This paper describes a new class of light transducer-based poly (vinylidene fluoride-trifluoroethylene-chlorotrifluoroethylene) (P(VDF-TrFE-CTFE)) terpolymer doped with 50% wt. phosphor particles that enables to efficiently transform light energy into an electrical signal. Broadband dielectric characterization together with experimental results on photo-electric conversion demonstrated high capacitance variation of the proposed composite under light exposure, confirming promising potential of our sensor device for application in retinal prostheses where the converted electrical signal can affect the biological activity of the neuron system. In addition to the benefit of being light-weight, having ultra-flexibility, and used in a simple process, the proposed photodetector composite leads to fast response and high sensibility in terms of photoelectrical coupling where significant increases in capacitance change of 78% and 25% have been recorded under blue and green light sources, respectively. These results demonstrated high-performance material design where phosphor filler contributes to promote charge-discharge efficiency as well as reduced dielectric loss in P(VDF-TrFE-CTFE), which facilitate the composite for flexible light transducer applications, especially in the medical environment.
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http://dx.doi.org/10.3390/polym12020349 | DOI Listing |
Sci Rep
December 2024
Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia.
The world is moving towards the utilization of hydrogen vehicle technology because its advantages are uniformity in power production, more efficiency, and high durability when compared to fossil fuels. So, in this work, the Proton Exchange Membrane Fuel Stack (PEMFS) device is selected for producing the energy for the hydrogen vehicle. The merits of this fuel technology are the possibility of operating less source temperature, and more suitability for stationery and transportation applications.
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December 2024
School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
A novel adaptive model-based motion control method for multi-UAV communication relay is proposed, which aims at improving the networks connectivity and the communications performance among a fleet of ground unmanned vehicles. The method addresses the challenge of relay UAVs motion control through joint consideration with unknown multi-user mobility, environmental effects on channel characteristics, unavailable angle-of-arrival data of received signals, and coordination among multiple UAVs. The method consists of two parts: (1) Network connectivity is constructed and communication performance index is defined using the minimum spanning tree in graph theory, which considers both the communication link between ground node and UAV, and the communication link between ground nodes.
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December 2024
School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, 434100, Hubei, China.
Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer.
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December 2024
Computer Science Department, Indiana University, Bloomington, IN, USA.
Pediatric diabetes I is an endemic and an especially difficult disease; indeed, at this point, there does not exist a cure, but only careful management that relies on anticipating hypoglycemia. The changing physiology of children producing unique blood glucose signatures, coupled with inconsistent activities, e.g.
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December 2024
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition.
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