This dataset corresponds to the paper Calibration of a Hail-Impact Energy Electroacoustic Sensor, submitted to IEEE Transactions in Instrumentation and Measurement by Florencia Blasina, Andrés Echarri, and Nicolás Pérez. 

The dataset corresponds to the voltage signals acquired regarding several steel-ball impacts on the proposed hail-sensor plate to calibrate it. 


Experimental measurement data was obtained utilizing RCbenchmark 1780 with full-range PWM signals. Measurements were made for two series of setups.

First series is related to low-voltage setups using the following T-MOTOR components: - motors: MN4014 400Kv, MN5212 340Kv, MN501-S 360Kv, U7 280Kv, MN6007 320Kv, P60 340Kv, MN701-S 280Kv; - ESC: Air 40A, Flame 40A, Flame 70A, Alpha 60A, Flame 100A; - propellers: P17×5.8, P18×6.1, P20×6, P22×6.6, P24×7.2, G26×8.5; - battery: 6-cell (6S) Lithium polymer (LiPo).


This report presents an end-to-end methodology for collecting datasets to recognize handwritten English alphabets in the Indian context by utilizing Inertial Measurement Units (IMUs) and leveraging the diversity present in the Indian writing style. The IMUs are utilized to capture the dynamic movement patterns associated with handwriting, enabling more accurate recognition of alphabets. The Indian context introduces various challenges due to the heterogeneity in writing styles across different regions and languages.


This is an indoor environment data set collected from our research team's laboratory, and the data is collected from the Intel RealSense D435i camera. There are a total of 12 datasets, each in the format of a `.bag` file in ROS packet format. Each file contains RGB images and IMU data.


Use of medical devices in the magnetic resonance environment is regulated by standards that include the ASTM-F2213 magnetically induced torque. This standard prescribes five tests. However, none can be directly applied to measure very low torques of slender lightweight devices such as needles. Methods: We present a variant of an ASTM torsional spring method that makes a “spring” of 2 strings that suspend the needle by its ends. The magnetically induced torque on the needle causes it to rotate. The strings tilt and lift the needle.


The project research team successfully established China's first Inertial Motion Tracking Dataset (IMTD), which can be widely used for artificial intelligence model training in fields such as satellite-free navigation, unmanned driving, and wearable devices. Based on the IMTD dataset, the motion tracking method proposed by Wang Yifeng, Zhao Yi, and others breaks through the limitations of traditional motion tracking and positioning technologies such as inertia, optics, GPS, and carrier phase.


Dataset containing Ultra-Wideband (UWB) Active-Passive Two-Way-Ranging (AP-TWR) protocol range estimates, using the Qorvo DW1000 chip-based Eliko UWB RTLS system. Data was captured in an industrial environment, at the premises of a thermoplastic pipe manufacturer Krah Pipes OÜ, located near Tallinn, Estonia in December 2022.


There are several non-idealities that can degrade magnetic Hall-effect sensors performance and impact related applications. Thus, a confidence weighted learning entropy (CWLE) is proposed as a fault-tolerant control strategy for field-oriented control (FOC) of permanent magnet synchronous machines (PMSM). It combines sensorless and sensor-based control, while capitalizing on their major advantages, such as operation from standstill and at lower speeds, fast dynamic response, and fault tolerance to encoder errors.


Smart homes contain programmable electronic devices (mostly IoT) that enable home automation. People who live in smart homes benefit from interconnected devices by controlling them either remotely or manually/autonomously. However, high interconnectivity comes with an increased attack surface, making the smart home an attractive target for adversaries. NCC Group and the Global Cyber Alliance recorded over 12,000 attacks to log into smart home devices maliciously. Recent statistics show that over 200 million smart homes can be subjected to these attacks.


The e-nose device used in this study was constructed using a gas sensor array, LCD display, micro air pumps for inhalation and exhalation, a microcontroller, and a mini-PC. Gas samples from the sample chamber were periodically drawn into the device through a hose. Each sample underwent a 30-hour sampling process at room temperature (25°C). The sampling frequency was 15 times per hour, resulting in 60 records per sample.