Compressive sensing is a new signal processing paradigm that aims to encode sparse signals by using far lower sampling rates than those in the traditional Nyquist approach. It helps acquire, store, fuse and process large data sets efficiently and accurately. This method, which links data acquisition, compression, dimensionality reduction and optimization, has attracted significant attention from researchers and engineers in various areas. This comprehensive reference develops a unified view on how to incorporate efficiently the idea of compressive sensing over assorted wireless network scenarios, interweaving concepts from signal processing, optimization, information theory, communications and networking to address the issues in question from an engineering perspective. It enables students, researchers and communications engineers to develop a working knowledge of compressive sensing, including background on the basics of compressive sensing theory, an understanding of its benefits and limitations, and the skills needed to take advantage of compressive sensing in wireless networks.

Das Buch vermittelt grundlegende Kenntnisse zur Synthese kombinatorischer (Schaltnetze) und sequentieller Schaltungen (Schaltwerke/Automaten) und wendet sich dabei vor allem an Studierende der Ingenieurwissenschaften.

Written using clear and accessible language, this text provides detailed coverage of the core mathematical concepts underpinning signal processing. All the core areas of mathematics are covered, including generalized inverses, singular value decomposition, function representation, and optimization, with detailed explanations of how basic concepts in these areas underpin the methods used to perform signal processing tasks. A particular emphasis is placed on the practical applications of signal processing, with numerous in-text practice questions and real-world examples illustrating key concepts, and MATLAB programs with accompanying graphical representations providing all the necessary computational background. This is an ideal text for graduate students taking courses in signal processing and mathematical methods, or those who want to establish a firm foundation in these areas before progressing to more advanced study.

Arduino 101 houses an Intel Curie module which offers a better performance at a lower power footprint. The module has two 32-bit MCUs - an x86 Intel Quark processor and an ARC EM4 processor along with 384kB flash memory and 80kB SRAM. These onboard MCUs combine a variety of new technologies including wireless communication via Bluetooth Low Energy, 6 axis motion sensor with an accelerometer, and a gyroscope. With this book, you will: Explore neural net pattern matching Have the Arduino learn gesture recognition Perfect for students, teachers, and hobbyists who need just enough information to get started with the Arduino 101.

A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. * Presents the necessary basic ideas from both digital signal processing and machine learning concepts * Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing * Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.

This book is intended to be a little different from other books in its coverage. There are a great many digital signal processing (DSP) books and signals and systems books on the market. Since most undergraduate courses begin with signals and systems and then move on in later years to DSP, I felt a need to combine the two into one book that was concise yet not too overburdening. This means that students need only purchase one book instead of two and at the same time see the flow of knowledge from one subject into the next. Like the rudiments of music, it starts at the very beginning with some elementary knowledge and builds on it chapter by chapter to advanced work by chapter 15. I have been teaching now for 38 years and always think it necessary to credit the pioneers of the subjects we teach and ask the question "How did we get to this present stage in technological achievement"? Therefore, in Chapter 1 I have given a concise history trying to not sway too much away from the subject area. This is followed by the rudimentary theory in increasing complexity. It has already been taught successfully to a class at Auckland University of Technology New Zealand.

Intelligent Computing for Interactive System Design provides a comprehensive resource on what has become the dominant paradigm in designing novel interaction methods, involving gestures, speech, text, touch and brain-controlled interaction, embedded in innovative and emerging human-computer interfaces. These interfaces support ubiquitous interaction with applications and services running on smartphones, wearables, in-vehicle systems, virtual and augmented reality, robotic systems, the Internet of Things (IoT), and many other domains that are now highly competitive, both in commercial and in research contexts. This book presents the crucial theoretical foundations needed by any student, researcher, or practitioner working on novel interface design, with chapters on statistical methods, digital signal processing (DSP), and machine learning (ML). These foundations are followed by chapters that discuss case studies on smart cities, brain-computer interfaces, probabilistic mobile text entry, secure gestures, personal context from mobile phones, adaptive touch interfaces, and automotive user interfaces. The case studies chapters also highlight an in-depth look at the practical application of DSP and ML methods used for processing of touch, gesture, biometric, or embedded sensor inputs. A common theme throughout the case studies is ubiquitous support for humans in their daily professional or personal activities. In addition, the book provides walk-through examples of different DSP and ML techniques and their use in interactive systems. Common terms are defined, and information on practical resources is provided (e.g., software tools, data resources) for hands-on project work to develop and evaluate multimodal and multi-sensor systems. In a series of in-chapter commentary boxes, an expert on the legal and ethical issues explores the emergent deep concerns of the professional community, on how DSP and ML should be adopted and used in socially appropriate ways, to most effectively advance human performance during ubiquitous interaction with omnipresent computers. This carefully edited collection is written by international experts and pioneers in the fields of DSP and ML. It provides a textbook for students and a reference and technology roadmap for developers and professionals working on interaction design on emerging platforms.

Fundamentals of Signal Processing for Sound and Vibration Engineers

Author: Kihong Shin and Joseph K. Hammond

"Fundamentals of Signal Processing for Sound and Vibration Engineers" is based on Joe Hammond's many years of teaching experience at the Institute of Sound and Vibration Research, University of Southampton. Whilst the applications presented emphasise sound and vibration, the book focusses on the basic essentials of signal processing that ensures its appeal as a reference text to students and practitioners in all areas of mechanical, automotive, aerospace and civil engineering.

Offers an excellent introduction to signal processing for students and professionals in the sound and vibration engineering field.

Split into two parts, covering deterministic signals then random signals, and offering a clear explanation of their theory and application together with appropriate MATLAB examples.

Provides an excellent study tool for those new to the field of signal processing.

Integrates topics within continuous, discrete, deterministic and random signals to facilitate better understanding of the topic as a whole.

Illustrated with MATLAB examples, some using 'real' measured data, as well as fifty MATLAB codes on an accompanying website.

In der hochbitratigen optischen Nachrichtentechnik ist es wichtig, parasitare induktive und kapazitive Einflusse auf die Funktion von Laser- und Fotodioden zu kompensieren. Wegen des nichtlinearen Charakters der u-i-Relationen der Induktivitaten, Kapazitaten und Widerstande ist es moeglich, Kompensationsverfahren gegen parasitare Effekte zu entwickeln oder die Nichtlinearitaten gezielt zur Signalubertragung einzusetzen. Reiner Thiele beweist, dass bei Applikation der vorgestellten Kompensationsverfahren kapazitive und induktive Influenzen auf die Grundfunktion der optoelektronischen Bauelemente vermeidbar sind, das Klemmenverhalten durch die u-i-Kennlinien von Laser- oder Fotodioden komplett erfasst wird und ungunstige Einflusse der Systemumgebung auf die optoelektronischen Schaltungen vermieden werden. Ausserdem stellt er Definitionen fur optoelektronische Grundstromkreise sowie ihre Berechnung fur die Applikation gleichartiger Laser- oder Fotodioden als Sende- bzw. Empfangsbauelemente der optischen Nachrichtentechnik vor. Der Autor: Prof. Dr.-Ing. Reiner Thiele lehrte an der Hochschule Zittau/Goerlitz und unterrichtet derzeit an der Staatlichen Studienakademie Bautzen.