Sensors

These are the 16-step phase-shift fringe images and five gray code fringe images that we collected and applied to the experiment. To facilitate the comparison experiments, we also supplement the average intensity images.
In order to better compare the experimental results, we collected the images of no target, horizontal discontinuous target and continuous target respectively, so that the results can better reflect all kinds of situations, so as to make the results more extensive and illustrative.

Ultra-wideband radar (UWB) is capable of perceiving the surroundings irrespective of the visibility due to its broad frequency spectrum. Therefore, UWB technology can be employed in mobile robots to perform simultaneous localization and mapping (SLAM) in vision-denied environments (e.g. smoke, fog, walls with reflective surfaces). We chose four different environments to teleoperate a TurtleBot2 nonholonomic robot equipped with Novelda X4M300 monostatic radar modules and RPLIDAR-A2 laser range scanner(s).

Supplementary video for the article "A Programmable CMOS DEP Chip for Cell Manipulation"

This video demonstrates the use of a CMOS DEP chip for particle manipulation, including particle patterning, concentration control, and single particle manipulation, all performed on the same chip and sample.

Measurement campaign was performed across the entire growing season of 2023. Seven Arduino+LoRaWAN sensors were measuring soil moisture, temperature and pH, as well as solar irradiance in the radius of about 35 km around Gdansk, Poland. Raw data were being collected with a nomadic gateway aboard an UAV and transferred to the cloud for analysis (package part A). Based on the physics informed analysis of the underlying measurement processes anomalies were classified and parametrized.

In our work, we propose an innovative system to accurately infer and track occluded target locations using mmWave beat frequency signals. Our approach combines a classic direction-finding method with advanced deep learning techniques, specifically a convolutional neural network (CNN), to enhance detection capabilities. The dataset includes raw beat frequency signal data from the TI IWR6843ISK rev B with TI mmWAVEICBOOST and the TI DCA1000EVM capture board. Corresponding ground truth data (target position) from the Realsense L515 RGB-D camera is also provided.

This data set contains the refractive index and reflectivity, emission intensity, and absorption length data for various materials utilitised in a SiPM-based Geant4 simulation platform. This simulation platform consists of the scintillation detector SiPM-3000 from Bridgeport Instruments, which contains a scintillator optically bonded to a silicon photomultiplier (SiPM) array within the detector housing. Scintillator crystals included in this study consist of GAGG:Ce, CLLBC:Ce, BGO, NaI:Tl, and CsI:Tl.

Sapphire fiber can withstand temperatures of around 2000°C, but it is multimoded, giving poor precision sensors. We have demonstrated a single-mode sapphire fiber Bragg grating temperature sensor operating up to 1200°C. A single-mode sapphire fiber was formed by writing a depressed cladding waveguide along the length of a 100-µm diameter sapphire fiber using a femtosecond laser. A second-order Bragg grating sensor was inscribed within the waveguide, achieving a reflectivity of >90% and a narrow bandwidth of <0.5 nm.

Dataset of a smart insole that are used to estimating stride length. Eight participants wore a pair of smart insoles and Gait Up. For a period of six minutes, each participant walked back and forth on a predefined 20-meter pathway in the indoor setting, and the numbers of round trips and strides taken were counted. The smart insole is equipped of five pressure sensors and one 3D accelerometer. The actual total distance was calculated by multiplying the number of round trips by 40 meters.

This is the dataset related to the article "Machine-learning-based Colorimetric Sensor on Smarthone for Salivary Uric Acid Detection". The dataset contains two types of images. The first type is the full-sized image captured by the sensor, which is used to evaluate the performance of the ROI detection. The second type is the reaction area from artificial saliva and clinical samples, which is used to train and test machine-learning models.