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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.
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TOWalk: A Multi-Modal Dataset for Real-World Movement Analysis
The TOWalk Dataset has been developed to support research on gait analysis, with a focus on leveraging data from head-worn sensors combined with other wearable devices. This dataset provides an extensive collection of movement data captured in both controlled laboratory settings and natural, unsupervised real-world conditions in Turin (Italy).
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Synthetic EEG Dataset for CNN Training: Clean and Artifact-Contaminated Signals
This dataset consists of synthetically generated EEG and EMG signals designed for training Convolutional Neural Networks (CNNs) in artifact detection and removal. The dataset includes both clean EEG signals and EEG signals contaminated with simulated EMG artifacts from various sources.
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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).
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This dataset contains signals collected on 7 different dates from 13 wired Ethernet network cards transmitted using the 100BASE-TX protocol. The signal is collected at the access point (switch side) using an oscilloscope with a sampling rate of 625Mbps and a sampling accuracy of 8 bits.
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1) RPaviaU-DPaviaC Dataset: The RPaviaU-DPaviaC dataset is constructed by amalgamating two publicly accessible HSI datasets: the ROSIS Pavia University (RPaviaU) scene and the DAIS Pavia Center (DPaviaC) scene. The RPaviaU dataset, featuring dimensions of 610 × 340 × 103, was acquired by the ROSIS HSI sensor over the terrain of the University of Pavia, Italy. Conversely, the DPaviaC dataset, with dimensions of 400 × 400 × 72, was collected using the DAIS sensor over the central area of Pavia City, Italy. These two scenes share a common set of seven land cover classes.
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Recently, machine learning models have seen considerable growth in size and popularity, lead-
ing to concerns regarding dataset privacy, especially around sensitive data containing personal information.
To address data extrapolation from model weights, various privacy frameworks ensure that the outputs of
machine learning models do not reveal their training data. However, this often results in diminished model
performance due to the necessary addition of noise to model weights. By enhancing models’ resistance to
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Cardiac functional imaging plays a crucial role in the detection, diagnosis, and prognosis of major cardiac diseases. Magnetocardiography (MCG) provides the benefits of non-invasive measurement and precise reflection of signals generated by the heart’s contraction and relaxation, and is gaining prominence in medical technology. However, due to various reasons, the reviewed dataset was not available and no standard dataset has been published on this topic.
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This dataset contains signals collected from 10 commercial-off-the-shelf Wi-Fi devices by an USRP X310 equipped with four receiving antennas. It comprises signals affected by various channel conditions, which is intended for use by the researchers in the development of a channel-robust RFFI system. The preprocessed preamble segments, estimated CFO values and device labels are provided. Please refer to the README document for more detailed information about the dataset.
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Synthetic Epileptic Spike EEG Database (SESED-WUT)
The database contains EEG, EMG, and EOG signals with artificially generated epileptic spikes. The recordings were performed using the g.USBamp 2.0 amplifier. Data were collected from 5 EEG channels (C3, Cz, C4, Fz, Fp1), 1 EOG channel (VEOG), and 3 EMG channels (Nape, Cheek, Jaw). The signals were sampled at 256 Hz and processed with a bandpass filter (0.1–100 Hz) and a notch filter (48–52 Hz).
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