Sorry, you need to enable JavaScript to visit this website.

Compressed Sensing and Sparse Signal Recovery

VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING


In recent years approximate message passing algorithms have gained a lot of attention and different versions have been proposed for coping with various system models. This paper focuses on vector approximate message passing (VAMP) for generalized linear models. While this algorithm is originally derived from a message passing point of view, we will review it from an estimation theory perspective and afterwards adapt it for a quantized compressed sensing application. Finally, numerical results are presented to evaluate the performance of the algorithm.

Paper Details

Authors:
Daniel Franz, Volker Kuehn
Submitted On:
21 November 2018 - 4:49am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

sample_poster.pdf

(64)

Subscribe

[1] Daniel Franz, Volker Kuehn, "VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3698. Accessed: Jun. 24, 2019.
@article{3698-18,
url = {http://sigport.org/3698},
author = {Daniel Franz; Volker Kuehn },
publisher = {IEEE SigPort},
title = {VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING},
year = {2018} }
TY - EJOUR
T1 - VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING
AU - Daniel Franz; Volker Kuehn
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3698
ER -
Daniel Franz, Volker Kuehn. (2018). VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING. IEEE SigPort. http://sigport.org/3698
Daniel Franz, Volker Kuehn, 2018. VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING. Available at: http://sigport.org/3698.
Daniel Franz, Volker Kuehn. (2018). "VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING." Web.
1. Daniel Franz, Volker Kuehn. VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3698

Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence


Most existing work in designing sensing matrices for compressive recovery is based on optimizing some quality factor, such as mutual coherence, average coherence or the restricted isometry constant (RIC), of the sensing matrix. In this paper, we report anomalous results that show that such a design is not always guaranteed to improve reconstruction results.

Paper Details

Authors:
Dhruv Shah, Alankar Kotwal, Ajit Rajwade
Submitted On:
20 November 2018 - 2:42am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

globalsip2018_poster_v3.pdf

(61)

Subscribe

[1] Dhruv Shah, Alankar Kotwal, Ajit Rajwade, "Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3670. Accessed: Jun. 24, 2019.
@article{3670-18,
url = {http://sigport.org/3670},
author = {Dhruv Shah; Alankar Kotwal; Ajit Rajwade },
publisher = {IEEE SigPort},
title = {Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence},
year = {2018} }
TY - EJOUR
T1 - Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence
AU - Dhruv Shah; Alankar Kotwal; Ajit Rajwade
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3670
ER -
Dhruv Shah, Alankar Kotwal, Ajit Rajwade. (2018). Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence. IEEE SigPort. http://sigport.org/3670
Dhruv Shah, Alankar Kotwal, Ajit Rajwade, 2018. Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence. Available at: http://sigport.org/3670.
Dhruv Shah, Alankar Kotwal, Ajit Rajwade. (2018). "Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence." Web.
1. Dhruv Shah, Alankar Kotwal, Ajit Rajwade. Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3670