Results 221 to 230 of about 2,999 (264)

AxC-CS: Approximate Computing for Hardware Efficient Compressed Sensing Encoder Design

2019 32nd IEEE International System-on-Chip Conference (SOCC), 2019
In this paper, we present an approximate computing framework for hardware-efficeint compressed sensing encoder design exploiting application-level error-resiliency, termed as AxC-CS (\underline {A}ppro\underline {x}imate \underline {C}omputing for \underline {C}ompressed \underline {S}ensing).
Wenfeng Zhao, Biao Sun, Jian Chen, Yajun Ha   +3 more
openaire   +1 more source

ECO CS: Energy consumption optimized compressive sensing in group sensor networks

Computer Networks, 2018
Abstract Compressive sensing (CS) is a widely employed technique in sensor networks for energy-efficient data transmission. In recent years, the group-based network structures, e.g., regionalized and clustered networks, have been proposed to work with compressive sensing to reduce the energy cost of boundary sensors.
Hao Yang 0002, Xiwei Wang
openaire   +1 more source

On the use of compressive sensing (CS) exploiting block sparsity for neural spike recording

2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2016
This paper presents a novel compressive sensing (CS) algorithm for neural spike recording that exploits the concept of block sparsity in both dictionary training and signal reconstruction. Initially, the block K-SVD (BK-SVD) algorithm is employed to train a block-sparsifying dictionary for neural spikes, followed by the block sparse Bayesian learning ...
Hossein Zamani, Hamid Bahrami 0002, Pedram Mohseni   +2 more
openaire   +1 more source

A new fast and parallel MRI framework based on contourlet and compressed sensing sensitivity encoding (CS-SENSE)

2016 International Conference on Machine Learning and Cybernetics (ICMLC), 2016
Compressed sensing MRI (CS-MRI) and compressed sensing sensitivity encoding (CS-SENSE) only include two regularization items, total variation (TV) and Wavelet, which leads to artifacts remaindering in 1-D random sampling. In order to improve the performance of them, a new regularization item-Contourlet is introduced to constrain the solution with the ...
Jie Song, Zhi-Wu Liao
openaire   +1 more source

Demonstration of a DMD-based Compressive Sensing (CS) Spectral Imaging System

CLEO:2011 - Laser Applications to Photonic Applications, 2011
We present a DMD-based spectral imaging system, which uses a DMD to impose CS measurements on the spatial/spectral information of the imaging scene. The original spatial/spectral information can be reconstructed from the CS measurements numerically.
Yuehao Wu, Iftekhar O. Mirza, Gonzalo R. Arce, Dennis W. Prather   +3 more
openaire   +1 more source

High-resolution MRI using compressed sensing-sensitivity encoding (CS-SENSE) for patients with suspected neurovascular compression syndrome: comparison with the conventional SENSE parallel acquisition technique

Clinical Radiology, 2019
To retrospectively compare sensitivity encoding (SENSE) and compressed sensing-sensitivity encoding (CS-SENSE) for high resolution (HR) cranial nerve magnetic resonance imaging (MRI) in a clinical population.Twenty consecutive patients who were clinically suspected of neurovascular compression syndrome (NVCS) were enrolled in this study.
S J, Cho, Y J, Choi, S R, Chung, J H, Lee, J H, Baek   +4 more
openaire   +2 more sources

MIM-CS: Message Importance Measure for Compressed Sensing

2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), 2021
Yuchen Shi, Pingyi Fan, Zheqi Zhu, Khaled B. Letaief   +3 more
openaire   +1 more source

CS-DeCNN: Deconvolutional Neural Network for Reconstructing Images from Compressively Sensed Measurements

2018
One important research point of compressive sensing (CS) is to restore a high-dimensional signal as completely as possible from its compressed form, which has much lower dimensionality than the original. Several methods have been employed to this end, including traditional iterative methods as well as recurrent approaches based on deep learning.
Wentao Wan 0001, Guohui Li 0001, Peng Pan 0001   +2 more
openaire   +1 more source

Image reconstruction in region-of-interest (or interior) digital tomosynthesis (DTS) based on compressed-sensing (CS)

Computer Methods and Programs in Biomedicine, 2017
Digital tomosynthesis (DTS) based on filtered-backprojection (FBP) reconstruction requires a full field-of-view (FOV) scan and relatively dense projections, which results in high doses for medical imaging purposes. To overcome these difficulties, we investigated region-of-interest (ROI) or interior DTS reconstruction where the x-ray beam span covers ...
Soyoung Park, Guna Kim, Hyosung Cho, Uikyu Je, Chulkyu Park, Kyuseok Kim, Hyunwoo Lim, Dongyeon Lee, Hunwoo Lee, SeokYoon Kang, Jeongeun Park 0003, Taeho Woo, Minsik Lee   +12 more
openaire   +2 more sources

Hybrid Compression Method Using Compressive Sensing (CS) Theory for Various Biometric Data and Biomedical Data

2018
A hybrid compression method based on compressive sensing (CS) theory proposed for various biometric data and biomedical data in this paper. The data compression method is designed using CS theory, discrete cosine transform (DCT), discrete wavelet transform (DWT), and singular value decomposition (SVD). In this method, first DCT and then DWT are applied
Rohit Thanki, Vedvyas Dwivedi, Komal Borisagar   +2 more
openaire   +1 more source