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13th International Conference on computer engineering and Networks, CENet 2023

作者： Li, Xuanyu Yang, Houqun Zhang, Xiaoying Yang, Dangui Huang, Jianqiang Gan, Lin College of Computer Science and Technology Hainan University Hainan Haikou570228 China Haikou Key Laboratory of Deep Learning and Big Data Application Technology Hainan University Hainan Haikou570228 China Hainan Haikou China Elmore Family School of Computer and Electrical Engineering Purdue University West Lafayette United States

ISBN: (纸本)9789819992386

Clustering is an important branch of unsupervised tasks, aiming at mining deeper relationships and patterns in data. The quality of feature representation based on image datasets often determines the upper limit of clustering tasks. In recent years, the application of deep learning in deep clustering representation learning module has learned better feature representation, which gradually overcomes the limitation of traditional shallow clustering facing high-dimensional unstructured data. Deep clustering has become a popular research direction in the unsupervised field in recent years, and high clustering performance has been obtained in the continuous deepening research. The existing deep clustering research is mainly oriented to various fields of artificial intelligence, including natural language processing (NLP), speech recognition (ASR), computer vision (CV), etc. We take the field of computer vision as the entry point, analyze and study the research progress of deep clustering in computer vision (CV), and deconstruct the deep clustering model into two parts: a detailed classification of the feature extraction module from the perspective of network model architecture, while the data space of clustering is used as the entry point to divide the clustering module, and discuss the deep clustering. Finally, we analyze the datasets and evaluation metrics commonly used in experiments to study deep clustering architectures in computer vision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Feature extraction

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Complexity based investigation in collaborative assembly scenarios via non intrusive techniques 4

Complexity based investigation in collaborative assembly sce...

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4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Panagou, Sotirios Sileo, Monica Papoutsakis, Konstantinos Fruggiero, Fabio Qammaz, Ammar Argyros, Antonis School of Engineering University of Basilicata Potenza85100 Italy Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion70013 Greece

Human and robot collaboration in assembly tasks is an integral part in modern manufactories. Robots provide advantages in both process and productivity with their repeatability and usability in different tasks, while human operators provide flexibility and can act as safeguards. However, process complexity increases which can lower the overall quality. Increased complexity can negatively influence decision making due to cognitive load on human operators, which can lead to lower quality, be it product, process or human work. Moreover, it can lead to safety risks, human-system error and accidents. In this work, we present the preliminary results on an experiment performed with student-participants, based on an assembly task. The experiment was set up to emulate an industrial assembly, and data collection was performed through qualitative and non-intrusive quantitative methods. Questionnaires were used to assess perceptual task complexity and cognitive load, while a stereo camera provided recordings for after-task analysis on process errors and human work quality based on a 3D skeleton-based human pose estimation and tracking method. The aim of the study is to investigate causes of errors and implications on quality. Future direction of the work is discussed. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Decision making

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IMUSHIELD: Defending against inaudible attacks via IMUs on Smartphones 2

IMUSHIELD: Defending against inaudible attacks via IMUs on S...

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2nd International Conference on Electronic Information Technology, EIT 2023

作者： Zhang, Zhenfei Li, Ping Zhu, Biaokai Dong, Chong Lv, Zhao Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Shanxi Police College China School of Electrical and Information Engineering Anhui University of Technology China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

Inaudible attack has brought growing concerns over security of voice assistants. With a well-designed inaudible signal, an adversary can force the voice assistant to execute commands inaudibly like "Siri, open the door". It is challenging to defend against ultrasonic attacks without modifying the hardware. In this paper, we proposed a light-weight system named IMUSHIELD to defend voice assistant against inaudible attack. By comparing the different response of signal from microphone and inertial measurement units (IMUs) to different frequencies on smartphones. IMUSHIELD is able to detect the attacks without modifying the hardware. We have prototyped our method on a number of smartphones and test the performance of IMUSHIELD comprehensively in the real world, the result shows that our average detection accuracy exceeded 90 %. © 2023 SPIE.

关键词： Smartphones

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Designing Advanced Aqueous Zinc-Ion Batteries:Principles,Strategies,and Perspectives

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Energy & Environmental Materials 2022年第3期5卷 823-851页

作者： Yan Li Zhouhao Wang Yi Cai Mei Er Pam Yingkui Yang Daohong Zhang Ye Wang Shaozhuan Huang Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education&Hubei Key Laboratory of Catalysis and Materials Science South-Central University for NationalitiesWuhan 430074China School of Materials Science and Engineering Nanyang Technological UniversitySingapore Singapore Department of Electrical&Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Key Laboratory of Material Physics Ministry of EducationSchool of Physics and MicroelectronicsZhengzhou UniversityZhengzhou 450052China

Aqueous zinc-ion batteries(AZIBs)are an appealing battery system due to their low cost,intrinsic safety,and environmental-friendliness,while their application is plagued by the obstacles from the cathode,electrolyte,and zinc *** the design principles and strategies toward the optimization of cathode,electrolyte,and zinc anode is crucial for the development of ***,we present a comprehensive analysis of the design principles and promising strategies toward the improvement of ***,the various reaction mechanisms are summarized and the existing issues associated with the cathode,electrolyte,and zinc anode are discussed to guide the rational design of ***,we provide an in-depth and comprehensive discussion on the design principles and strategies for the electrodes/electrolyte/separator optimization,and analyze the advantages and disadvantages of various ***,the design principles and strategies of the newly appeared conversion-type AZIBs,such as Zn-S battery and Zn-Se battery,are also discussed and *** effect of design strategies on the electrochemical performance and the relationship between the current issues and strategies are also unveiled in ***,some research trends and perspectives are provided for designing better AZIBs.

关键词： aqueous zinc-ion batteries cathode modification design principles electrolyte regulation zinc anode optimization

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Tuning Quantum Computing Privacy through Quantum Error Correction

Tuning Quantum Computing Privacy through Quantum Error Corre...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhong, Hui Ju, Keyi Sistla, Manojna Zhang, Xinyue Li, Aohan Qin, Xiaoqi Fu, Xin Pan, Miao University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Kennesaw State University Department of Computer Science MariettaGA30060 United States The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo1828585 Japan

ISBN: (纸本)9798350351255

Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. However, ensuring privacy within this quantum computing necessitates innovative approaches. Existing research has introduced the concept of quantum differential privacy (QDP) to protect data privacy in quantum computing by leveraging quantum noise. Yet, this method faces limitations due to the fixed and uncontrollable nature of the inherent noise, which directly affects the privacy budget of QDP. Addressing this critical gap, our study proposes a novel approach that utilizes quantum error correction (QEC) techniques not only to mitigate quantum computing errors but also to adjust QDP protection levels precisely. By selectively applying QEC to single or multiple qubit gates, we introduce a method to manipulate the quantum noise error rate effectively. Moreover, we derive a new formula for calculating the overall error rate in a quantum circuit and the adjusted privacy budget after QEC operation. Through extensive numerical simulations, we validate the efficacy of utilizing QEC in tuning privacy protection levels within quantum computing. © 2024 IEEE.

关键词： Quantum noise

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3D Object Detection Based on Spatially Adaptive Sparse Convolution

3D Object Detection Based on Spatially Adaptive Sparse Convo...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Hu, Jing Jing Fan, Yu Ling Zhang, Ping Duan, Chang Zhang, Chao Chen, Si Yu Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Information Southwest Petroleum University Chengdu China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province Luzhou China School of Computer Science And Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350391213

Autonomous driving has gradually gained attention in recent years due to its potential to reduce driver burden and improve safety, in which 3D object detection is the basis of the autonomous driving perception system, aiming at predicting the location, size, and class of 3D objects in the vicinity of an autonomous vehicle, which is an important guide for subsequent path planning, motion prediction, and so on. In particular, 3D object detection has difficulties in input point cloud data processing and multimodal fusion. Unlike the image distribution where pixels are regularly distributed in the image plane, the point cloud is a sparse and irregular 3D representation, so it is difficult to apply the traditional convolution directly on the point cloud for detection. In this paper, we carry out a method for 3D object detection for point clouds, using deformable convolution and sparse convolution as a tool to explore the feature extraction method of sparse convolution for irregular point clouds and propose a spatially adaptive sparse convolution-based method. © 2024 IEEE.

关键词： Motion planning

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MEMS-based twisted bilayer flat optics for multidimensional optical modulation and hyperimaging 14

MEMS-based twisted bilayer flat optics for multidimensional ...

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High Contrast Metastructures XIV 2025

作者： Tang, Haoning Lou, Beicheng Du, Fan Gao, Guangqi Zhang, Mingjie Ni, Xueqi Yacoby, Amir Cao, Yuan Fan, Shanhui Mazur, Eric School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States

Our MEMS-based twistoptics device enables precise control of interlayer gaps and twist angles in photonic crystals, offering high-accuracy, multidimensional light manipulation. This finding holds potential for applica... 详细信息

ISBN: (纸本)9781510685048

关键词： Nanophotonics

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Self-adaptive rolling motion for snake robots in unstructured environments based on torque control

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Biomimetic Intelligence & Robotics 2023年第3期3卷 59-66页

作者： Shihao Ma Fatao Qin Shufan Chen Longchuan Li Jianming Wang Zengzeng Wang Shuai Li Xuan Xiao School of Electronic and Information Engineering Tiangong UniversityTianjin 300382China College of Mechanical and Electrical Engineering Beijing University of Chemical TechnologyBeijing 100029China College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin 300382China School of Computer Science and Technology Tiangong UniversityTianjin 300382China

Snake robots have great potential for exploring and operating in challenging unstructured environments,such as rubble,caves,and narrow ***,due to the complexity and unpredictability of unstructured environments,designing a controller that can achieve adaptive motion is *** paper proposes a self-adaptive torque-based rolling controller for snake robots,enabling compliant motion in unstructured ***,a controller is designed to modify the torque of each motor by focusing on the different motion states of the rolling ***,an experimental platform is established for snake robots to verify the effectiveness of the ***,a series of rolling experiments are conducted using the torque-based rolling *** conclusion,the self-adaptive torque-based rolling controller enhances snake robot adaptability and mobility.

关键词： Snake robot Torque-based control Self-adaptive rolling gait Unstructured environments

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A Brain Tumor Segmentation Approach with Adaptive Threshold Optimization Numerical Spiking Neural P Systems

A Brain Tumor Segmentation Approach with Adaptive Threshold ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Dong, Jianping Zhang, Gexiang Rong, Haina Fortin, Giancarlo Chen, Min School of Automation Chengdu University of Information Technology Chengdu610225 China School of Electrical Engineering Southwest Jiaotong University Chengdu611756 China University of Calabria CS Rende87036 Italy School of Computer Science and Engineering South China University of Technology Guangzhou510640 China Pazhou Laboratory Guangzhou510640 China

ISBN: (纸本)9781665410205

Magnetic resonance imaging (MRI) with the high-resolution in computer-aided diagnostic technology is widely used to provide doctors with diagnostic advice, especially in brain tumor segmentation. In addition, MRI multi-sequence images of brain tumors also provide better image data support for studying brain tumor segmentation. In this paper, an adaptive threshold segmentation numerical optimization spiking neural P system (ATONSNPS or ATONSN P system) is designed to dynamically adjust the threshold quantity. In addition, the ATONSN P system and connectivity algorithm are combined to finish multi-sequence brain tumor segmentation. Experimental results on BraTS2019 show that the multi-sequence brain tumor segmentation approach can achieve more effective segmentation of brain tumor images comparing with several benchmark algorithms. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Adaptive Frequency Hopping for 5G New Radio mMTC Security

Adaptive Frequency Hopping for 5G New Radio mMTC Security

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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： Chan, Wai Ming Kwon, Hyuck M. Chou, Remi A. Love, David J. Fahmy, Sonia Hussain, Syed Rafiul Kim, Sang Wu Valk, Chris Vander Brinton, Christopher G. Marojevic, Vuk Pham, Khanh D. Kim, Taejoon The University of Kansas Department of Electrical Engineering and Computer Science LawrenceKS66045 United States Wichita State University Department of Electrical and Computer Engineering WichitaKS67260 United States School of Computing Wichita State University WichitaKS67260 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Purdue University Department of Computer Science West LafayetteIN47907 United States The Pennsylvania State University Department of Computer Science and Engineering State CollegePA16802 United States Iowa State University Department of Electrical and Computer Engineering AmesIA50011 United States Raytheon Bbn CambridgeMA02138 United States Mississippi State University Department of Ece StarkvilleMS39762 United States Air Force Research Laboratory AlbuquerqueNM87123 United States

ISBN: (纸本)9798350336504

3GPP standards (e.g., LTE and 5G New Radio (NR)) have revolutionized commercial broadband access. Unfortunately, the openness of 3GPP specifications and high-level of adaptability make fifth-generation (5G) networks increasingly sensitive to unsophisticated attacks in the network. While 5G NR has introduced frequency hopping (FH) for massive machine-type communications (mMTC), there has not been a unified approach to designing the FH patterns in practical 5G systems. In this paper, we investigate the multi-user FH design problem at the physical resource grid of the 5G communication networks. We present various criteria for FH pattern design and propose two algorithms that are implementable with low complexity. The first scheme utilizes known interference patterns to design a set of user equipment (UE) FH patterns that are immune to interfering attacks. The second scheme leverages interference statistics and minimizes the expected number of hits by the interferer. Through numerical simulations and analytical modeling, we demonstrate the efficacy of the proposed schemes, which outperform the uniform FH pattern and achieve near-optimal performance. © 2023 IEEE.

关键词： Frequency hopping

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