Results 301 to 310 of about 502,069 (324)

SPARSE COMPUTATION WITH PEI

International Journal of Foundations of Computer Science, 1999
PEI formalism has been designed to reason and develop parallel programs in the context of data parallelism. In this paper, we focus on the use of PEI to transform a program involving dense matrices into a new program involving sparse matrices, using the example of the matrix-vector product.
Frédérique Voisin, Guy-René Perrin
openaire   +1 more source

Sparse Autoencoder for Sparse Code Multiple Access

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2021
In the forthcoming 5G technology, Sparse Code Multiple Access (SCMA) is the most promising scheme that aims at improving spectral efficiency further and providing massive connectivity. The challenge behind implementing SCMA scheme is: constructing optimized codebooks in order to obtain minimum BER while keeping the receiver complexity minimum.
Medini Singh, Deepak Mishra 0002, M. Vani Devi   +2 more
openaire   +2 more sources

Non-linear sparse and group sparse classifier

2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2013
Recently there has been an interest in a new classification model, where it is assumed that the training samples for a particular class form a linear basis for any new test sample belonging to that class. This assumption led to two successful classification methods called the Sparse Classifier (SC) and the Group Sparse Classifier (GSC).
Angshul Majumdar, Rabab K. Ward
openaire   +1 more source

Removal of sparse noise from sparse signals

Signal Processing, 2019
Abstract In this paper, we propose two methods for signal denoising where both signal and noise are sparse but in different domains. First, an optimization problem is proposed which is non-convex and NP-hard due to the existence of l0 norm in its cost function. Then, we propose two approaches to approximate and solve it.
Nematollah Zarmehi, Farokh Marvasti
openaire   +2 more sources

Hyperspectral data unmixing using GNMF method and sparseness constraint

IEEE International Geoscience and Remote Sensing Symposium, 2013
Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers.
Roozbeh Rajabi, H. Ghassemian
semanticscholar   +1 more source

In Defense of Sparse Tracking: Circulant Sparse Tracker

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation.
Tianzhu Zhang 0001, Adel Bibi, Bernard Ghanem   +2 more
openaire   +2 more sources

Hidden Markov map matching through noise and sparseness

ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2009
P. Newson, J. Krumm
semanticscholar   +1 more source

Boundary Detection Using Double-Opponency and Spatial Sparseness Constraint

IEEE Transactions on Image Processing, 2015
Kai-Fu Yang, Shao-Bing Gao, Ce Guo, Chaoyi Li, Yong-Jie Li   +4 more
semanticscholar   +1 more source

Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering

IEEE Transactions on Image Processing, 2015
M. Mozerov, Joost van de Weijer
semanticscholar   +1 more source

Sparse Distributed Memory for Sparse Distributed Data

2022
Ruslan Vdovychenko, Vadim Tulchinsky
openaire   +1 more source