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ICASSP 2018

ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2018 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics. Visit ICASSP 2018.

DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION


In this paper, we propose a fully distributed approximate message passing (AMP) algorithm, which reconstructs an unknown vector from its linear measurements obtained at nodes in a network. The proposed algorithm is a distributed implementation of the centralized AMP algorithm, and consists of the local computation at each node and the global computation using communications between nodes. For the global computation, we propose a distributed algorithm named summation propagation to calculate a summation required in the AMP algorithm.

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Authors:
Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi
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20 April 2018 - 2:58am
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[1] Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi, "DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3088. Accessed: Apr. 21, 2018.
@article{3088-18,
url = {http://sigport.org/3088},
author = {Ryo Hayakawa; Ayano Nakai; Kazunori Hayashi },
publisher = {IEEE SigPort},
title = {DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION},
year = {2018} }
TY - EJOUR
T1 - DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION
AU - Ryo Hayakawa; Ayano Nakai; Kazunori Hayashi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3088
ER -
Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi. (2018). DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION. IEEE SigPort. http://sigport.org/3088
Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi, 2018. DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION. Available at: http://sigport.org/3088.
Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi. (2018). "DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION." Web.
1. Ryo Hayakawa, Ayano Nakai, Kazunori Hayashi. DISTRIBUTED APPROXIMATE MESSAGE PASSING WITH SUMMATION PROPAGATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3088

A CASCADED FRAMEWORK FOR MODEL-BASED 3D FACE RECONSTRUCTION


This paper presents a general framework for model-based 3D face reconstruction from a single image, which can incorporate mature face alignment methods and utilize their properties. In the proposed framework, the final model parameters, i.e., mostly including pose, identity and expression, are achieved by estimating updating the face landmarks and 3D face model parameter alternately. In addition, we propose the parameter augmented regression method (PARM) as an novel derivation of the framework.

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Authors:
Pengrui Wang, Wujun Che, Bo Xu
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20 April 2018 - 2:51am
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Poster_PARM_wang.pdf

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[1] Pengrui Wang, Wujun Che, Bo Xu, "A CASCADED FRAMEWORK FOR MODEL-BASED 3D FACE RECONSTRUCTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3087. Accessed: Apr. 21, 2018.
@article{3087-18,
url = {http://sigport.org/3087},
author = {Pengrui Wang; Wujun Che; Bo Xu },
publisher = {IEEE SigPort},
title = {A CASCADED FRAMEWORK FOR MODEL-BASED 3D FACE RECONSTRUCTION},
year = {2018} }
TY - EJOUR
T1 - A CASCADED FRAMEWORK FOR MODEL-BASED 3D FACE RECONSTRUCTION
AU - Pengrui Wang; Wujun Che; Bo Xu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3087
ER -
Pengrui Wang, Wujun Che, Bo Xu. (2018). A CASCADED FRAMEWORK FOR MODEL-BASED 3D FACE RECONSTRUCTION. IEEE SigPort. http://sigport.org/3087
Pengrui Wang, Wujun Che, Bo Xu, 2018. A CASCADED FRAMEWORK FOR MODEL-BASED 3D FACE RECONSTRUCTION. Available at: http://sigport.org/3087.
Pengrui Wang, Wujun Che, Bo Xu. (2018). "A CASCADED FRAMEWORK FOR MODEL-BASED 3D FACE RECONSTRUCTION." Web.
1. Pengrui Wang, Wujun Che, Bo Xu. A CASCADED FRAMEWORK FOR MODEL-BASED 3D FACE RECONSTRUCTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3087

PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION

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Authors:
Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki
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20 April 2018 - 2:22am
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0005294_poster

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[1] Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki, "PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3086. Accessed: Apr. 21, 2018.
@article{3086-18,
url = {http://sigport.org/3086},
author = {Hajime Yano; Toru Nakashika; Tetsuya Takiguchi; Yasuo Ariki },
publisher = {IEEE SigPort},
title = {PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION},
year = {2018} }
TY - EJOUR
T1 - PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION
AU - Hajime Yano; Toru Nakashika; Tetsuya Takiguchi; Yasuo Ariki
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3086
ER -
Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki. (2018). PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION. IEEE SigPort. http://sigport.org/3086
Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki, 2018. PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION. Available at: http://sigport.org/3086.
Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki. (2018). "PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION." Web.
1. Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki. PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3086

DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT


This paper presents a flexible framework for the challenging task of color-guided depth upsampling. Some state-of-the-art approaches apply an aligned RGB image for depth recovery. Unfortunately, these kinds of methods may result in texture copying artifacts and edge blurring artifacts. To address these difficulties, we propose an adaptive weighted least squares framework of choosing different guidance weight for variant conditions flexibly.

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Authors:
Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao
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20 April 2018 - 1:54am
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Depth map super-resolution, ToF, WLS, Patching-gradient method, De-noising

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[1] Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao, "DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3085. Accessed: Apr. 21, 2018.
@article{3085-18,
url = {http://sigport.org/3085},
author = {Yuyuan LI; Jiarui Sun; Bingshu Wang; Yong Zhao },
publisher = {IEEE SigPort},
title = {DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT},
year = {2018} }
TY - EJOUR
T1 - DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT
AU - Yuyuan LI; Jiarui Sun; Bingshu Wang; Yong Zhao
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3085
ER -
Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao. (2018). DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT. IEEE SigPort. http://sigport.org/3085
Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao, 2018. DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT. Available at: http://sigport.org/3085.
Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao. (2018). "DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT." Web.
1. Yuyuan LI, Jiarui Sun, Bingshu Wang, Yong Zhao. DEPTH SUPER-RESOLUTION USING JOINT ADAPTIVE WEIGHTED LEAST SQUARES AND PATCHING GRADIENT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3085

Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging


We address the problem of estimating the parameter of a Bernoulli process. This arises in many applications, including photon-efficient active imaging where each illumination period is regarded as a single Bernoulli trial. We introduce a framework within which to minimize the mean-squared error (MSE) subject to an upper bound on the mean number of trials. This optimization has several simple and intuitive properties when the Bernoulli parameter has a beta prior.

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Authors:
Safa C. Medin, John Murray-Bruce, Vivek K Goyal
Submitted On:
20 April 2018 - 2:03am
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ICASSP 2018 Poster

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[1] Safa C. Medin, John Murray-Bruce, Vivek K Goyal, "Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3084. Accessed: Apr. 21, 2018.
@article{3084-18,
url = {http://sigport.org/3084},
author = {Safa C. Medin; John Murray-Bruce; Vivek K Goyal },
publisher = {IEEE SigPort},
title = {Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging},
year = {2018} }
TY - EJOUR
T1 - Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging
AU - Safa C. Medin; John Murray-Bruce; Vivek K Goyal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3084
ER -
Safa C. Medin, John Murray-Bruce, Vivek K Goyal. (2018). Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging. IEEE SigPort. http://sigport.org/3084
Safa C. Medin, John Murray-Bruce, Vivek K Goyal, 2018. Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging. Available at: http://sigport.org/3084.
Safa C. Medin, John Murray-Bruce, Vivek K Goyal. (2018). "Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging." Web.
1. Safa C. Medin, John Murray-Bruce, Vivek K Goyal. Optimal Stopping Times for Estimating Bernoulli Parameters with Applications to Active Imaging [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3084

A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS

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20 April 2018 - 1:51am
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icassp_poster.pdf

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[1] , "A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3083. Accessed: Apr. 21, 2018.
@article{3083-18,
url = {http://sigport.org/3083},
author = { },
publisher = {IEEE SigPort},
title = {A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS},
year = {2018} }
TY - EJOUR
T1 - A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3083
ER -
. (2018). A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS. IEEE SigPort. http://sigport.org/3083
, 2018. A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS. Available at: http://sigport.org/3083.
. (2018). "A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS." Web.
1. . A NOVEL LSTM-BASED SPEECH PREPROCESSOR FOR SPEAKER DIARIZATION IN REALISTIC MISMATCH CONDITIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3083

HARD SHADOWS REMOVAL USING AN APPROXIMATE ILLUMINATION INVARIANT


Hard shadows detection and removal from foreground masks is a challenging step in change detection. This paper gives a simple and effective method to address hard shadows. There are inside portion and boundary portion in hard shadows. Pixel-wise neighborhood ratio is calculat¬ed to remove the most of inside shadow points. For the boundaries of shadow regions, we take advantage of color constancy to eliminate the edges of hard shadows and obtain relative accurate objects contours. Then, morphology processing is explored to enhance the integrity of objects.

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20 April 2018 - 1:47am
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BingshuWang_Poster_2018ICASSP.pdf

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[1] , "HARD SHADOWS REMOVAL USING AN APPROXIMATE ILLUMINATION INVARIANT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3082. Accessed: Apr. 21, 2018.
@article{3082-18,
url = {http://sigport.org/3082},
author = { },
publisher = {IEEE SigPort},
title = {HARD SHADOWS REMOVAL USING AN APPROXIMATE ILLUMINATION INVARIANT},
year = {2018} }
TY - EJOUR
T1 - HARD SHADOWS REMOVAL USING AN APPROXIMATE ILLUMINATION INVARIANT
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3082
ER -
. (2018). HARD SHADOWS REMOVAL USING AN APPROXIMATE ILLUMINATION INVARIANT. IEEE SigPort. http://sigport.org/3082
, 2018. HARD SHADOWS REMOVAL USING AN APPROXIMATE ILLUMINATION INVARIANT. Available at: http://sigport.org/3082.
. (2018). "HARD SHADOWS REMOVAL USING AN APPROXIMATE ILLUMINATION INVARIANT." Web.
1. . HARD SHADOWS REMOVAL USING AN APPROXIMATE ILLUMINATION INVARIANT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3082

Bayesian Sparse Signal Detection Exploiting Laplace Prior


In this paper, we consider the problem of sparse signal detection with compressed measurements in a Bayesian framework. Multiple nodes in the network are assumed to observe sparse signals. Observations at each node are compressed via random projections and sent to a centralized fusion center. Motivated by the fact that reliable detection of the sparse signals does not require complete signal reconstruction, we propose two computationally efficient methods for constructing decision statistics for detection.

ICASSP.pdf

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Authors:
Thakshila Wimalajeewa, Pramod K. Varshney
Submitted On:
20 April 2018 - 1:55am
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ICASSP.pdf

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[1] Thakshila Wimalajeewa, Pramod K. Varshney, "Bayesian Sparse Signal Detection Exploiting Laplace Prior", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3081. Accessed: Apr. 21, 2018.
@article{3081-18,
url = {http://sigport.org/3081},
author = {Thakshila Wimalajeewa; Pramod K. Varshney },
publisher = {IEEE SigPort},
title = {Bayesian Sparse Signal Detection Exploiting Laplace Prior},
year = {2018} }
TY - EJOUR
T1 - Bayesian Sparse Signal Detection Exploiting Laplace Prior
AU - Thakshila Wimalajeewa; Pramod K. Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3081
ER -
Thakshila Wimalajeewa, Pramod K. Varshney. (2018). Bayesian Sparse Signal Detection Exploiting Laplace Prior. IEEE SigPort. http://sigport.org/3081
Thakshila Wimalajeewa, Pramod K. Varshney, 2018. Bayesian Sparse Signal Detection Exploiting Laplace Prior. Available at: http://sigport.org/3081.
Thakshila Wimalajeewa, Pramod K. Varshney. (2018). "Bayesian Sparse Signal Detection Exploiting Laplace Prior." Web.
1. Thakshila Wimalajeewa, Pramod K. Varshney. Bayesian Sparse Signal Detection Exploiting Laplace Prior [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3081

Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing


Wireless distributed computing presents new opportunities to execute intelligent tasks on mobile devices for low-latency applications, by wirelessly aggregating the computation and storage resources among mobile devices. However, for low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency therein, we establish a novel interference alignment condition by exploiting the locally computed intermediate values as side information.

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Authors:
Yuanming Shi, Zhi Ding
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20 April 2018 - 1:48am
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ICASSP_poster.pdf

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[1] Yuanming Shi, Zhi Ding, "Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3080. Accessed: Apr. 21, 2018.
@article{3080-18,
url = {http://sigport.org/3080},
author = {Yuanming Shi; Zhi Ding },
publisher = {IEEE SigPort},
title = {Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing},
year = {2018} }
TY - EJOUR
T1 - Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing
AU - Yuanming Shi; Zhi Ding
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3080
ER -
Yuanming Shi, Zhi Ding. (2018). Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing. IEEE SigPort. http://sigport.org/3080
Yuanming Shi, Zhi Ding, 2018. Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing. Available at: http://sigport.org/3080.
Yuanming Shi, Zhi Ding. (2018). "Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing." Web.
1. Yuanming Shi, Zhi Ding. Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3080

On Sequential Random Distortion Testing of Non-Stationary Processes


Random distortion testing (RDT) addresses the problem of testing whether or not a random signal deviates by more than a specified tolerance from a fixed value. The test is non-parametric in the sense that the distribution of the signal under each hypothesis is assumed to be unknown. The signal is observed in independent and identically distributed (i.i.d) additive noise. The need to control the probabilities of false alarm and missed de- tection while reducing the number of samples required to make a decision leads to the SeqRDT approach.

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Authors:
Dominique Pastor, Vinod Sharma, Pramod K. Varshney
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20 April 2018 - 1:38am
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ICASSP18_Slides.pdf

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[1] Dominique Pastor, Vinod Sharma, Pramod K. Varshney, "On Sequential Random Distortion Testing of Non-Stationary Processes", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3079. Accessed: Apr. 21, 2018.
@article{3079-18,
url = {http://sigport.org/3079},
author = {Dominique Pastor; Vinod Sharma; Pramod K. Varshney },
publisher = {IEEE SigPort},
title = {On Sequential Random Distortion Testing of Non-Stationary Processes},
year = {2018} }
TY - EJOUR
T1 - On Sequential Random Distortion Testing of Non-Stationary Processes
AU - Dominique Pastor; Vinod Sharma; Pramod K. Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3079
ER -
Dominique Pastor, Vinod Sharma, Pramod K. Varshney. (2018). On Sequential Random Distortion Testing of Non-Stationary Processes. IEEE SigPort. http://sigport.org/3079
Dominique Pastor, Vinod Sharma, Pramod K. Varshney, 2018. On Sequential Random Distortion Testing of Non-Stationary Processes. Available at: http://sigport.org/3079.
Dominique Pastor, Vinod Sharma, Pramod K. Varshney. (2018). "On Sequential Random Distortion Testing of Non-Stationary Processes." Web.
1. Dominique Pastor, Vinod Sharma, Pramod K. Varshney. On Sequential Random Distortion Testing of Non-Stationary Processes [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3079

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