Unified Signal Theory is an indispensible textbook dealing with the theory of deterministic signals; a topic of fundamental interest to graduates and senior undergraduates in the areas of information engineering (telecommunications, control, systems theory and electronics), astronomy, oceanography, earth science, biology and medicine. The unified theory follows an innovative approach – that of combining all signal classes into just one. The fundamental signal operations (convolution, Fourier transform, linear systems, sampling and interpolation) are established simultaneously for all the signal classes. This unified approach avoids the repetition of similar concepts consequent on other approaches’ separate treatment of definitions and properties for each signal class. Modern wavelet ideas are developed in harmony with the rest of the text. Unified Signal Theory provides: • exercises and examples, to give the student practice; • solutions which are available for download and save the tutor time; and • a choice of two suggested reading paths depending on the level of the student, for an enhanced learning experience. The advantages of the unified approach are many: it permits a global vision of the topic, it is economical in teaching and learning, and it can be adjusted easily to fit new applications. This textbook presents the theory in five chapters, and goes on to demonstrate specific applications such as fast Fourier transform implementation, sampling and reconstructions of signals, and multicolor modulation systems, in a further six chapters. Mathematical concepts are introduced conceptually within the body of the book with more rigorous treatment being reserved for the appendices.

An outgrowth of the author's extensive experience teaching senior and graduate level students, this is both a thorough introduction and a solid professional reference. * Material covered has been developed based on a 35-year research program associated with such systems as the Landsat satellite program and later satellite and aircraft programs. * Covers existing aircraft and satellite programs and several future programs *An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Practical Signals Theory with MATLAB Applications is organized around applications, first introducing the actual behavior of specific signals and then using them to motivate the presentation of mathematical concepts. Tervo sequences the presentation of the major transforms by their complexity: first Fourier, then Laplace, and finally the z-transform. The goal is to help students who can’t visualize phenomena from an equation to develop their intuition and learn to analyze signals by inspection. Finally, most examples and problems are designed to use MATLAB, making the presentation more in line with modern engineering practice.

Signal theory provides a mathematical toolbox for modelling and analysis of physical systems. Stochastic processes are used to model more or less unknown signals. Signal theory has applications in communication engineering, signal processing, automatic control, medical engineering, and more. This book starts off by reviewing the needed background from probability theory and signals & systems. Then stochastic processes are introduced, both in discrete and continuous time, together with the important tools auto-correlation function and power-spectral density. Separate chapters focus on analysing linear filters, sampling & reconstruction, some non-linearities and modulation in terms of stochastic processes. The modulation chapter includes noise analyses of the considered modulation methods assuming that thermal noise dominates the picture. A separate chapter briefly covers multi-dimensional signals and systems as a preparation for image processing. A chapter on spectral estimation is included, which could be used as a basis for computer-based laborations. Problems are given at the end of each chapter. Hints and answers to most of those problems are provided in appendices. The book is suitable for an advanced course on engineering aspects of stochastic processes. It is written with an electrical engineering student in mind, but should be useful in other engineering disciplines as well. We do not dwell on the innermost details of stochastic processes. Instead we focus on the ability to deal with stochastic processes in situations that could very well be models of real-world problems. This book provides tools and understanding that can be used as a preparation for in-depth studies of subjects such as communication engineering, image and signal processing or analysis, and automatic control, just to mention a few. This book is intended for engineering students with a background in probability theory on one hand and signals and systems on the other hand. No prior knowledge of stochastic processes is assumed.

Increasing the noise immunity of complex signal processing systems is the main problem in various areas of signal processing. At the present time there are many books and periodical articles devoted to signal detection, but many important problems remain to be solved. New approaches to complex problems allow us not only to summarize investigations, but also to improve the quality of signal detection in noise. This book is devoted to fundamental problems in the generalized approach to signal processing in noise based on a seemingly abstract idea: the introduction of an additional noise source that does not carry any information about the signal in order to improve the qualitative performance of complex signal processing systems. Theoretical and experimental studies carried out by the author lead to the conclusion that the proposed generalized approach to signal processing in noise allows us to formulate a decision-making rule based on the determi nation of the jointly sufficient statistics of the mean and variance of the likelihood function (or functional). Classical and modern signal detection theories allow us to define only the sufficient statistic of the mean of the likelihood function (or functional). The presence of additional information about the statistical characteristics of the like lihood function (or functional) leads to better-quality signal detection in comparison with the optimal signal detection algorithms of classical and modern theories.

How understanding the signaling within social networks can change the way we make decisions, work with others, and manage organizations. How can you know when someone is bluffing? Paying attention? Genuinely interested? The answer, writes Alex Pentland in Honest Signals, is that subtle patterns in how we interact with other people reveal our attitudes toward them. These unconscious social signals are not just a back channel or a complement to our conscious language; they form a separate communication network. Biologically based “honest signaling,” evolved from ancient primate signaling mechanisms, offers an unmatched window into our intentions, goals, and values. If we understand this ancient channel of communication, Pentland claims, we can accurately predict the outcomes of situations ranging from job interviews to first dates. Pentland, an MIT professor, has used a specially designed digital sensor worn like an ID badge—a “sociometer”—to monitor and analyze the back-and-forth patterns of signaling among groups of people. He and his researchers found that this second channel of communication, revolving not around words but around social relations, profoundly influences major decisions in our lives—even though we are largely unaware of it. Pentland presents the scientific background necessary for understanding this form of communication, applies it to examples of group behavior in real organizations, and shows how by “reading” our social networks we can become more successful at pitching an idea, getting a job, or closing a deal. Using this “network intelligence” theory of social signaling, Pentland describes how we can harness the intelligence of our social network to become better managers, workers, and communicators.

A Primer of Signal Detection Theory is being reprinted to fill the gap in literature on Signal Detection Theory--a theory that is still important in psychology, hearing, vision, audiology, and related subjects. This book is intended to present the methods of Signal Detection Theory to a person with a basic mathematical background. It assumes knowledge only of elementary algebra and elementary statistics. Symbols and terminology are kept at a basic level so that the eventual and hoped for transfer to a more advanced text will be accomplished as easily as possible. Intended for undergraduate students at an introductory level, the book is divided into two sections. The first part introduces the basic ideas of detection theory and its fundamental measures. Its aim is to enable the reader to be able to understand and compute these measures. It concludes with a detailed analysis of a typical experiment and a discussion of some of the problems which can arise for the potential user of detection theory. The second section considers three more advanced topics: threshold theory, the extension of detection theory, and an examination of Thurstonian scaling procedures.

This book contains a number of problems of signal detection theory. A generalized observation model for signal detection problems is included. The model includes several interesting and common special cases such as those describing additive noise, multiplicative noise, and signal-dependent noise. The model can also describe composite signals in addition to the usual known (deterministic) signals and random (stochastic) signals. Locally optimum (LO) and locally optimum rank (LOR) detectors for known and random signals in the model are discussed, and original results are obtained. Other approaches to detection of signals are also discussed.

The requirement of causality in system theory is inevitably accompanied by the appearance of certain mathematical operations, namely the Riesz proj- tion,theHilberttransform,andthespectralfactorizationmapping.Aclassical exampleillustratingthisisthedeterminationoftheso-calledWiener?lter(the linear, minimum means square error estimation ?lter for stationary stochastic sequences [88]). If the ?lter is not required to be causal, the transfer function of the Wiener ?lter is simply given by H(?)=? (?)/? (?),where ? (?) xy xx xx and ? (?) are certain given functions. However, if one requires that the - xy timation ?lter is causal, the transfer function of the optimal ?lter is given by 1 ? (?) xy H(?)= P ,?? (??,?] . + [? ] (?) [? ] (?) xx + xx? Here [? ] and [? ] represent the so called spectral factors of ? ,and xx + xx? xx P is the so called Riesz projection. Thus, compared to the non-causal ?lter, + two additional operations are necessary for the determination of the causal ?lter, namely the spectral factorization mapping ? ? ([? ] ,[? ] ),and xx xx + xx? the Riesz projection P .