Micro / Nano Replication. Processes and Applications

This well-written book provides a clear and accessible treatment of the theory of discrete and continuous-time Markov chains, with an emphasis towards applications. The mathematical treatment is precise and rigorous without superfluous details, and the results are immediately illustrated in illuminating examples. This book will be extremely useful to anybody teaching a course on Markov processes. Jean-Fran?ois Le Gall, Professor at Universit? de Paris-Orsay, France. Markov processes is the class of stochastic processes whose past and future are conditionally independent, given their present state. They constitute important models in many applied fields. After an introduction to the Monte Carlo method, this book describes discrete time Markov chains, the Poisson process and continuous time Markov chains. It also presents numerous applications including Markov Chain Monte Carlo, Simulated Annealing, Hidden Markov Models, Annotation and Alignment of Genomic sequences, Control and Filtering, Phylogenetic tree reconstruction and Queuing networks. The last chapter is an introduction to stochastic calculus and mathematical finance. Features include: The Monte Carlo method, discrete time Markov chains, the Poisson process and continuous time jump Markov processes. An introduction to diffusion processes, mathematical finance and stochastic calculus. Applications of Markov processes to various fields, ranging from mathematical biology, to financial engineering and computer science. Numerous exercises and problems with solutions to most of them

This book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes. It combines classic topics such as construction of stochastic processes, associated filtrations, processes with independent increments, Gaussian processes, martingales, Markov properties, continuity and related properties of trajectories with contemporary subjects: integration with respect to Gaussian processes, Itȏ integration, stochastic analysis, stochastic differential equations, fractional Brownian motion and parameter estimation in diffusion models.

Micro-Cutting: Fundamentals and Applications comprehensively covers the state of the art research and engineering practice in micro/nano cutting: an area which is becoming increasingly important, especially in modern micro-manufacturing, ultraprecision manufacturing and high value manufacturing. This book provides basic theory, design and analysis of micro-toolings and machines, modelling methods and techniques, and integrated approaches for micro-cutting. The fundamental characteristics, modelling, simulation and optimization of micro/nano cutting processes are emphasized with particular reference to the predictabilty, producibility, repeatability and productivity of manufacturing at micro and nano scales. The fundamentals of micro/nano cutting are applied to a variety of machining processes including diamond turning, micromilling, micro/nano grinding/polishing, ultraprecision machining, and the design and implementation of micro/nano cutting process chains and micromachining systems. Key features • Contains contributions from leading global experts • Covers the fundamental theory of micro-cutting • Presents applications in a variety of machining processes • Includes examples of how to implement and apply micro-cutting for precision and micro-manufacturing Micro-Cutting: Fundamentals and Applications is an ideal reference for manufacturing engineers, production supervisors, tooling engineers, planning and application engineers, as well as machine tool designers. It is also a suitable textbook for postgraduate students in the areas of micro-manufacturing, micro-engineering and advanced manufacturing methods.

This book serves as a comprehensive source of asymptotic results for econometric models with deterministic exogenous regressors. Such regressors include linear (more generally, piece-wise polynomial) trends, seasonally oscillating functions, and slowly varying functions including logarithmic trends, as well as some specifications of spatial matrices in the theory of spatial models. The book begins with central limit theorems (CLTs) for weighted sums of short memory linear processes. This part contains the analysis of certain operators in Lp spaces and their employment in the derivation of CLTs. The applications of CLTs are to the asymptotic distribution of various estimators for several econometric models. Among the models discussed are static linear models with slowly varying regressors, spatial models, time series autoregressions, and two nonlinear models (binary logit model and nonlinear model whose linearization contains slowly varying regressors). The estimation procedures include ordinary and nonlinear least squares, maximum likelihood, and method of moments. Additional topical coverage includes an introduction to operators, probabilities, and linear models; Lp-approximable sequences of vectors; convergence of linear and quadratic forms; regressions with slowly varying regressors; spatial models; convergence; nonlinear models; and tools for vector autoregressions.

Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background. The book has the following features: Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy. Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities. Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares. The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website. Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing.

There s a buzzword that has quickly captured the imagination of product providers and investors alike: «hedge fund replication». In the broadest sense, replicating hedge fund strategies means replicating their return sources and corresponding risk exposures. However, there still lacks a coherent picture on what hedge fund replication means in practice, what its premises are, how to distinguish di erent approaches, and where this can lead us to. Serving as a handbook for replicating the returns of hedge funds at considerably lower cost, Alternative Beta Strategies and Hedge Fund Replication provides a unique focus on replication, explaining along the way the return sources of hedge funds, and their systematic risks, that make replication possible. It explains the background to the new discussion on hedge fund replication and how to derive the returns of many hedge fund strategies at much lower cost, it differentiates the various underlying approaches and explains how hedge fund replication can improve your own investment process into hedge funds. Written by the well known Hedge Fund expert and author Lars Jaeger, the book is divided into three sections: Hedge Fund Background, Return Sources, and Replication Techniques. Section one provides a short course in what hedge funds actually are and how they operate, arming the reader with the background knowledge required for the rest of the book. Section two illuminates the sources from which hedge funds derive their returns and shows that the majority of hedge fund returns derive from systematic risk exposure rather than manager «Alpha». Section three presents various approaches to replicating hedge fund returns by presenting the first and second generation of hedge fund replication products, points out the pitfalls and strengths of the various approaches and illustrates the mathematical concepts that underlie them. With hedge fund replication going mainstream, this book provides clear guidance on the topic to maximise returns.

A comprehensive and accessible presentation of probability and stochastic processes with emphasis on key theoretical concepts and real-world applications With a sophisticated approach, Probability and Stochastic Processes successfully balances theory and applications in a pedagogical and accessible format. The book’s primary focus is on key theoretical notions in probability to provide a foundation for understanding concepts and examples related to stochastic processes. Organized into two main sections, the book begins by developing probability theory with topical coverage on probability measure; random variables; integration theory; product spaces, conditional distribution, and conditional expectations; and limit theorems. The second part explores stochastic processes and related concepts including the Poisson process, renewal processes, Markov chains, semi-Markov processes, martingales, and Brownian motion. Featuring a logical combination of traditional and complex theories as well as practices, Probability and Stochastic Processes also includes: Multiple examples from disciplines such as business, mathematical finance, and engineering Chapter-by-chapter exercises and examples to allow readers to test their comprehension of the presented material A rigorous treatment of all probability and stochastic processes concepts An appropriate textbook for probability and stochastic processes courses at the upper-undergraduate and graduate level in mathematics, business, and electrical engineering, Probability and Stochastic Processes is also an ideal reference for researchers and practitioners in the fields of mathematics, engineering, and finance.

The second edition enhanced with new chapters, figures, and appendices to cover the new developments in applied mathematical functions This book examines the topics of applied mathematical functions to problems that engineers and researchers solve daily in the course of their work. The text covers set theory, combinatorics, random variables, discrete and continuous probability, distribution functions, convergence of random variables, computer generation of random variates, random processes and stationarity concepts with associated autocovariance and cross covariance functions, estimation theory and Wiener and Kalman filtering ending with two applications of probabilistic methods. Probability tables with nine decimal place accuracy and graphical Fourier transform tables are included for quick reference. The author facilitates understanding of probability concepts for both students and practitioners by presenting over 450 carefully detailed figures and illustrations, and over 350 examples with every step explained clearly and some with multiple solutions. Additional features of the second edition of Probability and Random Processes are: Updated chapters with new sections on Newton-Pepys’ problem; Pearson, Spearman, and Kendal correlation coefficients; adaptive estimation techniques; birth and death processes; and renewal processes with generalizations A new chapter on Probability Modeling in Teletraffic Engineering written by Kavitha Chandra An eighth appendix examining the computation of the roots of discrete probability-generating functions With new material on theory and applications of probability, Probability and Random Processes, Second Edition is a thorough and comprehensive reference for commonly occurring problems in probabilistic methods and their applications.

Providing a detailed overview of the fundamentals and latest developments in the field of energy autonomous microsystems, this book delivers an in-depth study of the applications in the fields of health and usage monitoring in aeronautics, medical implants, and home automation, drawing out the main specifications on such systems. Introductory information on photovoltaic, thermal and mechanical energy harvesting, and conversion, is given, along with the latest results in these fields. This book also provides a state of the art of ultra-low power sensor interfaces, digital signal processing and wireless communications. In addition, energy optimizations at the sensor node and sensors network levels are discussed, thus completing this overview. This book details the challenges and latest techniques available to readers who are interested in this field. A major strength of this book is that the first three chapters are application orientated and thus, by setting the landscape, introduce the technical chapters. There is also a good balance between the technical application, covering all the system-related aspects and, within each chapter, details on the physics, materials and technologies associated with electronics. Contents Introduction. Introduction to Energy Autonomous Micro
and Nano Systems and Presentation of Contributions, Marc Belleville and Cyril Condemine. 1. Sensors at the Core of Building Control, Gilles Chabanis, Laurent Chiesi, Hynek Raisigel,
Isabelle Ressejac and V?ronique Boutin. 2. Toward Energy Autonomous MedicalImplants, Raymond Campagnolo and Daniel Kroiss. 3. Energy Autonomous Systems in Aeronautic Applications, Thomas Becker, Jirka Klaue and Martin Kluge. 4. Energy Harvesting by Photovoltaic Effect, Emmanuelle Rouvi?re, Simon Perraud, Cyril Condemine and
Guy Waltisperger. 5. Mechanical Energy Harvesting, Ghislain Despesse, Jean Jacques Chaillout,
S?bastien Boisseau and Claire Jean-Mistral. 6. Thermal Energy Harvesting, Tristan Caroff, Emmanuelle Rouvi?re and J?r?me Willemin. 7. Lithium Micro-Batteries, Rapha?l Salot. 8. Ultra-Low-Power Sensors, Pascal Nouet, Norbert Dumas, Laurent Latorre and
Fr?d?rick Mailly. 9. Ultra-Low-Power Signal Processing in Autonomous Systems, Christian Piguet. 10. Ultra-Low-Power Radio Frequency Communications and Protocols, Eric Mercier. 11. Energy Management in an Autonomous Microsystem, Jean-Fr?d?ric Christmann, Edith Beigne, Cyril Condemine, J?r?me Willemin and Christian Piguet. 12. Optimizing Energy Efficiency of Sensor Networks, Olivier Sentieys and Olivier Berder.

A resource for probability AND random processes, with hundreds of worked examples and probability and Fourier transform tables This survival guide in probability and random processes eliminates the need to pore through several resources to find a certain formula or table. It offers a compendium of most distribution functions used by communication engineers, queuing theory specialists, signal processing engineers, biomedical engineers, physicists, and students. Key topics covered include: * Random variables and most of their frequently used discrete and continuous probability distribution functions * Moments, transformations, and convergences of random variables * Characteristic, generating, and moment-generating functions * Computer generation of random variates * Estimation theory and the associated orthogonality principle * Linear vector spaces and matrix theory with vector and matrix differentiation concepts * Vector random variables * Random processes and stationarity concepts * Extensive classification of random processes * Random processes through linear systems and the associated Wiener and Kalman filters * Application of probability in single photon emission tomography (SPECT) More than 400 figures drawn to scale assist readers in understanding and applying theory. Many of these figures accompany the more than 300 examples given to help readers visualize how to solve the problem at hand. In many instances, worked examples are solved with more than one approach to illustrate how different probability methodologies can work for the same problem. Several probability tables with accuracy up to nine decimal places are provided in the appendices for quick reference. A special feature is the graphical presentation of the commonly occurring Fourier transforms, where both time and frequency functions are drawn to scale. This book is of particular value to undergraduate and graduate students in electrical, computer, and civil engineering, as well as students in physics and applied mathematics. Engineers, computer scientists, biostatisticians, and researchers in communications will also benefit from having a single resource to address most issues in probability and random processes.