EE403: Digital Signal Processing
Berkeley數(shù)字信號(hào)處理視頻課程
（27講  ￥72）
Instructor: Professor Avideh Zakhor

        Digital Signal Processing是UC Berkeley電子工程專(zhuān)業(yè)高年級(jí)本科生課程。課程共27講，每講80分鐘左右，包括：.rm格式的課程視頻和與視頻課程內(nèi)容配套使用的完整的課程講義 ，且本課程視頻配有英文字幕。有了課程講義和視頻字幕讓您的學(xué)習(xí)更有效，在潛移默化中提高專(zhuān)業(yè)知識(shí)和英語(yǔ)能力。
        加州大學(xué)伯克萊分校（UC Berkeley）作為世界一流大學(xué)，有著世界頂級(jí)的大師，所設(shè)課程也都是精品中的精品，緊跟最新科技的進(jìn)展。本站推出的美國(guó)一流大學(xué)精品視頻課程套裝，讓您足不出戶就能一睹世界一流大學(xué)大師教學(xué)的風(fēng)采；聆聽(tīng)大師的聲音、拓展國(guó)際化的視野、與國(guó)際水平看齊、實(shí)現(xiàn)自我價(jià)值的提升。

1. Fast review of LTI systems, DTFT, sampling
2. Multirate signal processing, Bilateral Z Transform
3. Discrete Fourier Transform, Fast Fourier Transform
4. Quantization, finite word length effects
5. FIR and IIR filter design techniques
6. Filter banks, Wavelets
7. Multidimensional signal processing: Z transform, filter design, reconstruction from phase, magnitude, DFT
8. Applications: speech and video processing 
　

        A. V. Oppenheim and R. W. Schafer, Discrete Time Signal Processing, Second Edition, Prentice Hall, 1999.
　

Lecture 1: What is DSP? Intro to LTI systems
Lecture 2: The DTFT
Lecture 3: Basics of Sampling
Lecture 4: The Z-Transform
Lecture 5: Upsampling and Interpolation
Lecture 6: LTI
Lecture 7: Different Realization of LCC DE
Lecture 8: Flowgraphs/Realizations of IIR Filters
Lecture 9: Transposition Theorem
Lecture 10: Linear Phase Filtering
Lecture 11: Generalized Linear Phase Filter
Lecture 12: Filter Design
Lecture 13: FIR Filter Design Using Windows
Lecture 14: Optimal FIR Filter Design
Lecture 15: Optimal FIR Filter Design, continued
Lecture 16: Optimal FIR Filter Design, continued
Lecture 17: IIR Filter Design
Lecture 18: Bilinear Transformation
Lecture 19: IIR Filter Design Transformation
Lecture 20: Discrete Fourier Series
Lecture 21: DFT = Discrete Fourier Transform
Lecture 22: How to Use DFT to do Convolution
Lecture 23: Using DFT for Filtering Infinitely Long Sequences
Lecture 24: DCT and Its Relation to DFT
Lecture 25: Fast Fourier Transform
Lecture 26: Fouric Analysis of Signals using DFT
　

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