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Signal Processing Theory and Methods

Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes

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Authors:
Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä
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20 November 2017 - 9:21am
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[1] Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä, "Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2366. Accessed: Dec. 16, 2017.
@article{2366-17,
url = {http://sigport.org/2366},
author = {Anastasia Lavrenko; Florian Roemer; Giovanni Del Galdo; Reiner Thomä },
publisher = {IEEE SigPort},
title = {Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes},
year = {2017} }
TY - EJOUR
T1 - Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes
AU - Anastasia Lavrenko; Florian Roemer; Giovanni Del Galdo; Reiner Thomä
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2366
ER -
Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä. (2017). Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes. IEEE SigPort. http://sigport.org/2366
Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä, 2017. Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes. Available at: http://sigport.org/2366.
Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä. (2017). "Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes." Web.
1. Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo, Reiner Thomä. Multiband TDOA Estimation from Sub-Nyquist Samples with Distributed Sensing Nodes [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2366

1161: Image Super-Resolution Using Nonlocally Centralized Sparse Representation and Fields of Experts Priors

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Authors:
Wei zhou, Zhemin Duan
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10 November 2017 - 1:17am
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GlobalSIP_poster - zhangxiu.pdf

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[1] Wei zhou, Zhemin Duan, "1161: Image Super-Resolution Using Nonlocally Centralized Sparse Representation and Fields of Experts Priors", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2284. Accessed: Dec. 16, 2017.
@article{2284-17,
url = {http://sigport.org/2284},
author = {Wei zhou; Zhemin Duan },
publisher = {IEEE SigPort},
title = {1161: Image Super-Resolution Using Nonlocally Centralized Sparse Representation and Fields of Experts Priors},
year = {2017} }
TY - EJOUR
T1 - 1161: Image Super-Resolution Using Nonlocally Centralized Sparse Representation and Fields of Experts Priors
AU - Wei zhou; Zhemin Duan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2284
ER -
Wei zhou, Zhemin Duan. (2017). 1161: Image Super-Resolution Using Nonlocally Centralized Sparse Representation and Fields of Experts Priors. IEEE SigPort. http://sigport.org/2284
Wei zhou, Zhemin Duan, 2017. 1161: Image Super-Resolution Using Nonlocally Centralized Sparse Representation and Fields of Experts Priors. Available at: http://sigport.org/2284.
Wei zhou, Zhemin Duan. (2017). "1161: Image Super-Resolution Using Nonlocally Centralized Sparse Representation and Fields of Experts Priors." Web.
1. Wei zhou, Zhemin Duan. 1161: Image Super-Resolution Using Nonlocally Centralized Sparse Representation and Fields of Experts Priors [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2284

TIME SAMPLES SELECTION IN SPIRAL ACQUISITION FOR SPARSE MAGNETIC RESONANCE SPECTROSCOPIC IMAGING

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Authors:
Fabien Millioz, Maglie Viallon, Remy Prost, Helene Ratiney
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12 September 2017 - 10:28am
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posterICIP2017vf.pdf

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[1] Fabien Millioz, Maglie Viallon, Remy Prost, Helene Ratiney, "TIME SAMPLES SELECTION IN SPIRAL ACQUISITION FOR SPARSE MAGNETIC RESONANCE SPECTROSCOPIC IMAGING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1939. Accessed: Dec. 16, 2017.
@article{1939-17,
url = {http://sigport.org/1939},
author = {Fabien Millioz; Maglie Viallon; Remy Prost; Helene Ratiney },
publisher = {IEEE SigPort},
title = {TIME SAMPLES SELECTION IN SPIRAL ACQUISITION FOR SPARSE MAGNETIC RESONANCE SPECTROSCOPIC IMAGING},
year = {2017} }
TY - EJOUR
T1 - TIME SAMPLES SELECTION IN SPIRAL ACQUISITION FOR SPARSE MAGNETIC RESONANCE SPECTROSCOPIC IMAGING
AU - Fabien Millioz; Maglie Viallon; Remy Prost; Helene Ratiney
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1939
ER -
Fabien Millioz, Maglie Viallon, Remy Prost, Helene Ratiney. (2017). TIME SAMPLES SELECTION IN SPIRAL ACQUISITION FOR SPARSE MAGNETIC RESONANCE SPECTROSCOPIC IMAGING. IEEE SigPort. http://sigport.org/1939
Fabien Millioz, Maglie Viallon, Remy Prost, Helene Ratiney, 2017. TIME SAMPLES SELECTION IN SPIRAL ACQUISITION FOR SPARSE MAGNETIC RESONANCE SPECTROSCOPIC IMAGING. Available at: http://sigport.org/1939.
Fabien Millioz, Maglie Viallon, Remy Prost, Helene Ratiney. (2017). "TIME SAMPLES SELECTION IN SPIRAL ACQUISITION FOR SPARSE MAGNETIC RESONANCE SPECTROSCOPIC IMAGING." Web.
1. Fabien Millioz, Maglie Viallon, Remy Prost, Helene Ratiney. TIME SAMPLES SELECTION IN SPIRAL ACQUISITION FOR SPARSE MAGNETIC RESONANCE SPECTROSCOPIC IMAGING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1939

Phase Retrieval via Coordinate Descent


Phase retrieval refers to recovery of a signal-of-interest given only the intensity measurement samples and has wide applicability including important areas of astronomy, computational biology, crystallography, digital communications, electron microscopy, neutron radiography and optical imaging. The classical problem formulation is to restore the time-domain signal from its power spectrum observations, although the Fourier transform can be generalized to any linear mappings.

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28 June 2017 - 11:19pm
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[1] , "Phase Retrieval via Coordinate Descent", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1799. Accessed: Dec. 16, 2017.
@article{1799-17,
url = {http://sigport.org/1799},
author = { },
publisher = {IEEE SigPort},
title = {Phase Retrieval via Coordinate Descent},
year = {2017} }
TY - EJOUR
T1 - Phase Retrieval via Coordinate Descent
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1799
ER -
. (2017). Phase Retrieval via Coordinate Descent. IEEE SigPort. http://sigport.org/1799
, 2017. Phase Retrieval via Coordinate Descent. Available at: http://sigport.org/1799.
. (2017). "Phase Retrieval via Coordinate Descent." Web.
1. . Phase Retrieval via Coordinate Descent [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1799

Robust Matrix Completion via Alternating Projection


Matrix completion aims to find the missing entries from incomplete observations using the low-rank property. Conventional convex optimization based techniques minimize the nuclear norm subject to a constraint on the Frobenius norm of the residual. However, they are not robust to outliers and have a high computational complexity. Different from the existing schemes based on solving a minimization problem, we formulate matrix completion as a feasibility problem.

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19 June 2017 - 11:39pm
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AP_matrix_completion.pdf

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[1] , "Robust Matrix Completion via Alternating Projection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1798. Accessed: Dec. 16, 2017.
@article{1798-17,
url = {http://sigport.org/1798},
author = { },
publisher = {IEEE SigPort},
title = {Robust Matrix Completion via Alternating Projection},
year = {2017} }
TY - EJOUR
T1 - Robust Matrix Completion via Alternating Projection
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1798
ER -
. (2017). Robust Matrix Completion via Alternating Projection. IEEE SigPort. http://sigport.org/1798
, 2017. Robust Matrix Completion via Alternating Projection. Available at: http://sigport.org/1798.
. (2017). "Robust Matrix Completion via Alternating Projection." Web.
1. . Robust Matrix Completion via Alternating Projection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1798

PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING


Structured sparse representation has been recently found to achieve better efficiency and robustness in exploiting the target appearance model in tracking systems with both holistic and local information. Therefore, to better simultaneously discriminate multi-targets from their background, we propose a novel video-based multi-target tracking system that combines the particle probability hypothesis density (PHD) filter with discriminative group-structured dictionary learning.

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Authors:
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers
Submitted On:
22 March 2017 - 8:04am
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ICASSP2017-POSTER (1).pdf

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[1] Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers, "PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1780. Accessed: Dec. 16, 2017.
@article{1780-17,
url = {http://sigport.org/1780},
author = {Zeyu Fu; Pengming Feng; Syed Mohsen Naqvi; and Jonathon Chambers },
publisher = {IEEE SigPort},
title = {PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING},
year = {2017} }
TY - EJOUR
T1 - PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING
AU - Zeyu Fu; Pengming Feng; Syed Mohsen Naqvi; and Jonathon Chambers
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1780
ER -
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers. (2017). PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING. IEEE SigPort. http://sigport.org/1780
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers, 2017. PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING. Available at: http://sigport.org/1780.
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers. (2017). "PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING." Web.
1. Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers. PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1780

An M-Channel Critically Sampled Graph Filter Bank


We investigate an M-channel critically sampled filter bank for graph signals where each of the M filters is supported on a different subband of the graph Laplacian spectrum. We partition the graph vertices such that the mth set comprises a uniqueness set for signals supported on the mth subband. For analysis, the graph signal is filtered on each subband and downsampled on the corresponding set of vertices.

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Authors:
Yan Jin, David I Shuman
Submitted On:
8 March 2017 - 4:26pm
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[1] Yan Jin, David I Shuman, "An M-Channel Critically Sampled Graph Filter Bank", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1711. Accessed: Dec. 16, 2017.
@article{1711-17,
url = {http://sigport.org/1711},
author = {Yan Jin; David I Shuman },
publisher = {IEEE SigPort},
title = {An M-Channel Critically Sampled Graph Filter Bank},
year = {2017} }
TY - EJOUR
T1 - An M-Channel Critically Sampled Graph Filter Bank
AU - Yan Jin; David I Shuman
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1711
ER -
Yan Jin, David I Shuman. (2017). An M-Channel Critically Sampled Graph Filter Bank. IEEE SigPort. http://sigport.org/1711
Yan Jin, David I Shuman, 2017. An M-Channel Critically Sampled Graph Filter Bank. Available at: http://sigport.org/1711.
Yan Jin, David I Shuman. (2017). "An M-Channel Critically Sampled Graph Filter Bank." Web.
1. Yan Jin, David I Shuman. An M-Channel Critically Sampled Graph Filter Bank [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1711

Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery


Historically, sparse methods and neural networks, particularly modern deep learning methods, have been relatively disparate areas. Sparse methods are typically used for signal enhancement, compression,and recovery, usually in an unsupervised framework, while neural networks commonly rely on a supervised training set.

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Authors:
Scott Wisdom, Thomas Powers, James Pitton, Les Atlas
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8 March 2017 - 9:22am
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poster_icassp2017.pdf

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[1] Scott Wisdom, Thomas Powers, James Pitton, Les Atlas, "Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1706. Accessed: Dec. 16, 2017.
@article{1706-17,
url = {http://sigport.org/1706},
author = {Scott Wisdom; Thomas Powers; James Pitton; Les Atlas },
publisher = {IEEE SigPort},
title = {Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery},
year = {2017} }
TY - EJOUR
T1 - Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery
AU - Scott Wisdom; Thomas Powers; James Pitton; Les Atlas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1706
ER -
Scott Wisdom, Thomas Powers, James Pitton, Les Atlas. (2017). Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery. IEEE SigPort. http://sigport.org/1706
Scott Wisdom, Thomas Powers, James Pitton, Les Atlas, 2017. Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery. Available at: http://sigport.org/1706.
Scott Wisdom, Thomas Powers, James Pitton, Les Atlas. (2017). "Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery." Web.
1. Scott Wisdom, Thomas Powers, James Pitton, Les Atlas. Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1706

NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF

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7 March 2017 - 2:12pm
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posterICASSP_Gordana.pdf

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[1] , "NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1694. Accessed: Dec. 16, 2017.
@article{1694-17,
url = {http://sigport.org/1694},
author = { },
publisher = {IEEE SigPort},
title = {NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF},
year = {2017} }
TY - EJOUR
T1 - NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1694
ER -
. (2017). NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF. IEEE SigPort. http://sigport.org/1694
, 2017. NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF. Available at: http://sigport.org/1694.
. (2017). "NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF." Web.
1. . NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1694

SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY

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Authors:
Odysseas Pappas. Alin Achim. Dave Bull
Submitted On:
28 February 2017 - 6:49am
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icassp_2017_posterdraft.pptx

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[1] Odysseas Pappas. Alin Achim. Dave Bull, "SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1503. Accessed: Dec. 16, 2017.
@article{1503-17,
url = {http://sigport.org/1503},
author = {Odysseas Pappas. Alin Achim. Dave Bull },
publisher = {IEEE SigPort},
title = {SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY},
year = {2017} }
TY - EJOUR
T1 - SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY
AU - Odysseas Pappas. Alin Achim. Dave Bull
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1503
ER -
Odysseas Pappas. Alin Achim. Dave Bull. (2017). SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY. IEEE SigPort. http://sigport.org/1503
Odysseas Pappas. Alin Achim. Dave Bull, 2017. SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY. Available at: http://sigport.org/1503.
Odysseas Pappas. Alin Achim. Dave Bull. (2017). "SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY." Web.
1. Odysseas Pappas. Alin Achim. Dave Bull. SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1503

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