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| Preface and Acknowledgments | |
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| An Introduction to Signal Processing | |
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| Some Signal Processing History | |
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| The Signal Processing System | |
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| Describing Signals | |
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| Representation of Signals | |
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| Classification of Signals | |
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| Mathematical Description of Specific Signals | |
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| Continuous-Time Systems and Discrete-Time Systems | |
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| The Frequency Domain of Digital Signals and Systems | |
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| The Discrete-Time Fourier Transform | |
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| Example Calculations with the Discrete-Time Fourier Transform | |
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| Effects of Signal Length and Windowing on the Discrete-Time Fourier Transform | |
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| The Discrete Fourier Transform | |
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| Inverse Transforms | |
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| Signal Power in the Time and Frequency Domains | |
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| Random Noise in Signals | |
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| The Frequency Response of a Linear Time-Invariant DSP System | |
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| Finite Impulse Response Filter Design | |
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| General Concepts of FIR Filter Design | |
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| Phase Distortion and Linear Phase | |
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| The Ideal Window Design Method | |
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| Sampling Design of FIR Filters | |
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| Optimal FIR Design Methods in MATLAB | |
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| Infinite Impulse Response Filter Design | |
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| The General Concepts of IIR Filter Design | |
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| Design by Pole-Zero Location | |
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| Digital Realization of Classical Analog Filters | |
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| MATLAB IIR Design Tools | |
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| Coefficient Quantization with IIR Filters | |
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| Over-Sampling and Multi-Rate DSP Systems | |
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| Digital Anti-Aliasing | |
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| Down-sampling and Decimation | |
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| Up-Sampling and Interpolation | |
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| Sampling Rate Conversion by Rational Factors | |
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| Over-Sampling and Noise | |
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| Delta-Sigma Quantization | |
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| Correlation and Auto-correlation of Signals | |
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| The Cross-Correlation of Signals | |
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| Auto-correlation | |
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| Using Auto-correlation to Detect Signals in Noise | |
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| Detecting and Ranging a Return Echo Contaminated with Noise | |
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| Adaptive Filters | |
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| Theory of Adaptive Filters | |
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| The Adaptive Predictor | |
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| Adaptive System Identification | |
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| Basic Digital Signal Processing of Images | |
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| The Structure of Digital Images | |
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| Image Sampling, Quantization, and Aliasing | |
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| Arithmetic Operations on Image Matrices | |
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| Statistical Properties and Enhancement of Images | |
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| Image Filtering | |
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| Discrete Fourier Transform of Images | |
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| Case Study: JPEG Compression of Images | |
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| Wavelets | |
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| Non-Stationary Signals | |
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| Sub-Band Decomposition and Reconstruction of Signals | |
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| Analysis of Signals Using Wavelets | |
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| Signal Compression Using Wavelets | |
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| Computational Case Studies | |
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| Dual-Tone Multi-Frequency Signaling | |
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| Pattern Recognition in Images | |
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| Speech Processing: Compression and Synthesis | |
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| Echo Cancellation with Adaptive Filters | |
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| Wavelet De-Noising and Compression of Images | |
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| Other Case Studies Appendices | |
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| Complex Numbers | |
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| Imaginary Numbers | |
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| Why We Need Imaginary Numbers | |
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| Complex Numbers | |
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| Polar Form of a Complex Number and Euler?s Equation | |
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| Magnitude and Angle of a Complex Number | |
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| Complex Conjugate | |
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| Complex Exponential Forms of the Sine and Cosine Functions | |
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| Complex Functions | |
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| Working With Complex Numbers | |
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| A-to-D and D-to-A Conversion Methods | |
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| What Makes a DSP a DSP? | |
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| Mathematical Detail and Proofs | |
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| Fourier Analysis | |
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| The inverse DTFT | |
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| The inverse DFT | |
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| Statistical properties of digital signals: mean, variance, covariance, and expectation | |
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| The least-mean-squares algorithm to find the minimum of a function | |