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International Conference on computer Vision (ICCV)

作者： Jun Hoong Chan Bohan Yu Heng Guo Jieji Ren Zongqing Lu Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University School of Artificial Intelligence Beijing University of Posts and Telecommunications Osaka University School of Mechanical Engineering Shanghai Jiao Tong University

Illumination planning in photometric stereo aims to find a balance between surface normal estimation accuracy and image capturing efficiency by selecting optimal light configurations. It depends on factors such as the unknown shape and general reflectance of the target object, global illumination, and the choice of photometric stereo backbones, which are too complex to be handled by existing methods based on handcrafted illumination planning rules. This paper proposes a learning-based illumination planning method that jointly considers these factors via integrating a neural network and a generalized image formation model. As it is impractical to supervise illumination planning due to the enormous search space for ground truth light configurations, we formulate illumination planning using reinforcement learning, which explores the light space in a photometric stereo-aware and reward-driven manner. Experiments on synthetic and real-world datasets demonstrate that photometric stereo under the 20-light configurations from our method is comparable to, or even surpasses that of using lights from all available directions.

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Provably Secure Authenticated Key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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Real-time defect and freshness inspection on chicken eggs using hyperspectral imaging

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Food Control 2023年 150卷

作者： Chen, Shih-Yu Hsu, Shih-Hsun Ko, Chih-Yi Hsu, Kai-Hsun Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and Technology Yunlin 64002 Taiwan

Chicken eggs are a common raw material for cooking, as well as a common agricultural product. Eggs contain abundant nutrients such as oil and fat, protein, and vitamins. They are beneficial to human bodies. Residual dirt or breakage before or after the washing and selection procedure would affect the egg quality. Defect inspections for eggs were done manually in the past. As this process is labor intensive, fatigue-induced errors are likely to occur, leading to inconsistent quality. This study developed hyperspectral egg defect inspection technique (HEDIT) and hyperspectral egg freshness inspection technique (HEFIT), based on the perspectives of factory production lines and consumers. HEDIT and HEFIT combined band selections with deep neural network (DNN), one-dimensional convolutional neural network (1D-CNN), two-dimensional convolutional neural network (2D-CNN), three-dimensional convolutional neural network (3D-CNN), and MobileNet to implement real-time inspection using hyperspectral imaging (HSI). In the experiments, 1000 fresh egg samples and 800 defective egg samples were tested. The experimental results show that the overall accuracy of our proposed method was 99% in freshness and 100% in defect inspection. Additionally, in the defect inspection, the inspection time was 31 ms, and the overall accuracy remained at 95%, maintaining a certain degree of accuracy and sorting speed. These results are beneficial to subsequent applications and the commercialization of smart egg production. © 2023 Elsevier Ltd

关键词： Band selection Chicken eggs Convolutional neural network Deep learning Hyperspectral imaging Real-time defect inspection

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Multilingual Pre-training Model-Assisted Contrastive Learning Neural Machine Translation

Multilingual Pre-training Model-Assisted Contrastive Learnin...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shuo Sun Hong–xu Hou Zong–heng Yang Yi–song Wang National& Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Mongolian Information Processing Technology College of Computer Science Inner Mongolia University Hohhot China

Since pre-training and fine-tuning have been a successful paradigm in Natural Language Processing (NLP), this paper adopts the SOTA pre-training model-CeMAT as a strong assistant for low-resource ethnic language translation tasks. Aiming at the exposure bias problem in the fine-tuning process, we use the contrastive learning framework and propose a new contrastive examples generation method, which uses self- generated predictions as contrastive examples to expose the model to errors during inference. Moreover, in order to effectively utilize the limited bilingual data in low-resource tasks, this paper proposes a dynamic training strategy to fine-tune the model, and refines the model step by step by taking word embedding norm and uncertainty as the criteria of evaluate data and model respectively. Experimental results demonstrate that our method significantly improves the quality compared to the baselines, which fully verifies the effectiveness.

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Sensitivity Optimization of Impedance-Based Biosensor for Point-of-Care Applications

SSRN

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SSRN 2024年

作者： Jiang, Tianxiang Ye, Xuanjie Tian, Zuyuan Shaheen, Mohamed Abdelhamid, Ahmed Feng, Yiwei Li, Bingxuan Zhang, Yusheng Wang, Xihua Chen, Jie Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada National Engineering Research Center for Biomaterials College of Biomedical Engineering Sichuan University Sichuan Chengdu610064 China Academy for Engineering & Technology Fudan University Shanghai200433 China

The introduction of advanced lab-on-chip technologies, such as electrochemical biosensors, is promising for point-of-care testing. Impedimetric biosensors are of particular interest for a wide range of applications due to their low-cost fabrication requirements. Enhancing the sensitivity of such devices is currently a major challenge in the field. Here we describe sensitivity enhancements of an interdigitated electrode (IDE) impedimetric biosensor. The unique IDE design of our device allows for surface modification of antigens within the electrode gaps to allow highly selective antigen capture in biological samples. We have constructed a 3D model to simulate and study the impact of electrode geometry on impedance sensitivity. Simulation results revealed a linear trend between electrode inner gap distance and sensitivity, while electrode height and width showed non-linear trends. To demonstrate proof-of-concept, three IDE chip designs were tested with electrode gaps of 3 µm, 4 µm, and 5 µm. The chips were used to detect human anti-SARS-CoV-2 monoclonal antibody (mAb) in serum at concentrations ranging from 50 ng/mL to 500 ng/mL. The 3 µm design was shown to be the most sensitive, with successful detection of the mAb at the lowest tested concentration (50 mg/mL). This study provides a pathway to improving biosensor sensitivity through electrode geometric design, which can be further utilized in diverse electrode-based biosensors and optimization for multiple clinical and research applications beyond COVID-19. © 2024, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

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Explainable Artificial Intelligence Model for Electricity Usage Prediction in Nigeria

Explainable Artificial Intelligence Model for Electricity Us...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Grace Egbi Alilu Grace Chinenye Okorie Idowu Adetona Ayoade Ugochukwu Onyekachi Obonna Olusola Kunle Akinde Cosmas Ifeanyi Nwakanma Computer Science Hallmark University Ijebu-Itele Nigeria Computer Science and Engineering Obafemi Awolowo University Ile Ife Nigeria Electrical and Electronics Engineering Science University of Johannesburg Johannesburg South Africa Electrical and Electronics Engineering Federal University of Technology Oweri Nigeria Electrical and Electronics Engineering First Technical University Ibadan Nigeria ICT Convergence Research Center Kumoh National Institute of Technology Gumi South Korea

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

This study developed a model to predict electricity usage in Nigeria for policy and decision-making. Using XGBoost and LSTM, results showed XGBoost performed better with an RMSE of 0.089, MAPE of 18.51%, and R 2 of 0.8, making it the selected model. SHAP-based explainable AI (XAI) provided insights into key factors affecting electricity usage, with the hour of the day being the most significant, followed by the day, month, temperature, wind speed, cloud cover, pressure, and humidity. The model effectively predicts electricity usage and offers valuable insights for improving energy policies and decision-making.

关键词： Humanities Temperature Explainable AI Electricity Wind speed Clouds Decision making Humidity Predictive models Long short term memory

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Sustainable Solutions for Emerging Issues in Wireless technology

Sustainable Solutions for Emerging Issues in Wireless Techno...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Nidal A. Al-Dmour Saif. E. A. Alnawayseh Anaum Ihsan Muhammad Irfan Shagufta Kousar Dilawer Ali Tahir Abbas Department of Computer Engineering College of Engineering Mutah University Jordan Department of Electrical Engineering College of Engineering Mutah University Al-Karak Jordan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan National college of business administration economics (NCBA&E) Multan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science The TIMES Institute Multan Pakistan

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The rapid evolution of wireless technology has transformed communication, offering unprecedented accessibility and convenience. However, significant challenges like spectrum scarcity, security vulnerabilities, and environmental concerns must be addressed for sustainable growth. This paper provides an overview of these issues, including the impact of regulatory barriers and increased data demand on network infrastructure. It also suggests measures to support the development of effective wireless communication policies, ensuring sustainable progress as technologies advance towards 5G, MIMO, and IoT. The goal is to identify improvements for more robust wireless communication systems.

关键词： Wireless communication Privacy 5G mobile communication Spectral efficiency Power system management Modulation Communication system security Security Reliability Wireless fidelity

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Event-aided Semantic Scene Completion

arXiv

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arXiv 2025年

作者： Guo, Shangwei Shi, Hao Wang, Song Yin, Xiaoting Yang, Kailun Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Autonomous driving systems rely on robust 3D scene understanding. Recent advances in Semantic Scene Completion (SSC) for autonomous driving underscore the limitations of RGB-based approaches, which struggle under motion blur, poor lighting, and adverse weather. Event cameras, offering high dynamic range and low latency, address these challenges by providing asynchronous data that complements RGB inputs. We present DSEC-SSC, the first real-world benchmark specifically designed for event-aided SSC, which includes a novel 4D labeling pipeline for generating dense, visibility-aware labels that adapt dynamically to object motion. Our proposed RGB-Event fusion framework, EvSSC, introduces an Event-aided Lifting Module (ELM) that effectively bridges 2D RGB-Event features to 3D space, enhancing view transformation and the robustness of 3D volume construction across SSC models. Extensive experiments on DSEC-SSC and simulated SemanticKITTI-E demonstrate that EvSSC is adaptable to both transformer-based and LSS-based SSC architectures. Notably, evaluations on SemanticKITTI-C demonstrate that EvSSC achieves consistently improved prediction accuracy across five degradation modes and both In-domain and Out-of-domain settings, achieving up to a 52.5% relative improvement in mIoU when the image sensor partially fails. Additionally, we quantitatively and qualitatively validate the superiority of EvSSC under motion blur and extreme weather conditions, where autonomous driving is challenged. The established datasets and our codebase will be made publicly at https://***/Pandapan01/EvSSC. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Optimizing symmetric-sinusoidal microchannel heat sinks with butterfly prisms for high temperature superconducting applications: An ANN-based approach

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International Journal of Thermal Sciences 2025年 217卷

作者： Zhang, Jiacheng Ge, Baojun Gou, Zhide Zhang, Jiancheng Saeed, Abdullah Faisal, Khalid Ramanathan, Karthikeyan National Engineering Research Center of Large Electric Machines and Heat Transfer Technology School of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin150080 China Harbin Electric Machinery Company Limited Harbin150080 China State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Department of Electrical and Computer Engineering College of Engineering King Abdulaziz University P.O. Box 80204 Jeddah21589 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur50603 Malaysia

As electrical devices (EDs) advance in power density and miniaturization, microchannel heat sinks (MCHSs) gain extensive attention for efficient heat dissipation. Among these, the symmetric-sinusoidal microchannel heat sink (SMHS), which balances thermal and hydraulic performance, is emerging as one of the most promising cooling solution for EDs. Yet its performance breakthroughs are often constrained by high pressure drop (ΔP) losses and suboptimal heat transfer efficiency. This study proposes a SMHS integrated with butterfly prisms (SMHS-BP). By optimizing the structural and arrangement parameters of the BP (wing distance Du, tail distance Dv, and flow direction distance Dh), an artificial neural network (ANN) was employed to predict the Nusselt number and ΔP for different designs. The predictions, compared with simulation results, explore the heat transfer mechanisms of SMHS-BP and determine specific parameters for the overall ideal framework (OIF) and thermal ideal framework (TIF). Results demonstrate that the incorporation of BP significantly reduces fluid momentum decay at microchannel wave crests, promoting multidirectional fluid mixing and enhancing heat transfer. Furthermore, numerical simulations of the thermal behavior of the latest-generation high temperature superconducting synchronous condenser (HT-SSC) equipped with SMHS-BP demonstrated the promising application prospects of microchannels in superconducting power systems. © 2025 Elsevier Masson SAS

关键词： Supercooling

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DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction

arXiv

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arXiv 2024年

作者： Li, Siyu Lin, Jiacheng Shi, Hao Zhang, Jiaming Wang, Song Yao, You Li, Zhiyong Yang, Kailun The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The College of Computer Science Zhejiang University Hangzhou310027 China The USC Viterbi School of Engineering The University of Southern California Los AngelesCA90089 United States

Temporal information plays a pivotal role in Bird’s-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate temporal fusion method will cause the barrier of feature redundancy when constructing vectorized High-Definition (HD) maps. In this paper, we revisit the temporal fusion of vectorized HD maps, focusing on temporal instance consistency and temporal map consistency learning. To improve the representation of instances in single-frame maps, we introduce a novel method, DTCLMapper. This approach uses a dual-stream temporal consistency learning module that combines instance embedding with geometry maps. In the instance embedding component, our approach integrates temporal Instance Consistency Learning (ICL), ensuring consistency from vector points and instance features aggregated from points. A vectorized points pre-selection module is employed to enhance the regression efficiency of vector points from each instance. Then aggregated instance features obtained from the vectorized points preselection module are grounded in contrastive learning to realize temporal consistency, where positive and negative samples are selected based on position and semantic information. The geometry mapping component introduces Map Consistency Learning (MCL) designed with self-supervised learning. The MCL enhances the generalization capability of our consistent learning approach by concentrating on the global location and distribution constraints of the instances. Extensive experiments on well-recognized benchmarks indicate that the proposed DTCLMapper achieves state-of-the-art performance in vectorized mapping tasks, reaching 61.9% and 65.1% mAP scores on the nuScenes and Argoverse datasets, respectively. The source code is available at https://***/lynn-yu/DTCLMapper. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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