Signal Processing
These data is state estimation accuracy of the proposed algorithm When the equivalent measurement loss probability is 0.1
These data includes the position estimation accuracy and velocity estimation accuracy of the algorithm.
The data are explained below:
save_bikf_pos_p1, save_kf_pos_p1, save_okf_pos_p1, save_bakf_pos_p1, save_vakf_pos_p1 are the position accuracy of the BKF, KF, OKF, the proposed BAKF-GIWM and the VAKF-GIWM, respectively.
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Surface EMG (sEMG) signals collected during activities of daily life (ADL) provide better insights toward understanding neuromuscular disorders, persons with limb disabilities, aging adults and neuromotor deficits. Hand movement and control mechanism analysis may improve the design of prosthetic devices, realistic biomechanical hands, and rehabilitation therapy. We present a sEMG signal database corresponding to the Indian population.
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We present Vocal92, a multivariate Cappella solo singing and speech audio dataset spanning around 146.73 hours sourced from volunteers. To the best of our knowledge, this is the first dataset of its kind that specifically focuses on a cappella solo singing and speech. Furthermore, we use two current state-of-the-art models to construct the singer recognition baseline system.
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Typically, control strategies for legged robots have been developed to adapt their leg movements to deal with complex terrain. When the legs are extended in search of ground contact to support the robot body, this can result in the center of gravity (CoG) being raised higher from the ground and can lead to unstable locomotion if it deviates from the support polygon. An alternative approach is body adaptation, inspired by millipede/centipede locomotion behavior, which can result in low ground clearance and stable locomotion.
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Personal assistive devices for rehabilitation will be in increasing demand during the coming decades due to demographic change, i.e., an aging society. Among the elderly population, difficulty in walking is the most common problem. Even though there are commercially available lower limb exoskeleton systems, the coordination between user and device still needs to be improved to achieve versatile personalized gait. To tackle this issue, an advanced EXOskeleton framework for Versatile personalized gaIt generation with a Seamless user-exo interface (called "EXOVIS") is proposed in this study.
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This is a data set for Radio Frequency fingerprinting, which is a kind of identification of wireless devices based on their intrinsic physical features. The data set is composed by GSM bursts collected from 12 GSM mobile phones while transmitting. The samples have been collected using a Software Defined Radio with a sample rate at 20 MS/s. The content information has been removed from the bursts to remove the risk of bias due to content. The data set is in MATLAB format.
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In this dataset, based on a beam sweeping experiment in the 60 GHz band in an indoor environment, we provide the acquired IQ data samples (containing the announced TX antenna weighting vectors (AWV) index as information) for the given RX AWV index, location, and carrier frequency. We also include the information obtained after processing the PPDU in IQ data.
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Waterproof valve fault diagnosis test bench test data.
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Channel frequency response (CFR) dataset used for "In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks" paper (IEEE Transactions on Microwave Theory and Techniques, in print, doi: 10.1109/TMTT.2023.3256532, preprint link: https://arxiv.org/abs/2303.04470).
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To enable intelligent vehicles and transportation systems, the vehicles and relevant systems need to have the ability to sense environment and recognize objects. In order to benefit from the robustness of radar for sensing, knowing how to use the radar system for effective object recognition is critical. Observing this, we in this paper propose a novel deep learning-aided object recognition system for radar systems by combining the You only look once (YOLO) system with a proposed object recheck system.
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