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Sampling and Reconstruction

The Network Nullspace Property for Compressed Sensing of Big Data over Networks

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13 April 2018 - 9:38am
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ICASSP 2018 poster Hulsebos, Jung

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[1] , "The Network Nullspace Property for Compressed Sensing of Big Data over Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2488. Accessed: Oct. 17, 2018.
@article{2488-18,
url = {http://sigport.org/2488},
author = { },
publisher = {IEEE SigPort},
title = {The Network Nullspace Property for Compressed Sensing of Big Data over Networks},
year = {2018} }
TY - EJOUR
T1 - The Network Nullspace Property for Compressed Sensing of Big Data over Networks
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2488
ER -
. (2018). The Network Nullspace Property for Compressed Sensing of Big Data over Networks. IEEE SigPort. http://sigport.org/2488
, 2018. The Network Nullspace Property for Compressed Sensing of Big Data over Networks. Available at: http://sigport.org/2488.
. (2018). "The Network Nullspace Property for Compressed Sensing of Big Data over Networks." Web.
1. . The Network Nullspace Property for Compressed Sensing of Big Data over Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2488

Wavelet-Based Reconstruction for Unlimited Sampling


Self-reset analog-to-digital converters (ADCs) allow for digitization of a signal with a high dynamic range. The reset action is equivalent to a modulo operation performed on the signal. We consider the problem of recovering the original signal from the measured modulo-operated signal. In our formulation, we assume that the underlying signal is Lipschitz continuous. The modulo-operated signal can be expressed as the sum of the original signal and a piecewise-constant signal that captures the transitions. The reconstruction requires estimating the piecewise-constant signal.

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Authors:
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula
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12 April 2018 - 2:06pm
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ICASSP_2018_Sunil.pdf

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[1] Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula, "Wavelet-Based Reconstruction for Unlimited Sampling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2461. Accessed: Oct. 17, 2018.
@article{2461-18,
url = {http://sigport.org/2461},
author = {Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula },
publisher = {IEEE SigPort},
title = {Wavelet-Based Reconstruction for Unlimited Sampling},
year = {2018} }
TY - EJOUR
T1 - Wavelet-Based Reconstruction for Unlimited Sampling
AU - Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2461
ER -
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. (2018). Wavelet-Based Reconstruction for Unlimited Sampling. IEEE SigPort. http://sigport.org/2461
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula, 2018. Wavelet-Based Reconstruction for Unlimited Sampling. Available at: http://sigport.org/2461.
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. (2018). "Wavelet-Based Reconstruction for Unlimited Sampling." Web.
1. Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. Wavelet-Based Reconstruction for Unlimited Sampling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2461

Wavelet-Based Reconstruction for Unlimited Sampling


Self-reset analog-to-digital converters (ADCs) allow for digitization of a signal with a high dynamic range. The reset action is equivalent to a modulo operation performed on the signal. We consider the problem of recovering the original signal from the measured modulo-operated signal. In our formulation, we assume that the underlying signal is Lipschitz continuous. The modulo-operated signal can be expressed as the sum of the original signal and a piecewise-constant signal that captures the transitions. The reconstruction requires estimating the piecewise-constant signal.

Paper Details

Authors:
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula
Submitted On:
12 April 2018 - 2:06pm
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ICASSP_2018_Sunil.pdf

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[1] Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula, "Wavelet-Based Reconstruction for Unlimited Sampling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2460. Accessed: Oct. 17, 2018.
@article{2460-18,
url = {http://sigport.org/2460},
author = {Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula },
publisher = {IEEE SigPort},
title = {Wavelet-Based Reconstruction for Unlimited Sampling},
year = {2018} }
TY - EJOUR
T1 - Wavelet-Based Reconstruction for Unlimited Sampling
AU - Aniruddha Adiga; Basty Ajay Shenoy; Chandra Sekhar Seelamantula
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2460
ER -
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. (2018). Wavelet-Based Reconstruction for Unlimited Sampling. IEEE SigPort. http://sigport.org/2460
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula, 2018. Wavelet-Based Reconstruction for Unlimited Sampling. Available at: http://sigport.org/2460.
Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. (2018). "Wavelet-Based Reconstruction for Unlimited Sampling." Web.
1. Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula. Wavelet-Based Reconstruction for Unlimited Sampling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2460

DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION


It is of particular interest to reconstruct or estimate bandlimited graph signals, which are smoothly varying signals defined over graphs, from partial noisy measurements. However, choosing an optimal subset of nodes to sample is NP-hard. We formularize the problem as the experimental design of a linear regression model if we allow multiple measurements on a single node. By relaxing it to a convex optimization problem, we get the proportion of sample for each node given the budget of total sample size. Then, we use a probabilistic quantization to get the number of each node to be sampled.

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Authors:
Xuan Xie, Hui Feng, Junlian Jia, Bo Hu
Submitted On:
10 November 2017 - 8:32am
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GlobalSIP_Poster_XX_v2.pdf

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[1] Xuan Xie, Hui Feng, Junlian Jia, Bo Hu, "DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2290. Accessed: Oct. 17, 2018.
@article{2290-17,
url = {http://sigport.org/2290},
author = {Xuan Xie; Hui Feng; Junlian Jia; Bo Hu },
publisher = {IEEE SigPort},
title = {DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION},
year = {2017} }
TY - EJOUR
T1 - DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION
AU - Xuan Xie; Hui Feng; Junlian Jia; Bo Hu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2290
ER -
Xuan Xie, Hui Feng, Junlian Jia, Bo Hu. (2017). DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION. IEEE SigPort. http://sigport.org/2290
Xuan Xie, Hui Feng, Junlian Jia, Bo Hu, 2017. DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION. Available at: http://sigport.org/2290.
Xuan Xie, Hui Feng, Junlian Jia, Bo Hu. (2017). "DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION." Web.
1. Xuan Xie, Hui Feng, Junlian Jia, Bo Hu. DESIGN OF SAMPLING SET FOR BANDLIMITED GRAPH SIGNAL ESTIMATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2290

Phase Retrieval Based Deconvolution Algorithm in Optical Systems


In an optical imaging system, the retrieved image of an object is blurred by the point spread function (PSF) of the system,and cannot exactly represent the object. Deconvolution is an effective method to recover the object from the blurred image and improve the resolution of the optical system. But in real optical system, the detector only measures the intensity of the light, not the phase.

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Authors:
Shaohua Qin, Sebastian Berisha, David Mayerich and Zhu Han
Submitted On:
9 November 2017 - 11:20am
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Phase Retrieval Based Deconvolution Algorithm in Optical Systems

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[1] Shaohua Qin, Sebastian Berisha, David Mayerich and Zhu Han, " Phase Retrieval Based Deconvolution Algorithm in Optical Systems", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2265. Accessed: Oct. 17, 2018.
@article{2265-17,
url = {http://sigport.org/2265},
author = {Shaohua Qin; Sebastian Berisha; David Mayerich and Zhu Han },
publisher = {IEEE SigPort},
title = { Phase Retrieval Based Deconvolution Algorithm in Optical Systems},
year = {2017} }
TY - EJOUR
T1 - Phase Retrieval Based Deconvolution Algorithm in Optical Systems
AU - Shaohua Qin; Sebastian Berisha; David Mayerich and Zhu Han
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2265
ER -
Shaohua Qin, Sebastian Berisha, David Mayerich and Zhu Han. (2017). Phase Retrieval Based Deconvolution Algorithm in Optical Systems. IEEE SigPort. http://sigport.org/2265
Shaohua Qin, Sebastian Berisha, David Mayerich and Zhu Han, 2017. Phase Retrieval Based Deconvolution Algorithm in Optical Systems. Available at: http://sigport.org/2265.
Shaohua Qin, Sebastian Berisha, David Mayerich and Zhu Han. (2017). " Phase Retrieval Based Deconvolution Algorithm in Optical Systems." Web.
1. Shaohua Qin, Sebastian Berisha, David Mayerich and Zhu Han. Phase Retrieval Based Deconvolution Algorithm in Optical Systems [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2265

REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES

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Authors:
F. Kucharczak, C. Mory, O. Strauss, F. Comby, D. Mariano-Goulart
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15 February 2018 - 9:52am
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ICIP2017_sigport.pdf

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[1] F. Kucharczak, C. Mory, O. Strauss, F. Comby, D. Mariano-Goulart, "REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2098. Accessed: Oct. 17, 2018.
@article{2098-17,
url = {http://sigport.org/2098},
author = {F. Kucharczak; C. Mory; O. Strauss; F. Comby; D. Mariano-Goulart },
publisher = {IEEE SigPort},
title = {REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES},
year = {2017} }
TY - EJOUR
T1 - REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES
AU - F. Kucharczak; C. Mory; O. Strauss; F. Comby; D. Mariano-Goulart
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2098
ER -
F. Kucharczak, C. Mory, O. Strauss, F. Comby, D. Mariano-Goulart. (2017). REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES. IEEE SigPort. http://sigport.org/2098
F. Kucharczak, C. Mory, O. Strauss, F. Comby, D. Mariano-Goulart, 2017. REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES. Available at: http://sigport.org/2098.
F. Kucharczak, C. Mory, O. Strauss, F. Comby, D. Mariano-Goulart. (2017). "REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES." Web.
1. F. Kucharczak, C. Mory, O. Strauss, F. Comby, D. Mariano-Goulart. REGULARIZED SELECTION: A NEW PARADIGM FOR INVERSE BASED REGULARIZED IMAGE RECONSTRUCTION TECHNIQUES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2098

Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization

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Authors:
Feng Xi, Shengyao Chen, Zhong Liu
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13 March 2017 - 12:25am
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ICASSP2017.pdf

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[1] Feng Xi, Shengyao Chen, Zhong Liu, "Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1754. Accessed: Oct. 17, 2018.
@article{1754-17,
url = {http://sigport.org/1754},
author = {Feng Xi; Shengyao Chen; Zhong Liu },
publisher = {IEEE SigPort},
title = {Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization},
year = {2017} }
TY - EJOUR
T1 - Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization
AU - Feng Xi; Shengyao Chen; Zhong Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1754
ER -
Feng Xi, Shengyao Chen, Zhong Liu. (2017). Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization. IEEE SigPort. http://sigport.org/1754
Feng Xi, Shengyao Chen, Zhong Liu, 2017. Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization. Available at: http://sigport.org/1754.
Feng Xi, Shengyao Chen, Zhong Liu. (2017). "Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization." Web.
1. Feng Xi, Shengyao Chen, Zhong Liu. Super-resolution delay-Doppler estimation for sub-Nyquist radar via atomic norm minimization [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1754

Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study


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.

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Authors:
Tiffany Moy, Upamanyu Madhow, Naveen Verma
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8 March 2017 - 3:42am
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Comp_Info_Acq_ICASSP17_Poster.zip

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[1] Tiffany Moy, Upamanyu Madhow, Naveen Verma, "Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1702. Accessed: Oct. 17, 2018.
@article{1702-17,
url = {http://sigport.org/1702},
author = {Tiffany Moy; Upamanyu Madhow; Naveen Verma },
publisher = {IEEE SigPort},
title = {Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study},
year = {2017} }
TY - EJOUR
T1 - Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study
AU - Tiffany Moy; Upamanyu Madhow; Naveen Verma
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1702
ER -
Tiffany Moy, Upamanyu Madhow, Naveen Verma. (2017). Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study. IEEE SigPort. http://sigport.org/1702
Tiffany Moy, Upamanyu Madhow, Naveen Verma, 2017. Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study. Available at: http://sigport.org/1702.
Tiffany Moy, Upamanyu Madhow, Naveen Verma. (2017). "Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study." Web.
1. Tiffany Moy, Upamanyu Madhow, Naveen Verma. Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1702

RECOVERY OF SPARSE SIGNALS VIA BRANCH AND BOUND LEAST-SQUARES

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Authors:
Haris Vikalo
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8 March 2017 - 1:20am
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BBLS2017.pdf

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[1] Haris Vikalo, "RECOVERY OF SPARSE SIGNALS VIA BRANCH AND BOUND LEAST-SQUARES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1699. Accessed: Oct. 17, 2018.
@article{1699-17,
url = {http://sigport.org/1699},
author = {Haris Vikalo },
publisher = {IEEE SigPort},
title = {RECOVERY OF SPARSE SIGNALS VIA BRANCH AND BOUND LEAST-SQUARES},
year = {2017} }
TY - EJOUR
T1 - RECOVERY OF SPARSE SIGNALS VIA BRANCH AND BOUND LEAST-SQUARES
AU - Haris Vikalo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1699
ER -
Haris Vikalo. (2017). RECOVERY OF SPARSE SIGNALS VIA BRANCH AND BOUND LEAST-SQUARES. IEEE SigPort. http://sigport.org/1699
Haris Vikalo, 2017. RECOVERY OF SPARSE SIGNALS VIA BRANCH AND BOUND LEAST-SQUARES. Available at: http://sigport.org/1699.
Haris Vikalo. (2017). "RECOVERY OF SPARSE SIGNALS VIA BRANCH AND BOUND LEAST-SQUARES." Web.
1. Haris Vikalo. RECOVERY OF SPARSE SIGNALS VIA BRANCH AND BOUND LEAST-SQUARES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1699

Compressed sensing and optimal denoising of monotone signals

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5 March 2017 - 11:18am
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icassp17-poster.pdf

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[1] , "Compressed sensing and optimal denoising of monotone signals", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1636. Accessed: Oct. 17, 2018.
@article{1636-17,
url = {http://sigport.org/1636},
author = { },
publisher = {IEEE SigPort},
title = {Compressed sensing and optimal denoising of monotone signals},
year = {2017} }
TY - EJOUR
T1 - Compressed sensing and optimal denoising of monotone signals
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1636
ER -
. (2017). Compressed sensing and optimal denoising of monotone signals. IEEE SigPort. http://sigport.org/1636
, 2017. Compressed sensing and optimal denoising of monotone signals. Available at: http://sigport.org/1636.
. (2017). "Compressed sensing and optimal denoising of monotone signals." Web.
1. . Compressed sensing and optimal denoising of monotone signals [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1636

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