Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memris...
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Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.
Visible light communications (VLC) enable wireless data transmission using existing light-emitting diode (LED) illumination. This experimental study evaluates the performance of multiple input and multiple output (MIM...
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Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,...
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Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electricalprogramming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.
This study introduces two novel hybrid machine-learning architectures for multilabel anomaly detection in electrocardiograms (EKGs): a 1D modified ResNet combined with a transformer encoder and an equivalent 2D ResNet...
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The traditional plasma etching process for defining micro-LED pixels could lead to significant sidewall *** near sidewall regions act as non-radiative recombination centers and paths for current leakage,significantly ...
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The traditional plasma etching process for defining micro-LED pixels could lead to significant sidewall *** near sidewall regions act as non-radiative recombination centers and paths for current leakage,significantly deteriorating device *** this study,we demonstrated a novel selective thermal oxidation(STO)method that allowed pixel definition without undergoing plasma damage and subsequent dielectric *** annealing in ambient air oxidized and reshaped the LED structure,such as p-layers and InGaN/GaN multiple quantum ***,the pixel areas beneath the pre-deposited SiO_(2)layer were selectively and effectively *** was demonstrated that prolonged thermal annealing time enhanced the insulating properties of the oxide,significantly reducing LED leakage ***,applying a thicker SiO_(2)protective layer minimized device resistance and boosted device efficiency *** the STO method,InGaN green micro-LED arrays with 50-,30-,and 10-μm pixel sizes were manufactured and *** results indicated that after 4 h of air annealing and with a 3.5-μm SiO_(2)protective layer,the 10-μm pixel array exhibited leakage currents density 1.2×10^(-6)A/cm^(2)at-10 V voltage and a peak on-wafer external quantum efficiency of~6.48%.This work suggests that the STO method could become an effective approach for future micro-LED manufacturing to mitigate adverse LED efficiency size effects due to the plasma etching and improve device ***-LEDs fabricated through the STO method can be applied to micro-displays,visible light communication,and optical interconnect-based *** planar pixel geometry will provide more possibilities for the monolithic integration of driving circuits with ***,the STO method is not limited to micro-LED fabrication and can be extended to design other III-nitride devices,such as photodetectors,laser diodes,high-electron-mobility transistors
In the realm of coal mining, safety and risk management stand as paramount challenges, underscored by the inherent perils like structural failures and the presence of hazardous gases such as methane. Recognizing the u...
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This study examines the impact of environmental, social, and governance (ESG) factors on economic investment from a statistical perspective, aiming to develop a tested investment strategy that capitalizes on the conne...
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Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the a...
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Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d
This paper discusses a method for classification of breast cancer imaging data through the application of an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) for hyperparameter optim...
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