This is the calibration procedure and experimental data of a 4PPa-2PaR parallel mechanism, main_simulation.m and main_experiment.m are simulation and experimental programs, respectively, and end_measurement.xlsx is the experimental data measured by the laser tracker


This dataset consists of measurements from a foot-mounted inertial measurement unit (IMU). In total, we provide data from five different test subjects travelling over more than 7.6 km. The data are combined with various forms of ground truth positioning information that can be used to evaluate the accuracy of a zero-velocity-aided, foot-mounted inertial navigation system (INS).


Herein, we provide inertial data (produced from an inertial measurement unit) collected in three different test environments. Each environment contains multiple trajectories, and we provide the raw, foot-mounted inertial measurements for each trajectory. Additionally, we include forms of ground truth for each trajectory (the form of ground truth varies within each test environment). For each trajectory, we include processed results that can be used for benchmarking other systems. Our processed results (six degree-of-freedom trajectory estimates) are generated from a baseline zero-velocity-aided inertial navigation system (INS).

Once the dataset has been downloaded, it should be unpacked into the PyShoe repository in order to use the available tools with the provided dataset: To do so, unzip the results and data folders into the main directory. The correct folder structure is as follows:

| - - pyshoe

|        | - - data

|                | - - hallway

|                | - - stairs

|                | - - vicon

|         | - - results

Following this, we refer you to the readme instructions in the Github repository for detailed instructions on how the data can be used with our open-source INS. See the individual readme files within the various data subdirectories to understand how the dataset is formatted.


Clamp-on ultrasonic transit time difference is used extensively to calculate the volumetric flow rate of a fluid through a pipe. The operating principle is that waves travelling along a path that is generally against the flow direction take longer to travel the same path than waves travelling along the same path in the opposite direction. The transit time difference between the waves travelling in opposite directions can be used to calculate the flow rate through the pipe, by applying suitable mathematical correction factors.


The uploaded data are for the paper: "A Wearable Skin Temperature Monitoring System for Early Detection of Infections". Baseline kin temperature measurement data from all 5 volunteers (subjects) who wore the wearable band for 3-5 days are included along with 5-day temperature measurement data with anomalies of one volunteer who wore both the smart band and a heating pad. Augmented data generated using the methods described in the paper for COVID-19 infection anomaly detection are also included 


Packet delivery ratio data collected for the article Wireless-Sensor Network Topology Optimization in Complex Terrain: A Bayesian Approach. Published in the IEEE Internet of Things Journal. 


<p>The technique of electrical impedance tomography (EIT) has been recognized as a promising method to design tactile sensors with continuous sensing capability over a large area. The mechanism of electrical impedance tomography allows reconstructing tactile information within the sensing area based on measurements made only at the boundary. However, spatial performance of EIT-based tactile sensors has demonstrated location dependency in previous reports, which severely affects correct interpretation of tactile stimuli.


Human activity recognition (HAR) has been one of the most prevailing and persuasive research topics in different fields for the past few decades. The main idea is to comprehend individuals’ regular activities by looking at bits of knowledge accumulated from people and their encompassing living environments based on sensor observations. HAR has a great impact on human-robot collaborative work, especially in industrial works. In compliance with this idea, we have organized this year’s Bento Packaging Activity Recognition Challenge.

Last Updated On: 
Thu, 07/01/2021 - 02:09
Citation Author(s): 
Sayeda Shamma Alia, Kohei Adachi, Paula Lago, Nazmun Nahid, Haru Kaneko, Sozo Inoue

This study presents a dataset that comprises the magnetic field, Wi-Fi, and the data from the inertial measurement unit (IMU) sensors of the smartphone including accelerometer, gyroscope, and barometer. First, the important

characteristics of both the Wi-Fi and the magnetic field that require further investigation are highlighted, and later the data are collected. The data are collected over a longer period spanning approximately five years involving five


For instructions, kindly check the following paper which is published in IEEE Access:

"MagWi: Benchmark Dataset for Long Term Magnetic Field And Wi-Fi Data Involving Heterogeneous Smartphones, Multiple Orientations, Spatial Diversity and Multi-floor Buildings".

For further queries, contact at


The data set is collected using Neurosky MindWave 2.0 Headset. It uses a single dry electrode placed at FP-1 position for the acquisition of EEG signals. The data is collected from Healthy Individuals and Epileptic Patients performing different Activities of Daily Living (ADLs) in an unconstraint environment. 


The data files are stored in a comma-separated value (.csv) format.

60 sample files of activities performed by healthy individuals and 30 sample files of activities performed by epileptic patients are present in two separate folders in the .zip file.

The sampling frequency of the headset is 512Hz and each activity is performed for a duration of 20 seconds. Every data file contains raw EEG data in a single column.  

Disclaimer: This data was collected ethically with the consent of relevant local research committees. The anonymity of subjects and confidentiality of their mental health conditions was ensured.


The Objects Mosaic Hyperspectral Database contains 10,666 hyperspectral cubes of size 256x256x29 in the 420-700nm spectral range. This original hyperspectral database of real objects was experimentally acquired as described in the paper "SHS-GAN: Synthetic enhancement of a natural hyperspectral database", by J. Hauser, G. Shtendel, A. Zeligman, A. Averbuch, and M. Nathan, in the IEEE Transactions on Computational Imaging.

In addition, the database contains the SHS-GAN algorithm, which enables to generate synthetic database of hyperspectral images.