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ICASSP 2019

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website

CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES


The problem of impulse noise mitigation is considered when videos encoded using a SoftCast based Linear Video Coding scheme are transmitted using an OFDM scheme over a wideband channel prone to impulse noise A Fast Bayesian Matching Pursuit algorithm is employed for impulse noise mitigation This approach requires the provisioning of some OFDM subchannels to estimate the impulse noise locations and amplitudes Provisioned subchannels cannot be used to transmit data and lead to a decrease of the nominal decoded video quality at receivers in absence of impulse noise Using a phenomenological mod

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Authors:
Shuo Zheng, Marco Cagnazzo, Michel Kieffer
Submitted On:
16 May 2019 - 6:45pm
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[1] Shuo Zheng, Marco Cagnazzo, Michel Kieffer, "CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4541. Accessed: Sep. 21, 2019.
@article{4541-19,
url = {http://sigport.org/4541},
author = {Shuo Zheng; Marco Cagnazzo; Michel Kieffer },
publisher = {IEEE SigPort},
title = {CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES},
year = {2019} }
TY - EJOUR
T1 - CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES
AU - Shuo Zheng; Marco Cagnazzo; Michel Kieffer
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4541
ER -
Shuo Zheng, Marco Cagnazzo, Michel Kieffer. (2019). CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES. IEEE SigPort. http://sigport.org/4541
Shuo Zheng, Marco Cagnazzo, Michel Kieffer, 2019. CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES. Available at: http://sigport.org/4541.
Shuo Zheng, Marco Cagnazzo, Michel Kieffer. (2019). "CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES." Web.
1. Shuo Zheng, Marco Cagnazzo, Michel Kieffer. CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4541

Nuclei Segmentation in Histopathology Images


Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, adaptivity and generalizability. Popular Deep Convolutional Neural Networks (DCNNs) have recently been utilized for nuclei segmentation, outperforming \textit{traditional} approaches that exploit color and texture features in combination with shallow classifiers or segmentation algorithms.

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Authors:
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard
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16 May 2019 - 11:13am
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[1] Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard, "Nuclei Segmentation in Histopathology Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4539. Accessed: Sep. 21, 2019.
@article{4539-19,
url = {http://sigport.org/4539},
author = {Deniz Mercadier Sayin; Beril Besbinar; Pascal Frossard },
publisher = {IEEE SigPort},
title = {Nuclei Segmentation in Histopathology Images},
year = {2019} }
TY - EJOUR
T1 - Nuclei Segmentation in Histopathology Images
AU - Deniz Mercadier Sayin; Beril Besbinar; Pascal Frossard
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4539
ER -
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. (2019). Nuclei Segmentation in Histopathology Images. IEEE SigPort. http://sigport.org/4539
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard, 2019. Nuclei Segmentation in Histopathology Images. Available at: http://sigport.org/4539.
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. (2019). "Nuclei Segmentation in Histopathology Images." Web.
1. Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. Nuclei Segmentation in Histopathology Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4539

On the Computability of the Secret Key Capacity Under Rate Constraints


Secret key generation refers to the problem of generating a common secret key without revealing any information about it to an eavesdropper. All users observe correlated components of a common source and can further use a rate-limited public channel for discussion which is open to eavesdroppers. This paper studies the Turing computability of the secret key capacity with a single rate-limited public forward transmission. Turing computability provides fundamental performance limits for today’s digital computers.

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Authors:
Holger Boche, Rafael F. Schaefer, and H. Vincent Poor
Submitted On:
16 May 2019 - 4:42am
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[1] Holger Boche, Rafael F. Schaefer, and H. Vincent Poor, "On the Computability of the Secret Key Capacity Under Rate Constraints", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4537. Accessed: Sep. 21, 2019.
@article{4537-19,
url = {http://sigport.org/4537},
author = {Holger Boche; Rafael F. Schaefer; and H. Vincent Poor },
publisher = {IEEE SigPort},
title = {On the Computability of the Secret Key Capacity Under Rate Constraints},
year = {2019} }
TY - EJOUR
T1 - On the Computability of the Secret Key Capacity Under Rate Constraints
AU - Holger Boche; Rafael F. Schaefer; and H. Vincent Poor
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4537
ER -
Holger Boche, Rafael F. Schaefer, and H. Vincent Poor. (2019). On the Computability of the Secret Key Capacity Under Rate Constraints. IEEE SigPort. http://sigport.org/4537
Holger Boche, Rafael F. Schaefer, and H. Vincent Poor, 2019. On the Computability of the Secret Key Capacity Under Rate Constraints. Available at: http://sigport.org/4537.
Holger Boche, Rafael F. Schaefer, and H. Vincent Poor. (2019). "On the Computability of the Secret Key Capacity Under Rate Constraints." Web.
1. Holger Boche, Rafael F. Schaefer, and H. Vincent Poor. On the Computability of the Secret Key Capacity Under Rate Constraints [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4537

A History-based Stopping Criterion in Recursive Bayesian State Estimation


In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for greater gains in sensing cost.

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Authors:
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus
Submitted On:
15 May 2019 - 9:57pm
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[1] Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus, "A History-based Stopping Criterion in Recursive Bayesian State Estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4536. Accessed: Sep. 21, 2019.
@article{4536-19,
url = {http://sigport.org/4536},
author = {Yeganeh M. Marghi; Aziz Kocanaogullari; Murat Akcakaya; Deniz Erdomus },
publisher = {IEEE SigPort},
title = {A History-based Stopping Criterion in Recursive Bayesian State Estimation},
year = {2019} }
TY - EJOUR
T1 - A History-based Stopping Criterion in Recursive Bayesian State Estimation
AU - Yeganeh M. Marghi; Aziz Kocanaogullari; Murat Akcakaya; Deniz Erdomus
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4536
ER -
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. (2019). A History-based Stopping Criterion in Recursive Bayesian State Estimation. IEEE SigPort. http://sigport.org/4536
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus, 2019. A History-based Stopping Criterion in Recursive Bayesian State Estimation. Available at: http://sigport.org/4536.
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. (2019). "A History-based Stopping Criterion in Recursive Bayesian State Estimation." Web.
1. Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. A History-based Stopping Criterion in Recursive Bayesian State Estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4536

A History-based Stopping Criterion in Recursive Bayesian State Estimation


In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for greater gains in sensing cost.

Paper Details

Authors:
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus
Submitted On:
15 May 2019 - 9:57pm
Short Link:
Type:
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[1] Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus, "A History-based Stopping Criterion in Recursive Bayesian State Estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4535. Accessed: Sep. 21, 2019.
@article{4535-19,
url = {http://sigport.org/4535},
author = {Yeganeh M. Marghi; Aziz Kocanaogullari; Murat Akcakaya; Deniz Erdomus },
publisher = {IEEE SigPort},
title = {A History-based Stopping Criterion in Recursive Bayesian State Estimation},
year = {2019} }
TY - EJOUR
T1 - A History-based Stopping Criterion in Recursive Bayesian State Estimation
AU - Yeganeh M. Marghi; Aziz Kocanaogullari; Murat Akcakaya; Deniz Erdomus
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4535
ER -
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. (2019). A History-based Stopping Criterion in Recursive Bayesian State Estimation. IEEE SigPort. http://sigport.org/4535
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus, 2019. A History-based Stopping Criterion in Recursive Bayesian State Estimation. Available at: http://sigport.org/4535.
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. (2019). "A History-based Stopping Criterion in Recursive Bayesian State Estimation." Web.
1. Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. A History-based Stopping Criterion in Recursive Bayesian State Estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4535

Statistical rank selection for incomplete low-rank matrices

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Authors:
Rui Zhang, Alexander Shapiro, Yao Xie
Submitted On:
15 May 2019 - 7:09pm
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[1] Rui Zhang, Alexander Shapiro, Yao Xie, "Statistical rank selection for incomplete low-rank matrices", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4534. Accessed: Sep. 21, 2019.
@article{4534-19,
url = {http://sigport.org/4534},
author = {Rui Zhang; Alexander Shapiro; Yao Xie },
publisher = {IEEE SigPort},
title = {Statistical rank selection for incomplete low-rank matrices},
year = {2019} }
TY - EJOUR
T1 - Statistical rank selection for incomplete low-rank matrices
AU - Rui Zhang; Alexander Shapiro; Yao Xie
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4534
ER -
Rui Zhang, Alexander Shapiro, Yao Xie. (2019). Statistical rank selection for incomplete low-rank matrices. IEEE SigPort. http://sigport.org/4534
Rui Zhang, Alexander Shapiro, Yao Xie, 2019. Statistical rank selection for incomplete low-rank matrices. Available at: http://sigport.org/4534.
Rui Zhang, Alexander Shapiro, Yao Xie. (2019). "Statistical rank selection for incomplete low-rank matrices." Web.
1. Rui Zhang, Alexander Shapiro, Yao Xie. Statistical rank selection for incomplete low-rank matrices [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4534

Statistical rank selection for incomplete low-rank matrices

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Authors:
Rui Zhang, Alexander Shapiro, Yao Xie
Submitted On:
15 May 2019 - 7:09pm
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[1] Rui Zhang, Alexander Shapiro, Yao Xie, "Statistical rank selection for incomplete low-rank matrices", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4533. Accessed: Sep. 21, 2019.
@article{4533-19,
url = {http://sigport.org/4533},
author = {Rui Zhang; Alexander Shapiro; Yao Xie },
publisher = {IEEE SigPort},
title = {Statistical rank selection for incomplete low-rank matrices},
year = {2019} }
TY - EJOUR
T1 - Statistical rank selection for incomplete low-rank matrices
AU - Rui Zhang; Alexander Shapiro; Yao Xie
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4533
ER -
Rui Zhang, Alexander Shapiro, Yao Xie. (2019). Statistical rank selection for incomplete low-rank matrices. IEEE SigPort. http://sigport.org/4533
Rui Zhang, Alexander Shapiro, Yao Xie, 2019. Statistical rank selection for incomplete low-rank matrices. Available at: http://sigport.org/4533.
Rui Zhang, Alexander Shapiro, Yao Xie. (2019). "Statistical rank selection for incomplete low-rank matrices." Web.
1. Rui Zhang, Alexander Shapiro, Yao Xie. Statistical rank selection for incomplete low-rank matrices [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4533

PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS


In this paper, we analyze the asymptotic performance of a convex optimization-based discrete-valued vector reconstruction from linear measurements. We firstly propose a box-constrained version of the conventional sum of absolute values (SOAV) optimization, which uses a weighted sum of L1 regularizers as a regularizer for the discrete-valued vector. We then derive the asymptotic symbol error rate (SER) performance of the box-constrained SOAV (Box-SOAV) optimization theoretically by using convex Gaussian min-max theorem.

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Authors:
Ryo Hayakawa, Kazunori Hayashi
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15 May 2019 - 5:52pm
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[1] Ryo Hayakawa, Kazunori Hayashi, "PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4532. Accessed: Sep. 21, 2019.
@article{4532-19,
url = {http://sigport.org/4532},
author = {Ryo Hayakawa; Kazunori Hayashi },
publisher = {IEEE SigPort},
title = {PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS},
year = {2019} }
TY - EJOUR
T1 - PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS
AU - Ryo Hayakawa; Kazunori Hayashi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4532
ER -
Ryo Hayakawa, Kazunori Hayashi. (2019). PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS. IEEE SigPort. http://sigport.org/4532
Ryo Hayakawa, Kazunori Hayashi, 2019. PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS. Available at: http://sigport.org/4532.
Ryo Hayakawa, Kazunori Hayashi. (2019). "PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS." Web.
1. Ryo Hayakawa, Kazunori Hayashi. PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4532

Sample Space-Time Covariance Estimation


Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations. The variance of the estimation links directly to previously explored perturbation of the analytic eigenvalues and eigenspaces of a parahermitian cross-spectral density matrix when estimated from finite data.

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Authors:
Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss
Submitted On:
15 May 2019 - 4:53pm
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[1] Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss, "Sample Space-Time Covariance Estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4531. Accessed: Sep. 21, 2019.
@article{4531-19,
url = {http://sigport.org/4531},
author = {Connor Delaosa; Jennifer Pestana; Nicholas J. Goddard; Sam Somasundaram; Stephan Weiss },
publisher = {IEEE SigPort},
title = {Sample Space-Time Covariance Estimation},
year = {2019} }
TY - EJOUR
T1 - Sample Space-Time Covariance Estimation
AU - Connor Delaosa; Jennifer Pestana; Nicholas J. Goddard; Sam Somasundaram; Stephan Weiss
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4531
ER -
Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss. (2019). Sample Space-Time Covariance Estimation. IEEE SigPort. http://sigport.org/4531
Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss, 2019. Sample Space-Time Covariance Estimation. Available at: http://sigport.org/4531.
Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss. (2019). "Sample Space-Time Covariance Estimation." Web.
1. Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss. Sample Space-Time Covariance Estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4531

Aggregation Graph Neural Networks


Graph neural networks (GNNs) regularize classical neural networks by exploiting the underlying irregular structure supporting graph data, extending its application to broader data domains. The aggregation GNN presented here is a novel GNN that exploits the fact that the data collected at a single node by means of successive local exchanges with neighbors exhibits a regular structure. Thus, regular convolution and regular pooling yield an appropriately regularized GNN.

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Authors:
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro
Submitted On:
15 May 2019 - 2:34pm
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[1] Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro, "Aggregation Graph Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4528. Accessed: Sep. 21, 2019.
@article{4528-19,
url = {http://sigport.org/4528},
author = {Fernando Gama; Antonio G. Marques; Geert Leus; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Aggregation Graph Neural Networks},
year = {2019} }
TY - EJOUR
T1 - Aggregation Graph Neural Networks
AU - Fernando Gama; Antonio G. Marques; Geert Leus; Alejandro Ribeiro
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4528
ER -
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro. (2019). Aggregation Graph Neural Networks. IEEE SigPort. http://sigport.org/4528
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro, 2019. Aggregation Graph Neural Networks. Available at: http://sigport.org/4528.
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro. (2019). "Aggregation Graph Neural Networks." Web.
1. Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro. Aggregation Graph Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4528

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