The EKF-based dynamic magnetic field calibration algorithm uses gyroscope data to calibrate magnetic field data. The magnetic field data can be well calibrated to the surface of the magnetic field sphere, which is superior to the traditional ellipsoid fitting algorithm.
This dataset is for the manuscript "TCAD model for TeraFET detectors operating in a large dynamic range" submitted to IEEE Transactions on Terahertz Science and Technology.
The pressure sensors are represented by black circles, which are located in the three zones of each foot. For the left foot: S1 and S2 cover the forefoot area. S3, S4, and S5 the midfoot area. S6 and S7 the rearfoot or heel area. Similarly, for the right foot: S8 and S9 represent the forefoot area. S10, S11, S12 the midfoot area. S13 and S14 the heel area. The values of each sensor are read by the analog inputs of an Arduino mega 2560.
In robotic grasping and manipulation, force feedback is one of the most important factors. In the absence of force feedback, force control and compliant grasping is almost impossible. In this study a novel Vibrational Haptic feedback system is designed. The system gives individual digit awareness of a multipronged robotic gripper to the user.
Empirical line methods (ELM) are frequently used to correct images from aerial remote sensing. Remote sensing of aquatic environments captures only a small amount of energy because the water absorbs much of it. The small signal response of the water is proportionally smaller when compared to the other land surface targets.
This dataset presents some resources and results of a new approach to calibrate empirical lines combining reference calibration panels with water samples. We optimize the method using python algorithms until reaches the best result.
The dataset is used in the paper entitled "A distributed Fog node assessment model by using Fuzzy rules learned by XGBoost" as fuzzy rules extracted by XGboost
AoT for Smart Society provides solutions of industry 4.0 standards in which contains custom-built multisensory wearable suit with cloud connectivity interfaced Artificial Intelligent techniques and Machine Learning algorithms in order to detect, to monitor and to analyze biofeedback control and visualization during human daily activities.
The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes. Five Xsens MTx units are used on the torso, arms, and legs.
This paper presents a road vehicle recognition and classification approach for intelligent transportation systems. This approach uses a roadside installed low cost magnetometer and associated data collection system. The system measures the magnetic field changing, detects passing vehicles and recognizes vehicle types.
The present dataset is based on implementing of 3 approaches with respect to the acquisition of driver data. The same one that we propose to use a sensor of concentration of alcohol in the environment (physiological), a sensor that measure the temperature of the defined points on driver’s face (biological) and another one that allows to identify and recognize the thickness of the pupil (visual characteristics).
