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Signal Processing Theory and Methods

Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing


In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data.

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Authors:
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi
Submitted On:
4 December 2018 - 11:04pm
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GlobalSIP2018_talk.pdf

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[1] Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi, "Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3840. Accessed: Dec. 13, 2018.
@article{3840-18,
url = {http://sigport.org/3840},
author = {Gonzalo Cucho-Padin; Yue Wang; Lara Waldrop; Zhi Tian; Farzad Kamalabadi },
publisher = {IEEE SigPort},
title = {Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing},
year = {2018} }
TY - EJOUR
T1 - Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing
AU - Gonzalo Cucho-Padin; Yue Wang; Lara Waldrop; Zhi Tian; Farzad Kamalabadi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3840
ER -
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. (2018). Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing. IEEE SigPort. http://sigport.org/3840
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi, 2018. Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing. Available at: http://sigport.org/3840.
Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. (2018). "Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing." Web.
1. Gonzalo Cucho-Padin, Yue Wang, Lara Waldrop, Zhi Tian, Farzad Kamalabadi. Efficient RFI detection in radio astronomy based on Compressive Statistical Sensing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3840

MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING


In this paper, we discuss the problem of modeling a graph signal on a directed graph when observing only partially the graph signal. The graph signal is recovered using a learned graph filter. The novelty is to use the random walk operator associated to an ergodic random walk on the graph, so as to define and learn a graph filter, expressed as a polynomial of this operator. Through the study of different cases, we show the efficiency of the signal modeling using the random walk operator compared to existing methods using the adjacency matrix or ignoring the directions in the graph.

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Authors:
Harry Sevi, Gabriel Rilling, Pierre Borgnat
Submitted On:
27 November 2018 - 9:53am
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Borgnat_talk_GlobalSIP_2018.pdf

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[1] Harry Sevi, Gabriel Rilling, Pierre Borgnat, "MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3812. Accessed: Dec. 13, 2018.
@article{3812-18,
url = {http://sigport.org/3812},
author = {Harry Sevi; Gabriel Rilling; Pierre Borgnat },
publisher = {IEEE SigPort},
title = {MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING},
year = {2018} }
TY - EJOUR
T1 - MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING
AU - Harry Sevi; Gabriel Rilling; Pierre Borgnat
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3812
ER -
Harry Sevi, Gabriel Rilling, Pierre Borgnat. (2018). MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING. IEEE SigPort. http://sigport.org/3812
Harry Sevi, Gabriel Rilling, Pierre Borgnat, 2018. MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING. Available at: http://sigport.org/3812.
Harry Sevi, Gabriel Rilling, Pierre Borgnat. (2018). "MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING." Web.
1. Harry Sevi, Gabriel Rilling, Pierre Borgnat. MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3812

Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs

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Authors:
Madeleine S. Kotzagiannidis, Mike E. Davies
Submitted On:
8 December 2018 - 1:41pm
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MKotzagiannidisglobalsip2018.pdf

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[1] Madeleine S. Kotzagiannidis, Mike E. Davies, "Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3802. Accessed: Dec. 13, 2018.
@article{3802-18,
url = {http://sigport.org/3802},
author = {Madeleine S. Kotzagiannidis; Mike E. Davies },
publisher = {IEEE SigPort},
title = {Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs},
year = {2018} }
TY - EJOUR
T1 - Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs
AU - Madeleine S. Kotzagiannidis; Mike E. Davies
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3802
ER -
Madeleine S. Kotzagiannidis, Mike E. Davies. (2018). Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs. IEEE SigPort. http://sigport.org/3802
Madeleine S. Kotzagiannidis, Mike E. Davies, 2018. Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs. Available at: http://sigport.org/3802.
Madeleine S. Kotzagiannidis, Mike E. Davies. (2018). "Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs." Web.
1. Madeleine S. Kotzagiannidis, Mike E. Davies. Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3802

PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES

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Authors:
Hanwei Wu, Qiwen Wang, Markus Flierl
Submitted On:
27 November 2018 - 6:31pm
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global_poster.pdf

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global_poster.pdf

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[1] Hanwei Wu, Qiwen Wang, Markus Flierl, " PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3796. Accessed: Dec. 13, 2018.
@article{3796-18,
url = {http://sigport.org/3796},
author = {Hanwei Wu; Qiwen Wang; Markus Flierl },
publisher = {IEEE SigPort},
title = { PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES},
year = {2018} }
TY - EJOUR
T1 - PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES
AU - Hanwei Wu; Qiwen Wang; Markus Flierl
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3796
ER -
Hanwei Wu, Qiwen Wang, Markus Flierl. (2018). PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES. IEEE SigPort. http://sigport.org/3796
Hanwei Wu, Qiwen Wang, Markus Flierl, 2018. PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES. Available at: http://sigport.org/3796.
Hanwei Wu, Qiwen Wang, Markus Flierl. (2018). " PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES." Web.
1. Hanwei Wu, Qiwen Wang, Markus Flierl. PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3796

Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals


In this paper we consider the generalized approxi- mate message passing (GAMP) algorithm for recovering a sparse signal from modulo samples of randomized projections of the unknown signal. The modulo samples are obtained by a self-reset (SR) analog to digital converter (ADC). Additionally, in contrast to previous work on SR ADC, we consider a scenario where the compressed sensing (CS) measurements (i.e., randomized projections) are sent through a communication channel, namely an additive white Gaussian noise (AWGN) channel before being quantized by a SR ADC.

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Authors:
Osman Musa, Peter Jung, Norbert Goertz
Submitted On:
26 November 2018 - 3:57pm
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globalsip2018-poster.pdf

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[1] Osman Musa, Peter Jung, Norbert Goertz, " Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3788. Accessed: Dec. 13, 2018.
@article{3788-18,
url = {http://sigport.org/3788},
author = {Osman Musa; Peter Jung; Norbert Goertz },
publisher = {IEEE SigPort},
title = { Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals},
year = {2018} }
TY - EJOUR
T1 - Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals
AU - Osman Musa; Peter Jung; Norbert Goertz
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3788
ER -
Osman Musa, Peter Jung, Norbert Goertz. (2018). Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals. IEEE SigPort. http://sigport.org/3788
Osman Musa, Peter Jung, Norbert Goertz, 2018. Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals. Available at: http://sigport.org/3788.
Osman Musa, Peter Jung, Norbert Goertz. (2018). " Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals." Web.
1. Osman Musa, Peter Jung, Norbert Goertz. Generalized Approximate Message Passing for Unlimited Sampling of Sparse Signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3788

Generation of Correlated PSK Waveforms Using Complex Gaussian Random Variables


This work proposes a direct method to generate phase shift keying (PSK) symbols with desired correlation properties by mapping complex Gaussian random variables. The relationship between the cross-correlation of Gaussian and PSK symbols is derived in closed-form. This non-iterative approach outputs finite-alphabet constant-modulus waveforms capable of matching desired transmit beampatterns.

Poster.pdf

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Authors:
Seifallah Jardak, Sajid Ahmed, Mohamed-Slim Alouini
Submitted On:
22 November 2018 - 7:49pm
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Poster.pdf

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[1] Seifallah Jardak, Sajid Ahmed, Mohamed-Slim Alouini, "Generation of Correlated PSK Waveforms Using Complex Gaussian Random Variables", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3724. Accessed: Dec. 13, 2018.
@article{3724-18,
url = {http://sigport.org/3724},
author = {Seifallah Jardak; Sajid Ahmed; Mohamed-Slim Alouini },
publisher = {IEEE SigPort},
title = {Generation of Correlated PSK Waveforms Using Complex Gaussian Random Variables},
year = {2018} }
TY - EJOUR
T1 - Generation of Correlated PSK Waveforms Using Complex Gaussian Random Variables
AU - Seifallah Jardak; Sajid Ahmed; Mohamed-Slim Alouini
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3724
ER -
Seifallah Jardak, Sajid Ahmed, Mohamed-Slim Alouini. (2018). Generation of Correlated PSK Waveforms Using Complex Gaussian Random Variables. IEEE SigPort. http://sigport.org/3724
Seifallah Jardak, Sajid Ahmed, Mohamed-Slim Alouini, 2018. Generation of Correlated PSK Waveforms Using Complex Gaussian Random Variables. Available at: http://sigport.org/3724.
Seifallah Jardak, Sajid Ahmed, Mohamed-Slim Alouini. (2018). "Generation of Correlated PSK Waveforms Using Complex Gaussian Random Variables." Web.
1. Seifallah Jardak, Sajid Ahmed, Mohamed-Slim Alouini. Generation of Correlated PSK Waveforms Using Complex Gaussian Random Variables [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3724

LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION


The rapid convergence rate, high fidelity learning outcome and low computational cost are key targets in solving the learning problem of the complex physical system. Guided

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Authors:
Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song
Submitted On:
18 November 2018 - 4:23pm
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GlobalSIP2018_FWI 16.07.19.pdf

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[1] Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song, "LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3680. Accessed: Dec. 13, 2018.
@article{3680-18,
url = {http://sigport.org/3680},
author = {Rui Xie; Fangyu Li; Zengyan Wang; WenZhan Song },
publisher = {IEEE SigPort},
title = {LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION},
year = {2018} }
TY - EJOUR
T1 - LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION
AU - Rui Xie; Fangyu Li; Zengyan Wang; WenZhan Song
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3680
ER -
Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song. (2018). LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION. IEEE SigPort. http://sigport.org/3680
Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song, 2018. LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION. Available at: http://sigport.org/3680.
Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song. (2018). "LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION." Web.
1. Rui Xie, Fangyu Li, Zengyan Wang, WenZhan Song. LARGE SCALE RANDOMIZED LEARNING GUIDED BY PHYSICAL LAWS WITH APPLICATIONS IN FULL WAVEFORM INVERSION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3680

Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence


Most existing work in designing sensing matrices for compressive recovery is based on optimizing some quality factor, such as mutual coherence, average coherence or the restricted isometry constant (RIC), of the sensing matrix. In this paper, we report anomalous results that show that such a design is not always guaranteed to improve reconstruction results.

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Authors:
Dhruv Shah, Alankar Kotwal, Ajit Rajwade
Submitted On:
20 November 2018 - 2:42am
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globalsip2018_poster_v3.pdf

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[1] Dhruv Shah, Alankar Kotwal, Ajit Rajwade, "Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3670. Accessed: Dec. 13, 2018.
@article{3670-18,
url = {http://sigport.org/3670},
author = {Dhruv Shah; Alankar Kotwal; Ajit Rajwade },
publisher = {IEEE SigPort},
title = {Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence},
year = {2018} }
TY - EJOUR
T1 - Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence
AU - Dhruv Shah; Alankar Kotwal; Ajit Rajwade
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3670
ER -
Dhruv Shah, Alankar Kotwal, Ajit Rajwade. (2018). Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence. IEEE SigPort. http://sigport.org/3670
Dhruv Shah, Alankar Kotwal, Ajit Rajwade, 2018. Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence. Available at: http://sigport.org/3670.
Dhruv Shah, Alankar Kotwal, Ajit Rajwade. (2018). "Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence." Web.
1. Dhruv Shah, Alankar Kotwal, Ajit Rajwade. Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3670

SAVE - Space Alternating Variational Estimation for Sparse Bayesian Learning

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Authors:
Dirk Slock
Submitted On:
6 August 2018 - 9:13am
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[1] Dirk Slock, "SAVE - Space Alternating Variational Estimation for Sparse Bayesian Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3384. Accessed: Dec. 13, 2018.
@article{3384-18,
url = {http://sigport.org/3384},
author = {Dirk Slock },
publisher = {IEEE SigPort},
title = {SAVE - Space Alternating Variational Estimation for Sparse Bayesian Learning},
year = {2018} }
TY - EJOUR
T1 - SAVE - Space Alternating Variational Estimation for Sparse Bayesian Learning
AU - Dirk Slock
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3384
ER -
Dirk Slock. (2018). SAVE - Space Alternating Variational Estimation for Sparse Bayesian Learning. IEEE SigPort. http://sigport.org/3384
Dirk Slock, 2018. SAVE - Space Alternating Variational Estimation for Sparse Bayesian Learning. Available at: http://sigport.org/3384.
Dirk Slock. (2018). "SAVE - Space Alternating Variational Estimation for Sparse Bayesian Learning." Web.
1. Dirk Slock. SAVE - Space Alternating Variational Estimation for Sparse Bayesian Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3384

Data-Driven Nonparametric Hypothesis Testing

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Authors:
Yixian Liu, Yingbin Liang, Shuguang Cui
Submitted On:
20 April 2018 - 12:55am
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icassp2018_poster_liuyixian.pdf

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[1] Yixian Liu, Yingbin Liang, Shuguang Cui, "Data-Driven Nonparametric Hypothesis Testing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3069. Accessed: Dec. 13, 2018.
@article{3069-18,
url = {http://sigport.org/3069},
author = {Yixian Liu; Yingbin Liang; Shuguang Cui },
publisher = {IEEE SigPort},
title = {Data-Driven Nonparametric Hypothesis Testing},
year = {2018} }
TY - EJOUR
T1 - Data-Driven Nonparametric Hypothesis Testing
AU - Yixian Liu; Yingbin Liang; Shuguang Cui
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3069
ER -
Yixian Liu, Yingbin Liang, Shuguang Cui. (2018). Data-Driven Nonparametric Hypothesis Testing. IEEE SigPort. http://sigport.org/3069
Yixian Liu, Yingbin Liang, Shuguang Cui, 2018. Data-Driven Nonparametric Hypothesis Testing. Available at: http://sigport.org/3069.
Yixian Liu, Yingbin Liang, Shuguang Cui. (2018). "Data-Driven Nonparametric Hypothesis Testing." Web.
1. Yixian Liu, Yingbin Liang, Shuguang Cui. Data-Driven Nonparametric Hypothesis Testing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3069

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