EM solving and sampling parameters of a simulation example.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"},{slug:"intechopen-identified-as-one-of-the-most-significant-contributor-to-oa-book-growth-in-doab-20210809",title:"IntechOpen Identified as One of the Most Significant Contributors to OA Book Growth in DOAB"}]},book:{item:{type:"book",id:"6129",leadTitle:null,fullTitle:"Kalman Filters - Theory for Advanced Applications",title:"Kalman Filters",subtitle:"Theory for Advanced Applications",reviewType:"peer-reviewed",abstract:"This book presents recent issues on theory and practice of Kalman filters, with a comprehensive treatment of a selected number of concepts, techniques, and advanced applications. From an interdisciplinary point of view, the contents from each chapter bring together an international scientific community to discuss the state of the art on Kalman filter-based methodologies for adaptive/distributed filtering, optimal estimation, dynamic prediction, nonstationarity, robot navigation, global navigation satellite systems, moving object tracking, optical communication systems, and active power filters, among others. The theoretical and methodological foundations combined with extensive experimental explanation make this book a reference suitable for students, practicing engineers, and researchers in sciences and engineering.",isbn:"978-953-51-3828-0",printIsbn:"978-953-51-3827-3",pdfIsbn:"978-953-51-4038-2",doi:"10.5772/intechopen.68249",price:139,priceEur:155,priceUsd:179,slug:"kalman-filters-theory-for-advanced-applications",numberOfPages:314,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"7341ee991eeb22a10a8b14bcd53a24b6",bookSignature:"Ginalber Luiz de Oliveira Serra",publishedDate:"February 21st 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6129.jpg",numberOfDownloads:21378,numberOfWosCitations:30,numberOfCrossrefCitations:36,numberOfCrossrefCitationsByBook:7,numberOfDimensionsCitations:50,numberOfDimensionsCitationsByBook:8,hasAltmetrics:0,numberOfTotalCitations:116,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 18th 2017",dateEndSecondStepPublish:"May 9th 2017",dateEndThirdStepPublish:"August 5th 2017",dateEndFourthStepPublish:"November 3rd 2017",dateEndFifthStepPublish:"January 2nd 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"11032",title:"Dr.",name:"Ginalber Luiz",middleName:"De Oliveira",surname:"Serra",slug:"ginalber-luiz-serra",fullName:"Ginalber Luiz Serra",profilePictureURL:"https://mts.intechopen.com/storage/users/11032/images/3436_n.jpg",biography:"Dr. Ginalber L. O. Serra was born in São Luis, Maranhão, Brazil, in 1976. He received the B.Sc. and M. Sc. degrees in electrical engineering from Federal University of Maranhão, Maranhão, Brazil, in 1999 and 2001, respectively. He received the Ph.D. degree in electrical engineering from State University of Campinas (UNICAMP), Campinas, Brazil, on September 2005. He has finished his Postdoctoral research on multivariable neuro-fuzzy adaptive control of nonlinear systems at the Department of Machines, Components and Intelligent Systems, State University of Campinas, Campinas, Brazil, on September 2006 and he has served as researcher with the Department of Electrical Engineering at the University of Santiago, Santiago, Chile, in 2007. Currently, Dr. Serra is with the Department of ElectroElectronics at Federl Institute of Education, Cience and Technology, São Luis, Maranhão, Brazil. Dr. Serra has served as Reviewer for many prestigious international journals, including the IEEE TRANSACTIONS ON FUZZY SYSTEMS, IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS - PART B: CYBERNETICS, IEE CONTROL THEORY AND APPLICATIONS, and for papers from {\\\\em IEEE Conferences}. His research interests include fuzzy systems, neural networks and genetic algorithms applied to identification and control of nonlinear systems.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Instituto Federal do Maranhão",institutionURL:null,country:{name:"Brazil"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1401",title:"Control Theory",slug:"applied-mathematics-control-theory"}],chapters:[{id:"58317",title:"A Reference Recursive Recipe for Tuning the Statistics of the Kalman Filter",doi:"10.5772/intechopen.71961",slug:"a-reference-recursive-recipe-for-tuning-the-statistics-of-the-kalman-filter",totalDownloads:1463,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The philosophy and the historical development of Kalman filter from ancient times to the present is followed by the connection between randomness, probability, statistics, random process, estimation theory, and the Kalman filter. A brief derivation of the filter is followed by its appreciation, aesthetics, beauty, truth, perspectives, competence, and variants. The menacing and notorious problem of specifying the filter initial state, measurement, and process noise covariances and the unknown parameters remains in the filter even after more than five decades of enormous applications in science and technology. Manual approaches are not general and the adaptive ones are difficult. The proposed reference recursive recipe (RRR) is simple and general. The initial state covariance is the probability matching prior between the Frequentist approach via optimization and the Bayesian filtering. The filter updates the above statistics after every pass through the data to reach statistical equilibrium within a few passes without any optimization. Further many proposed cost functions help to compare the present and earlier approaches. The efficacy of the present RRR is demonstrated by its application to a simulated spring, mass, and damper system and a real airplane flight data having a larger number of unknown parameters and statistics.",signatures:"Mudambi R Ananthasayanam",downloadPdfUrl:"/chapter/pdf-download/58317",previewPdfUrl:"/chapter/pdf-preview/58317",authors:[{id:"209685",title:"Prof.",name:"Mudambi",surname:"Ananthasayanam",slug:"mudambi-ananthasayanam",fullName:"Mudambi Ananthasayanam"}],corrections:null},{id:"58052",title:"The Error Covariance Matrix Inflation in Ensemble Kalman Filter",doi:"10.5772/intechopen.71960",slug:"the-error-covariance-matrix-inflation-in-ensemble-kalman-filter",totalDownloads:1289,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The estimation accuracy of ensemble forecast errors is crucial to the assimilation results for all ensemble-based schemes. The ensemble Kalman filter (EnKF) is a widely used scheme in land surface data assimilation, without using the adjoint of a dynamical model. In EnKF, the forecast error covariance matrix is estimated as the sampling covariance matrix of the ensemble forecast states. However, past researches on EnKF have found that it can generally lead to an underestimate of the forecast error covariance matrix, due to the limited ensemble size, as well as the poor initial perturbations and model error. This can eventually result in filter divergence. Therefore, using inflation to further adjust the forecast error covariance matrix becomes increasingly important. In this chapter, a new structure of the forecast error covariance matrix is proposed to mitigate the problems with limited ensemble size and model error. An adaptive procedure equipped with a second-order least squares method is applied to estimate the inflation factors of forecast and observational error covariance matrices. The proposed method is tested on the well-known atmosphere-like Lorenz-96 model with spatially correlated observational systems. The experiment results show that the new structure of the forecast error covariance matrix and the adaptive estimation procedure lead to improvement of the analysis states.",signatures:"Guocan Wu and Xiaogu Zheng",downloadPdfUrl:"/chapter/pdf-download/58052",previewPdfUrl:"/chapter/pdf-preview/58052",authors:[{id:"210688",title:"Dr.",name:"Guocan",surname:"Wu",slug:"guocan-wu",fullName:"Guocan Wu"}],corrections:null},{id:"57977",title:"Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics",doi:"10.5772/intechopen.71900",slug:"unscented-kalman-filter-for-state-and-parameter-estimation-in-vehicle-dynamics",totalDownloads:1948,totalCrossrefCites:10,totalDimensionsCites:11,hasAltmetrics:0,abstract:"Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers’ safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for high-precision measurements of the vehicle’s states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces.",signatures:"Mark Wielitzka, Alexander Busch, Matthias Dagen and Tobias\nOrtmaier",downloadPdfUrl:"/chapter/pdf-download/57977",previewPdfUrl:"/chapter/pdf-preview/57977",authors:[{id:"122290",title:"Dr.",name:"Tobias",surname:"Ortmaier",slug:"tobias-ortmaier",fullName:"Tobias Ortmaier"},{id:"201140",title:"M.Sc.",name:"Mark",surname:"Wielitzka",slug:"mark-wielitzka",fullName:"Mark Wielitzka"},{id:"202801",title:"M.Sc.",name:"Matthias",surname:"Dagen",slug:"matthias-dagen",fullName:"Matthias Dagen"},{id:"222868",title:"MSc.",name:"Alexander",surname:"Busch",slug:"alexander-busch",fullName:"Alexander Busch"}],corrections:null},{id:"58292",title:"Sensitivity-Based Adaptive SRUKF for State, Parameter, and Covariance Estimation on Mechatronic Systems",doi:"10.5772/intechopen.72470",slug:"sensitivity-based-adaptive-srukf-for-state-parameter-and-covariance-estimation-on-mechatronic-system",totalDownloads:1340,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Since the initial developments in the state-space theory in the 1950s and 1960s, the state estimation has become an extensively researched and applied discipline. All systems that can be modelled mathematically are candidates for state estimators. The state estimators reconstruct the states that represent internal conditions and status of a system at a specific instant of time using a mathematical model and the information received from the system sensors. Moreover, the estimator can be extended for system parameter estimation. The resulting Kalman filter (KF) derivatives for state and parameter estimation also require knowledge about the noise statistics of measurements and the uncertainties of the system model. These are often unknown, and an inaccurate parameterization may lead to decreased filter performance or even divergence. Additionally, insufficient system excitation can cause parameter estimation drifts. In this chapter, a sensitivity-based adaptive square-root unscented KF (SRUKF) is presented. This filter combines a SRUKF and the recursive prediction-error method to estimate system states, parameters and covariances online. Moreover, local sensitivity analysis is performed to prevent parameter estimation drifts, while the system is not sufficiently excited. The filter is evaluated on two testbeds based on an axis serial mechanism and compared with the joint state and parameter UKF.",signatures:"Mauro Hernán Riva, Mark Wielitzka and Tobias Ortmaier",downloadPdfUrl:"/chapter/pdf-download/58292",previewPdfUrl:"/chapter/pdf-preview/58292",authors:[{id:"122290",title:"Dr.",name:"Tobias",surname:"Ortmaier",slug:"tobias-ortmaier",fullName:"Tobias Ortmaier"},{id:"201140",title:"M.Sc.",name:"Mark",surname:"Wielitzka",slug:"mark-wielitzka",fullName:"Mark Wielitzka"},{id:"210832",title:"M.Sc.",name:"Mauro",surname:"Riva",slug:"mauro-riva",fullName:"Mauro Riva"}],corrections:null},{id:"57768",title:"Kalman Filters for Parameter Estimation of Nonstationary Signals",doi:"10.5772/intechopen.71874",slug:"kalman-filters-for-parameter-estimation-of-nonstationary-signals",totalDownloads:1543,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"An adaptive Taylor-Kalman filter with PSO tuning for tracking nonstationary signal parameters in a noisy environment with primary focus on time-varying power signals has been presented in this piece of work. In order to deal with the dynamic envelope of the power signal, second-order Taylor expansion has been used such that the Taylor coefficients are updated with the PSO-tuned Taylor-Kalman Filter algorithm. In addition to this, for fast convergence, a self-adaptive particle swarm optimization technique has been used for obtaining the optimal values of model and measurement error covariances of the Kalman filter. The proposed algorithm is linear and therefore has less computational burden, which is easier to be implemented on a hardware platform like DSP processor or FPGA. The proposed PSO-tuned Taylor-Kalman filter exhibits robust tracking capabilities even under changing signal dynamics, immune to critical noise conditions, harmonic contaminations, and also reveals excellent convergence properties.",signatures:"Sarita Nanda",downloadPdfUrl:"/chapter/pdf-download/57768",previewPdfUrl:"/chapter/pdf-preview/57768",authors:[{id:"209587",title:"Dr.",name:"Sarita",surname:"Nanda",slug:"sarita-nanda",fullName:"Sarita Nanda"}],corrections:null},{id:"57455",title:"Kalman Filter Models for the Prediction of Individualised Thermal Work Strain",doi:"10.5772/intechopen.71205",slug:"kalman-filter-models-for-the-prediction-of-individualised-thermal-work-strain",totalDownloads:1204,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,abstract:"It is important to monitor and assess the physiological strain of individuals working in hot environments to avoid heat illness and performance degradation. The body core temperature (Tc) is a reliable indicator of thermal work strain. However, measuring Tc is invasive and often inconvenient and impractical for real-time monitoring of workers in high heat strain environments. Seeking a better solution, the main aim of the present study was to investigate the Kalman filter method to enable the estimation of heat strain from non-invasive measurements (heart rate (HR) and chest skin temperature (ST)) obtained ‘online’ via wearable body sensors. In particular, we developed two Kalman filter models. First, an extended Kalman filter (EFK) was implemented in a cubic state space modelling framework (HR versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR and ST) as the time update model. Under the second model, the online Kalman filter (OFK) approach builds up the time update equation depending only on the initial value of Tc and the latest value of the exogenous variables. Both models were trained and validated using data from laboratory- and outfield-based heat strain profiling studies in which subjects performed a high intensity military foot march. While both the EKF and OKF models provided satisfactory estimates of Tc, the results showed an overall superior performance of the OKF model (overall root mean square error, RMSE = 0.31°C) compared to the EKF model (RMSE = 0.45°C).",signatures:"Jia Guo, Ying Chen, Weiping Priscilla Fan, Si Hui Maureen Lee,\nJunxian Ong, Poh Ling Tan, Yu Li Lydia Law, Kai Wei Jason Lee and\nKok-Yong Seng",downloadPdfUrl:"/chapter/pdf-download/57455",previewPdfUrl:"/chapter/pdf-preview/57455",authors:[{id:"171298",title:"Dr.",name:"Kok-Yong",surname:"Seng",slug:"kok-yong-seng",fullName:"Kok-Yong Seng"},{id:"209402",title:"Dr.",name:"Ying",surname:"Chen",slug:"ying-chen",fullName:"Ying Chen"},{id:"209404",title:"Dr.",name:"Jia",surname:"Guo",slug:"jia-guo",fullName:"Jia Guo"},{id:"220688",title:"Ms.",name:"Weiping Priscilla",surname:"Fan",slug:"weiping-priscilla-fan",fullName:"Weiping Priscilla Fan"},{id:"220689",title:"Ms.",name:"Si Hui Maureen",surname:"Lee",slug:"si-hui-maureen-lee",fullName:"Si Hui Maureen Lee"},{id:"220690",title:"Mr.",name:"Junxian",surname:"Ong",slug:"junxian-ong",fullName:"Junxian Ong"},{id:"220691",title:"Ms.",name:"Poh Ling",surname:"Tan",slug:"poh-ling-tan",fullName:"Poh Ling Tan"},{id:"220692",title:"Ms.",name:"Yu Li Lydia",surname:"Law",slug:"yu-li-lydia-law",fullName:"Yu Li Lydia Law"},{id:"220693",title:"Dr.",name:"Kai Wei Jason",surname:"Lee",slug:"kai-wei-jason-lee",fullName:"Kai Wei Jason Lee"}],corrections:null},{id:"57512",title:"Application of Kalman Filtering in Dynamic Prediction for Corporate Financial Distress",doi:"10.5772/intechopen.71616",slug:"application-of-kalman-filtering-in-dynamic-prediction-for-corporate-financial-distress",totalDownloads:1388,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter aims to dynamically improve the method of predicting financial distress based on Kalman filtering. Financial distress prediction (FDP) is an important study area of corporate finance. The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the state-space method, we establish two models that are used to describe the dynamic process and discriminant rules of financial distress, respectively, that is, a process model and a discriminant model. These two models collectively are called dynamic prediction models for financial distress. The operation of the dynamic prediction is achieved by Kalman filtering algorithm, and further, a general n-step-ahead prediction algorithm based on Kalman filtering is derived for prospective prediction. We also conduct an empirical study for China’s manufacturing industry, and the results have proved the accuracy and advance of predicting financial distress in such case.",signatures:"Qian Zhuang",downloadPdfUrl:"/chapter/pdf-download/57512",previewPdfUrl:"/chapter/pdf-preview/57512",authors:[{id:"209794",title:"Dr.",name:"Qian",surname:"Zhuang",slug:"qian-zhuang",fullName:"Qian Zhuang"}],corrections:null},{id:"57651",title:"Predicting Collisions in Mobile Robot Navigation by Kalman Filter",doi:"10.5772/intechopen.71653",slug:"predicting-collisions-in-mobile-robot-navigation-by-kalman-filter",totalDownloads:1116,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The growing trend of the use of robots in many areas of daily life makes it necessary to search for approaches to improve efficiency in tasks performed by robots. For that reason, we show, in this chapter, the application of the Kalman filter applied to the navigation of mobile robots, specifically the Time-to-contact (TTC) problem. We present a summary of approaches that have been taken to address the TTC problem. We use a monocular vision-based approach to detect potential obstacles and follow them over time through their apparent size change. Our approach collects information about obstacle data and models the behavior while the robot is approaching the obstacle, in order to predict collisions. We highlight some characteristics of the Kalman filter applied to our problem. Finally, we show of our results applied to sequences composed of 210 frames in different real scenarios. The results show a fast convergence of the model to the data and good fit even with noisy measures.",signatures:"Angel Sánchez, Homero Ríos, Gustavo Quintana and Antonio Marín",downloadPdfUrl:"/chapter/pdf-download/57651",previewPdfUrl:"/chapter/pdf-preview/57651",authors:[{id:"210685",title:"MSc.",name:"Angel",surname:"Sanchez Garcia",slug:"angel-sanchez-garcia",fullName:"Angel Sanchez Garcia"},{id:"211071",title:"Dr.",name:"Homero",surname:"Rios Figueroa",slug:"homero-rios-figueroa",fullName:"Homero Rios Figueroa"},{id:"211072",title:"Dr.",name:"Antonio",surname:"Marin Hernandez",slug:"antonio-marin-hernandez",fullName:"Antonio Marin Hernandez"},{id:"219509",title:"Dr.",name:"Gustavo",surname:"Quintana Carapia",slug:"gustavo-quintana-carapia",fullName:"Gustavo Quintana Carapia"}],corrections:null},{id:"59091",title:"Efficient Matrix-Free Ensemble Kalman Filter Implementations: Accounting for Localization",doi:"10.5772/intechopen.72465",slug:"efficient-matrix-free-ensemble-kalman-filter-implementations-accounting-for-localization",totalDownloads:1111,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter discusses efficient and practical matrix-free implementations of the ensemble Kalman filter (EnKF) in order to account for localization during the assimilation of observations. In the EnKF context, an ensemble of model realizations is utilized in order to estimate the moments of its underlying error distribution. Since ensemble members come at high computational costs (owing to current operational model resolutions) ensemble sizes are constrained by the hundreds while, typically, their error distributions range in the order of millions. This induces spurious correlations in estimates of prior error correlations when these are approximated via the ensemble covariance matrix. Localization methods are commonly utilized in order to counteract this effect. EnKF implementations in this context are based on a modified Cholesky decomposition. Different flavours of Cholesky-based filters are discussed in this chapter. Furthermore, the computational effort in all formulations is linear with regard to model resolutions. Experimental tests are performed making use of the Lorenz 96 model. The results reveal that, in terms of root-mean-square-errors, all formulations perform equivalently.",signatures:"Elias David Niño Ruiz, Rolando Beltrán Arrieta and Alfonso Manuel\nMancilla Herrera",downloadPdfUrl:"/chapter/pdf-download/59091",previewPdfUrl:"/chapter/pdf-preview/59091",authors:[{id:"107082",title:"Prof.",name:"Elias",surname:"Nino-Ruiz",slug:"elias-nino-ruiz",fullName:"Elias Nino-Ruiz"}],corrections:null},{id:"58238",title:"Kalman Filters for Reference Current Generation in Shunt Active Power Filter (APF)",doi:"10.5772/intechopen.72467",slug:"kalman-filters-for-reference-current-generation-in-shunt-active-power-filter-apf-",totalDownloads:1654,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Shunt active power filter (APF) method have been used by many researchers as a solution in reducing the harmonics creating by the non-liner loads. Therefore, this research is targeted to design and implement a three-phase shunt APF employing Kalman filter estimator. Conventionally, low-pass filter (LPF) is used to filter out the unwanted DC component of the non-linear load to produce the sinusoidal waveform called the reference current. However, when applying LPF it contributes with the phase shift and high transient at the supply current. Therefore, to reduce these problems, the digital Kalman filter estimator is used to replace the LPF for generating the reference current. Details on the investigation between conventional and proposed methods under simulation based on Matlab Simulink platform and experimental that are made for two types of load, namely, three-phase rectifier with RC-load and three-phase induction motor, are presented. The performance criteria of the shunt APF are determined by the supply current waveform, total harmonic distortion (THD), harmonic spectrum and power quality measurements, which were also obtained by simulation and experimental. In conclusion, by employing Kalman filter estimator for generating the reference current, it reduces the time delay and high transient current at the power supply and, thus, improved the overall THD from 0.1 to 0.42% compared to the LPF.",signatures:"Ahmad Shukri Bin Abu Hasim, Syed Mohd Fairuz Bin Syed Mohd\nDardin and Zulkifilie Bin Ibrahim",downloadPdfUrl:"/chapter/pdf-download/58238",previewPdfUrl:"/chapter/pdf-preview/58238",authors:[{id:"208972",title:"Dr.",name:"Syed Mohd Fairuz",surname:"Syed Mohd Dardin",slug:"syed-mohd-fairuz-syed-mohd-dardin",fullName:"Syed Mohd Fairuz Syed Mohd Dardin"},{id:"209475",title:"Dr.",name:"Ahmad Shukri",surname:"Abu Hasim",slug:"ahmad-shukri-abu-hasim",fullName:"Ahmad Shukri Abu Hasim"},{id:"218276",title:"Prof.",name:"Zulkifilie",surname:"Ibrahim",slug:"zulkifilie-ibrahim",fullName:"Zulkifilie Ibrahim"}],corrections:null},{id:"57692",title:"Applications of Kalman Filters for Coherent Optical Communication Systems",doi:"10.5772/intechopen.71617",slug:"applications-of-kalman-filters-for-coherent-optical-communication-systems",totalDownloads:1477,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, we review various applications of Kalman filtering for coherent optical communication systems. First, we briefly discuss the principles of Kalman filter and its variations including extended Kalman filter (EKF) and adaptive Kalman filter (AKF). Later on, we illustrate the applicability of Kalman filters for joint tracking of several optical transmission impairments, simultaneously, by formulating the state space model (SSM) and detailing the principles. A detailed methodology is presented for the joint tracking of linear and nonlinear phase noise along with amplitude noise using EKF. Also, approaches to enhance the performance obtained by EKF by combining with other existing digital signal processing (DSP) techniques are presented. Frequency and phase offset estimation using a two stage linear Kalman filter (LKF)/EKF is also discussed. A cascaded structure of LKF and EKF by splitting the SSM to jointly mitigate the effects of polarization, phase and amplitude noise is also presented. The numerical analysis concludes that the Kalman filter based approaches outperform the conventional methods with better tracking capability and faster convergence besides offering more feasibility for real-time implementations.",signatures:"Lalitha Pakala and Bernhard Schmauss",downloadPdfUrl:"/chapter/pdf-download/57692",previewPdfUrl:"/chapter/pdf-preview/57692",authors:[{id:"210340",title:"M.Sc.",name:"Lalitha",surname:"Pakala",slug:"lalitha-pakala",fullName:"Lalitha Pakala"},{id:"210654",title:"Prof.",name:"Bernhard",surname:"Schmauss",slug:"bernhard-schmauss",fullName:"Bernhard Schmauss"}],corrections:null},{id:"57673",title:"Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design",doi:"10.5772/intechopen.71731",slug:"kalman-filter-for-moving-object-tracking-performance-analysis-and-filter-design",totalDownloads:3144,totalCrossrefCites:12,totalDimensionsCites:19,hasAltmetrics:0,abstract:"This chapter presents Kalman filters for tracking moving objects and their efficient design strategy based on steady-state performance analysis. First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.",signatures:"Kenshi Saho",downloadPdfUrl:"/chapter/pdf-download/57673",previewPdfUrl:"/chapter/pdf-preview/57673",authors:[{id:"209334",title:"Associate Prof.",name:"Kenshi",surname:"Saho",slug:"kenshi-saho",fullName:"Kenshi Saho"}],corrections:null},{id:"57985",title:"Distributed Kalman Filter",doi:"10.5772/intechopen.71941",slug:"distributed-kalman-filter",totalDownloads:1337,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The continuing trend toward connected sensors (“internet of things” and” ubiquitous computing”) drives a demand for powerful distributed estimation methodologies. In tracking applications, the distributed Kalman filter (DKF) provides an optimal solution under Kalman filter conditions. The optimal solution in terms of the estimation accuracy is also achieved by a centralized fusion algorithm, which receives all associated measurements. However, the centralized approach requires full communication of all measurements at each time step, whereas the DKF works at arbitrary communication rates since the calculation is fully distributed. A more recent methodology is based on ”accumulated state density” (ASD), which augments the states from multiple time instants to overcome spatial cross-correlations. This chapter explains the challenges in distributed tracking. Then, possible solutions are derived, which include the DKF and ASD approach.",signatures:"Felix Govaers",downloadPdfUrl:"/chapter/pdf-download/57985",previewPdfUrl:"/chapter/pdf-preview/57985",authors:[{id:"209490",title:"Dr.",name:"Felix",surname:"Govaers",slug:"felix-govaers",fullName:"Felix Govaers"}],corrections:null},{id:"57804",title:"Consensus-Based Distributed Filtering for GNSS",doi:"10.5772/intechopen.71138",slug:"consensus-based-distributed-filtering-for-gnss",totalDownloads:1371,totalCrossrefCites:4,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Kalman filtering in its distributed information form is reviewed and applied to a network of receivers tracking Global Navigation Satellite Systems (GNSS). We show, by employing consensus-based data-fusion rules between GNSS receivers, how the consensus-based Kalman filter (CKF) of individual receivers can deliver GNSS parameter solutions that have a comparable precision performance as their network-derived, fusion center dependent counterparts. This is relevant as in the near future the proliferation of low-cost receivers will give rise to a significant increase in the number of GNSS users. With the CKF or other distributed filtering techniques, GNSS users can therefore achieve high-precision solutions without the need of relying on a centralized computing center.",signatures:"Amir Khodabandeh, Peter J.G. Teunissen and Safoora Zaminpardaz",downloadPdfUrl:"/chapter/pdf-download/57804",previewPdfUrl:"/chapter/pdf-preview/57804",authors:[{id:"116970",title:"Prof.",name:"Peter",surname:"Teunissen",slug:"peter-teunissen",fullName:"Peter Teunissen"},{id:"210691",title:"Dr.",name:"Amir",surname:"Khodabandeh",slug:"amir-khodabandeh",fullName:"Amir Khodabandeh"},{id:"210714",title:"Dr.",name:"Safoora",surname:"Zaminpardaz",slug:"safoora-zaminpardaz",fullName:"Safoora Zaminpardaz"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"2286",title:"Frontiers in Advanced Control Systems",subtitle:null,isOpenForSubmission:!1,hash:"43dae6480e55f2985ce8c4a1860e6dca",slug:"frontiers-in-advanced-control-systems",bookSignature:"Ginalber Luiz de Oliveira Serra",coverURL:"https://cdn.intechopen.com/books/images_new/2286.jpg",editedByType:"Edited by",editors:[{id:"11032",title:"Dr.",name:"Ginalber Luiz",surname:"Serra",slug:"ginalber-luiz-serra",fullName:"Ginalber Luiz Serra"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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The concepts of traveling wave and standing wave can be used to categorize antenna types with corresponding dominant propagation mode for radiating. Traveling-wave antennas [1] often have long electrical length in the main propagation path, and the EM energy radiating proportion prevails over the reflected proportion in the propagation along the main path. In contrast, in standing-wave antennas, the EM energy reflects many times in the main propagation path. This causes the standing-wave or resonance phenomena and increases the EM field intensities with appropriate periodic excitation source, and this facilitates to increase the radiating proportion for a certain bandwidth (BW). The standing-wave antennas often have high Q-factor and with narrow BW, while these characteristics are opposite for the traveling-wave antennas. However, this categorization for the antennas is relative because the propagation mode for radiating depends on the actual structure and is only dominant over a certain band of frequencies. Traveling-wave antennas have been the research subject of many reports, and Vivaldi antennas [2] are the typical branch of traveling-wave antennas.
To model EM fields, characterize structural/operational features and optimize the performances for traveling-wave antennas, various approaches have been implemented. Transverse EM (TEM)-mode transmission line models have been used to describe the propagating and radiating mechanism of these antennas. For example, stepped-width transmission line slots connected end to end were used for the Vivaldi antenna, and the effect of the stepped discontinuities was solved by a power continuity criterion in [3], and in [4], a design process with least-square optimization was implemented for the calculation of input impedance and power division at the junctions of a stepped line by a transmission matrix chain. However, simplicity of the models constrained the accuracy and practical application of such methods.
Model accuracy improvements based on approximation to the conical slot lines [5, 6] yield electric field distributions and radiating fields with Green’s functions. Diffraction at the end of the radiating slot and lateral edges was incorporated by a weight pattern for each edge. These improvements achieved the better predictions than the TEM-mode transmission line models.
While, in general, the EM propagation knowledge in a specific antenna structure is required for modeling, numerical three-dimensional EM solvers segment and discrete a structural space into a meshed volume of cells adapted to the material and geometric properties of the structure. Space, time and/or frequency distributions of EM energy in the volume are established,
Parallel improvements in EM wave theory, material characterization, numerical techniques, fast algorithms, and high-performance computing have realized faster, more accurate EM solvers for highly complex and real-life problems [7]. The improvements have even supported the design of complicated antipodal Vivaldi antennas from fractal fern leaf-shaped geometries [8]. By using EM solvers with feasible processes for design optimization, a variety of traveling-wave antennas with diverse EM responses have been proposed. Optimization of the conventional geometry and modifications/additions are the common methods to attain improved antenna performances such as in [9, 10, 11, 12]. To explain the complex EM characteristics of the antenna geometries and the effects from the added elements in [10, 11, 12], direct observations of EM near-field vectors and of metal surface currents were implemented.
In a new near-field propagation research, an analysis method for the propagation of distributed near fields from a full-wave EM solver for the typical traveling-wave antenna has been proposed [13]. With an adequately accurate data set of near field in the time and/or frequency domains for the antenna structure corresponding to a excitation condition, the EM fields in key regions can be evaluated and quantified to expose the relationships between the geometric properties and space, time and/or frequency EM energy distributions. The correlation characteristics reveal causal relationships of the geometric properties to the EM field propagation process.
Because the EM fields on the specific regions of the structure were observed and analysed based on time-domain impulse response analysis, the results were not be affected by superpositions of excited periodic cycles such as the conventional frequency-analysis method, it revealed the propagation processes of EM energy clusters and geometry-property influence details on the structure. Moreover, observations of EM responses at a consecutive point set along the dominant EM energy flows were implemented to analyse propagation progresses and the scattering components between the sections in the structure. This avoided locality in observation and permitted to overcome analysis bandwidth limits. Super-resolution algorithms such as MUltiple SIgnal Classification (MUSIC) were also useful [14] to tackle these limits.
The rendered details in the space, time and/or frequency of the results are a powerful feature of the analysis method for the antenna design. The quantitative and qualitative analyses can be implemented to characterize for a subpart of the time and/or frequency EM energy response at each position in space and propagation mechanism dependency on a particular part of the structure. Design and optimization methods, built on such analysis, respond for refined adjustment of the structures. This approach reveals a new, deeper perspective in the hierarchy of antenna and related system design.
In this method, CST Microwave Studio (MWS) is used as the EM solver in the time domain. Structural, mesh and near-field vector data are extracted for the near-field propagation analysis process. This section presents the features of these data as well as sampling problems and their solutions.
The hexahedral meshing algorithm is used within time-domain MWS solver to generate mesh cells adapted to material and geometric properties to represent the input structure and background space. MWS solves and establishes the near-field distributions on this meshed representation rather than the input structure. Structures such as Vivaldi antennas contain diagonal components in the Vivaldi-curved edges that are not parallel with any of the coordinate axes. This causes a stepped or staircase mesh structure to represent these structural components.
The algorithm in the structural data sampling step extracts the meshing lines and the material matrix data of the whole simulation space from the MWS solver, and it reconstructs the meshed structure of the main elements of the structure for the next sampling and analysis steps. Specifically, based on the material matrix data, the metal cells of the meshed metal patches of the Vivaldi antenna are identified. These cells are merged together into a reconstructed structure of the meshed metal patch. Figure 1 shows the top surface of a small piece of the reconstructed metal patch with the staircase meshed structure for the Vivaldi edge. The reconstructed structure becomes basic for the field data sampling step.
Reconstructed structure and mapping of the Vivaldi edge points to the smooth polynomial curve.
The complexity and size of an EM simulation are governed by the detail and size of the structure and surrounding space, and the frequency band of interest. These simulating features affect the number of mesh cells and the time step of the EM solving. To achieve high spatial resolution for the analysis, the excitation signal BW must be adequate, and it can be larger than the operational BW of the antenna. To guarantee convergence condition, the time step can be much smaller than the time step corresponding to the excitation signal Nyquist BW. Therefore, it does not need to sample all the computed near-field data in the simulating time. The duration of the EM solving depends on the volume of the simulation space, degree of EM energy stored in the structure, the length of the excitation signal and accuracy requirement of the analysis, especially for low-frequency or long-delay-time components.
An example of EM solving and sampling parameters for a Vivaldi antenna structure is presented in Table 1. EM field vector data are sampled in the simulating space and time. Each vector is represented by three scalar components. With single-precision floating point format, each scalar component uses 32 bits in size. The total size of the sampled EM near-field data can reach hundreds of gigabytes.
Parameters | Values |
---|---|
Simulation space volume | |
Simulation BW | |
Number of mesh cells | |
Time duration | |
Sampling time step | |
Total size of EM near-field data |
EM solving and sampling parameters of a simulation example.
The Vivaldi edges are the main parts of this traveling-wave antenna structure, and a significant proportion of EM energy concentrates along these edges. Space, time and frequency distributions of the EM fields along these edges contain the most important information about the EM energy propagation; therefore, accurate sampling of field data in these regions is essential.
The sampling is based on fitting a nine-order polynomial smooth curve on the stepped structure of the reconstructed structure from the meshed structure, as shown Figure 1. As expected, this curve represents the Vivaldi curve of the input structure, at which there is a transition in material property from conductor to dielectric/vacuum. Therefore, there is also a corresponding transition in the field vectors at the edge. To preserve this attribute in the sampled data, the EM field vectors at the edge points of the reconstructed structure are mapped to the nearest points on the smooth curve, and these represent the field vectors on the Vivaldi edge of the input structure. The tangential and normal vector components of the EM field vectors can also be divided from the sampled vectors as shown in Figure 1. A three-dimensional interpolation from the field vector data at mesh cell vertices around the mapped point is also a method for the sampling. However, because of the dependence of interpolated result on distance to the mapped point and the difference between field vectors inside the conducting patch and field vectors at the edge of the patch (especially in the vector direction), there can be a significant error in the interpolation result if a mapped point closes to a mesh cell vertex inside the conducting patch.
The field vector data for the lateral and end edges of the conducting patches are sampled by extracting the vector data directly on mesh cell vertices corresponding to these edges. The field vector data at positions in dielectric/vacuum volumes are sampled by interpolated data from the nearest mesh cell vertices.
With the time-domain-based method (TDbM), the analysis is started by an EM simulation with an impulse excitation signal (e.g., Gaussian signal) covering a certain analysis BW. The observed signals are directly extracted from the EM-simulating data. The EM responses on the structure can be analyzed directly in the time domain or can be transformed to the frequency domain by the discrete Fourier transform (DFT). An ineffectiveness of the analysis method is that the EM solver must be rerun whenever there is a change in the analysis BW, and the frequency-domain-based method (FDbM) is a solution proposed to improve the analysis performance.
By the FDbM, the EM solver was only run once with an excitation signal covering a wide enough BW of all analysis sub-bandwidth segments. Then, transformations to the frequency domain for the excitation signal and the observed signals are implemented based on DFT. To compensate the unequal in magnitude and phase of the frequency components of the excitation signal, an equalization is implemented for the frequency components of the observed signals.
Then, the compensated observed signals are transformed into the time domain by inverse DFT. Thus, when the excitation signal is processed and transformed to the time domain, all of its frequency components are equal in magnitude and phase; it is a full Nyquist BW impulse signal. The compensated observed signals in time domain are the responses of this impulse. However, in practical, the compensation can gain noise levels excessively for low-energy-level frequency components for an observed signal with a certain signal-to-noise ratio (SNR), especially at the upper end of the simulating band. Therefore, the responses with full Nyquist BW cannot be achieved in practice. Rectangular, Gaussian and Kaiser windows are used to limit the analysis sub-bandwidth segments in this work.
A frequency-modulated continuous wave (FMCW) is chosen for the excitation signal. This linear chirp sweeps from 0 to 180 GHz in 0.5 ns and covering a BW of 0–210 GHz. Figure 2 shows this excitation signal and the impulse signals corresponding to the different windows in the time and frequency domains. The impulses and responses in Figure 3 are the result of process using a Gaussian window in a low-pass analysis band of 0–64 GHz and a band-pass analysis band of 20–90 GHz (−20 dB BW is standard) in the time and frequency domains. The response signals are curve normal vector component of the E-field at points along a Vivaldi edge located a distance
The FMCW excitation signal and FDbM impulse signals with different windows in the (a) time and (b) frequency domains.
The FDbM impulse signal and responses in the (a) time and (b) frequency domains at the points along the Vivaldi edge.
The differences between the TDbM and the FDbM results are also examined. For TDbM, the EM simulation uses a Gaussian impulse excitation signal with a BW of 0–64 GHz in time domain. For FDbM, the EM simulation uses an FMCW excitation signal with a BW of 0–210 GHz in time domain, and a Gaussian window with a low-pass BW of 0–64 GHz is used in the analysis frequency limitation. The impulse signal pair, the response pairs, and their errors are shown in Figure 4. These comparisons demonstrate a good agreement between the TDbM and FDbM.
The impulse and responses pairs with (a) TDbM and FDbM and (b) the corresponding errors.
As a well-known super-resolution algorithm, MUSIC is often used for signal analysis [15, 16], especially in cases of the overlap of many signals and/or limitation in analysis BW. The MUSIC algorithm is applied in this work for ToA or time delay estimation of dominant components/clusters in the observed signals.
The signal applied at the antenna excitation port is
In general,
where
The channel impulse response in the frequency domain is
Considering noise (as white noise from EM solver errors) and with a sufficient SNR of the signals in the analysis band, an estimation of
where
Thus,
where
The MUSIC algorithm is applied to solve this problem in this work.
The matrix form of
where
The analysis band limitation is implemented by a rectangular window:
where
A Toeplitz data matrix
where
A covariance matrix
With the
where
where
This MUSIC algorithm is applied to directly analyze the observed signals (curve normal components of the E-field vectors at the points a distance
Analysis of expected single component
The structural models with excitation ports of (a) a Vivaldi antenna and (b) a slot line.
In the case of
MUSIC parameters are set as
The MUSIC spectra and FDbM impulse response signals at the points along the slot line edge with analysis bands of (a) 0–60, (b) 0–90, (c) 20–60, and (d) 20–90 GHz.
The estimated propagation velocities based on the FDbM response signal peaks and the MUSIC spectra in different analysis bands along the slot line edge.
Figure 6 shows that the FDbM response signal shape changes with increasing
Analysis of multi-components
The analysis is observed at a point
The MUSIC spectra and FDbM response signals at a point
With the larger
The near-field propagation analysis is implemented on the slot line and Vivaldi structures, as shown in Figure 5. A Rogers RT5880 (
The open-ended slot line consists of two copper patches with length
where
where
Parameters | Slot line | Vivaldi antenna |
---|---|---|
Parameters of the slot line and Vivaldi antenna.
The EM simulations were implemented in time domain with an open boundary condition. The distances from the structures to most of the boundaries are 5 mm, except at the radiation aperture boundary
The
The intensity distributions are examined based on the maximum (over time) of the impulse responses of EM field vector magnitudes in the two analysis bands as shown in Figure 9. Noticeably, the strongest EM field intensities distribute along the slot line and Vivaldi slot, especially at the slot conduction edges. Figure 9(b) and (c) shows that because of the discontinuity in the structure of the reconstructed meshed Vivaldi slot, as mentioned in the Section 2.1.1, at these discontinuous locations, there are abrupt changes in the spatial distribution of field intensities. This is an expression of scattering phenomena in the propagation along the tapered structure of the Vivaldi slot.
The maximum EM field vector magnitudes on the conducting plane of (a) a slot line and (b) and (c) a Vivaldi antenna in the analysis bands of (a) and (b) 0–30 GHz and (c) 0–60 GHz.
These plots also reveal other features of the propagation, for example while the E-field intensity increases at the endpoints
At each point in space, the impulse response analysis result is a time distribution of clusters, and this distribution reveals propagation path information such as the number of paths, time delay, and attenuation characteristic. If the time distribution of a certain cluster can be identified for each point in space, then the space distribution information of this cluster is determined. This information reveals the effects of the structure’s spatial characteristics on the cluster’s propagation.
In propagation characterization, the most important cluster is the earliest- or first- ToA cluster. This cluster is formed by the propagating EM energy flows from the source over the shortest path with the fastest velocity. In this analysis, the ToA of the first cluster of the EM field vector magnitudes is estimated for each point in the conducting plane based on a local peak-finding algorithm. Direction and magnitude information of the field vectors corresponding to the first clusters is derived as illustrated in Figures 10 and 11. These results reveal how the first clusters of EM fields depend on material and spatial characteristics of the structure. Figure 12 presents the space distribution of first-cluster ToAs or propagation times from the source. ToA contour lines play the role of two-dimensional wavefront and present visually the effects of the material and spatial characteristics of the structure on the clusters’ propagations.
The EM field vectors at the time of the first peaks on the conducting plane of (a) a slot line and (b) a Vivaldi antenna in the analysis bands of 0–30 GHz.
The first-peak EM field vector magnitudes on the conducting plane of (a) a slot line and (b) and (c) a Vivaldi antenna in the analysis bands of (a) and (b) 0–30 GHz and (c) 0–60 GHz.
The ToAs of the first clusters based on the first peaks of the EM field vector magnitudes on the conducting plane of (a) a slot line and (b) and (c) a Vivaldi antenna in the analysis bands of (a) and (b) 0–30 GHz and (c) 0–60 GHz.
However, due to spreading of the analysis impulse in time and the overlapping of multiple EM flows, the total response signals can be canceled, flat or not distinguishable as separate clusters. In this case, ToA of the clusters cannot be estimated exactly, or the clusters cannot be distinguished in the time domain, and this can lead to significant estimation errors in the analysis results.
As described in Section 2.2.2, the MUSIC algorithm is used in this analysis for estimation of the ToA. The results of estimated ToAs are shown Figure 13 based on the peaks of three MUSIC spectrum components summation of EM field vectors in the analysis band of 0–60 GHz and with MUSIC parameters
The ToAs based on the MUSIC spectra of EM field vectors on the conducting plane of a Vivaldi antenna in the analysis band of 0–60 GHz.
In this section, detailed near-field examinations of the slot line and Vivaldi slot conducting edge propagation are made along the
The stepped or staircase meshed structure of the Vivaldi antenna is different from the constant distance between the two parallel slot edges of the slot line. This structural feature is the cause of abrupt changes in intensity of the EM responses at transition positions along the Vivaldi edges as seen in Figures 9 and 11. In this section, this is investigated thoroughly based on the first-peak field vector magnitude analysis versus the distance from source
Figure 14 shows that the propagations on the slot line and the Vivaldi slot are similar in the first segment
The first peak of the EM field vector magnitudes on the edge of the slot line and Vivaldi slot versus the distance from source
To observe the propagation progress/process along the edges, the EM response signals versus time and versus distance
Slot line
(a) The vector magnitude signals and (b) the curve normal/surface normal vector component signals of the EM fields on the edge of the slot line versus time and versus distance from source
The vector magnitude signals of the EM fields on the edge of the Vivaldi slot versus time and versus distance from source
Some key features of the propagation process can be disclosed from Figure 15 with/without a reference to the original magnitudes in Figure 14. Specifically, the main cluster transferring from the source through the slot line path in the
Vivaldi slot
Besides the A, B and C clusters being similar to the slot line edge response, along the Vivaldi edge, the first-order scattering components affect significantly to the propagation process. Particularly, scattering of the A and B clusters at the transition positions forms a series of clusters, such as the F and G clusters illustrated in Figure 16. Additionally, the C cluster is also scattered at these transition positions when traveling in the
The H cluster in Figure 16 reveals a noticeable feature in the Vivaldi slot propagation process. The accumulation and combination of second- and higher-order scattering components arriving from continuous spatial sections form the H cluster. Examples of the spatial sections creating second- and/or third-order scattering components are illustrated in Figure 17. Assume that point
Examples of paths of second- and third-order scattering components on the end of the Vivaldi taper.
The plots of the maximum and first EM cluster intensity distributions in Figures 9(b) and (c), 10(b), 11(b) and (c), and 14 reveal important regions/points in the Vivaldi structure with high-density EM flow and associated first-order scattering, which contribute to the main EM energy proportion of the total radiation. As seen in Figure 14, the scattering degree at the transition positions can be evaluated primarily by 3–6.5 dB abrupt changes in magnitude. This scattering degree can also be assessed based on the cluster intensities in Figure 16 and with a reference to the first-cluster intensities in Figure 14. The magnitude reduction along the Vivaldi edges in Figure 14 indicates the EM energy transfer out of the antenna conducting element into the free space.
The propagation of EM first clusters in the structures can be observed and quantified in terms of field vector direction and magnitude of first clusters based on Figures 10 and 11 and ToA of first clusters based on Figures 12 or 13. All three space distributions of field vector direction, magnitude, and ToA of first clusters in these figures provide adequate information of propagation of EM first clusters in the observed space, and the important features of the propagation can be recognized. For example, as seen in Figures 10(b), 11 and 12(b) and (c), and also in Figure 13, the propagation process of first clusters on the metal plane of the Vivaldi antenna and the degree of local EM flows were revealed. These also revealed intensity of scattering fields at the antenna aperture, and geometric features of the field propagation were also revealed such as the flare effect of the EM flow propagating away from the Vivaldi patches at the aperture. This is a significant factor in the reduction of antenna directivity.
The propagation progress investigation on the main propagation path, such as the results shown in Figures 15 and 16, not only provides more details of propagation of the first clusters, but it also reveals formation of other clusters due to scattering at the detail elements of the structure. Additionally, features of the formed clusters are also observed and evaluated. Based on these information, higher-order scattering components and corresponding propagation paths can be recognized or inferred. For example, the H cluster formation in Figure 16 reveals the higher-order scattering at the lateral edge and the Vivaldi edge, as illustrated in Figure 17. The scattering on the lateral edge causes partial radiation in unwanted directions and reduces the total directional characteristic.
Based on this knowledge about mechanism in propagation and radiation on the structure and influences of the structural detail elements, the solutions can be proposed to increase the advantage features and to reduce the disadvantage features of the propagation to improve the total responses of the antenna. For example, methods to reduce the flare effect at the Vivaldi antenna aperture are adjustment of the Vivaldi p-factor, introduction of a core element, conversion of the antenna into a double-slot structure in [11], and insertion of a material structure at the antenna aperture to adjust the directions and/or velocities of the local EM flows. The estimated results for magnitude, ToA, velocity, and direction of EM flows propagation at specific regions at conducting edges and/or in the radiating aperture support the effectiveness of these refined adjustment methods. Another example solution for the higher-order scattering on the lateral edge was proposed. By addition of 450 ripples on the lateral edge in [11], a part of this energy is redirected into the antenna end-fire direction. This improves the antenna gain in a certain frequency band. It also reduces the EM energy portion coming back to the source, thereby improving the S11 characteristic.
In the perfective of considering propagation to be the fundamental basic of dynamic electromagnetic phenomena, the comprehensive space/time/frequency analysis method of near-field propagation was proposed. Based on this, detailed knowledge of behavior and physical effects of the structures and/or objects to dynamic electromagnetics was revealed, and these also are basic for the refined optimization in design. With the fundamental basic and generality, applicability of this analysis technique is not limited to the example cases of this work. Besides Vivaldi antennas, this analysis technique can be applied for the analysis, designs, and optimization of any sort of traveling-wave antennas or transmission structures.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\\n\\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\\n\\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nOAI-PMH
\\n\\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\\n\\nLicense
\\n\\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\\n\\nPeer Review Policies
\\n\\nAll scientific works are Peer Reviewed prior to publishing. Read more
\\n\\nOA Publishing Fees
\\n\\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\\n\\nDigital Archiving Policy
\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n\\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\\n\\nOpen Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
\\n\\nOpen Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
\\n\\nWe aim at improving the quality and availability of scholarly communication by promoting and practicing:
\\n\\n\\n"}]'},components:[{type:"htmlEditorComponent",content:'
The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\n\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\n\nOpen Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
\n\nOpen Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
\n\nWe aim at improving the quality and availability of scholarly communication by promoting and practicing:
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/105692",hash:"",query:{},params:{id:"105692"},fullPath:"/profiles/105692",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()