Results 271 to 280 of about 20,444,531 (305)

Sparse Array Signal Processing

2023
This dissertation details three approaches for direction-of-arrival (DOA) estimation or beamforming in array signal processing from the perspective of sparsity. In the first part of this dissertation, we consider sparse array beamformer design based on the alternating direction method of multipliers (ADMM); in the second part of this dissertation, the ...
openaire   +1 more source

MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention

Neural Information Processing Systems
The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase. Due to the quadratic complexity of the attention computation, it takes 30
Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, Qianhui Wu, Xufang Luo, Surin Ahn, Zhenhua Han, Amir H. Abdi, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu   +11 more
semanticscholar   +1 more source

Applications of sparse signal processing

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016
Sparse signal processing has found various applications in different research areas where the sparsity of the signal of interest plays a significant role in addressing their ill-posedness. In this invited paper, we give a brief review of a number of such applications in inverse scattering of microwave medical imaging, compressed video sensing, and ...
Masoumeh Azghani, Farokh Marvasti
openaire   +1 more source

Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

IEEE Transactions on Image Processing, 2006
We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries.
Michael Elad, M. Aharon
semanticscholar   +1 more source

Sparse Multimodal Gaussian Processes

2017
Gaussian processes (GPs) are effective tools in machine learning. Unfortunately, due to their unfavorable scaling, a more widespread use has probably been impeded. By leveraging sparse approximation methods, sparse Gaussian processes extend the applicability of GPs to a richer data. Multimodal data are common in machine learning applications.
Qiuyang Liu, Shiliang Sun
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Session details: Session 8B: Sparse Processing

Micro, 2021

semanticscholar   +1 more source

Sparse Sampling in Array Processing

2001
Sparsely sampled irregular arrays and random arrays have been used or proposed in several fields such as radar, sonar, ultrasound imaging, and seismics. We start with an introduction to array processing and then consider the combinatorial problem of finding the best layout of elements in sparse 1-D and 2-D arrays.
S. Holm, A. Austeng, K. Iranpour, J.-F. Hopperstad   +3 more
openaire   +1 more source

Parallel sparse filtering for intelligent fault diagnosis using acoustic signal processing

Neurocomputing, 2021
Shanshan Ji, Baokun Han, Zongzhen Zhang, Jinrui Wang, Bo Lu, Jiawei Yang, Xingxing Jiang   +6 more
semanticscholar   +1 more source

Low Sidelobe Sparse Array Processing

Digital Signal Processing, 2002
Abstract Foster, S., Low Sidelobe Sparse Array Processing, Digital Signal Processing 12 (2002) 360–371 The coherent multiweight beamformer (CMWB) recently proposed by the author is analyzed from the point of view of its co-array structure. It is shown that for certain classes of sparse arrays it is possible to design CMWB beamformers to an ...
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Evaluation of radiating-source parameters by measurements in Faraday cages and sparse processing

, 2017
N. Munic, M. Stevanovic, A. Djordjević, A. Kovacevic   +3 more
semanticscholar   +1 more source