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Adaptive compressive sensing for low power wireless sensors

Published: 20 May 2014 Publication History

Abstract

Compressive sensing has been demonstrated as an appealing technique in the implementation of low-power sensors. This work studies the feasibility and potential power savings by adaptively adjusting the sampling rates in compressive sensing operations, which is referred to as adaptive compressive sensing in this paper. The results reveal that the sparsity of many biomedical sensor signals varies over time and hence it is possible to perform such adaptive operations. The study also shows that the adaptive operation can lead to significant reduction on sensor node power consumption

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  • (2024)Development of wearable sensors performance in medical systemTHE 5TH INTERNATIONAL CONFERENCE ON BUILDINGS, CONSTRUCTION, AND ENVIRONMENTAL ENGINEERING: BCEE510.1063/5.0237191(020109)Online publication date: 2024
  • (2020)Energy-Efficient and QoS-Aware Link Adaptation With Resource Allocation for Periodical Monitoring Traffic in SmartBANsIEEE Access10.1109/ACCESS.2020.29662708(13476-13488)Online publication date: 2020
  • (2019)An Efficient Strategy for Online Performance Monitoring of Datacenters via Adaptive SamplingIEEE Transactions on Cloud Computing10.1109/TCC.2016.26034737:1(155-169)Online publication date: 1-Jan-2019
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    cover image ACM Conferences
    GLSVLSI '14: Proceedings of the 24th edition of the great lakes symposium on VLSI
    May 2014
    376 pages
    ISBN:9781450328166
    DOI:10.1145/2591513
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 20 May 2014

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    Author Tags

    1. compressive sensing
    2. low power
    3. power estimation
    4. sensor

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    May 21 - 23, 2014
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    GLSVLSI '14 Paper Acceptance Rate 49 of 179 submissions, 27%;
    Overall Acceptance Rate 312 of 1,156 submissions, 27%

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    View all
    • (2024)Development of wearable sensors performance in medical systemTHE 5TH INTERNATIONAL CONFERENCE ON BUILDINGS, CONSTRUCTION, AND ENVIRONMENTAL ENGINEERING: BCEE510.1063/5.0237191(020109)Online publication date: 2024
    • (2020)Energy-Efficient and QoS-Aware Link Adaptation With Resource Allocation for Periodical Monitoring Traffic in SmartBANsIEEE Access10.1109/ACCESS.2020.29662708(13476-13488)Online publication date: 2020
    • (2019)An Efficient Strategy for Online Performance Monitoring of Datacenters via Adaptive SamplingIEEE Transactions on Cloud Computing10.1109/TCC.2016.26034737:1(155-169)Online publication date: 1-Jan-2019
    • (2018)Sparse Coding Enables the Reconstruction of High-Fidelity Images and Video from Retinal Spike TrainsProceedings of the International Conference on Neuromorphic Systems10.1145/3229884.3229892(1-5)Online publication date: 23-Jul-2018
    • (2018)A Survey on Efficient Power Consumption in Adaptive Wireless Sensor NetworksWireless Personal Communications: An International Journal10.1007/s11277-018-5678-5101:1(101-117)Online publication date: 1-Jul-2018
    • (2015)Low-power technologies for wearable telecare and telehealth systems: A reviewBiomedical Engineering Letters10.1007/s13534-015-0174-25:1(1-9)Online publication date: 12-Apr-2015

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