Sensors
this is a dataset for Human-Robot Physical Contact Classification. We used the UR10e six-axis robotic arm as the data collection object and the official tool, RTDE, as the data acquisition tool. Regarding the labels of the dataset, we categorize Human-Robot physical contact into three types: no contact, intentional contact, and collision, based on common occurrences in Human-Robot collaborative tasks. The dataset contains 2375 non-repetitive data entries with valid Human-Robot physical contact information, and each entry includes the motion data of the robotic arm within 1 second.
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The IAMCV Dataset was acquired as part of the FWF Austrian Science Fund-funded Interaction of Autonomous and Manually-Controlled Vehicles project. It is primarily centred on inter-vehicle interactions and captures a wide range of road scenes in different locations across Germany, including roundabouts, intersections, and highways. These locations were carefully selected to encompass various traffic scenarios, representative of both urban and rural environments.
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460 Views
To evaluate the proposed odometry method based on 360-degree cameras, we build a new dataset using an Insta-360 One X2 device, which provided high-resolution 4K images captured at a rate of 30 frames per second and 500\,Hz IMU measurements from its built-in IMU.Different from previous datasets, we have set the illumination brightness and the camera movements as the condition variable. The sequence can be classified based on changes in illumination speed or camera movement intensity.
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588 Views
This dataset corresponds to the near field (NF) measurements of a 70 cm diameter offset reflector antenna, fed with a circularly polarized helix antenna, working at 4650 MHz [1,2].
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332 Views
Object tracking systems within closed environments employ light detection and ranging (LiDAR) to address privacy and confidentiality. Data collection occurred in two distinct scenarios. The goal of scenario one is to detect the locations of multiple objects from various locations on a flat surface in a closed environment. The second scenario describes the effectiveness of the technique in detecting multiple objects by using LiDAR data obtained from a single, fixed location.
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418 Views
The enhanced dataset is a sophisticated collection of simulated data points, meticulously designed to emulate real-world data as collected from wearable Internet of Things (IoT) devices. This dataset is tailored for applications in safety monitoring, particularly for women, and is ideal for developing machine learning models for distress or danger detection.
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1712 Views
Underwater sampling of heart rate for sports training has growing attention recently because of the availability of new sensors able to gather data while the user is swimming. Namely, optical sensor for the wrist and strap sensor for the chest. Underwater data transmission is not an option, forcing the analysis to be done off-line. Thus, movement and distance from heart could infuence the gap between data from sensors.
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172 Views
Gas monitoring plays a crucial role in our intelligent societies. Thus we build those stepwise-collected data for gas concentration modeling in dynamic setting which can boost training data collection. First column is time, you have 8 columns for different sensors, including GM402B, GM702B, GM512B, GM302B, MICS5914, MICS5914, MICS4514, and MICS4514. The remaining 3 columns is recorded as the flow rate, which the flow rate of methane and the oxygen-nitrogen mixture is read as L/min, while hydrogen is read as mL/min.
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206 Views
The dataset, developed at the National Institute of Neurology and Neurosurgery in Mexico, encapsulates crucial gait biomarkers associated with neurodegenerative diseases. This invaluable compilation serves as a comprehensive resource for understanding and analyzing the distinctive gait patterns exhibited by patients grappling with neurological disorders. By delving into these intricate biomarkers, researchers gain insights into the nuanced manifestations of conditions impacting the nervous system.
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194 Views
This paper presents a simple yet novel two-dimensional modelling approach for approximating the coupling coefficient between neighbouring inductors as a function of co-planar separation and relative angular displacement. The approach employs simple geometric arguments to predict the effective magnetic flux between inductors. Two extreme coil geometry regimes are considered; planar coils (i.e. on printed circuit board), and solenoid coils, each with asymmetric ferrite cores about the central magnetic plane of the inductor.
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136 Views