Results 261 to 270 of about 3,801,818 (306)

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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Sampling and processing of color signals

Proceedings of 1st International Conference on Image Processing, 2002
The paper discusses the sampling of color spectra and its effect on the accuracy of derived properties such as CIE tristimulus values and color rendering indices. The effect of aliasing and common mathematical operations are discussed. >
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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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Signals and Sampling

2004
One- or multidimensional sampling of sensor information is the origin of natural- source digital multimedia signals. The methodologies of sampling have an eminent impact on any subsequent processing steps, including encoding and content analysis of signals.
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Multichannel sampling for multiband signals

Signal Processing, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Sampling and quantization of bilevel signals

Pattern Recognition Letters, 1993
Abstract Results are presented on the relationship between sampling interval, number of quantization levels, and the point spread function of the digitizer in order to achieve detection of light and dark areas of the input signal.
Theo Pavlidis, Minghua Chen, Eugene Joseph   +2 more
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Sampling of Signals in Energy Domain

2005 IEEE Conference on Emerging Technologies and Factory Automation, 2006
The paper introduces a new event-based criterion for discrete representations of continuous-time signals, which can be used in intelligent sensors to report the current state of the observed object. The criterion is based on the control of the sampling error energy. The analytic approximation of the mean sampling rate for a given sampling resolution is
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On Sampling of Bandlimited Graph Signals

2018
The signal processing on graphs has been widely used in various fields, including machine learning, classification and network signal processing, in which the sampling of bandlimited graph signals plays an important role. In this paper, we discuss the sampling of bandlimited graph signals based on the theory of function spaces, which is consistent with
Mo Han, Jun Shi 0003, Yiqiu Deng, Weibin Song   +3 more
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Compressive Sampling for Signal Classification

2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Compressive sampling (CS), also called compressed sensing, entails making observations of an unknown signal by projecting it onto random vectors. Recent theoretical results show that if the signal is sparse (or nearly sparse) in some basis, then with high probability such observations essentially encode the salient information in the signal.
Haupt, J., Castro, R.M., Nowak, R., Fudge, G., Yeh, A.   +4 more
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