Showing posts with label B. (figures)-S-S. Sengupta. Show all posts
Showing posts with label B. (figures)-S-S. Sengupta. Show all posts

2016-12-10

Neural Networks and Applications by S. Sengupta (IIT Kharagpur)

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source: nptelhrd    2009年9月22日
Electronics - Neural Networks and Applications by Prof. S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur.

Lec-1 Introduction to Artificial Neural Networks 53:50
Lec-2 Artificial Neuron Model and Linear Regression 58:28
Lec-3 Gradient Descent Algorithm 56:35
Lec-4 Nonlinear Activation Units and Learning Mechanisms 58:09
Lec-5 Learning Mechanisms-Hebbian,Competitive,Boltzmann 57:16
Lec-6 Associative memory 58:58
Lec-7 Associative Memory Model 57:16
Lec-8 Condition for Perfect Recall in Associative Memory 59:59
Lec-9 Statistical Aspects of Learning 54:08
Lec-10 V.C. Dimensions: Typical Examples 57:44
Lec-11 Importance of V.C. Dimensions Structural Risk Minimization 45:47
Lec-12 Single-Layer Perceptions 56:13
Lec-13 Unconstrained Optimization: Gauss-Newtons Method 59:17
Lec-14 Linear Least Squares Filters 57:58
Lec-15 Least Mean Squares Algorithm 52:21
Lec-16 Perceptron Convergence Theorem 55:29
Lec-17 Bayes Classifier & Perceptron: An Analogy 56:55
Lec-18 Bayes Classifier for Gaussian Distribution 55:51
Lec-19 Back Propagation Algorithm 55:35
Lec-20 Practical Consideration in Back Propagation Algorithm 57:09
Lec-21 Solution of Non-Linearly Separable Problems Using MLP 57:32
Lec-22 Heuristics For Back-Propagation 58:05
Lec-23 Multi-Class Classification Using Multi-layered Perceptrons 56:11
Lec-24 Radial Basis Function Networks: Cover's Theorem 56:49
Lec-25 Radial Basis Function Networks: Separability&Interpolation 57:24
Lec-26 Radial Basis Function as ill-Posed Surface Reconstruc 57:58
Lec-27 Solution of Regularization Equation: Greens Function 55:44
Lec-28 Use of Greens Function in Regularization Networks 57:14
Lec-29 Regularization Networks and Generalized RBF 48:47
Lec-30 Comparison Between MLP and RBF 54:09
Lec-31 Learning Mechanisms in RBF 54:37
Lec-32 Introduction to Principal Components and Analysis 56:38
Lec-33 Dimensionality reduction Using PCA 54:17
Lec-34 Hebbian-Based Principal Component Analysis 50:23
Lec-35 Introduction to Self Organizing Maps 39:05
Lec-36 Cooperative and Adaptive Processes in SOM 52:15
Lec-37 Vector-Quantization Using SOM 52:02

2016-12-08

Digital Voice and Picture Communication by S. Sengupta (IIT Kharagpur)

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source: nptelhrd    2008年10月10日
Electronics - Digital Voice and Picture Communication by Prof. S. Sengupta, Department of Electronics and Electrical Communication Engg, IIT Kharagpur.

Lecture - 1 Introduction 54:53
Lecture - 2 Speech Production Model 57:47
Lecture - 3 Speech Coding : Objectives and Requirements 58:24
Lecture - 4 Quantizers for Speech Signal 51:53
Lecture - 5 mew - Law and Optimum Quantizer 51:47
Lecture - 6 Adaptive Quantizer 51:38
Lecture - 7 Differential Quantization 52:49
Lecture - 8 LDM and ADM 56:44
Lecture - 9 Differential PCM and Adaptive Prediction 54:29
Lecture - 10 Linear Prediction of Speech 50:17
Lecture - 11 Computational Aspects of LPC parameters 55:18
Lecture - 12 Cholesky Decomposition 54:31
Lecture - 13 Lattice Formulation of LPC Coefficient 54:13
Lecture - 14 Linear Predictive Synthesizer 53:27
Lecture - 15 LPC Vocoder 44:03
Lecture - 16 Introduction to Image and Video Coding 56:49
Lecture - 17 Lossy Image Compression : DCT 56:25
Lecture -18 DCT Quantization and Limitations 55:18
Lecture - 19 Theory of Wavelets 58:57
Lecture -20 Discrete Wavelet Transforms 55:13
Lecture - 21 DWT on the Images and its Encoding 55:59
Lecture -22 Embedded Zero Tree Wavelet Encoding 58:10
Lecture - 23 Video Coding : Basic Building Blocks 48:53
Lecture - 24 Motion Estimate Techniques 50:13
Lecture - 25 Fast Motion Estimation Techniques 59:51
Lecture - 26 Video Coding Standards 55:09
Lecture - 27 Advanced Coding Aspects 52:41
Lecture - 28 Audio Coding: Basic Concepts 56:31
Lecture - 29 Audio Coding AC - 3 59:05
Lecture - 30 AC -3 Decoder 55:56
Lecture - 31 MPEG - 1 Audio Coding 57:54
Lecture - 32 Introduction to VoIP 52:35
Lecture -33 VoIP Signaling : H.323 Protocol 56:19
Lecture - 34 H.323 Call Controls and Enhancements 49:39
Lecture - 35 Interworking with PSTN Limitations and Solution 48:33
Lecture -36 Multiplexing Schemes 53:13
Lecture - 37 H.323:Multiplexing:Header Compression and BW 56:44
Lecture - 38 ISDN Video Conferencing 58:24
Lecture -39 Video Conferencing : SIP Protocol 51:20
Lecture - 40 4G Multimedia Conferencing 57:14