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Flat Optics: Components to Systems, FLATOPTICS 2021 - Part of OSA Optical Design and Fabrication 2021

作者： Zhang, Yifei Fowler, Clayton Liang, Junhao Azhar, Bilal Shalaginov, Mikhail Y. Deckoff-Jones, Skylar An, Sensong Chou, Jeffrey B. Roberts, Christopher M. Liberman, Vladimir Kang, Myungkoo Ríos, Carlos Richardson, Kathleen A. Rivero-Baleine, Clara Gu, Tian Zhang, Hualiang Hu, Juejun Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA United States Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA United States College of Optics and Photonics Department of Materials Science and Engineering University of Central Florida OrlandoFL United States Materials Research Laboratory Massachusetts Institute of Technology CambridgeMA United States Missiles and Fire Control Lockheed Martin Corporation OrlandoFL United States

We report an electrically reconfigurable metasurface platform based on optical phase change materials. Multi-cycle reversible switching is realized using conductive micro-heaters. Large-contrast spectral tuning and be... 详细信息

ISBN: (纸本)9781557528209

关键词： Phase change materials

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Research on Multi-dimensional Bilingual Teaching Model of computer Courses Supported by Artificial Intelligence 17th

Research on Multi-dimensional Bilingual Teaching Model of Co...

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17th International Conference on computer science and Education, ICCSE 2022

作者： Zhao, Changhua Mi, Chunqiao Tangbo School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924486

Aiming at the problems of the integration of computer course teaching with international teaching and engineering certification, this study designs a multi-dimensional teaching mode of computer courses through the support of Artificial Intelligence. It analyzes the teaching resources, learning services, teaching process and teaching means involved in computer course teaching. Taking the course of "computer Introduction" as an example, this study explores the multi-dimensional bilingual teaching mode supported by Artificial Intelligence, trains computer talents who meet the ability requirements of the era of Artificial Intelligence, and provides ideas for the teaching innovation of other courses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

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Application of an Efficient Blockchain PBFT Optimization Algorithm for Environmental Monitoring

Application of an Efficient Blockchain PBFT Optimization Alg...

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computer science and Blockchain (CCSB), International Conference on

作者： Manyu ZHAO Wei LIU Kai HE School of Information and Electronic Engineering Shandong Technology and Business University Yantai Shandong China Key Laboratory of Sensing Technology and Control in Universities of Shandong Yantai Shandong China School of Computer Science and Technology(School of Artificial Intelligence) Shandong Technology and Business University Yantai Shandong China

Blockchain technology has changed the centralized management model of the existing environmental monitoring system by virtue of its decentralized and non-tampering characteristics. However, with the increase in the amount of monitoring data, the environmental monitoring blockchain system is faced with a large number of computing and communication overhead caused by data consensus, which affects the normal collection, calculation, and upload of environmental monitoring data. Aiming at the above problems, this paper proposes a credit grouping Byzantine fault-tolerant(CGBFT) mechanism which is suitable for environmental monitoring blockchain scenarios. First, the reliability analysis is performed based on the authenticity and integrity of the data transmitted by the monitoring nodes to obtain the credit value of each node as the basis for selecting the master node. Second, the consistency protocol of PBFT is optimized for the characteristics of the environmental monitoring blockchain system to meet the data consensus strategy of high throughput and reliable fault tolerance. The scheme analysis shows that the improved PBFT consensus mechanism can effectively solve the problems of high communication overhead and low efficiency in the system performance.

关键词： computer science Fault tolerance System performance Computational modeling Fault tolerant systems Throughput Blockchains

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Beamforming Design in Cell-Free Massive MIMO Integrated Sensing and Communication Systems

Beamforming Design in Cell-Free Massive MIMO Integrated Sens...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Weihao Mao Yang Lu Jingxian Liu Bo Ai Zhangdui Zhong Zhiguo Ding School of Computer and Information Technology Beijing Jiaotong University Beijing China Ericsson(China) Communications Company Ltd. Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Department of Electrical and Electronic Engineering The University of Manchester Manchester U.K.

This paper investigates the beamforming design in the Cell-Free massive multi-input multi-output (MIMO) integrated sensing and communication (ISAC) system, termed as the CF-ISAC system, in presence of the channel state information (CSI) estimation error. The beamforming design is formulated into a sensing beampattern matching mean square error minimization problem under the constraints of the power budgets of the access points (APs) and the ergodic rate requirements of the users. A computationally tractable lower bound of the ergodic rate over the imperfect CSI is derived based on the Jensen's Inequality, and then a successive convex approximation based algorithm is proposed to solve the considered problem. Numerical results illustrate the beampatterns for different direction of arrival estimations of the targets. The advantage of the CF-ISAC system for radar sensing is revealed based on the relative location between the AP and the target.

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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Simulations and Experiments of Time-Varying Takagi Factorization via Zhang Neural Network 5

Simulations and Experiments of Time-Varying Takagi Factoriza...

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5th IEEE Advanced Information technology, Electronic and Automation control Conference, IAEAC 2021

作者： Tang, Zanyu Zeng, Tianyu Li, Zhenyu Chen, Jianrong Zhang, Yunong Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China College of Information Science and Engineering Jishou University Jishou416000 China School of Public Health Administration Youjiang Medical University for Nationalities Baise533000 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

The problem of Takagi factorization (TF) for complex time-varying symmetric matrix is proposed in this paper. To solve this difficult and challenging problem, a continuous-time TF (CTTF) model of derivative equation system form is proposed via Zhang neural network (ZNN) method. For the sake of digital hardware implementation, we also build up the corresponding discrete-time (DT) ZNN solution models with different precisions of the derivative equation system discretization. Specifically, an eight-instant DTTF (EIDTTF) model are proposed. We also comparatively use two other multi-instant (e.g., four-instant and six-instant, i.e., FIDTTF and SIDTTF, respectively) models by discretizing the CTTF model. The effective solution step-size intervals of the proposed DTTF models are further discussed lastly. The results of simulations and experiments substantiate the correctness, effectiveness and superiority of the proposed CTTF and DTTF models. © 2021 IEEE.

关键词： Complex networks

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Visualization Study on Operating Performance of a Dual Compensation Chamber Loop Heat Pipe Under Acceleration Condition

SSRN

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SSRN 2022年

作者： Xie, Yongqi Liu, Siyuan Zhang, Hongxing Wu, Hongwei Fang, Zhen School of Aeronautic Science and Engineering Beihang University Beijing100191 China Beijing Key Laboratory of Space Thermal Control Technology China Academy of Space Technology Beijing100094 China School of Physics Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom

In this article, a novel visual dual compensation chamber loop heat pipe (DCCLHP) under acceleration conditions was experimentally investigated. The working fluid was deionized water and the wick material was sintered nickel powder. Visual windows were installed on both compensation chambers (CCs) and condenser in order to observe the vapor and liquid distribution. The operating performance and physical mechanism of the proposed DCCLHP under both direction A and B at different heat loads and acceleration magnitudes were analysed in a systematic manner. In the current study, the heat load varies from 30 W to 130 W and the acceleration magnitude ranges from 1g to 15g. Experimental results revealed that: (i) The larger the heat load, the higher the operating temperature. Obviously waving of the vapor-liquid interface in the CC is observed at direction A. Bubbles generated in the CCs and the vapor-liquid interface moves back and forth in the condenser during temperature oscillation at both 70 W and 90 W for the case of 13g and direction B. (ii) Under direction B, the DCCLHP presents lower operating temperature and higher thermal conductance. The maximum temperature is 143.2 o C at 5g with heat load of 90 W under direction A. The maximum thermal conductance is 1.70 W/K at 13g and 130 W under direction B. (iii) In general, the operating temperature shows a trend of decreasing first and then increasing with the increase of acceleration. Whereas the thermal conductance shows an opposite behavior. The transition acceleration, namely the acceleration magnitude at the minimum temperature, is 13g for the case of direction A. However, under direction B, the large heat load can result in a large transition acceleration. (iv) Intermittent spattering of liquid drops is observed in the CCs at 15g under direction A. The flow pattern under direction A is different with that under direction B. Multiple segments of the liquid and vapor phase alternately distribute and stratified flow fo

关键词： Heat pipes

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Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification

arXiv

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arXiv 2022年

作者： Liu, Haojie Xia, Daoxun Jiang, Wei Xu, Chao Huzhou Institute Zhejiang University Hangzhou310027 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Big Data and Computer Science and also with Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang550025 China

Visible-infrared person re-identification (VI-ReID) is a challenging and essential task, which aims to retrieve a set of person images over visible and infrared camera views. In order to mitigate the impact of large modality discrepancy existing in heterogeneous images, previous methods attempt to apply generative adversarial network (GAN) to generate the modality-consisitent data. However, due to severe color variations between the visible domain and infrared domain, the generated fake cross-modality samples often fail to possess good qualities to fill the modality gap between synthesized scenarios and target real ones, which leads to sub-optimal feature representations. In this work, we address cross-modality matching problem with Aligned Grayscale Modality (AGM), an unified dark-line spectrum that reformulates visible-infrared dual-mode learning as a gray-gray single-mode learning problem. Specifically, we generate the grasycale modality from the homogeneous visible images. Then, we train a style tranfer model to transfer infrared images into homogeneous grayscale images. In this way, the modality discrepancy is significantly reduced in the image space. In order to reduce the remaining appearance discrepancy, we further introduce a multi-granularity feature extraction network to conduct feature-level alignment. Rather than relying on the global information, we propose to exploit local (head-shoulder) features to assist person Re-ID, which complements each other to form a stronger feature descriptor. Comprehensive experiments implemented on the mainstream evaluation datasets include SYSU-MM01 and RegDB indicate that our method can significantly boost cross-modality retrieval performance against the state of the art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI Systems

arXiv

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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Real-Time AIoT for UAV Antenna Interference Detection via Edge-Cloud Collaboration

arXiv

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arXiv 2024年

作者： Dong, Jun Cheng, Jintao Wu, Jin Zhang, Chengxi Zhao, Shunyi Tang, Xiaoyu School of Data Science and Engineering Xingzhi College South China Normal University Shanwei516600 China School of Physics South China Normal University Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Key Laboratory of Advanced Control for Light Industry Processes Ministry of Education Jiangnan University Wuxi214122 China School of Electronics and Information Engineering Xingzhi College South China Normal University Shanwei516600 China

In the fifth-generation (5G) era, eliminating communication interference sources is crucial for maintaining network performance. Interference often originates from unauthorized or malfunctioning antennas, and radio monitoring agencies must address numerous sources of such antennas annually. Unmanned aerial vehicles (UAVs) can improve inspection efficiency. However, the data transmission delay in the existing cloud-only (CO) artificial intelligence (AI) mode fails to meet the low latency requirements for real-time performance. Therefore, we propose a computer vision-based AI of Things (AIoT) system to detect antenna interference sources for UAVs. The system adopts an optimized edge-cloud collaboration (ECC+) mode, combining a keyframe selection algorithm (KSA), focusing on reducing end-to-end latency (E2EL) and ensuring reliable data transmission, which aligns with the core principles of ultra-reliable low-latency communication (URLLC). At the core of our approach is an end-to-end antenna localization scheme based on the tracking-by-detection (TBD) paradigm, including a detector (EdgeAnt) and a tracker (AntSort). EdgeAnt achieves state-of-the-art (SOTA) performance with a mean average precision (mAP) of 42.1% on our custom antenna interference source dataset, requiring only 3 million parameters and 14.7 GFLOPs. On the COCO dataset, EdgeAnt achieves 38.9% mAP with 5.4 GFLOPs. We deployed EdgeAnt on Jetson Xavier NX (TRT) and Raspberry Pi 4B (NCNN), achieving real-time inference speeds of 21.1 (1088) and 4.8 (640) frames per second (FPS), respectively. Compared with CO mode, the ECC+ mode reduces E2EL by 88.9%, increases accuracy by 28.2%. Additionally, the system offers excellent scalability for coordinated multiple UAVs inspections. The detector code is publicly available at https://***/SCNURISLAB/EdgeAnt. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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