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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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A group of 10 healthy subjects without any upper limb pathologies participated in the data collection process. A total of 8 activities are performed by each subject. The measurement setup consists of a 5-channel Noraxon Ultium wireless sEMG sensor system. Representative muscle sites of the forearm are identified and self-adhesive Ag/AgCl dual electrodes are placed. The signal (sEMG) recorded during an ADL activity is segmented into functional phases: 1) rest 2) action and 3) release. During the rest phase, the subject is instructed to rest the muscles in a natural way.
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159 Views
Modern, industrial automation is unthinkable without wireless communications. Thereby, wireless links provide the necessary flexibility for industrial real-time applications. On the other side, these applications need at the same time a wireless communications link that works ultra-reliably. In communications systems, unreliability can be traced back to the fading behavior of the wireless radio channel as the medium between the communicating entities.
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This dataset contains electrocardiography, electromyography, accelerometer, gyroscope and magnetometer signals that were measured in different scenarios using wearable equipment on 13 subjects:
- Weight movement in a horizontal position at an angle of approximately 45°.
- Vertical movement of the weights from the table to the floor and back.
- Moving the weights vertically from the table to the head and back.
- Rotational movement of the wrist while holding the weights with the arm extended, see Figure ~\ref{fig2}.
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This paper addresses the problem of dynamic multi-objective optimization problems (DMOPs), by demonstrating new approaches to change prediction strategies within an evolutionary algorithm paradigm. Because the objectives of such problems change over time, the Pareto optimal set (PS) and Pareto optimal front (PF) are also dynamic.
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Ultra-wideband radar (UWB) is capable of perceiving the surroundings irrespective of the visibility due to its broad frequency spectrum. Therefore, UWB technology can be employed in mobile robots to perform simultaneous localization and mapping (SLAM) in vision-denied environments (e.g. smoke, fog, walls with reflective surfaces). We chose four different environments to teleoperate a TurtleBot2 nonholonomic robot equipped with Novelda X4M300 monostatic radar modules and RPLIDAR-A2 laser range scanner(s).
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This dataset is utilized for the research of blind identification of CPM signal modulation order. The signal parameters in the dataset range as follows: modulation index from 0.125 to 1, modulation order of 2, 4, 8, pulse types including REC, RC, SRC, TFM, and GMSK, and correlation lengths of 1 to 8. The signals are oversampled by a factor of 10, transmitted through an additive white Gaussian noise (AWGN) channel, and the signal-to-noise ratio (SNR) ranges from 0 to 30dB.
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