Results 241 to 250 of about 322,181 (285)

Sparse Multiple Kernel Learning for Signal Processing Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
In many signal processing applications, grouping of features during model development and the selection of a small number of relevant groups can be useful to improve the interpretability of the learned parameters. While a lot of work based on linear models has been reported to solve this problem, in the last few years, multiple kernel learning has come
Niranjan, Subrahmanya, Yung C, Shin
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Sparse Image and Signal Processing

2010
This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators.
Starck, Jean-Luc, Murtagh, Fionn, Fadili, Jalal M.   +2 more
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Waveform Design for Sparse Signal Processing in Radar

2021 IEEE Radar Conference (RadarConf21), 2021
In the past decades, there has been an extensive research interest in the areas of both waveform diversity/design and advanced signal processing algorithms departing from the more classical solutions based on Linear Frequency Modulated (LFM) pulses and Matched Filters (MF).
Anitori, L., Ender, J.
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Strong Impossibility Results for Sparse Signal Processing

IEEE Signal Processing Letters, 2014
This letter derives strong impossibility results for several sparse signal processing problems. It is shown that regardless of the allowed error probability in identifying the salient support set (as long as this probability is below one), the required number of measurements is almost the same as that required for the error probability to be ...
Tan, Vincent Y.F., Atia, George K.
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Signal processing with the sparseness constraint

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
An overview is given of the role of the sparseness constraint in signal processing problems. It is shown that this is a fundamental problem deserving of attention. This is illustrated by describing several applications where sparseness of solution is desired.
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Radar sparse signal processing by non-negative least-squares estimation

IET Conference Proceedings, 2023
Increasing the granularity of the solution space for many inverse problems results in ill-posedness. Detection of objects that areclose in the solution space e.g. in range or Doppler is significantly improved if a-priori information, such as sparsity, can be used.Alternatively to traditional sparse signal processing algorithms priors, in this paper, we
Mouri Sardarabadi1, A., Coutino, M., Uysal1, F., Kotti, S.E., Anitori, L.   +4 more
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Digital alias-free signal processing methodology for sparse multiband signals

2013 6th International Congress on Image and Signal Processing (CISP), 2013
In this paper, we present a method of reconstructing multiband signals with the arbitrary Spectrum Support Function. The signal reconstruction method is based on the theorem of Minimum Energy Reconstruction which is extended for multiband signals. The method allows the use of low sampling rates close to the Landau rate to achieve the goal.
Dongdong Qu, Jiuling Jia, Jian Zhou
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Sparse-signal processing on information-based range grid

2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014
Radar obtains its parameters on an estimation grid whose cell size is related to resolution of underlying radar processing. Existing radar exploits a regular grid (i.e. constant resolution) although the resolution changes with stronger echoes at shorter ranges.We compute radar resolution from the intrinsic geometrical structure of data models that is ...
Edwin de Jong, Radmila Pribic
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Finite Frames for Sparse Signal Processing

2013
Over the last decade, considerable progress has been made toward developing new signal processing methods to manage the deluge of data caused by advances in sensing, imaging, storage, and computing technologies. Most of these methods are based on a simple but fundamental observation: high-dimensional data sets are typically highly redundant and live on
Waheed U. Bajwa, Ali Pezeshki
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Sparse sampling of non-stationary signal for radar signal processing

2013 IEEE International Conference on Communications Workshops (ICC), 2013
Estimating the spectrogram of non-stationary signal relates to many important applications in radar signal processing. In recent years, coprime sampling and array attract attention for their potential of sparse sensing with derivative to estimate autocorrelation coefficients with all lags, which could in turn calculate the power spectrum density.
null Qiong Wu, Qilian Liang
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