Signal Processing

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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:
Sat, 06/16/2018 - 23:17
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: Jun. 22, 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
Data Format:
 
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[1] , "A Manitoban Speech Dataset", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2KM16. Accessed: Jun. 22, 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:
Sat, 06/16/2018 - 23:05
DOI:
10.21227/H2VW5Z
Data Format:
Links:
 
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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: Jun. 22, 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

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
Data Format:
Links:
 
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[1] , "Real-life Power Quality Sags ", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2K88D. Accessed: Jun. 22, 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

UnderMp3Cover


Mp3 is a very popular audio format and hence it can be a good host for carrying hidden messages. Therefore, different steganography methods have been proposed for mp3 hosts. UnderMp3Cover is one of such algorithms and has some important advantage over other comparable methods. First, the popular steganography method mp3stego, works directly on non-compressed samples. Therefore, using covers that have been compressed before could lead to serious degradation of its security. UnderMp3Cover does not have this important limitation.

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Citation Author(s):
Submitted by:
Hamzeh Ghasemzadeh
Last updated:
Thu, 06/07/2018 - 13:37
DOI:
10.21227/H2SX0S
Data Format:
 
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[1] , "UnderMp3Cover", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2SX0S. Accessed: Jun. 22, 2018.
@data{h2sx0s-18,
doi = {10.21227/H2SX0S},
url = {http://dx.doi.org/10.21227/H2SX0S},
author = { },
publisher = {IEEE Dataport},
title = {UnderMp3Cover},
year = {2018} }
TY - DATA
T1 - UnderMp3Cover
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2SX0S
ER -
. (2018). UnderMp3Cover. IEEE Dataport. http://dx.doi.org/10.21227/H2SX0S
, 2018. UnderMp3Cover. Available at: http://dx.doi.org/10.21227/H2SX0S.
. (2018). "UnderMp3Cover." Web.
1. . UnderMp3Cover [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2SX0S
. "UnderMp3Cover." doi: 10.21227/H2SX0S

IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification


This database consists of the data used for the 2018 IEEE Signal Processing Cup.  This iteration of the Signal Processing Cup was a forensic camera model identification challenge.  Teams of undergraduate students were tasked with building a system capable of determining type of camera (manufacturer and model) that captured a digital image without relying on metadata.

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Citation Author(s):
Submitted by:
Matthew Stamm
Last updated:
Sat, 06/16/2018 - 23:18
DOI:
10.21227/H2XM2P
Links:
 
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[1] , "IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2XM2P. Accessed: Jun. 22, 2018.
@data{h2xm2p-18,
doi = {10.21227/H2XM2P},
url = {http://dx.doi.org/10.21227/H2XM2P},
author = { },
publisher = {IEEE Dataport},
title = {IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification},
year = {2018} }
TY - DATA
T1 - IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2XM2P
ER -
. (2018). IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification. IEEE Dataport. http://dx.doi.org/10.21227/H2XM2P
, 2018. IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification. Available at: http://dx.doi.org/10.21227/H2XM2P.
. (2018). "IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification." Web.
1. . IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2XM2P
. "IEEE Signal Processing Cup 2018 Database - Forensic Camera Model Identification." doi: 10.21227/H2XM2P

RFID 3D Indoor Localization for Tag and Tag-free Target Based on Interference


The system obtained interference information from the measurement signal, solved the problem of phase wrapping, and got the accurate coordinates of target. The tags and tag-free items including shrimp chips, cola and instant noodles were taken as target respectively in experiment.

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Dataset Details

Citation Author(s):
Submitted by:
Meng Liu
Last updated:
Tue, 06/19/2018 - 05:46
DOI:
10.21227/H2V09H
Data Format:
 
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[1] , "RFID 3D Indoor Localization for Tag and Tag-free Target Based on Interference", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2V09H. Accessed: Jun. 22, 2018.
@data{h2v09h-18,
doi = {10.21227/H2V09H},
url = {http://dx.doi.org/10.21227/H2V09H},
author = { },
publisher = {IEEE Dataport},
title = {RFID 3D Indoor Localization for Tag and Tag-free Target Based on Interference},
year = {2018} }
TY - DATA
T1 - RFID 3D Indoor Localization for Tag and Tag-free Target Based on Interference
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2V09H
ER -
. (2018). RFID 3D Indoor Localization for Tag and Tag-free Target Based on Interference. IEEE Dataport. http://dx.doi.org/10.21227/H2V09H
, 2018. RFID 3D Indoor Localization for Tag and Tag-free Target Based on Interference. Available at: http://dx.doi.org/10.21227/H2V09H.
. (2018). "RFID 3D Indoor Localization for Tag and Tag-free Target Based on Interference." Web.
1. . RFID 3D Indoor Localization for Tag and Tag-free Target Based on Interference [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2V09H
. "RFID 3D Indoor Localization for Tag and Tag-free Target Based on Interference." doi: 10.21227/H2V09H

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.  

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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: Jun. 22, 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).

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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: Jun. 22, 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.

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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: Jun. 22, 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

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