The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.

The book explains in a comprehensive way the basic terms of communication engineering, giving a proper amount of the needed mathematical background and explanations of the physical nature of the problems. The theory of communication sciences is explained by using knowledge and examples from real-world applications. The information is presented in a way that is understandable also for those who are not directly involved in communication sciences, but would like to learn more about them.

This proceedings set contains 85 selected full papers presented at the 3rd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences - MCO 2015, held on May 11-13, 2015 at Lorraine University, France. The present part I of the 2 volume set includes articles devoted to Combinatorial optimization and applications, DC programming and DCA: thirty years of Developments, Dynamic Optimization, Modelling and Optimization in financial engineering, Multiobjective programming, Numerical Optimization, Spline Approximation and Optimization, as well as Variational Principles and Applications.

Extended versions of awarded contributions of the International Conference on Systems, Analysis and Automatic Control, Barcelona 2014. Among the topics are: Adaptive Control, Predictive Control, Fuzzy Logic Control, System Identification, Expert and Knowledge Based Systems, Nonlinear Systems, Human-Machine Systems, Intelligent User Interface, Human-Machine Design and Evaluation, Learning Control, Uncertain Systems, Supervision.

The subject of Digital Signal Processing (DSP) is enormously complex, involving many concepts, probabilities, and signal processing that are woven together in an intricate manner. To cope with this scope and complexity, many DSP texts are often organized around the "numerical examples" of a communication system. With such organization, readers can see through the complexity of DSP, they learn about the distinct concepts and protocols in one part of the communication system while seeing the big picture of how all parts fit together. From a pedagogical perspective, our personal experience has been that such approach indeed works well.Based on the authors' extensive experience in teaching and research, Digital Signal Processing: a breadth-first approach is written with the reader in mind. The book is intended for a course on digital signal processing, for seniors and undergraduate students. The subject has high popularity in the field of electrical and computer engineering, and the authors consider all the needs and tools used in analysis and design of discrete time systems for signal processing.Key features of the book include:* The extensive use of MATLAB based examples to illustrate how to solve signal processing problems. The textbook includes a wealth of problems, with solutions* Worked-out examples have been included to explain new and difficult concepts, which help to expose the reader to real-life signal processing problems* The inclusion of FIR and IIR filter design further enrich the contents

Compressed Sensing (CS) is a promising method that recovers the sparse and compressible signals from severely under-sampled measurements. CS can be applied to wireless communication to enhance its capabilities. As this technology is proliferating, it is possible to explore its need and benefits for emerging applicationsCompressive Sensing for Wireless Communication provides:* A clear insight into the basics of compressed sensing* A thorough exploration of applying CS to audio, image and computer vision* Different dimensions of applying CS in Cognitive radio networks* CS in wireless sensor network for spatial compression and projection* Real world problems/projects that can be implemented and tested* Efficient methods to sample and reconstruct the images in resource constrained WMSN environmentThis book provides the details of CS and its associated applications in a thorough manner. It lays a direction for students and new engineers and prepares them for developing new tasks within the field of CS. It is an indispensable companion for practicing engineers who wish to learn about the emerging areas of interest.

This book presents models and procedures to design pipeline analog-to-digital converters, compensating for device inaccuracies, so that high-performance specs can be met within short design cycles. These models are capable of capturing and predicting the behavior of pipeline data converters within less than half-a-bit deviation, versus transistor-level simulations. As a result, far fewer model iterations are required across the design cycle. Models described in this book accurately predict transient behaviors, which are key to the performance of discrete-time systems and hence to the performance of pipeline data converters.

Signal Measurement and Estimation Techniques for Micro and Nanotechnology discusses micro, nano and robotic cells and gives a state-of-the-art presentation of the different techniques and solutions to measure and estimate signals at the micro and nano scale. New technologies and applications such as micromanipulation (artificial components, biological objects), micro-assembly (MEMS, MOEMS, NEMS) and material and surface force characterization are covered. The importance of sensing at the micro and nano scale is presented as a key issue in control systems, as well as for understanding the physical phenomena of these systems. The book also: Explains issues that make signal measurement and estimation techniques difficult at the micro-nano-scale and offers solutions Discusses automated micro-assembly, and control of micro-nano robotic devices Presents and links signal measurement and estimation techniques for micro-nano scale systems with microfabrication methods, sensors integration and control schemes Signal Measurement and Estimation Techniques for Micro and Nanotechnology is a must-read for researchers and engineers working in MEMS and control systems.

Low power wide area network (LPWAN) is a promising solution for long range and low power Internet of Things (IoT) and machine to machine (M2M) communication applications. The LPWANs are resource-constrained networks and have critical requirements for long battery life, extended coverage, high scalability, and low device and deployment costs. There are several design and deployment challenges such as media access control, spectrum management, link optimization and adaptability, energy harvesting, duty cycle restrictions, coexistence and interference, interoperability and heterogeneity, security and privacy, and others. LPWAN Technologies for IoT and M2M Applications is intended to provide a one-stop solution for study of LPWAN technologies as it covers a broad range of topics and multidisciplinary aspects of LPWAN and IoT. Primarily, the book focuses on design requirements and constraints, channel access, spectrum management, coexistence and interference issues, energy efficiency, technology candidates, use cases of different applications in smart city, healthcare, and transportation systems, security issues, hardware/software platforms, challenges, and future directions.

This book is primarily intended for junior-level students who take the courses on 'signals and systems'. It may be useful as a reference text for practicing engineers and scientists who want to acquire some of the concepts required for signal proce- ing. The readers are assumed to know the basics about linear algebra, calculus (on complex numbers, differentiation, and integration), differential equations, Laplace R transform, and MATLAB . Some knowledge about circuit systems will be helpful. Knowledge in signals and systems is crucial to students majoring in Electrical Engineering. The main objective of this book is to make the readers prepared for studying advanced subjects on signal processing, communication, and control by covering from the basic concepts of signals and systems to manual-like introduc- R R tions of how to use the MATLAB and Simulink tools for signal analysis and lter design. The features of this book can be summarized as follows: 1. It not only introduces the four Fourier analysis tools, CTFS (continuous-time Fourier series), CTFT (continuous-time Fourier transform), DFT (discrete-time Fourier transform), and DTFS (discrete-time Fourier series), but also illuminates the relationship among them so that the readers can realize why only the DFT of the four tools is used for practical spectral analysis and why/how it differs from the other ones, and further, think about how to reduce the difference to get better information about the spectral characteristics of signals from the DFT analysis.

Low-power sensors and their applications in various fields ranging from military to civilian lives have made tremendous progress in the recent years. Low-power and extended battery life are the key focuses for long term, reliable and easy operation of these sensors. Sensors and Low Power Signal Processing provides a general overview of a sensor's working principle and a discussion of the emerging sensor technologies including chemical, electro-chemical and MEMS based sensors. Also included is a discussion on design challenges associated with low-power analog circuits and the schemes to overcome them. Finally, a short discussion of some of the simple wireless telemetry schemes best suited for low-power sensor applications and sensor packaging issues is discussed. Applications and sensor prototypes included are environmental monitoring, health care monitoring and issues related to the development of sensor prototypes and associated electronics to achieve high signal-to-noise ratio will also be presented.

One of the important issues of Scientific Visualization is the utilization of the broad bandwidth of the human sensory system in steering and interpreting complex processes and simulations involving voluminous data sets across diverse scientific disciplines. This book presents the state-of-the-art in visualization techniques both as an overview for the inquiring scientist, and as a solid platform from which developers may extend existing techniques or devise new ones to meet the specific needs of their problems. A secondary goal in crafting this volume has been to provide a vehicle for teaching of state-of-the-art techniques in scientific visualization. The first part of the book covers the application areas fluid flow visualization in medicine, and environmental protection. The second set of chapters explain fundamentals of scientific visualization. It comprises contributions on data structuring and data administration, data modeling, and rendering. A final section is devoted to auditory representation of scientific data.

A smart camera is an integrated machine vision system which, in addition to image capture circuitry, includes a processor, which can extract information from images without need for an external processing unit, and interface devices used to make results available to other devices. This book provides content on smart cameras for an interdisciplinary audience of professionals and students in embedded systems, image processing, and camera technology. It serves as a self-contained, single-source reference for material otherwise found only in sources such as conference proceedings, journal articles, or product data sheets. Coverage includes the 50 year chronology of smart cameras, their technical evolution, the state-of-the art, and numerous applications, such as surveillance and monitoring, robotics, and transportation.

A problem-solving approach to statistical signal processing for practicing engineers, technicians, and graduate students This book takes a pragmatic approach in solving a set of common problems engineers and technicians encounter when processing signals. In writing it, the author drew on his vast theoretical and practical experience in the field to provide a quick-solution manual for technicians and engineers, offering field-tested solutions to most problems engineers can encounter. At the same time, the book delineates the basic concepts and applied mathematics underlying each solution so that readers can go deeper into the theory to gain a better idea of the solution s limitations and potential pitfalls, and thus tailor the best solution for the specific engineering application. Uniquely, Statistical Signal Processing in Engineering can also function as a textbook for engineering graduates and post-graduates. Dr. Spagnolini, who has had a quarter of a century of experience teaching graduate-level courses in digital and statistical signal processing methods, provides a detailed axiomatic presentation of the conceptual and mathematical foundations of statistical signal processing that will challenge students analytical skills and motivate them to develop new applications on their own, or better understand the motivation underlining the existing solutions. Throughout the book, some real-world examples demonstrate how powerful a tool statistical signal processing is in practice across a wide range of applications. * Takes an interdisciplinary approach, integrating basic concepts and tools for statistical signal processing * Informed by its author s vast experience as both a practitioner and teacher * Offers a hands-on approach to solving problems in statistical signal processing * Covers a broad range of applications, including communication systems, machine learning, wavefield and array processing, remote sensing, image filtering and distributed computations * Features numerous real-world examples from a wide range of applications showing the mathematical concepts involved in practice * Includes MATLAB code of many of the experiments in the book Statistical Signal Processing in Engineering is an indispensable working resource for electrical engineers, especially those working in the information and communication technology (ICT) industry. It is also an ideal text for engineering students at large, applied mathematics post-graduates and advanced undergraduates in electrical engineering, applied statistics, and pure mathematics, studying statistical signal processing.

This proceedings set contains 85 selected full papers presentedat the 3rd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences - MCO 2015, held on May 11-13, 2015 at Lorraine University, France. The present part II of the 2 volume set includes articles devoted to Data analysis and Data mining, Heuristic / Meta heuristic methods for operational research applications, Optimization applied to surveillance and threat detection, Maintenance and Scheduling, Post Crises banking and eco-finance modelling, Transportation, as well as Technologies and methods for multi-stakeholder decision analysis in public settings.

An Introduction to Audio Content Analysis Enables readers to understand the algorithmic analysis of musical audio signals with AI-driven approaches An Introduction to Audio Content Analysis serves as a comprehensive guide on audio content analysis explaining how signal processing and machine learning approaches can be utilized for the extraction of musical content from audio. It gives readers the algorithmic understanding to teach a computer to interpret music signals and thus allows for the design of tools for interacting with music. The work ties together topics from audio signal processing and machine learning, showing how to use audio content analysis to pick up musical characteristics automatically. A multitude of audio content analysis tasks related to the extraction of tonal, temporal, timbral, and intensity-related characteristics of the music signal are presented. Each task is introduced from both a musical and a technical perspective, detailing the algorithmic approach as well as providing practical guidance on implementation details and evaluation. To aid in reader comprehension, each task description begins with a short introduction to the most important musical and perceptual characteristics of the covered topic, followed by a detailed algorithmic model and its evaluation, and concluded with questions and exercises. For the interested reader, updated supplemental materials are provided via an accompanying website. Written by a well-known expert in the music industry, sample topics covered in Introduction to Audio Content Analysis include: Digital audio signals and their representation, common time-frequency transforms, audio features Pitch and fundamental frequency detection, key and chord Representation of dynamics in music and intensity-related features Beat histograms, onset and tempo detection, beat histograms, and detection of structure in music, and sequence alignment Audio fingerprinting, musical genre, mood, and instrument classification An invaluable guide for newcomers to audio signal processing and industry experts alike, An Introduction to Audio Content Analysis covers a wide range of introductory topics pertaining to music information retrieval and machine listening, allowing students and researchers to quickly gain core holistic knowledge in audio analysis and dig deeper into specific aspects of the field with the help of a large amount of references.

Synthesis of Computational Structures for Analog Signal Processing focuses on analysis and design of analog signal processing circuits. The author presents a multitude of design techniques for improving the performances of analog signal processing circuits, and proposes specific implementation strategies that can be used in CMOS technology. The author's discussion proceeds from the perspective of signal processing as it relates to analog. Included are coverage of low-power design, portable equipment, wireless nano-sensors and medical implantable devices. The material is especially appropriate for researchers and specialists in the area of analog and mixed-signal CMOS VLSI design, as well as postgraduate or Ph.D. students working on analog microelectronics.

"Think DSP: Digital Signal Processing in Python" is an introduction to signal processing and system analysis using a computational approach. The premise of this book (like the others in the Think X series) is that if you know how to program, you can use that skill to learn other things. By the end of the first chapter, you'll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Subsequent chapters follow a logical progression that develops the important ideas incrementally, with a focus on applications.

Signal processing arises in the design of such diverse systems as communications, sonar, radar, electrooptical, navigation, electronic warfare and medical imaging systems. It is also used in many physical sciences, such as geophysics, acoustics, and meteorology, among many others. The common theme is to extract and estimate the desired signals, which are mixed with a variety of noise sources and disturbances. Signal processing involves system analysis, random processes, statistical inferences, and software and hardware implementation. The purpose of this book is to provide an elementary, informal introduction, as well as a comprehensive account of principles of random signal processing, with emphasis on the computational aspects. This book covers linear system analysis, probability theory, random signals, spectral analysis, estimation, filtering, and detection theory. It can be used as a text for a course in signal processing by under graduates and beginning graduate students in engineering and science and also by engineers and scientists engaged in signal analysis, filtering, and detection. Part of the book has been used by the author while teaching at the State University of New York at Buffalo and California State University at Long Beach. An attempt has been made to make the book self-contained and straight forward, with the hope that readers with varied backgrounds can appreciate and apply principles of signal processing. Chapter 1 provides a brief review of linear analysis of deterministic signals."

This volumes discusses various aspects regarding the capacity/achievable data rate of stationary Rayleigh fading channels. First, it analyses bounds on the achievable data rate with zero-mean proper Gaussian input symbols, which are capacity achieving in the coherent case, i.e., in case of perfect channel knowledge at the receiver. These bounds are tight in the sense that the difference between the upper and the lower bound is bounded for all SNRs. The lower bound converges to the coherent capacity for asymptotically small channel dynamics. Furthermore, these bounds are extended to the case of multiple-input multiple-output (MIMO) channels and to the case of frequency selective channels. In a further part, the present work studies the achievable rate with receivers based on synchronized detection and a code-aided channel estimation. For a specific type of such a receiver an approximate upper bound on the achievable rate is derived. The comparison of this approximate upper bound and the achievable data rate with receivers using synchronized detection based on a solely pilot based channel estimation gives an approximate upper bound on the possible gain by using this kind of code-aided channel estimation in comparison to the conventional receiver using a solely pilot based channel estimation. In addition, the achievable data rate with an optimal joint processing of pilot and data symbols is studied and a lower bound on the achievable rate for this case is derived. In this context, it is also shown which part of the mutual information of the transmitter and the receiver is discarded when using the conventional receiver with synchronized detection based on a solely pilot based channel estimation.

This work deals with the instrumental measurement methods for the perceived quality of transmitted speech. These measures simulate the speech perception process employed by human subjects during auditory experiments. The measure standardized by the International Telecommunication Union (ITU), called "Wideband-Perceptual Speech Quality Evaluation (WB-PESQ)", is not able to quantify all these perceived characteristics on a unidimensional quality scale, the Mean Opinion Score (MOS) scale. Recent experimental studies showed that subjects make use of several perceptual dimensions to judge about the quality of speech signals. In order to represent the signal at a higher stage of perception, a new model, called "Diagnostic Instrumental Assessment of Listening quality (DIAL)", has been developed. It includes a perceptual and a cognitive model which simulate the whole quality judgment process. Except for strong discontinuities, DIAL predicts very well speech quality of different speech processing and transmission systems, and it outperforms the WB-PESQ.

The accurate determination of the speech spectrum, particularly for short frames, is commonly pursued in diverse areas including speech processing, recognition, and acoustic phonetics. With this book the author makes the subject of spectrum analysis understandable to a wide audience, including those with a solid background in general signal processing and those without such background. In keeping with these goals, this is not a book that replaces or attempts to cover the material found in a general signal processing textbook. Some essential signal processing concepts are presented in the first chapter, but even there the concepts are presented in a generally understandable fashion as far as is possible. Throughout the book, the focus is on applications to speech analysis; mathematical theory is provided for completeness, but these developments are set off in boxes for the benefit of those readers with sufficient background. Other readers may proceed through the main text, where the key results and applications will be presented in general heuristic terms, and illustrated with software routines and practical "show-and-tell" discussions of the results. At some points, the book refers to and uses the implementations in the Praat speech analysis software package, which has the advantages that it is used by many scientists around the world, and it is free and open source software. At other points, special software routines have been developed and made available to complement the book, and these are provided in the Matlab programming language. If the reader has the basic Matlab package, he/she will be able to immediately implement the programs in that platform---no extra "toolboxes" are required.

This book provides anyone needing a primer on random signals and processes with a highly accessible introduction to these topics. It assumes a minimal amount of mathematical background and focuses on concepts, related terms and interesting applications to a variety of fields. All of this is motivated by numerous examples implemented with MATLAB, as well as a variety of exercises at the end of each chapter.

The book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations such as sequential Monte Carlo methods, Markov Chain Monte Carlo filters, Rao Blackwellization, to the biologically inspired paradigm of Neural Networks and decomposition techniques such as Empirical Mode Decomposition, Independent Component Analysis and Singular Spectrum Analysis. The book is directed to the research students, professors, researchers and practitioners interested in exploring the advanced techniques in intelligent signal processing and data mining paradigms.