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Medical Internet of Things Techniques Practices and Applications

Medical Internet of Things Techniques Practices and Applications

In recent years the Medical Internet of Things (MIoT) has emerged as one of the most helpful technological gifts to mankind. With the incredible development in data science big data technologies IoT and embedded systems it is now possible to collect a huge amount of sensitive and personal data compile it and store it through cloud or edge computing techniques. However important concerns remain about security and privacy the preservation of sensitive and personal data and the efficient transfer storage and processing of MIoT-based data. Medical Internet of Things: Techniques Practices and Applications is an attempt to explore new ideas and novel techniques in the area of MIoT. The book is composed of fifteen chapters discussing basic concepts issues challenges case studies and applications in MIoT. This book offers novel advances and applications of MIoT in a precise and clear manner to the research community to achieve in-depth knowledge in the field. This book will help those interested in the field as well as researchers to gain insight into different concepts and their importance in multifaceted applications of real life. This has been done to make the book more flexible and to stimulate further interest in the topic. Features: A systematic overview of concepts in Medical Internet of Things (MIoT) is included. Recent research and some pointers on future advancements in MIoT are discussed. Examples and case studies are included. It is written in an easy-to-understand style with the help of numerous figures and datasets. This book serves as a reference book for scientific investigators who are interested in working on MIoT as well as researchers developing methodology in this field. It may also be used as a textbook for postgraduate-level courses in computer science or information technology. | Medical Internet of Things Techniques Practices and Applications

GBP 130.00
1

Metabolomics Practical Guide to Design and Analysis

Metabolomics Practical Guide to Design and Analysis

Metabolomics is the scientific study of the chemical processes in a living system environment and nutrition. It is a relatively new omics science but the potential applications are wide including medicine personalized medicine and intervention studies food and nutrition plants agriculture and environmental science. The topics presented and discussed in this book are based on the European Molecular Biology Organization (EMBO) practical courses in metabolomics bioinformatics taught to those working in the field from masters to postgraduate students PhDs postdoctoral and early PIs. The book covers the basics and fundamentals of data acquisition and analytical technologies but the primary focus is data handling and data analysis. The mentioning and usage of a particular data analysis tool has been avoided; rather the focus is on the concepts and principles of data processing and analysis. The material has been class-tested and includes lots of examples computing and exercises. Key Features:Provides an overview of qualitative /quantitative methods in metabolomicsOffers an introduction to the key concepts of metabolomics including experimental design and technologyCovers data handling processing analysis data standards and sharingContains lots of examples to illustrate the topicsIncludes contributions from some of the leading researchers in the field of metabolomics with extensive teaching experiences | Metabolomics Practical Guide to Design and Analysis

GBP 44.99
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Statistical Design and Analysis of Stability Studies

Statistical Design and Analysis of Stability Studies

The US Food and Drug Administration's Report to the Nation in 2004 and 2005 indicated that one of the top reasons for drug recall was that stability data did not support existing expiration dates. Pharmaceutical companies conduct stability studies to characterize the degradation of drug products and to estimate drug shelf life. Illustrating how stability studies play an important role in drug safety and quality assurance Statistical Design and Analysis of Stability Studies presents the principles and methodologies in the design and analysis of stability studies. After introducing the basic concepts of stability testing the book focuses on short-term stability studies and reviews several methods for estimating drug expiration dating periods. It then compares some commonly employed study designs and discusses both fixed and random batch statistical analyses. Following a chapter on the statistical methods for stability analysis under a linear mixed effects model the book examines stability analyses with discrete responses multiple components and frozen drug products. In addition the author provides statistical methods for dissolution testing and explores current issues and recent developments in stability studies. To ensure the safety of consumers professionals in the field must carry out stability studies to determine the reliability of drug products during their expiration period. This book provides the material necessary for you to perform stability designs and analyses in pharmaceutical research and development.

GBP 44.99
1

Bioequivalence and Statistics in Clinical Pharmacology

Bioequivalence and Statistics in Clinical Pharmacology

Maintaining a practical perspective Bioequivalence and Statistics in Clinical Pharmacology Second Edition explores statistics used in day-to-day clinical pharmacology work. The book is a starting point for those involved in such research and covers the methods needed to design analyze and interpret bioequivalence trials; explores when how and why these studies are performed as part of drug development; and demonstrates the methods using real world examples. Drawing on knowledge gained directly from working in the pharmaceutical industry the authors set the stage by describing the general role of statistics. Once the foundation of clinical pharmacology drug development regulatory applications and the design and analysis of bioequivalence trials are established including recent regulatory changes in design and analysis and in particular sample-size adaptation they move on to related topics in clinical pharmacology involving the use of cross-over designs. These include but are not limited to safety studies in Phase I dose-response trials drug interaction trials food-effect and combination trials QTc and other pharmacodynamic equivalence trials proof-of-concept trials dose-proportionality trials and vaccines trials. This second edition addresses several recent developments in the field including new chapters on adaptive bioequivalence studies scaled average bioequivalence testing and vaccine trials. Purposefully designed to be instantly applicable Bioequivalence and Statistics in Clinical Pharmacology Second Edition provides examples of SAS and R code so that the analyses described can be immediately implemented. The authors have made extensive use of the proc mixed procedures available in SAS.

GBP 44.99
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Model-Assisted Bayesian Designs for Dose Finding and Optimization Methods and Applications

Model-Assisted Bayesian Designs for Dose Finding and Optimization Methods and Applications

Bayesian adaptive designs provide a critical approach to improve the efficiency and success of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they form the basis for the development and success of subsequent phase II and III trials. The objective of this book is to describe the state-of-the-art model-assisted designs to facilitate and accelerate the use of novel adaptive designs for early phase clinical trials. Model-assisted designs possess avant-garde features where superiority meets simplicity. Model-assisted designs enjoy exceptional performance comparable to more complicated model-based adaptive designs yet their decision rules often can be pre-tabulated and included in the protocol—making implementation as simple as conventional algorithm-based designs. An example is the Bayesian optimal interval (BOIN) design the first dose-finding design to receive the fit-for-purpose designation from the FDA. This designation underscores the regulatory agency's support of the use of the novel adaptive design to improve drug development. Features Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials Describes the up-to-date theory and practice for model-assisted designs Presents many practical challenges issues and solutions arising from early-phase clinical trials Illustrates with many real trial applications Offers numerous tips and guidance on designing dose finding and optimization trials Provides step-by-step illustrations of using software to design trials Develops a companion website (www. trialdesign. org) to provide freely available easy-to-use software to assist learning and implementing model-assisted designs Written by internationally recognized research leaders who pioneered model-assisted designs from the University of Texas MD Anderson Cancer Center this book shows how model-assisted designs can greatly improve the efficiency and simplify the design conduct and optimization of early-phase dose-finding trials. It should therefore be a very useful practical reference for biostatisticians clinicians working in clinical trials and drug regulatory professionals as well as graduate students of biostatistics. Novel model-assisted designs showcase the new KISS principle: Keep it simple and smart! | Model-Assisted Bayesian Designs for Dose Finding and Optimization Methods and Applications

GBP 84.99
1

Face Detection and Recognition Theory and Practice

Face Detection and Recognition Theory and Practice

Face detection and recognition are the nonintrusive biometrics of choice in many security applications. Examples of their use include border control driver’s license issuance law enforcement investigations and physical access control. Face Detection and Recognition: Theory and Practice elaborates on and explains the theory and practice of face detection and recognition systems currently in vogue. The book begins with an introduction to the state of the art offering a general review of the available methods and an indication of future research using cognitive neurophysiology. The text then:Explores subspace methods for dimensionality reduction in face image processing statistical methods applied to face detection and intelligent face detection methods dominated by the use of artificial neural networksCovers face detection with colour and infrared face images face detection in real time face detection and recognition using set estimation theory face recognition using evolutionary algorithms and face recognition in frequency domainDiscusses methods for the localization of face landmarks helpful in face recognition methods of generating synthetic face images using set estimation theory and databases of face images available for testing and training systemsFeatures pictorial descriptions of every algorithm as well as downloadable source code (in MATLAB®/PYTHON) and hardware implementation strategies with code examplesDemonstrates how frequency domain correlation techniques can be used supplying exhaustive test resultsFace Detection and Recognition: Theory and Practice provides students researchers and practitioners with a single source for cutting-edge information on the major approaches algorithms and technologies used in automated face detection and recognition. | Face Detection and Recognition Theory and Practice

GBP 59.99
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Mathematics and Music Composition Perception and Performance

Mathematics and Music Composition Perception and Performance

Mathematics and Music: Composition Perception and Performance Second Edition includes many new sections and more consistent expectations of a student’s experience. The new edition of this popular text is more accessible for students with limited musical backgrounds and only high school mathematics is required. The new edition includes more illustrations than the previous one and the added sections deal with the XronoMorph rhythm generator musical composition and analyzing personal performance. The text teaches the basics of reading music explaining how various patterns in music can be described with mathematics providing mathematical explanations for musical scales harmony and rhythm. The book gives students a deeper appreciation showing how music is informed by both its mathematical and aesthetic structures. Highlights of the Second Edition: Now updated for more consistent expectations of students’ backgrounds More accessible for students with limited musical backgrounds Full-color presentation Includes more thorough coverage of spectrograms for analyzing recorded music Provides a basic introduction to reading music Features new coverage of building and evaluating rhythms | Mathematics and Music Composition Perception and Performance

GBP 42.99
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Embedded and Networking Systems Design Software and Implementation

Embedded and Networking Systems Design Software and Implementation

Embedded and Networking Systems: Design Software and Implementation explores issues related to the design and synthesis of high-performance embedded computer systems and networks. The emphasis is on the fundamental concepts and analytical techniques that are applicable to a range of embedded and networking applications rather than on specific embedded architectures software development or system-level integration. This system point of view guides designers in dealing with the trade-offs to optimize performance power cost and other system-level non-functional requirements. The book brings together contributions by researchers and experts from around the world offering a global view of the latest research and development in embedded and networking systems. Chapters highlight the evolution and trends in the field and supply a fundamental and analytical understanding of some underlying technologies. Topics include the co-design of embedded systems code optimization for a variety of applications power and performance trade-offs benchmarks for evaluating embedded systems and their components and mobile sensor network systems. The book also looks at novel applications such as mobile sensor systems and video networks. A comprehensive review of groundbreaking technology and applications this book is a timely resource for system designers researchers and students interested in the possibilities of embedded and networking systems. It gives readers a better understanding of an emerging technology evolution that is helping drive telecommunications into the next decade. | Embedded and Networking Systems Design Software and Implementation

GBP 77.99
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Image and Video Compression Fundamentals Techniques and Applications

Image and Video Compression Fundamentals Techniques and Applications

Image and video signals require large transmission bandwidth and storage leading to high costs. The data must be compressed without a loss or with a small loss of quality. Thus efficient image and video compression algorithms play a significant role in the storage and transmission of data. Image and Video Compression: Fundamentals Techniques and Applications explains the major techniques for image and video compression and demonstrates their practical implementation using MATLAB® programs. Designed for students researchers and practicing engineers the book presents both basic principles and real practical applications. In an accessible way the book covers basic schemes for image and video compression including lossless techniques and wavelet- and vector quantization-based image compression and digital video compression. The MATLAB programs enable readers to gain hands-on experience with the techniques. The authors provide quality metrics used to evaluate the performance of the compression algorithms. They also introduce the modern technique of compressed sensing which retains the most important part of the signal while it is being sensed. | Image and Video Compression Fundamentals Techniques and Applications

GBP 59.99
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Big Data and Social Science Data Science Methods and Tools for Research and Practice

Big Data and Social Science Data Science Methods and Tools for Research and Practice

Big Data and Social Science: Data Science Methods and Tools for Research and Practice Second Edition shows how to apply data science to real-world problems covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences statistics and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data apply data science methods and tools to the data and recognize and respond to data errors biases and limitations. Features: Takes an accessible hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available workbooks with data code and practical programming exercises through Binder and GitHub New to the Second Edition: Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner. | Big Data and Social Science Data Science Methods and Tools for Research and Practice

GBP 52.99
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Games Gambling and Probability An Introduction to Mathematics

Games Gambling and Probability An Introduction to Mathematics

Many experiments have shown the human brain generally has very serious problems dealing with probability and chance. A greater understanding of probability can help develop the intuition necessary to approach risk with the ability to make more informed (and better) decisions. The first four chapters offer the standard content for an introductory probability course albeit presented in a much different way and order. The chapters afterward include some discussion of different games different ideas that relate to the law of large numbers and many more mathematical topics not typically seen in such a book. The use of games is meant to make the book (and course) feel like fun! Since many of the early games discussed are casino games the study of those games along with an understanding of the material in later chapters should remind you that gambling is a bad idea; you should think of placing bets in a casino as paying for entertainment. Winning can obviously be a fun reward but should not ever be expected. Changes for the Second Edition: New chapter on Game Theory New chapter on Sports Mathematics The chapter on Blackjack which was Chapter 4 in the first edition appears later in the book. Reorganization has been done to improve the flow of topics and learning. New sections on Arkham Horror Uno and Scrabble have been added. Even more exercises were added! The goal for this textbook is to complement the inquiry-based learning movement. In my mind concepts and ideas will stick with the reader more when they are motivated in an interesting way. Here we use questions about various games (not just casino games) to motivate the mathematics and I would say that the writing emphasizes a just-in-time mathematics approach. Topics are presented mathematically as questions about the games themselves are posed. Table of Contents Preface1. Mathematics and Probability 2. Roulette and Craps: Expected Value 3. Counting: Poker Hands 4. More Dice: Counting and Combinations and Statistics 5. Game Theory: Poker Bluffing and Other Games 6. Probability/Stochastic Matrices: Board Game Movement 7. Sports Mathematics: Probability Meets Athletics 8. Blackjack: Previous Methods Revisited 9. A Mix of Other Games 10. Betting Systems: Can You Beat the System? 11. Potpourri: Assorted Adventures in Probability Appendices Tables Answers and Selected Solutions Bibliography Biography Dr. David G. Taylor is a professor of mathematics and an associate dean for academic affairs at Roanoke College in southwest Virginia. He attended Lebanon Valley College for his B. S. in computer science and mathematics and went to the University of Virginia for his Ph. D. While his graduate school focus was on studying infinite dimensional Lie algebras he started studying the mathematics of various games in order to have a more undergraduate-friendly research agenda. Work done with two Roanoke College students Heather Cook and Jonathan Marino appears in this book! Currently he owns over 100 different board games and enjoys using probability in his decision-making while playing most of those games. In his spare time he enjoys reading cooking coding playing his board games and spending time with his six-year-old dog Lilly. | Games Gambling and Probability An Introduction to Mathematics

GBP 82.99
1

Extreme Value Modeling and Risk Analysis Methods and Applications

Extreme Value Modeling and Risk Analysis Methods and Applications

Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics eliminating the need to sort through the massive amount of literature on the subject. After reviewing univariate extreme value analysis and multivariate extremes the book explains univariate extreme value mixture modeling threshold selection in extreme value analysis and threshold modeling of non-stationary extremes. It presents new results for block-maxima of vine copulas develops time series of extremes with applications from climatology describes max-autoregressive and moving maxima models for extremes and discusses spatial extremes and max-stable processes. The book then covers simulation and conditional simulation of max-stable processes; inference methodologies such as composite likelihood Bayesian inference and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel applications of extreme value modeling including financial investments insurance and financial risk management weather and climate disasters clinical trials and sports statistics. Risk analyses related to extreme events require the combined expertise of statisticians and domain experts in climatology hydrology finance insurance sports and other fields. This book connects statistical/mathematical research with critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists. | Extreme Value Modeling and Risk Analysis Methods and Applications

GBP 44.99
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Bioinformatics and Computational Biology Technological Advancements Applications and Opportunities

Bioinformatics and Computational Biology Technological Advancements Applications and Opportunities

Bioinformatics and Computational Biology: Technological Advancements Applications and Opportunities is an invaluable resource for general and applied researchers who analyze biological data that is generated at an unprecedented rate at the global level. After careful evaluation of the requirements for current trends in bioinformatics and computational biology it is anticipated that the book will provide an insightful resource to the academic and scientific community. Through a myriad of computational resources algorithms and methods it equips readers with the confidence to both analyze biological data and estimate predictions. The book offers comprehensive coverage of the most essential and emerging topics: Cloud-based monitoring of bioinformatics multivariate data with cloud platforms Machine learning and deep learning in bioinformatics Quantum machine learning for biological applications Integrating machine learning strategies with multiomics to augment prognosis in chronic diseases Biomedical engineering Next generation sequencing techniques and applications Computational systems biology and molecular evolution While other books may touch on some of the same issues and nuances of biological data analysis they neglect to feature bioinformatics and computational biology exclusively and as exhaustively. This book's abundance of several subtopics related to almost all of the regulatory activities of biomolecules from where real data is being generated brings an added dimension. | Bioinformatics and Computational Biology Technological Advancements Applications and Opportunities

GBP 120.00
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Advanced Wireless Communication and Sensor Networks Applications and Simulations

Advanced Wireless Communication and Sensor Networks Applications and Simulations

This book covers wireless communication security issues advanced wireless sensor networks (WSNs) routing protocols of WSNs with cross-layer solutions emerging trends in the advanced WSNs power management distributed sensing and data gathering techniques for WSNs WSNs security applications research of advanced WSNs with simulation results and simulation tools for WSNs. Features: Covers technologies supporting advanced wireless communication systems sensor networks and the conceptual development of the subject Discusses advanced data gathering and sharing/ distributed sensing techniques with its business applicability Includes numerous worked-out mathematical equations and formulas as well as essential principles including figures illustrations algorithms and flow charts Provides pervasive background knowledge including both wireless communications and WSNs Covers wireless networks as well as sensor network models in detail This book is aimed at graduate students researchers and academics working in the field of computer science wireless communication technology and advanced WSNs. | Advanced Wireless Communication and Sensor Networks Applications and Simulations

GBP 110.00
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Generative Adversarial Networks and Deep Learning Theory and Applications

Generative Adversarial Networks and Deep Learning Theory and Applications

This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks which includes creating new tools and methods for processing text images and audio. A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology including computer vision security multimedia and advertisements image generation image translation text-to-images synthesis video synthesis generating high-resolution images drug discovery etc. Features: Presents a comprehensive guide on how to use GAN for images and videos. Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network Intrusion detection using GAN Highlights the inclusion of gaming effects using deep learning methods Examines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutions The book addresses scientific aspects for a wider audience such as junior and senior engineering undergraduate and postgraduate students researchers and anyone interested in the trends development and opportunities in GAN and Deep Learning. The material in the book can serve as a reference in libraries accreditation agencies government agencies and especially the academic institution of higher education intending to launch or reform their engineering curriculum | Generative Adversarial Networks and Deep Learning Theory and Applications

GBP 140.00
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Introduction to Modeling and Simulation with MATLAB and Python

Introduction to Modeling and Simulation with MATLAB and Python

Introduction to Modeling and Simulation with MATLAB and Python is intended for students and professionals in science social science and engineering that wish to learn the principles of computer modeling as well as basic programming skills. The book content focuses on meeting a set of basic modeling and simulation competencies that were developed as part of several National Science Foundation grants. Even though computer science students are much more expert programmers they are not often given the opportunity to see how those skills are being applied to solve complex science and engineering problems and may also not be aware of the libraries used by scientists to create those models. The book interleaves chapters on modeling concepts and related exercises with programming concepts and exercises. The authors start with an introduction to modeling and its importance to current practices in the sciences and engineering. They introduce each of the programming environments and the syntax used to represent variables and compute mathematical equations and functions. As students gain more programming expertise the authors return to modeling concepts providing starting code for a variety of exercises where students add additional code to solve the problem and provide an analysis of the outcomes. In this way the book builds both modeling and programming expertise with a just-in-time approach so that by the end of the book students can take on relatively simple modeling example on their own. Each chapter is supplemented with references to additional reading tutorials and exercises that guide students to additional help and allows them to practice both their programming and analytical modeling skills. In addition each of the programming related chapters is divided into two parts – one for MATLAB and one for Python. In these chapters the authors also refer to additional online tutorials that students can use if they are having difficulty with any of the topics. The book culminates with a set of final project exercise suggestions that incorporate both the modeling and programming skills provided in the rest of the volume. Those projects could be undertaken by individuals or small groups of students. The companion website at http://www. intromodeling. com provides updates to instructions when there are substantial changes in software versions as well as electronic copies of exercises and the related code. The website also offers a space where people can suggest additional projects they are willing to share as well as comments on the existing projects and exercises throughout the book. Solutions and lecture notes will also be available for qualifying instructors. | Introduction to Modeling and Simulation with MATLAB® and Python

GBP 44.99
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Deep and Shallow Machine Learning in Music and Audio

Deep and Shallow Machine Learning in Music and Audio

Providing an essential and unique bridge between the theories of signal processing machine learning and artificial intelligence (AI) in music this book provides a holistic overview of foundational ideas in music from the physical and mathematical properties of sound to symbolic representations. Combining signals and language models in one place this book explores how sound may be represented and manipulated by computer systems and how our devices may come to recognize particular sonic patterns as musically meaningful or creative through the lens of information theory. Introducing popular fundamental ideas in AI at a comfortable pace more complex discussions around implementations and implications in musical creativity are gradually incorporated as the book progresses. Each chapter is accompanied by guided programming activities designed to familiarize readers with practical implications of discussed theory without the frustrations of free-form coding. Surveying state-of-the art methods in applications of deep neural networks to audio and sound computing as well as offering a research perspective that suggests future challenges in music and AI research this book appeals to both students of AI and music as well as industry professionals in the fields of machine learning music and AI. | Deep and Shallow Machine Learning in Music and Audio

GBP 44.99
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Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

In engineering and quality control various situations including process validation and design verification require equivalence and noninferiority tests. Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers presents methods for using validation and verification test data to demonstrate equivalence and noninferiority in engineering and applied science. The book covers numerous tests drawn from the author’s more than 30 years of work in a range of industrial settings. It provides computational formulas for the tests methods to determine or justify sample sizes and formulas to calculate power and operating characteristic curves. The methods are accessible using standard statistical software and do not require complicated programming. The book also includes computer code and screen shots for SAS R and JMP. This book provides you with a guide to performing validation and verification tests that demonstrate the adequacy of your process system or product. It will help you choose the best test for your application.

GBP 59.99
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Spatiotemporal Patterns in Ecology and Epidemiology Theory Models and Simulation

Spatiotemporal Patterns in Ecology and Epidemiology Theory Models and Simulation

Although the spatial dimension of ecosystem dynamics is now widely recognized the specific mechanisms behind species patterning in space are still poorly understood and the corresponding theoretical framework is underdeveloped. Going beyond the classical Turing scenario of pattern formation Spatiotemporal Patterns in Ecology and Epidemiology: Theory Models and Simulation illustrates how mathematical modeling and numerical simulations can lead to greater understanding of these issues. It takes a unified approach to population dynamics and epidemiology by presenting several ecoepidemiological models where both the basic interspecies interactions of population dynamics and the impact of an infectious disease are explicitly considered. The book first describes relevant phenomena in ecology and epidemiology provides examples of pattern formation in natural systems and summarizes existing modeling approaches. The authors then explore nonspatial models of population dynamics and epidemiology. They present the main scenarios of spatial and spatiotemporal pattern formation in deterministic models of population dynamics. The book also addresses the interaction between deterministic and stochastic processes in ecosystem and epidemic dynamics discusses the corresponding modeling approaches and examines how noise and stochasticity affect pattern formation. Reviewing the significant progress made in understanding spatiotemporal patterning in ecological and epidemiological systems this resource shows that mathematical modeling and numerical simulations are effective tools in the study of population ecology and epidemiology. | Spatiotemporal Patterns in Ecology and Epidemiology Theory Models and Simulation

GBP 59.99
1

Design and Analysis of Experiments and Observational Studies using R

Design and Analysis of Experiments and Observational Studies using R

Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions. R is used to implement designs and analyse the data collected. Features: Classical experimental design with an emphasis on computation using tidyverse packages in R. Applications of experimental design to clinical trials A/B testing and other modern examples. Discussion of the link between classical experimental design and causal inference. The role of randomization in experimental design and sampling in the big data era. Exercises with solutions. Instructor slides in RMarkdown a new R package will be developed to be used with book and a bookdown version of the book will be freely available. The proposed book will emphasize ethics communication and decision making as part of design data analysis and statistical thinking. | Design and Analysis of Experiments and Observational Studies using R

GBP 82.99
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Discrete and Continuous Fourier Transforms Analysis Applications and Fast Algorithms

Discrete and Continuous Fourier Transforms Analysis Applications and Fast Algorithms

Long employed in electrical engineering the discrete Fourier transform (DFT) is now applied in a range of fields through the use of digital computers and fast Fourier transform (FFT) algorithms. But to correctly interpret DFT results it is essential to understand the core and tools of Fourier analysis. Discrete and Continuous Fourier Transforms: Analysis Applications and Fast Algorithms presents the fundamentals of Fourier analysis and their deployment in signal processing using DFT and FFT algorithms. This accessible self-contained book provides meaningful interpretations of essential formulas in the context of applications building a solid foundation for the application of Fourier analysis in the many diverging and continuously evolving areas in digital signal processing enterprises. It comprehensively covers the DFT of windowed sequences various discrete convolution algorithms and their applications in digital filtering and filters and many FFT algorithms unified under the frameworks of mixed-radix FFTs and prime factor FFTs. A large number of graphical illustrations and worked examples help explain the concepts and relationships from the very beginning of the text. Requiring no prior knowledge of Fourier analysis or signal processing this book supplies the basis for using FFT algorithms to compute the DFT in a variety of application areas. | Discrete and Continuous Fourier Transforms Analysis Applications and Fast Algorithms

GBP 56.99
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Sound and Robotics Speech Non-Verbal Audio and Robotic Musicianship

Sound and Robotics Speech Non-Verbal Audio and Robotic Musicianship

Sound in human–robot interaction currently encompasses a wide range of approaches and methodologies not easily classified analyzed or compared among projects. This edited book covers the state of the art in sound and robotics aiming to gather existing approaches in a combined volume. Collecting chapters from world-leading academic and industry authors Sound and Robotics: Speech Non-Verbal Audio and Robotic Musicianship explores how robots can communicate through speech non-verbal audio and music. The first set of chapters explores how robots use verbal communication considering the possibilities of speech for human–robot interaction. The second section shifts to roles of non-verbal communication in HRI including consequential sound sonification and audio cues. The third and final section describes current approaches to robotic musicianship and their evaluation. This book is primarily aimed at HRI researchers ranging from those who have never used sound to those very experienced with sound. Alongside robotic researchers this book will present avenues for a diverse range of musicians composers and sound designers to become introduced to the world of HRI and learn of potential creative directions in robotics. | Sound and Robotics Speech Non-Verbal Audio and Robotic Musicianship

GBP 44.99
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Geospatial Health Data Modeling and Visualization with R-INLA and Shiny

Geospatial Health Data Modeling and Visualization with R-INLA and Shiny

Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden understand geographic and temporal patterns identify risk factors and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulating and transforming point areal and raster data Bayesian hierarchical models for disease mapping using areal and geostatistical data Fitting and interpreting spatial and spatio-temporal models with the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches Creating interactive and static visualizations such as disease maps and time plots Reproducible R Markdown reports interactive dashboards and Shiny web applications that facilitate the communication of insights to collaborators and policymakers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia cancer in Scotland and USA and air pollution in Spain. Examples in the book focus on health applications but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology ecology demography or criminology. The book provides clear descriptions of the R code for data importing manipulation modelling and visualization as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students researchers and practitioners. | Geospatial Health Data Modeling and Visualization with R-INLA and Shiny

GBP 84.99
1

Statistical Design Monitoring and Analysis of Clinical Trials Principles and Methods

Statistical Design Monitoring and Analysis of Clinical Trials Principles and Methods

Statistical Design Monitoring and Analysis of Clinical Trials Second Edition concentrates on the biostatistics component of clinical trials. This new edition is updated throughout and includes five new chapters. Developed from the authors’ courses taught to public health and medical students residents and fellows during the past 20 years the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. The book begins with ethical and safety principles core trial design concepts the principles and methods of sample size and power calculation and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials covering monitoring safety futility and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures phase 2/3 seamless design and trials with predictive biomarkers exploit multiple testing procedures and explain the concept of estimand intercurrent events and different missing data processes and describe how to analyze incomplete data by proper multiple imputations. This text reflects the academic research commercial development and public health aspects of clinical trials. It gives students and practitioners a multidisciplinary understanding of the concepts and techniques involved in designing monitoring and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students and researchers in (bio)statistics epidemiology medicine pharmacy and public health. | Statistical Design Monitoring and Analysis of Clinical Trials Principles and Methods

GBP 82.99
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Basic Experimental Strategies and Data Analysis for Science and Engineering

Basic Experimental Strategies and Data Analysis for Science and Engineering

Every technical investigation involving trial-and-error experimentation embodies a strategy for deciding what experiments to perform when to quit and how to interpret the data. This handbook presents several statistically derived strategies which are more efficient than any intuitive approach and will get the investigator to their goal with the fewest experiments give the greatest degree of reliability to their conclusions and keep the risk of overlooking something of practical importance to a minimum. Features:Provides a comprehensive desk reference on experimental design that will be useful to practitioners without extensive statistical knowledgeFeatures a review of the necessary statistical prerequisitesPresents a set of tables that allow readers to quickly access various experimental designsIncludes a roadmap for where and when to use various experimental design strategiesShows compelling examples of each method discussedIllustrates how to reproduce results using several popular software packages on a supplementary websiteFollowing the outlines and examples in this book should quickly allow a working professional or student to select the appropriate experimental design for a research problem at hand follow the design to conduct the experiments and analyze and interpret the resulting data. John Lawson and John Erjavec have a combined 25 years of industrial experience and over 40 years of academic experience. They have taught this material to numerous practicing engineers and scientists as well as undergraduate and graduate students. | Basic Experimental Strategies and Data Analysis for Science and Engineering

GBP 44.99
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