Signal Processing

CREATE: Multimodal Dataset for Unsupervised Learning and Generative Modeling of Sensory Data from a Mobile Robot


The CREATE database is composed of 14 hours of multimodal recordings from a mobile robotic platform based on the iRobot Create.

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Citation Author(s):
Submitted by:
Simon Brodeur
Last updated:
Wed, 03/28/2018 - 10:44
DOI:
10.21227/H2M94J
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[1] , "CREATE: Multimodal Dataset for Unsupervised Learning and Generative Modeling of Sensory Data from a Mobile Robot", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2M94J. Accessed: Apr. 23, 2018.
@data{h2m94j-18,
doi = {10.21227/H2M94J},
url = {http://dx.doi.org/10.21227/H2M94J},
author = { },
publisher = {IEEE Dataport},
title = {CREATE: Multimodal Dataset for Unsupervised Learning and Generative Modeling of Sensory Data from a Mobile Robot},
year = {2018} }
TY - DATA
T1 - CREATE: Multimodal Dataset for Unsupervised Learning and Generative Modeling of Sensory Data from a Mobile Robot
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2M94J
ER -
. (2018). CREATE: Multimodal Dataset for Unsupervised Learning and Generative Modeling of Sensory Data from a Mobile Robot. IEEE Dataport. http://dx.doi.org/10.21227/H2M94J
, 2018. CREATE: Multimodal Dataset for Unsupervised Learning and Generative Modeling of Sensory Data from a Mobile Robot. Available at: http://dx.doi.org/10.21227/H2M94J.
. (2018). "CREATE: Multimodal Dataset for Unsupervised Learning and Generative Modeling of Sensory Data from a Mobile Robot." Web.
1. . CREATE: Multimodal Dataset for Unsupervised Learning and Generative Modeling of Sensory Data from a Mobile Robot [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2M94J
. "CREATE: Multimodal Dataset for Unsupervised Learning and Generative Modeling of Sensory Data from a Mobile Robot." doi: 10.21227/H2M94J

A Manitoban Speech Dataset


      The following dataset consists of utterances, recorded using 24 volunteers raised in the Province of Manitoba, Canada. To provide a repeatable set of test words that would cover all of the phonemes, the Edinburg Machine Readable Phonetic Alphabet (MRPA) [KiGr08], consisting of 44 words is used. Each recording consists of one word uttered by the volunteer and recorded in one continuous session.

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Dataset Details

Citation Author(s):
Submitted by:
Sina Sedigh
Last updated:
Wed, 03/28/2018 - 10:49
DOI:
10.21227/H2KM16
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[1] , "A Manitoban Speech Dataset", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2KM16. Accessed: Apr. 23, 2018.
@data{h2km16-18,
doi = {10.21227/H2KM16},
url = {http://dx.doi.org/10.21227/H2KM16},
author = { },
publisher = {IEEE Dataport},
title = {A Manitoban Speech Dataset},
year = {2018} }
TY - DATA
T1 - A Manitoban Speech Dataset
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2KM16
ER -
. (2018). A Manitoban Speech Dataset. IEEE Dataport. http://dx.doi.org/10.21227/H2KM16
, 2018. A Manitoban Speech Dataset. Available at: http://dx.doi.org/10.21227/H2KM16.
. (2018). "A Manitoban Speech Dataset." Web.
1. . A Manitoban Speech Dataset [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2KM16
. "A Manitoban Speech Dataset." doi: 10.21227/H2KM16

Decoding local field potentials for neural interfaces


This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/

The DataPort Repository contains the data used primarily for generating Figure 1.

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Citation Author(s):
Submitted by:
Thomas Hall
Last updated:
Wed, 03/28/2018 - 10:53
DOI:
10.21227/H2VW5Z
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[1] , "Decoding local field potentials for neural interfaces", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2VW5Z. Accessed: Apr. 23, 2018.
@data{h2vw5z-17,
doi = {10.21227/H2VW5Z},
url = {http://dx.doi.org/10.21227/H2VW5Z},
author = { },
publisher = {IEEE Dataport},
title = {Decoding local field potentials for neural interfaces},
year = {2017} }
TY - DATA
T1 - Decoding local field potentials for neural interfaces
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2VW5Z
ER -
. (2017). Decoding local field potentials for neural interfaces. IEEE Dataport. http://dx.doi.org/10.21227/H2VW5Z
, 2017. Decoding local field potentials for neural interfaces. Available at: http://dx.doi.org/10.21227/H2VW5Z.
. (2017). "Decoding local field potentials for neural interfaces." Web.
1. . Decoding local field potentials for neural interfaces [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2VW5Z
. "Decoding local field potentials for neural interfaces." doi: 10.21227/H2VW5Z

Source Separation for Simultaneous Seismic Data Acquisition


This is the data competion hosted by the IEEE Machine Learning for Signal Processing (MLSP) Technical Committee as part of the 27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2017), Tokyo, Japan. This year the competion is based on a dataset kindly provided Petroleum Geo-Systems (PGS), on source separation for seismic data acquistion. 


The submission period for this data competition has ended.

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Dataset Details

Citation Author(s):
Submitted by:
Yuejie Chi
Last updated:
Wed, 03/28/2018 - 10:46
DOI:
10.21227/H2TP46
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[1] , "Source Separation for Simultaneous Seismic Data Acquisition", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2TP46. Accessed: Apr. 23, 2018.
@data{h2tp46-17,
doi = {10.21227/H2TP46},
url = {http://dx.doi.org/10.21227/H2TP46},
author = { },
publisher = {IEEE Dataport},
title = {Source Separation for Simultaneous Seismic Data Acquisition},
year = {2017} }
TY - DATA
T1 - Source Separation for Simultaneous Seismic Data Acquisition
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2TP46
ER -
. (2017). Source Separation for Simultaneous Seismic Data Acquisition. IEEE Dataport. http://dx.doi.org/10.21227/H2TP46
, 2017. Source Separation for Simultaneous Seismic Data Acquisition. Available at: http://dx.doi.org/10.21227/H2TP46.
. (2017). "Source Separation for Simultaneous Seismic Data Acquisition." Web.
1. . Source Separation for Simultaneous Seismic Data Acquisition [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2TP46
. "Source Separation for Simultaneous Seismic Data Acquisition." doi: 10.21227/H2TP46

Real-life Power Quality Sags


The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.

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Dataset Details

Citation Author(s):
Submitted by:
Juan Jose Gonza...
Last updated:
Wed, 03/28/2018 - 10:54
DOI:
10.21227/H2K88D
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[1] , "Real-life Power Quality Sags ", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2K88D. Accessed: Apr. 23, 2018.
@data{h2k88d-17,
doi = {10.21227/H2K88D},
url = {http://dx.doi.org/10.21227/H2K88D},
author = { },
publisher = {IEEE Dataport},
title = {Real-life Power Quality Sags },
year = {2017} }
TY - DATA
T1 - Real-life Power Quality Sags
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2K88D
ER -
. (2017). Real-life Power Quality Sags . IEEE Dataport. http://dx.doi.org/10.21227/H2K88D
, 2017. Real-life Power Quality Sags . Available at: http://dx.doi.org/10.21227/H2K88D.
. (2017). "Real-life Power Quality Sags ." Web.
1. . Real-life Power Quality Sags [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2K88D
. "Real-life Power Quality Sags ." doi: 10.21227/H2K88D

Human Neck Activity Classification, Wearable Antenna S21 Data


These .s2p files contain the S-parameters measured between two on-neck antennas for multiple test subjects acting out four activites. Each files is one trial of measurement, containing 20 seconds of data sampled at 200 Hz.

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Citation Author(s):
Submitted by:
Drew Bresnahan
Last updated:
Wed, 04/18/2018 - 18:47
DOI:
10.21227/H2BM11
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[1] , "Human Neck Activity Classification, Wearable Antenna S21 Data", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2BM11. Accessed: Apr. 23, 2018.
@data{h2bm11-18,
doi = {10.21227/H2BM11},
url = {http://dx.doi.org/10.21227/H2BM11},
author = { },
publisher = {IEEE Dataport},
title = {Human Neck Activity Classification, Wearable Antenna S21 Data},
year = {2018} }
TY - DATA
T1 - Human Neck Activity Classification, Wearable Antenna S21 Data
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2BM11
ER -
. (2018). Human Neck Activity Classification, Wearable Antenna S21 Data. IEEE Dataport. http://dx.doi.org/10.21227/H2BM11
, 2018. Human Neck Activity Classification, Wearable Antenna S21 Data. Available at: http://dx.doi.org/10.21227/H2BM11.
. (2018). "Human Neck Activity Classification, Wearable Antenna S21 Data." Web.
1. . Human Neck Activity Classification, Wearable Antenna S21 Data [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2BM11
. "Human Neck Activity Classification, Wearable Antenna S21 Data." doi: 10.21227/H2BM11

experiments results in paper "Never Worry about Occlusions: A Regional Color Histogram Based Occlusion Estimating Agency to Deal with Occlusions in Tracking"


Visual tracking methods have achieved a successful development in recent years. Especially the Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. The advancement in DCF tracking performance is predominantly attributed to powerful features and sophisticated online learning formulations. However, it would come to some troubles if the tracker learns the samples indiscriminately.

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Dataset Details

Citation Author(s):
Submitted by:
Kaiwen Jiang
Last updated:
Wed, 04/04/2018 - 10:21
DOI:
10.21227/H2CW8R
Data Format:
 
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[1] , "experiments results in paper "Never Worry about Occlusions: A Regional Color Histogram Based Occlusion Estimating Agency to Deal with Occlusions in Tracking"", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2CW8R. Accessed: Apr. 23, 2018.
@data{h2cw8r-18,
doi = {10.21227/H2CW8R},
url = {http://dx.doi.org/10.21227/H2CW8R},
author = { },
publisher = {IEEE Dataport},
title = {experiments results in paper "Never Worry about Occlusions: A Regional Color Histogram Based Occlusion Estimating Agency to Deal with Occlusions in Tracking"},
year = {2018} }
TY - DATA
T1 - experiments results in paper "Never Worry about Occlusions: A Regional Color Histogram Based Occlusion Estimating Agency to Deal with Occlusions in Tracking"
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2CW8R
ER -
. (2018). experiments results in paper "Never Worry about Occlusions: A Regional Color Histogram Based Occlusion Estimating Agency to Deal with Occlusions in Tracking". IEEE Dataport. http://dx.doi.org/10.21227/H2CW8R
, 2018. experiments results in paper "Never Worry about Occlusions: A Regional Color Histogram Based Occlusion Estimating Agency to Deal with Occlusions in Tracking". Available at: http://dx.doi.org/10.21227/H2CW8R.
. (2018). "experiments results in paper "Never Worry about Occlusions: A Regional Color Histogram Based Occlusion Estimating Agency to Deal with Occlusions in Tracking"." Web.
1. . experiments results in paper "Never Worry about Occlusions: A Regional Color Histogram Based Occlusion Estimating Agency to Deal with Occlusions in Tracking" [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2CW8R
. "experiments results in paper "Never Worry about Occlusions: A Regional Color Histogram Based Occlusion Estimating Agency to Deal with Occlusions in Tracking"." doi: 10.21227/H2CW8R

Wi-Fi signal strength measurements from smartphone for various hand gestures


The dataset is an extensive collection of labeled high-frequency Wi-Fi Radio Signal Strength (RSS) measurements corresponding to multiple hand gestures made near a smartphone under different spatial and data traffic scenarios. We open source the software code and an Android app (Winiff) to create this dataset, which is available at Github (https://github.com/mohaseeb/wisture). The dataset is created using an artificial traffic induction (between the phone and the access point) approach to enable useful and meaningful RSS value

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Dataset Details

Citation Author(s):
Submitted by:
Ramviyas Parasuraman
Last updated:
Mon, 01/08/2018 - 21:53
DOI:
10.21227/H2C362
Data Format:
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[1] , "Wi-Fi signal strength measurements from smartphone for various hand gestures", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2C362. Accessed: Apr. 23, 2018.
@data{h2c362-18,
doi = {10.21227/H2C362},
url = {http://dx.doi.org/10.21227/H2C362},
author = { },
publisher = {IEEE Dataport},
title = {Wi-Fi signal strength measurements from smartphone for various hand gestures},
year = {2018} }
TY - DATA
T1 - Wi-Fi signal strength measurements from smartphone for various hand gestures
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2C362
ER -
. (2018). Wi-Fi signal strength measurements from smartphone for various hand gestures. IEEE Dataport. http://dx.doi.org/10.21227/H2C362
, 2018. Wi-Fi signal strength measurements from smartphone for various hand gestures. Available at: http://dx.doi.org/10.21227/H2C362.
. (2018). "Wi-Fi signal strength measurements from smartphone for various hand gestures." Web.
1. . Wi-Fi signal strength measurements from smartphone for various hand gestures [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2C362
. "Wi-Fi signal strength measurements from smartphone for various hand gestures." doi: 10.21227/H2C362

joint filter approach reliable power system state estimation


This is the Smulation Data for Power System State Estimation.

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Citation Author(s):
Submitted by:
Yang Yu
Last updated:
Fri, 03/09/2018 - 08:23
DOI:
10.21227/H2M634
Data Format:
 
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[1] , "joint filter approach reliable power system state estimation", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2M634. Accessed: Apr. 23, 2018.
@data{h2m634-17,
doi = {10.21227/H2M634},
url = {http://dx.doi.org/10.21227/H2M634},
author = { },
publisher = {IEEE Dataport},
title = {joint filter approach reliable power system state estimation},
year = {2017} }
TY - DATA
T1 - joint filter approach reliable power system state estimation
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2M634
ER -
. (2017). joint filter approach reliable power system state estimation. IEEE Dataport. http://dx.doi.org/10.21227/H2M634
, 2017. joint filter approach reliable power system state estimation. Available at: http://dx.doi.org/10.21227/H2M634.
. (2017). "joint filter approach reliable power system state estimation." Web.
1. . joint filter approach reliable power system state estimation [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2M634
. "joint filter approach reliable power system state estimation." doi: 10.21227/H2M634

Long-Term Spectral Pseudo-Entropy (LTSPE) Feature


This is the source code (MATLAB) of the LTSPE feature.

 

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Citation Author(s):
Submitted by:
mohammad rasoul...
Last updated:
Fri, 12/01/2017 - 08:44
DOI:
10.21227/H2G05K
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[1] , "Long-Term Spectral Pseudo-Entropy (LTSPE) Feature", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2G05K. Accessed: Apr. 23, 2018.
@data{h2g05k-17,
doi = {10.21227/H2G05K},
url = {http://dx.doi.org/10.21227/H2G05K},
author = { },
publisher = {IEEE Dataport},
title = {Long-Term Spectral Pseudo-Entropy (LTSPE) Feature},
year = {2017} }
TY - DATA
T1 - Long-Term Spectral Pseudo-Entropy (LTSPE) Feature
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2G05K
ER -
. (2017). Long-Term Spectral Pseudo-Entropy (LTSPE) Feature. IEEE Dataport. http://dx.doi.org/10.21227/H2G05K
, 2017. Long-Term Spectral Pseudo-Entropy (LTSPE) Feature. Available at: http://dx.doi.org/10.21227/H2G05K.
. (2017). "Long-Term Spectral Pseudo-Entropy (LTSPE) Feature." Web.
1. . Long-Term Spectral Pseudo-Entropy (LTSPE) Feature [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2G05K
. "Long-Term Spectral Pseudo-Entropy (LTSPE) Feature." doi: 10.21227/H2G05K

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