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IEEE Conference on Global Communications (GLOBECOM)

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

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

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.

关键词： Privacy Differential privacy Quantum computing Error analysis Noise Qubit Error correction Protection Quantum circuit Tuning

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Catboost3D: A Novel CatBoost-based Approach for Efficient Classification of 3D Models 1

Catboost3D: A Novel CatBoost-based Approach for Efficient Cl...

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1st International Conference on Computing, Internet of Things and Microwave systems, ICCIMS 2024

作者： Bouksim, Mohcine Madani, Mohamed Amine Zakani, Fatima Rafii Arhid, Khadija Gadi, Taoufiq Aboulfatah, Mohamed National School of Applied Sciences Mohammed First University SmartICT Lab Oujda Morocco National School of Applied Sciences Mohammed First University Engineering Sciences Laboratory LSI Oujda Morocco SOVIA team National School of Applied Sciences University of Abdelmalek Essaâdi. Lab. of Applied Sciences Al Hoceima Morocco Higher School of Technology Cadi Ayyad University Lab. of Processes Signals Industrial Systems and Computer science Safi Morocco University of Hassan I. MISI Laboratory Faculty of Science and Technology Settat Morocco

ISBN: (纸本)9798350351736

The classification of 3D models consists in grouping similar objects in predefined classes. This task is challenging and requires results that align with human perception. This paper introduces a new classification method for 3D models, using the CatBoost classifier, a machine learning algorithm that has shown good results in various domains. Our approach addresses the challenge of accurately categorizing 3D models without needing a large database for training and using the 3D models directly without passing by intermediate representation. We trained the CatBoost classifier using the dihedral angle, shape index, and shape diameter function to create a model able to classify each query object to the correct class. We demonstrate, through extensive experimentation, that our method not only accurately classifies 3D models into their respective categories but also effectively handles the complexity and variability inherent in 3D data. © 2024 IEEE.

关键词： 3D modeling

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Natural Human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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Digital Twin with Delay-Optimal Relay-Assisted Federated Learning

Digital Twin with Delay-Optimal Relay-Assisted Federated Lea...

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International Conference on computer and Communications (ICCC)

作者： Yongyi Tang Ni Zhang Kunlun Wang George K. Karagiannidis School of Communication and Electronic Engineering East China Normal University Shanghai China Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In this paper, we propose a secure federated learning (FL) system in the digital twin (DT) based on relay technique and cooperative interference, which realizes accurate real-time prediction of physical entities. First, an optimization problem aiming to minimize the total delay of each FL iteration is established, in which the local device performs model uploading by non-orthogonal multiple access (NOMA) technique. Then, in order to solve this non-convex problem, a layered optimization algorithm is proposed, the local processing rate of each device, the transmission power of the relay and the interference signal of the jammer are obtained. Finally, numerical results show that the algorithm is effective and the relay-based FL security scheme can be effectively applied to DT.

关键词： Performance evaluation NOMA Federated learning Simulation Interference Digital twins Delays Relays Jamming Optimization

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Nefis: A Network Coding Based Flexible Device-to-Device Video Streaming Scheme

SSRN

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SSRN 2024年

作者： Yin, Jun Wen, Jiaxin Zhu, Ming Li, Yulong Wang, Lei The Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China The Department of Electrical and Computer Engineering University of Nevada Las VegasNV89154 United States The School of Computer Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China

Wireless device-to-device (D2D) communication has empowered efficient and convenient video sharing among neighboring devices. However, the mobility and diversity of user devices, combined with dynamically shifting channel conditions, make these processes susceptible to environmental interference. In this paper, we introduce an adaptive network coding scheme tailored for D2D raw source video streaming in the scalable video coding (SVC) format. Video frames are partitioned into equalsized fragments and encoded into streaming packets with the addition of certain redundancy. To address the observed deficiencywhere decoding throughput of coded video frames is generally lower than that of the encoding operation on mobile device platforms, we introduce a parallel decoding algorithm based on LU decomposition. Furthermore, we propose a flexible adjustment mechanism, named Nefis, which dynamically adapts the networkcoding redundancy and stream resolution based on the statistical model established to predict current network conditions and video streaming quality. Implementation on Android platform demonstrates that Nefis reduce redundancy in bandwidth usage during streaming process and enhance resilience to network dynamics. Experimental results also conclusively demonstrate the feasibility and advantages of network coding technology in D2D streaming applications. Nevertheless, achieving these advantages requires carefully designed encoding and decoding mechanisms. © 2024, The Authors. All rights reserved.

关键词： Wi-Fi

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A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

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computers, Materials & Continua 2022年第11期73卷 3983-4002页

作者： Abdullah AAsiri Tariq Ali Ahmad Shaf Muhammad Aamir Muhammad Shoaib Muhammad Irfan Hassan A.Alshamrani Fawaz F.Alqahtani Osama M.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia

Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to *** resonance imaging methods capture skull images that contain healthy and affected *** checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic ***,auto segmentation of the affected part is needed to facilitate ***,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor *** CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region *** proposed method was evaluated on a publicly available BraTS2020 *** statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice ***,our method achieved an accuracy value of 0.98,which is most prominent than existing ***,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

关键词： Brain tumor support vector machine convolutional neural network BraTS classification

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Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation

arXiv

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arXiv 2024年

作者： Zhou, Hanyu Chang, Yi Shi, Zhiwei Yan, Wending Chen, Gang Tian, Yonghong Yan, Luxin The National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China The Huawei International Co. Ltd. Singapore The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China The School of Electrical Engineering and Computer Science Peking University Beijing China

Optical flow has made great progress in clean scenes, while suffers degradation under adverse weather due to the violation of the brightness constancy and gradient continuity assumptions of optical flow. Typically, existing methods mainly adopt domain adaptation to transfer motion knowledge from clean to degraded domain through one-stage adaptation. However, this direct adaptation is ineffective, since there exists a large gap due to adverse weather and scene style between clean and real degraded domains. Moreover, even within the degraded domain itself, static weather (e.g., fog) and dynamic weather (e.g., rain) have different impacts on optical flow. To address above issues, we explore synthetic degraded domain as an intermediate bridge between clean and real degraded domains, and propose a cumulative homogeneous-heterogeneous adaptation framework for real adverse weather optical flow. Specifically, for clean-degraded transfer, our key insight is that static weather possesses the depth-association homogeneous feature which does not change the intrinsic motion of the scene, while dynamic weather additionally introduces the heterogeneous feature which results in a significant boundary discrepancy in warp errors between clean and degraded domains. For synthetic-real transfer, we figure out that cost volume correlation shares a similar statistical histogram between synthetic and real degraded domains, benefiting to holistically aligning the homogeneous correlation distribution for synthetic-real knowledge distillation. Under this unified framework, the proposed method can progressively and explicitly transfer knowledge from clean scenes to real adverse weather. In addition, we further collect a real adverse weather dataset with manually annotated optical flow labels and perform extensive experiments to verify the superiority of the proposed method. Both the code and the dataset will be available at https://***/hyzhouboy/CH2DA-Flow. Copyright © 2024, The Authors

关键词： Distillation

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