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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Sun, Yuhao Zhang, Shixin Shan, Jianhua Zhao, Lei Wang, Xiangbo Sun, Fuchun Yang, Yiyong Fang, Bin Anhui University of Technology School of Mechanical Engineering China Tsinghua University Institute for Artificial Intelligence Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Beijing100084 China School of Engineering and Technology Beijing100083 China Hebei University School of Quality and Technology Supervising Baoding China

ISBN: (纸本)9798350325706

Vision-based tactile sensors (VBTS) leverages visual modality to present high-resolution tactile information. The vision-based sensing mechanism has good adaptability to robot manipulation because of available information capture and data processing. However, the simulation of VBTS is a challenging problem because it deals with computer graphics and elasto-plastic deformation. In this paper, we propose a simulation approach for VBTS using a rendering approach based on path tracing. The rendering method can be integrated with existing VBTS and robotic simulators for the simulation of robotic systems. In addition, this method can control the rendering efficiency and quality by controlling the number of voxels in the fitting deformation area to meet the requirements of efficient robot training. The experimental results verify that our method can provide high-quality images at low rendering efficiency or reduce image quality to improve rendering efficiency. Our work has the potential to advance the sim-to-real research on VBTS. © 2023 IEEE.

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Achieving adaptively secure data access control with privacy protection for lightweight IoT devices

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science China(Information sciences) 2021年第6期64卷 144-157页

作者： Zhitao GUAN Wenti YANG Liehuang ZHU Longfei WU Ruimiao WANG School of Control and Computer Engineering North China Electric Power University School of Computer Beijing Institute of Technology Department of Mathematics and Computer Science Fayetteville State University

The Internet of things(IoT) technology has been used in a wide range of fields, ranging from industrial manufacturing to daily lives. The IoT system contains numerous resource-constrained lightweight devices such as wireless sensors and radio frequency identification(RFID) tags. A massive amount of sensitive data is generated and transmitted by these devices to a variety of users. The complexity of the IoT system places a high demand on security. Therefore, it is necessary to develop an encryption scheme with access control to provide flexible and secure access to the sensitive data. The ciphertext policy attribute-based encryption(CP-ABE) scheme is a potential solution. However, the long ciphertext as well as the slow encryption and decryption operations in traditional ABE schemes make it inappropriate for most IoT systems,which require low latency and contain many devices with limited memory size and computing capability. In this paper, we propose a modified CP-ABE scheme with constant length of ciphertext and low computation overhead in the encryption and decryption phases. Additionally, our scheme is proven to be adaptively secure under the standard model. Moreover, two enhanced schemes are developed to prevent authorized users from leaking data and protect the privacy of data owners by combining chameleon hash, bloom filters and CP-ABE, respectively. Finally, the experimental evaluation and analysis prove the feasibility of our scheme.

关键词： IoT CP-ABE constant-size ciphertexts adaptively secure privacy protection

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Performance Analysis of Disruptive Instances in Cloud Environment

Performance Analysis of Disruptive Instances in Cloud Enviro...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Pranab Nandy Rounak Saha Anurag Satpathy Sandip Chakraborty Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka Surathkal India Department of Computer Science and Engineering Cloud and Smart System Services Lab Missouri University of Science and Technology USA Department of Computer Science and Engineering Cloud and Smart System Services Lab Indian Institute of Technology Kharagpur India

Virtualization enables the service providers (SPs) to logically partition the resources into virtual machines (VM) instances. Real-world SPs such as Amazon, Google, Microsoft Azure, IBM, and Oracle provide different flavors of VM instances, such as on-demand, reserved, and low-cost or spot, depending on the type of application hosted. The on-demand instances are short-term and typically incur a higher cost than reserved instances that are provisioned for a longer duration at a discounted rate. Low-cost or spot instances are cost-effective compared to on-demand but are reclaimable by the SPs. The SPs often claim that the on-demand and spot instances achieve similar performance, but it is far from that. This paper studies the performance of spot instances via rigorous experimentation over commercial SPs such as Amazon AWS and Microsoft Azure. Real-world evaluations affirm that spot instances perform poorly compared to their on-demand counterpart concerning memory, CPU, disk read, and write operations. We identify such instances as disruptive and name them so because it does not fulfill the performance, durability, and flexibility expectations like an on-demand instance having the same configuration. We also perform hypothesis testing over the experimental data obtained to corroborate our claim further.

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Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss

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Mathematical Biosciences and engineering 2024年第4期21卷 5521-5535页

作者： Ran, Zhongnan Jiang, Mingfeng Li, Yang Wang, Zhefeng Wu, Yongquan Ke, Wei Xia, Ling School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Department of Cardiology Beijing Anzhen Hospital Capital Medical University No. 2 Anzhen Road Chaoyang District Beijing100029 China School of Applied Sciences Macao Polytechnic Institute China Department of Biomedical Engineering Zhejiang University Hangzhou310027 China

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively. © 2024 the Author(s).

关键词： Diseases

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Task Assignment of Heterogeneous Robots Based on Large Model Prompt Learning 1

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2nd International Conference on Cognitive Computation and systems, ICCCS 2023

作者： Deng, Mingfang Wang, Ying Lu, Lingyun Zhao, Huailin Yuan, Xiaohu School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China School of Physics and Electronic Information Yantai University Shandong Yantai China Nanjing Research Institute of Electronic Engineering Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

In order to ensure that the heterogeneous robot clusters performing the task can complete the target search at the specified location of the target object that the user needs to search according to their own field of view capabilities, we propose a task assignment algorithm for heterogeneous indoor robot clusters based on the robot’s own field of view constraints. In particular, the heterogeneous robot clusters need to be parsed by linguistic commands to obtain the assignment results. Therefore, we solve the task assignment of heterogeneous robot clusters by performing cue learning on a large model to achieve the maximum utilization of heterogeneous robots while satisfying the field-of-view constraint;the simulation verifies the effectiveness of the task assignment of this method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Learning systems

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Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

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computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.Vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta Velmurugan Subbiah Parvathy Gobi Ramasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Christ UniversityBangalore560029India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time ***,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,*** paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data *** AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT *** AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast *** addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature ***,softmax layer is applied to perform classification *** effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

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50-km Weakly-coupled Transmission with L-band 4-LP-mode FM-EDFA

50-km Weakly-coupled Transmission with L-band 4-LP-mode FM-E...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Huang, Shuailuo Zhu, Baolong Qiao, Gang Gao, Yuyang Zuo, Mingqing Shen, Shikui Wang, Zelin He, Yongqi Chen, Zhangyuan Li, Juhao Chen, Zhangyuan Li, Juhao China Unicom Research Institute Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Fundamental Network Technology China Mobile Research Institute Beijing China Pengcheng National Laboratory Shenzhen China

ISBN: (纸本)9798350379266

关键词： Pulse amplitude modulation

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Privacy Protection for Federated Learning in OFDM Aided Over-the-Air Computation system

Privacy Protection for Federated Learning in OFDM Aided Over...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wang, Yujuan Fan, Rongfei Hu, Han Zhang, Ning Beijing Institute of Technology The School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology The School of Information and Electronics Beijing100081 China University of Windsor The Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada

ISBN: (纸本)9798350345384

Federated learning (FL) an emerging distributed and privacy-protecting machine learning paradigm. In FL, training is completed through iterative local training and gradient aggregation among multiple mobile devices (MDs) and the edge server (ES). Over-the-air computation can help to achieve fast aggregation when multiple MDs need to offload their local gradients to the ES. Orthogonal frequency division modulation (OFDM) can further speed up aggregation through enabling simultaneous symbol transmission over multiple fading blocks. In this paper, we focus on the FL system with OFDM aided over-the-air computation technique implemented. To overcome the challenge of non-linear distortion at the amplifier of OFDM transmitter and protect the privacy of each individual MU, we propose a new aggregation method, which normalizes the local gradient first and then add uniformly distributed artificial noise, before it is offloaded to the ES. With our proposed aggregation method, the performance of differential privacy (DP) is analyzed, which disclose the quantization relationship between the variance of artificial noise and the level of privacy protection. We also analyze the peak power at OFDM transmitter by our proposed method, which is shown to be surely less than traditional method. Numerical results verifies the convergence and effectiveness of our proposed strategy. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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Development of China's spaceborne SAR satellite,processing strategy,and application:take Gaofen-3 series as an example

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地球空间信息科学学报（英文版） 2024年第1期27卷 221-236页

作者： Guo Zhang Shunyao Wang Zhenwei Chen Yuzhi Zheng Ruishan Zhao Taoyang Wang Yu Zhu Xinzhe Yuan Wei Wu Weitao Chen State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Law Wuhan UniversityWuhanChina School of Geomatics Liaoning Technical UniversityFuxinChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Institute of Spacecraft System Engineering China Academy of Space TechnologyBeijingChina National Disaster Reduction Center of China Ministry of Emergency ManagementBeijingChina School of Computer Science China University of GeosciencesWuhanChina Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural Resources of the People's Republic of ChinaBeijingChina

After mastering the key technologies of manufacturing spaceborne Synthetic Aperture Radar(SAR),China's SAR satellites have been successfully launched into *** the only civil microwave satellite listed in the"National High-resolution Earth Observation system Major Project,"the Gaofen-3(GF-3)01 satellite is the first C-band multi-polarization SAR satellite with a resolution of 1 ***-3 series satellites stand out among civil SAR satellites worldwide because of their high resolution,wide swath,high geometric radiation quality,multiple imaging modes,and long operation *** GF-3 series satellites as an example,this study introduces the development of China's civil SAR satellites,as well as their processing strategies and *** success of the GF-3 series satellites shows that China's SAR remote sensing technology has stepped into a new era of high-quality and high-precision Earth observations.

关键词： High-resolution Synthetic Aperture Radar(SAR) GaoFen-3(GF-3)satellites earth observation

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