Sensors

The Jackal UGV, from Clearpath Robotics, was used as the data collecting platform. This skid-steer four-wheel-drive vehicle comes with an onboard IMU, two DC motors with encoders that measure wheel angular speeds, and current sensors that measure motor current outputs. On each side of the robot, the front wheel and back wheel are jointed with a gearbox and so spin together at the same rate and direction. The IMU provided vehicle attitude measurements in terms of Euler angles, as well as linear acceleration and angular rate of the vehicle body in three Euclidean axes.

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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.

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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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A wide range of wearable sensors exist on the market for continuous physiological health monitoring. The type and scope of health data that can be gathered is a function of the sensor modality. Blumio presents a dataset of synchronized data from a reference blood pressure device along with several wearable sensor types: PPG, applanation tonometry, and the Blumio millimeter-wave radar. Data collection was conducted under set protocol with subjects seated at rest. 115 study subjects were included (age range 20-67 years), resulting in over 19 hours of data acquired.

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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.

 

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