The data provided corresponds to the open-source codes and reference images used in a computer interface for real-time gait biofeedback using a Wearable Integrated Sensor System for Data Acquisition.The details of the system can be found in the study titled Computer interface for real-time gait biofeedback using a Wearable Integrated Sensor System for Data Acquisition, accepted for publication in the IEEE Transactions on Human-Machine Systems journal. 

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This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.

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The dataset contains 4600 samples of 12 different hand-movement gestures. Data were collected from four different people using the FMCW AWR1642 radar. Each sample is saved as a CSV file associated with its gesture type.

Instructions: 

Dataset is divided into 12 separate folders associated to different gesture types. Each folder contains gesture samples saved as a CSV file. First line of the CSV file is a headline describing the columns of data: FrameNumber, ObjectNumber, Range, Velocity, PeakValue, x coordinate, y coordinate. In order to read the gestures into matrix representation copy all 12 folders into single folder called “data”. Copy the “read_gesture.py” script to the same folder as “data” and run it. Script will convert CSV files of given gesture type into the numpy matrix.

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This dataset consists of temporal and temperature drift characteristics of Si3N4-gate iSFET andsupplementary files

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Extensive experimental measurement campaigns of more than 30,000 data points of end-to-end latency measurements for the following network architecture schemes is available:

  • Unlicensed IoT (standalone LoRa)
  • Cellular IoT (standalone LTE-M)
  • Concatenated IoT (LoRa interfaced with LTE-M)

Download Data.zip to access all relevant files for the open data measurements.

Related Paper:

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Data from simulations in which different radiation search strategies are compared.

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Description

This data set contains 100,000 pcd files taken by LiDAR, a 3-D image sensor, of a vehicle orbiting an indoor field.

Data Acquisition

The indoor field was built as a 1/60 scale model of an intersection, where two vehicles kept moving along pre-fixed tracks independently of each other.

The size of the vehicles was 0.040 m  × 0.035 m × 0.240 m 

We captured the indoor field by two LiDAR sensor units, which was commercialized by Velodyne.

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This dataset contains three simulation models: cantilever_transducer.mph, simply_supported_beam_transducer.mph and optimized_ simply_supported_beam_transducer.mph.

The cantilever_transducer.mph is a cantilever transducer model with a fixed center, which is used to compare its sensitivity-frequency response with that of the optimized simply supported beam transducer model.

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The S3 dataset contains the behaviour (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones.

 

Instructions: 

The data set is compressed into a zip file. Please unzip this file in the desired place and inside the main folder, you will find the file Readme.md with the instructions and the details of the database.

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