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2019 IEEE Conference on Sustainable Utilization and Development in Engineering and Technologies, CSUDET 2019

作者： Najih, Abdulmawla Alhaddad, Syed Rahman Ramli, Abd Hashim, S.J. Albannai, Nabila University Putra Malaysia Upm Department of Computer Communication Systems Engineering Serdang Selangor Darul Ehsan43400 Malaysia University Putra Malaysia Department of Computer Communication Systems Engineering Malaysia Kastamonu University Faculty of Engineering and Architecture Kastamonu Turkey

ISBN: (纸本)9781728132761

The use of biometric features, to authenticate users of different applications, is growing rapidly in recent years, according to the high sensitivity of the protected information and the good security that biometric authentication provides. In this study, a method is proposed to measure the similarity between two face images, using convolutional neural networks, directly from the raw pixel values of the input image. As the neural network is used for matching instead of classifications, modifying the users that the proposed method can recognize is a matter of adding or removing model images of the user's faces, without the need to retrain the model. The similarity between the face image and every model image is measured in order to select the user with the highest similarity to the input image as the recognized user, where that similarity measure is compared to a threshold value in order to authenticate that user. The evaluation results of the proposed method, using ORL shows that the proposed method has 99.48% accuracy with 0.22% False Acceptance Rate (FAR) and 3.2% False Rejection Rate (FRR). Hence, the proposed method has been able to maintain high accuracy while eliminating the vulnerabilities of biometric authentication systems imposed by the use of separate stages for features extraction and similarity measurement. © 2019 IEEE.

关键词： Neural networks

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Analysis Of Concrete Cracks And Fatigue In Smart Cities Using Yolov3

Analysis Of Concrete Cracks And Fatigue In Smart Cities Usin...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： U. Archana Sarvdaman Sharma Sitesh Kumar Singh Sureshkumar R Senthilkumar P T. Harish Babu Department of Computer Science and Design R.M.K. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia Department of Electronics and Communication Engineering VEL Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai KL School of Architecture Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electrical and Electronics Engineering Sethu Institute of Technology Tamilnadu India

This study focuses on the application of the YOLOv3 (You Only Look Once version 3) object detection algorithm for the analysis of concrete cracks and fatigue within Smart Cities. Concrete infrastructure plays a crucial role in urban environments, and its structural integrity is paramount for ensuring public safety and sustainable development. The proposed approach utilizes YOLOv3 to identify and localize instances of concrete cracks and signs of fatigue in images and videos captured from various urban settings. By leveraging deep learning techniques, this research aims to enhance the efficiency and accuracy of monitoring and maintaining concrete structures, contributing to the overall resilience and longevity of Smart Cities. The findings of this study present valuable insights into the potential of utilizing advanced computer vision methods for proactive infrastructure management in the urban landscape.

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Joint LED selection and precoding optimization for multiple-user multiple-cell VLC systems

arXiv

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arXiv 2021年

作者： Yang, Yang Yang, Yujie Chen, Mingzhe Feng, Chunyan Xia, Hailun Cui, Shuguang Poor, H. Vincent The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The Chinese University of Hong Kong Shenzhen518172 China

This paper proposes a hybrid dimming scheme based on joint LED selection and precoding design (TASP-HD) for multiple-user (MU) multiple-cell (MC) visible light communications (VLC) systems. In TASP-HD, both the LED selection and the precoding of each cell can be dynamically adjusted to reduce the intra- and inter-cell interferences while satisfying illumination constraints. First, a MU-MC-VLC system model is established, and then a sum-rate maximization problem under dimming level and illumination uniformity constraints is formulated. In this studied problem, the indices of activated LEDs and precoding matrices are optimized, which result in a complex non-convex mixed integer problem. To solve this problem, the original problem is separated into two subproblems. The first subproblem, which maximizes the sum-rate of users via optimizing the LED selection with a given precoding matrix, is a mixed integer problem solved by the penalty method. With the optimized LED selection matrix, the second subproblem which focuses on the maximization of the sum-rate via optimizing the precoding matrix is solved by the Lagrangian dual method. Finally, these two subproblems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario under a dimming level of 70%, the mean bandwidth efficiency of TASPHD is 4.8 bit/s/Hz and 7.13 bit/s/Hz greater than AD and DD, respectively. © 2021, CC BY-SA.

关键词： Light emitting diodes

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset

A Horizontal Federated Learning Approach to IoT Malware Traf...

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International Conference on Advanced communication Technology (ICACT)

作者： Phuc Hao Do Tran Duc Le Vladimir Vishnevsky Aleksandr Berezkin Ruslan Kirichek The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia University of Science and Technology - The University of Danang Da Nang Viet Nam Information Technology Faculty The University of Danang - University of Science and Technology Danang Vietnam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Department of Programming Engineering and Computer Science Bonch Bruevich Saint Petersburg State University of Telecommunications

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy.

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Near Sensors Computation based on Embedded Machine Learning for Electronic Skin

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Procedia Manufacturing 2020年 52卷 295-300页

作者： Ali Ibrahim Hamoud Younes Mohamad Alameh Maurizio Valle Department of Electrical Electronic and Telecommunications Engineering and Naval Architecture University of Genova via Opera pia 11a 16145 Genova Italy Department of Computer and Communication Engineering Lebanese International University 146404 Mazraa Beirut Lebanon

The electronic skin system is usually made of distributed tactile sensors integrated with an embedded electronic system for tactile data decoding. Meaningful information e.g. texture classification and pattern recognition can be decoded from tactile data by employing machine learning methods. Near sensors computation using embedded machine learning algorithms may enable the electronic skin system to be used in various application domains such as wearable Internet of Things devices, prosthetics, and robotics. However, embedding machine-learning algorithms is constrained by the high computational complexity of Machine Learning methods. This poses relevant challenges on 1) real-time operation and 2) very low (e.g. pJ/op) power/energy consumption due to the limited energy budget available on wearable/portable systems. In this perspective, the paper presents our recent achievements in the implementation of embedded machine learning methods for near sensors tactile data processing. The paper provides an overview about the implementation on dedicated hardware platforms. Finally, efficient techniques for embedded machine learning highlighting the challenges and perspectives are discussed with major emphasis on energy-efficient intelligent electronic skin systems.

关键词： Near Sensors Computation Embedded Machine Learning Smart Electronic Skin System Energy Efficinet Systems Real Time Response

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Photo-volatic Systems on Rooftops of Buildings;Kaoshiung City's Renewable Energy Endeavor

Photo-volatic Systems on Rooftops of Buildings;Kaoshiung Cit...

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2019 IEEE International Conference on Computation, communication and Engineering, ICCCE 2019

作者： Wu, Min-Chang Huang, Chi-Ming Chiang, Chun-Chang Kao, Kuo-Sheng Cheng, Da-Long Li, Yen-Yi Liu, Chung-Ang Chung, Po-Ren Bureau of Public Works Kaohsiung City Government Taiwan SHU-TE University Department of Computer and Communication Taiwan SHU-TE University Graduate School of Architecture and Interior Design Taiwan National Cheng-Kung University Department of Architecture Taiwan

ISBN: (纸本)9781728131122

The Kaohsiung city government adopted an action with a series of policies to increase solar energy production. Firstly, it established an operating network by forming an inter-departmental task force to promote photovoltaic (PV) systems in the public and private sectors. It also linked professional organizations with local communities and set up a dedicated hotline for consultation and inquiries. Secondly, it strengthened its photovoltaic infrastructure through formal statutes and regulations. This includes the announcement of a series of official solar photovoltaic policies, the promulgation of a floor area ratio incentive, and the enforcement of mandatory policies to increase photovoltaic implementation. Thirdly, it designated target sites to serve as examples of the project. These include installing photovoltaic systems on public rooftops, building photovoltaic demonstration zones, transforming illegal structures into solar power providers, and assisting the fishing and agricultural sectors in building photovoltaic systems. This study looks at the evidence of achievements for which the city has promoted scientifically meaningful architectural aesthetics in its innovative green buildings and energy technology, as well as the success it has seen pushing the policies through a bottom-up structure. In conclusion, it can be said that, using a model based on a project developed by our institute, the Kaohsiung city government has successfully made the renewable energy become an integral part of Kaohsiung's lifestyle, creating a threefold win for the government, the institute, and the people of Kaohsiung. © 2019 IEEE.

关键词： Solar energy

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Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless communications

arXiv

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arXiv 2020年

作者： Yang, Helin Xiong, Zehui Zhao, Jun Niyato, Dusit Xiao, Liang Wu, Qingqing School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Information and Communication Engineering Key Laboratory of Digital Fujian on IoT Communication Architecture and Security Technology Xiamen University Xiamen361005 China Department of Electrical and Computer Engineering National University of Singapore Singapore119260 Singapore

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)’s beamforming and the IRS’s reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Beyond game environments: Evolutionary algorithms with parameter space noise for task-oriented dialogue policy exploration

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Neurocomputing 2025年 648卷

作者： Qingxin Xiao Yangyang Zhao Lingwei Dang Yun Hao Le Che Qingyao Wu South China University of Technology School of Software Engineering Guangzhou 510006 Guangdong PR China Institute for Super Robotics(Huangpu) Guangzhou 510700 Guangdong PR China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha 410114 Hunan PR China South China University of Technology School of Architecture Guangzhou 510006 Guangdong PR China

Reinforcement learning (RL) has achieved significant success in task-oriented dialogue (TOD) policy learning. Nevertheless, training dialogue policy through RL faces a critical challenge: insufficient exploration, which leads to the policy getting trapped in local optima. Evolutionary algorithms (EAs) enhance exploration breadth by maintaining and selecting diverse individuals, and they often add parameter space noise among different individuals to simulate mutation, thereby increasing exploration depth. This approach has proven to be an effective method for enhancing RL exploration and has shown promising results in game domains. However, previous research has not analyzed its effectiveness in TOD dialogue policy. Given the substantial differences between gaming contexts and TOD dialogue policy, this paper explores and validates the efficacy of EAs in TOD dialogue policy, investigating the effects of different evolutionary cycles and various noise strategies across different dialogue tasks to determine which combination of evolutionary cycle and noise strategy is most suitable for TOD dialogue policy. Additionally, we propose an adaptive noise evolution method that dynamically adjusts noise scales to improve exploration efficiency. Experiments on the MultiWOZ dataset demonstrate significant performance improvements, achieving state-of-the-art results in both on-policy and off-policy settings.

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Unified modeling language code generation from diagram images using multimodal large language models

Machine Learning with Applications

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Machine Learning with Applications 2025年 20卷

作者： Bates, Averi Vavricka, Ryan Carleton, Shane Shao, Ruosi Pan, Chongle School of Computer Science University of Oklahoma 110 W. Boyd St. NormanOK United States Enterprise Architecture MapLarge 1201 Peachtree Street NE Building 400 Suite 1750 AtlantaGA United States School of Communication Florida State University 4100 University Center Building C TallahasseeFL United States

The Unified Modeling Language is a standardized visual language widely used for modeling and documenting the design of software systems. Although many tools are available that generate UML diagrams from UML code, generating executable UML code from image-based UML diagrams remains challenging. This paper proposes a new approach to generate UML code using a large multimodal language model automatically. Synthetic UML activity and sequence diagram datasets were created to train and test the model. We compared the standard fine-tuning with LoRA techniques to optimize base models. The experiments measured the code generation accuracy across different model sizes and training strategies. These results demonstrated that domain-adapted MM-LLMs perform for UML code generation automation, whereby, at the best model, it achieved BLEU and SSIM of 0.779 and 0.942 on sequence diagrams. This will enable the modernization of legacy systems and decrease the manual effort put into software development workflows. © 2025

关键词： Software design

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Lightweight Task-Oriented Semantic communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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