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A microsecond pulse power module (MPPM) conversion system with high performance is proposed in this manuscript (TPEL-Reg-2023-05-1116) for low-voltage lithium battery-powered portable DBD plasma source devices. High performance is not only reflected in the proposed MPPM has excellent driving performance for small DBD electrode discharge, but also reflected in the optimization of the system volume parameters.
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<p>Legged robots have demonstrated high potential when dealing with rough terrain, for which an efficient motion planner becomes crucial. This paper presents a novel approach for quadrupedal robot motion planning on rough terrain, which is both conceptually straightforward and computationally efficient. Implementing the concept of workspace constitutes the cornerstone of this method: both body poses and swing-leg footholds are chosen within their corresponding workspace.
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This dataset investigates the suitability of different filters for Learning-Based Motion Magnification (LBMM) and examines the impact of filter parameters on output results. The study finds that the Butterworth filter produces satisfactory results, while the analysis of IIR filters is unsatisfactory due to computational and memory limitations. Additionally, the efficacy of IIR filters for image processing and the reliability of FIR filters are called into question.
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The Autofocus Projector Dataset is a collection of 555 images and 150 videos captured while projecting images and videos with varying levels of Gaussian blur. The dataset includes images and videos of different blur levels, ranging from fully focused to the maximum levels of left and right Gaussian blur as per the projector's specifications. The dataset was recorded using a Redmi Note 11T 5G mobile camera with a 50 MP, f/1.8, 26mm (wide) sensor, PDAF image camera, and 1080p@30 fps video camera.
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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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In this study, we present advances on the development of proactive control for online individual user adaptation in a welfare robot guidance scenario, with the integration of three main modules: navigation control, visual human detection, and temporal error correlation-based neural learning. The proposed control approach can drive a mobile robot to autonomously navigate in relevant indoor environments. At the same time, it can predict human walking speed based on visual information without prior knowledge of personality and preferences (i.e., walking speed).
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Oral health problems are closely associated with the analysis of dental tissue changes and the stomatologic treatment that follows. The associated paper explores the use of diffuse reflectance spectroscopy in the detection of dental tissue disorders. The data set includes 78 out of 343 measurements of teeth spectra in the wavelength range from 400 to 1700 nm. The proposed methodology focuses on computational and statistical methods and the use of these methods for the classification of dental tissue into two classes (healthy and unhealthy) by estimating the probability of class membership.
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