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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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Improved Single DC Source Hybrid Neutral Point Clamped Converter with 13 Levels

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IEEE Transactions on Power Electronics 2025年

作者： Zakzewski, Daniel Resalayyan, Rakesh Khaligh, Alireza University of Maryland Department of Electrical and Computer Engineering The Institute for Systems Research United States

In this paper, a 13-level circuit topology is proposed, which features a single dc source, extended linear modulation range, and a high ratio of output voltage levels to semiconductor devices. The proposed converter, which consists of a series-connected floating H-bridge (FHB) supplementing a five-level active neutral point clamped converter (5L-ANPC), can generate 13 pole voltage levels in the normal modulation region and 15 levels in the extended modulation region. The key performance improvements offered by the proposed topology are enhanced power quality, reduced losses, and increased power density. Whereas existing schemes with a similar circuit structure regulate the FHB to one-eighth of the dc-link voltage and only achieve nine levels, the proposed joint-phase redundancy balancing approach regulates the FHB stage to one-twelfth of the total dc-link voltage on a switching-frequency basis, thereby increasing output power quality while reducing the converter's cost and size without the need for multiple isolated supplies. Further, the proposed modulation scheme also allows for a switching frequency reduction of the higher voltage blocking 5L-ANPC stage by shifting the high-frequency switching to the low voltage blocking FHB stage, thus reducing losses. This work also evaluates operation in the topology's extended linear modulation range which is shown to be increased to 0.612 times the dc-link voltage (modulation index, M = 1.223) without limitations on load power factors, compared to the typical limit of to 0.577 times the dc-link voltage (M = 1.154) for non-boosting topologies such as 3L-ANPC and 5L-ANPC. The proposed converter uses boosting states enabled by the FHB in this extended modulation region, which increases the number of output levels to 15. The effectiveness of the proposed topology and capacitor balancing scheme is validated using experimental and analytical results. Based on experimental and analytical results, the proposed topology is shown to h

关键词： Model predictive control

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Application scenarios for simultaneous optical power and data transfer

Application scenarios for simultaneous optical power and dat...

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Optical Power Delivery 2025

作者： Nacke, Richard Pellegrino, Carmine Lackner, David Siefer, Gerald Majlesein, Behnaz Younus, Othman Osahon, Isaac N.O. Tavakkolnia, Iman Bett, Andreas W. Haas, Harald Helmers, Henning Fraunhofer Institute for Solar Energy Systems ISE Freiburg Germany LiFi Research and Development Centre Electrical Engineering Division University of Cambridge Cambridge United Kingdom Albert Ludwigs University Institute of Physics Freiburg Germany

ISBN: (纸本)9781510684669

Optical power transfer and optical data communication are versatile technologies with numerous applications, utilizing both fiber-coupled systems and free-space optical links. The integration of these technologies into a single link simplifies system design while combining the benefits of simultaneous power delivery and data communication for receiving systems. This study evaluates various applications by examining their power demands, data rate requirements, and external factors such as link medium and operational distance. A graphical representation is provided to facilitate application targeting for specific groups, dictating the needed system requirements. Three key application clusters are identified: A) Internet of Things connectivity (free-space, ~10 m, 0.2-10 W), B) network infrastructure (fiber/free-space/underwater, 10 m – 1 km, 10-100 W), and C) moving objects (free-space, 100 m – 500 km, 1-10 kW). © 2025 SPIE.

关键词： Optical links

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Decoding the future: exploring and comparing ABE standards for cloud, IoT, blockchain security applications

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Multimedia Tools and Applications 2025年第13期84卷 12299-12327页

作者： Singamaneni, Kranthi Kumar Yadav, Kusum Aledaily, Arwa N. Viriyasitavat, Wattana Dhiman, Gaurav Kaur, Amandeep Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology T.S Gandipet Hyderabad500075 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand School of Sciences and Emerging Technologies Jagat Guru Nanak Dev Punjab State Open University Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 173212 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India MEU Research Unit Middle East University Amman Jordan

Data security poses a significant challenge in cloud computing, the Internet of Things (IoT), and blockchain applications. Attribute-Based Encryption (ABE) has emerged as a promising cryptographic solution for secure data sharing and access management in these contexts. This research article focuses on enhancing security in cloud, IoT, and blockchain applications through a comprehensive examination and comparison of various ABE standards. The study begins with an in-depth literature review on ABE requirements, offering insights into the evolving landscape, challenges, and trends. This review serves as the foundation for the research, which aims to compare ABE standards based on cryptographic techniques, encryption/decryption processes, and access control rules, considering criteria like flexibility, efficiency, scalability, and security. Statistical analyses complement the comparative study, generating empirical data on ABE standard performance. Key metrics, including key size, storage overhead, encryption/decryption time, and computational complexity, are considered to identify trends and draw meaningful conclusions. The research also explores use cases for different ABE models within blockchain, IoT, and cloud computing applications, pinpointing scenarios where specific ABE models excel. The study contributes to advancing access control and secure data-sharing technologies in specified domains, aiding researchers, practitioners, and developers in selecting the most appropriate encryption method. By systematically outlining advantages and disadvantages of various Cipher Text ABE standards, the research facilitates informed decision-making. Additionally, practical use cases ensure alignment between ABE models and the distinctive traits and requirements of each domain, expanding the toolset for enhancing data security in blockchain, IoT, and cloud computing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springe

关键词： Internet of things

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Monocular vision navigation system for UAV autonomous mission: a real-time window-based obstacle avoidance approach

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Neural Computing and Applications 2025年 1-18页

作者： Javaid, Abdulrahman Alnaser, Mustafa Baroudi, Uthman Alfaraj, Amjad Research and Development Department Yokogawa Saudi Arabia Company Al Khobar Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Center for Intelligent Secure Systems King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia

The need for autonomous unmanned aerial vehicles (UAVs) is rapidly increasing for various industrial applications today. However, the realization of UAV autonomy requires a careful mix of Artificial Intelligence (AI) and computer vision (CV) techniques to be implemented to address end-user challenges and satisfy field requirements. This study aims to develop an intelligent system for UAV to achieve autonomy with standard low-cost hardware components. We have infused state-of-the-art AI and CV technologies into our developed system architecture to improve autonomous navigational capabilities of UAVs. The proposed architecture has been implemented and tested on the DJI Tello UAV equipped only with a monocular camera on-board and an inertial measurement unit (IMU). We focused on solving multiple practical technical problems that have arisen during this study due to the hardware limitations of the UAV. The study investigated methods for representing obstacles within an environmental map, which are crucial for effective avoidance and path planning, such as their edges, width, and shape, to ensure accurate detection and navigation. The experiments were carried out in an environment that included dynamic obstacles that were not considered in the map. The experimental results showed outstanding performance in terms of successful autonomous navigation for a wide range of light intensity. These results show that the proposed approach has a high potential to achieve autonomous UAV operation in the field with low hardware and energy requirements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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Design and development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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Fruit Ripeness Detection using RipeNet 3

Fruit Ripeness Detection using RipeNet

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Bhavana, Kapa Sumathi, R. Vanaja, Kandregula Manjula, S. Srinivasulu, T. Divya, Siripuri Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Department of Information Technology Andhra Pradesh Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331509675

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don't buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure. © 2025 IEEE.

关键词： Fruits

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research on Biomedical engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Graph neural networks

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Towards a Methodology for the Definition and Evaluation of Enterprises through Modeling and Simulation: Application to Product development

Towards a Methodology for the Definition and Evaluation of E...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Mueller, Sean Ryan Pinon Fischer, Olivia J. Bagdatli, Burak Cimtalay, Selcuk Mavris, Dimitri N. Faveto, Alberto Boulbene, Benjamin Mazzoldi, Antonio Croue, Nicolas Aerospace Systems Design Laboratory School of Aerospace Engineering Georgia Institute of Technology AtlantaGA United States Capgemini Engineering Research & Development Aix-en-Provence France Capgemini Engineering Bristol United Kingdom Capgemini Engineering BostonMA United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Enterprises today have to navigate a rapidly evolving landscape driven by technological advancements, intensifying global competition, and shifting market dynamics. To remain competitive, organizations must adopt agile and digital solutions that enhance operational efficiency, optimize processes, and accelerate time-to-market. This research introduces a comprehensive methodology designed to evaluate enterprise performance across a variety of scenarios, with a specific focus on product development processes. The proposed methodology rests on three pillars: modeling, simulation, and their integration. Leveraging Model-based systems engineering (MBSE) and the Unified Architecture Framework (UAF), the methodology provides a structured approach to model organizational elements such as processes, resources (tools, personnel), and goals, establishing a cohesive enterprise architecture. A holistic agent based simulation facilitates a granular analysis of the enterprise operations. The simulation captures dynamic interactions and emergent behaviors, enabling quantitative evaluations of the individual impacts of digital tools, processes and human resources on organizational outcomes. The seamless integration between the modeling and simulation environments is ensured through the use of a centralized Authoritative Source of Truth (ASOT), maintaining consistency and traceability. This end-to-end approach enables scenario-based assessments and quantitative evaluations of alternative configurations, addressing the limitations of traditional EA frameworks by capturing dynamic interactions and emergent behaviors. This methodology therefore lays the groundwork for the further development of Digital Twins of Organizations (DTOs), one that facilitates strategic decision-making and optimization in complex enterprise environments. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Commerce

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