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» Evaluation of a High Performance Code Compression Method
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DCC
2000
IEEE
15 years 7 months ago
Multi-Resolution Adaptation of the SPIHT Algorithm for Multiple Description
Multiple description codes are data compression algorithms designed with the goal of minimizing the distortion caused by data loss in packet-based or diversity communications syst...
Nedeljko Varnica, Michael Fleming, Michelle Effros
CORR
2011
Springer
259views Education» more  CORR 2011»
14 years 9 months ago
The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding vs. Dynamic Range
Compressive sensing (CS) exploits the sparsity present in many common signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, ...
Mark A. Davenport, Jason N. Laska, John R. Treichl...
123
Voted
CORR
2010
Springer
92views Education» more  CORR 2010»
15 years 2 months ago
On Network-Error Correcting Convolutional Codes under the BSC Edge Error Model
Convolutional network-error correcting codes (CNECCs) are known to provide error correcting capability in acyclic instantaneous networks within the network coding paradigm under sm...
K. Prasad, B. Sundar Rajan
FLAIRS
2008
15 years 4 months ago
State Space Compression with Predictive Representations
Current studies have demonstrated that the representational power of predictive state representations (PSRs) is at least equal to the one of partially observable Markov decision p...
Abdeslam Boularias, Masoumeh T. Izadi, Brahim Chai...
113
Voted
ICASSP
2011
IEEE
14 years 6 months ago
Combined compressed sensing and parallel mri compared for uniform and random cartesian undersampling of K-space
Both compressed sensing (CS) and parallel imaging effectively reconstruct magnetic resonance images from undersampled data. Combining both methods enables imaging with greater und...
Daniel S. Weller, Jonathan R. Polimeni, Leo Grady,...