Torrent Signal Processing First Solutions
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Bookmarks, highlights and notes sync across all your devices. Smart study tools such as note sharing and subscription, review mode, and Microsoft OneNote integration. Search and navigate content across your entire Bookshelf library. Interactive notebook and read-aloud functionality. Look up additional information online by highlighting a word or phrase. Digital signal processing (DSP) has been applied to a very wide range of applications. This includes voice processing, image processing, digital communications, the transfer of data over the internet, image and data compression, etc.
Jun 05, 2015 Solutions to Exercises in Chapter 2. 5 June, 2015 - 09:47. Solution to Exercise 2.6.1. In the first case, order does not matter; in the second it does. Introduction to Digital Signal Processing. Introduction to Computer Organization. Computer Architecture. Representing Numbers. Exercise 5.2.1. McClellan, Schafer, and Yoder, Signal Processing First, ISBN 0-13-065562-7. Homework - A large collection of homework problems, most with solutions. The original band-limited signal can be reconstructed by convolving this with the sampled signal ˆx(t), which eliminates the periodicity of the frequency domain introduced by the sampling process: x(t) = h(t)∗xˆ(t) Note that sampling h(t) gives the impulse function: h(t) s(t) = δ(t).
Engineers who develop DSP applications today, and in the future, will need to address many implementation issues including mapping algorithms to computational structures, computational efficiency, power dissipation, the effects of finite precision arithmetic, throughput and hardware implementation. It is not practical to cover all of these in a single text. However, this text emphasizes the practical implementation of DSP algorithms as well as the fundamental theories and analytical procedures that form the basis for modern DSP applications.Digital Signal Processing: Principles, Algorithms and System Design provides an introduction to the principals of digital signal processing along with a balanced analytical and practical treatment of algorithms and applications for digital signal processing. It is intended to serve as a suitable text for a one semester junior or senior level undergraduate course. It is also intended for use in a following one semester first-year graduate level course in digital signal processing. It may also be used as a reference by professionals involved in the design of embedded computer systems, application specific integrated circuits or special purpose computer systems for digital signal processing, multimedia, communications, or image processing.
Key Features. List of Tables. References. List of Figures. References.
About the Authors. Preface. Chapter 1: Introduction to Digital Signal Processing.
Abstract. 1.1. Introduction.
1.2. Deterministic and Random Signals. 1.3. Mathematical Representation of Signals.
1.4. Typical Signal Processing Operations. 1.5. Signal Processing with Matlab. 1.6. Problems for Chapter 1. 1.7.
Matlab Problems for Chapter 1. References. Chapter 2: Fundamental DSP Concepts. Abstract. 2.1. Introduction.
2.2. Representing Discrete Time Signals. 2.3. Elementary Discrete Time Signals. 2.4. Classification of Discrete Time Signals. 2.5.
Transformation of Signals. 2.6. Operations on Sequences. 2.7. Sampling Continuous Time Signals. 2.8.
Discrete Time Convolution. 2.9. Output Response of a Discrete Time System to a Given Input Sequence. 2.10. Difference Equation Representation. 2.11. Normalized Frequency Representation.
2.12. Periodicity for Discrete Time Sequences. 2.13. Interconnection of Systems. 2.14. Discrete Time Correlation. 2.15.
The Z Transform. 2.16. Properties of the Z Transform. 2.17.
System Transfer Function. 2.18. Poles and Zeros.
2.19. The Difference Equation and the Transfer Function. 2.20. The Z Transform of a Sequence.
2.21. Bounded Input, Bounded Output Stability. 2.22. The Inverse Z Transform. 2.23. Systems with Complex Poles. 2.24.
Systems with Multiple Poles. 2.25. Nonzero Initial Conditions. 2.26. Reconstruction of Discrete Time Signals. 2.27.
Sample Rate Conversion by Interpolation. 2.28. Sample Rate Conversion by Decimation. 2.29. Non-Integer Ratio Interpolation or Decimation.
2.30. Problems for Chapter 2. 2.31. Matlab Problems for Chapter 2.
References. Chapter 3: Frequency Domain Analysis. Abstract. 3.1. Frequency Analysis of Discrete Time Signals.
3.2. Frequency Response Characteristics. 3.3. Frequency Response Estimates using Pole–Zero Plots. 3.4. Frequency Filtering. 3.5.
Frequency Domain Sampling. 3.6. The Discrete Fourier Transform. 3.7. Discrete Fourier Transform Examples. 3.8. Convolution Using the DFT.
3.9. Filtering of Long Data Sequences. 3.10. The Discrete Cosine Transform. 3.11. Fast Fourier Transform Algorithms.
3.12. Problems for Chapter 3. 3.13. Matlab Problems for Chapter 3. References.
Chapter 4: Design of Digital Filters. Abstract. 4.1. Filter Specifications. 4.2.
Causality Constraints. 4.3. Symmetry and Antisymmetry for FIR Filters.
4.4. Window Based FIR Filter Design. 4.5. Fixed Window Functions. 4.6.
Dsp First Online
Design of FIR Filters Using Matlab. 4.7. Comparison of FIR Filter Designs. 4.8. Design by Simulating the Differentiator. 4.9.
Impulse Invariant Design. 4.10. Design using the Bilinear Transformation. 4.11.
Design using Analog Filter Prototypes. 4.12. Frequency Transformations. 4.13. Frequency Transformation Procedures.
4.14. Design of IIR Filters using Matlab. 4.15. Problems for Chapter 4. 4.16.
Matlab Problems for Chapter 4. References. Chapter 5: Implementation of Discrete Time Systems. Abstract. 5.1.
Structures for the Realization of FIR Systems. 5.2. FIR Lattice Implementation. 5.3. Cascade Implementation of FIR Filters.
5.4. Structures for the Realization of IIR Systems.
5.5. All Pass Digital Filters. 5.6. All Pole IIR Lattice Structure. 5.7. IIR Lattice Structure.
5.8. Implementing IIR Filters Using Matlab. 5.9. State Space Realization. 5.10. Problems for Chapter 5. 5.11.
Matlab Problems for Chapter 5. References. Chapter 6: Finite Word Length Effects. Abstract. 6.1. Introduction.
6.2. Representation of Floating Point Numbers. 6.3.
Computational Errors due to Rounding. 6.4. Two's Complement Representation of Numbers. 6.5.
Analytical Basis for Two's Complement Numbers. 6.6. Scaling FIR Digital Filters. 6.7. Scaling IIR Digital Filters.
6.8. Scaling the State Space Representation. 6.9.
Statistical Analysis of Word Length Effects. 6.10. Problems for Chapter 6. 6.11. Matlab Problems for Chapter 6.
References. Chapter 7: Multirate Digital Signal Processing. Abstract. 7.1. Conceptual View of Modifying Sampling Rate. 7.2.
Frequency Representation of the Upsampler. 7.3. Frequency Interpretation of the Downsampler. 7.4. Multirate Structures for Sampling Rate Conversion. 7.5. Frequency-Based Interpolation using the FFT.
7.6. Sampling Rate Conversion by a Rational Number. Corporate seal stamp template psd form. 7.7. Noble Identities. 7.8. Interpolation using a Polyphase Filter. 7.9.
Decimation using a Polyphase Filter. 7.10. Multistage Decimator. 7.11. Multirate Filter Banks. 7.12.
Multilevel Filter Banks. 7.13. Problems for Chapter 7. References. Chapter 8: Digital Signal Processing Systems Design. Abstract.
8.1. Introduction.
8.2. General Purpose Processors. 8.3. Programmable Digital Signal Processor. 8.4. Field Programmable Gate Array. 8.5.
Application Specific Computing Systems. 8.6. System on a Chip. 8.7. Computational Structures. 8.8. Parallel Algorithm Expressions.
8.9. Pipeline Implementation of DSP Systems.
8.10. Moving Delays Around. 8.11. Retiming of Cellular Arrays. 8.12. Computational Cells for Arrays.
8.13. Pipelining Techniques. 8.14.
FIR Filter Computational Structures. 8.15. Interleave FIR Digital Filter. 8.16. Linear Phase FIR Filter. 8.17.
Second Order IIR Filter Section. 8.18. Block Implementation of IIR Digital Filters.
8.19. Matrix–Vector Multiplication. 8.20. Problems for Chapter 8. 8.21. Matlab Problems for Chapter 8. References.
Chapter 9: Hardware Implementation. Abstract. 9.1. Introduction. 9.2. Pipeline FIR Filter Implementation. 9.3.
IIR Filter Implementation. References. Chapter 10: Multidimensional Discrete Time Systems. Abstract. 10.1. Introduction. 10.2.
Multidimensional Sequences. 10.3. Multidimensional Systems. 10.4. Two-Dimensional Convolution. 10.5.
Cascade and Parallel Connection of Systems. 10.6.
Stability of M-D Systems. 10.7. Regions of Support. 10.8. Frequency Domain Characteristics. 10.9.
The 2-D Impulse Response. 10.10. The 2-D Fourier Transform.
10.11. 2-D Convolution. 10.12. The Discrete Cosine Transform.
References. Appendix A: Matlab Code for Chapter 3. A.1. Generate a Test Sequence.
Appendix B: Matlab Code for Chapter 4. B.1. Hamming Window Design.
Appendix C: Matlab Code for Chapter 5. C.1. IIR Second Order Sections. C.2.
Vector Implementation of IIR Filter. C.3. Linear Phase IIR Filter. Appendix D: Matlab Code for Chapter 6.
D.1. Scaling of FIR Filters. D.2. Output Overflow Scaling of FIR Filters.
D.3. Scaling of IIR Filters. D.4. Output Overflow Scaling of IIR Filters. D.5.
Scaling the State Space Model. D.6. Scaling IIR SOS in Direct Form II. D.7. The Fixed Point State Space Model.
Appendix E: Matlab Code for Chapter 7. E.1.
Polyphase Filter with Downsampling. E.2. Polyphase Filter with Upsampling. Appendix F: Matlab Code for Chapter 8. F.1.
Multiply/Accumulate Cellular Array. F.2.
Multiply/Accumulate FIR Pipeline. F.3. A Second Input Sample Sequence. F.4. Multiply/Add Computational Array. F.5. Multiply/Add FIR Pipeline.
F.6. Interleave FIR Filter Cellular Array. F.7. Interleave FIR Filter Pipeline.
F.8. Linear Phase FIR Cellular Array. F.9. Computational Cell for the Linear Phase FIR Filter.
F.10. Implementation of the FIR Linear Phase Filter. Appendix G: Matlab Code for Chapter 9.
G.1. FIR Interleave Computational Cell. G.2. FIR Interleave Array. G.3. FIR Interleave Array Test.
G.4. Fixed Point FIR Interleave Cell. G.5. Fixed Point FIR Interleave Array. G.6.
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Fixed Point FIR Interleave Test. G.7. Two's Complement FIR Interleave Cell. G.8. Two's Complement FIR Interleave Array. G.9.
Two's Complement FIR Interleave Array Test. G.10. Two's Complement of Integer Numbers. G.11.
Inverse Two's Complement. G.12. Two's Complement Multiplication. Bibliography. Index.
Alexander is a Professor of Electrical and Computer Engineering at North Carolina State University. He served as Interim Provost and Vice-Chancellor for Academic Affairs (2011-2013) and as Interim Dean of the College of Engineering at North Carolina Agricultural and Technical (A & T) State University (2009-2011) while of leave of absence from North Carolina State University. He received the B. Degree in Electrical Engineering from North Carolina A & T State University and he received the M. Degree in Engineering and the Ph. In Electrical Engineering from the University of New Mexico. He served as an officer in the U.
Air Force (highest rank of captain), he was a Member of Technical Staff at Sandia Laboratories, Albuquerque, NM and he was previously Chair of the Department of Electrical Engineering at North Carolina A & T State University. His research interests include digital signal processing (DSP), genomic signal processing, parallel algorithms and special purpose multiprocessor architectures for DSP. He has taught courses in DSP, DSP architecture and fundamentals of logic systems design at North Carolina State University since 1982.Dr. Alexander is a senior life member of IEEE, a member of Sigma Xi and he is registered as a professional engineer in North Carolina.
Cranos M.Williams is an Associate Professor of Electrical and Computer Engineering at North Carolina State University. He received the B. Degree in Electrical Engineering from North Carolina A & T State University and he received the M. Degrees in Electrical Engineering from North Carolina State University. His research focuses on developing computational and analytical solutions for modeling and understanding the combinatorial interactions of biomolecular, physiological, and structural processes that impact plant growth, development, and adaptation.
His research involves the development of methodologies familiar to other areas of electrical and computer engineering (e.g. Computational intelligence, system identification, uncertainty propagation, experimental design, nonlinear systems analysis, control, and signal processing) to predict the impact that internal (genetic) and external (environmental) perturbations have on overall plant response (e.g. Biomass, cellulose content, and plant cell wall strength). He has taught courses in analytical foundations of electrical and computer engineering, DSP, and analysis of nonlinear complex systems at North Carolina State University since 2008.Dr. Williams is a member of IEEE.