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Titlebook: Digital Signal Processing with Field Programmable Gate Arrays; Uwe Meyer-Baese Textbook 20042nd edition Springer-Verlag Berlin Heidelberg

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发表于 2025-3-21 16:27:30 | 显示全部楼层 |阅读模式
书目名称Digital Signal Processing with Field Programmable Gate Arrays
编辑Uwe Meyer-Baese
视频video
丛书名称Signals and Communication Technology
图书封面Titlebook: Digital Signal Processing with Field Programmable Gate Arrays;  Uwe Meyer-Baese Textbook 20042nd edition Springer-Verlag Berlin Heidelberg
描述.Field-Programmable Gate Arrays (FPGAs) are revolutionizing digital signal processing as novel FPGA families are replacing ASICs and PDSPs for front-end digital signal processing algorithms. So the efficient implementation of these algorithms is critical and is the main goal of this book. It starts with an overview of today‘s FPGA technology, devices, and tools for designing state-of-the-art DSP systems. A case study in the first chapter is the basis for more than 30 design examples throughout. The following chapters deal with computer arithmetic concepts, theory and the implementation of FIR and IIR filters, multirate digital signal processing systems, DFT and FFT algorithms, and advanced algorithms with high future potential. Each chapter contains exercises. The VERILOG source code and a glossary are given in the appendices, while the accompanying CD-ROM contains the examples in VHDL and Verilog code as well as the newest Altera "Baseline" software. This edition has a new chapter on adaptive filters, new sections on division and floating point arithmetics, an up-date to the current Altera software, and some new exercises. .
出版日期Textbook 20042nd edition
关键词Elektrische Schaltungen; FPGA; Flip-Flop; Hardwarebeschreibungssprache; Signals; VHDL; Verilog; adaptive Fi
版次2
doihttps://doi.org/10.1007/978-3-662-06728-4
isbn_ebook978-3-662-06728-4Series ISSN 1860-4862 Series E-ISSN 1860-4870
issn_series 1860-4862
copyrightSpringer-Verlag Berlin Heidelberg 2004
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发表于 2025-3-21 21:17:48 | 显示全部楼层
Computer Arithmetic, [21, 22, 23, 24, 25]. We will first discuss possible number representations, (e.g., fixed-point or floating-point), then basic operations like adder and multiplier, and finally efficient implementation of more difficult operations such as square roots, and the computation of trigonometric functions
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Multirate Signal Processing, are referred to as . systems. In this chapter, two typical examples will illustrate decimation and interpolation in multirate DSP systems. We will then introduce polyphase notation, and will discuss some efficient decimator designs. At the end of the chapter we will discuss filter banks and a quite
发表于 2025-3-22 08:29:27 | 显示全部楼层
Adaptive Filters,, i.e., they were LTI systems. However, many real-world signals we find in typical DSP fields like speech processing, communications, radar, sonar, seismology, or biomedicine, require that the “optimal” filter or system coefficients need to be adjusted over time depending on the input signal. If the
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Multirate Signal Processing,en introduce polyphase notation, and will discuss some efficient decimator designs. At the end of the chapter we will discuss filter banks and a quite new, highly celebrated addition to the DSP toolbox: wavelet analysis.
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Textbook 20042nd editionnd digital signal processing algorithms. So the efficient implementation of these algorithms is critical and is the main goal of this book. It starts with an overview of today‘s FPGA technology, devices, and tools for designing state-of-the-art DSP systems. A case study in the first chapter is the b
发表于 2025-3-22 23:48:28 | 显示全部楼层
1860-4862 or front-end digital signal processing algorithms. So the efficient implementation of these algorithms is critical and is the main goal of this book. It starts with an overview of today‘s FPGA technology, devices, and tools for designing state-of-the-art DSP systems. A case study in the first chapte
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发表于 2025-3-23 06:50:49 | 显示全部楼层
Security Awareness: Brave New Worldismology, or biomedicine, require that the “optimal” filter or system coefficients need to be adjusted over time depending on the input signal. If the parameter changes slowly compared with the sampling frequency we can compute a “better” estimation for our optimal coefficients and adjust the filter appropriate.
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