Randomness and Elements of Decision Theory Applied to Signals
portes grátis
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
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.