Artificial synapses based on electrolyte gated transistors with conductance modulation characteristics have demonstrated their great potential in emulating the memory functions in the human brain for neuromorphic computing. While previous...
Electronic devices capable of faithfully emulating the fashion of biological neurons to encode information through firing neural spike trains with unique temporal patterns are the building blocks of neuromorphic hardware and result in various bionic electronic systems. Unfortunately, state-of-theart artificial spiking neurons are generally built with tens of transistors interconnected with complex circuits, which are bulky, energy-hungry, and need software assistance, not suitable for large-scale integration and efficient implementations. [6,7] Exploration of emerging electronic elements with a simple structure that can naturally exhibit rich neuronal spiking behaviors using the inherent physical attributes is thus highly pursued, which will lead to compact and functional neuromorphic systems.Memristors [8] are two-terminal resistive devices showing resistance switching (RS) effects whose mechanism arises from such as Mott phase transition, [9,10] ion migration, [11][12][13][14] ferroelectric polarization, [15,16] and spin polarization, [17,18] etc. Recent studies [19][20][21][22] have shown that, Mott memristors driven by the electronic current induced insulator-metal phase transition, can exhibit volatile RS behaviors and be leveraged to emulate the opening/closing dynamics of ion channels on neuronal membrane. By grouping a couple of Mott memristors with capacitors and resistors through carefully
The recent emergence of various smart wearable electronics has furnished the rapid development of human-computer interaction, medical health monitoring technologies, etc. Unfortunately, processing redundant motion and physiological data acquired by multiple wearable sensors using conventional off-site digital computers typically result in serious latency and energy consumption problems. In this work, a multi-gate electrolyte-gated transistor (EGT)-based reservoir device for efficient multi-channel near-sensor computing is reported. The EGT, exhibiting rich short-term dynamics under voltage modulation, can implement nonlinear parallel integration of the time-series signals thus extracting the temporal features such as the synchronization state and collective frequency in the inputs. The flexible EGT integrated with pressure sensors can perform on-site gait information analysis, enabling the identification of motion behaviors and Parkinson's disease. This near-sensor reservoir computing system offers a new route for rapid analysis of the motion and physiological signals with significantly improved efficiency and will lead to robust smart flexible wearable electronics.
Aiming to reduce the difficulty of managing and motivating knowledge workers (k-workers), and promote the psychological well-being of them in Chinese hospitals, this study examines how k-workers’ leader–member exchange (LMX) influences their task performance and the mediation effect of organizational citizenship behavior (OCB). Through a self-administered survey, valid questionnaires were collected from 384 k-workers in Chinese hospitals, and partial least squares structural equation modeling was employed for data analysis. The findings show that LMX is positively related to OCB and task performance, and that OCB mediates the relationship between LMX and task performance. This research has theoretical implications and also provides practical suggestions on how to manage, motivate, and inspire k-workers, and promote the psychological well-being of them, and finally enhance the organizational performance in Chinese hospitals.
The interference of noise will cause the degradation of image quality, which can have a negative impact on the subsequent image processing and visual effect. Although the existing image denoising algorithms are relatively perfect, their computational efficiency is restricted by the performance of the computer, and the computational process consumes a lot of energy. In this paper, we propose a method for image denoising and recognition based on multi-conductance states of memristor devices. By regulating the evolution of Pt/ZnO/Pt memristor wires, 26 continuous conductance states were obtained. The image feature preservation and noise reduction are realized via the mapping between the conductance state and the image pixel. Furthermore, weight quantization of convolutional neural network is realized based on multi-conductance states. The simulation results show the feasibility of CNN for image denoising and recognition based on multi-conductance states. This method has a certain guiding significance for the construction of high-performance image noise reduction hardware system.
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