicin-and-its-analogues",totalDownloads:711,totalCrossrefCites:1,authors:[null]},{id:"72029",title:"Capsicum Seeds as a Source of Bioactive Compounds: Biological Properties, Extraction Systems, and Industrial Application",slug:"-em-capsicum-em-seeds-as-a-source-of-bioactive-compounds-biological-properties-extraction-systems-an",totalDownloads:667,totalCrossrefCites:2,authors:[{id:"161326",title:"Prof.",name:"Jesus",surname:"Simal-Gandara",slug:"jesus-simal-gandara",fullName:"Jesus Simal-Gandara"},{id:"317263",title:"Dr.",name:"Miguel Ángel",surname:"Prieto Lage",slug:"miguel-angel-prieto-lage",fullName:"Miguel Ángel Prieto Lage"}]},{id:"72095",title:"Use of Capsaicin for Nonlethal Technology",slug:"use-of-capsaicin-for-nonlethal-technology",totalDownloads:565,totalCrossrefCites:0,authors:[null]},{id:"71447",title:"The Effects of Consumption of Capsicum on Some Neurobehavioural Parameters",slug:"the-effects-of-consumption-of-em-capsicum-em-on-some-neurobehavioural-parameters",totalDownloads:451,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247865",firstName:"Jasna",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247865/images/7225_n.jpg",email:"jasna.b@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Gas sensors are generally defined as devices to detect or measure concentration of (bio-) chemicals in gaseous state. The demands of highly sensitive gas sensors for industry, environmental monitoring, safety, biomedicine, and pharmaceutics have provoked the intensive research interests in micro-electromechanical systems (MEMS) and nanotechnology. The nanotechnology in conjunction with MEMS has created huge potential to build highly sensitive, low cost, compact gas sensors with low power consumption. (Graf et al.,2006; Panchanpakesan et al.,2006; Udrea et al.,2007; Maeng et al., 2008-b)
An example of MEMS sensor system integrated with nanostructured sensing materials. The darkish areas are covered with multi-walled CNTs.
There exist various types of MEMS gas sensors: two terminal resistance type, field-effect transistor (FET) type, capacitance type, and surface acoustic wave (SAW) type, etc. Among them, the two terminal resistance type gas sensors are most widely used and investigated as the manufacturing process is very cost effective. In the manufacturing of two terminal resistance type gas sensors, semiconducting materials such as SnO2, ZnO and WO3 are used as sensing elements. Among them, nanocryatalline SnO2 has been focused in gas sensor applications as this material was first commercialized for industrial resistance type gas sensors. (Göpel & Schierbaum,1995; Cheng et al.,2004) As the demand for sensors both highly sensitive and very compact increases, however, the problems of this material have been revealed. The conventional semiconducting materials need high temperature operation for enhancement of the sensitivity. If the grain size of the sensing elements becomes very small, the coalescence of the grains is inevitable and this leads to the change of the base-line resistance of the sensors. (Shukla et al.,2003) Another problem is the thickness and uniformity control of the semiconducting sensing materials. The conventional sensors adopt thick layer of sensing materials and the control of the thickness uniformity is not difficult. In the MEMS gas sensors, ultrathin layer of nanostructured sensing materials is needed as this facilitates both the high sensitive detection of gases and the low power operation of the sensors. (Udrea et al., 2009) The thickness uniformity control of extremely thin films is not as easy as the thick film cases. Recent development of metal oxide semiconducting nanowires, nanobelts, and nanosheets overcomes the problem of coalescence of materials by thermal heating and accelerates the progress of MEMS gas sensors. (Park et al., 2010; Moon et al., 2010; Maeng et al., 2011) However, the thickness uniformity control problem still remains.
The use of semiconducting CNTs as gas sensing elements instead of metal oxide nano-materals proposed couple of decades before and have been studied intensively due to their inherent properties such as high strength, high electrical and thermal conductivity and high surface-to-volume ratio. In this chapter, the recent development of two terminal resistance type single-walled carbon nanotube (SWCNT) network gas sensors will be reviewed.
The early work by Kong et al. showed that the electrical resistance of individual semiconducting SWCNTs dramatically changes when exposed to gaseous molecules such as nitrogen dioxide, ammonia, and oxygen under applied gate voltages. These FET type SWCNT gas sensors exhibited very good sensitivity and fast response time at room temperature. The electrical properties of individual metallic SWCNTs in various chemical environments were also investigated under gate modulation. The resulting electrical conductance changes turned out to be negligible compared to the semiconducting counterparts. (Kong, J. Et al.(2000))
It is difficult to pick up an individual semiconducting SWCNT from as-grown samples, which are mixtures of both metallic and semiconducting SWCNTs. Furthermore, the alignment of an individual semiconducting SWCNT between pre-patterned electrodes is very complicated process. Therefore, the individual semiconducting FET type SWCNT gas sensor is impractical. More practical way of making semiconducting SWCNT sensors is to use mat or network of SWCNTs, which consist of both metallic and semiconducting SWCNTs, as sensing elements.
Direct growth of SWCNTs network from selectively arranged catalyst by chemical vapor deposition (CVD) has been atempted by various authors.(Qi et al., 2003; Stardermann, et al.,2004; Wongwiriyapan et al., 2006)
Qi et al. fabricated SWCNT FET sensors by using direct CVD growth technique at 900º C and reported that all the devices showed highly sensitive chemical gatings, which indicates that the charge transfer is predominantly through semiconducting SWCNTs. This cannot be easily understood. The nanotubes formed at the devices are mixture of both semiconducting and metallic SWCNTs as the chirality control of SWCNTs is impossible for present day CVD technology. Since the average conductance of a metallic SWCNT is about two orders of magnitude higher than that of semiconducting counterpart( Liang et al.,2007), the main charge transfer would be through metallic SWCNTs for many of the devices.
a) Optical image of an array of SWCNT network devices. (b) Optical image of one device. The black regions contain catalyst patterned on top of opposing Mo source and drain electrodes. (c) Scanning electron microscopy (SEM) image of a SWCNT network bridging two opposing Mo electrodes in a device. Excepted from
Stadermann et al. grew SWCNT networks directly onto silicon dioxide substrates using CVD. Gold was deposited onto the surface and patterned to form the electrodes. The devices were tested as FETs by applying sufficient back gate voltage (10V) to show conductance changes between 1% and 50%. These data demonstrate that, in a network of SWCNT between two electrodes, a large part of the current is carried by a small number of highly conductive metallic connections, and the connections involving semiconducting tubes, even though much more numerous, carry a comparable or smaller portion of the current.
Wongwiriyapan et al. synthesized SWCNT film directly on a substrate by thermal CVD. Vertically aligned SWCNTs with a high density were grown at 750º C, while horizontally lying SWCNT networks with a low density were grown in the temperature range 800–950º C. Figure 3 shows the varieties of SWCNT networks depending on the growth temperatures. Figure 5. in conjunction with Figure 4. shows the relationship between the surface coverage of SWCNTs and the normalized sensor response: the SWCNT networks with the lowest density exhibited the highest normalized sensor response. Here the normalized sensor response is defined as
SEM image of SWCNT network synthesized at different teperatures: (a)750º C, (b)850º C, (c)900º C and (d)950º C. Excepted from
Surface coverage of SWCNTs synthesized at different growth temperatures. Excepted from
Normalized sensor response to NO2 of SWCNTs systhesized at A: 750º C, B:850º C, C:900º C, D:950º C. Excepted from
This direct growth of SWCNT networks by CVD exhibits complexity with low yield in manufacturing point of view. Furthermore, the SWCNT network sensors can be fabricated on conventional bulky sensor platforms only. The maximum temperature achieved by MEMS gas sensor platform is limited below 700º C at present. (Haque et al., 2008-a, 2008-b)
The most widely reported SWCNT gas sensor is drop-deposited SWCNT network sensor. (Li, et al.,2003; Lu et al.,2006) In this case, the purified SWCNTs are generally dispersed in solutions (surfactants) and then drop-deposited onto interdigitated area of electrodes pre-patterned on the insulating substrate. Figure 6 shows the SEM image of a SWCNT network device fabricated by the drop-deposition method. Figure 7 shows the normalized sensing response of a SWCNT network sensor for various concentrations of NO2 gas at room temperature. Here 10 minute UV light illumination was introduced to accelerate NO2 desorption.
SEM image of SWCNT network bridging two gold electrodes. Excepted from
Normalized sensor response of a drop-deposited SWCNT network sensor upon exposure to various concentration of NO2 gas. Excepted from
This type of fabrication of network sensor is preferred at the research level as it is very simple. But, as shown in Figure 6 the deposited SWCNT network exhibits non-uniformity, which leads to poor reproducibility of the sensor. Thus, this method is not adequate for mass-production.
In order to enhance non-local uniformity of the SWCNT network sensors, Cho et al. used vacuum filtration method. In this process, 30 nm average pore size AAO filter membrane was installed inside the vacuum filteration system. Then, vacuum filtration of SWCNT solution containing 1% surfactant (SDS) was achieved as shown in Figure 8(a). The SWCNT network deposited on the AAO, then were transferred onto sensor platform as shown in Figure 8(b). By changing the weight of SWCNTs added to the solution, the thickness of the SWCNT network was controlled. As shown in Figure 9, it was revealed that the lower the SWCNT density, the higher the sensitivity of the SWCNT network sensor. The study by Cho et al. gives important information: The SWCNT network sensors must be very thin in order to get high sensitivity. (Cho et al., 2006) Hu et al. reported ultrathin uniform SWCNT network deposition by vacuum filtration method. However, no gas sensors have ever reported by them. (Hu et al., 2004)
Schematic illustration of (a) the vacuum filtration method using AAO filter membrane and (b) PDMS mold transfer of SWCNT network. Excepted from
SEM images of the SWCNT network formed on the filter membrane. The initial SWCNT densities used to form each of the thin film were (a) 0.04mg/ml, (b)0.08mg/ml, (c)0.12mg/ml, and (d)0.16mg/ml. The tool bar all indicate 2μm. Excepted from
Normalized sensor responses to the 50% NH3 gas diluted in N2 for various sensors with different nanotube densities. Excepted from
To form very uniform and thin SWCNT network, AC dielectrophoresis technique was introduced.(Suehiro et al., 2005; Lee et al., 2006) By using the technique,FET and resistance type sensors were fabricated and tested for various gases.
a) Optical image of a SWCNT network sensor. (b) AFM image of SWCNT network deposited between the Au gap by ac dielectrophoresis. Excepted from
Dependence of channel current upon gate bias of a SWCNT network FET fabricated by ac dielectrophoresis. The FET characteristics were measured at different SOCl2 concentrations. Small on/of ratio (~1) and vertical shift confirms that the charge transfer between SWCNT network and SOCl2 occurs mainly through metallic pathways. Excepted from
Figure 11. exhibits a SWCNT network FET device fabricated by Lee et al. and Figure 12. shows its perforamance under both gate modulation and gas adsorption. From the observation that gate voltage does not modulate the drain current Lee et al. reached a conclusion that the semiconducting nanotubes do not mainly contribute to the signal. They attributed this to the high density of the nanotube network. If the nanotube density is too high, the signal transduction occurs through metallic pathways, which leads to poor sensitivity.
SEM image of SWCNT network device fabricated by ac dielectrophresis for 3 hours. The inset is magnified image near an electrode corner. Excepted from
Figure 13. exhibits a SWCNT network device fabricated by Suehiro et al.. By monitoring impedence of the devices during the ac dielectrophresis Suehiro et al. controlled the assembly of SWCNT and reached a conclusion that the semiconducting SWCNT
The dependence of NO2 gas concentration upon the normalized sensor response of a SWCNT network gas sensor fabricated by ac dielectrophresis. Excepted from
Recently, ultra uniform and thin SWCNT network sensors fabricated by ‘surface-programmed assembly’ technique were reported by several authors.(Wang et al.,2009; Tran et al.,2008; Maeng et al.,2008-a) As this technique enables very precise control of nanotube density, it is expected that semiconducting nanotubes contribute to the sensing mechanism as is predicted by percolation theory which leads to high sensitivity. In addition, this technique is expected to facilitate wafer scale production of SWCNT sensor.
Wang et al. reported SWCNT sensors fabricated by using self-assembled monolayer (SAM) of hydrophilic 3- aminoprophiltrimethysilane(APS) as shown in Figure 15. In this process very thin SWCNT network patterns were selectively formed on the functionalized region. From the DMMP detection experiments, they revealed that the sensitivity reaches a maximum when networks of overlapped nanotubes disappear, i.e. the nanotubes are assembled to form a monolayer network. This conclusion supports the observations by Cho et al. that the thinner the SWCNT network the higher the sensitivity of the sensor.(Cho et al., 2006) It is further revealed that SWCNT monolayer with maximum connectivity is ideal for making highest sensitivity gas sensors.
a) Optical image of the electrode arrays, where deposited SWCNT networks are separated by Au electrodes. (b) SEM image of SWCNT networks bridging Au electrodes deposited on Si/SiO2 substrate. (c) An enlarged SEM image of (b). Excepted from Wang et al.,2006. © IOP Publishing Ltd
Tran et al. fabricated SWCNT sensors on both functionalized substrate with hydrophilic monolayer of 3-aminoprophilethoxysilane(APTES) and bare substrate as shown in Figure 16.
SEM image of (a) the sensor device which consist of interdigitate electrodes and SWCNTs (b) the SWCNT networks formed on bare surface (c) the SWCNt networks formed on the APTES-treated surface. Excepted from
Then, they tried to compare NO2 detection response of the two sensors as shown in Figure 17. The response time of the functionalized substrate-based gas sensor is very fast (a few seconds), while that of the bare substrate-based one is very slow(hundreds of seconds). It is conjectured that the interaction between APTES and sidewall of SWCNTs gives easier accessibility to the gas molecules. The sensitivity of the sensor on APTES- treated substrate is also shown to be higher than that on bare substrate.
Comparision of the normalized sensor reponses to 10 ppm NO2 gas between the SWCNT sensor on a bare and APTES-treated surface. Excepted from
Maeng et al. reported highly uniform SWCNT monolayer sensors fabricated by using hydrophobic octadecyltrichlorosiline (OST) surface functionalization. This time, SWCNTs are selectively assembled on non-functionalized or bare surface regions of the substrates. The atomic force microscopy (AFM) image of a SWCNT junction shows a low-density monolayer of SWCNT network between the Au/Ti electrodes. (Figure 18)
Schematic illustrations of (a) top view and (b) side-view depicting the structure of the SWCNT network sensor. (c) Optical image of the 10 x 10 array of SWCNT network sensors. (d) AFM topography image of a SWCNT sensor. Excepted from Maeng et al.,2008-a. © American Institute of Physics
Figure 19 shows the normalized responses of typical SWCNT monolayer network sensor fabricated on bare surface to various NO2 gas concentrations at room temperature. If we compare Figure 19 with Figure 17, it seems that SWCNT monolayer sensor formed on bare substrate exhibits higher sensitivity than that formed on APTES-functionalized substrate for the same concentration of NO2. This is contradictory to the observation by Tran et al.. It is likely that Tran et al. could not optimize the sensor fabrication conditions and further researches are necessary to determine whether or not SWCNT monolayer sensor formed on bare substrate assumes higher sensitivity than that formed on APTES-functionalized substrate. Considerably high sensitivity of the sensor indicates that semiconducting SWCNTs are involved in the sensing as percolation theory describes.
Figure 20(a) shows the responses of various SWCNT monolayer network sensors to 500 ppb NO2 gas at room temperature. Figure 20(b) shows the dependence of sensitivity upon base-line conductance. Unexpectedly, the sensitivity of SWCNT network sensors linearly proportional to the inverse of based-line conductance as shown in Figure 20(b).
The dependence of normalized sensing response of a SWCNT network sensor to the concentration of exposed NO2 gas at room temperature. Excepted from
In order to construct a conduction model of the SWCNT network sensor, Suehiro et al. assumed that the base-line conductance G0 = Nt g0, where Nt is the total number of SWCNTs and g0 is the average conductance of one SWCNT. It was implicitly assumed that all SWCNT had the same electrical property. After NO2 adsorption, the conductance of individual SWCNT is further assumed to be increases by ∆g on average, which leads to the total increase of sensor conductance ∆G = Nt ∆g. From this relations, they further derived the relation that normalized sensor response ∆G/G0 = ∆g/ g0. However, they gave no thought to the fact that the random network both follows percolation theory and consists of very different types of SWCNTs. According to the percolation theory, G0∝(Nt -Nc)ν when all the nanotubes have the similar electrical conductance. Here Nc =1/π(4.236/Ltube)2 is the critical number corresponding to percolation threshold, and ν ~1.94 is the critical exponent. (Stauffer, 1985) If some of the constituents have totally different electrical conductances as was the case of SWCNT network, the calculation becomes a bit complicated.
a) Normalized sensor response ∆G/G0 of SWCNT network sensor to 500 ppb NO2 gas. (b) Sensitivity Rs vs base-line conductance G0 graph. (Inset) Sensitivity Rs lineraly proportional to the inverse of the base-line conductance. Excepted from
The sensing characteristics shown in Figure 20 can be attributed to both the mixing nature of metallic and semiconducting SWCNTs and the randomly assembled network. Even though the SWCNT monolayer network is so uniformly deposited that each devices have equal number of SWCNTs and fixed ratio of metallic SWCNT/semiconducting SWCNT, the fluctuations of the base-line conductance can occur depending on the numbers of metallic SWCNTs which contact with electrodes. If the width of the electrode is comparable to the size of nanotubes as was in the case of the work by Maeng et al. the numbers may differ from device to device. Now, it should be explained how the difference of base-line conductance of devices influence the sensitivity of the devices.
where
By using the percolation theory,
where
For a specific gas concentration, the achievable maximum value of ∆
where
To secure the reproducibility of the SWCNT monolayer network sensor, the size of the electrodes must be large enough so that the numbers of metallic SWCNTs contacting with electrodes are averaged out. The SWCNT monolayer sensor is very promising not only in the perspective of sensitivity but also in the perspective of sensor operation. Even though the response time of semiconducting SWCNT sensor is about one order of magnitude faster than conventional metal oxide sensors, the recovery time of the sensor is about one order of magnitude slower than the metal oxide counterparts. This slow recovery time of the SWCNT sensors are due to irreversible binding of gas molecules on the SWCNT surface and is a primary hurdle for most SWCNT sensors that have appeared in the literature to date. MEMS-based metal oxide sensors usually adopt microheaters to enhance gas adsorption rate at moderate temperature range (200 ~ 400º C) and the desorption rate at higher temperature range. The integration of microheater with SWCNT network sensor will be also very useful for acceleration of desorption rate of adsorbed species even though some authors suggest to use UV source for the same purpose. (Li et al., 2003; Karthigeyan, et al., 2008; Maeng, et al., 2008-a) The use of microheaters is highly recommendable as the characteristics of SWCNT devices are affected by humidity. (Sung et al, 2006) Carbon nanotubes are also known to exhibit higher sensitivity at moderately elevated temperature range.(Valentini, et al.,2003) It is, thus, desirable for SWCNTs network to become monolayer so that the temperature of microheater and the nanotubes coincides during the operation. In this regards, the attempt to fabricate mass-production scale SWCNT sensors by depositing SWCNT monolayer onto mocroheaters of the MEMS sensor platforms is much needed and to be explored in the future.
In this chapter, an attempt has been made to provide overview of resistance and FET type SWCNT network gas sensors. In the thick SWCNT network sensor, the conduction of charge carriers occurs primarily through metallic-only pathways and the sensor sensitivity is poor. As the thickness of the sensor is reduced, the sensitivity tends to be enhanced. There exist a critical point where main conduction begin to occur through semiconducting-metallic mixed pathways. The critical point is determined by the density of SWCNT network. When the network becomes non-overlapping monolayer, the semiconducting nanotubes contribute to the electrical conduction so significantly that sensitivity reaches a maximum value. The behavior of the sensor sensitivity can be explained satisfactorily by the percolation theory at this condition.
This work was supported by the RIC program of MKE in Woosuk university.
Object tracking is defined as a problem of estimating the object’s trajectory, done by means of a video image. There are several tools for tracking objects and are used in various fields of research, such as computer vision, digital video processing, and autonomous vehicle navigation [1]. With the emergence of high-performance computers, high-resolution cameras, and the growing use of so-called autonomous systems that, in addition to these items, require specialized tracking algorithms, increasingly accurate and robust for automatic video analysis, has currently been the target of numerous research on the development of new object tracking techniques [2, 3].
Object tracking techniques are applicable to motion-based reconnaissance cases [4], automatic surveillance systems [5], pedestrian flow monitoring in crosswalks [6], traffic control [7], and autonomous vehicular navigation [8]. Problems of this type are highly complex due to the characteristics of the object and the environment, generating many variables, which impairs performance and makes the application of tracking algorithms unfeasible to real-world situations. Some approaches seek to resolve this impasse by simplifying the problem, reducing the number of variables [9]. This process, in most cases, does not generate good results [10, 11], making it even more difficult to identify the main attributes to be selected to perform a task [12, 13].
Most of the object tracking problems occur in open environments, so-called uncontrolled [14]. The complexity of these problems has attracted the interest of the scientific community and generated numerous applied research in various fields of research. Current approaches, such as the ones that use convolutional neural networks—CNN, deal well with the high number of variables of these types of problems, providing space–temporal information of the tracked objects, through three-dimensional convolutions [15, 16, 17]. This ends up creating an enormous number of learnable parameters, which ends up generating an overfitting [11]. A solution to reduce this number of learnable parameters was combining space–time data, extracted using the optical flow algorithm, used in the Two-Stream technique [18, 19, 20]. However, this technique presents good results only for large datasets, showing itself to be inefficient for small datasets [15, 21].
In recent years, research using machine learning has been applied to tracking problems, gaining notoriety due to the excellent results obtained in complex environments and attribute extraction [21, 22, 23]. Deep learning stands out among these techniques for presenting excellent results to unsupervised learning problems, [24], object identification [25], semantic segmentation [26]. Random trees are also examples of machine learning techniques, and their excellent results, due to their precision and great capacity to handle a large volume of data and low overfitting tendency [27, 28], and widely used in research areas such as medicine, in the prediction of hereditary diseases [29], agriculture to increase the productivity of a given plantation crop and in astronomy, acting on the improvement of images captured by telescopes, in the spectrum electromagnetic radiation not visible to the human eye [30]. The possibilities of applications, and new trends and research related to machine learning techniques, with particular attention to random trees, allow the development of algorithms that can be combined with existing ones, in the case of optical flow algorithms, (belonging to computational field of view) taken advantage of in this way, the advantages of each [31, 32, 33].
Developing an algorithm whose objective is to track objects, using the particular advantages of these techniques in a combined way, justifies creating a tracking algorithm that combines the optical flow technique, adapted in this work in terms of the Gaussian curvature associated with a minimal surface, with a random trees waiting for it to capture on this surface a minimum number of optical flow vectors that characterize the moving object, accurately and with low computational cost, contributing not only in the fields of computational vision but in other branches of science, such as in medicine, it can help in the early identification of infarctions.
Due to the large number of studies related to the technique of object tracking, only a small number surrounding this theme will be addressed. The focus of this project is not to make a thorough study on the state of the art. With this in this item, the main works in the literature, associated with the tracking of objects, will be presented. Among the various approaches used for this context, we highlight those focused on the techniques of optical flow, and others belonging to machine learning, such as those that use identifications of patterns, which allow relating, framing, and justifying the development of this proposal and its importance, through its contribution, to the state of the art.
Object tracking is defined as a process that allows you to uniquely estimate and associate the movements of objects with consecutive image frames. The objects considered can be from one, the set of pixels belonging to a region of the image. The detection of pixels is done by a motion detector or objects, which allows to locate objects with similar characteristics that move, between consecutive frames.
These characteristics of the object to be tracked are compared with the characteristics of a reference object modeled by a classifier over a limited region of the so-called region of interest frame, where the probability of detection of the object is greater. Thus, according to [33], the detector of traced objects, locate several objects on the different parts of the region of interest and performs the comparison of these objects with the reference object. This process is performed for each frame and each object detected, candidate to be recognized as the greatest possible similarity, to the reference object can be represented, through a set of fixed-size characteristics, extracted from this region containing a set of pixels, which can be represented by a numerical array of data.
Thus, mathematically, the region containing a set of pixels belonging to the regions of the object of interest, where the characteristics that allow to test whether the region of the frame, in which the object to be traced is, is given by:
where,
According to the works of [34, 35], learning methods are used to adapt the changes of movement and other characteristics such as geometric aspect and appearance of the tracked object. These methods are usually used adaptive tracked object trackers and detectors. The following will be presented other types of object trackers, found in the literature.
According to [36], a classifier can be defined with a
The classifier aims to determine the best way to discriminate the data classes, on the space of characteristics. The test data form a set containing the characteristics of the candidate objects, which have not yet been classified. The position of the object to be tracked in the frame is defined as the position corresponding to the highest response of the detector of the object to be tracked on the
where the variable
Offline-trained classifiers are generally employed in object detectors designed to detect all new objects of interest that enter the camera’s field of view [37]. The training set
In [38], trackers that use the detection tracking technique deal with object tracking, as a binary classification problem whose goal is to find the best function
In [39], were developed trackers that used detectors of objects to be tracked, formed by classifiers in committee formed by binary classifiers said weak. For [40], a binary classifier is defined as a classifier, used in problems where the class
A classifier is said to be weak, when it has a probability of “hitting” a given data class, only slightly higher than a random classifier. The detector of the object to be tracked must separate the crawled object from the other objects and the environment. Its purpose and determine the position of the tracked object, according to the equations (1)–(3)
For [43], the term monitoring system, refers to the process of monitoring and autonomous control, without human intervention. This type of system has the function of detecting, classifying, tracking, analyzing, and interpreting the behavior of objects of interest. In [44, 45], this technique was used combined with statistical techniques for controlling people’s access to a specific location. It was also observed the use of intelligent monitoring systems, applied to building, port, or ship security [46, 47].
The functions comprised by a monitoring system are so-called low- and high-level tasks. Among some high-level tasks, we highlight the analysis, interpretation and description of behavior, the recognition of gestures, and the decision between the occurrence or not of a threat. Performing high-level tasks require that for each frame, the system needs to perform low-level tasks, which involve direct manipulation of the image pixels [48, 49, 50, 51, 52, 53, 54, 55, 56]. As an example, we highlight the processes of noise elimination, detection of connected components, and obtain information on the location and geometric aspect of the object of interest.
A monitoring system consists of five main components, which are presented in Figures 9. Some monitoring systems may not contain all components. The initial detector aims to detect the pixel regions of each frame that have a significant probability of containing an object to be tracked. This detector can be formed by a motion detector that detects all moving objects based on models of objects previously recorded in a database or based on characteristics extracted offline [40, 41]. The information obtained by the initial detector is processed by an image processor], which will have the function of eliminating noise, segmenting, and detecting the connected components.
The regions containing the most relevant pixels are analyzed and then classified as objects of interest by the classifier [50, 51, 52, 53, 54]. Objects of interest are modeled and are now called reference objects so that the tracker determines its position frame by frame [55, 56]. The information obtained by the initial detector is processed by an image processor], which will have the function of eliminating noise, segmenting, and detecting the connected components.
A tracker, an integral part of a detector, is defined as a function that allows estimating the position of objects at each consecutive frame, through and defines the region of the object of interest, for each
Main component of a monitoring.
Several techniques that allow the calculation to have been developed in recent years to calculate the optical flow vector [57]. These methods are grouped according to their main characteristics and the approach used for the calculation of the optical flow. Thus, the differential methods performed in the studies in [56], the methods d and calculation of the optical flow through the frequency domain [46] the phase correlation methods [58], and the method of association between regions [59].
The method proposed in [56], allows the calculation of the optical flow for each point around a neighborhood of pixels. In [60], it is also considered a neighborhood of pixels, but in this case, the calculation of the optical flow is performed geometrically. In the work presented by [61] it is adding of the restrictions of regularization. In [62] turn active compare performance analyses were performed between the various algorithms and optical flow present in the literature.
This technique is considered robust for detaining and tracking moving objects from your images, both those captured by fixed or mobile cameras. This gives this technique, but high computational cost makes most practical applications unfeasible. Thus, to reduce this complexity, techniques of increasing resolutions were adopted in [63]. Also, for the same purpose, we used the techniques of subsampling on some of the pixels belonging to the object of interest to obtain optical flow [52].
Other authors also use a point of interest detector to select the best pixels for tracking and calculate the optical flow on these points [52, 64]. The reduction in the number of points to be tracked is associated with a decrease in computational complexity, so in [52] the points of interest were selected using the FAST algorithm [64].
The method developed by Lucas-Kanade [56], it is a differential method and widely used in the literature and having variations modifications. It allows you to estimate the optical flow for each point
where
New variations of the techniques were being proposed to make the calculation of the optical flow faster and faster. In [65] a tracker was proposed based on the algorithm of [56]. The translation of a point represented by a grid of rectangular sized pixels 25 × 25, was calculated and its validity is evaluated by calculating the SSD1 in the grid pixels in
In [51] objects were detected by subtracting the image from the environment and removed the movement of the camera with the calculation algorithm of the optical flow vector proposed by [56]. In the studies carried out in [66, 67], they showed that the reliability of the estimated optical flow reduced the case of some points of the object of interest whose optical flow cannot be represented by the same matrix given by the related transformation
In [67] they also modified Lucas - Kanade’s algorithm [56] by inserting the Hessian matrix in the calculation of the value of the variation of the related transformation
Already in the proposal presented in [68] was the development of algorithm to detect people in infrared images that combines the information of the value of pixels with a method of motion detection. The algorithm forms a relevant pixel map by applying thresholding segmentation. While the camera is still, an image
The method for tracking swimmers presented in [46], uses the information of the movement pattern by the optical flow and the appearance of the water that is modeled by a MoG.2 This allows you to calculate an optical flow vector for each pixel of the video independently of the other, through
In [69], a method was presented that incorporated physical restrictions to the calculation of optical flow. The tracker uses the constraints to extract the moving pixels with a lower failure rate. The calculation can be impaired when occlusions occur or when the environment has low light. The operator defines the physical constraints and selects the points of the
In [70], the points that are tracked with the optical flow are defined by applying the Canny edge detector on the pixels of the reference pixel map. Pixels that produce a high response to the Canny detector are the selected points.
In [43], optical flow is used as a characteristic for tracking the contour of the object. The contour is shifted in small steps until the position in which the optical flow vectors are homogeneous is found.
In [64], they performed an estimate of the translation and orientation of the reference object by calculating the optical flow of the pixels belonging to its silhouette. The coordinates of the centroid position are defined by minimizing the Hausdorff distance between the mean of the optical flow vectors of the reference object and the candidate object to be chosen as the object of interest.
Optical flow is defined as a dense vector field associated with the movement and apparent velocity of an object, given by the translation of pixels from consecutive frames in an image region. It can be calculated from the brightness restriction, considered constant, from the corresponding pixels in consecutive frames.
Mathematically be a pixel
So that equation (7) is called optical flow restriction and where the terms
The number of variables in equation (6) is greater than that of equations, which does not allow estimating components and vector, and determining a single solution for the optical flow restriction equation. With this, Lucas and Kanade proposed a solution to solve this problem. The solution method proposed by them considers the constant flow in a region formed by a set of pixels
Passing the set of equations given by equation (8) to the matrix form we have:
Using the least squares method, in the system of equations (9) in the form of matricial, the same can be solved. Therefore, the optical flow
Where:
Therefore, one has that:
Thus:
This method has a reduced computational cost to determine optical flow estimation when compared to other methods because it is simple, that is, it is since the region in which the variation of light intensity between pixels is minimal has a size
To calculate the optical flow over the size region
Where the terms
In view of the small variations present and accumulated along the vector field associated with the optic flow, which cause an additional error in equation (13), a regularization adjustment was made, given by equation (14):
Thus, combining equations (13) and (14), the error
where
where
and replacing the coefficients
whereas
It is possible to reduce the data system by (17), such as:
where the term
Where
Therefore, isolating terms
The Algorithm 1 is a pseudocode to generate the proposed optical flow vector, through equations (24) and (25) and that allow estimating the speed and position of an object, through a sequence of video images.
Begin
For I = 1…N do
Convert images to a gray tone
Calculate the partial derivatives of 1°and 2°orders of
Calculate constants
Calculate the discretized Laplacians of
Calculating Gaussian curvature
Calculate flow components (
End For
End
Developed by Breiman [63] in the mid-2000s, and later revised in [71] random trees are considered one of the best-supervised learning methods used in data prediction and classification. Due to its simplicity, low computational cost, great potential to deal with a large volume of data, and still present great accuracy of results, currently this method has become very popular being applied in various fields of science as data science [72]. Bioinformatics, Ecology, in real-life systems and recognition of 3D objects. In recent years, several studies have been conducted with the objective of making the technique more elaborate and seeking new practical applications [73, 74, 75].
Many studies were carried out with the aim of narrowing the existing gap between theory and practice can be seen in [58, 76, 77, 78]. Among the main components of random tree forests, one can highlight the bagging method [63], and the criterion of classification and regression called cart
Bagging (a bootstrap-aggregating contraction) is an aggregation scheme, which generates samples through the bootstrap method, from the original dataset. These methods are nonparametric and belong to the Monte Carlos method class [80], treating the sample as a finite population. Still, these methods are used when the distribution of the target population is not specified, and the sample is the only information available. How in this way a predictor of each sample is constructed, so that the decision is made through an average, and is more effective computational procedures to improve the indexable estimates, especially for large sets of high-dimensional data, where finding a good model in one step is impossible due to the complexity and scale of the problem. As for the cart-split criterion, it originates from the CART program [63], and is used in the construction of individual trees to choose the best cuts perpendicular to the Axes. However, while bagging and the CART division scheme are key elements in the random forest, both are difficult to mathematically analyze and are a very promising field for both theoretical and practical research.
In general, the set of trees is organized in the form of {
The methodology employed consisted of combining the optical flow algorithm in terms of Gaussian curvature, developed in this work together with the technique of random forest. The language used for the development of this algorithm was the MATLAB programming language, executed on a 64-bit 8th generation notebook, CORE i7. The input data is a video extension Avi, lasting 5 min of a vehicle and two cyclists, circulating in the vicinity of the beach of Costa Nova, in the locality of Ilhavo, in Aveiro, Portugal. The video was fragmented into a set of frames, analyzed two by two by the algorithm for the generation of the vector field of optical flow. After that, the resulting image associated with the flow and a minimal surface region, given by the Gaussian curvature. Next on this surface, the random trees analyzed which vectors presented important characteristics to characterize in an “optimal” way, the movement of the object (see Figure 2).
Representative model of operation of a random forest.
After finishing the process of analysis of the movement of the objects, the execution times and accuracy of the results obtained by the proposed algorithm were compared in relation to the algorithms of Lucas Kanade, Horn and Shunck, Farneback and Lucas Kanade with or without Gaussian filter, allowing to validate the results obtained. After that, the implementation of the developed algorimo began.
Figure 3 shows the vehicle and the two cyclists that were used to collect the image to which the results proposed in this work were obtained so that the choice was random on the right side. A graphical representation of the vector field of optical flow generated by the sequence of two consecutive frames, over 5 minutes of video is shown.
(a) Left side: vehicle shift between moments
On the right side of Figure 3, the optical flow associated with the movement of the vehicle between the time intervals from
The region with the highest horizontal vector density in Figure 3 is located on the left side, in blue. It is also observed that the number of vectors in this region, despite being spaced, starting from the center to the left, is greater in relation to the number of vectors on the right side. It is also possible, through it, to visually evaluate the movement behavior of the considered objects. This region, containing a higher vector density, corresponds to the current direction in which the object is heading and its predicted displacement. It is also possible to observe that this vector density increases towards the left side, passing through the central part, coming from the right, clearly indicating the direction of movement of the object, that is, the object moves to the left. In Figure 4, this process can be understood more clearly.
Prediction and actual displacement of the object obtained through the optical flow.
In a similar way to the one mentioned in Figure 3, on the right side of Figure 5, the optical flow generated by the displacement of the moving vehicle is represented, between the instants
Object remains on the right side, but with a medium offset to the right and displacement estimate still to the left.
It is possible to observe a small increase in the vector density to the left, but that has a great influence on the determination of the real and predicted position of the object in the considered time intervals. The Object continues with its actual movement to the left, as well as the predicted movement of the object to the left. However, he showed a slight movement to the left (direction where the cyclists are).
In Figure 6, a small variation of the optical flow is observed again in the associated movement between the instants
Object remains on the right side, but with a slight shift to the right and offset estimate to the left.
In Figure 7, there was no optical flow variation in the associated movement between the time intervals
Object moving and keeping on the left for consecutive frames.
In Figure 8, the vehicle can be seen completely overtaking the two cyclists and approaching another vehicle in the opposite direction in the upper part of the image (left). The variation of the optical flow vector field remains the same. This indicates that the vehicle continues its trajectory, on the left side to the cyclists, however without posing a danger of collision for the other vehicle in the opposite direction.
Object with unchanged offset pattern.
This item will show how the performance evaluation of the proposed algorithm and accuracy was performed in relation to the Algorithms of Luca and Kanade, with or without Gaussian filter, Horn and Schunck, and Farneback.
The algorithm allowed to show on the display in real-time the displacement of the object on the right side and the set of vectors capable of representing the movement of the real-time or accumulated indicating the tendency, in this case, of the direction that the object should perform. This process was carried out in a similar way, using the other algorithms to make it possible to compare them. The behavior of the proposed algorithm and the other will be graphically shown.
The technique developed in this work allowed to generate an optical flow considering important geometric properties allowing to identify similar categories of moving objects and same characteristics. These geometric properties are intrinsically associated with the curvature of the object’s surface in three-dimensional space, called Gaussian curvature, in this case in a 2D image.
The modified optical flow, considering these properties, generated a dense optical flow, allowing the generation of a band, describing a track on the 2D plane. This allowed tracking the movement of the considered object. In the same Figure 8, it is possible to observe that at each time interval in which the object was monitored, the dispositions of the vectors for the left and right sides, as shown in Figures 3–7 were responsible for drawing the track associated with the displaced and that allowed tracking the object as it moves.
Figure 9 shows the vehicle that, when moving, generated the optical flow. In Figures 10 and 11, the variations of the optical flow between two-time intervals,
Variation of the optical flow of the moving object.
Vehicle movement.
Object moving to the left side.
In the following items, the implementations of the Lucas and Kanade algorithms without or with a Gaussian filter, Horn and Schunck, and Farneback will be shown, using as input data the same sequence of video images used in the algorithm developed in this work. For each, the performance and accuracy obtained will be verified.
For each of the 5 algorithms, 1 frame is shown containing 4 figures, with 2 upper and 2 lower. In each frame, the figure at the top left shows the variation of the vector field between two frames. The right frame, on the other hand, corresponds to the variation of the object’s movement in real-time. The lower ones, except for the proposed algorithm, correspond to the number of points on the right or the left, and with this, the movement will occur to the side that has the greatest number of points. In the case of the proposed algorithm, the process will take place through the analysis of vector density. So, to the side where there is greater vector density, this is the side to which the movement will be occurring (see Figures 12–20).
Object moving to the right side.
Variation of the optical flow of the moving object.
Vehicle movement.
Object moving to the left side.
Object moving to the right side.
Variation of the optical flow of the moving object.
Vehicle movement.
Object moving to the left side.
Object moving to the right side.
Comparing the results presented by the algorithms, it is observed that in the developed model, it was possible to see a dense vector trail of the object, with a slight tendency of displacement to the left, as it continues its movement. In the other models, this was not possible, and it is necessary to resort to a score of points, in the lower table. This process is also possible in the proposed model, but not necessary, which means a reduction in computational cost (see Figures 21–28).
Variation of the optical flow of the moving object.
Vehicle movement.
Object moving to the right side.
Object moving to the left side.
Variation of the optical flow of the moving
Vehicle movement.
Object moving to the left side.
Object moving to the left side.
Comparing the results, it is observed that the Farneback algorithm also presents high vector density. But the proposed model, as previously said, presents a well-defined vector trail which suggests the non-use of the point count in the lower frame, which does not occur for the Farneback algorithm, indicating higher computational cost, which can affect the accuracy of this algorithm when compared to the proposed algorithm.
Comparing the Horn and Schunck algorithm, a low vector density is observed when compared to the proposed algorithm, which indicates lower accuracy when compared to the proposed algorithm.
Although the two techniques of Lucas and Kanade, are faster applications, indicating low computational cost when compared to the proposed algorithm, the factor of low vector density results in low precision in relation to the proposed method.
The proposed method presented good results, showing to be accurate and reasonable speed. This allows this application to be used in critical problems, i.e., to real-world problems. However, it presented limitations that could be verified when compared to the model with Lucas and Kanade, with a Gaussian filter, which is faster and presents good accuracy.
The proposed Method reached only approximately 50% execution speed in relation to the Lucas and Kanade Method, which motivates further improvements to the Method. The technique presented can be applied to other fields of research as in cardiology due to presenting great precision when submitted to small region, which is important because it can be applied with the objective of predicting infarctions and as a current contribution, for the state of the art is to characterize the optical flow in terms of Gaussian curvature, that makes it possible to highlight fields of research such as computational vision and differential geometry.
The authors of this work would like to thank the Institute of Electronics and Informatics Engineering of Aveiro, the Telecommunications Institute of Aveiro, and the University of Aveiro for the financial, technical-administrative, and structural support provided that allowed the accomplishment of this work.
Edited by Jan Oxholm Gordeladze, ISBN 978-953-51-3020-8, Print ISBN 978-953-51-3019-2, 336 pages,
\nPublisher: IntechOpen
\nChapters published March 22, 2017 under CC BY 3.0 license
\nDOI: 10.5772/61430
\nEdited Volume
This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\\n\\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\\n\\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\\n\\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\\n\\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\\n\\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\\n\\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\\n\\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\\n\\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\\n\\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\\n\\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\\n\\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
\\n"}]'},components:[{type:"htmlEditorComponent",content:'This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\n\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\n\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\n\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\n\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\n\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\n\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\n\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\n\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\n\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\n\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\n\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
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In such cases, the said graph theory and matrix method provides very suitable and fruitful solutions to make the decision to its final effective extent. The further improvements and the outcome enhancement can also be revealed through the use of combined practice of graph theory results along with some artificial intelligence-inspired logics and practices such as fuzzy logic, artificial neural network, etc. The significance and applicability of said method in vast fields of science, engineering, and research are also proved. Nowadays, our manufacturing sectors are getting up to date through the applications of artificial intelligence and several software-based directions. This is all to enhance the overall machine system performance with a view to improve desired performance characteristics of the process under the study. 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The use of clinical DSS in AF management is ubiquitous, starting from detection of AF through sophisticated electrophysiology treatment procedures, all the way to monitoring the patient's health during follow‐ups. Most of the software associated with AF DSS are developed based on signal processing, image processing, and artificial intelligence techniques. The chapter begins with a brief description of DSS in general and then introduces DSS that are used for various clinical applications. The chapter continues with a background on AF and some relevant mechanisms. 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The prosthesis design, control, and architecture completely changed with the change in the amputation level. The transradial amputee stump design, electronics, battery, and circuit placement change significantly with the change of the residual arm of the amputee. This leads to designing the prostheses with the focus of the amputation level and ease of customization. Recent development in the 3D printing and open source prosthetic design leads the user to choose, modify, and print the prostheses with the required sets of functionalities. In this chapter, a brief introduction of the prostheses has been given, starting with the types of prostheses according to the level of amputation and functionality. Then, the state-of-the-art prostheses available commercially and under research will be introduced. Afterward, the 3D printed prostheses are discussed. 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Because this type of comparison is not present in the literature yet, this work wants to place the emphasis on the fact that till now there is not a unique approach to solve problems of geometry assurance and no approach can be defined as better than another, in terms of results.",book:{id:"5379",slug:"computer-aided-technologies-applications-in-engineering-and-medicine",title:"Computer-aided Technologies",fullTitle:"Computer-aided Technologies - Applications in Engineering and Medicine"},signatures:"Andrea Corrado and Wilma Polini",authors:[{id:"16151",title:"Dr.",name:"Wilma",middleName:null,surname:"Polini",slug:"wilma-polini",fullName:"Wilma Polini"},{id:"190416",title:"Ph.D. Student",name:"Andrea",middleName:null,surname:"Corrado",slug:"andrea-corrado",fullName:"Andrea Corrado"}]},{id:"67441",title:"Human-Computer/Device Interaction",slug:"human-computer-device-interaction",totalDownloads:764,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Any interaction refers to the communication between two or more entities (be it abstract/conceptual or physical entity). Successful interaction is equated from the properties of each entity involved in the interaction as well as the capabilities of the interacting entities. With the diversified use and application of computers and specialized devices for specific tasks, such as biomechanical and biomedical devices, interaction design needs to further study the context of the tasks as well. Moreover, with the inclusion of embedded systems and smart devices, instead of focusing only on the hardware performance, the computer architecture needs to consider the opportunities. Especially, HCI can be improved as the current technologies are giving an opportunity for building smart interaction where the user interacts with devices implicitly and in less obtrusive way. In light of this, the design and architecture of an engineered product need to strive for making the product usable and used while making it useful to the user. And this can be achieved if interaction design is dictated by scrutinizing the user model with respects to the usability attributes in view of the context of its task as well as the platform capabilities and constraints as discussed in this chapter.",book:{id:"8347",slug:"computer-architecture-in-industrial-biomechanical-and-biomedical-engineering",title:"Computer Architecture in Industrial, Biomechanical and Biomedical Engineering",fullTitle:"Computer Architecture in Industrial, Biomechanical and Biomedical Engineering"},signatures:"Dagmawi Lemma Gobena",authors:[{id:"284850",title:"Dr.",name:"Dagmawi Lemma",middleName:null,surname:"Gobena",slug:"dagmawi-lemma-gobena",fullName:"Dagmawi Lemma Gobena"}]},{id:"67484",title:"Human Behavior Modeling: The Necessity of Narrative",slug:"human-behavior-modeling-the-necessity-of-narrative",totalDownloads:932,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"As progress is made in the development of artificial intelligent mechanisms to assist human research into aspects of industrial, biomechanical and biomedical engineering, the conceptualization of mental behavior of human entities become more vital and more central to the success of any interaction between machine and humans. This discussion explores one of the most important features of human behavior, the fundamental and irreversible concept of narrative. The narrative is the essential construct for the theoretical understanding and presentation of human communication, including formal and informal logic, emotional wonder and desperation, noble and selfish biases, nationalism and globalist politics, and any form of spiritualism. This presentation offers a working definition of human narrative and proposes its basic structure that must be represented by any computer system which is required to deal with human behavior.",book:{id:"8347",slug:"computer-architecture-in-industrial-biomechanical-and-biomedical-engineering",title:"Computer Architecture in Industrial, Biomechanical and Biomedical Engineering",fullTitle:"Computer Architecture in Industrial, Biomechanical and Biomedical Engineering"},signatures:"Roger Parker",authors:[{id:"284043",title:"Ph.D.",name:"Roger",middleName:null,surname:"Parker",slug:"roger-parker",fullName:"Roger Parker"}]},{id:"66967",title:"Decision-Making in Real-Life Industrial Environment through Graph Theory Approach",slug:"decision-making-in-real-life-industrial-environment-through-graph-theory-approach",totalDownloads:841,totalCrossrefCites:9,totalDimensionsCites:9,abstract:"The approach called as “graph theory and matrix approach” has been well employed in numerous research studies with a view to perform the decision-making while the situation is becoming perplexed type or where there is a very strong relative importance of one parameter over another. In such cases, the said graph theory and matrix method provides very suitable and fruitful solutions to make the decision to its final effective extent. The further improvements and the outcome enhancement can also be revealed through the use of combined practice of graph theory results along with some artificial intelligence-inspired logics and practices such as fuzzy logic, artificial neural network, etc. The significance and applicability of said method in vast fields of science, engineering, and research are also proved. Nowadays, our manufacturing sectors are getting up to date through the applications of artificial intelligence and several software-based directions. This is all to enhance the overall machine system performance with a view to improve desired performance characteristics of the process under the study. Few sections of this chapter has also elaborated the utility of the artificial intelligence-inspired fuzzy logic-based decision system which has already been a part of previous researches.",book:{id:"8347",slug:"computer-architecture-in-industrial-biomechanical-and-biomedical-engineering",title:"Computer Architecture in Industrial, Biomechanical and Biomedical Engineering",fullTitle:"Computer Architecture in Industrial, Biomechanical and Biomedical Engineering"},signatures:"Ravi Pratap Singh, Ravinder Kataria and Sandeep Singhal",authors:[{id:"265010",title:"Dr.",name:"Ravi Pratap",middleName:null,surname:"Singh",slug:"ravi-pratap-singh",fullName:"Ravi Pratap Singh"},{id:"265017",title:"Dr.",name:"Ravinder",middleName:null,surname:"Kataria",slug:"ravinder-kataria",fullName:"Ravinder Kataria"},{id:"271624",title:"Dr.",name:"Sandeep",middleName:null,surname:"Singhal",slug:"sandeep-singhal",fullName:"Sandeep Singhal"}]}],onlineFirstChaptersFilter:{topicId:"716",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:317,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He is currently the Director of the Postgraduate Program in Implantology of the Bioface/UCAM/PgO (Montevideo, Uruguay), Director of the Cathedra of Biotechnology of the Catholic University of Murcia (Murcia, Spain), an Extraordinary Full Professor of the Catholic University of Murcia (Murcia, Spain) as well as the Director of the private center of research Biotecnos – Technology and Science (Montevideo, Uruguay). Applied biomaterials, cellular and molecular biology, and dental implants are among his research interests. He has published several original papers in renowned journals. 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She holds a degree in Dentistry from the Federal University of Alfenas (UNIFAL), while her specialization and professional improvement in Stomatology took place at Hospital Heliopolis (São Paulo, SP). Her qualifications are: a specialist in Dental Imaging and Radiology, Master in Dentistry (Periodontics) from the University of São Paulo (FORP-USP, Ribeirão Preto, SP), and Doctor (Ph.D.) in Dentistry (Stomatology Clinic) from Hospital São Lucas of the Pontifical Catholic University of Rio Grande do Sul (HSL-PUCRS, Porto Alegre, RS). She held a postdoctoral internship at the Federal University from Jequitinhonha and Mucuri Valleys (UFVJM, Diamantina, MG). She is currently a member of the Brazilian Society for Dental Research (SBPqO) and the Brazilian Society of Stomatology and Pathology (SOBEP). 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Her Ph.D. research work on the soft tissue-implant interface at the University of Sheffield has yielded several important publications in the key implant journals. She was awarded an Excellent Exchange Award by the University of Sheffield which gave her the opportunity to work at the famous Faculty of Dentistry of the University of Gothenburg, Sweden, under the tutelage of Prof. Peter Thomsen. In 2016, she was appointed as a visiting scholar at UCLA, USA, with attachment in Hospital Dentistry, and involvement in research work related to zirconia implant. In 2016, her contribution to dentistry was recognized by the Royal College of Surgeon of Edinburgh with her being awarded a Fellowship in Dental Surgery. She has authored numerous papers published both in local and international journals. She was the Editor of the Malaysian Dental Journal for several years. 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His passion for teaching then led him to join the faculty of dentistry at University Malaya and he has since became a valuable lecturer and clinical specialist in the Department of Restorative Dentistry. He is currently the removable prosthodontic undergraduate year 3 coordinator, head of the undergraduate module on occlusion and a member of the multidisciplinary team for the TMD clinic. He has previous membership in the British Society for Restorative Dentistry, the Malaysian Association of Aesthetic Dentistry and he is currently a lifetime member of the Malaysian Association for Prosthodontics. Currently, he is also the examiner for the Restorative Specialty Membership Examinations, Royal College of Surgeons, England. He has authored and co-authored handful of both local and international journal articles. 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She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",institutionURL:null,country:{name:"Turkey"}}}]},{type:"book",id:"7139",title:"Current Approaches in Orthodontics",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7139.jpg",slug:"current-approaches-in-orthodontics",publishedDate:"April 10th 2019",editedByType:"Edited by",bookSignature:"Belma Işık Aslan and Fatma Deniz Uzuner",hash:"2c77384eeb748cf05a898d65b9dcb48a",volumeInSeries:2,fullTitle:"Current Approaches in Orthodontics",editors:[{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null}]},{type:"book",id:"7572",title:"Trauma in Dentistry",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7572.jpg",slug:"trauma-in-dentistry",publishedDate:"July 3rd 2019",editedByType:"Edited by",bookSignature:"Serdar Gözler",hash:"7cb94732cfb315f8d1e70ebf500eb8a9",volumeInSeries:3,fullTitle:"Trauma in Dentistry",editors:[{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. 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