Microprocessors In Signal Processing Measurement And Control
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Microprocessors in Signal Processing, Measurement and Control
Author: S.G. Tzafestas
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
In racent years the LSI technology has witnessed a revoluti onary development, and allowed substantial reductions in the size and cost of digital logic circuitry. Computer system building blocks have progressed from the level of discrete components to the level of complex ICs involving many logic circuits on a single "chip". The invention and wide applica tions of microprocessors have changed the philosophy of the signal processing, measurement and control engineering fields. The microprocessor-based digital signal processing systems and controllers have replaced the conventional ones based on standard analog and digital computing equipment. The first microprocessors and "on-chip" computers have appeared towards the end of 71 beginning 72. Their evolution since then and the number of applications, in which they have been utilized, have both been extremely spectacular. New system concepts and hardware/software tools are steadily under development to sup port the microprocessor in its multiple and complex tasks. The goal of this book is to provide a cohesive and well-balan ced set of contributions dealing with important aspects and applications of microprocessors to signal processing, measu rement and system control. The majority of contributions in clude sufficient review material and present rather complete treatments of the respective topics.
Microprocessors in Robotic and Manufacturing Systems
Author: S.G. Tzafestas
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Microprocessors play a dominant role in computer technology and have contributed uniquely in the development of many new concepts and design techniques for modem industrial systems. This contribution is excessively high in the area of robotic and manufacturing systems. However, it is the editor's feeling that a reference book describing this contribution in a cohesive way and covering the major hardware and software issues is lacking. The purpose of this book is exactly to fill in this gap through the collection and presentation of the experience of a number of experts and professionals working in different academic and industrial environments. The book is divided in three parts. Part 1 involves the first four chapters and deals with the utilization of microprocessors and digital signal processors ( DSPs ) for the computation of robot dynamics. The emphasis here is on parallel computation with particular problems attacked being task granularity, task allocation/scheduling and communication issues. Chapter I, by Zheng and Hemami, is concerned with the real-time multiprocessor computation of torques in robot control systems via the Newton-Euler equations. This reduces substantially the height of the evaluation tree which leads to more effective parallel processing. Chapter 2, by D'Hollander, examines thoroughly the automatic scheduling of the Newton-Euler inverse dynamic equations. The automatic program decomposition and scheduling techniques developed are embedded in a tool used to generate multiprocessor schedules from a high-level language program.
Computational Intelligence in Systems and Control Design and Applications
Author: S.G. Tzafestas
language: en
Publisher: Springer Science & Business Media
Release Date: 2001-11-30
This book contains thirty timely contributions in the emerging field of Computational Intelligence (CI) with reference to system control design and applications. The three basic constituents ofCI are neural networks (NNs). fuzzy logic (FL) I fuzzy reasoning (FR). and genetic algorithms (GAs). NNs mimic the distributed functioning of the human brain and consist of many. rather simple. building elements (called artificial neurons) which are controlled by adaptive parameters and are able to incorporate via learning the knowledge provided by the environment, and thus respond intelligently to new stimuli. Fuzzy logic (FL) provides the means to build systems that can reason linguistically under uncertainty like the human experts (common sense reasoning). Both NNs and FL I FR are among the most widely used tools for modeling unknown systems with nonlinear behavior. FL suits better when there is some kind of knowledge about the system. such as, for example, the linguistic information of a human expert. On the other hand. NNs possess unique learning and generalization capabilities that allow the user to construct very accurate models of nonlinear systems simply using input-output data. GAs offer an interesting set of generic tools for systematic random search optimization following the mechanisms of natural genetics. In hybrid Computational Intelligence - based systems these three tools (NNs, FL, GAs) are combined in several synergetic ways producing integrated tools with enhanced learning, generalization. universal approximation. reasoning and optimization abilities.