Microelectromechanical system (MEMS) inertial sensors have become ubiquitous in modern society. Built into mobile telephones, gaming consoles, virtual reality headsets, we use such sensors on a daily basis. They also have applications in medical therapy devices, motion- capture filming, traffic monitoring systems, and drones. While providing accurate measurements over short time scales, this diminishes over longer periods. To date, this problem has been resolved by combining them with additional sensors and models. This adds both expense and size to the devices. This tutorial focuses on the signal processing aspects of position and orientation estimation using inertial sensors. It discusses different modelling choices and a selected number of important algorithms that engineers can use to select the best options for their designs. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. Engineers, researchers, and students deploying MEMS inertial sensors will find that this tutorial is an essential monograph on how to optimize their designs.

There is a wealth of literature and books available to engineers starting to understand what machine learning is and how it can be used in their everyday work. This presents the problem of where the engineer should start. The answer is often "for a general, but slightly outdated introduction, read this book; for a detailed survey of methods based on probabilistic models, check this reference; to learn about statistical learning, this text is useful" and so on. This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment, encompassing recent developments and pointers to the literature for further study. A Brief Introduction to Machine Learning for Engineers is the entry point to machine learning for students, practitioners, and researchers with an engineering background in probability and linear algebra.

Sensors are becoming increasingly omnipresent throughout society. These sensors generate a billion gigabytes of data every day. With the availability of immense computing power at central locations, the local storage and transmission of the data to a central location becomes the bottleneck in the real-time processing of the mass of data. Recently compressed sensing has emerged as a technique to alleviate these problems, but much of the data is blindly discarded without being examined to achieve acceptable throughput rates. Sparse Sensing for Statistical Inference introduces and reviews a new technique called Sparse Sensing that reduces the amount of data that must be collected to start with, proving an efficient and cost-effective method for data collection. This monograph provides the reader with a comprehensive overview of this technique and a framework that can be used by researchers and engineers in implementing the technique in practical sensing systems.

As a major breakthrough in artificial intelligence, deep learning has achieved impressive success on solving grand challenges in many fields including speech recognition, natural language processing, computer vision, image and video processing, and multimedia. This monograph provides a historical overview of deep learning and focuses on its applications in object recognition, detection, and segmentation, which are key challenges of computer vision and have numerous applications to images and videos. Specifically the topics covered under object recognition include image classification on ImageNet, face recognition, and video classification. In detection, the monograph covers general object detection on ImageNet, pedestrian detection, face landmark detection (face alignment), and human landmark detection (pose estimation). Finally, within segmentation, it covers the most recent progress on scene labeling, semantic segmentation, face parsing, human parsing, and saliency detection. Concrete examples of these applications explain the key points that make deep learning outperform conventional computer vision systems. Deep Learning in Object Recognition, Detection, and Segmentation provides a comprehensive introductory overview of a topic that is having major impact on many areas of research in signal processing, computer vision, and machine learning. This is a must-read for students and researchers new to these fields.

Video Coding is the second part of the two-part monograph Fundamentals of Source and Video Coding by Wiegand and Schwarz. This part describes the application of the techniques described in the first part to video coding. In doing so it provides a description of the fundamentals concepts of video coding and, in particular, the signal processing in video encoders and decoders.

The human visual system has evolved to have the ability to selectively focus on the most relevant parts of a visual scene. This mechanism, referred to as visual attention, has been the focus of several neurological and psychological studies in the past few decades. These studies have inspired several computational visual attention models which have been successfully applied to problems in computer vision and robotics. Computational Visual Attention Models provides a comprehensive survey of the state-of-the- art in computational visual attention modelling with a special focus on the latest trends. By reviewing several models published since 2012, the theoretical advantages and disadvantages of each approach are discussed. In addition, existing methodologies to evaluate computational models through the use of eye-tracking data along with the visual attention performance metrics used are described. The shortcomings in existing approaches and approaches to overcome them are also covered. Finally, a subjective evaluation for benchmarking existing visual attention metrics is presented and open problems in visual attention are highlighted. This monograph provides the reader with an in-depth survey of the research conducted to date in computational visual attention models and provides the basis for further research in this exciting area.

Despite the different nature of financial engineering and electrical engineering, both areas are intimately connected on a mathematical level. The foundations of financial engineering lie on the statistical analysis of numerical time series and the modeling of the behavior of the financial markets in order to perform predictions and systematically optimize investment strategies. Similarly, the foundations of electrical engineering, for instance, wireless communication systems, lie on statistical signal processing and the modeling of communication channels in order to perform predictions and systematically optimize transmission strategies. Both foundations are the same in disguise. It is often the case in science that the same or very similar methodologies are developed and applied independently in different areas. A Signal Processing Perspective of Financial Engineering is about investment in financial assets treated as a signal processing and optimization problem. It explores such connections and capitalizes on the existing mathematical tools developed in wireless communications and signal processing to solve real-life problems arising in the financial markets in an unprecedented way. It provides straightforward and systematic access to financial engineering for researchers in signal processing and communications so that they can understand problems in financial engineering more easily and may even apply signal processing techniques to handle some financial problems.

H Robust design is an advancing technology which aims to achieve the system design purpose under intrinsic random fluctuation and external disturbance. This book introduces several robust design methods, some of which include linear to nonlinear systems and frequency to time domain. This book provides not only a complete theoretical development and application of H robust design over the last three decades, but also an integrated platform for control, signal processing, communication, systems and synthetic biology. Based on the theoretical H robust design results, the authors also give some practical design examples to illustrate the procedure and validate the performance of the proposed H method with computational simulations and tables.

Covariance matrices have found applications in many diverse areas. These include beamforming in array processing; portfolio analysis in finance; classification of data and the handling of high-frequency data. Structured Robust Covariance Estimation considers the estimation of covariance matrices in non-standard conditions including heavy-tailed distributions and outlier contamination. Prior knowledge on the structure of these matrices is exploited in order to improve the estimation accuracy. The distributions, structures and algorithms are all based on an extension of convex optimization to manifolds. It also provides a self-contained introduction and survey of the theory known as geodesic convexity. This is a generalized form of convexity associated with positive definite matrix variables. The fundamental g-convex sets and functions are detailed, along with the operations that preserve them, and their application to covariance estimation. This monograph will be of interest to researchers and students working in signal processing, statistics and optimization.

This research monograph has the following main theme. Given an interpolation function, which is supposed to determine an estimate of the unknown signal value, the reader can use the traditional approach (traditional interpolation function) to estimate the numerical value of the signal. Alternatively, the reader can follow the theoretical developments offered in the book and so design, on the basis of the unified theory described in the book, three new interpolation functions with improved approximation capabilities. That means, that under the unified theory, the book offers three new classes of interpolation functions with improved approximation capabilities of the true and unknown signal to estimate. These works were published in the year 2011 and submitted for peer review and they are now presented to the public through this new publication. There are likely to be three main types of readership of this research monograph. The primary readership is composed of users of libraries which may adopt the book as reference. The secondary readership is composed of the population of instructors/professors of a course in one of applied mathematics, signal-image interpolation, signal-image processing, biomedical imaging and/or biomedical engineering academic disciplines. In such case, the book can be used as an additional educational resource to be available both to undergraduate and graduate students, in order to assign homework and/or projects to be included in the coursework. The tertiary readership is composed of apprentices and/or passionate of math. In such case, the book would be used to employ time while following the desire of intellectual enrichment. The apprentices and/or passionate of math would study the methodology of the unified theory, would apply the unified theory such to design new interpolation functions, and should there be the desire of furthering the interest, the apprentice and/or passionate would proceed further to the analysis of the results, and possibly into the discussion and the dissemination of the knowledge made out of this book.

If information theory and estimation theory are thought of as two scientific languages, then their key vocabularies are information measures and estimation measures, respectively. The basic information measures are entropy, mutual information and relative entropy. Among the most important estimation measures are mean square error (MSE) and Fisher information. Playing a paramount role in information theory and estimation theory, those measures are akin to mass, force and velocity in classical mechanics, or energy, entropy and temperature in thermodynamics. The Interplay Between Information and Estimation Measures is intended as handbook of known formulas which directly relate to information measures and estimation measures. It provides intuition and draws connections between these formulas, highlights some important applications, and motivates further explorations. The main focus is on such formulas in the context of the additive Gaussian noise model, with lesser treatment of others such as the Poisson point process channel. Also included are a number of new results which are published here for the first time. Proofs of some basic results are provided, whereas many more technical proofs already available in the literature are omitted. In 2004, the authors of this monograph found a general differential relationship commonly referred to as the I-MMSE formula. In this book a new, complete proof for the I-MMSE formula is developed, which includes some technical details omitted in the original papers relating to this. It concludes by highlighting the impact of the information-estimation relationships on a variety of information-theoretic problems of current interest, and provide some further perspective on their applications.

Deep Learning provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been benefitting from recent research efforts, including natural language and text processing, information retrieval, and multimodal information processing empowered by multitask deep learning. This is a timely and important book for researchers and students with an interest in deep learning methodology and its applications in signal and information processing.

This book aims to serve as an introductory text on algebraic coding theory. The contents are suitable for a final year undergraduate and beginning graduate course in Electrical Engineering. The material will give the reader knowledge of coding fundamentals essential for a deeper understanding of state-of-the-art coding systems. This book will also serve as a quick reference for students or practitioners who have not been exposed to the topic, but need it for specific applications such as cryptography and communications. The book will cover linear error-correcting block codes from elementary principles, going through cyclic codes and then covering some finite field algebra, Goppa codes, algebraic decoding algorithms and applications in public-key cryptography and private-key cryptography. Three appendices will cover the Gilbert bound and some related derivations, a derivation of the Mac Williamsa identities based on the probability of undetected error, and the finite field Fourier transform and the Euclidean algorithm for polynomials."

ArchiMate(R), an Open Group Standard, is an open and independent modelling language for Enterprise Architecture that is supported by different tool vendors and consulting firms. ArchiMate provides instruments to enable enterprise architects to describe, analyze, and visualize the relationships among business domains in an unambiguous way. This book provides the official specification of ArchiMate 2.1 from The Open Group. ArchiMate 2.1 is a maintenance update to ArchiMate 2.0, addressing comments raised since the introduction of ArchiMate 2.0 in 2012.The ArchiMate 2.1 Standard supports modelling throughout the TOGAF(R) Architecture Development Method (ADM). The intended audience is threefold: * Enterprise Architecture practitioners, such as architects (e.g. application, information, process, infrastructure, and, obviously, enterprise architects), senior and operational management, project leaders, and anyone committed to work within the reference framework defined by the Enterprise Architecture. * Those who intend to implement ArchiMate in a software tool; they will find a complete and detailed description of the language in this book. * The academic community, on which we rely for amending and improving the language, based on state-of-the-art research results in the enterprise architecture field.