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High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing

Light(Science & Applications) 2025年第1期14卷 227-237页

作者： Xiangyan Meng Nuannuan Shi Guojie Zhang Junshen Li Ye Jin Shiyou Sun Yichen Shen Wei Li Ninghua Zhu Ming Li Key Laboratory of Optoelectronic Materials and Devices Institute of SemiconductorsChinese Academy of SciencesBeijing100083China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of SciencesBeijing100049China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing100049China China Academy of Space Technology(Xi’an) Xi’anShaanxi710100China WeChat Pay Lab 33 Shenzhen Tencent Computer System Co.Ltd.Shenzhen518054China Lightelligence Group Hangzhou311121China

The burgeoning volume of parameters in artificial neural network models has posed substantial challenges to conventional tensor computing *** from the available optical multidimensional information entropy,optical intelligent computing is used as an alternative solution to address the emerging challenges of electrical *** limitations,in terms of device size and photonic integration scale,have hindered the performance of optical ***,an ultrahigh computing density optical tensor processing unit(OTPU),which is grounded in an individual microring resonator(MRR),is introduced to respond to these *** the independent tuning of multiwavelength lasers,the operational capabilities of an MRR are orchestrated,culminating in the formation of an optical tensor *** design facilitates the execution of tensor convolution operations via the lightwave and microwave multidomain hybrid multiplexing in terms of the time,wavelength,and frequency of *** experimental results for the MRR-based OTPU show an extraordinary computing density of 34.04 TOPS/***,the achieved accuracy rate in recognizing MNIST handwritten digits was 96.41%.These outcomes signify a significant advancement toward the realization of high-performance optical tensor processing chips.

关键词： tuning resonator tensor

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Semantic-Aware Prompt Learning for Multimodal Sarcasm Detection

Semantic-Aware Prompt Learning for Multimodal Sarcasm Detect...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Guangjin Wang, Bao Xu, Fuyong Zhu, Zhenfang Wang, Peipei Wang, Ru Liu, Peiyu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model's "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model's ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： KL divergence multi-view semantic representation multimodal sarcasm detection semantic representation enhancement

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Light Shift Detection Using a Multiple Photodetection Method Suitable for a Chip-Scale Atomic Clock

Light Shift Detection Using a Multiple Photodetection Method...

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IEEE International Frequency Control Symposium

作者： Masahiro Fukuoka Shigeyoshi Goka Department of Electrical Engineering and Computer Science Graduate School of System Design Tokyo Metropolitan University Tokyo Japan

SummaryIn this study, we demonstrated light shift detection in a running atomic clock using the multi-photodetection method. This method enables the observation of atomic resonances with different intensities using the intensity distribution of the laser beam. As it requires only two photodiodes to detect light shifts, it is easily applicable to miniature vapor-cell-based atomic clocks such as chip-scale atomic clocks and miniature atomic clocks.

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NARX Neural Network Modeling of Batch Distillation Process

NARX Neural Network Modeling of Batch Distillation Process

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11th IEEE International Conference on system engineering and Technology, ICSET 2021

作者： Putri, Adi Novitarini MacHbub, Carmadi Hidayat, Egi Muhammad Idris School of Electrical Engineering and Informatics Institut Teknologi Control Computer System Research Group Bandung Indonesia

ISBN: (纸本)9781665437660

The batch distillation model is highly nonlinear due to the influence of mass and composition of the initial material to be separated. It is also caused by the thermodynamics of the system which is not ideal. Therefore, analytical batch distillation modeling does not fulfill superposition theory, or is nonlinear. Thus, any well-established linear control schemes can not be applied here. Meanwhile, modeling a system using the Nonlinear Autoregressive with eXogenous inputs (NARX) is quite widely used today because of its ability to represent non-linear system dynamics. This paper propose a system identification using NARX Neural Network (NARX-NN) to modeling batch distillation process. In order to prove the superiority of NARX-NN's accuracy in the system identification process, a comparison with linear ARMA models is done. In this study, ARMA approximation was carried out in two ways. The first way is to use a neural network, while the second method is through approximation to the discrete transfer function. The validation results show that the NARX-NN achieves significantly better fit compared to the linear models. NARX-NN and ARMA-NN were compared and their MSE ratio for delay input and output 1 and 3, respectively have the smallest value, i.e 1.71e-04 © 2021 IEEE.

关键词： Thermodynamics Analytical models system dynamics Conferences Neural networks Transfer functions systems engineering and theory

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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Internet of Things for Agriculture Industry: A Bibliometric Analysis in the Last Five Years 11

Internet of Things for Agriculture Industry: A Bibliometric ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Maulana, Fairuz Iqbal Puspitasari, Chasandra Hamim, Miftahul Adi, Puput Dani Prasetyo Arifuddin, Rahman Widartha, Vandha Pradwiyasma School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Business School Bina Nusantara University Entrepreneurship Business Creation Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Pukyong National University Department of Information System Busan Korea Republic of

ISBN: (纸本)9798350389654

With the arrival of new technologies, the farm business is always changing. It is one of the most important in the world. The Internet of Things (loT) is one of the most exciting new technologies of the past few years. It could completely change the way we farm. We look at the literature about loT in agriculture over the last five years in this study. According to our findings, the use of loT in agriculture has been steadily increasing over the previous five years. This research provides a bibliometric evaluation of academic papers covering the years 2018-2022, with an emphasis on the Internet of Things (loT) in the agricultural sector. The analysis is performed using data obtained from the Scopus database. 120 documents were chosen and evaluated to acquire insights on the present state of research in this topic. Co-citation analysis identified wearable technology, farmers, loT devices, food, and wireless sensor networks (WSNs) as prominent research subjects in the agricultural industry's use of the Internet of Things (loT). The findings can provide valuable insights for scholars, practitioners, funding agencies, and policymakers in comprehending the patterns and focal points in this domain. The authors and institutes highlighted in this analysis are useful resources for collaborating and staying updated on the newest advances in the loT for Agriculture Industry. © 2024 IEEE.

关键词： Agricultural economics

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Ionic liquid multistate resistive switching characteristics in two terminal soft and flexible discrete channels for neuromorphic computing

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Microsystems & Nanoengineering 2022年第3期8卷 153-161页

作者： Muhammad Umair Khan Jungmin Kim Mahesh Y.Chougale Chaudhry Muhammad Furqan Qazi Muhammad Saqib Rayyan Ali Shaukat Nobuhiko P.Kobayashi Baker Mohammad Jinho Bae Hoi-Sing Kwok Department of Ocean System Engineering Jeju National University102 JejudaehakroJeju 63243Republic of Korea Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788UAE System on Chip Center Khalifa UniversityAbu Dhabi 127788UAE Department of Electronics and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina State Key Laboratory on Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Baskin School of Engineering Univerity of California Santa Cruz1156 High StreetSanta CruzCA 95064USA

By exploiting ion transport phenomena in a soft and flexible discrete channel,liquid material conductance can be controlled by using an electrical input signal,which results in analog neuromorphic *** paper proposes an ionic liquid(IL)multistate resistive switching device capable of mimicking synapse analog behavior by using IL BMIM FeCL_(4) and H_(2)O into the two ends of a discrete polydimethylsiloxane(PDMs)*** spike rate-dependent plasticity(SRDP)and spike-timing-dependent plasticity(STDP)behavior are highly stable by modulating the input ***,the discrete channel device presents highly durable performance under mechanical bending and *** the obtained parameters from the proposed ionic liquid-based synaptic device,convolutional neural network simulation runs to an image recognition task,reaching an accuracy of 84%.The bending test of a device opens a new gateway for the future of soft and flexible brain-inspired neuromorphic computing systems for various shaped artificial intelligence applications.

关键词： computing gateway state

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MULTIMODAL LEARNING TO IMPROVE CARDIAC LATE MECHANICAL ACTIVATION DETECTION FROM CINE MR IMAGES

arXiv

引用

arXiv 2024年

作者： Xing, Jiarui Wu, Nian Bilchick, Kenneth C. Epstein, Frederick H. Zhang, Miaomiao Department of Electrical and Computer Engineering University of Virginia United States Department of Computer Science University of Virginia United States Department of Biomedical Engineering University of Virginia Health System United States School of Medicine University of Virginia Health System United States

This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs. Our framework consists of two major components: (i) a DENSE-supervised strain network leveraging latent motion features learned from a registration network to predict myocardial strains;and (ii) a LMA network taking advantage of the predicted strain for effective LMA detection. Experimental results show that our proposed work substantially improves the performance of strain analysis and LMA detection from cine CMR images, aligning more closely with the achievements of DENSE. © 2024, CC BY.

关键词： Strain

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Generation of tunable superchiral spot in metal-insulator-metal waveguide

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Chinese Optics Letters 2023年第1期21卷 125-130页

作者：庄涛胡海峰詹其文 School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Zhangjiang Laboratory Shanghai 201204China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

The chiral feature of an optical field can be evaluated by the parameter of g-factor enhancement,which is helpful to enhance chiroptic signals from a chiral *** this work,the superchiral spot has been theoretically proposed in metal-insulator-metal *** g-factor enhancement of the superchiral spot can be enhanced by 67-fold more than that of circularly polarized light,and the spot is confined in the deep wavelength scale along each spatial ***,the position of the superchiral spot can be tuned by manipulating the incident *** tunable superchiral spot may find applications in chiral imaging and sensing.

关键词： circular dichroism superchiral spot radially polarized beam metal-insulator-metal waveguide

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Unit-Level AC Induced Magnetic Behavior in the Low-Frequency Regime: Measurement and Characterization

Unit-Level AC Induced Magnetic Behavior in the Low-Frequency...

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2022 ESA Workshop on Aerospace EMC, Aerospace EMC 2022

作者： Spantideas, Sotirios T. Giannopoulos, Anastasios E. Baklezos, Anargyros T. Nikolopoulos, Christos D. Nicoletto, Marco Marziali, Ilario Boschetti, Demis Capsalis, Christos N. National Technical University of Athens School of Electrical and Computer Engineering 15780 Greece Hellenic Mediterranean University Department of Electronic Engineering Crete73133 Greece Thales Alenia Space Italia Domain Exploration and Science: Electrical System and EMC Turin10146 Italy

ISBN: (纸本)9789082684742

In this paper, the measurement and characterization of the unit-level, low-frequency induced magnetic response for purposes of magnetic cleanliness is presented. Firstly, we concretely describe the test measurement procedure, including the configuration of the measurement setup and the specifications required to accurately obtain the weak induced magnetic signature of a unit. Furthermore, the post-processing pipeline of the measured data for denoising purposes is illustrated through measurements of representative spacecraft equipment (RF Switch unit). Finally, the frequency domain characterization of the induced unit magnetization is performed based on dipole fitting modeling and the induced magnetization tensor is calculated as a linear combination for different orientations of the applied magnetic field. © 2022 ESA ESTEC.

关键词： Magnetization

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