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Biomedical signal processing

Enhancing the Reliability of Large-Scale Multiuser Molecular Communication Systems

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Authors:
Maheshi Buddhinee Dissanayake, Yansha Deng, Arumugam Nallanathan, Maged Elkashlan, and Urbashi Mitra
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20 June 2018 - 9:29am
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[1] Maheshi Buddhinee Dissanayake, Yansha Deng, Arumugam Nallanathan, Maged Elkashlan, and Urbashi Mitra, "Enhancing the Reliability of Large-Scale Multiuser Molecular Communication Systems", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3252. Accessed: Aug. 21, 2018.
@article{3252-18,
url = {http://sigport.org/3252},
author = {Maheshi Buddhinee Dissanayake; Yansha Deng; Arumugam Nallanathan; Maged Elkashlan; and Urbashi Mitra },
publisher = {IEEE SigPort},
title = {Enhancing the Reliability of Large-Scale Multiuser Molecular Communication Systems},
year = {2018} }
TY - EJOUR
T1 - Enhancing the Reliability of Large-Scale Multiuser Molecular Communication Systems
AU - Maheshi Buddhinee Dissanayake; Yansha Deng; Arumugam Nallanathan; Maged Elkashlan; and Urbashi Mitra
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3252
ER -
Maheshi Buddhinee Dissanayake, Yansha Deng, Arumugam Nallanathan, Maged Elkashlan, and Urbashi Mitra. (2018). Enhancing the Reliability of Large-Scale Multiuser Molecular Communication Systems. IEEE SigPort. http://sigport.org/3252
Maheshi Buddhinee Dissanayake, Yansha Deng, Arumugam Nallanathan, Maged Elkashlan, and Urbashi Mitra, 2018. Enhancing the Reliability of Large-Scale Multiuser Molecular Communication Systems. Available at: http://sigport.org/3252.
Maheshi Buddhinee Dissanayake, Yansha Deng, Arumugam Nallanathan, Maged Elkashlan, and Urbashi Mitra. (2018). "Enhancing the Reliability of Large-Scale Multiuser Molecular Communication Systems." Web.
1. Maheshi Buddhinee Dissanayake, Yansha Deng, Arumugam Nallanathan, Maged Elkashlan, and Urbashi Mitra. Enhancing the Reliability of Large-Scale Multiuser Molecular Communication Systems [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3252

Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals


In this paper, we present completely automated cardiac anomaly detection for remote screening of cardio-vascular abnormality using Phonocardiogram (PCG) or heart sound signal. Even though PCG contains significant and vital cardiac health information and cardiac abnormality signature, the presence of substantial noise does not guarantee highly effective analysis of cardiac condition. Our proposed method intelligently identifies and eliminates noisy PCG signal and consequently detects pathological abnormality condition. We further present a unified model of hybrid feature selection method.

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Authors:
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal
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27 April 2018 - 2:44am
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[1] Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, "Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3184. Accessed: Aug. 21, 2018.
@article{3184-18,
url = {http://sigport.org/3184},
author = {Arijit Ukil; Soma Bnadyopadhyay; Chetanya Puri; Rituraj Singh; Arpan Pal },
publisher = {IEEE SigPort},
title = {Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals},
year = {2018} }
TY - EJOUR
T1 - Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals
AU - Arijit Ukil; Soma Bnadyopadhyay; Chetanya Puri; Rituraj Singh; Arpan Pal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3184
ER -
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal. (2018). Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals. IEEE SigPort. http://sigport.org/3184
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, 2018. Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals. Available at: http://sigport.org/3184.
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal. (2018). "Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals." Web.
1. Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal. Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3184

EPILEPTIC STATE SEGMENTATION WITH TEMPORAL-CONSTRAINED CLUSTERING

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24 April 2018 - 7:37am
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[1] , "EPILEPTIC STATE SEGMENTATION WITH TEMPORAL-CONSTRAINED CLUSTERING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3165. Accessed: Aug. 21, 2018.
@article{3165-18,
url = {http://sigport.org/3165},
author = { },
publisher = {IEEE SigPort},
title = {EPILEPTIC STATE SEGMENTATION WITH TEMPORAL-CONSTRAINED CLUSTERING},
year = {2018} }
TY - EJOUR
T1 - EPILEPTIC STATE SEGMENTATION WITH TEMPORAL-CONSTRAINED CLUSTERING
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3165
ER -
. (2018). EPILEPTIC STATE SEGMENTATION WITH TEMPORAL-CONSTRAINED CLUSTERING. IEEE SigPort. http://sigport.org/3165
, 2018. EPILEPTIC STATE SEGMENTATION WITH TEMPORAL-CONSTRAINED CLUSTERING. Available at: http://sigport.org/3165.
. (2018). "EPILEPTIC STATE SEGMENTATION WITH TEMPORAL-CONSTRAINED CLUSTERING." Web.
1. . EPILEPTIC STATE SEGMENTATION WITH TEMPORAL-CONSTRAINED CLUSTERING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3165

Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography


Estimation of conduction velocity (CV) is an important task in the analysis of surface electromyography (sEMG). The problem can be framed as estimation of a time-varying delay (TVD) between electrode recordings. In this paper we present an algorithm which incorporates information from multiple electrodes into a single TVD estimation. The algorithm uses a common all-pass filter to relate two groups of signals at a local level.

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Authors:
Adrian Bingham, Thierry Blu, Beth Jelfs
Submitted On:
22 April 2018 - 12:58am
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[1] Adrian Bingham, Thierry Blu, Beth Jelfs, "Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3128. Accessed: Aug. 21, 2018.
@article{3128-18,
url = {http://sigport.org/3128},
author = {Adrian Bingham; Thierry Blu; Beth Jelfs },
publisher = {IEEE SigPort},
title = {Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography},
year = {2018} }
TY - EJOUR
T1 - Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography
AU - Adrian Bingham; Thierry Blu; Beth Jelfs
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3128
ER -
Adrian Bingham, Thierry Blu, Beth Jelfs. (2018). Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography. IEEE SigPort. http://sigport.org/3128
Adrian Bingham, Thierry Blu, Beth Jelfs, 2018. Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography. Available at: http://sigport.org/3128.
Adrian Bingham, Thierry Blu, Beth Jelfs. (2018). "Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography." Web.
1. Adrian Bingham, Thierry Blu, Beth Jelfs. Time-Varying Delay Estimation using Common Local All-Pass Filters with Application to Surface Electromyography [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3128

An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings

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Authors:
Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT
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19 April 2018 - 4:47pm
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[1] Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT, "An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3026. Accessed: Aug. 21, 2018.
@article{3026-18,
url = {http://sigport.org/3026},
author = {Jean-Baptiste SCHIRATTI; Jean-Eudes LE DOUGET; Michel LE VAN QUYEN; Slim ESSID; Alexandre GRAMFORT },
publisher = {IEEE SigPort},
title = {An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings},
year = {2018} }
TY - EJOUR
T1 - An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings
AU - Jean-Baptiste SCHIRATTI; Jean-Eudes LE DOUGET; Michel LE VAN QUYEN; Slim ESSID; Alexandre GRAMFORT
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3026
ER -
Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT. (2018). An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings. IEEE SigPort. http://sigport.org/3026
Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT, 2018. An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings. Available at: http://sigport.org/3026.
Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT. (2018). "An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings." Web.
1. Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT. An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3026

Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes


Spike sorting is the process of assigning each detected neuronal spike in an extracellular recording to its putative source neuron. A linear filter design is proposed where the filter output allows for threshold-based spike sorting of high-density neural probe data. The proposed filter design is based on optimizing the signal-to-peak-interference ratio for each detectable neuron in a data-driven way.

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Authors:
Jasper Wouters, Fabian Kloosterman, Alexander Bertrand
Submitted On:
15 April 2018 - 9:21am
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[1] Jasper Wouters, Fabian Kloosterman, Alexander Bertrand, "Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2894. Accessed: Aug. 21, 2018.
@article{2894-18,
url = {http://sigport.org/2894},
author = {Jasper Wouters; Fabian Kloosterman; Alexander Bertrand },
publisher = {IEEE SigPort},
title = {Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes},
year = {2018} }
TY - EJOUR
T1 - Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes
AU - Jasper Wouters; Fabian Kloosterman; Alexander Bertrand
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2894
ER -
Jasper Wouters, Fabian Kloosterman, Alexander Bertrand. (2018). Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes. IEEE SigPort. http://sigport.org/2894
Jasper Wouters, Fabian Kloosterman, Alexander Bertrand, 2018. Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes. Available at: http://sigport.org/2894.
Jasper Wouters, Fabian Kloosterman, Alexander Bertrand. (2018). "Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes." Web.
1. Jasper Wouters, Fabian Kloosterman, Alexander Bertrand. Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2894

A PRAGMATIC AUTHENTICATION SYSTEM USING ELECTROENCEPHALOGRAPHY SIGNALS


EEG-based authentication is an emerging research field. In this work, a realistic authentication system using Electroencephalography signals, was developed aiming to show that brain signals contain sufficient information to be used in security systems. The dataset used was composed of 29 users on 4 different days via the cheap Neurosky Mindwave headset with a single dry electrode, and 10 users on 3 different days via Emotiv with 14 electrodes. Various techniques, features, and algorithms were examined to achieve the highest security.

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Authors:
Ayman Khalafallah, Aly Ibrahim, Bahieeldeen Shehab, Hisham Raslan, Omar Eltobgy, Shady Elbaroudy
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15 April 2018 - 1:24am
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[1] Ayman Khalafallah, Aly Ibrahim, Bahieeldeen Shehab, Hisham Raslan, Omar Eltobgy, Shady Elbaroudy, "A PRAGMATIC AUTHENTICATION SYSTEM USING ELECTROENCEPHALOGRAPHY SIGNALS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2882. Accessed: Aug. 21, 2018.
@article{2882-18,
url = {http://sigport.org/2882},
author = {Ayman Khalafallah; Aly Ibrahim; Bahieeldeen Shehab; Hisham Raslan; Omar Eltobgy; Shady Elbaroudy },
publisher = {IEEE SigPort},
title = {A PRAGMATIC AUTHENTICATION SYSTEM USING ELECTROENCEPHALOGRAPHY SIGNALS},
year = {2018} }
TY - EJOUR
T1 - A PRAGMATIC AUTHENTICATION SYSTEM USING ELECTROENCEPHALOGRAPHY SIGNALS
AU - Ayman Khalafallah; Aly Ibrahim; Bahieeldeen Shehab; Hisham Raslan; Omar Eltobgy; Shady Elbaroudy
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2882
ER -
Ayman Khalafallah, Aly Ibrahim, Bahieeldeen Shehab, Hisham Raslan, Omar Eltobgy, Shady Elbaroudy. (2018). A PRAGMATIC AUTHENTICATION SYSTEM USING ELECTROENCEPHALOGRAPHY SIGNALS. IEEE SigPort. http://sigport.org/2882
Ayman Khalafallah, Aly Ibrahim, Bahieeldeen Shehab, Hisham Raslan, Omar Eltobgy, Shady Elbaroudy, 2018. A PRAGMATIC AUTHENTICATION SYSTEM USING ELECTROENCEPHALOGRAPHY SIGNALS. Available at: http://sigport.org/2882.
Ayman Khalafallah, Aly Ibrahim, Bahieeldeen Shehab, Hisham Raslan, Omar Eltobgy, Shady Elbaroudy. (2018). "A PRAGMATIC AUTHENTICATION SYSTEM USING ELECTROENCEPHALOGRAPHY SIGNALS." Web.
1. Ayman Khalafallah, Aly Ibrahim, Bahieeldeen Shehab, Hisham Raslan, Omar Eltobgy, Shady Elbaroudy. A PRAGMATIC AUTHENTICATION SYSTEM USING ELECTROENCEPHALOGRAPHY SIGNALS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2882

Stochastic Dynamical Systems Based Latent Structure Discovery in High-dimensional Time Series


The brain encodes information by neural spiking activities, which can be described by time series data as spike counts. Latent Vari- able Models (LVMs) are widely used to study the unknown factors (i.e. the latent states) that are dependent in a network structure to modulate neural spiking activities. Yet, challenges in performing experiments to record on neuronal level commonly results in rela- tively short and noisy spike count data, which is insufficient to de- rive latent network structure by existing LVMs. Specifically, it is difficult to set the number of latent states.

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Authors:
Rosa H.M. Chan
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14 April 2018 - 9:06pm
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[1] Rosa H.M. Chan, "Stochastic Dynamical Systems Based Latent Structure Discovery in High-dimensional Time Series", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2865. Accessed: Aug. 21, 2018.
@article{2865-18,
url = {http://sigport.org/2865},
author = {Rosa H.M. Chan },
publisher = {IEEE SigPort},
title = {Stochastic Dynamical Systems Based Latent Structure Discovery in High-dimensional Time Series},
year = {2018} }
TY - EJOUR
T1 - Stochastic Dynamical Systems Based Latent Structure Discovery in High-dimensional Time Series
AU - Rosa H.M. Chan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2865
ER -
Rosa H.M. Chan. (2018). Stochastic Dynamical Systems Based Latent Structure Discovery in High-dimensional Time Series. IEEE SigPort. http://sigport.org/2865
Rosa H.M. Chan, 2018. Stochastic Dynamical Systems Based Latent Structure Discovery in High-dimensional Time Series. Available at: http://sigport.org/2865.
Rosa H.M. Chan. (2018). "Stochastic Dynamical Systems Based Latent Structure Discovery in High-dimensional Time Series." Web.
1. Rosa H.M. Chan. Stochastic Dynamical Systems Based Latent Structure Discovery in High-dimensional Time Series [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2865

FUNCTIONAL CONNECTIVITY STATES OF THE BRAIN USING RESTRICTED BOLTZMANN MACHINES


Recent work on resting-state functional magnetic resonance imaging (rs-fMRI) suggests that functional connectivity (FC) is dynamic. A variety of machine learning and signal processing tools have been applied to the study of dynamic functional connectivity networks (dFCNs) of the brain, by identifying a small number of network states that describe the dynamics of connectivity during rest. Recently, deep learning (DL) methods have been applied to neuroimaging data for learning generative models.

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14 April 2018 - 7:20am
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[1] , "FUNCTIONAL CONNECTIVITY STATES OF THE BRAIN USING RESTRICTED BOLTZMANN MACHINES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2817. Accessed: Aug. 21, 2018.
@article{2817-18,
url = {http://sigport.org/2817},
author = { },
publisher = {IEEE SigPort},
title = {FUNCTIONAL CONNECTIVITY STATES OF THE BRAIN USING RESTRICTED BOLTZMANN MACHINES},
year = {2018} }
TY - EJOUR
T1 - FUNCTIONAL CONNECTIVITY STATES OF THE BRAIN USING RESTRICTED BOLTZMANN MACHINES
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2817
ER -
. (2018). FUNCTIONAL CONNECTIVITY STATES OF THE BRAIN USING RESTRICTED BOLTZMANN MACHINES. IEEE SigPort. http://sigport.org/2817
, 2018. FUNCTIONAL CONNECTIVITY STATES OF THE BRAIN USING RESTRICTED BOLTZMANN MACHINES. Available at: http://sigport.org/2817.
. (2018). "FUNCTIONAL CONNECTIVITY STATES OF THE BRAIN USING RESTRICTED BOLTZMANN MACHINES." Web.
1. . FUNCTIONAL CONNECTIVITY STATES OF THE BRAIN USING RESTRICTED BOLTZMANN MACHINES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2817

Poster for ICCASP 2018


Identification of cell subclasses using single-cell RNA-Sequencing (scRNA-Seq) data is of paramount importance since it uncovers the hidden biological processes within the cell population. While the nonnegative matrix factorization (NMF) model has been reported to be effective in various unsupervised clustering tasks, it may still produce inappropriate results for some scRNA-Seq datasets with heterogeneous structures. In this paper, we propose the use of an orthogonally constrained NMF (ONMF) model for the subclass identification problem of scRNA-Seq datasets.

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Authors:
Peng Wu, Manqi Zhou, Tsung-Hui Chang, Song Wu
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14 April 2018 - 4:24am
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ICCASP Poster for paper 3809

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[1] Peng Wu, Manqi Zhou, Tsung-Hui Chang, Song Wu, "Poster for ICCASP 2018", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2812. Accessed: Aug. 21, 2018.
@article{2812-18,
url = {http://sigport.org/2812},
author = { Peng Wu; Manqi Zhou; Tsung-Hui Chang; Song Wu },
publisher = {IEEE SigPort},
title = {Poster for ICCASP 2018},
year = {2018} }
TY - EJOUR
T1 - Poster for ICCASP 2018
AU - Peng Wu; Manqi Zhou; Tsung-Hui Chang; Song Wu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2812
ER -
Peng Wu, Manqi Zhou, Tsung-Hui Chang, Song Wu. (2018). Poster for ICCASP 2018. IEEE SigPort. http://sigport.org/2812
Peng Wu, Manqi Zhou, Tsung-Hui Chang, Song Wu, 2018. Poster for ICCASP 2018. Available at: http://sigport.org/2812.
Peng Wu, Manqi Zhou, Tsung-Hui Chang, Song Wu. (2018). "Poster for ICCASP 2018." Web.
1. Peng Wu, Manqi Zhou, Tsung-Hui Chang, Song Wu. Poster for ICCASP 2018 [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2812

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