Discrete-time signal processing

Dataset asscociated with a paper to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence

"The perils and pitfalls of block design for EEG classification experiments"

The paper has been accepted and is in production.

We will upload the dataset when the paper is published.

This is a placeholder so we can obtain a DOI to include in the paper.

64 views
  • Artificial Intelligence
  • Last Updated On: 
    Fri, 04/24/2020 - 16:39

    This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. In the ‘5D_Data_Extractor.py’ file this can be set up to 6-Dimension, by the ‘fields_included’ variable. Two folders are included, one ready for preprocessing (‘subjects raw’) and the other already preprocessed ‘subjects preprocessed’.

    113 views
  • Machine Learning
  • Last Updated On: 
    Fri, 05/29/2020 - 16:24

    These uploaded video files show the results of distributed multi-vehicle SLAM in three cases:1, simulated scenario;2, UTIAS dataset;3, Victoria park dataset.

    39 views
  • Discrete-time signal processing
  • Last Updated On: 
    Wed, 02/19/2020 - 08:12

    Time-Scale Modification (TSM) is a well-researched field, however no effective objective measure of quality exists. This paper details the creation, subjective evaluation and analysis of a dataset, for use in the development of an objective measure of quality for TSM. Comprising two parts, the training component contains 88 source files processed using six TSM methods at 10 time-scales, while the testing component contains 20 source files processed using three additional methods at four time-scales.

    175 views
  • Machine Learning
  • Last Updated On: 
    Tue, 06/02/2020 - 01:08

    This dataset contains the actual sensor and calculated process variables in a winder station in a paper mill. Several Process variables change in time with the change of the rewind diameter. I provided the process data for two sets, in future I will add more data. Advanced time series forcasting techniques can be used to estimate many process variables considering the rewind diameter as the time axis.

    159 views
  • Machine Learning
  • Last Updated On: 
    Tue, 10/08/2019 - 06:23

    Urban flooding is a common problem across the world. In India, it leads to casualties every year, and financial loss to the tune of tens of billions of rupees. The damage done due to flooding can be mitigated if the locations deserving attention are known. This will enable an effective emergency response, and provide enough information for the construction of appropriate storm water drains to mitigate the effect of floods. In this work, a new technique to detect flooding level is introduced, which requires no additional equipment, and consequent installation and maintenance costs.

    86 views
  • Machine Learning
  • Last Updated On: 
    Mon, 01/06/2020 - 23:27

    Typically, a paper mill comprises three main stations: Paper machine, Winder station, and Wrapping station. The Paper machine produces paper with particular grammage in gsm (gram per square meter). The typical grammage classes in our paper mill are 48 gsm, 50 gsm, 58 gsm, 60 gsm, 68 gsm, 70 gsm. The Winder station takes a paper spool that is about 6 m width as it’s input and transfers is to customized paper rolls with particular diameter and width.

    161 views
  • Artificial Intelligence
  • Last Updated On: 
    Tue, 10/08/2019 - 06:26

    This dataset shows the amount of water used by a company in southern China from 2016 to 2017.

    167 views
  • Discrete-time signal processing
  • Last Updated On: 
    Sun, 09/29/2019 - 00:13

    S&P 500 index of monthly data of bull/bear markets

    279 views
  • Nonlinear signal processing
  • Last Updated On: 
    Mon, 08/19/2019 - 04:52

    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.

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

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