Differentiation of Basmati and non-Basmati types.
\\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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\r\n\r\n\tThis book will represent one of the first educational contributions to the need to consolidate an emerging research line aimed at learning by and for education for a critical, responsible, and committed citizenship with its social environment, and the acquisition of skills for democratic culture.
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Rice is an important crop as half of the world’s population depends on it. The year 1966 was declared as Year of Rice by FAO and again in 2002, United Nations General Assembly declared 2004 as International Year of Rice. Milled rice is the third most produced crop after wheat and maize in world. Rice cultivation is source of employment to billions of people in rice cultivating countries. The global rice consumption is dominated by the countries in the Asia-Pacific region, like China, India, Indonesia, Bangladesh, and Vietnam. There are different types of rice cultivated around the world, but if we talk about global rice trade, then there are four types: long grained
India has nearly 300 indigenous collections of aromatic rice varieties. These varieties falls under different types based on kernel length:
This group mainly constitutes of small, medium and long grained types of aromatic rice. The center of diversity of non-Basmati types of aromatic rice of India is located in Himalayan foothills; Indian states of Uttar Pradesh and Bihar; and Tarai region of Nepal [5]. Few famous and locally cultivated varieties of this group with respective kernel length are listed below (Table 2) along with their area of cultivation. There are a total of eight non-Basmati types of aromatic rice with GI tag in India; names of such varieties are mentioned in bold and italicized letters in the table (Table 2).
Kernel dimensions | Basmati type | Non-Basmati type |
---|---|---|
Kernel length | 6.4–7.6 mm | 5.2–5.4 mm |
Kernel length/breadth ratio before cooking | 3.5–4.2 | 3.3–3.5 |
Kernel length/breadth ratio after cooking | 4.9–5.6 | 2.95–3.8 |
Elongation ratio of kernel length | 1.7–1.83 | 1.4–1.57 |
Elongation ratio of kernel breadth | 1.26–1.33 | 1.31–1.61 |
Differentiation of Basmati and non-Basmati types.
States of India | Small grained (≤5.51 mm) | Medium grained (5.51–6.60 mm) | Long grained (6.61–7.51 mm) |
---|---|---|---|
Andhra Pradesh | — | Jeeragasamba | — |
Assam | — | — | |
Bihar | Badshah Bhog, Deobhog, | Gopal Bhog, Champaran Basmati (Lal), Champaran Basmati (Kali), Champaran Basmati (Bhini), Bhilahi Basmati, Amod, Abdul, Kesar, Sonachur | Baikani |
Himachal Pradesh | Achhu, Begrui, Panarsa (local) | Baldhar Basmati, Madhumati, Mushkan, Seond Basmati | |
Kerela | — | — | |
Madhya Pradesh | Chinore, Dubrej, Kalimooch, Bishnubhog, Badshah Bhog, Tulsi-Manjari | Chatri, Modhuri, Vishnu Parag | Laloo |
Maharashtra | Ambemohar, | Prabhavati | — |
Manipur | — | — | |
Uttar Pradesh | Adamchini, Badshah Pasand, Bindli, Bhartaphool, Dhania, Chhoti Chinnawar, Laungchoor, Jeerabattis, Kanak Jeeri, Yuvraj, Moongpholi, Rambhog, Ramjawain | Karmuhi, Kesar, Kesarparsom, Sonachur, Tilakchandan, Kalanamak, Vishnu Bhog | Type-3, Hansraj, Nagina-12, Safeda, Kalasukhdas, Tapovan Basmati, Type-9, Duniapat Dabraj |
West Bengal | Kanakchur | — |
Indigenous aromatic varieties cultivated in different states of India.
Italicized and bold genotypes names indicate that they hold GI tag.
This group includes slender and long to very long grained type of aromatic rice. It is indigenous to Himalayan foothills. The word Basmati is derived from Sanskrit word,
Hence, any aromatic rice in India can be called as Basmati only when it meets the minimum standards given in aforementioned table (Table 3). Apart from, quality standards, there is another writ under
Traditional Basmati: these are pureline selection from the existed Basmati landraces which are six in number (Table 4).
Evolved Basmati: Evolved Basmati varieties are developed through hybridization or any other breeding methods in such way that at least one of the parents used to develop them was a Traditional Basmati Variety or pureline Basmati variety (Table 4).
Traits | Value |
---|---|
Average pre-cooked milled rice length | >6.61 mm |
Average pre-cooked milled rice breadth | <2 mm |
Average length/breadth ratio of pre-cooked milled rice | >3.5 |
Average cooked rice length | >12 mm |
Average pre-cooked rice length/pre-cooked rice length (elongation ratio) | >1.7 |
Average volume expansion ratio | >3.5 |
Amylose content range | 20–25% |
Alkali spreading value range | 4–7 |
Minimum brown rice recovery | 76% |
Minimum milled rice recovery | 65% |
Minimum head rice recovery | 45% |
Aroma | Present |
Texture of cooked grain (without surface bursting of cooked rice kernel), non-stickiness, tenderness, good taste and mouth feel | Present |
Minimum standard of Basmati rice quality traits.
Traditional Basmati | Year of release | Pedigree |
---|---|---|
Basmati-217 | 1973 | Pureline selection from local landraces of Punjab |
Basmati-370 | 1976 | Pureline selection from local landraces of Punjab (now in Pakistan) |
Type-3 | 1978 | Pureline selection from Dehraduni Basmati |
Ranbir Basmati | 1996 | Pureline selection from Basmati-370 |
Taraori Basmati | 1996 | Pureline selection from Karnal local |
Basmati-386 | 1997 | Pureline selection from local landraces of Punjab |
Evolved Basmati | Pedigree | |
Punjab Basmati-1 | 1984 | Sona/Basmati370 |
Pusa Basmati-1 | 1989 | Pusa150/Karnal Local |
Kasturi | 1989 | Basmati370/CR 88-17-1-5 |
Haryana-1 | 1991 | Sona/Basmati370 |
Mahi Sugandha | 1995 | BK-79/Basmati370 |
Improved Pusa Basmati-1 | 2007 | Pusa Basmati-1 |
Pusa Basmati-1121 | 2008 | Pusa 614-1-2/Pusa 614-2-4-3 (sister line of PB-1) |
Vallabh Basmati-22 | 2009 | — |
Pusa Basmati-6 | 2010 | PB-1/PB-1121 |
Punjab Basmati-2 | 2012 | — |
CSR-30 | 2012 | Buraratha 4-10/Pak Basmati |
HUBR10-9 | 2013 | Taraori Basmati/Jaya |
Vallabh Basmati-21 | 2013 | — |
Pusa Basmati-1509 | 2013 | Pusa Basmati-1301/Pusa Basmati-1121 |
Basmati-564 | 2015 | — |
Vallabh Basmati-23 | 2015 | — |
Vallabh Basmati-24 | 2015 | — |
Pusa Basmati-1609 | 2015 | PRR78/C101A51 |
Pant Basmati-1 | 2016 | PB-1/IET-12603 |
Pant Basmati-2 | 2016 | — |
Punjab Basmati-3 | 2016 | B-386/IET-17948/B-386 |
Pusa Basmati-1637 | 2016 | MAS derived NIL from PB-1 |
Pusa Basmati-1728 | 2016 | MAS derived NIL from PB-6 |
Pusa Basmati-1718 | 2019 | PB 1121/SPS97//PB1121*IRBB59 |
Given the importance of Basmati; in year 2008
World trade of aromatic rice mostly includes Basmati and Jasmine types of varieties and India is leading exporter of Basmati in International market. In year 2019–2020, India has exported 4.45 million MT of Basmati to Iran, Saudi Arab, Iraq, UAE, Kuwait (major countries which import Basmati from India), US, UK, Singapore and Malaysia; earning 4,330.68 million USD. Major export of Basmati from India is headed to Asian countries (Middle East) followed by Western Europe [7]. Besides, milled Basmati, parboiled Basmati called as Sella Basmati rice in India and Middle East; and Cooked Basmati in United Kingdom [8] is also exported from India. Nearly half of the exported Basmati to Gulf countries (Saudi Arabia, Kuwait, and UAE) as well as UK and USA, includes Sella types of Basmati [7]. Earlier, Traditional Basmati viz., Basmati-370 and Taraori Basmati dominated the export of Basmati from India. In early 1990s, an evolved Basmati variety PB-1 replaced them and ruled Basmati export. Currently, PB1121 is the major Basmati variety exported from India; which has an exceptional kernel length (approximately 9 mm) and elongation ratio of 2.7 [9]. Cooked kernels of PB1121 attain a maximum length of 21.0 mm to 21.5 mm which maximum known in any rice germplasm [9]. It occupies 47% of Basmati growing area, followed by PB 1509 (26%), PB-6 (9%) and PB-1 (8%); (APEDA, [10], Basmati Survey Report,
In India, the systematic rice breeding program started with the establishment of agricultural organizations like ICAR (Indian Council of Agricultural Research, Delhi) in 1929; NRRI (National Rice Research Institute, Orissa) in 1946; Directorate of Rice Research; and Agricultural universities [3]. Aromatic rice breeding program was initiated at research stations:
Breeding for aromatic rice varieties is a complex task which is attributed to its quality traits. In a study Khush and Juliano [11] gave three reasons which adversely affect the aromatic rice breeding programs, 1) number of breeding objectives are more; 2) lack of equipment to measure grain quality and; 3) selection indices are not well defined. In present time the second problem has been overcome due to development of different equipments, software etc. to measure to the quality attributes of aromatic rice. Still breeding for aromatic rice is a complicated task, attributed to reasons outlined in the following paragraph; after reviewing work of famous scientists on aromatic rice:
Rice is staple food to half of world population, and in scenario of increasing population; increasing yield become the prime objective of any varietal development program; accordingly less emphasis is made on quality rice (aromatic rice).
Aromatic rice and
Aromatic rice yield poor [6, 13, 14, 15]; photoperiod sensitive [14].
Environmental factors viz., climate, soil, temperature; and cultural practices affect the grain quality of aromatic rice [6, 15].
Aromatic rice grow and express quality traits best in their indigenous area only [6].
Some of the most common breeding methods practiced to develop aromatic rice varieties in India are listed below:
Pureline selection is oldest breeding method used in development of new aromatic rice varieties. Breeding for Basmati rice started with pureline selection in 1920’s at two research stations; Kala Shah Kaku (Punjab state, now in Pakistan) and Nagina (Uttar Pradesh, India) [3]. The very first Basmati variety Basmati-370 was developed through pureline selection in 1933, at Kala Shah Kaku Research station by Late Sardar Mohammad. Few other Basmati varieties developed at these two research station were Basmati-217, Type-3, Type-23, N-10-B, N-12, Muskan, Begumi and Hansraj [8]. Among these, Basmati-217, Type-3 and Basmati-370 are still recognized as Basmati variety in India. A list of aromatic varieties (other than Basmati) developed through pureline selection is given below (Table 5).
New variety developed | From | Traits improved | State of India |
---|---|---|---|
3 new genotypes | Jeeraksala | Improved agronomic and yield potential | Kerala |
14 new genotypes | Kalanamak | Improved agronomic and yield potential | Uttar Pradesh |
C435 | Jeerege Sanna | Early maturing | Karnataka |
K441 | Kakasali | Early maturing | Karnataka |
DP33 | Krishna Pasangi | Early maturing | Karnataka |
Madhuri Selection A | Madhuri | Perform well in delayed planting conditions | Madhya Pradesh |
N-10B | Hansraj | Better quality and high yielding | Uttar Pradesh |
N-12 | Safeda | Better quality and high yielding | Uttar Pradesh |
Type-9 | Dimnepet | Better quality and high yielding | Uttar Pradesh |
Type-1 | Ramjeevan | Better quality and high yielding | Uttar Pradesh |
Type-23 | Kalasukhdas | Better quality and high yielding | Uttar Pradesh |
Sugandha | Pureline selection from local Basmati | Bihar |
It is a very common breeding method utilized in development of a crop variety. Hybridization is a process in which crosses are made between two varieties of same species (inter-varietal hybridization); between two different species of same genus (inter-specific); between two different genera of same family (inter-generic). For self pollinated crop like rice, hybridization program is followed via Pedigree selection, Bulk Method, and Convergent Breeding to develop new varieties. Introduction of dwarfing gene and development of hybridization techniques in 1960s, augmented the Basmati development program Siddiq et al. [7] and other aromatic varieties too. Short, medium and long grained aromatic rice were developed at different agricultural research Institution in India (Table 6). PB1 is the first, high yielding, and semi-dwarf Basmati variety, developed through convergent breeding method in 1989 [9].
Variety name | Kernel type | Parents |
---|---|---|
Kusuma (LS) | Long | TN-1/Basmati-370 |
PAU 29-295 | Very long | Basmati-370/Hamsa |
GR101 | Very long | IR8/Pankhali 203 |
PNR-546 | Long | PNR-125-2/PNR130-2 |
Narendra Sugandha Dhan NDR-6093 | Long | NDR 637/Type-3 |
Ketkijoha | Medium | Savitri/Badsh abhog |
Nua kalajeera | Short | Pureline selection for Kalajeera |
Nua Dhusara | Medium | Pureline selection for Dhusara |
Nua Chinikamini | Short | |
CR Dhan 907 | Medium | Dubraj/Pusa 44 |
CR Sugandh Dhan 908 | Medium | Swarna/Geetanjali |
CR Sugandh Dhan 909 | Medium | Pankaj/Podum oni |
CR Sugandh Dhan 910 | Medium | Swarna/Geetanjali |
Gangawati Ageti | Long | Gaurav x Kalinga III |
HUBR-2-1 | Long | HBR92/Pusa Basmati/Kasturi |
Aromatic varieties developed through hybridization in India.
Source: http://drdpat.bih.nic.in.
Outstanding achievements of Hybrid rice breeding in China encouraged Indian plant breeders to employ hybrid breeding in aromatic rice too. In India, hybrid breeding in aromatic rice was initiated first in Basmati germpalsm. CMS lines like:
Mutation breeding is a useful method to produce genetic variability in crop. In mutation breeding, whole plant/plant part/seed are subjected to mutagen (physical or chemical). This method has been widely applied in developing new varieties in different crops including rice. In aromatic rice, mutation breeding is used to bring desirable change in quality traits. Many mutants line have been developed from several aromatic rice genotypes including Basmati (especially Basmati-370) but only few of them are cultivated. In India mutant lines have been developed in genotypes viz., Kalimoonch-6, Bindli, Kamal Local, Type-9, NP-49, T412, Kalanamak, Gobindbhog, Badshapasand and Basmati-370. Mutants showing certain desirable trait (dwarf stature, lodging resistance, early maturing) are conserved to be used in future breeding program. One such institute is NRRI, Cuttack, India which is maintaining more than 100 mutant lines having certain desirable traits of aromatic rice. Geetanjali and ADT 41 aromatic rice varieties were developed at NRRI; these are mutant lines of Basmati-370. A-201 aromatic variety of USA was developed by using PI457920 mutant; this mutant was developed from Basmati-370 of Pakistan.
In recent years, application of molecular breeding techniques has increased in field of plant breeding. Biotechnological tools viz., NGS, GWAS, MAS, and QTL mapping etc. have been utilized at larger scale in studies related to plant breeding. In India, only two aromatic varieties (Basmati type) have been developed by using molecular breeding method.
Aromatic rice is not only special food in India, but they are culturally auspicious too. Indian consumers prefer aromatic rice over non-aromatic rice. Basmati is premium quality of aromatic rice of India and it is mainly cultivated for export purposes. There is a huge demand of Basmati in International market, but in India, demand of aromatic rice is not limited only to Basmati; rather many non-Basmati indigenous varieties are cultivated, and are very popular among locals. A major portion of non-Basmati types of aromatic varieties had been gradually lost in course of time due to aftermath of Green Revolution in India. Unfortunately, those lost aromatic germplasm inherited enriched quality traits on par with traditional Basmati types. In context of present scenario of aromatic rice in India, there is a need to emphasize on the under-utilized non-Basmati varieties rather only aiming to develop more and more of Basmati types.
"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)
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\\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.
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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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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. 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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. 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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. 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