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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

Detection of Pilot-Hopping Sequences for Grant-Free Random Access in Massive MIMO Systems

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Authors:
Ema Becirovic, Emil Björnson, Erik G. Larsson
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17 May 2019 - 3:46am
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presentation_ICASSP.pdf

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[1] Ema Becirovic, Emil Björnson, Erik G. Larsson, "Detection of Pilot-Hopping Sequences for Grant-Free Random Access in Massive MIMO Systems", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4544. Accessed: May. 30, 2020.
@article{4544-19,
url = {http://sigport.org/4544},
author = {Ema Becirovic; Emil Björnson; Erik G. Larsson },
publisher = {IEEE SigPort},
title = {Detection of Pilot-Hopping Sequences for Grant-Free Random Access in Massive MIMO Systems},
year = {2019} }
TY - EJOUR
T1 - Detection of Pilot-Hopping Sequences for Grant-Free Random Access in Massive MIMO Systems
AU - Ema Becirovic; Emil Björnson; Erik G. Larsson
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4544
ER -
Ema Becirovic, Emil Björnson, Erik G. Larsson. (2019). Detection of Pilot-Hopping Sequences for Grant-Free Random Access in Massive MIMO Systems. IEEE SigPort. http://sigport.org/4544
Ema Becirovic, Emil Björnson, Erik G. Larsson, 2019. Detection of Pilot-Hopping Sequences for Grant-Free Random Access in Massive MIMO Systems. Available at: http://sigport.org/4544.
Ema Becirovic, Emil Björnson, Erik G. Larsson. (2019). "Detection of Pilot-Hopping Sequences for Grant-Free Random Access in Massive MIMO Systems." Web.
1. Ema Becirovic, Emil Björnson, Erik G. Larsson. Detection of Pilot-Hopping Sequences for Grant-Free Random Access in Massive MIMO Systems [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4544

Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems


In this paper, the importance of analytic phase of the speech signal in automatic speaker verification systems is demonstrated in the context of replay spoof attacks. In order to accurately detect the replay spoof attacks, effective feature representations of speech signals are required to capture the distortion introduced due to the intermediate playback/recording devices, which is convolutive in nature.

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Authors:
Shaik Mohammad Rafi B, Sri Rama Murty K
Submitted On:
16 May 2019 - 10:16pm
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Poster of ICASSP19

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[1] Shaik Mohammad Rafi B, Sri Rama Murty K, "Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4543. Accessed: May. 30, 2020.
@article{4543-19,
url = {http://sigport.org/4543},
author = {Shaik Mohammad Rafi B; Sri Rama Murty K },
publisher = {IEEE SigPort},
title = {Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems},
year = {2019} }
TY - EJOUR
T1 - Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems
AU - Shaik Mohammad Rafi B; Sri Rama Murty K
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4543
ER -
Shaik Mohammad Rafi B, Sri Rama Murty K. (2019). Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems. IEEE SigPort. http://sigport.org/4543
Shaik Mohammad Rafi B, Sri Rama Murty K, 2019. Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems. Available at: http://sigport.org/4543.
Shaik Mohammad Rafi B, Sri Rama Murty K. (2019). "Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems." Web.
1. Shaik Mohammad Rafi B, Sri Rama Murty K. Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4543

Single Image Interpolation Exploiting Semi-local Similarity


This paper explores the modeling and exploitation of semi-local similarity in natural images to address the ill-posed nature of image interpolation. Our approach distinguishes itself from prior approaches by direct and careful use of semi-local similar patches to interpolate each individual patch. Our work uses a simple, parallelizable algorithm without the need to solve complicated optimization problems. Experimental results demonstrate that our interpolated images achieve significantly higher objective and subjective quality compared with those from state-of-the-art algorithms.

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16 May 2019 - 8:32pm
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Single Image Interpolation Exploiting Semi-local Similarity.pdf

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[1] , "Single Image Interpolation Exploiting Semi-local Similarity", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4542. Accessed: May. 30, 2020.
@article{4542-19,
url = {http://sigport.org/4542},
author = { },
publisher = {IEEE SigPort},
title = {Single Image Interpolation Exploiting Semi-local Similarity},
year = {2019} }
TY - EJOUR
T1 - Single Image Interpolation Exploiting Semi-local Similarity
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4542
ER -
. (2019). Single Image Interpolation Exploiting Semi-local Similarity. IEEE SigPort. http://sigport.org/4542
, 2019. Single Image Interpolation Exploiting Semi-local Similarity. Available at: http://sigport.org/4542.
. (2019). "Single Image Interpolation Exploiting Semi-local Similarity." Web.
1. . Single Image Interpolation Exploiting Semi-local Similarity [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4542

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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PPT_ICASSP_2019.pdf

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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: May. 30, 2020.
@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
Submitted On:
16 May 2019 - 11:13am
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MercadierBesbinarFrossard_ICASSP2019_presentation.pdf

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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: May. 30, 2020.
@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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icassp19_skg_talk.pdf

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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: May. 30, 2020.
@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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P_Marghi_Kocan_ICASSP_2019.pdf

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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: May. 30, 2020.
@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
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P_Marghi_Kocan_ICASSP_2019.pdf

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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: May. 30, 2020.
@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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ICASSP2019.pdf

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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: May. 30, 2020.
@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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ICASSP2019.pdf

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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: May. 30, 2020.
@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

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