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Secure Enhanced IoT-WLAN Authentication Protocol With Efficient Fast Reconnection

IEEE Transactions on Mobile Computing 2025年

作者： Wang, Weizheng Xie, Qipeng Han, Zhaoyang Su, Chunhua Rodrigues, Joel J. P. C. Wu, Kaishun City University of Hong Kong Department of Computer Science Hong Kong Guangzhou China Nanjing Forestry University College of Information Science & Technology Nanjing China University of Aizu Division of Computer Science Fukushima965–8580 Japan Federal University of Piauí Post-Graduation Program in Electrical Engineering Teresina Brazil

The increasing integration of Internet of Things (IoT) devices in Wireless Local Area Networks (WLANs) necessitates robust and efficient authentication mechanisms. While existing IoT authentication protocols address certain security concerns, they often fail to provide comprehensive protection against threats such as perfect forward secrecy violations, insider attacks, and key compromise impersonation, or impose significant computational and communication overhead on resource-constrained IoT systems. This paper presents a novel Extensible Authentication Protocol (EAP) based scheme for IoT-WLAN environments that addresses these security challenges while maintaining cost-effectiveness. Our approach utilizes elliptic curve cryptography and incorporates advanced features including perfect forward secrecy, strong identity protection, and explicit key confirmation. We provide a thorough security analysis using informal heuristics, formal methods (Random Oracle Model and BAN Logic), and automated verification with ProVerif. Performance evaluations demonstrate that our protocol achieves lower communication, storage, and computational costs compared to state-of-the-art solutions, with an average 79.6% reduction in computation time. A detailed comparison with existing schemes highlights the efficiency and enhanced security features of our proposed authentication mechanism for IoT-WLAN deployments. © 2002-2012 IEEE.

关键词： Authentication Protocol

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Comparative Analysis of Real-Time Multi-Person Pose Detection in electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Zong-Rong Wu Pin-Hsuan Lin Chin-Hsiung Chen Wei Chien Yu-Cheng Chang School of Art and Design Sanming University Fujian China Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Business Administration National Central University Taoyuan Taiwan Department of Electrical Engineering New Taipei Municipal Tamsui Commercial Industrial Vocational Senior High School New Taipei Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy.

关键词： Training Accuracy Microcontrollers Neural networks Real-time systems Safety Internet of Things

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Sentiment Analysis of User Review on Indonesian Food and Beverage Group using Machine Learning Techniques

Sentiment Analysis of User Review on Indonesian Food and Bev...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Yanfi Yanfi Yaya Heryadi Lukas Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy.

关键词： Support vector machines Sentiment analysis Analytical models Machine learning algorithms Food industry Companies Classification algorithms

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Global-Local Active Contour for Segmenting Images with Simultaneous Inhomogeneity and Texture

SSRN

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SSRN 2024年

作者： Shao, Pei-Chiang Department of Applied Mathematics Chung Yuan Christian University Zhongli District Taoyuan City320314 Taiwan Undergraduate Program in College of Electrical Engineering and Computer Science Chung Yuan Christian University Zhongli District Taoyuan City320314 Taiwan

Severe intensity inhomogeneity (InH) and complex real-world textures cause great difficulties and become two important issues in image segmentation and object extraction applications. Plenty of methods are proposed to tackle these issues, however, they usually perform poorly on images possess both InH and texture. In this paper, we propose a region-based active contour model for segmenting images with simultaneous inhomogeneity and texture. A new bias field possesses both global and local smoothness properties is proposed to handle the InH issue, and a pixel inhomogeneity factor (PIF) is employed to jointly deal with InH and textured images. We also employ the total variation (TV) regularization to further smooth and denoise the transformed PIF image, making the segmentation process to have the nice properties of global convergence and independence of contour initialization. A large number of numerical experiments and comparisons are conducted to demonstrate the effective performance of the newly proposed method. © 2024, The Authors. All rights reserved.

关键词： Textures

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All-optical image denoising using a diffractive visual processor

arXiv

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arXiv 2023年

作者： Işıl, Çağatay Gan, Tianyi Ardic, F. Onuralp Mentesoglu, Koray Digani, Jagrit Karaca, Huseyin Chen, Hanlong Li, Jingxi Mengu, Deniz Jarrahi, Mona Akşit, Kaan Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States University College London Department of Computer Science London United Kingdom

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images – implemented at the speed of light propagation within a thin diffractive visual processor that axially spans Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Deep Reinforcement Learning for Mapless Robot Navigation Systems

Deep Reinforcement Learning for Mapless Robot Navigation Sys...

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IEEE Latin American Robotics Symposium, LARS

作者： Iure Rosa L. Oliveira Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Núcleo de Especialização em Robótica (NERo) Universidade Federal de Viçosa Viçosa MG Brazil

DRL has emerged as a promising approach for mobile robot navigation in unknown environments without a prior map. However, the performance of DRL methods for this task varies greatly, depending on the choice of algorithm, state representation, and training procedure. In this paper we explore various cutting-edge DRL algorithms, such as policy-, value-, and actor-critic-based approaches. Our results demonstrate the effectiveness of the ranging sensor approach, which achieves robust navigation policies capable of generalizing to unseen virtual environments with a high success rate. We combine Behavior Cloning with Imitation Learning to expedite the training process, leveraging expert demonstrations and reinforcement learning. Our methodology enables faster training while enhancing the learning efficiency and performance of the robot, obtaining better results in terms of crash and success rate, and being able to reach a cumulative reward of approximately 12000.

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Development of Mobile Robot Education Kit Prototype for Elementary School Students

Development of Mobile Robot Education Kit Prototype for Elem...

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2021 International Conference on computer System, Information Technology, and electrical engineering, COSITE 2021

作者： Priandana, Karlisa Margaretha, Friska Sinaga, Doli Aryo Dani Kusumoputro, Benyamin Bogor Agricultural University Computer Science Department Bogor Indonesia Bogor Agricultural University Vocational School of Computer Engineering Program Bogor Indonesia Research Development Interactive Robotics Bogor Indonesia Universitas Indonesia Dept of Electrical Engineering Depok Indonesia

ISBN: (纸本)9781665425094

Electronic technologies are growing very rapidly, especially those related to automation and robotics. Robotics technology is currently being developed and implemented in various fields including military, search and rescue, education, transportation, sports, and many other fields. Various researches on robotics technology continue to be carried out by various universities in the world including in Indonesia. To increase the interest of the young generation in the field of robotics, an attractive educational kit especially for elementary school level is necessary. This work aims to develop a robotics education kit in the form of a unique and interesting mobile robot as a learning media for elementary school students. The robot was developed using an Arduino microcontroller and can be operated by elementary school students using an easy and user-friendly blocks programming. The testing shows that the developed robotics education kit has been able to function properly, in accordance with the design and the specified requirements. © 2021 IEEE.

关键词： Mobile robots

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Q-Learning based Local Path Planning for UAVs with Different Priorities

Q-Learning based Local Path Planning for UAVs with Different...

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IEEE Latin American Robotics Symposium, LARS

作者： Kevin B. de Carvalho O. B. Hiago Batista Leonardo A. Fagundes-Júnior Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Núcleo de Especialização em Robótica (NERo) Universidade Federal de Viçosa Viçosa MG Brazil

Path planning is a crucial component of autonomous navigation and frequently demands different priorities such as path length, safety, or energy consumption, with the latter being particularly important in the context of unmanned autonomous vehicles. In many applications, the agent may have to react to environment shifting. Algorithms such as geometric and dynamic programming as well as techniques such as artificial potential fields have been employed to tackle this local planning problem. In recent years, machine learning has gained more evidence in many research fields due to its flexibility and generalization capabilities. In this study, we propose a Q-learning-based approach to local planning, which weighs three crucial factors- path length, safety, and energy consumption- that can be freely adjusted by the user to suit its application’s needs. The performance of the proposed method was tested in simulated static and dynamic scenarios as well as benchmarked with a baseline approach. The results show that it can perform well in both kinds of environments without struggling with the commom pitfalls of other local planning algorithms.

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Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning

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Biomedical engineering Frontiers 2022年第1期3卷 422-436页

作者： Bijie Bai Hongda Wang Yuzhu Li Kevin de Haan Francesco Colonnese Yujie Wan Jingyi Zuo Ngan B.Doan Xiaoran Zhang Yijie Zhang Jingxi Li Xilin Yang Wenjie Dong Morgan Angus Darrow Elham Kamangar Han Sung Lee Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos Angeles 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCAUSA Computer Science Department University of CaliforniaLos AngelesCAUSA Physics and Astronomy Department University of CaliforniaLos AngelesCA 90095USA Translational Pathology Core Laboratory University of CaliforniaLos AngelesCA 90095USA Statistics Department University of CaliforniaLos AngelesCA 90095USA Department of Pathology and Laboratory Medicine University of California at DavisSacramentoCA 95817USA Department of Surgery University of CaliforniaLos AngelesCA 90095USA

The immunohistochemical(IHC)staining of the human epidermal growth factor receptor 2(HER2)biomarker is widely practiced in breast tissue analysis,preclinical studies,and diagnostic decisions,guiding cancer treatment and investigation of ***2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist,which typically takes one day to prepare in a laboratory,increasing analysis time and associated ***,we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images,matching the standard HER2 IHC staining that is chemically performed on the same tissue *** efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis,in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images(WSIs)to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts.A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail,membrane clearness,and absence of staining artifacts with respect to their immunohistochemically stained *** virtual HER2 staining framework bypasses the costly,laborious,and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.

关键词： HER2 consuming Deep

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Characterizations of Second-order Nonlinear Optical Susceptibility for ϵ-phase Gallium Oxide

Characterizations of Second-order Nonlinear Optical Suscepti...

引用

2022 Compound Semiconductor Week, CSW 2022

作者： Zhou, Jingan Zhao, Xuan Xu, Rui Fu, Kai Li, Tao Xu, Mingfei He, Ziyi Robinson, Jacob T. Zhu, Hanyu Zhang, Xiaodong Zhao, Yuji Rice University Department of Electrical and Computer Engineering HoustonTX77005 United States Rice University Smalley-Curl Institute Applied Physics Graduate Program HoustonTX77005 United States Rice University Department of Materials Science and NanoEngineering HoustonTX77005 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85287 United States Suzhou Institute of Nano-tech and Nano-bionics CAS Key Laboratory of Nanodevices and Applications Suzhou215123 China

ISBN: (数字)9781665453400

ISBN: (纸本)9781665453400

In this work, we report the second-order nonlinear optical susceptibility χ(2) for epsilon phase Gallium Oxide (ϵ-Ga2O3) thin film on sapphire. ϵ-Ga2O3 exhibits hexagonal P63mc space group symmetry, which is a non-centrosymmetric structure with non-zero second order susceptibility. By focusing a pulsed laser beam on to a polished ϵ-Ga2O3 thin film, we collected the generated second harmonic photons with an ultra-sensitive femtowatt photodetector, obtaining effective second-order nonlinear optical susceptibility of χ(2) = 4.89×10-3 pm/V at wavelength of 800 nm. Two different measurement systems collecting reflected and transmitted photons were applied separately to make the result more convincing. The wavelength dependence from 790 nm to 900 nm and polarization dependence from TM mode to TE mode were measured as well. These results will be helpful for the design and fabrication of Ga2O3 based integrated photonics platform in the infrared-visible spectral range. © 2022 IEEE.

关键词： Gallium compounds

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