This advanced course, a sequel to the first volume of this lecture series on topos quantum theory, delves deeper into the theory, addressing further technical aspects and recent advances. These include, but are not limited to, the development of physical quantities and self-adjoint operators; insights into the quantization process; the description of an alternative, covariant version of topos quantum theory; and last but not least, the development of a new concept of spacetime. The book builds on the concepts introduced in the first volume (published as Lect. Notes Phys. 868), which presents the main building blocks of the theory and how it could provide solutions to interpretational problems in quantum theory, such as: What are the main conceptual issues in quantum theory? And how can these issues be solved within a new theoretical framework of quantum theory? These two volumes together provide a complete, basic course on topos quantum theory, offering a set of mathematical tools to readers interested in tackling fundamental issues in quantum theory in general, and in quantum gravity in particular. From the reviews of the first volume: The book is self-contained and can be used as a textbook or self-study manual teaching the usage of category theory and topos theory, in particular in theoretical physics or in investigating the foundations of quantum theory in mathematically rigorous terms. [The] book is a very welcome contribution. Frank Antonsen, Mathematical Reviews, December, 2013.

"Synthesis is one of the distinguishing features of chemistry as a science. It is the synthetic chemist who uses their intimate understanding of molecular structure and properties to produce materials of great value to society. Thus, it is important for the student of chemistry to study synthesis and acquire a working knowledge that enables them to address the following question - given a specific molecular target, how does one formulate a plan for its synthesis? This synthesis primer provides students who have taken introductory organic chemistry, with a set of tools that can be used to plan a synthesis. It may be used in conjunction with a synthetic methods course or it can stand alone"--

This introduction to quantum mechanics is intended for undergraduate students of physics, chemistry, and engineering with some previous exposure to quantum ideas. Following in Heisenberg's and Dirac's footsteps, this book is centered on the concept of the quantum state as an embodiment of all experimentally available information about a system, and its representation as a vector in an abstract Hilbert space. This conceptual framework and formalism are introduced immediately, and developed throughout the first four chapters, while the standard Schr¿inger equation does not appear until Chapter 5. The book grew out of lecture notes developed by the author over fifteen years of teaching at the undergraduate level. In response to numerous requests by students, material is presented with an unprecedented level of detail in both derivation of technical results and discussion of their physical significance. The book is written for students to enjoy reading it, rather than to use only as a source of formulas and examples. The colloquial and personal writing style makes it easier for readers to connect with the material. Additionally, readers will find short, relatable snippets about the "founding fathers" of quantum theory, their difficult historical circumstances, personal failings and triumphs, and often tragic fate. This textbook, complete with extensive original end-of-chapter exercises, is recommended for use in one- or two-semester courses for upper level undergraduate and beginning graduate students in physics, chemistry, or engineering.

"This textbook strengthens students' computational skills, teaches them how to prove theorems, and helps them understand proofs. Designed for junior and senior undergraduates, the book includes historical background for many concepts and theorems. It begins with the calculus of one variable and progresses to multivariable calculus in the second half of the book. An introductory section in each chapter reviews the material covered in earlier calculus courses, easing students into the more serious material. A solutions manual is available with qualifying course adoption"--

This book is designed to serve as a core text for courses in advanced engineering mathematics required by many engineering departments. The style of presentation is such that the student, with a minimum of assistance, can follow the step-by-step derivations. Liberal use of examples and homework prob­lems aid the student in the study of the topics presented. Ordinary differential equations, including a number of physical applica­tions, are reviewed in Chapter One. The use of series methods are presented in Chapter Two, Subsequent chapters present Laplace transforms, matrix theory and applications, vector analysis, Fourier series and transforms, partial differential equations, numerical methods using finite differences, complex vari­ables, and wavelets. The material is presented so that four or five subjects can be covered in a single course, depending on the topics chosen and the completeness of coverage. Incorporated in this textbook is the use of certain computer software packages. Short tutorials on Maple, demonstrating how problems in engineering mathematics can be solved with a computer algebra system, are included in most sections of the text. Problems have been identified at the end of sections to be solved specifically with Maple, and there are computer laboratory activities, which are more difficult problems designed for Maple. In addition, MATLAB and Excel have been included in the solution of problems in several of the chapters. There is a solutions manual available for those who select the text for their course. This text can be used in two semesters of engineering mathematics. The many helpful features make the text relatively easy to use in the classroom.

"Modern and comprehensive, the new seventh edition of award-winning author, Dennis G. Zill's Advanced Engineering Mathematics is a compendium of topics that are most often covered in courses in engineering mathematics, and is extremely flexible to meet the unique needs of courses ranging from ordinary differential equations, to vector calculus, to partial differential equations. A key strength of this best-selling text is the author's emphasis on differential equations as mathematical models, discussing the constructs and pitfalls of each. An accessible writing style and robust pedagogical aids guide students through difficult concepts with thoughtful explanations, clear examples, interesting applications, and contributed project problems"--

Annotation Solve challenging data science problems by mastering cutting-edge machine learning techniques in PythonAbout This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approachWho This Book Is ForThis title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you!Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models togetherIn DetailDesigned to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data.The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce.This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano.By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering.Style and approachThis book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.

In 1988, James Orbinski, then a medical student in his twenties, embarked on a year-long research trip to Rwanda, a trip that would change who he would be as a doctor and as a man. Investigating the conditions of pediatric AIDS in Rwanda, James confronted widespread pain and suffering, much of it preventable, much of it occasioned by political and economic corruption. Fuelled by the injustice of what he had seen in Rwanda, Orbinski helped establish the Canadian chapter of Médecins Sans Frontières (Doctors Without Borders/MSF). As a member of MSF he travelled to Peru during a cholera epidemic, to Somalia during the famine and civil war, and to Jalalabad, Afghanistan. In April 1994, James answered a call from the MSF Amsterdam office. Rwandan government soldiers and armed militias of extremist Hutus had begun systematically to murder Tutsis. While other foreigners were evacuated from Rwanda, Orbinski agreed to serve as Chef de Mission for MSF in Kigali. As Rwanda descended into a hell of civil war and genocide, he and his team worked tirelessly, tending to thousands upon thousands of casualties. In fourteen weeks 800,000 men, women and children were exterminated. Half a million people were injured, and millions were displaced. The Rwandan genocide was Orbinski’s undoing. Confronted by indescribable cruelty, he struggled to regain his footing as a doctor, a humanitarian and a man. In the end he chose not to retreat from the world, but resumed his work with MSF, and was the organization’s president when it was awarded the Nobel Peace Prize in 1999.

Completely updated and revised edition of the bestselling guide to artificial intelligence, updated to Python 3.8 and TensorFlow 2, with seven new chapters that cover RNNs, AI & Big Data, fundamental use cases, machine learning data pipelines, chatbots, Big Data, and more.

This book introduces SpecDB, an intelligent database created to represent and host software specifications in a machine-readable format, based on the principles of artificial intelligence and unit testing database operations. SpecDB is demonstrated via two automated intelligent tools. The first automatically generates database constraints from a rule-base in SpecDB. The second is a reverse engineering tool that logs the actual execution of the program from the code.

Elementary Differential Equations and Boundary Value Problems 11e, like its predecessors, is written from the viewpoint of the applied mathematician, whose interest in differential equations may sometimes be quite theoretical, sometimes intensely practical, and often somewhere in between.

Calculus , Second Edition,was written so that students can read it. The engaging style, clear presentation, detailed examples, and just-in-time margin notes will help students learn and retain concepts.

This book studies the approximate solutions of optimization problems in the presence of computational errors. A number of results are presented on the convergence behavior of algorithms in a Hilbert space; these algorithms are examined taking into account computational errors. The author illustrates that algorithms generate a good approximate solution, if computational errors are bounded from above by a small positive constant. Known computational errors are examined with the aim of determining an approximate solution. Researchers and students interested in the optimization theory and its applications will find this book instructive and informative. This monograph contains 16 chapters; including a chapters devoted to the subgradient projection algorithm, the mirror descent algorithm, gradient projection algorithm, the Weiszfelds method, constrained convex minimization problems, the convergence of a proximal point method in a Hilbert space, the continuous subgradient method, penalty methods and Newton's method.

Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance. Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that'll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you'll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis. This practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you'll get equipped with XAI and anomaly detection knowledge that'll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you'll learn how to quantify and assess their explainability. By the end of this deep learning book, you'll be able to build a variety of deep learning XAI models and perform validation to assess their explainability.

"Debunks cherished theories of mainstream consensus and reveals the deeper mysteries of the science of the unseen"--

This book provides undergraduate physics majors and students of related sciences with a sound basic understanding of electronics and how it is used, principally in the physical sciences. While today few science students go on to careers that demand an ability to design and build electronic circuits, many will use and rely on electronics. As scientists, they will require an appropriate level of fundamental knowledge that enables them, for example, to understand what electronic equipment is doing, to correctly interpret the measurements obtained, and to appreciate the numerous links between electronics and how it is practiced, and other areas of science. Discussing electronics in the broader context and from the point of view of the scientist, this book is intended for students who are not planning to become electronics specialists. It has been written in a relatively informal, personal style and includes detailed examples, as well as some "outside the box" material to inspire thought and creativity. A selection of relevant exercises is included at the end of each chapter.

"We are what we eat: this aphorism contains a profound truth about civilization, one that has played out on the world historical stage over many millennia of human endeavor.

"This book covers key aspects of modern communications; specifically, it covers analog and digital signals and analog and digital transmissions, analog and digital filters, the response of a communications system in time domain and in frequency domain, the spectral analysis, and the analog and digital modulations. To discuss these topics in depth, the authors introduce principles of operations, appropriate technologies, and design issues of communications devices and systems. They also describe the Laplace transform and Fourier transform and their applications. In addition, the book discusses the methods and techniques relating to description of communications systems in their entirety. It also provides a deeper understanding of issues by explaining the need for a specific task, showing the possible approaches to meeting the challenge, discussing the proper method to pursue, and considering the implementations of what seems to be the best possible solution"--

Combining science and psychology, an empowering guide helps individuals break away from the cycle of negative behaviors by revealing how new thinking and new beliefs can change one's behavior, emotional reactions, and habit-forming patterns.

This book focuses on conceptual frameworks that are helpful in understanding the basics of electronics - what the feedback system is, the principle of an oscillator, the operational working of an amplifier, and other relevant topics. It also provides an overview of the technologies supporting electronic systems, like OP-AMP, transistor, filter, ICs, and diodes. It consists of seven chapters, written in an easy and understandable language, and featuring relevant block diagrams, circuit diagrams, valuable and interesting solved examples, and important test questions. Further, the book includes up-to-date illustrations, exercises, and numerous worked examples to illustrate the theory and to demonstrate their use in practical designs.

"This unique, practical guide for postdoctoral researchers and graduate students explains how to build and perfect the necessary research tools and working skills to build a career in academia and beyond. It is based on successful training workshops run by the authors: first, it describes the tools needed for independent research, from writing papers to applying for academic jobs; it then introduces skills to thrive in a new job, including managing and interacting with others, designing a taught course and giving a good lecture; and it concludes with a section on managing your career, from how to manage stress to understanding the higher education system. Packed with helpful features encouraging readers to apply the theory to their individual situation, the book is also illustrated throughout with real-world case studies to enable readers to learn from others' experience. It is a vital handbook for everyone seeking to make a successful scientific career"--

"Thoroughly updated with 50% new material, this introductory textbook covers the basic principles and latest research on the causes, growth, detection, and treatment of cancers. Student learning is supported with a stepwise presentation, 'Pause and Recap' boxes, end-of-chapter review questions, and additional online resources"--

"The standard textbook on electricity and magnetism for junior/senior undergraduate students in electrodynamics, electricity and magnetism, and electrical engineering. It includes new problems (including several computational problems in Mathematica), worked examples, figures, and updated references to accessible literature"--

"This book is in no way a medical textbook; it only aims at providing a wide in-depth thinking about the growing life expectancy in the current digital age"--

Take your Python text processing skills to another level by learning about the latest natural language processing and machine learning techniques with this full color guide Key Features Learn how to acquire and process textual data and visualize the key findings Obtain deeper insight into the most commonly used algorithms and techniques and understand their tradeoffs Implement models for solving real-world problems and evaluate their performance Book Description With the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code. A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions. By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation. What you will learn Understand fundamental concepts of machine learning for text Discover how text data can be represented and build language models Perform exploratory data analysis on text corpora Use text preprocessing techniques and understand their trade-offs Apply dimensionality reduction for visualization and classification Incorporate and fine-tune algorithms and models for machine learning Evaluate the performance of the implemented systems Know the tools for retrieving text data and visualizing the machine learning workflow Who this book is for This book is for professionals in the area of computer science, programming, data science, informatics, business analytics, statistics, language technology, and more who aim for a gentle career shift in machine learning for text. Students in relevant disciplines that seek a textbook in the field will benefit from the practical aspects of the content and how the theory is presented. Finally, professors teaching a similar course will be able to pick pertinent topics in terms of content and difficulty. Beginner-level knowledge of Python programming is needed to get started with this book.

Interested in developing embedded systems? Since they don't tolerate inefficiency, these systems require a disciplined approach to programming. This easy-to-read guide helps you cultivate good development practices based on classic software design patterns and new patterns unique to embedded programming. You'll learn how to build system architecture for processors, not operating systems, and discover techniques for dealing with hardware difficulties, changing designs, and manufacturing requirements. Written by Elecia White, an expert who's created embedded systems ranging from DNA scanners to children's toys, this book is ideal for intermediate and experienced programmers, no matter what platform you use. This expanded update includes new chapters on IoT and networked sensors, motors and movement, and data handling strategies. Optimize your system to reduce cost and increase performance. Develop an architecture that makes your software robust in resource-constrained environments. Explore sensors, displays, motors, and other I/O devices. Reduce RAM and power consumption, code space, and processor cycles. Learn how to interpret schematics, datasheets, and power requirements. Discover how to implement complex mathematics and machine learning on small processors. Design effective embedded systems for IoT and networked sensors.

Presents an introduction to the methods used in signal processing. This book is designed to impart the mathematical techniques used in modeling signals and systems, encompassing standard mathematical tools, as well as techniques such as wavelets and neural networks. C++ and Java implementations furnish these descriptions.

"This textbook introduces the field of mechanics, commonly offered at universities in Germany as part of structured courses in theoretical physics. Within the framework of elementary Newtonian mechanics, the basic concepts - such as trajectory curves, mass point, equations of motion, reference frames - are introduced. The focus is placed on the Lagrangian formalism (Lagrangian equations of the first and second kind, Hamilton's principle, conservation laws, Noether's theorem, etc.) and its most important applications (e.g.: motion in a central potential, dynamics of a rigid body and harmonic oscillations). The Hamilton formalism is then introduced in a compact way and continuum mechanics is presented through illustrative examples, such as string vibrations, beam bending, elementary hydrodynamics, sound waves, and more. The book also deals in detail with special relativity (principle of relativity by Einstein, length contraction, time dilation, relativistic equation of motion, production of heavy particles, twin paradox, etc.), and is supplemented with an appendix that examines the relation between the Newtonian force and the Minkowksi force"--

Microbiology An Introduction, 12E (Edition 12th) authored by genard j. tortora, an expert in PATHOLOGY. This edition comprises 955 pages of invaluable medical knowledge published by PEARSON in , tailored for medical professionals preparing for international exams. Joining our brand-new collection, this book is available for online purchase. Enhance your medical knowledge journey by exploring our store for more medical books and products!

Annotation This work provides a comprehensive, up-to-date description of the mechanisms of microbial infection and the pathogenesis of infectious disease. Presented in a clear, accessible style, it deals in an integrated manner with the spectrum of microorganisms, bringing together molecular biology, pathology, and immunology to explain the mechanisms for spread, immune response, and recovery.

This textbook offers clear explanations of optical spectroscopic phenomena and shows how spectroscopic techniques are used in modern molecular and cellular biophysics and biochemistry. The topics covered include electronic and vibrational absorption, fluorescence, resonance energy transfer, exciton interactions, circular dichroism, coherence and dephasing, ultrafast pump-probe and photon-echo spectroscopy, single-molecule and fluorescence-correlation spectroscopy, Raman scattering, and multiphoton absorption. This revised and updated edition provides expanded discussions of quantum optics, metal-ligand charge-transfer transitions, entropy changes during photoexcitation, electron transfer from excited molecules, normal-mode calculations, vibrational Stark effects, studies of fast processes by resonance energy transfer in single molecules, and two-dimensional electronic and vibrational spectroscopy. The explanations are sufficiently thorough and detailed to be useful for researchers and graduate students and advanced undergraduates in chemistry, biochemistry and biophysics. They are based on time-dependent quantum mechanics, but are developed from first principles with a clarity that makes them accessible to readers with little prior training in this field. Extra topics and highlights are featured in special boxes throughout the text. The author also provides helpful exercises for each chapter.

"Some said it was a miracle drug; others called it the cure. A drug known simply as "GDNF" seemed to do the impossible by actually reversing the effects of Parkinson's disease. The miracle ended in the fall of 2004 when Amgen Inc. abruptly halted clinical trials of GDNF and denied access to the drug. In "Monkeys in the Middle", investigative reporter Nick Nelson recounts the true story of the brave patients who took on the world's biggest biotechnology company for the right to be kept alive"--Editorial

The sixth edition of this highly successful textbook provides a detailed introduction to Monte Carlo simulation in statistical physics, which deals with the computer simulation of many-body systems in condensed matter physics and related fields of physics and beyond (traffic flows, stock market fluctuations, et cetera). Using random numbers generated by a computer, these powerful simulation methods calculate probability distributions, making it possible to estimate the thermodynamic properties of various systems. The book describes the theoretical background of these methods, enabling newcomers to perform such simulations and to analyse their results. It features a modular structure, with two chapters providing a basic pedagogic introduction plus exercises suitable for university courses; the remaining chapters cover major recent developments in the field. This edition has been updated with two new chapters dealing with recently developed powerful special algorithms and with finite size scaling tools for the study of interfacial phenomena, which are important for nanoscience. Previous editions have been highly praised and widely used by both students and advanced researchers.

"With this accessible, introductory guide, you will quickly learn how to use and apply optical spectroscopy and optical microscopy techniques. Focusing on day-to-day implementation and offering practical lab tips throughout, it provides step-by-step instructions on how to select the best technique for a particular application, how to set up and customize new optical systems, and how to analyze optical data. You will gain an intuitive understanding of the full range of standard optical techniques, from fluorescence and Raman spectroscopy to super resolution microscopy. Understand how to navigate around an optics lab with clear descriptions of the most common optical components and tools. Including explanations of basic optics and photonics, and easy-to-understand mathematics, this is an invaluable resource for graduate students, instructors, researchers and professionals who use or teach optical measurements in laboratories"--

This textbook presents an introduction to the use of probability in physics, treating introductory ideas of both statistical physics and of statistical inference, as well the importance of probability in information theory, quantum mechanics, and stochastic processes, in a unified manner. The book also presents a harmonised view of frequentist and Bayesian approaches to inference, emphasising their complementary value. The aim is to steer a middle course between the "cookbook" style and an overly dry mathematical statistics style. The treatment is driven by real physics examples throughout, but developed with a level of mathematical clarity and rigour appropriate to mid-career physics undergraduates. Exercises and solutions are included.

The author "...shows why pushy, hovering 'tiger parents' and permissive 'jellyfish parents' actually hinder self-motivation. She proposes a powerful new parenting model: the intelligent, playful, highly social dolphin. Dolphin parents focus on maintaining balance in their children's lives to gently, yet authoritatively guide them toward lasting health, happiness, and success"-- Front dust jacket flap.

This textbook offers a clear and comprehensive introduction to statistical physics, one of the core components of advanced undergraduate physics courses. It follows on naturally from the previous volumes in this series, using methods of probability theory and statistics to solve physical problems. The first part of the book gives a detailed overview on classical statistical physics and introduces all mathematical tools needed. The second part of the book covers topics related to quantized states, gives a thorough introduction to quantum statistics, followed by a concise treatment of quantum gases. Ideally suited to undergraduate students with some grounding in quantum mechanics, the book is enhanced throughout with learning features such as boxed inserts and chapter summaries, with key mathematical derivations highlighted to aid understanding. The text is supported by numerous worked examples and end of chapter problem sets. About the Theoretical Physics series Translated from the renowned and highly successful German editions, the eight volumes of this series cover the complete core curriculum of theoretical physics at undergraduate level. Each volume is self-contained and provides all the material necessary for the individual course topic. Numerous problems with detailed solutions support a deeper understanding. Nolting is famous for his refined didactical style and has been referred to as the "German Feynman" in reviews.

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size-- small enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary.

This eighth edition preserves the popular format and style of the previous editions. It is a standard right triangle approach to trigonometry, providing a smooth and integrated development of the six trigonometric functions from point-on-the-terminal-side, to right triangle, to circular function definitions. Nearly every section is written so that it can be discussed in a typical class session. The focus of the text is on understanding the definitions and principles of trigonometry and their applications to problem solving.

Wireless Communication Networks and Systems covers all types of wireless communications, from satellite and cellular to local and personal area networks. Organised into four easily comprehensible, reader-friendly parts, it presents a clear and comprehensive overview of the field of wireless communications. For those who are new to the topic, the book explains basic principles and fundamental topics concerning the technology and architecture of the field. Numerous figures and tables help clarify discussions, and each chapter includes a list of keywords, review questions, homework problems, and suggestions for further reading. The book includes an extensive online glossary, a list of frequently used acronyms, and a reference list. A diverse set of projects and other student exercises enables instructors to use the book as a component in a varied learning experience, tailoring courses to meet their specific needs.

"Reissued by Cambridge University Press, the second edition of this definitive textbook provides an unrivaled introduction to the theoretical and practical fundamentals of wireless communications. Supported by slides and solutions for instructors, it is the ideal foundation for senior undergraduate and graduate courses in wireless communications"--