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The last of a three-volume set which provides a collection of application reports on the TMS320 family of DSP microprocessors. Volume III focuses on floating-point applications.
This volume presents the fundamentals of data signal processing, ranging from data conversion to z-transforms and spectral analysis. In addition to presenting basic theory and describing the devices, the material is complemented by real examples in specific case studies.
Provides an introduction to the nature, synthesis and transformation of sound which forms the basis of digital sound processing for music and multimedia. Background information in computer techniques is included so that you can write computer algorithms to realise new processes central to your own musical and sound processing ideas. Finally, material is inlcuded to explain the way in which people contribute to the development of new kinds of performance and composition systems. Key features of the book include: · Contents structured into free-standing parts for easy navigation · `Flow lines' to suggest alternative paths through the book, depending on the primary interest of the reader. · Practical examples are contained on a supporting website. Digital Sound Processing can be used by anyone, whether from an audio engineering, musical or music technology perspective. Digital sound processing in its various spheres - music technology, studio systems and multimedia - are witnessing the dawning of a new age. The opportunities for involvement in the expansion and development of sound transformation, musical performance and composition are unprecedented. The supporting website (www.york.ac.uk/inst/mustech/dspmm.htm) contains working examples of computer techniques, music synthesis and sound processing.
This book lays out the concepts necessary to understand how a computer works. For reasons of clarity, the authors have deliberately chosen examples that apply to machines from all eras, without having to water down the contents of the book. This choice helps to show how techniques, concepts and performances have evolved since the first computers. The book is divided into five parts. The first four, which are of increasing difficulty, are the core of the book: “Elements of a Basic Architecture”, “Programming Model and Operation”, “Memory Hierarchy”, “Parallelism and Performance Enhancement”. The final part provides hints and solutions to the exercises in the book as well as appendices. The reader may approach each part independently based on their prior knowledge and goals.
Designed for senior electrical engineering students, this textbook explores the theoretical concepts of digital signal processing and communication systems by presenting laboratory experiments using real-time DSP hardware. Each experiment begins with a presentation of the required theory and concludes with instructions for performing them. Engineering students gain experience in working with equipment commonly used in industry. This text features DSP-based algorithms for transmitter and receiver functions.
Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been organized into four subject areas that cover the two major categories of this book; the areas are: analog circuits for neural networks, digital implementations of neural networks, neural networks on multiprocessor systems and applications, and VLSI machines for artificial intelligence. The topics that are covered in each area are briefly introduced below.