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Sampling and Reconstruction

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: Apr. 24, 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
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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: Apr. 24, 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
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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: Apr. 24, 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: Apr. 24, 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
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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: Apr. 24, 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: Apr. 24, 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: Apr. 24, 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

Structure of the Set of Signals With Strong Divergence of the Shannon Sampling Series


It is known that there exist signals in Paley-Wiener space $PW_\pi^1$ of bandlimited signals with absolutely integrable Fourier transform, for which the peak value of the Shannon sampling series diverges unboundedly. In this paper we analyze the structure of the set of signals which lead to strong divergence. Strong divergence is closely linked to the existence of adaptive methods. We prove that there exists an infinite dimensional closed subspace of $PW_\pi^1$, all signals of which, except the zero signal, lead to strong divergence of the peak value of the Shannon sampling series.

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Authors:
Holger Boche, Ullrich J. Mönich, Ezra Tampubolon
Submitted On:
2 March 2017 - 5:48am
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icassp2017_structure_poster.pdf

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[1] Holger Boche, Ullrich J. Mönich, Ezra Tampubolon, "Structure of the Set of Signals With Strong Divergence of the Shannon Sampling Series", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1579. Accessed: Apr. 24, 2017.
@article{1579-17,
url = {http://sigport.org/1579},
author = {Holger Boche; Ullrich J. Mönich; Ezra Tampubolon },
publisher = {IEEE SigPort},
title = {Structure of the Set of Signals With Strong Divergence of the Shannon Sampling Series},
year = {2017} }
TY - EJOUR
T1 - Structure of the Set of Signals With Strong Divergence of the Shannon Sampling Series
AU - Holger Boche; Ullrich J. Mönich; Ezra Tampubolon
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1579
ER -
Holger Boche, Ullrich J. Mönich, Ezra Tampubolon. (2017). Structure of the Set of Signals With Strong Divergence of the Shannon Sampling Series. IEEE SigPort. http://sigport.org/1579
Holger Boche, Ullrich J. Mönich, Ezra Tampubolon, 2017. Structure of the Set of Signals With Strong Divergence of the Shannon Sampling Series. Available at: http://sigport.org/1579.
Holger Boche, Ullrich J. Mönich, Ezra Tampubolon. (2017). "Structure of the Set of Signals With Strong Divergence of the Shannon Sampling Series." Web.
1. Holger Boche, Ullrich J. Mönich, Ezra Tampubolon. Structure of the Set of Signals With Strong Divergence of the Shannon Sampling Series [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1579

Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems


Sampling and reconstruction of bandlimited graph signals have well-appreciated merits for dimensionality reduction, affordable storage, and online processing of streaming network data. However, these parsimonious signals are oftentimes encountered with high-dimensional linear inverse problems. Hence, interest shifts from reconstructing the signal itself towards instead approximating the input to a prescribed linear operator efficiently.

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Authors:
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro
Submitted On:
8 December 2016 - 12:32am
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[1] Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1420. Accessed: Apr. 24, 2017.
@article{1420-16,
url = {http://sigport.org/1420},
author = {Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems},
year = {2016} }
TY - EJOUR
T1 - Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems
AU - Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1420
ER -
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. IEEE SigPort. http://sigport.org/1420
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, 2016. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. Available at: http://sigport.org/1420.
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems." Web.
1. Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1420

Sampling solutions of Schrödinger equations on combinatorial graphs


We consider functions on a weighted combinatorial graph G (finite or countable) whose evolution in time −∞ < t < ∞ is governed by the Schro ̈dinger type equation ∂g(t, v)/∂t = i∆g(t, v), v ∈ V (G), with the combinatorial Laplace operator on the right side.

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8 December 2016 - 3:51pm
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Global2016.pdf

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[1] , "Sampling solutions of Schrödinger equations on combinatorial graphs", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1417. Accessed: Apr. 24, 2017.
@article{1417-16,
url = {http://sigport.org/1417},
author = { },
publisher = {IEEE SigPort},
title = {Sampling solutions of Schrödinger equations on combinatorial graphs},
year = {2016} }
TY - EJOUR
T1 - Sampling solutions of Schrödinger equations on combinatorial graphs
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1417
ER -
. (2016). Sampling solutions of Schrödinger equations on combinatorial graphs. IEEE SigPort. http://sigport.org/1417
, 2016. Sampling solutions of Schrödinger equations on combinatorial graphs. Available at: http://sigport.org/1417.
. (2016). "Sampling solutions of Schrödinger equations on combinatorial graphs." Web.
1. . Sampling solutions of Schrödinger equations on combinatorial graphs [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1417

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