Signal Processing

The dataset has 150 three-second sampling motor current signals from each synthetically-prepared motors. There are five motors with respective fault condition - bearing axis deviation (F1), stator coil inter-turn short circuit (F2), rotor broken strip (F3), outer bearing ring damage (F4), and healthy (H). The motors are run under five coupling loads - 0, 25, 50, 75, and 100%. The sampling signals are collected and processed into frequency occurrence plots (FOPs). Each image has a label, for example F2_L50_130, where F2 is the fault condition, L50 is the coupling load condition.

  • Sensors
  • Last Updated On: 
    Fri, 05/24/2019 - 22:27

    We study the Picture-level Just Noticeable Difference (PJND) of symmetrically and asymmetrically compressed stereoscopic images for JPEG2000 and H.265 intra coding. We conduct interactive subjective quality assessment tests to determine the PJND point using both a pristine image and a distorted image as a reference.

  • Signal Processing
  • Last Updated On: 
    Wed, 05/15/2019 - 22:31

    The files contain the research data. The noisy unprocessed audio data and processed audio data using the proposed speech enhancement method are uploaded. 

  • Standards Research Data
  • Last Updated On: 
    Thu, 05/09/2019 - 13:57

    Two files are provided. In the first one, there are the power signals obtained from the current and voltage measurements made with our own acquisition system (with a sampling frequency of 5 kHz). They correspond to the switching on and off of 12 home electrical appliances randomly switched on and off during 1 hour by using relay modules and resulting in 1200 events.

    In the second file, the time instants of these events are all reported.

  • Smart Grid
  • Last Updated On: 
    Wed, 05/01/2019 - 09:35

    The SWINSEG dataset contains 115 nighttime images of sky/cloud patches along with their corresponding binary ground truth maps The ground truth annotation was done in consultation with experts from Singapore Meteorological Services. All images were captured in Singapore using WAHRSIS, a calibrated ground-based whole sky imager, over a period of 12 months from January to December 2016. All image patches are 500x500 pixels in size, and were selected considering several factors such as time of the image capture, cloud coverage, and seasonal variations.

  • Energy
  • Last Updated On: 
    Tue, 02/18/2020 - 11:08

    The dataset, includes raw data, observations and biometric data from our case study with an individual with DMD, controlling for the first time an active hand orthosis.

  • Biomedical and Health Sciences
  • Last Updated On: 
    Thu, 10/17/2019 - 10:05

    Our goal is to find whether a convolutional neural network (CNN) performs better than the existing blind algorithms for image denoising, and, if yes, whether the noise statistics has an effect on the performance gap. We performed automatic identification of noise distribution, over a set of nine possible distributions, namely, Gaussian, log-normal, uniform, exponential, Poisson, salt and pepper, Rayleigh, speckle and Erlang. Next, for each of these noisy image sets, we compared the performance of FFDNet, a CNN based denoising method, with noise clinic, a blind denoising algorithm.

  • Communications
  • Last Updated On: 
    Fri, 12/20/2019 - 05:33

    Our Signing in the Wild dataset consists of various videos harvested from YouTube containing people signing in various sign languages and doing so in diverse settings, environments, under complex signer and camera motion, and even group signing. This dataset is intended to be used for sign language detection.


  • Communications
  • Last Updated On: 
    Sat, 02/23/2019 - 10:49

    The steganography and steganalysis of audio, especially compressed audio, have drawn increasing attention in recent years, and various algorithms are proposed. However, there is no standard public dataset for us to verify the efficiency of each proposed algorithm. Therefore, to promote the study field, we construct a dataset including 33038 stereo WAV audio clips with a sampling rate of 44.1 kHz and duration of 10s. And, all audio files are from the Internet through data crawling, which is for a better simulation of a real detection environment.

  • Signal Processing
  • Last Updated On: 
    Tue, 11/12/2019 - 10:38

    This dataset collection contains eleven datasets used in Locally Linear Embedding and fMRI feature selection in psychiatric classification.

    The datasets given in the Links section are reduced subsets of those contained in their respective tar files (a consequence of Mendeley Data's 10GB limitation).

    The Linked datasets (not the tar files) contain just the MATLAB file and the resting state image (or block-design fMRI for the MRN dataset), where appropriate.

  • Neuroscience
  • Last Updated On: 
    Sun, 02/17/2019 - 13:07