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Synthesis and Optimization of DSP Algorithms describes approaches taken to synthesising structural hardware descriptions of digital circuits from high-level descriptions of Digital Signal Processing (DSP) algorithms. The book contains: -A tutorial on the subjects of digital design and architectural synthesis, intended for DSP engineers, -A tutorial on the subject of DSP, intended for digital designers, -A discussion of techniques for estimating the peak values likely to occur in a DSP system, thus enabling an appropriate signal scaling. Analytic techniques, simulation techniques, and hybrids are discussed. The applicability of different analytic approaches to different types of DSP design is covered, -The development of techniques to optimise the precision requirements of a DSP algorithm, aiming for efficient implementation in a custom parallel processor. The idea is to trade-off numerical accuracy for area or power-consumption advantages. Again, both analytic and simulation techniques for estimating numerical accuracy are described and contrasted. Optimum and heuristic approaches to precision optimisation are discussed, -A discussion of the importance of the scheduling, allocation, and binding problems, and development of techniques to automate these processes with reference to a precision-optimized algorithm, -Future perspectives for synthesis and optimization of DSP algorithms. A wide body of literature exists covering separately the areas of DSP, hardware design, and design automation. This book brings together the fields, concentrating on those transformations, optimizations, and design techniques that would usually be considered to cross the domain boundaries between "pure DSP" and "pure digital implementation". As such, this book forms a valuable contribution to the existing literature. Synthesis and Optimization of DSP Algorithms is of use both to researchers and students in the field of design automation for DSP systems, and to those wishing to implement state-of-the-art techniques within an Electronic Design Automation framework.
Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied math- ematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, air- line crew scheduling, corporate planning, computer-aided design and man- ufacturing, database query design, cellular telephone frequency assignment, constraint directed reasoning, and computational biology. Furthermore, combinatorial optimization problems occur in many diverse areas such as linear and integer programming, graph theory, artificial intelligence, and number theory. All these problems, when formulated mathematically as the minimization or maximization of a certain function defined on some domain, have a commonality of discreteness. Historically, combinatorial optimization starts with linear programming. Linear programming has an entire range of important applications including production planning and distribution, personnel assignment, finance, alloca- tion of economic resources, circuit simulation, and control systems. Leonid Kantorovich and Tjalling Koopmans received the Nobel Prize (1975) for their work on the optimal allocation of resources. Two important discover- ies, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These algo- rithms have had a profound effect in combinatorial optimization. Many polynomial-time solvable combinatorial optimization problems are special cases of linear programming (e.g. matching and maximum flow). In addi- tion, linear programming relaxations are often the basis for many approxi- mation algorithms for solving NP-hard problems (e.g. dual heuristics).
This clinician's guide to the In Search of Recovery book and workbook helps men learn how to achieve a high level of commitment to change their behavior and thinking. The guide is structured to facilitate therapists who direct group therapy.
This book considers problems of optimization arising in the design of electromagnetic radiators and receivers. The authors develop a systematic general theory that can be applied to a wide class of structures. The theory is illustrated with familiar, simple examples and indications of how the results can be applied to more complicated structures. The final chapter introduces techniques from multicriteria optimization in antenna design. The material is intended for a dual audience of mathematicians and mathematically-sophisticated engineers. References to both the mathematics and engineering literature help guide the reader through the necessary mathematical background.
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