Reliable fatigue assessment is desired in many different fields and environments. An efficient fatigue evaluation tool is promising in reducing fatal errors and economic loss in industrial settings. This dataset contains electroencephalographic (EEG) signals obtainedfrom an 8-channel OpenBCI headset, as well as biometric measurements obtained from the Empatica E4 wristband. Signals obtained from the E4 include: Photopletismography (PPG), heart rate, inter-beat interval (IBI), skin temperature and Electrodermic Activity(EDA).

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A dataset asscociated with paper “Learning-based Sparse Data Reconstruction for Compressed Data Aggregation in IoT Networks” in IEEE Internet of Things Journal. Five different structured sparse models (SSMs) are considered in the synthesized dataset, including random sparse (Sparse Model A), row sparse (Sparse Model B), row-sparse with embedded element-sparse (Sparse Model C), row-sparse plus element-spares (Sparse Model D) and block diagonal sparse (Block Sparse or group sparse).

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Feature tables and source code for Camargo et al. A Machine Learning Strategy for Locomotion Classification and Parameter Estimation using Fusion of Wearable Sensors. Transactions on Biomedical Engineering. 2021

Instructions: 

The feature tables used for this paper can be found in ‘Classification.zip’ and ‘Regression.zip’, while source code is found in ‘CombinedLocClassAndParamEst-sourcecode.zip’. To get started, download all the files into a single folder and unzip them. Within ‘CombinedLocClassAndParamEst-master’, the folder ‘sf_analysis’ contains the main code to run, split into ‘Classification’ and ‘Regression’ code folders. There is also a 'README.md' file within the source code with more information and dependencies. If you’d like to just regenerate plots and results from the paper, then move all contents of the ‘zz_results_published’ folders (found under the feature table folders) up one folder so they are just within the ‘Classification’ or ‘Regression’ data folders. Go into the source code, find the ‘analysis’ folders, and run any ‘analyze*.m’ script with updated ‘datapath’ variables to point to the results folders you just moved.

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CMSO CFAR NN classifier

Instructions: 

See example file

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Trained NN

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This matlab code allows to reproduce some of the constrained and unconstrained dynamic identification techniques for open-chain robots. Below you can find an summary of the underlying research.

 

Instructions: 

See attached PDF file. Please download and unzip code and data to reproduce our research.

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Noise control is required to ensure crew habitability onboard an offshore platform. Applying noise prediction is important to identify the potential noise problem at the early stage of the offshore platform design to avoid costly retrofitting in the implementation stage. Noise data were collected. The 4 output targets are namely: spatial sound pressure level (SPL), spatial average SPL, structure-borne noise and airborne noise at different octave frequencies (e.g. 125Hz, 250Hz, 500Hz, 1000Hz, 2000Hz, 4000Hz, 8000 Hz).

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