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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

Science China(Information Sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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Spiking Token Mixer: An Event-Driven Friendly Former Structure for Spiking Neural Networks 38

Spiking Token Mixer: An Event-Driven Friendly Former Structu...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Deng, Shikuang Wu, Yuhang Du, Kangrui Gu, Shi University of Electronic Science and Technology of China China College of Computing Georgia Institute of Technology United States Shenzhen Institute for Advanced Study UESTC China

Spiking neural networks (SNNs), inspired by biological processes, use spike signals for inter-layer communication, presenting an energy-efficient alternative to traditional neural networks. To realize the theoretical advantages of SNNs in energy efficiency, it is essential to deploy them onto neuromorphic chips. On clock-driven synchronous chips, employing shorter time steps can enhance energy efficiency but reduce SNN performance. Compared to the clock-driven synchronous chip, the event-driven asynchronous chip achieves much lower energy consumption but only supports some specific network operations. Recently, a series of SNN projects have achieved tremendous success, significantly improving the SNN's performance. However, event-driven asynchronous chips do not support some of the proposed structures, making it impossible to integrate these SNNs into asynchronous hardware. In response to these problems, we propose the Spiking Token Mixer (STMixer) architecture, which consists exclusively of operations supported by asynchronous scenarios, including convolutional, fully connected layers and residual paths. Our series of experiments also demonstrates that STMixer achieves performance on par with spiking transformers in synchronous scenarios with very low timesteps. This indicates its ability to achieve the same level of performance with lower power consumption in synchronous scenarios. The codes are available at https://***/brain-intelligence-lab/STMixer_demo. © 2024 Neural information processing systems foundation. All rights reserved.

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A Novel Framework for Human Chronic Disease Prediction Using CNN and Adaptive Dragon Fly Optimization (ADFO) Algorithm

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SN Computer Science 2025年第4期6卷 1-18页

作者： Ragu, B. Perumal, S. Department of Computer Science School of Computing Sciences Technology and Advanced Studies (VISTAS) Vels Institute of Science Tamil Nadu Chennai 600117 India

Chronic diseases in human beings are long-lasting health disorders that will progress slowly over months or a lifetime. The chronic diseases can affect various parts or systems of the body and are non-acute in nature. The chronic disease does not occur suddenly and shall not be resolved quickly. However, the spreading of the disease to other parts of the body and the effectiveness of the disease shall be suppressed by providing proper medical treatment from the earlier stages. The World Health Organization has expressed the significance of the treatment of chronic disease, stating that around 41 million deaths, representing 71% of all deaths occurring every year due to chronic diseases. This necessitates the earlier detection of human chronic diseases like cancer, cardiovascular disease and diabetes mellitus. The major challenge associated with chronic disease is that it does not express valid symptoms at the earlier stage and needs advanced technology for the earlier detection process with high accuracy. The existing system of chronic disease predictions falls short in diagnosis and detection at the earlier stages, whereas capable of detecting at stage 3 or stage 4, which were considered the advanced stage of the chronic disease. To overcome this challenge, this proposed work introduces a novel framework encompassing the Convolutional Neural Networks (CNN) and the Adaptive Dragon Fly Optimization algorithm for the earlier prediction process. The proposed work was trained with datasets of various chronic diseases individually to prove the system’s adaptability. In addition, the performance of the proposed work is analyzed in terms of accuracy, Precision, recall and F score, which were compared with the performance of the existing state-of-the-art methodologies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive dragon fly optimization algorithm Cancer Cardiovascular disease Convolutional neural networks Human chronic disease

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Blood Flow Modeling in Stenosed Arteries Using CFD Solver 9th

Blood Flow Modeling in Stenosed Arteries Using CFD Solver

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9th International and 49th National Conference on Fluid Mechanics and Fluid Power, FMFP 2022

作者： Praharaj, Priyambada Sonawane, Chandrakant Kumar, Vikas Symbiosis Institute of Technology Pune412115 India High Performance Computing-Scientific and Engineering Application Centre for Development of Advanced Computing Pune411008 India

ISBN: (纸本)9789819971763

Simulation of blood flow in arteries is an extensive topic of research to study various hemodynamic factors in CFD. This paper studies two different degrees of stenosis (less than 50 and 80% by flow area) that have been extracted from real images. The pulsatile nature of the blood flow has been considered. Changes in velocity flow patterns and wall shear stress for the two cases have been studied. Results show that the peak velocity increases drastically as the stenosis area increases to 80%. Negative velocity near the walls of the artery shows flow separation. This value also increases with the increase in stenosis area. This is seen in almost all regions of the cardiac cycle except in the accelerating flow region. With the increase in stenosis area, wall shear stress (WSS) rises drastically. Negative wall shear stress seen downstream of stenosis also indicates recirculation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computational fluid dynamics

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C-PPT: A Channel-Wise Prototypical Part Transformer for Interpretable Perioperative Complication Prediction with Blood Pressure 27th

C-PPT: A Channel-Wise Prototypical Part Transformer for Inte...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Jingwei Yang, Xiaodong Chen, Yiqiang Sun, Ruizhe Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9783031783401

Continuous monitoring of multi-channel blood pressure during the perioperative period is crucial for predicting complications. ProtoPNet has garnered attention as powerful tools for providing interpretable support for complication prediction, enabling clinicians to better understand the impact of blood pressure trajectories on complications. However, existing prototype-based models for complication prediction tasks based on channel-wise blood pressure fail to recognize the unique characteristics of individual channels such as systolic pressure, diastolic pressure, and mean arterial pressure. Instead, they treat them as a unified entity for prediction and interpretation, resulting in performance degradation. To address this issue, we proposed the Channel-wise Prototypical Part Transformer (C-PPT). Firstly, we match the encoded data independently for each channel using a ProtoPNet, allowing for the extraction of unique features. Secondly, we enhance the prototypes by incorporating distance information between prototypes in the loss function. Finally, we optimize the influence of different channels on the results using pre-set weights. Experimental results conducted on a real dataset of perioperative blood pressure and cardiovascular adverse events classification tasks in a hospital setting demonstrate that our proposed method effectively interprets the relationship between blood pressure trajectories in different channels and the occurrence of cardiovascular adverse events, outperforming other benchmark models on relevant metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Blood pressure

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Fully Distributed Nash Equilibrium Seeking for High-Order Players With Actuator Limitations

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1434-1444页

作者： Maojiao Ye Qing-Long Han Lei Ding Shengyuan Xu the School of Automation Nanjing University of Science and TechnologyNanjing 210094China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China

This paper explores the problem of distributed Nash equilibrium seeking in games, where players have limited knowledge on other players' actions. In particular, the involved players are considered to be high-order integrators with their control inputs constrained within a pre-specified region. A linear transformation for players' dynamics is firstly utilized to facilitate the design of bounded control inputs incorporating multiple saturation functions. By introducing consensus protocols with adaptive and time-varying gains, the unknown actions for players are distributively estimated. Then, a fully distributed Nash equilibrium seeking strategy is exploited, showcasing its remarkable properties: (1) ensuring the boundedness of control inputs;(2) avoiding any global information/parameters;and (3) allowing the graph to be directed. Based on Lyapunov stability analysis, it is theoretically proved that the proposed distributed control strategy can lead all the players' actions to the Nash equilibrium. Finally, an illustrative example is given to validate effectiveness of the proposed method.

关键词： Actuator limitation directed networks games Nash equilibrium

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Optimization of Resource Allocation in the Cloud Environment using Regression Models 5

Optimization of Resource Allocation in the Cloud Environment...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Nathiya, R. Preethika, S. K. Piramu VEL'S Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331504403

Cloud computing is a developing technology that separates computations from infrastructures making it far more effective to traditional self-hosted computing methods. It saves operating costs and has a positive environmental impact by limiting resource waste, excessive provisioning, and consumption of energy. It also helps to improve the quality of service and user experience by decreasing delays, boosting throughput, and preventing bottlenecks. This study investigates the effectiveness of using regression models to direct cloud resource allocation. The cloud environment is well-equipped to both minimize cloud costs more efficiently and respond to changing demands by making decisions based on real-time data. © 2024 IEEE.

关键词： Cloud platforms

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Attack-Resilient Distributed Cooperative Control of Virtually Coupled High-Speed Trains via Topology Reconfiguration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1066-1068页

作者： Shunyuan Xiao Xiaohua Ge Qing Wu the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the School of Engineering and Technology Central Queensland UniversityRockhampton QLD 4701Australia

Dear Editor,This letter addresses the resilient distributed cooperative control problem of a virtually coupled train convoy under stochastic disturbances and cyber *** main purpose is to achieve distributed coordinati... 详细信息

关键词： prescribed Speed trains

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IP-geolocater:a more reliable IP geolocation algorithm based on router error training

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Frontiers of Computer Science 2022年第1期16卷 101-111页

作者： Shuodi ZU Xiangyang LUO Fan ZHANG State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Zhengzhou Science and Technology Institute Zhengzhou450001China

Location based services(LBS)are widely utilized,and determining the location of users’IP is the foundation for *** by unstable delay and insufficient landmarks,the existing geolocation algorithms have problems such as low geolocation accuracy and uncertain geolocation error,difficult to meet the requirements of LBS for accuracy and reliability.A new IP geolocation algorithm based on router error training is proposed in this manuscript to improve the accuracy of geolocation results and obtain the current geolocation error ***,bootstrapping is utilized to divide the landmark data into training set and verification set,and/24 subnet distribution is utilized to extend the training ***,the path detection is performed on nodes in the three data sets respectively to extract the metropolitan area network(MAN)of the target city,and the geolocation result and error of each router in MAN are obtained by training the detection ***,the MAN is utilized to get the target’s *** on China’s 24,254 IP geolocation experiments,the proposed algorithm has higher geolocation accuracy and lower median error than existing typical geolocation algorithms LBG,SLG,NNG and RNBG,and in most cases the difference is less than 10km between estimated error and actual error.

关键词： estimated error geolocation path detection network measurement

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Adaptive Random Evolution Whale Optimization Algorithm (AREWOA) Based Workflow Scheduling in a Virtualized Cloud Environment 15

Adaptive Random Evolution Whale Optimization Algorithm (AREW...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Nathiya, R. Preethika, S.K. Piramu VEL'S Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350370249

Cloud workflow task scheduling refers to assigning workflow tasks submitted by users to appropriate computer resources for execution. The workflow scheduler assigns the user request to the available Virtual Machine (VM) hosted on cloud servers during scheduling. However, while limiting energy consumption, the workflow application processing schedule length (makespan) and associated costs must stay within the users budget and deadline. The tasks are chosen using the optimal scheduling technique to reduce Energy Consumption (EC) and maximize Resource Utilization (RU) without compromising system performance. In this paper, Adaptive Random Evolution Whale Optimization Algorithm (AREWOA) based scheduling is proposed to reduce EC, maximize RU, and workflow execution time. AREWOA is performed based on the dual-operation strategy partnership. Initially, the Gaussian distribution is used in the randomization procedure to raise the variety of the population. Secondly, a new skip step factor is introduced to enhance the optimizer capability to solve local optimum, and specific reinforcement is applied to the hunter process to enhance the whale's exclusive exploration or exploitation skills. An adaptive weight factor is applied to maintain algorithm stability and a balance among exploration and exploitation. Dynamic provisioning of infinite heterogeneous resources, AREWOA is thought to demonstrate cloud-based Infrastructure-as-a-Service (IaaS). This approach is simulated in Cloud Simulator and the results shows that the efficiency of the methods. © 2024 IEEE.

关键词： Adaptive Random Evolution Whale Optimization Algorithm (AREWOA) Cloud computing (CC) Cloud Workflow Virtualization VM migration Workflow scheduling

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