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International Conference on Emerging Technologies, ICET

作者： Iqra Mueed Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing “GikiLenS”, an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure.

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Improved Adaptive Fairness-Indexed Technique for Congestion Control in Underwater Networks

Improved Adaptive Fairness-Indexed Technique for Congestion ...

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Communication, Control and Intelligent systems (CCIS)

作者： Balaji Vijayan V K S Manojee Mohammed Ibrahim M Nivethitha R Thamaraimanalan T Ranjithkumar M Department of Information Science and Engineering HKBK College of Engineering Bengaluru Karnataka India Department of Information Technology Kongunadu College of Engineering and Technology Trichy Tamilnadu India Department of Computer Science and Engineering Sona College of Technology Salem Tamilnadu India Department of Computer Science and Engineering K.S.R. College of Engineering Thiruchengode Tamilnadu India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India Department of Computer Science and Business Systems Knowledge Institute of Technology Salem Tamilnadu India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

In underwater acoustic sensor networks (UASNs), effective congestion control is essential for ensuring reliable data transmission and resource efficiency, especially in the face of challenging environmental factors such as water velocity, pressure, temperature, and salinity. This paper introduces a novel Adaptive Fairness-Indexed Congestion Control (AFICC) mechanism that employs Q-learning to dynamically adjust transmission rates among nodes, ensuring equitable resource distribution and minimizing congestion. Our simulations, conducted with 100 nodes at depths of up to 1000 meters, reveal that AFICC significantly outperforms two existing algorithms: Rate-Based Congestion Control (RBCC) and Energy-Aware Congestion Control (EACC). Specifically, AFICC achieved a Packet Delivery Ratio (PDR) of 93%, compared to 85% for RBCC and 80% for EACC. Additionally, the End-to-End Delay was reduced to an average of 150 milliseconds with AFICC, while RBCC and EACC recorded delays of 220 milliseconds and 210 milliseconds, respectively. AFICC also demonstrated a 25% improvement in Energy Efficiency over EACC and maintained a Jain’s Fairness Index of 0.92, surpassing the 0.82 and 0.78 indices achieved by RBCC and EACC, respectively. These results confirm that AFICC offers a robust framework for congestion control in UASNs, effectively adapting to fluctuating underwater conditions while ensuring reliable communication and equitable resource allocation.

关键词： Q-learning Salinity (geophysical) Dynamic scheduling Energy efficiency Delays Temperature control Resource management Reliability Underwater acoustics Water resources

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Exploring the Implications of IoT Integration in Urban Infrastructures for Sustainable Smart Cities

Exploring the Implications of IoT Integration in Urban Infra...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sharma, Priti Bamini, J. Vijayalakshmi, S. Vijayalakshmi, N. Meenambika, A. Singh, Vijendra Pratap Jagannath International Management School Vasant Kunj Department of Information Technology New Delhi India Sri Ramakrishna College of Arts & Science Department of Management Studies Coimbatore India Cmr Institute of Technology Department of Cse Bengaluru India Sns College of Technology Department of Cse Coimbatore India Psna College of Engineering and Technology Computer Science and Business Systems Dindigul India Mahatma Gandhi Kashi Vidyapith Department of Computer Science & Applications U.P. Varanasi India

ISBN: (纸本)9798350348910

Global cities are undergoing revolutionary changes thanks to the widespread adoption of Internet of Things (IoT) technologies. In order to shed light on the Internet of Things (IoT) potential to bring about a new era of sustainable smart cities, this study explores the myriad consequences of incorporating IoT into urban infrastructures. In this study, we used a variety of research approaches. Key performance factors for sustainability, efficiency, and user happiness were analyzed using the quantitative data obtained from 45 smart city initiatives throughout the world using IoT devices. Thirty urban planners, IoT experts, and legislators were interviewed semi-structured to glean qualitative insights on the perceived benefits, obstacles, and methods for successful integration. Preliminary findings point to considerable benefits in energy conservation, reduced traffic congestion, and cost savings due to IoT applications including real-time energy management, smart traffic solutions, and predictive maintenance. These applications not only improved city life, but also helped reduce carbon emissions. However, major obstacles were uncovered. The most common worries were on the safety of one's data, the invasion of one's privacy, and the potential widening of socioeconomic gaps caused by unequal access to technology. The need for consistent legislation, open data policies, and inclusive urban planning was a common theme among respondents. The study proposes a strategic framework for implementing IoT in urban areas based on the findings. It places an emphasis on group decision-making, broad consultation, and strict regulation. Citizens, technologists, and urban planners all have a role in ensuring that smart city development is sustainable, secure, and ultimately beneficial for all people. The Internet of Things has the potential to drastically improve city infrastructures. While its advantages can greatly improve urban sustainability and efficiency, overcoming obstacles and

关键词： Smart city

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A Dual-view Semi-supervised Learning Framework for Combinatorial Few-shot Fault Diagnosis

A Dual-view Semi-supervised Learning Framework for Combinato...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Yumeng Liu Wenzhang Zhong Ke Yan Ling Tian Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

The accurate and comprehensive fault diagnosis is pivotal for the maintenance of mechanical equipment, which may directly impact the safety in production and avoid unnecessary losses of equipment. With the pervasively adopted sensing components, sensing data can be collected from mechanical equipment and analyzed for fault diagnosis. Previous solutions apply sophisticated deep learning models for such fault diagnosis tasks and have achieved impressive performance. However, these methods usually ignore the existences of diverse characteristics of faults, and formulate the fault diagnosis as a simple classification problem. Considering necessity of comprehensive and multi-view fault diagnosis, this work proposes a novel dual-view learning framework for combinatorial mechanical fault classification. The framework includes dual encoders to simultaneously learn the feature representation tied with the position and size of faults on equipment. Then these features are mutually mixed for better representation and the mixed features are merged and feed into deep neural networks for further representation learning. The query samples are classified through metric-based comparison to search for the closest type of composite fault. Moreover, the proposed framework also applies typical metric-based meta learning method to handle the issue of small training datasets, and adopts semi-supervised learning method to make use of both labeled and unlabeled samples. Finally, the experimental results on public datasets show that the proposed method can outperform previous solutions and achieve state-of-the-art performance.

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Modelling and Control of a Trailer Sprayer for Precision Spraying

Modelling and Control of a Trailer Sprayer for Precision Spr...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： André Baltazar Filipe N. Santos Antόnio Paulo Moreira Salviano Pinto Soares M. J. C. S. Reis José Boaventura Cunha CRIIS-Centre for Robotics in Industry and Intelligent Systems INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto Portugal ECT—School of Science and Technology UTAD—University of Trás-os-Montes and Alto Douro Vila Real Portugal FEUP—Faculty of Engineering University of Porto Porto Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) Portugal Intelligent Systems Associate Laboratory (LASI) Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Precision spraying in agriculture is crucial for optimizing the application of pesticides while minimizing environmental impact. Despite significant advancements in control models for spraying systems, predictive control algorithms were not used. This paper addresses this gap by proposing a real-time control framework that integrates predictive control strategies to ensure consistent pressure output in a trailer sprayer. Based on information from various sensors, the framework anticipates and adapts to dynamic environmental conditions, enhancing accuracy and sustainability in spraying practices. A methodology is developed to define a proportional valve model. Based on this valve model, the predictive control model optimizes valve movements to minimize errors between predicted and reference pressures, thereby improving spraying efficiency. This study demonstrates the viability of predictive control in improving precision spraying systems applicable to autonomous robots, encouraging future advances in agricultural spraying technologies.

关键词： Adaptation models Spraying Predictive models Valves Real-time systems Agriculture Sensors

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Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions: a Test Bed for Safe Optimal Merging

arXiv

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arXiv 2023年

作者： Sabouni, Ehsan Sabbir Ahmad, H.M. Xiao, Wei Cassandras, Christos G. Li, Wenchao Division of Systems Engineering Department of Electrical & Computer Engineering Boston University BostonMA United States Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States

We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints. It has been shown that such problems can be solved through a combination of tractable optimal control problems and Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved on line over discrete time steps. However, guaranteeing the feasibility of the CBF-based QP method within each discretized time interval requires the careful selection of time steps which need to be sufficiently small. This creates computational requirements and communication rates between agents which may hinder the controller’s application to real CAVs. In this paper, we overcome this limitation by adopting an event-triggered approach for CAVs in a conflict area such that the next QP is triggered by properly defined events with a safety guarantee. We present a laboratory-scale test bed we have developed to emulate merging roadways using mobile robots as CAVs which can be used to demonstrate how the event-triggered scheme is computationally efficient and can handle measurement uncertainties and noise compared to time-driven control while guaranteeing safety. © 2023, CC BY.

关键词： Merging

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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All in One Framework for Multimodal Re-identification in the Wild

arXiv

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arXiv 2024年

作者： Li, He Ye, Mang Zhang, Ming Du, Bo National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Luojia Laboratory Wuhan University Wuhan China Guangzhou Urban Planning Design Survey Research Institute Guangzhou China

In Re-identification (ReID), recent advancements yield noteworthy progress in both unimodal and cross-modal retrieval tasks. However, the challenge persists in developing a unified framework that could effectively handle varying multimodal data, including RGB, infrared, sketches, and textual information. Additionally, the emergence of large-scale models shows promising performance in various vision tasks but the foundation model in ReID is still blank. In response to these challenges, a novel multimodal learning paradigm for ReID is introduced, referred to as All-in-One (AIO), which harnesses a frozen pre-trained big model as an encoder, enabling effective multimodal retrieval without additional fine-tuning. The diverse multimodal data in AIO are seamlessly tokenized into a unified space, allowing the modality-shared frozen encoder to extract identity-consistent features comprehensively across all modalities. Furthermore, a meticulously crafted ensemble of cross-modality heads is designed to guide the learning trajectory. AIO is the first framework to perform all-in-one ReID, encompassing four commonly used modalities. Experiments on cross-modal and multimodal ReID reveal that AIO not only adeptly handles various modal data but also excels in challenging contexts, showcasing exceptional performance in zero-shot and domain generalization scenarios. Code will be available at: https://***/lihe404/AIO. © 2024, CC BY.

关键词： Modal analysis

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An MA-PPO based Task Scheduling Method for Computing First Networks 22

An MA-PPO based Task Scheduling Method for Computing First N...

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Proceedings of the 2022 5th International Conference on Telecommunications and Communication engineering

作者： Jialu Zhao Kun Xie Huiyue Sun Chengcheng Li Yasheng Zhang School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications China The 54th Research Institute of CETC China

ISBN: (纸本)9781450397797

Aiming at converging computing and network resources, computing first networking (CFN) provides the computing task scheduling solution along with routing policy based on real-time computing and network information, to achieve the optimal utilization of overall resources and improve user experience. Most of the existing schemes consider computing and network resources separately to obtain their optimal results, lacking a load balancing mechanism that comprehensively considers both. In view of the above, this paper proposes an MA-PPO-based service scheduling algorithm for CFN. Based on the current computing and network resource status, scheduling strategies combined with computing task allocation and routing planning are flexibly generated for each computing task to achieve the overall optimization of computing and network resources utilization. The experimental results show that the algorithm can effectively improve the utilization of computing and network resources, shorten task processing time, and improve user experience.

关键词： task scheduling routing planning computing first networking MA-PPO

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Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles

arXiv

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arXiv 2023年

作者： Ahmad, H.M. Sabbir Sabouni, Ehsan Xiao, Wei Cassandras, Christos G. Li, Wenchao Division of Systems Engineering Department of Electrical & Computer Engineering Boston University BostonMA United States Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in collisions and traffic jams. In addition, uncooperative (but not necessarily adversarial) CAVs can also induce similar adversarial effects on the traffic network. We propose a decentralized resilient control and coordination scheme that mitigates the effects of adversarial attacks and uncooperative CAVs by utilizing a trust framework. Our trust-aware scheme can guarantee safe collision free coordination and mitigate traffic jams. Simulation results validate the theoretical guarantee of our proposed scheme, and demonstrate that it can effectively mitigate adversarial effects across different traffic scenarios. © 2023, CC0.

关键词： Traffic congestion

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