Author(s): Arpit Yadav
Machine learning (ML) provides the high end automation of data processing for the wide range of human brain with machine interfacing. Deep machine learning (DML) performs like human brain to achieve automated features extraction, reducing the dimension of the complex data set. Analog signal processing (ASP) need much higher energy efficiency than digital signal processing (DSP), presenting a way for overcoming of these limitations. This paper have reviewed ML techniques which propose analogue memory which can be essential component for learning system. Discussed about unsupervised learning system for different computation node in DML. In addition, also discussed about ultra-low-power circuit to provide similarity measures in analogue signal processing and technique matched with latest development in VLSI, ULSI for CMOS transistor with compact technology. The face recognition studied, based on Hidden Markov Models (HMMs) and discrete wavelet transform (DWT). A sequence of overlapping sub-images is extracted from each face image computing the DWT coefficients for each of them.
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