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ICASSP 2018

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.

DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE


The electroencephalography classifier is the most important component of brain-computer interface based systems. There are two major problems hindering the improvement of it. First, traditional methods do not fully exploit multimodal information. Second, large-scale annotated EEG datasets are almost impossible to acquire because biological data acquisition is challenging and quality annotation is costly. Herein, we propose a novel deep transfer learning approach to solve these two problems.

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Authors:
Chuanqi Tan, Fuchun Sun, Wenchang Zhang
Submitted On:
12 April 2018 - 11:40am
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Poster Chuanqi.pdf

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[1] Chuanqi Tan, Fuchun Sun, Wenchang Zhang, "DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2408. Accessed: Aug. 17, 2019.
@article{2408-18,
url = {http://sigport.org/2408},
author = {Chuanqi Tan; Fuchun Sun; Wenchang Zhang },
publisher = {IEEE SigPort},
title = {DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE},
year = {2018} }
TY - EJOUR
T1 - DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE
AU - Chuanqi Tan; Fuchun Sun; Wenchang Zhang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2408
ER -
Chuanqi Tan, Fuchun Sun, Wenchang Zhang. (2018). DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE. IEEE SigPort. http://sigport.org/2408
Chuanqi Tan, Fuchun Sun, Wenchang Zhang, 2018. DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE. Available at: http://sigport.org/2408.
Chuanqi Tan, Fuchun Sun, Wenchang Zhang. (2018). "DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE." Web.
1. Chuanqi Tan, Fuchun Sun, Wenchang Zhang. DEEP TRANSFER LEARNING FOR EEG-BASED BRAIN COMPUTER INTERFACE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2408

CORRELATION-BASED FACE DETECTION FOR RECOGNIZING FACES IN VIDEOS

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12 April 2018 - 11:36am
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HENG_POSTER.pdf

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[1] , "CORRELATION-BASED FACE DETECTION FOR RECOGNIZING FACES IN VIDEOS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2407. Accessed: Aug. 17, 2019.
@article{2407-18,
url = {http://sigport.org/2407},
author = { },
publisher = {IEEE SigPort},
title = {CORRELATION-BASED FACE DETECTION FOR RECOGNIZING FACES IN VIDEOS},
year = {2018} }
TY - EJOUR
T1 - CORRELATION-BASED FACE DETECTION FOR RECOGNIZING FACES IN VIDEOS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2407
ER -
. (2018). CORRELATION-BASED FACE DETECTION FOR RECOGNIZING FACES IN VIDEOS. IEEE SigPort. http://sigport.org/2407
, 2018. CORRELATION-BASED FACE DETECTION FOR RECOGNIZING FACES IN VIDEOS. Available at: http://sigport.org/2407.
. (2018). "CORRELATION-BASED FACE DETECTION FOR RECOGNIZING FACES IN VIDEOS." Web.
1. . CORRELATION-BASED FACE DETECTION FOR RECOGNIZING FACES IN VIDEOS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2407

Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform


Diagnosis of melanoma is fraught with uncertainty, and discordance rates among physicians remain high because of the lack of a definitive criterion. Motivated by this challenge, this paper first introduces the Patch Weyl transform (PWT), a 2-dimensional variant of the Weyl transform. It then presents a method for classifying pump-probe images of melanocytic lesions based on the PWT coefficients.

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Authors:
Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank
Submitted On:
12 April 2018 - 11:47am
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ICASSP Presentations.pdf

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[1] Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank, "Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2406. Accessed: Aug. 17, 2019.
@article{2406-18,
url = {http://sigport.org/2406},
author = {Qiang Qiu; Edward Bosch; Andrew Thompson; Francisco E. Robles; Guillermo Sapiro; Warren S. Warren; Robert Calderbank },
publisher = {IEEE SigPort},
title = {Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform},
year = {2018} }
TY - EJOUR
T1 - Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform
AU - Qiang Qiu; Edward Bosch; Andrew Thompson; Francisco E. Robles; Guillermo Sapiro; Warren S. Warren; Robert Calderbank
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2406
ER -
Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank. (2018). Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform. IEEE SigPort. http://sigport.org/2406
Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank, 2018. Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform. Available at: http://sigport.org/2406.
Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank. (2018). "Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform." Web.
1. Qiang Qiu, Edward Bosch, Andrew Thompson, Francisco E. Robles, Guillermo Sapiro, Warren S. Warren, Robert Calderbank. Classifying Pump-probe Images of Melanocytic Lesions using the Weyl Transform [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2406

Linear classification in speech-based objective differential diagnosis of Parkinsonism

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Authors:
Gongfeng Li, Khalid Daoudi, Jiri Klempir, Jan Rusz
Submitted On:
12 April 2018 - 11:33am
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Poster-Icassp18.pdf

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[1] Gongfeng Li, Khalid Daoudi, Jiri Klempir, Jan Rusz, "Linear classification in speech-based objective differential diagnosis of Parkinsonism", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2405. Accessed: Aug. 17, 2019.
@article{2405-18,
url = {http://sigport.org/2405},
author = {Gongfeng Li; Khalid Daoudi; Jiri Klempir; Jan Rusz },
publisher = {IEEE SigPort},
title = {Linear classification in speech-based objective differential diagnosis of Parkinsonism},
year = {2018} }
TY - EJOUR
T1 - Linear classification in speech-based objective differential diagnosis of Parkinsonism
AU - Gongfeng Li; Khalid Daoudi; Jiri Klempir; Jan Rusz
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2405
ER -
Gongfeng Li, Khalid Daoudi, Jiri Klempir, Jan Rusz. (2018). Linear classification in speech-based objective differential diagnosis of Parkinsonism. IEEE SigPort. http://sigport.org/2405
Gongfeng Li, Khalid Daoudi, Jiri Klempir, Jan Rusz, 2018. Linear classification in speech-based objective differential diagnosis of Parkinsonism. Available at: http://sigport.org/2405.
Gongfeng Li, Khalid Daoudi, Jiri Klempir, Jan Rusz. (2018). "Linear classification in speech-based objective differential diagnosis of Parkinsonism." Web.
1. Gongfeng Li, Khalid Daoudi, Jiri Klempir, Jan Rusz. Linear classification in speech-based objective differential diagnosis of Parkinsonism [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2405

Robust Recognition of Speech with Background Music in Acoustically Under-Resourced Scenarios


This paper addresses the task of Automatic Speech Recognition
(ASR) with music in the background. We consider two different
situations: 1) scenarios with very small amount of labeled training
utterances (duration 1 hour) and 2) scenarios with large amount of
labeled training utterances (duration 132 hours). In these situations,
we aim to achieve robust recognition. To this end we investigate
the following techniques: a) multi-condition training of the acoustic
model, b) denoising autoencoders for feature enhancement and c)

Paper Details

Authors:
Jiri Malek, Jindrich Zdansky, Petr Cerva
Submitted On:
12 April 2018 - 11:32am
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ICASSP2018_Paper1052_MalekZdanskyCerva.pdf

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[1] Jiri Malek, Jindrich Zdansky, Petr Cerva, "Robust Recognition of Speech with Background Music in Acoustically Under-Resourced Scenarios", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2404. Accessed: Aug. 17, 2019.
@article{2404-18,
url = {http://sigport.org/2404},
author = {Jiri Malek; Jindrich Zdansky; Petr Cerva },
publisher = {IEEE SigPort},
title = {Robust Recognition of Speech with Background Music in Acoustically Under-Resourced Scenarios},
year = {2018} }
TY - EJOUR
T1 - Robust Recognition of Speech with Background Music in Acoustically Under-Resourced Scenarios
AU - Jiri Malek; Jindrich Zdansky; Petr Cerva
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2404
ER -
Jiri Malek, Jindrich Zdansky, Petr Cerva. (2018). Robust Recognition of Speech with Background Music in Acoustically Under-Resourced Scenarios. IEEE SigPort. http://sigport.org/2404
Jiri Malek, Jindrich Zdansky, Petr Cerva, 2018. Robust Recognition of Speech with Background Music in Acoustically Under-Resourced Scenarios. Available at: http://sigport.org/2404.
Jiri Malek, Jindrich Zdansky, Petr Cerva. (2018). "Robust Recognition of Speech with Background Music in Acoustically Under-Resourced Scenarios." Web.
1. Jiri Malek, Jindrich Zdansky, Petr Cerva. Robust Recognition of Speech with Background Music in Acoustically Under-Resourced Scenarios [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2404

EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS


Many smart devices now support high-quality speech communication services at super-wide bandwidths. Often, however, speech quality is degraded when they are used with networks or devices which lack super-wideband support. Artificial bandwidth extension can then be used to improve speech quality. While approaches to wideband extension have been reported previously, this paper proposes an approach to super-wide bandwidth extension.

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Submitted On:
12 April 2018 - 11:37am
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ICASSP2018_SWBE.pdf

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[1] , "EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2403. Accessed: Aug. 17, 2019.
@article{2403-18,
url = {http://sigport.org/2403},
author = { },
publisher = {IEEE SigPort},
title = {EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS},
year = {2018} }
TY - EJOUR
T1 - EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2403
ER -
. (2018). EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS. IEEE SigPort. http://sigport.org/2403
, 2018. EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS. Available at: http://sigport.org/2403.
. (2018). "EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS." Web.
1. . EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2403

COMPLEXITY REDUCTION OF EIGENVALUE DECOMPOSITION-BASED DIFFUSE POWER SPECTRAL DENSITY ESTIMATORS USING THE POWER METHOD


In noisy and reverberant environments speech enhancement techniques such as the multi-channel Wiener filter (MWF) can be used to improve speech quality and intelligibility. Assuming that reverberation and ambient noise can be modeled as diffuse sound fields, such techniques require an estimate of the diffuse power spectral density (PSD). Recently a multi-channel diffuse PSD estimator based on the eigenvalue decomposition (EVD) of the prewhitened signal PSD matrix was proposed.

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Authors:
Marvin Tammen, Ina Kodrasi, Simon Doclo
Submitted On:
12 April 2018 - 11:31am
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ICASSP2018_Tammenetal.pdf

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[1] Marvin Tammen, Ina Kodrasi, Simon Doclo, "COMPLEXITY REDUCTION OF EIGENVALUE DECOMPOSITION-BASED DIFFUSE POWER SPECTRAL DENSITY ESTIMATORS USING THE POWER METHOD", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2402. Accessed: Aug. 17, 2019.
@article{2402-18,
url = {http://sigport.org/2402},
author = {Marvin Tammen; Ina Kodrasi; Simon Doclo },
publisher = {IEEE SigPort},
title = {COMPLEXITY REDUCTION OF EIGENVALUE DECOMPOSITION-BASED DIFFUSE POWER SPECTRAL DENSITY ESTIMATORS USING THE POWER METHOD},
year = {2018} }
TY - EJOUR
T1 - COMPLEXITY REDUCTION OF EIGENVALUE DECOMPOSITION-BASED DIFFUSE POWER SPECTRAL DENSITY ESTIMATORS USING THE POWER METHOD
AU - Marvin Tammen; Ina Kodrasi; Simon Doclo
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2402
ER -
Marvin Tammen, Ina Kodrasi, Simon Doclo. (2018). COMPLEXITY REDUCTION OF EIGENVALUE DECOMPOSITION-BASED DIFFUSE POWER SPECTRAL DENSITY ESTIMATORS USING THE POWER METHOD. IEEE SigPort. http://sigport.org/2402
Marvin Tammen, Ina Kodrasi, Simon Doclo, 2018. COMPLEXITY REDUCTION OF EIGENVALUE DECOMPOSITION-BASED DIFFUSE POWER SPECTRAL DENSITY ESTIMATORS USING THE POWER METHOD. Available at: http://sigport.org/2402.
Marvin Tammen, Ina Kodrasi, Simon Doclo. (2018). "COMPLEXITY REDUCTION OF EIGENVALUE DECOMPOSITION-BASED DIFFUSE POWER SPECTRAL DENSITY ESTIMATORS USING THE POWER METHOD." Web.
1. Marvin Tammen, Ina Kodrasi, Simon Doclo. COMPLEXITY REDUCTION OF EIGENVALUE DECOMPOSITION-BASED DIFFUSE POWER SPECTRAL DENSITY ESTIMATORS USING THE POWER METHOD [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2402

UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY

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12 April 2018 - 11:29am
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Underwater Optical Sensor Networks Localization

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[1] , "UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2400. Accessed: Aug. 17, 2019.
@article{2400-18,
url = {http://sigport.org/2400},
author = { },
publisher = {IEEE SigPort},
title = {UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY},
year = {2018} }
TY - EJOUR
T1 - UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2400
ER -
. (2018). UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY. IEEE SigPort. http://sigport.org/2400
, 2018. UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY. Available at: http://sigport.org/2400.
. (2018). "UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY." Web.
1. . UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2400

DNN BASED EMBEDDINGS FOR LANGUAGE RECOGNITION


In this work, we present a language identification (LID) system based on embeddings. In our case, an embedding is a fixed-length vector (similar to i-vector) that represents the whole utterance, but unlike i-vector it is designed to contain mostly information relevant to the target task (LID). In order to obtain these embeddings, we train a deep neural network (DNN) with sequence summarization layer to classify languages.

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Authors:
Alicia Lozano-Diez, Oldrich Plchot, Pavel Matejka, Joaquin Gonzalez-Rodriguez
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12 April 2018 - 11:29am
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Poster Embeddings LID NIST LRE 2017 Lozano et al.

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[1] Alicia Lozano-Diez, Oldrich Plchot, Pavel Matejka, Joaquin Gonzalez-Rodriguez, "DNN BASED EMBEDDINGS FOR LANGUAGE RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2399. Accessed: Aug. 17, 2019.
@article{2399-18,
url = {http://sigport.org/2399},
author = {Alicia Lozano-Diez; Oldrich Plchot; Pavel Matejka; Joaquin Gonzalez-Rodriguez },
publisher = {IEEE SigPort},
title = {DNN BASED EMBEDDINGS FOR LANGUAGE RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - DNN BASED EMBEDDINGS FOR LANGUAGE RECOGNITION
AU - Alicia Lozano-Diez; Oldrich Plchot; Pavel Matejka; Joaquin Gonzalez-Rodriguez
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2399
ER -
Alicia Lozano-Diez, Oldrich Plchot, Pavel Matejka, Joaquin Gonzalez-Rodriguez. (2018). DNN BASED EMBEDDINGS FOR LANGUAGE RECOGNITION. IEEE SigPort. http://sigport.org/2399
Alicia Lozano-Diez, Oldrich Plchot, Pavel Matejka, Joaquin Gonzalez-Rodriguez, 2018. DNN BASED EMBEDDINGS FOR LANGUAGE RECOGNITION. Available at: http://sigport.org/2399.
Alicia Lozano-Diez, Oldrich Plchot, Pavel Matejka, Joaquin Gonzalez-Rodriguez. (2018). "DNN BASED EMBEDDINGS FOR LANGUAGE RECOGNITION." Web.
1. Alicia Lozano-Diez, Oldrich Plchot, Pavel Matejka, Joaquin Gonzalez-Rodriguez. DNN BASED EMBEDDINGS FOR LANGUAGE RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2399

FLEXIBLE MULTI-GROUP SINGLE CARRIER MODULATION: OPTIMAL SUBCARRIER GROUPING AND RATE MAXIMIZATION

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Authors:
Yifei Yang, Shuowen Zhang, Joni Polili Lie, Rui Zhang
Submitted On:
12 April 2018 - 11:28am
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ICASSP_FMGSC.pdf

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[1] Yifei Yang, Shuowen Zhang, Joni Polili Lie, Rui Zhang, "FLEXIBLE MULTI-GROUP SINGLE CARRIER MODULATION: OPTIMAL SUBCARRIER GROUPING AND RATE MAXIMIZATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2398. Accessed: Aug. 17, 2019.
@article{2398-18,
url = {http://sigport.org/2398},
author = {Yifei Yang; Shuowen Zhang; Joni Polili Lie; Rui Zhang },
publisher = {IEEE SigPort},
title = {FLEXIBLE MULTI-GROUP SINGLE CARRIER MODULATION: OPTIMAL SUBCARRIER GROUPING AND RATE MAXIMIZATION},
year = {2018} }
TY - EJOUR
T1 - FLEXIBLE MULTI-GROUP SINGLE CARRIER MODULATION: OPTIMAL SUBCARRIER GROUPING AND RATE MAXIMIZATION
AU - Yifei Yang; Shuowen Zhang; Joni Polili Lie; Rui Zhang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2398
ER -
Yifei Yang, Shuowen Zhang, Joni Polili Lie, Rui Zhang. (2018). FLEXIBLE MULTI-GROUP SINGLE CARRIER MODULATION: OPTIMAL SUBCARRIER GROUPING AND RATE MAXIMIZATION. IEEE SigPort. http://sigport.org/2398
Yifei Yang, Shuowen Zhang, Joni Polili Lie, Rui Zhang, 2018. FLEXIBLE MULTI-GROUP SINGLE CARRIER MODULATION: OPTIMAL SUBCARRIER GROUPING AND RATE MAXIMIZATION. Available at: http://sigport.org/2398.
Yifei Yang, Shuowen Zhang, Joni Polili Lie, Rui Zhang. (2018). "FLEXIBLE MULTI-GROUP SINGLE CARRIER MODULATION: OPTIMAL SUBCARRIER GROUPING AND RATE MAXIMIZATION." Web.
1. Yifei Yang, Shuowen Zhang, Joni Polili Lie, Rui Zhang. FLEXIBLE MULTI-GROUP SINGLE CARRIER MODULATION: OPTIMAL SUBCARRIER GROUPING AND RATE MAXIMIZATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2398

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