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Sampling and Reconstruction

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: Nov. 23, 2017.
@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: Nov. 23, 2017.
@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
Submitted On:
15 September 2017 - 4:08am
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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: Nov. 23, 2017.
@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
Submitted On:
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: Nov. 23, 2017.
@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
Submitted On:
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: Nov. 23, 2017.
@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
Submitted On:
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: Nov. 23, 2017.
@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: Nov. 23, 2017.
@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

Low Rank Phase Retrieval

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Authors:
Seyedehsara Nayer, Namrata Vaswani, Yonina C. Eldar
Submitted On:
3 March 2017 - 3:47pm
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main2.pdf

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[1] Seyedehsara Nayer, Namrata Vaswani, Yonina C. Eldar, "Low Rank Phase Retrieval", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1617. Accessed: Nov. 23, 2017.
@article{1617-17,
url = {http://sigport.org/1617},
author = {Seyedehsara Nayer; Namrata Vaswani; Yonina C. Eldar },
publisher = {IEEE SigPort},
title = {Low Rank Phase Retrieval},
year = {2017} }
TY - EJOUR
T1 - Low Rank Phase Retrieval
AU - Seyedehsara Nayer; Namrata Vaswani; Yonina C. Eldar
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1617
ER -
Seyedehsara Nayer, Namrata Vaswani, Yonina C. Eldar. (2017). Low Rank Phase Retrieval. IEEE SigPort. http://sigport.org/1617
Seyedehsara Nayer, Namrata Vaswani, Yonina C. Eldar, 2017. Low Rank Phase Retrieval. Available at: http://sigport.org/1617.
Seyedehsara Nayer, Namrata Vaswani, Yonina C. Eldar. (2017). "Low Rank Phase Retrieval." Web.
1. Seyedehsara Nayer, Namrata Vaswani, Yonina C. Eldar. Low Rank Phase Retrieval [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1617

Demixing Sparse Signals via Convex Optimization


We consider demixing a pair of sparse signals in orthonormal basis via convex optimization. Theoretically, we characterize the condition under which the solution of the convex optimization problem correctly demixes the true signal components. In specific, we introduce the local subspace coherence to characterize how a basis vector is coherent with a signal subspace, and show that the convex optimization approach succeeds if the subspaces of the true signal components avoid high local subspace coherence.

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Authors:
Yi Zhou, Yingbin Liang
Submitted On:
2 March 2017 - 10:55am
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icassp2017e.pdf

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[1] Yi Zhou, Yingbin Liang, "Demixing Sparse Signals via Convex Optimization", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1586. Accessed: Nov. 23, 2017.
@article{1586-17,
url = {http://sigport.org/1586},
author = {Yi Zhou; Yingbin Liang },
publisher = {IEEE SigPort},
title = {Demixing Sparse Signals via Convex Optimization},
year = {2017} }
TY - EJOUR
T1 - Demixing Sparse Signals via Convex Optimization
AU - Yi Zhou; Yingbin Liang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1586
ER -
Yi Zhou, Yingbin Liang. (2017). Demixing Sparse Signals via Convex Optimization. IEEE SigPort. http://sigport.org/1586
Yi Zhou, Yingbin Liang, 2017. Demixing Sparse Signals via Convex Optimization. Available at: http://sigport.org/1586.
Yi Zhou, Yingbin Liang. (2017). "Demixing Sparse Signals via Convex Optimization." Web.
1. Yi Zhou, Yingbin Liang. Demixing Sparse Signals via Convex Optimization [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1586

Energy Blowup for Truncated Stable LTI Systems


In this paper we analyze the convergence behavior of a sampling based system approximation process, where the time variable is in the argument of the signal and not in the argument of the bandlimited impulse response. We consider the Paley-Wiener space $PW_\pi^2$ of bandlimited signals with finite energy and stable linear time-invariant (LTI) systems, and show that there are signals and systems such that the approximation process diverges in the $L^2$-norm, i.e., the norm of the signal space. We prove that the sets of signals and systems creating divergence are jointly spaceable, i.e., there exists an infinite dimensional closed subspace of $PW_\pi^2$ and an infinite dimensional closed subspace of the space of all stable LTI systems, such that the approximation process diverges for any non-zero pair of signal and system from these subspaces.

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Authors:
Holger Boche, Ullrich Mönich
Submitted On:
2 March 2017 - 5:57am
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icassp2017_energy_poster.pdf

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[1] Holger Boche, Ullrich Mönich, "Energy Blowup for Truncated Stable LTI Systems", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1580. Accessed: Nov. 23, 2017.
@article{1580-17,
url = {http://sigport.org/1580},
author = {Holger Boche; Ullrich Mönich },
publisher = {IEEE SigPort},
title = {Energy Blowup for Truncated Stable LTI Systems},
year = {2017} }
TY - EJOUR
T1 - Energy Blowup for Truncated Stable LTI Systems
AU - Holger Boche; Ullrich Mönich
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1580
ER -
Holger Boche, Ullrich Mönich. (2017). Energy Blowup for Truncated Stable LTI Systems. IEEE SigPort. http://sigport.org/1580
Holger Boche, Ullrich Mönich, 2017. Energy Blowup for Truncated Stable LTI Systems. Available at: http://sigport.org/1580.
Holger Boche, Ullrich Mönich. (2017). "Energy Blowup for Truncated Stable LTI Systems." Web.
1. Holger Boche, Ullrich Mönich. Energy Blowup for Truncated Stable LTI Systems [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1580

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