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Design and Implementation of Signal Processing Systems

Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors


Quantum computers threaten to break public-key cryptography schemes such as DSA and ECDSA in polynomial time, which poses an imminent threat to secure signal processing.
Ring learning with error (RLWE) lattice-based cryptography (LBC) is one of the most promising families of post-quantum cryptography (PQC) schemes in terms of efficiency and versatility. Two conventional methods to compute polynomial multiplication, the most compute-intensive routine in the RLWE schemes, are convolutions and Number Theoretic Transform (NTT).

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Authors:
Hamid Nejatollahi, Sina Shahhosseini, Rosario Cammarota, Nikil Dutt
Submitted On:
12 February 2020 - 8:32pm
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[1] Hamid Nejatollahi, Sina Shahhosseini, Rosario Cammarota, Nikil Dutt, "Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4985. Accessed: Apr. 07, 2020.
@article{4985-20,
url = {http://sigport.org/4985},
author = {Hamid Nejatollahi; Sina Shahhosseini; Rosario Cammarota; Nikil Dutt },
publisher = {IEEE SigPort},
title = {Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors},
year = {2020} }
TY - EJOUR
T1 - Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors
AU - Hamid Nejatollahi; Sina Shahhosseini; Rosario Cammarota; Nikil Dutt
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4985
ER -
Hamid Nejatollahi, Sina Shahhosseini, Rosario Cammarota, Nikil Dutt. (2020). Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors. IEEE SigPort. http://sigport.org/4985
Hamid Nejatollahi, Sina Shahhosseini, Rosario Cammarota, Nikil Dutt, 2020. Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors. Available at: http://sigport.org/4985.
Hamid Nejatollahi, Sina Shahhosseini, Rosario Cammarota, Nikil Dutt. (2020). "Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors." Web.
1. Hamid Nejatollahi, Sina Shahhosseini, Rosario Cammarota, Nikil Dutt. Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4985

Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems


Resistive switching memory technologies (RRAM) are seen by most of the scientific community as an enabler for Edge-level applications such as embedded deep Learning, AI or signal processing of audio and video signals. However, going beyond a ``simple'' replacement of eFlash in micro-controller and introducing RRAM inside the memory hierarchy is not a straightforward move. Indeed, integrating a RRAM technology inside the cache hierarchy requires higher endurance requirement than for eFlash replacement, and thus necessitates relaxed programming conditions.

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Authors:
Alexandre Levisse, Marco Rios, Miguel Peon, David Atienza
Submitted On:
4 February 2020 - 8:19am
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[1] Alexandre Levisse, Marco Rios, Miguel Peon, David Atienza, "Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4971. Accessed: Apr. 07, 2020.
@article{4971-20,
url = {http://sigport.org/4971},
author = {Alexandre Levisse; Marco Rios; Miguel Peon; David Atienza },
publisher = {IEEE SigPort},
title = {Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems},
year = {2020} }
TY - EJOUR
T1 - Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems
AU - Alexandre Levisse; Marco Rios; Miguel Peon; David Atienza
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4971
ER -
Alexandre Levisse, Marco Rios, Miguel Peon, David Atienza. (2020). Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems. IEEE SigPort. http://sigport.org/4971
Alexandre Levisse, Marco Rios, Miguel Peon, David Atienza, 2020. Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems. Available at: http://sigport.org/4971.
Alexandre Levisse, Marco Rios, Miguel Peon, David Atienza. (2020). "Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems." Web.
1. Alexandre Levisse, Marco Rios, Miguel Peon, David Atienza. Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4971

STOCHASTIC DATA-DRIVEN HARDWARE RESILIENCE TO EFFICIENTLY TRAIN INFERENCE MODELS FOR STOCHASTIC HARDWARE IMPLEMENTATIONS


Machine-learning algorithms are being employed in an increasing range of applications, spanning high-performance and energy-constrained platforms. It has been noted that the statistical nature of the algorithms can open up new opportunities for throughput and energy efficiency, by moving hardware into design regimes not limited to deterministic models of computation. This work aims to enable high accuracy in machine-learning inference systems, where computations are substantially affected by hardware variability.

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Authors:
Bonan Zhang, Lung-Yen Chen, Naveen Verma
Submitted On:
10 May 2019 - 12:07am
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[1] Bonan Zhang, Lung-Yen Chen, Naveen Verma, "STOCHASTIC DATA-DRIVEN HARDWARE RESILIENCE TO EFFICIENTLY TRAIN INFERENCE MODELS FOR STOCHASTIC HARDWARE IMPLEMENTATIONS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4265. Accessed: Apr. 07, 2020.
@article{4265-19,
url = {http://sigport.org/4265},
author = {Bonan Zhang; Lung-Yen Chen; Naveen Verma },
publisher = {IEEE SigPort},
title = {STOCHASTIC DATA-DRIVEN HARDWARE RESILIENCE TO EFFICIENTLY TRAIN INFERENCE MODELS FOR STOCHASTIC HARDWARE IMPLEMENTATIONS},
year = {2019} }
TY - EJOUR
T1 - STOCHASTIC DATA-DRIVEN HARDWARE RESILIENCE TO EFFICIENTLY TRAIN INFERENCE MODELS FOR STOCHASTIC HARDWARE IMPLEMENTATIONS
AU - Bonan Zhang; Lung-Yen Chen; Naveen Verma
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4265
ER -
Bonan Zhang, Lung-Yen Chen, Naveen Verma. (2019). STOCHASTIC DATA-DRIVEN HARDWARE RESILIENCE TO EFFICIENTLY TRAIN INFERENCE MODELS FOR STOCHASTIC HARDWARE IMPLEMENTATIONS. IEEE SigPort. http://sigport.org/4265
Bonan Zhang, Lung-Yen Chen, Naveen Verma, 2019. STOCHASTIC DATA-DRIVEN HARDWARE RESILIENCE TO EFFICIENTLY TRAIN INFERENCE MODELS FOR STOCHASTIC HARDWARE IMPLEMENTATIONS. Available at: http://sigport.org/4265.
Bonan Zhang, Lung-Yen Chen, Naveen Verma. (2019). "STOCHASTIC DATA-DRIVEN HARDWARE RESILIENCE TO EFFICIENTLY TRAIN INFERENCE MODELS FOR STOCHASTIC HARDWARE IMPLEMENTATIONS." Web.
1. Bonan Zhang, Lung-Yen Chen, Naveen Verma. STOCHASTIC DATA-DRIVEN HARDWARE RESILIENCE TO EFFICIENTLY TRAIN INFERENCE MODELS FOR STOCHASTIC HARDWARE IMPLEMENTATIONS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4265

SVM-based Seal Imprint Verification Using Edge Difference

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9 May 2019 - 9:29pm
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[1] , "SVM-based Seal Imprint Verification Using Edge Difference", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4250. Accessed: Apr. 07, 2020.
@article{4250-19,
url = {http://sigport.org/4250},
author = { },
publisher = {IEEE SigPort},
title = {SVM-based Seal Imprint Verification Using Edge Difference},
year = {2019} }
TY - EJOUR
T1 - SVM-based Seal Imprint Verification Using Edge Difference
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4250
ER -
. (2019). SVM-based Seal Imprint Verification Using Edge Difference. IEEE SigPort. http://sigport.org/4250
, 2019. SVM-based Seal Imprint Verification Using Edge Difference. Available at: http://sigport.org/4250.
. (2019). "SVM-based Seal Imprint Verification Using Edge Difference." Web.
1. . SVM-based Seal Imprint Verification Using Edge Difference [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4250

ENHANCING BEAMFORMED FINGERPRINT OUTDOOR POSITIONING WITH HIERARCHICAL CONVOLUTIONAL NEURAL NETWORKS

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Authors:
Joao Gante, Gabriel Falcao, Leonel Sousa
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8 May 2019 - 2:55pm
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[1] Joao Gante, Gabriel Falcao, Leonel Sousa, "ENHANCING BEAMFORMED FINGERPRINT OUTDOOR POSITIONING WITH HIERARCHICAL CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4130. Accessed: Apr. 07, 2020.
@article{4130-19,
url = {http://sigport.org/4130},
author = {Joao Gante; Gabriel Falcao; Leonel Sousa },
publisher = {IEEE SigPort},
title = {ENHANCING BEAMFORMED FINGERPRINT OUTDOOR POSITIONING WITH HIERARCHICAL CONVOLUTIONAL NEURAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - ENHANCING BEAMFORMED FINGERPRINT OUTDOOR POSITIONING WITH HIERARCHICAL CONVOLUTIONAL NEURAL NETWORKS
AU - Joao Gante; Gabriel Falcao; Leonel Sousa
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4130
ER -
Joao Gante, Gabriel Falcao, Leonel Sousa. (2019). ENHANCING BEAMFORMED FINGERPRINT OUTDOOR POSITIONING WITH HIERARCHICAL CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/4130
Joao Gante, Gabriel Falcao, Leonel Sousa, 2019. ENHANCING BEAMFORMED FINGERPRINT OUTDOOR POSITIONING WITH HIERARCHICAL CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/4130.
Joao Gante, Gabriel Falcao, Leonel Sousa. (2019). "ENHANCING BEAMFORMED FINGERPRINT OUTDOOR POSITIONING WITH HIERARCHICAL CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Joao Gante, Gabriel Falcao, Leonel Sousa. ENHANCING BEAMFORMED FINGERPRINT OUTDOOR POSITIONING WITH HIERARCHICAL CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4130

Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction


Deep learning models have been shown to be capable of extracting high-level representations from the increasing amount of customer-level data generated via fast-growing financial activities. In financial data, dynamic features that evolve with time are commonly observed. However, such time dependencies are often ignored in classical classification models. In this study, we propose to learn a Recurrent Neural Network (RNN) feature extractor with GRU on credit card payment history to leverage the time dependencies embedded in these dynamic features.

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Authors:
Te-Cheng Hsu, Shing-Tzuo Liou, Yun-Ping Wang, Yung-Shun Huang, Che Lin
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8 May 2019 - 9:27am
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[1] Te-Cheng Hsu, Shing-Tzuo Liou, Yun-Ping Wang, Yung-Shun Huang, Che Lin, "Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4093. Accessed: Apr. 07, 2020.
@article{4093-19,
url = {http://sigport.org/4093},
author = {Te-Cheng Hsu; Shing-Tzuo Liou; Yun-Ping Wang; Yung-Shun Huang; Che Lin },
publisher = {IEEE SigPort},
title = {Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction},
year = {2019} }
TY - EJOUR
T1 - Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction
AU - Te-Cheng Hsu; Shing-Tzuo Liou; Yun-Ping Wang; Yung-Shun Huang; Che Lin
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4093
ER -
Te-Cheng Hsu, Shing-Tzuo Liou, Yun-Ping Wang, Yung-Shun Huang, Che Lin. (2019). Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction. IEEE SigPort. http://sigport.org/4093
Te-Cheng Hsu, Shing-Tzuo Liou, Yun-Ping Wang, Yung-Shun Huang, Che Lin, 2019. Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction. Available at: http://sigport.org/4093.
Te-Cheng Hsu, Shing-Tzuo Liou, Yun-Ping Wang, Yung-Shun Huang, Che Lin. (2019). "Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction." Web.
1. Te-Cheng Hsu, Shing-Tzuo Liou, Yun-Ping Wang, Yung-Shun Huang, Che Lin. Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4093

GPU-BASED IMPLEMENTATION OF BELIEF PROPAGATION DECODING FOR POLAR CODES


Belief Propagation (BP) decoding provides soft outputs and features high-level parallelism. In this paper, we propose an optimized software BP decoder for polar codes on graphics processing units (GPUs). A full-parallel decoding architecture for codes with length n ≤ 2048 is presented to simultaneously update n/2 processing elements (PEs) within each stage and achieve high on-chip memory utilization by using

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Authors:
Zhanxian Liu, Rongke Liu, Zhiyuan Yan, Ling Zhao
Submitted On:
8 May 2019 - 1:19am
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[1] Zhanxian Liu, Rongke Liu, Zhiyuan Yan, Ling Zhao, "GPU-BASED IMPLEMENTATION OF BELIEF PROPAGATION DECODING FOR POLAR CODES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4008. Accessed: Apr. 07, 2020.
@article{4008-19,
url = {http://sigport.org/4008},
author = {Zhanxian Liu; Rongke Liu; Zhiyuan Yan; Ling Zhao },
publisher = {IEEE SigPort},
title = {GPU-BASED IMPLEMENTATION OF BELIEF PROPAGATION DECODING FOR POLAR CODES},
year = {2019} }
TY - EJOUR
T1 - GPU-BASED IMPLEMENTATION OF BELIEF PROPAGATION DECODING FOR POLAR CODES
AU - Zhanxian Liu; Rongke Liu; Zhiyuan Yan; Ling Zhao
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4008
ER -
Zhanxian Liu, Rongke Liu, Zhiyuan Yan, Ling Zhao. (2019). GPU-BASED IMPLEMENTATION OF BELIEF PROPAGATION DECODING FOR POLAR CODES. IEEE SigPort. http://sigport.org/4008
Zhanxian Liu, Rongke Liu, Zhiyuan Yan, Ling Zhao, 2019. GPU-BASED IMPLEMENTATION OF BELIEF PROPAGATION DECODING FOR POLAR CODES. Available at: http://sigport.org/4008.
Zhanxian Liu, Rongke Liu, Zhiyuan Yan, Ling Zhao. (2019). "GPU-BASED IMPLEMENTATION OF BELIEF PROPAGATION DECODING FOR POLAR CODES." Web.
1. Zhanxian Liu, Rongke Liu, Zhiyuan Yan, Ling Zhao. GPU-BASED IMPLEMENTATION OF BELIEF PROPAGATION DECODING FOR POLAR CODES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4008

ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)

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Authors:
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps
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7 May 2019 - 6:58pm
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[1] Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps, "ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3966. Accessed: Apr. 07, 2020.
@article{3966-19,
url = {http://sigport.org/3966},
author = {Tharshini Gunendradasan; Saad Irtza; Eliathamby Ambikairajah; Julien Epps },
publisher = {IEEE SigPort},
title = {ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)},
year = {2019} }
TY - EJOUR
T1 - ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)
AU - Tharshini Gunendradasan; Saad Irtza; Eliathamby Ambikairajah; Julien Epps
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3966
ER -
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. (2019). ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION). IEEE SigPort. http://sigport.org/3966
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps, 2019. ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION). Available at: http://sigport.org/3966.
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. (2019). "ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)." Web.
1. Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION) [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3966

ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)

Paper Details

Authors:
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps
Submitted On:
7 May 2019 - 6:58pm
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[1] Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps, "ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3965. Accessed: Apr. 07, 2020.
@article{3965-19,
url = {http://sigport.org/3965},
author = {Tharshini Gunendradasan; Saad Irtza; Eliathamby Ambikairajah; Julien Epps },
publisher = {IEEE SigPort},
title = {ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)},
year = {2019} }
TY - EJOUR
T1 - ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)
AU - Tharshini Gunendradasan; Saad Irtza; Eliathamby Ambikairajah; Julien Epps
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3965
ER -
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. (2019). ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION). IEEE SigPort. http://sigport.org/3965
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps, 2019. ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION). Available at: http://sigport.org/3965.
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. (2019). "ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)." Web.
1. Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION) [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3965

Adaptive Multi-Trace Carving Based on Dynamic Programming


In this work, we study the problem of tracking multiple frequency components in a noisy signal using a spectrogram-based method. Previous approaches such as image processing based or hidden Markov model-based methods may not be capable of tracking multiple frequency components, may require extensive training, and may be time-consuming. To address these issues, we propose an accurate and efficient method named Adaptive Multi-Trace Carving (AMTC) for tracking multiple frequency traces by iterative forward and backward dynamic programming and adaptive trace compensation.

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Authors:
Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu
Submitted On:
7 March 2019 - 10:50am
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[PDF] AMTC Asilomar 2018 poster

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[1] Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu, "Adaptive Multi-Trace Carving Based on Dynamic Programming", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3845. Accessed: Apr. 07, 2020.
@article{3845-19,
url = {http://sigport.org/3845},
author = {Qiang Zhu; Mingliang Chen; Chau-Wai Wong; Min Wu },
publisher = {IEEE SigPort},
title = {Adaptive Multi-Trace Carving Based on Dynamic Programming},
year = {2019} }
TY - EJOUR
T1 - Adaptive Multi-Trace Carving Based on Dynamic Programming
AU - Qiang Zhu; Mingliang Chen; Chau-Wai Wong; Min Wu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3845
ER -
Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu. (2019). Adaptive Multi-Trace Carving Based on Dynamic Programming. IEEE SigPort. http://sigport.org/3845
Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu, 2019. Adaptive Multi-Trace Carving Based on Dynamic Programming. Available at: http://sigport.org/3845.
Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu. (2019). "Adaptive Multi-Trace Carving Based on Dynamic Programming." Web.
1. Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu. Adaptive Multi-Trace Carving Based on Dynamic Programming [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3845

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