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刘全慧
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回复@杨正瓴：Sparse Signal Processing
作者/authors， M Azghani, F Marvasti
摘要/abstract， Conventional sampling techniques are based on Shannon-Nyquist theory which states that the required sampling rate for perfect recovery of a band-limited signal is at least twice its bandwidth. The band-limitedness property of the signal plays a significant role in the design of conventional sampling and reconstruction systems. As the natural signals are not necessarily band-limited, a low-pass filter is applied to the signal prior to its sampling for the purpose of antialiasing. Most of the signals we are faced with are sparse rather than band-limited (or low pass). It means that they have a small number of non-zero coefficients in some domain such as time, discrete cosine transform (DCT), discrete wavelet transform (DWT), or discrete fourier transform (DFT). This characteristic of the signal is the foundation for the emerging of a new signal sampling theory called Compressed Sampling, an extension of random...
作者/authors， M Azghani, F Marvasti
摘要/abstract， Conventional sampling techniques are based on Shannon-Nyquist theory which states that the required sampling rate for perfect recovery of a band-limited signal is at least twice its bandwidth. The band-limitedness property of the signal plays a significant role in the design of conventional sampling and reconstruction systems. As the natural signals are not necessarily band-limited, a low-pass filter is applied to the signal prior to its sampling for the purpose of antialiasing. Most of the signals we are faced with are sparse rather than band-limited (or low pass). It means that they have a small number of non-zero coefficients in some domain such as time, discrete cosine transform (DCT), discrete wavelet transform (DWT), or discrete fourier transform (DFT). This characteristic of the signal is the foundation for the emerging of a new signal sampling theory called Compressed Sampling, an extension of random...
08-19 16:30
刘全慧
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08-19 19:17
