Results 211 to 220 of about 478,778 (267)

Compressed Signal Processing on Nyquist-Sampled Signals

IEEE Transactions on Computers, 2016
Pattern-recognition algorithms from the domain of machine learning play a prominent role in embedded sensing systems, in order to derive inferences from sensor data. Very often, such systems face severe energy constraints. The focus of this work is to mitigate the computational energy by exploiting a form of compression which preserves a similarity ...
Jie Lu, Naveen Verma, Niraj K. Jha
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Ginibre sampling and signal reconstruction

2016 IEEE International Symposium on Information Theory (ISIT), 2016
The spatial distribution of sensing nodes plays a crucial role in signal sampling and reconstruction via wireless sensor networks. Although homogeneous Poisson point process (PPP) model is widely adopted for its analytical tractability, it cannot be considered a proper model for all experiencing nodes.
ZABINI, FLAVIO, Conti, Andrea
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Structured sampling of structured signals

2013 IEEE Global Conference on Signal and Information Processing, 2013
The paper considers structured sampling of structured signals, more specifically, using block diagonal (BD) measurement matrices to sense signals with uniform partitions that share the same sparsity profile. This model arises in distributed compressive sensing systems. In general, the fact that the number of nonzero entries in the measurement matrix is
Bo Li 0027, Athina P. Petropulu
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Analytic sampling of bandpass signals

Signal Processing, 1992
Abstract This paper investigates the simultaneous sampling of a bandpass signal and its Hilbert transform. A simultaneous sample of a bandpass signal and its Hilbert transform is equivalent to a complex sample of the analytic signal. Therefore, this type of sampling is called analytic sampling.
S. C. Scoular, William J. Fitzgerald 0001   +1 more
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Compressive sampling of correlated signals

2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011
The recently developed theory of Compressive sensing (CS) has shown that sparse signals can be reconstructed from a much smaller number of measurements than their bandwidth suggests. In this paper we present a sampling scheme to acquire ensembles of correlated signals at a sub-Nyquist rate.
Ali Ahmed 0004, Justin K. Romberg
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Compressive Sampling for Signal Detection

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
Compressive sampling (CS) refers to a generalized sampling paradigm in which observations are inner products between an unknown signal vector and user-specified test vectors. Among the attractive features of CS is the ability to reconstruct any sparse (or nearly sparse) signal from a relatively small number of samples, even when the observations are ...
Jarvis D. Haupt, Robert D. Nowak
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