This dataset of 7200 channels is generated at different locations in the room area of 30x15x4 m3, where the locations are separated by 0.25m in both horizontal and vertical directions. Each AP uses 10 dBm TX power and 2D BF. In the concurrent mmWave BT scenario, all APs are operating, while in the single mmWave BT scenario, we consider a single AP fixed on the center of the room’s ceiling

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This dataset is simulation output for our work on model swapping. It represents runs of a simulation where we swapped out the L1 cache model with different simple statistical models. 

Instructions: 

To use this dataset, please view the ModelSwapping repo. This data should be downloaded and placed in the DataPMBS20/ directory. 

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This dataset contains EEG signals from 73 subjects (42 healthy; 31 disabled) using an ERP-based speller to control different brain-computer interface (BCI) applications. The demographics of the dataset can be found in info.txt. Additionally, you will find the results of the original study broken down by subject, the code to build the deep-learning models used in [1] (i.e., EEG-Inception, EEGNet, DeepConvNet, CNN-BLSTM) and a script to load the dataset.

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552 Views

Nurse Care Activity Recognition

Instructions: 

This dataset consists of two folders: training and testing

The training folder contains data collected in the lab and data from two users collected in the nursing home. They are separated in folders and there is one labels file for each.

 

The testing folder contains data for only the nursing home from the same users as the training folder but different days.

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Drive test measurements of deployed LTE base stations located at and around the campus of The Technical Unversity of Denmark. Metrics of signal quality are obtained using TSMW equipment with a vehicle driving around the area. In addition to the radio measurements, a GNSS receiver is utilized for additional localization metrics. Approximate altitude information is provided by the GNSS. 

Instructions: 

The csv contains the necessary headers for the data. This includes specifically the following columns:

 

[Date,Time,UTC,Latitude,Longitude,Altitude,Speed,Heading, #Sat, EARFCN, Frequency, PCI, MCC, MNC, TAC, CI, eNodeB-ID, cellID, BW,SymPerSlot,Power,SINR,RSRP,RSRQ,4G-Drift,Sigma-4G-Drift,TimeOfArrival,TimeOfArrivalFN]

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203 Views

FallAllD is a large open dataset of human falls and activities of daily living simulated by 15 participants. FallAllD consists of 26420 files collected using three data-loggers worn on the waist, wrist and neck of the subjects. Motion signals are captured using an accelerometer, gyroscope, magnetometer and barometer with efficient configurations that suit the potential applications e.g. fall detection, fall prevention and human activity recognition.

Instructions: 

Data files are stored in comma-separated values (csv) format. We developed two tools for encapsulating the data. The first one, namely FallAllD_Files_to_Matlab_Struct, is a MATLAB script that converts the dataset into a MATLAB structure stored as a ”.mat” file. The structure contains 8 fields {SubjectID, ActivityID, TrialNo, Device, Acc, Gyr, Mag, Bar}. The second tool, namely FallAllD_Files_to_Python_Struct, is a Python script that converts the dataset into a Pandas dataframe stored in hdf (”.h5”) or pickle (”.pkl”) formats. The dataframe has the same fields as the MATLAB structure.

To get familiar with FallAllD, use the MATLAB script Plot_FallAllD_Register to show any register of this dataset. 

If you use this dataset, please cite the following publication:

M. Saleh, M. Abbas and R. L. B. Jeannès, "FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications," in IEEE Sensors Journal, doi: 10.1109/JSEN.2020.3018335.

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