Photoactive Monolayer MoS₂ for Spiking Neural Networks Enabled Machine Vision Applications

Advanced Materials Technologies, Год журнала: 2025, Номер unknown

Опубликована: Апрель 23, 2025

Abstract Neuromorphic vision and information processing is a strongly emerging avenue for next‐generation computing sensing technologies. Spiking neural networks (SNNs), which mimic the spiking behavior of biological neurons, offer energy efficiency plausibility. The leaky integrate‐and‐fire (LIF) model fundamental building block SNNs, where incoming electrical pulses are integrated until threshold reached, triggering spike resetting membrane potential. While several photoactive materials have been explored basic functionalities, realization accurate LIF neuron including replication potential its application in neuromorphic tasks remain unexplored. This work demonstrates chemical vapor deposition grown visible‐active, atomically thin molybdenum disulfide (MoS 2 ) monolayers replicating charging discharging states neurons through their photoelectrical response. Gate voltage modulation employed to swiftly reset neuron, enabling dynamics fast system responsiveness. key photoresponse parameters MoS embedded into SNN that achieves 75% accuracy after 15 epochs static 80% 60 dynamic tasks. 2D representing step development real‐time, efficient computing.

Язык: Английский

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Deep Learning based Network based Intrusion Detection System in Industrial Internet of Things

Опубликована: Янв. 4, 2024

The Industrial Internet of Things (IIoT) is the result integrating (IoT) into critical industries, especially in industrial and production settings. things has many benefits, but security privacy remain major concerns. current intrusion detection systems (IDS) for have encountered a number problems, including inability to handle different kinds attacks, dependence on traditional datasets, lack attention imbalanced datasets. In addressing these challenges, this study proposes implementation deep learning-based network system designed identify diverse types attacks within environments. And improve effectiveness Network Intrusion Detection System (NIDS), suggested model combines Convolutional Neural (CNN) with Auto Encoder (AE) techniques. improves outcomes by reducing data features using an autoencoder. Additionally, Networks possess ability automatically extract intricate patterns from complex data, such as traffic. hybrid exhibits commendable performance Edge_IIoT dataset, achieving accuracy 92.34%, precision 91.69%, recall 90.28%, F1 score 89.08%.

Язык: Английский

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A Violet‐Light‐Responsive ReRAM Based on Zn₂SnO₄/Ga₂O₃ Heterojunction as an Artificial Synapse for Visual Sensory and In‐Memory Computing

Advanced Electronic Materials, Год журнала: 2024, Номер unknown

Опубликована: Окт. 9, 2024

Abstract Due to the imitation of neural functionalities human brain via optical modulation resistance states, photoelectric resistive random access memory (ReRAM) devices attract extensive attraction for synaptic electronics and in‐memory computing applications. In this work, a ReRAM (PSR) structure ITO/Zn 2 SnO 4 /Ga O 3 /ITO/glass with simple fabrication process is reported imitate plasticity. Electrically induced long‐term potentiation/depression (LTP/D) behavior indicates fulfillment fundamental requirement artificial neuron devices. Classification three‐channeled images corrupted different levels (0.15–0.9) Gaussian noise achieved by simulating convolutional network (CNN). The violet light (405 nm) illumination generates excitatory post current (EPSC), which influenced persistent photoconductivity (PPC) effect after discontinuing excitation. As an device, PSR able some basic functions such as multi‐levels linearly increasing trend, learning‐forgetting‐relearning behavior. same device also shows emulation visual persistency optic nerve skin‐damage warning. This executes high‐pass filtering function demonstrates its potential in image‐sharpening process. These findings provide avenue develop oxide semiconductor‐based multifunctional advanced systems.

Язык: Английский

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Deep Learning for Blood Cells Classification Based on Multispectral Imaging for Improved Accuracy

Опубликована: Июль 24, 2024

Язык: Английский

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