Fundamentals of convolutional coding second editionWiley
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出版社: Institute of Electrical and Electronics Engineers, 摘要: Electrical Engineering Fundamentals of Convolutional Coding A volume in the IEEE Press Series on Digital and Mobile Communication John B. Anderson, Series Editor Convolutional codes, among the main error control codes, are routinely used ...
• Fundamentals of Convolutional Coding, R. Johannesson. received. One says it is a single error correcting code. (2) W. Cary Huffman and Vera Pless, Fundamentals of Error"Correcting Codes, Cambridge. University. However, the RS codes have two drawbacks: 1) Received erroneous codewords ...
Understand the different categories of learning: supervised, unsupervised, semi-supervised Learn how to work with and preprocess image data Gain an understanding for all the components in a convolutional neural network (CNN) Learn exactly how convolutions work with image data to detect patterns Underst...
13.3 Convolutional Codes 288 13.4 Trellis Coded Modulation 297 13.5 Bit Interleaved Coded Modulation (BICM) 301 13.6 Turbo Codes 302 13.7 Low-Density Parity-Check Codes 306 13.8 Polar Codes 310 13.9 Comparison of Capacity-Approaching Codes 314 13.10 Coding for the Fading Channel 315 13.1...
[130] developed an active placement method without closed-loop control by training multiple convolutional neural networks, thereby significantly reducing the time consumption of active assembly. In general, the combination of vision and deep neural networks helps reduce manual labor while maintaining the ...
Error Control Coding In this chapter, we discuss a number of codes for error control. Only block codes are treated here. Discussion on convolutional codes will be deferred unti... S Lin - 《Fundamentals & Applications》 被引量: 491发表: 1983年 Genetic algorithms in materials design and proces...
In the category of classical models, we introduce linear/logistic regressions, decision trees and tree ensembles, and support vector machines (SVMs). Our investigation into deep learning models includes multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (...
Finally, the fully connected layer, typically a DNN, performs classification on the features extracted by the series of convolutional and pooling layers. An example architecture of DCNN is illustrated in Figure 2. Features learned at different layers of DCNN correspond to different levels of ...