Compressive information acquisition with hardware impairments and constraints: A case study

S. Gopalakrishnan; T. Moy; U. Madhow; Naveen Verma

doi:10.1109/ICASSP.2017.7953323

Compressive information acquisition with hardware impairments and constraints: A case study

S. Gopalakrishnan, T. Moy, U. Madhow, Naveen Verma

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Compressive information acquisition is a natural approach for low-power hardware front ends, since most natural signals are sparse in some basis. Key design questions include the impact of hardware impairments (e.g., nonlinearities) and constraints (e.g., spatially localized computations) on the fidelity of information acquisition. Our goal in this paper is to obtain specific insights into such issues through modeling of a Large Area Electronics (LAE)-based image acquisition system. We show that compressive information acquisition is robust to stochastic nonlinearities, and that appropriately designed spatially localized computations are effective, by evaluating the performance of reconstruction and classification based on the information acquired.

Original language	English (US)
Title of host publication	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6075-6079
Number of pages	5
ISBN (Electronic)	9781509041176
DOIs	https://doi.org/10.1109/ICASSP.2017.7953323
State	Published - Jun 16 2017
Event	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States Duration: Mar 5 2017 → Mar 9 2017

Other

Other	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/Territory	United States
City	New Orleans
Period	3/5/17 → 3/9/17

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2017.7953323

Cite this

Gopalakrishnan, S., Moy, T., Madhow, U., & Verma, N. (2017). Compressive information acquisition with hardware impairments and constraints: A case study. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 6075-6079). [7953323] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7953323

@inproceedings{36f8710b03094230afb80c10c17e34ca,

title = "Compressive information acquisition with hardware impairments and constraints: A case study",

abstract = "Compressive information acquisition is a natural approach for low-power hardware front ends, since most natural signals are sparse in some basis. Key design questions include the impact of hardware impairments (e.g., nonlinearities) and constraints (e.g., spatially localized computations) on the fidelity of information acquisition. Our goal in this paper is to obtain specific insights into such issues through modeling of a Large Area Electronics (LAE)-based image acquisition system. We show that compressive information acquisition is robust to stochastic nonlinearities, and that appropriately designed spatially localized computations are effective, by evaluating the performance of reconstruction and classification based on the information acquired.",

author = "S. Gopalakrishnan and T. Moy and U. Madhow and Naveen Verma",

year = "2017",

month = jun,

day = "16",

doi = "10.1109/ICASSP.2017.7953323",

language = "English (US)",

pages = "6075--6079",

booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",

}

Gopalakrishnan, S, Moy, T, Madhow, U & Verma, N 2017, Compressive information acquisition with hardware impairments and constraints: A case study. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings., 7953323, Institute of Electrical and Electronics Engineers Inc., pp. 6075-6079, 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, New Orleans, United States, 3/5/17. https://doi.org/10.1109/ICASSP.2017.7953323

Compressive information acquisition with hardware impairments and constraints : A case study. / Gopalakrishnan, S.; Moy, T.; Madhow, U. et al.

2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 6075-6079 7953323.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Compressive information acquisition with hardware impairments and constraints

T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017

AU - Gopalakrishnan, S.

AU - Moy, T.

AU - Madhow, U.

AU - Verma, Naveen

PY - 2017/6/16

Y1 - 2017/6/16

N2 - Compressive information acquisition is a natural approach for low-power hardware front ends, since most natural signals are sparse in some basis. Key design questions include the impact of hardware impairments (e.g., nonlinearities) and constraints (e.g., spatially localized computations) on the fidelity of information acquisition. Our goal in this paper is to obtain specific insights into such issues through modeling of a Large Area Electronics (LAE)-based image acquisition system. We show that compressive information acquisition is robust to stochastic nonlinearities, and that appropriately designed spatially localized computations are effective, by evaluating the performance of reconstruction and classification based on the information acquired.

AB - Compressive information acquisition is a natural approach for low-power hardware front ends, since most natural signals are sparse in some basis. Key design questions include the impact of hardware impairments (e.g., nonlinearities) and constraints (e.g., spatially localized computations) on the fidelity of information acquisition. Our goal in this paper is to obtain specific insights into such issues through modeling of a Large Area Electronics (LAE)-based image acquisition system. We show that compressive information acquisition is robust to stochastic nonlinearities, and that appropriately designed spatially localized computations are effective, by evaluating the performance of reconstruction and classification based on the information acquired.

UR - http://www.scopus.com/inward/record.url?scp=85023760342&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85023760342&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2017.7953323

DO - 10.1109/ICASSP.2017.7953323

M3 - Conference contribution

AN - SCOPUS:85023760342

SP - 6075

EP - 6079

BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 5 March 2017 through 9 March 2017

ER -

Compressive information acquisition with hardware impairments and constraints: A case study

Abstract

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this