26.1 Introduction 26.2 Carriers and channels form water soluble paths through the membrane 26.3 Ion channels are selective 26.4 Neurotransmitters control channel activity 26.5 G proteins may activate or inhibit target proteins 26.6 G proteins function by dissociation of the trimer 26.7 Growth factor receptors are protein kinases 26.8 Receptors are activated by dimerization 26.9 Receptor kinases activate signal transduction pathways

Digital Signal Processing • Signal & System • DSP system • Description for DSP FIR Filter Design & Implement • Digital Filter • Specification Design • Hardware Implementation Some Examples • Digital Down Converter • Central Processing Unit

3.1 Sparsity: Applications and Development 3.2 Sparsity Rendering Algorithms 3.3 EM 3.4 Variational Bayes 3.5 Sparse Signal Recovery: Performance PK 3.6 Other Applications for Bayes methods

Part 1: Singular Value Decomposition (SVD) and Applications Part 2: Principle Component Analysis (PCA) and Applications Part 3: Non-Negative Matrix Factorization (NNMF) and Applications

◆2.1 Discrete-Time Signals: Sequences ◆High and Low Frequencies in Discrete-time signal ◆2.2 Discrete-Time Systems ◆Memoryless (memory); Linear; Time-Invariant; Causality; Stability(BIBO) ◆2.3 Linear Time-Invariant (LTI) Systems ◆LTI Systems:Convolution( 系统适用吗?) ◆2.4 Properties of LTI Systems ◆Stability and Causality of LTI systems;FIR and IIR systems ◆2.5 Linear Constant-Coefficient Difference Equations ◆The output for a given input is not uniquely specified. Auxiliary conditions are required; initial-rest conditions ◆2.6 Frequency-Domain Representation of Discrete￾Time Signals and systems ◆ Eigenfunction and Eigenvalue for LTI systems ◆2.7 Representation of Sequences by Fourier Transforms ◆2.8 Symmetry Properties of the Fourier Transform ◆2.9 Fourier Transform Theorems ◆2.10 Discrete-Time Random Signals

一,名词解释 核孔复合体 肌醇磷脂信号通路 过氧化物酶体 细胞质基质 Signal Sequence a nd Signal patch

 S1. Filters: devices of signal processing  Classic Filter  Optimal Filter  Adaptive Filter  S2. Adaptive filtering applications  S3. Stochastic processes  Partial characteristic: Autocorrelation Matrix (ACM)  PSD  Linear parametric model  S4. Mean Square Information Space

◆8.0 Introduction ◆8.1 Representation of Periodic Sequence: the Discrete Fourier Series ◆8.2 Properties of the Discrete Fourier Series ◆8.3 The Fourier Transform of Periodic Signal ◆8.4 Sampling the Fourier Transform ◆8.5 Fourier Representation of Finite-Duration Sequence: the Discrete Fourier Transform ◆8.6 Properties of the Discrete Fourier Transform ◆8.7 Linear Convolution using the Discrete Fourier Transform ◆8.8 the discrete cosine transform (DCT)

84.1 Introduction 84.2 The Analog and Digital Signal Interface 84.3 Analog Signal Conditioning 84.4 Sample-and-Hold and A/D Techniques in Data Acquisition

HIGH RESOLUTION SIGNAL CONDITIONING ADCs Walt Kester, James Bryant, Joe Buxton The trend in ADCs and DACs is toward higher speeds and higher resolutions at reduced power levels. Modern data converters generally operate