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Environmental Temperature Monitoring system Featuring Printed Temperature Sensor and a Flexible, Printed WiFi Antenna

Environmental Temperature Monitoring System Featuring Printe...

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Microwave, Antennas & Circuits (ICMAC), International conference on

作者： Rana muhammad Bilal Firas Fatani Mohammad Vaseem Sakandar Rauf Atif Shamim Department of Electrical and Computer Engineering CEMSE Division King Abdullah University of Science and Technology (KAUST) Kingdom of Saudi Arabia

ISBN: (数字)9798331518424

ISBN: (纸本)9798331518431

With the increasing need for scalable, energy-efficient sensing solutions in smart cities and buildings, traditional temperature monitoring systems face limitations due to higher manufacturing costs and rigid designs. Printed fabrication offers a flexible and economical alternative that enhances potential for volume deployment, ubiquitous integration and the increased spatial resolution of monitoring. In this work, we present a novel environmental temperature monitoring system that leverages printed fabrication for antenna and sensor for such applications. A high measured sensitivity (i.e. 3.1% Temperature coefficient of resistance) is shown for the designed printed temperature sensor. A measured 93MHz bandwidth coverage is shown for the designed printed, flexible dual-patch array WiFi antenna.

关键词： Temperature sensors Temperature measurement Antenna measurements Fabrication Smart cities Sensors Electrical resistance measurement Monitoring Wireless fidelity Antenna arrays

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Sustainable Safeguarding: Implementing Fraud Defense systems for Authorized Push Payments Through Robotics Process Automation and AI integration 2nd

Sustainable Safeguarding: Implementing Fraud Defense Systems...

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2nd International conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Kotagiri, Anudeep Bammidi, Tirupathi Rao Robotics Process Automation CGI Inc. HuntersvilleNC United States Mphasis Corp. Application Service Delivery New York United States

ISBN: (纸本)9783031717284

This research paper investigates the development and implementation of a robust fraud defense system for Authorized Push Payments (APP) by seamlessly integrating Robotics Process Automation (RPA) and Artificial Intelligence (AI) technologies. Titled "Sustainable Safeguarding", the paper explores the synergy between RPA and AI in fortifying APP transactions against fraudulent activities. The research delves into the intricacies of creating a sophisticated defense mechanism while ensuring sustainability by minimizing environmental impact. Through a comprehensive analysis of real-world scenarios, the paper demonstrates the effectiveness of the proposed system in enhancing security and protecting financial transactions. The integration of sustainable practices underscores a forward-thinking approach, aligning technological advancements with environmental responsibility. This research contributes to the ongoing discourse on the intersection of financial technology, security, and sustainability, offering insights for both industry practitioners and researchers navigating the evolving landscape of digital finance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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Optimized Mutual Authentication and AES Encryption Framework for Enhanced Security in Multi-Server Environments

Optimized Mutual Authentication and AES Encryption Framework...

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Computational Intelligence, Communication technology and Networking (CICTN), International conference on

作者： Soham Sunil Kulkarni Anant Kumar Suket Gakhar Vipin Kumar Sharma Sneha Arora Department of Computer Science University of California Irvine Irvine USA Department of Computer Science and Engineering Manipal Institute of Technology Manipal India Department of Computer Science and Engineering Kurukshetra University Kurukshetra India Department of Electronics and Communication Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

This paper introduces a secure authentication framework by applying Mutual Authentication Coupled with Optimised AES (Advanced Encryption Standard) Encryption to improve the security of the multi-server environment. By addressing inefficiencies in traditional mutual authentication protocols without encryption, this framework significantly improves both performance and resilience. Encryption time was reduced by 25% and authentication time by 16.67% while CPU (Central Processing Unit) usage was reduced by 23.08%, with a corresponding 21.43% improvement in memory efficiency. The proposed system demonstrates a 70% improvement in attack resistance. Dynamic revocation mechanisms mitigate unauthorized access and malicious activity in real time. The protocol is designed for resource-constrained environments to ensure secure, scalable, and efficient communication in multi-server architectures, further increasing system reliability and user trust in cloud-based services.

关键词： Resistance Cloud computing Protocols Authentication Encryption Central Processing Unit Reliability Usability Standards Resilience

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Optimised Data Encryption and Search Efficiency Framework Using Memory Modules for IoT Applications

Optimised Data Encryption and Search Efficiency Framework Us...

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Computational Intelligence, Communication technology and Networking (CICTN), International conference on

作者： Suket Gakhar Soham Sunil Kulkarni Anant Kumar Vipin Kumar Sharma Sneha Arora Department of Computer Science and Engineering Kurukshetra University Kurukshetra India Department of Computer Science University of California Irvine Irvine USA Department of Computer Science and Engineering Manipal Institute of Technology Manipal India Department of Electronics and Communication Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The proliferation of IoT devices necessitates secure and efficient mechanisms for data encryption and retrieval. This paper presents an optimized framework that leverages AES encryption integrated with memory modules to enhance security and operational efficiency. The proposed system introduces a dynamic key generation mechanism to mitigate key reuse vulnerabilities and incorporates a memory module to achieve reductions in encryption and search execution times by 80% and 79%, respectively, compared to traditional methods. The framework also ensures secure cloud storage of encrypted data and supports privacy-preserving retrieval using trapdoor functions. A Java-based implementation validates the system's scalability, robustness, and platform independence for real-world IoT applications. By addressing key challenges such as data integrity, confidentiality, and low-latency operations, the proposed framework offers a comprehensive solution tailored to the evolving needs of the IoT ecosystem.

关键词： Cloud computing Scalability Ecosystems Memory modules Data retrieval Encryption Internet of Things Low latency communication Secure storage Optimization

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Design of signal acquisition and waveform generation based on CX8242K

Design of signal acquisition and waveform generation based o...

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2nd International conference on Frontiers of Applied Optics and Computer Engineering, AOCE 2025

作者： Huang, Zhen Wu, Bing Yu, Qingfa Dong, Huijuan No.38 Research Institute of China Electronic Technology Group Corporation Hefei China Key Laboratory of Aperture Array and Space Exploration Hefei China

ISBN: (纸本)9781510689404

Aiming at the design requirements of multi-channel high-speed signal acquisition in phased array antenna systems, a mid-frequency signal acquisition board based on RF direct sampling transceiver chip is designed and implemented, which realizes signal acquisition and digital waveform generation of 2 ADCs and 2 DACs. This paper introduces the hardware and software design of the high-speed signal acquisition board, and builds a testing system to verify its performance indicators. The ADC has a signal-to-noise ratio better than 55dB and a spurious free dynamic range better than 65dBc at a sampling rate of 1.6GHz in dual channel working mode. The DAC has an output power greater than - 5dBm and a spurious free dynamic range better than 60dBc at a conversion rate of 3.2GHz in dual channel working mode. This design adopts a modular design approach, expands the dynamic range of transmission and reception, improves product configuration flexibility, enhances system integration, and reduces overall power consumption and cost. © The Authors.

关键词： integration testing

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Red Panda Optimization Technique for Voltage Control and Electric Vehicle Reactive Power Management in Smart Micro Grids

Red Panda Optimization Technique for Voltage Control and Ele...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Sivaramkrishnan, M. Ramkumar, M.Siva Uday, Kaparthi Kanan, Mohammad Hemananth, B. Giri, Jayant Karpagam College of Engnieering Dept of EEE India SNS College of Technology Dept of ECE India CVR College of Engineering Ibrahimpatanam EIE Department. Hyderabad India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Sri Eshwar College of Engineeering Department of EEE Coimbatore641 202 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

The integration of Electric Vehicles (EV) with Smart Micro Grids (SMGs) is supposed to perform an effective voltage control and active reactive power management for the sake of securing the solidity of the system. Managing current ripple in interleaved boost converters finally is a constant design or control challenge, demanding approaches that allow the performance to be sustained against the load and input variations. To address these challenges, this paper proposes a hybrid approach for voltage control and EV reactive power management in smart MGs, optimizing grid stability and energy efficiency. The proposed method integrates the Red Panda Optimization (RPO). The aim is to improve the economic performance of the MG integrated with EVs by minimize the Mean Absolute Error (MAE). RPO is utilized to optimize the controller parameters for enhancing voltage control in smart MGs. By then, the suggested method is implemented on MATLAB platform and evaluated with various existing approaches such as Spider Monkey Optimization (SMO), Particle Swarm Optimization (PSO), Stochastic Multi Objective Optimization (SMOO), and Hunting Prey Optimization Algorithm (HPOA). The proposed RPO method achieves the MAE at 0.021, demonstrating a significant improvement in voltage control and reactive power management efficiency in SMGs compared to other optimization methods © 2025 IEEE.

关键词： Hybrid vehicles

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Innovating NCAAA Accreditation: A Smart Education Management system Powered by Generative Pre-trained Transformer

Innovating NCAAA Accreditation: A Smart Education Management...

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Advanced Innovations in Smart Cities (ICAISC), International conference on

作者： Gilang Aulia Muhamad Bassma Saleh Alsulami Khalid Omar Thabit Department of Computer Science King Abdulaziz University Jeddah Kingdom of Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

This study introduces a smart education management system designed to enhance the accreditation processes mandated by the National Commission for Academic Accreditation & Assessment (NCAAA) for postgraduate programs. By leveraging Generative Pre-trained Transformer (GPT) models, the system efficiently generates comprehensive course reports and dynamic assessment questions, aligning them with the cognitive levels defined in Bloom's Taxonomy. Automating these critical but labor-intensive tasks significantly improves the efficiency of accreditation processes in academic settings. Initial testing within the Computer Science Department at King Abdulaziz University (KAU) demonstrated the system's robustness and scalability, indicating its potential for broader application across universities. The integration of Artificial Intelligence (AI) not only streamlines administrative processes but also improves the quality of educational assessments through alignment with Bloom's Taxonomy. This study emphasizes the transformative potential of AI in educational accreditation, setting a new benchmark for future research and development in integrating AI technologies for smart education management systems.

关键词： Technological innovation Smart cities Scalability Taxonomy Educational technology Benchmark testing Transformers Accreditation Artificial intelligence Standards

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Deep Learning-Based Floating Debris Detection for Marine Pollution Monitoring

Deep Learning-Based Floating Debris Detection for Marine Pol...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Aswathy, K.P. Sabarimalai Manikandan, M. Cochin University of Science And Technology Department of Electronics Kerala Kochi682022 India Indian Institute of Technology Palakkad Department of Electrical Engineering Kerala Palakkad678623 India

ISBN: (纸本)9798331505745

Marine pollution generated by floating debris is dangerous to aquatic ecosystems and can increase risk factors of marine organisms, necessitating robust debris detection systems for monitoring and management. This paper proposes a floating debris identification system based on the you only look once, version 8 (YOLOv8) model, which is trained on 4000 annotated images classified as dense-debris, sparse-debris, single or isolated debris and partial debris portions. The performance of the model is tested under different objects, image qualities and orientations by introducing three different types of noises which are Gaussian, speckle and salt-and-pepper at increasing densities. To increase the adaptability, noise reduction techniques such as Gaussian and moving average filters are used in this study for achieving a higher detection accuracy. On raw images, the YOLOv8 model outperformed current YOLO versions as well as state-of-the-art models as cascade region-based convolutional neural networks (R-CNN), YOLOv5, optimized YOLOv4-tiny model and YOLOv7, with a precision of 82.85%, recall of 91.10% and mean average precision at 0.5 intersection over union (mAP@0.5) of 97%. The performance of the model is decreased under noisy environments, but the use of filters improved the accuracy significantly, obtaining the accuracy of 96.57% and precision of 98.98% in low-density Gaussian noise by applying the Gaussian filter. Compared to previous YOLO versions, the YOLOv8-based approach showed significant increases in precision, recall and mAP, making it a better solution for debris identification. Results demonstrate that the YOLOv8 model's can achieve a higher accuracy and computational efficiency in real-time marine debris identification under complicated and noisy environments. Evaluation results demonstrate the effectiveness of the proposed method for automatic marine pollution monitoring and management applications. © 2025 IEEE.

关键词： Marine pollution

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability 2

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Katru, Chandrasekhar Rao Srinivasan, Sathish Kumar, Mohan Yadwad, Sunita Satish, G. Maranan, Ramya Lead Software Engineer Indian LandSC29707 United States Machine Learning & AI Development Oracle Cloud Infrastructure Division San FranciscoCA United States Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Parul institute of Engineering and Technology Parul University Computer science and Engineering Faculty of Engineering and Technology Gujarat India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Telangana Sheriguda India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance. © 2025 IEEE.

关键词： Infrastructure as a service (IaaS)

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Hausa Handwriting Character Recognition Using CNN and Tesseract

Hausa Handwriting Character Recognition Using CNN and Tesser...

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Advanced Innovations in Smart Cities (ICAISC), International conference on

作者： Muhammad Khamis Dauda Umar Musa Hassanin Al-Barhamtoshy Department of Information Technology Faculty of Computing and Information Technology Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The Hausa language, spoken by over 64 million people, is a vital part of West Africa's cultural and social fabric. Despite its importance, handwritten Hausa script recognition has little concern due to its tonal nature, unique characters, and limited datasets. This study tackles these challenges by creating a dataset and evaluating two models commonly used in handwritten recognition: a Convolutional Neural Network (CNN) and Tesseract Optical Character Recognition (OCR). The custom CNN, trained on data from 30 volunteers with varied writing styles, achieved an accuracy of 96%, with strong precision, recall, and F1-score metrics. In contrast, Tesseract OCR struggled, particularly with Hausa-specific characters like Ɓ, Ƙ, Ƴ, and ɗ, achieving lowest accuracy for these classes. Although it had non-zero recognition in some characters, its performance was inconsistent, indicating difficulties in generalization. This work provides a scalable solution for handwritten Hausa character recognition, contributing to research on low-resource African languages and paving the way for better access to Hausa handwritten literature and integration into natural language processing applications.

关键词： Handwriting recognition Accuracy Biological system modeling Optical character recognition Writing Natural language processing Fabrics Character recognition Convolutional neural networks Cultural differences

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