Astronomical instrumentation and related fields have seen remarkable evolution in recent decades, driving the need for advanced signal acquisition and processing techniques. Current experiments demand readout capabilities beyond traditional approaches, leading to the adoption of a wideband instrumentation system architecture for high-speed Radio Frequency (RF) measurements.
This dataset was collected to support research on the screening and diagnosis of Diabetic Peripheral Neuropathy (DPN) and Cardiac Autonomic Neuropathy (CAN) using wearable sensor technology. It includes synchronized data from gait analysis and physiological signals such as electrocardiogram (ECG), heart rate variability (HRV), and inertial measurement units (IMUs) obtained from individuals with and without DPN and CAN.
Data from acoustic condition monitoring of a milling process. During the process, several test parts were milled and acoustic data was recorded using a MEMS microphone.
One file shows two different tools used during milling, one for rough (face) milling with five cutting edges, and one with four cutting edges.
The other files show the data from different parts being milled, with the tool having four cutting edges. There were seven parts milled in total.
As the parts progress, it can be seen that the acoustic data changes, indicating wear and damage in the tool.
This dataset contains electrocardiography (ECG) data recorded under controlled laboratory conditions. The primary objective of the measurement was to evaluate and compare different types of electrodes made from conductive textile materials in terms of their signal quality and performance. The dataset includes recordings from both conventional adhesive electrodes and textile electrodes to assess their suitability for ECG monitoring.
The dataset contains thousands of signal frames from 60 ZigBee devices. The devices and USRP N210 were placed on two movable cabinets on wheels with a maximum distance of 3 meters between them. Due to distance constraints, a coaxial attenuator with an attenuation of 60 dB was fitted at the receiver in order to obtain signals at low SNR. The SNR of the received signal is changed by adjusting the distance between the two cabinets, which can be reduced to about 0 dB when the distance is about 3 metres.
The field data set is collected in Hangzhou, Zhejiang Province, China. A linear array of 48 Fairfield Nodal ZLand 3C 5 Hz seismic sensors with approximately 5-m spacing was deployed to continuously record cultural noise from 10:00 am to about 02:45 pm, 24 June, 2019 (Beijing time) at a sampling frequency of 1000 Hz. The raw data includes 48 data files with the size of 201,312 kb and a file of the location information of all the receivers. Noted that the two horizontal components of sensors are oriented in the inline and crossline directions.
As an important component of inertial guidance and navigation, micro-electro-mechanical-system (MEMS) gyroscope is widely used in many fields. However, the accumulation of noise errors limits the long-term accuracy and further application of MEMS gyroscope. This paper proposes a novel denoising method for MEMS gyroscope based on interpolated complementary ensemble local mean decomposition with adaptive noise (ICELMDAN) and gated recurrent unit-unscented Kalman filter (GRU-UKF).
FLAME2-DT (Forest Fire Detection Dataset with Dual-modality Labels) is a comprehensive multi-modal dataset specifically designed for UAV-based forest fire detection research. The dataset consists of 1,280 paired RGB-thermal infrared images captured by a Mavic 2 Enterprise Advanced UAV system, with high-resolution (640×512) and precise pixel-level annotations for both fire and smoke regions. This dataset addresses critical challenges in forest fire detection by providing paired multi-modal data that captures the complementary characteristics of visible light and thermal imaging.
Repeated Route Naturalistic Driving Dataset (R2ND2) is a dual-perspective dataset for driver behavior analysis constituent of vehicular data collected using task-specific CAN decoding sensors using OBD port and external sensors, and (b) gaze-measurements collected using industry-standard multi-camera gaze calibration and collection system. Our experiment is designed to consider the variability associated with driving experience that depends on the time of day and provides valuable insights into the correlation of these additional metrics on driver behavior.
The JU-Impact Radiomap Dataset is a comprehensive dataset designed for research and development in indoor positioning systems. It comprises 5431 instances characterized by readings from 105 static Wi-Fi Access Points (APs) and spans 152 distinct virtual grids. Each virtual grid represents a 1x1 square meter area, derived by dividing a physical floor of a university building into reference coordinate points (x, y). The dataset was collected over a period of 21 days using four mobile devices: Samsung Galaxy Tab, Moto G, Redmi Note 4, and Google Pixel.
