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Multiscaled PVA Bionanocomposite Films: Characterisation and Nanoscale Modelling
by Yu Dong Mohanad MousaThis book highlights a novel and holistic approach to multiscaled PVA bionanocomposite films used for electrical sensing, medical and packaging applications. With a combination of material characterization and modeling to understand the effect of nanoparticle size and shape, as well as 3D interphase properties and features such as interphase modulus and nanoscale dimensions, this book substantiates how excellent mechanical and thermal properties of these materials are achieved. Also it addresses the importance of using economical and ecofriendly bionanocomposites as potential green materials to support the goal of environmental sustainability with multifunctional properties.
Multisensor Attitude Estimation: Fundamental Concepts and Applications (Devices, Circuits, and Systems)
by Hassen Fourati, Djamel Eddine Chouaib Belkhiat and Krzysztof IniewskiThere has been an increasing interest in multi-disciplinary research on multisensor attitude estimation technology driven by its versatility and diverse areas of application, such as sensor networks, robotics, navigation, video, biomedicine, etc. Attitude estimation consists of the determination of rigid bodies’ orientation in 3D space. This research area is a multilevel, multifaceted process handling the automatic association, correlation, estimation, and combination of data and information from several sources. Data fusion for attitude estimation is motivated by several issues and problems, such as data imperfection, data multi-modality, data dimensionality, processing framework, etc. While many of these problems have been identified and heavily investigated, no single data fusion algorithm is capable of addressing all the aforementioned challenges. The variety of methods in the literature focus on a subset of these issues to solve, which would be determined based on the application in hand. Historically, the problem of attitude estimation has been introduced by Grace Wahba in 1965 within the estimate of satellite attitude and aerospace applications. This book intends to provide the reader with both a generic and comprehensive view of contemporary data fusion methodologies for attitude estimation, as well as the most recent researches and novel advances on multisensor attitude estimation task. It explores the design of algorithms and architectures, benefits, and challenging aspects, as well as a broad array of disciplines, including: navigation, robotics, biomedicine, motion analysis, etc. A number of issues that make data fusion for attitude estimation a challenging task, and which will be discussed through the different chapters of the book, are related to: 1) The nature of sensors and information sources (accelerometer, gyroscope, magnetometer, GPS, inclinometer, etc.); 2) The computational ability at the sensors; 3) The theoretical developments and convergence proofs; 4) The system architecture, computational resources, fusion level.
Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing
by Ni-Bin Chang Kaixu BaiIn the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.
Multisensor Data Fusion: From Algorithms and Architectural Design to Applications (Devices, Circuits, and Systems #43)
by Hassen FouratiMultisensor Data Fusion: From Algorithms and Architectural Design to Applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge techniques drawn from a broad array of disciplines. Featuring contributions from the world’s leading data fusion researchers and academicians, this authoritative book: Presents state-of-the-art advances in the design of multisensor data fusion algorithms, addressing issues related to the nature, location, and computational ability of the sensors Describes new materials and achievements in optimal fusion and multisensor filters Discusses the advantages and challenges associated with multisensor data fusion, from extended spatial and temporal coverage to imperfection and diversity in sensor technologies Explores the topology, communication structure, computational resources, fusion level, goals, and optimization of multisensor data fusion system architectures Showcases applications of multisensor data fusion in fields such as medicine, transportation's traffic, defense, and navigation Multisensor Data Fusion: From Algorithms and Architectural Design to Applications is a robust collection of modern multisensor data fusion methodologies. The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of the problems that can be faced during its execution.
Multisensor Fusion Estimation Theory and Application
by Yuanqing Xia Lu Jiang Liping YanThis book focuses on the basic theory and methods of multisensor data fusion state estimation and its application. It consists of four parts with 12 chapters. In Part I, the basic framework and methods of multisensor optimal estimation and the basic concepts of Kalman filtering are briefly and systematically introduced. In Part II, the data fusion state estimation algorithms under networked environment are introduced. Part III consists of three chapters, in which the fusion estimation algorithms under event-triggered mechanisms are introduced. Part IV consists of two chapters, in which fusion estimation for systems with non-Gaussian but heavy-tailed noises are introduced. The book is primarily intended for researchers and engineers in the field of data fusion and state estimation. It also benefits for both graduate and undergraduate students who are interested in target tracking, navigation, networked control, etc.
Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System: An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017) (Lecture Notes in Electrical Engineering #501)
by Sukhan Lee Hanseok Ko Songhwai OhThis book includes selected papers from the 13th IEEE International Conference on Multisensor Integration and Fusion for Intelligent Systems (MFI 2017) held in Daegu, Korea, November 16–22, 2017. It covers various topics, including sensor/actuator networks, distributed and cloud architectures, bio-inspired systems and evolutionary approaches, methods of cognitive sensor fusion, Bayesian approaches, fuzzy systems and neural networks, biomedical applications, autonomous land, sea and air vehicles, localization, tracking, SLAM, 3D perception, manipulation with multifinger hands, robotics, micro/nano systems, information fusion and sensors, and multimodal integration in HCI and HRI. The book is intended for robotics scientists, data and information fusion scientists, researchers and professionals at universities, research institutes and laboratories.
Multispectral Image Analysis Using the Object-Oriented Paradigm (Remote Sensing Applications Series)
by Kumar NavulurBringing a fresh new perspective to remote sensing, object-based image analysis is a paradigm shift from the traditional pixel-based approach. Featuring various practical examples to provide understanding of this new modus operandi, Multispectral Image Analysis Using the Object-Oriented Paradigm reviews the current image analysis methods and demonstrates advantages to improve information extraction from imagery.This reference describes traditional image analysis techniques, introduces object-oriented technology, and discusses the benefits of object-based versus pixel-based classification. It examines the creation of object primitives using image segmentation approaches and the use of various techniques for object classification. The author covers image enhancement methods, how to use ancillary data to constrain image segmentation, and concepts of semantic grouping of objects. He concludes by addressing accuracy assessment approaches. The accompanying downloadable resources present sample data that enable the use of different approaches to problem solving.Integrating remote sensing techniques and GIS analysis, Multispectral Image Analysis Using the Object-Oriented Paradigm distills new tools to extract information from remotely sensed data.
Multispectral Image Sensors Using Metasurfaces (Progress in Optical Science and Photonics #17)
by Xin He Paul Beckett Ranjith R UnnithanThis book presents how metasurfaces are exploited to develop new low-cost single sensor based multispectral cameras. Multispectral cameras extend the concept of conventional colour cameras to capture images with multiple color bands and with narrow spectral passbands. Images from a multispectral camera can extract significant amount of additional information that the human eye or a normal camera fails to capture and thus have important applications in precision agriculture, forestry, medicine, object identifications, and classifications. Conventional multispectral cameras are made up of multiple image sensors each externally fitted with a narrow passband wavelength filters, optics and multiple electronics. The need for multiple sensors for each band results in a number of problems such as being bulky, power hungry and suffering from image co-registration problems which in turn limits their wide usage. The above problems can be eliminated if a multispectral camera is developed using one single image sensor.
Multitemporal Earth Observation Image Analysis: Remote Sensing Image Sequences (ISTE Invoiced)
by Clément Mallet Nesrine ChehataEarth observation has witnessed a unique paradigm change in the last decade with a diverse and ever-growing number of data sources. Among them, time series of remote sensing images has proven to be invaluable for numerous environmental and climate studies. Multitemporal Earth Observation Image Analysis provides illustrations of recent methodological advances in data processing and information extraction from imagery, with an emphasis on the temporal dimension uncovered either by recent satellite constellations (in particular the Sentinels from the European Copernicus programme) or archival aerial images available in national archives. The book shows how complementary data sources can be efficiently used, how spatial and temporal information can be leveraged for biophysical parameter estimation, classification of land surfaces and object tracking, as well as how standard machine learning and state-of-the-art deep learning solutions can solve complex problems with real-world applications.
Multitemporal Remote Sensing
by Yifang BanWritten by world renowned scientists, this book provides an excellent overview of a wide array of methods and techniques for the processing and analysis of multitemporal remotely sensed images. These methods and techniques include change detection, multitemporal data fusion, coarse-resolution time series processing, and interferometric SAR multitemporal processing, among others. A broad range of multitemporal datasets are used in their methodology demonstrations and application examples, including multispectral, hyperspectral, SAR and passive microwave data. This book features a variety of application examples covering both land and aquatic environments. Land applications include urban, agriculture, habitat disturbance, vegetation dynamics, soil moisture, land surface albedo, land surface temperature, glacier and disaster recovery. Aquatic applications include monitoring water quality, water surface areas and water fluctuation in wetland areas, spatial distribution patterns and temporal fluctuation trends of global land surface water, as well as evaluation of water quality in several coastal and marine environments. This book will help scientists, practitioners, students gain a greater understanding of how multitemporal remote sensing could be effectively used to monitor our changing planet at local, regional, and global scales.
Multitemporal Remote Sensing: Methods and Applications (Remote Sensing and Digital Image Processing #20)
by Yifang BanWritten by world renowned scientists, this book provides an excellent overview of a wide array of methods and techniques for the processing and analysis of multitemporal remotely sensed images. These methods and techniques include change detection, multitemporal data fusion, coarse-resolution time series processing, and interferometric SAR multitemporal processing, among others. A broad range of multitemporal datasets are used in their methodology demonstrations and application examples, including multispectral, hyperspectral, SAR and passive microwave data. This book features a variety of application examples covering both land and aquatic environments. Land applications include urban, agriculture, habitat disturbance, vegetation dynamics, soil moisture, land surface albedo, land surface temperature, glacier and disaster recovery. Aquatic applications include monitoring water quality, water surface areas and water fluctuation in wetland areas, spatial distribution patterns and temporal fluctuation trends of global land surface water, as well as evaluation of water quality in several coastal and marine environments.This book will help scientists, practitioners, students gain a greater understanding of how multitemporal remote sensing could be effectively used to monitor our changing planet at local, regional, and global scales.
Multivariable Predictive Control: Applications in Industry
by Sandip K. LahiriA guide to all practical aspects of building, implementing, managing, and maintaining MPC applications in industrial plants Multivariable Predictive Control: Applications in Industry provides engineers with a thorough understanding of all practical aspects of multivariate predictive control (MPC) applications, as well as expert guidance on how to derive maximum benefit from those systems. Short on theory and long on step-by-step information, it covers everything plant process engineers and control engineers need to know about building, deploying, and managing MPC applications in their companies. MPC has more than proven itself to be one the most important tools for optimising plant operations on an ongoing basis. Companies, worldwide, across a range of industries are successfully using MPC systems to optimise materials and utility consumption, reduce waste, minimise pollution, and maximise production. Unfortunately, due in part to the lack of practical references, plant engineers are often at a loss as to how to manage and maintain MPC systems once the applications have been installed and the consultants and vendors’ reps have left the plant. Written by a chemical engineer with two decades of experience in operations and technical services at petrochemical companies, this book fills that regrettable gap in the professional literature. Provides a cost-benefit analysis of typical MPC projects and reviews commercially available MPC software packages Details software implementation steps, as well as techniques for successfully evaluating and monitoring software performance once it has been installed Features case studies and real-world examples from industries, worldwide, illustrating the advantages and common pitfalls of MPC systems Describes MPC application failures in an array of companies, exposes the root causes of those failures, and offers proven safeguards and corrective measures for avoiding similar failures Multivariable Predictive Control: Applications in Industry is an indispensable resource for plant process engineers and control engineers working in chemical plants, petrochemical companies, and oil refineries in which MPC systems already are operational, or where MPC implementations are being considering.
Multivariate Analysis and Machine Learning Techniques: Feature Analysis in Data Science Using Python (Transactions on Computer Systems and Networks)
by Srikrishnan SundararajanThis book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques – probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensiveintroduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications. The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.
Multivariate Quality Control: Theory and Applications (Quality and Reliability)
by Camil Fuchs Ron S. KenettProvides a theoretical foundation as well as practical tools for the analysis of multivariate data, using case studies and MINITAB computer macros to illustrate basic and advanced quality control methods. This work offers an approach to quality control that relies on statistical tolerance regions, and discusses computer graphic analysis highlightin
Multivariate Statistical Machine Learning Methods for Genomic Prediction
by José Crossa Osval Antonio Montesinos López Abelardo Montesinos LópezThis book is open access under a CC BY 4.0 licenseThis open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Multivariate Statistical Process Control
by Zhiqiang Ge Zhihuan SongGiven their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas. Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Multiview Machine Learning
by Shiliang Sun Liang Mao Ziang Dong Lidan WuThis book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis. Incorporating multiple views to improve the generalization performance, multiview learning is also known as data fusion or data integration from multiple feature sets. This self-contained book is applicable for multi-modal learning research, and requires minimal prior knowledge of the basic concepts in the field. It is also a valuable reference resource for researchers working in the field of machine learning and also those in various application domains.
Mundania: How and Where Technologies Are Made Ordinary
by Robert WillimDigital services, platforms and arrangements are often promoted as smooth and convenient, smart or intelligent. When introduced, devices can appear utterly fascinating or awkward, even disquieting. Eventually, however, they soon disappear in the muddle of everyday life. This is how Mundania takes form. Based on original research, this book uses the concept of mundania to better understand technological change. Scholar-artist Robert Willim deftly unpacks the interplay between everyday life and the immense complexity of technological infrastructures. Offering imaginative new insights into our relationship with technology, this book will appeal to readers in a range of fields from science and technology studies and media studies to the arts.
Municipal Landfill Leachate Management (Environmental Science and Engineering)
by Maryam Pazoki Reza GhasemzadehThis book is divided into seven chapters, which address various leachate landfill management issues such as the quality, quantity and management of municipal landfill leachate, together with new methods. There are many methods available for the treatment and management of municipal landfill leachate. The waste management methods presented here can be applied in most third-world countries, due to the lack of waste separation and high organic content of waste. The book provides descriptions and a hierarchy of waste management, reviews the history of solid waste disposal, and covers a range of topics, including: leachate and gas generation in landfills; natural attenuation landfills; landfill site selection; leachate and stormwater management, collection and treatment; landfill gas management; landfill cover requirements; leachate collection; types of natural treatment systems; and design procedure and considerations. In closing, it provides an overview of the current solid waste management status in Iran.
Municipal Sewage Sludge Management: A Reference Text on Processing, Utilization and Disposal, Second Edition, Volume IV
by Joseph Malina Cecil Lue-Hing David R. Zenz Prakasam Tata Richard Kuchenrither Bernard Sawyer W. W. Eckenfelder J. F. Malina J. W. PattersonFROM THE PREFACEThis textbook explains and discusses many of the unit operations used for processing municipal sewage sludge. It also contains valuable information on the available methods for final disposition of this sludge. This textbook can be used for planning, designing, and implementing municipal sewage sludge management projects.
Municipal Solid Waste Management in Asia and the Pacific Islands: Challenges and Strategic Solutions
by Masaru Tanaka Pariatamby AgamuthuSolid waste management issues, technologies and challenges are dynamic. More so, in developing and transitory nations in Asia. This book, written by Asian experts in solid waste management, explores the current situation in Asian countries including Pacific Islands. There are not many technical books of this kind, especially dedicated to this region of the world. The chapters form a comprehensive, coherent investigation in municipal solid waste (MSW) management, including, definitions used, generation, sustainable waste management system, legal framework and impacts on global warming. Several case studies from Asian nations are included to exemplify the real situation experienced. Discussions on MSW policy in these countries and their impacts on waste management and minimization (if any) are indeed an eye-opener. Undoubtedly, this book would be a pioneer in revealing the latest situation in the Asian region, which includes two of the world's most dynamic nations in the economic growth. It is greatly envisaged to form an excellent source of reference in MSW management in Asia and Pacific Islands. This book will bridge the wide gap in available information between the developed and transitory/developing nations.
Municipal Solid Waste Management in Developing Countries
by Sunil KumarThis book contains detailed and structured approaches to tackling practical decision-making troubles using economic consideration and analytical methods in Municipal solid waste (MSW) management. Among all other types of environmental burdens, MSW management is still a mammoth task, and the worst part is that a suitable technique to curb the situation in developing countries has still not emerged. Municipal Solid Waste Management in Developing Countries will help fill this information gap based on information provided by field professionals. This information will be helpful to improve and manage solid waste systems through the application of modern management techniques. It covers all the fundamental concepts of MSWM; the various component systems, such as collection, transportation, processing, and disposal; and their integration. This book also discusses various component technologies available for the treatment, processing, and disposal of MSW. Written in view of actual scenarios in developing countries, it provides knowledge to develop solutions for prolonged problems in these nations. It is mainly for undergraduate and postgraduate students, research scholars, professionals, and policy makers.
Municipal Solid Wastes: Problems and Solutions
by Robert E. Landreth Paul A. RebersEnvironmental scientists and engineers are faced with the challenge of how to manage increasing amounts of solid waste. Furthermore, waste management officials are constantly faced with the question "Which option is the most appropriate one in this situation, and how does it compare to other options?" For these individuals, and for the general public, Municipal Solid Wastes: Problems and Solutions helps to answer this and other questions by presenting the issues of waste handling and disposal-from general management concepts to specific techniques. Each topic is carefully reviewed: problems are presented, and possible solutions are discussed. Legislation that affects recycling and disposal is covered.
Municipal Stormwater Management
by Thomas N. Debo Andrew ReeseDesigned to be a stand alone desktop reference for the Stormwater manager, designer, and planner, the bestselling Municipal Stormwater Management has been expanded and updated. Here is what's new in the second edition: New material on complying with the NPDES program for Phase II and in running a stormwater quality programThe latest information on
Muon Spin Spectroscopy: Methods and Applications in Chemistry and Materials Science
by Donald G. Fleming Iain McKenzie Paul W. PercivalMuon Spin Spectroscopy An introduction to muon spin spectroscopy with a focus on applications in chemistry and materials science Muon Spin Spectroscopy: Methods and Applications in Chemistry and Materials Science delivers a robust and practical discussion of the areas in muon spin spectroscopy most relevant to chemistry and materials science. In this text readers will find the background details of muonium chemistry, as well as descriptions of applications in a variety of topics of varying complexity, from chemical reactivity in the gas phase to condensed matter and biological systems. The text covers material ranging from the historical background to recent technological and theoretical developments in the field. Readers will also find: An introduction to muon beams and spin spectroscopy, including discussions of spin polarization and muon decayComprehensive explanations of the formation of chemical states incorporating muonsPractical discussions of chemical reactivity and dynamics testing rate theory in the gas phase, including the influence of the potential energy surfaceComprehensive treatments of muoniated free radicals, spin relaxation studies, and muonium chemistry and chemical kinetics in condensed phases Ideal for practicing spectroscopists, physical chemists, and surface chemists, Muon Spin Spectroscopy: Methods and Applications in Chemistry and Materials Science will also benefit students of materials science and chemistry.