Signal Processing

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RSSI-Based Indoor Localization with the Internet of Things


RSSI-Dataset

The RSSI-Dataset provides a comprehensive set of Received Signal Strength Indication (RSSI) readings from within two indoor office buildings. Four wireless technologies were used:

  • Zigbee (IEEE 802.15.4),
  • WiFi (IEEE 802. 11),
  • Bluetooth Low Energy (BLE) and
  • Long Range Area-Wide Network (LoRaWAN).

For experimentation Arduinos Raspberry Pi, XBees, Gimbal beacons Series 10 and Dragino LoRa Shield were also used.  

License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Submitted by:
PETROS SPACHOS
Last updated:
Wed, 05/30/2018 - 20:51
DOI:
10.21227/H25D5X
Data Format:
Links:
 
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[1] , "RSSI-Based Indoor Localization with the Internet of Things", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H25D5X. Accessed: Jul. 21, 2018.
@data{h25d5x-18,
doi = {10.21227/H25D5X},
url = {http://dx.doi.org/10.21227/H25D5X},
author = { },
publisher = {IEEE Dataport},
title = {RSSI-Based Indoor Localization with the Internet of Things},
year = {2018} }
TY - DATA
T1 - RSSI-Based Indoor Localization with the Internet of Things
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H25D5X
ER -
. (2018). RSSI-Based Indoor Localization with the Internet of Things. IEEE Dataport. http://dx.doi.org/10.21227/H25D5X
, 2018. RSSI-Based Indoor Localization with the Internet of Things. Available at: http://dx.doi.org/10.21227/H25D5X.
. (2018). "RSSI-Based Indoor Localization with the Internet of Things." Web.
1. . RSSI-Based Indoor Localization with the Internet of Things [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H25D5X
. "RSSI-Based Indoor Localization with the Internet of Things." doi: 10.21227/H25D5X

Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion


Electroencephalography (EEG) signal data was collected from twelve healthy subjects with no known musculoskeletal or neurological deficits (mean age 25.5 ± 3.7, 11 male, 1 female, 1 left handed, 11 right handed) using an EGI Geodesics© Hydrocel EEG 64-Channel spongeless sensor net. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the University of Wisconsin-Milwaukee (17.352).

License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Submitted by:
Joshua Myszewski
Last updated:
Sat, 05/19/2018 - 12:01
DOI:
10.21227/H2F66Q
Data Format:
 
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[1] , "Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2F66Q. Accessed: Jul. 21, 2018.
@data{h2f66q-18,
doi = {10.21227/H2F66Q},
url = {http://dx.doi.org/10.21227/H2F66Q},
author = { },
publisher = {IEEE Dataport},
title = {Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion},
year = {2018} }
TY - DATA
T1 - Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2F66Q
ER -
. (2018). Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion. IEEE Dataport. http://dx.doi.org/10.21227/H2F66Q
, 2018. Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion. Available at: http://dx.doi.org/10.21227/H2F66Q.
. (2018). "Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion." Web.
1. . Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2F66Q
. "Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion." doi: 10.21227/H2F66Q

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.

License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Submitted by:
Drew Bresnahan
Last updated:
Wed, 04/18/2018 - 18:47
DOI:
10.21227/H2BM11
Data Format:
 
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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: Jul. 21, 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.

License: Creative Commons Attribution

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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: Jul. 21, 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

License: Creative Commons Attribution

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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:
Links:
 
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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: Jul. 21, 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.

License: Creative Commons Attribution

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Dataset Details

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: Jul. 21, 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.

 

License: Creative Commons Attribution

Dataset Files

No Data files have been uploaded.

OPEN ACCESS Dataset Details

Citation Author(s):
Submitted by:
mohammad rasoul...
Last updated:
Sat, 06/16/2018 - 23:16
DOI:
10.21227/H2G05K
Data Format:
 
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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: Jul. 21, 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

Long-Term Multi-Band Mean-Crossing Rate (LTMBMCR) Feature


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

 

License: Creative Commons Attribution

Dataset Files

No Data files have been uploaded.

OPEN ACCESS Dataset Details

Citation Author(s):
Submitted by:
mohammad rasoul...
Last updated:
Sat, 06/16/2018 - 23:19
DOI:
10.21227/H2NW6G
Data Format:
 
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[1] , "Long-Term Multi-Band Mean-Crossing Rate (LTMBMCR) Feature", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2NW6G. Accessed: Jul. 21, 2018.
@data{h2nw6g-17,
doi = {10.21227/H2NW6G},
url = {http://dx.doi.org/10.21227/H2NW6G},
author = { },
publisher = {IEEE Dataport},
title = {Long-Term Multi-Band Mean-Crossing Rate (LTMBMCR) Feature},
year = {2017} }
TY - DATA
T1 - Long-Term Multi-Band Mean-Crossing Rate (LTMBMCR) Feature
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2NW6G
ER -
. (2017). Long-Term Multi-Band Mean-Crossing Rate (LTMBMCR) Feature. IEEE Dataport. http://dx.doi.org/10.21227/H2NW6G
, 2017. Long-Term Multi-Band Mean-Crossing Rate (LTMBMCR) Feature. Available at: http://dx.doi.org/10.21227/H2NW6G.
. (2017). "Long-Term Multi-Band Mean-Crossing Rate (LTMBMCR) Feature." Web.
1. . Long-Term Multi-Band Mean-Crossing Rate (LTMBMCR) Feature [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2NW6G
. "Long-Term Multi-Band Mean-Crossing Rate (LTMBMCR) Feature." doi: 10.21227/H2NW6G

Activities of Daily Living


Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living). 

License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Submitted by:
Walid Gomaa
Last updated:
Wed, 11/22/2017 - 09:06
DOI:
10.21227/H2PS74
Data Format:
 
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[1] , "Activities of Daily Living", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2PS74. Accessed: Jul. 21, 2018.
@data{h2ps74-17,
doi = {10.21227/H2PS74},
url = {http://dx.doi.org/10.21227/H2PS74},
author = { },
publisher = {IEEE Dataport},
title = {Activities of Daily Living},
year = {2017} }
TY - DATA
T1 - Activities of Daily Living
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2PS74
ER -
. (2017). Activities of Daily Living. IEEE Dataport. http://dx.doi.org/10.21227/H2PS74
, 2017. Activities of Daily Living. Available at: http://dx.doi.org/10.21227/H2PS74.
. (2017). "Activities of Daily Living." Web.
1. . Activities of Daily Living [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2PS74
. "Activities of Daily Living." doi: 10.21227/H2PS74

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:
Tue, 05/01/2018 - 15:07
DOI:
10.21227/H2TP46
Data Format:
 
Cite

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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: Jul. 21, 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

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