Randomness and Elements of Decision Theory Applied to Signals

Randomness and Elements of Decision Theory Applied to Signals

Terebes, Romulus; Cislariu, Mihaela; Miclea, Andreia; Borda, Monica; Ilea, Ioana; Malutan, Raul; Barburiceanu, Stefania

Springer Nature Switzerland AG

12/2022

242

Mole

Inglês

9783030903169

15 a 20 dias

403

Descrição não disponível.
Introduction in Matlab.- Random variables.- Probability distributions.- Joint random variables.- Random processes.- Binary pseudo-noise sequence generator.- Markov processes.- Noise in telecommunication systems.- Decision systems in noisy transmission channels.- Audio signals denoising using Independent Component Analysis.- Texture classification based on statistical models.- Histogram equalization.- PCM and DPCM.- NN and kNN supervised classification algorithms.- Supervised deep learning classification algorithms.- Texture feature extraction and classification using the Local Binary Patterns operator.
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Random variables;Random prcoesses;Random Signals;Probability distributions;Joint random variables;pseudo-noise sequence generator;Markov processes;Decision systems;denoising;Histogram equalization;Pulse code modulation;kNN supervised classification;Convolutional Neural Network