Datasets

subject: Electrical and Computer Engineering

Total is 18 Results
RP1D (Clustering Method Using 1D Random Projections)

10.4231/R74F1NN4

Mireille Boutin ORCID logo , Sangchun Han

11/12/2014

The source code of RP1D (Clustering Method Using 1D Random Projections).

Clustering Computer Science Electrical and Computer Engineering Random Projection Source Code

Code and Dataset for TARP Detection Benchmarks

10.4231/R7ST7MVC

Kelsie Larson , Mireille Boutin ORCID logo

05/16/2017

The TARP method uses random projections, followed by threshold classifications, to construct receiver-operating characteristic curves and uncover underlying structure in the given data.

Electrical and Computer Engineering Machine Learning Receiver Operating Characteristics ROC curve Signal Processing Target Detection

Signal Processing Toolbox for Simultaneously Acquired fMRI and EEG

10.4231/R7DB7ZSC

Rodrigo Castellanos , Zhongming Liu ORCID logo

06/07/2016

This Matlab toolbox includes signal processing functions to remove gradient and pulse artifacts in EEG data recorded simultaneously with fMRI. It is distributed as a GUI plugin for EEGLAB.

Bioinformatics Biomedical Engineering Electrical and Computer Engineering fMRI-EEG independent component analysis Signal Processing Singular value decomposition

Signal Processing Toolbox for Simultaneously Acquired fMRI and EEG

10.4231/R7QF8QT4

Rodrigo Castellanos , Zhongming Liu ORCID logo

07/27/2015

This Matlab toolbox includes signal processing functions to remove gradient and pulse artifacts in EEG data recorded simultaneously with fMRI. It is distributed as a GUI plugin for EEGLAB.

Bioinformatics Biomedical Engineering Electrical and Computer Engineering fMRI-EEG independent component analysis Signal Processing Singular value decomposition

Code and Dataset for Pattern Recognition Benchmarks

10.4231/R7610X8M

Jonas Hepp , Mireille Boutin ORCID logo , Yellamraju Tarun

02/03/2016

This code computed a sequence of bounds for the error rate of a pattern recognition method. The bounds correspond to the error rate that one would expect to achieve by simply selecting features at random and thresholding the feature (TARP) approach.

classification Computer Science Electrical and Computer Engineering feature evaluation image processing image recognition Pattern Recognition Pedestrian Classification

Code and Dataset for Pattern Recognition Benchmarks

10.4231/R7G73BPN

Jonas Hepp , Mireille Boutin ORCID logo , Yellamraju Tarun

12/12/2016

This code computed a sequence of bounds for the error rate of a pattern recognition method. The bounds correspond to the error rate that one would expect to achieve by simply selecting features at random and thresholding the feature (TARP) approach.

classification Computer Science Electrical and Computer Engineering feature evaluation image processing image recognition Pattern Recognition Pedestrian Classification

A Machine Learning Approach to Design of Aperiodic, Clustered-Dot Halftone Screens via Direct Binary Search

10.4231/AMGQ-0T59

Itamar Roth , Jan Allebach , Jiayin Liu ORCID logo , Orel Bat Mor , Oren Haik , Shani Gat , Tal Frank , Yitzhak Yitzhaky

06/01/2022

This dataset contains two parts: one has halftone patches that were used to predicts the quality level and scale using machine learning methods. The second part contains full versions of halftone images so viewers can zoom in to see the details.

direct binary search Electrical and Computer Engineering Halftone screen Machine Learning

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