Animals possess various functional systems such as sensory, nervous, and motor systems, which show effective cooperation in order to realize complicated and intelligent behaviors. This inspires rational designs for the integration of individual electronic devices to exhibit a series of functions, such as sensing, memory, and feedback. Inspired by the fact that humans can monitor and memorize various body motions, a motion memory device is developed to mimic this biological process. In this work, mechanical hybrid substrates are introduced, in which rigid memory devices and stretchable strain sensors are integrated into a single module, which enables them to work cooperatively in the wearable state. When attached to the joints of limbs, the motion memory device can detect the deformations caused by limb motions and simultaneously store the corresponding information in the memory device. This work would be valuable in materials design and electronics technology toward the realization of wearable and multifunctional electronic modules.
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http://dx.doi.org/10.1002/adma.201701780 | DOI Listing |
ACS Cent Sci
December 2024
Division of Chemistry and Chemical Engineering, Arthur Amos Noyes Laboratory of Chemical Physics, California Institute of Technology, Pasadena, California 91125, United States.
Spin-lattice relaxation constitutes a key challenge for the development of quantum technologies, as it destroys superpositions in molecular quantum bits (qubits) and magnetic memory in single molecule magnets (SMMs). Gaining mechanistic insight into the spin relaxation process has proven challenging owing to a lack of spectroscopic observables and contradictions among theoretical models. Here, we use pulse electron paramagnetic resonance (EPR) to profile changes in spin relaxation rates ( ) as a function of both temperature and magnetic field orientation, forming a two-dimensional data matrix.
View Article and Find Full Text PDFSci Rep
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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December 2024
Department of Communications and Electronics, Delta University for Science and Technology, Mansoura, Egypt.
Human activity recognition (HAR) is one of the most important segments of technology advancement in applications of smart devices, healthcare systems & fitness. HAR uses details from wearable sensors that capture the way human beings move or engage with their surrounding. Several researchers have thus presented different ways of modeling human motion, and some have been as follows: Many researchers have presented different methods of modeling human movements.
View Article and Find Full Text PDFJ Chem Phys
December 2024
Department of Physics, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
In this study, we present a comprehensive analysis of the motion of a tagged monomer within a Gaussian semiflexible polymer model. We carefully derived the generalized Langevin equation (GLE) that governs the motion of a tagged central monomer. This derivation involves integrating out all the other degrees of freedom within the polymer chain, thereby yielding an effective description of the viscoelastic motion of the tagged monomer.
View Article and Find Full Text PDFNano Lett
December 2024
Tianjin Key Laboratory for Rare Earth Materials and Applications, Center for Rare Earth and Inorganic Functional Materials, School of Materials Science and Engineering, Nankai University, 300350 Tianjin, China.
Architectures based on a magnetic domain wall (DW) can store and process information at a high speed in a nonvolatile manner with ultra-low power consumption. Recently, transition-metal rare earth metal alloy-based ferrimagnets have attracted a considerable amount of attention for the ultrafast current-driven DW motion. However, the high-speed DW motion is subject to film inhomogeneity and device edge defects, causing challenges in controlling the DW motion and hindering practical application.
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