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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 ieee.

关键词： Cloud platforms

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Attention-Driven Emotion Recognition in EEG: A Transformer-Based Approach With Cross-Dataset Fine-Tuning

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ieee Access 2025年 13卷 69369-69394页

作者： Ghous, Ghulam Najam, Shaheryar Alshehri, Mohammed Alshahrani, Abdulmonem AlQahtani, Yahya Jalal, Ahmad Liu, Hui Riphah International University Department of Electrical and Computer Engineering Islamabad44000 Pakistan Bahria University Department of Electrical Engineering Islamabad44000 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia King Khalid University Department of Informatics and Computer Systems Abha61421 Saudi Arabia Air University Faculty of Computer Science and AI Islamabad44000 Pakistan Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of University of Bremen Cognitive Systems Laboratory Bremen28359 Germany Guodian Nanjing Automation Company Ltd. Nanjing210032 China

Emotion recognition from EEG (electroencephalogram) signals is crucial in mental health diagnostics and human-computer interaction but is often hindered by high dimensionality, noise, and complex temporal dependencies in the data. This paper presents a novel approach that integrates transformer models, attention mechanisms, and transfer learning to enhance emotion recognition accuracy from EEG signals. The proposed methodology consists of two phases: Attention Enhanced Base Model Development (AE-BMD) and Cross-Dataset Fine Tuning Adaptation (CD-FTA). In the AE-BMD phase, the base model is developed and trained on the SEED-IV dataset (15 participants, 62 EEG channels), achieving an accuracy of 84%, with an average precision of 84.75%, recall of 84% and F1-score of 84%. This phase employs optimized feature extraction from key EEG frequency bands (Delta, Theta, Alpha, Beta, Gamma) using techniques such as MFCC, GFCC, power spectral density, and Hjorth parameters. A transformer encoder with integrated spectral and temporal attention mechanisms captures intricate patterns and long-range dependencies within the EEG signals. In the CD-FTA phase, the model undergoes fine-tuning on the SEED-V dataset (20 participants, 62 channels) leading to an improved accuracy of 90%, with an average precision of 90.6%, recall of 90.6%, and F1-score of 90.6%. The model’s generalization is further validated on the MPED dataset (23 participants, 62 channels, seven emotion classes), achieving 79%, with an average precision of 79.3%, recall of 79.3% and F1-score of 79.1% across diverse emotional states. This cross-dataset adaptation leverages transfer learning to enhance the model’s generalization across different emotional states and EEG datasets. Experimental results show that the proposed approach outperforms traditional methods, achieving superior accuracy and robustness in emotion recognition tasks. This work advances emotion recognition systems by addressing challenges in EEG signal proc

关键词： Electroencephalography

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CTPU Based MetaMetarial Behaviour as a Soft Strain Sensor

CTPU Based MetaMetarial Behaviour as a Soft Strain Sensor

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2025 ieee international conference on Mechatronics, ICM 2025

作者： Mutlu, Rahim Asghar, Umar Odriscoll, Ciara Yildirim, Tanju Southern Cross University Faculty of Science and Engineering Lismore Australia School of Engineering University of Wollongong in Dubai Robotics and Automation Research Group United Arab Emirates School of Engineering University of Wollongong in Dubai Robotics and Automation Research Group Dubai United Arab Emirates

ISBN: (纸本)9798331533892

The surge in flexible and wearable electronics, fueled by advancements in smart technologies and growing market demand, has highlighted the importance of auxetic sensors for applications in healthcare, medical rehabilitation, soft robotics, and human-machine interfaces. For widespread adoption, these sensors must offer high sensitivity, substantial stretchability, and long-term durability. This study explores the possibilities of realizing such wearable soft sensors based on conductive thermoplastic polyurethane (cTPU) 3D printing enhanced with auxetic mechanical metamaterials. Following simultaneous empirical mechanical and electrical measurements, auxetic soft sensor designs were compared with numerical simulations. Results suggest that such soft sensors based on metamaterials are great candidates for applications in healthcare and robotics when realized with 3D printing. © 2025 ieee.

关键词： Flexible electronics

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Text Summarization in local language

Text Summarization in local language

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Joglekar, Pushkar Bhosle, Sanket Bomble, Gaurav Bhosale, Shourya Bokil, Siddhant Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331529833

This research focuses on abstractive text summarization techniques for regional languages, specifically Hindi. It employs a Transformer-based model to generate rephrased summaries from datasets of local language news articles. Preprocessing includes tokenization and embedding creation with FastText. Summarization quality is evaluated using ROUGE scores, precision, recall, F1-Score, and human assessment. The study addresses challenges related to Hindi syntax and semantics, aiming to improve regional language processing by providing concise, coherent news summaries and enhancing information access. © 2025 ieee.

关键词： Semantics

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Exploring the Capabilities and Limitations of Generative AI Applications, Challenges, and Future Directions

Exploring the Capabilities and Limitations of Generative AI ...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Gulbir Meenakshi Sharma, Poonam Bhardwaj, Anjul Graphic Era Hill University Department of Computer Science and Engineering Haldwani Campus Uttarakhand Nanital India Mmict&bm Mullana Haryana Ambala India Divine Institute of Management & Studies Dept. of Computer Application Ziradei Bihar Siwan India

ISBN: (纸本)9798331508685

Unprecedented capabilities for content generation, predictive analytics, and automation are made available by the introduction of Generative Artificial Intelligence (AI) technologies, which uses in a new age of industrial innovation. The purpose of this study is to investigate the transformational potential of generative AI from the perspective of exploring its capabilities and limitations and highlighting its role in the development of future technological developments. With the help of this study, we want to highlight important unsolved issues in contemporary generative AI paradigms. These challenges need to be addressed in order to further improve the capabilities, variety, and dependability of these concepts. Our goal is to give researchers with significant insights that will allow them to explore successful research areas, which will ultimately lead to the creation of generative AI systems that are more robust and accessible. © 2025 ieee.

关键词： Generative adversarial networks

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Design and implementation of engineering integrated practical training management system under the background of engineering education 24

Design and implementation of engineering integrated practica...

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5th international conference on computer science and Management Technology, ICCSMT 2024

作者： Xue, Tianbao Lan, Quanxiang School of Mathematics and Computer Science Panzhihua University Sichuan Panzhihua China

ISBN: (纸本)9798400709999

With the promotion of global economic integration, internationalised engineering education has become an important way for colleges and universities to improve their teaching quality and international competitiveness. In order to cultivate students' ability to solve complex engineering problems, many colleges and universities adopt the mode of integrated practical training projects. However, this model has the characteristics of long duration, multi-stage tasks, multi-dimensional assessment, etc. The use of traditional management methods will lead to management chaos, low teaching efficiency, and is difficult to adapt to modern teaching needs. For this reason, this paper designs and develops an intelligent, automation engineering integrated practical training management system based on B/S architecture. The system adopts SpringBoot, Vue, MySQL and Redis technologies to optimize the management process of the practical training project by automating the management of the project, marking scheme and grade analysis. In addition, the system has real-time performance analysis and feedback functions, which improves the efficiency of teaching management and provides strong support for universities to cultivate composite talents with innovative ability and comprehensive engineering literacy. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Efficiency

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Fabrication of Sensorsless Automated Guided Vehicle for Small Warehouse Rack Management

Fabrication of Sensorsless Automated Guided Vehicle for Smal...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Sahu, Neelesh Kumar Chaturvedi, Piyush Gupta, Vaishali Patel, Ruchi Verma, Ashok Shrivastava, Megha Gyan Ganga Institute of Technology and Sciences Artificial Intelligence and Robotics MP Jabalpur India IPS Academy Institute of Engineering & Science Department of Computer Sc. & Engg. MP Indore India Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Medicaps University Department of Mathematics MP Indore India

ISBN: (纸本)9798331512965

Automated guided vehicles (AGVs) are an essential part of today's logistics networks because they save time, reduce wear and maintenance expenses, and maximize efficiency in route *** creation of AGVthat is capable of effectively managing warehouse racks without the utilization of sensors is the primary emphasis of this project. In order to travel and carry out duties, the AGVruns on predetermined pathways and makes use of a button-based interface. For the purpose of offering an efficient and costeffective solution for the automation of small warehouses, it is outfitted with a lever mechanism that is operated by a servo motor and features the ability to connect and move racks. © 2025 ieee.

关键词： Automatic guided vehicles

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Quantum Analysis of LESCA 38

Quantum Analysis of LESCA

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38th international conference on VLSI Design, VLSID 2025

作者： Chakraborty, Sumanta Mandal, Debajyoti Islam, Sk Hafizul Techno International New Town Department of Computer Science and Engineering West Bengal Kolkata India Indian Institute of Information Technology Kalyani Department of Computer Science and Engineering West Bengal Kalyani India

ISBN: (纸本)9798331522445

This paper explores the complexity measurements of applying Grover's search algorithm on a dynamic, key-dependent, lightweight stream cipher, LESCA (LightwEight Stream Cipher Algorithm). For this purpose, we present our algorithms to implement LESCA in quantum paradigm, probably the first, to the best of our knowledge. After that, we provide the cost measurements of the quantum implementation of LESCA and the quantum cost for applying Grover's search on LESCA. We show that LESCA meets the quantum security levels 1, 3, and 5 defined by National Institute of Standards and Technology (NIST). © 2025 ieee.

关键词： Quantum computers

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Next-Generation Embedded Systems with Synergies VLSI with Deep Learning: Design Methodologies, Optimization Techniques, and Real-World Applications

Next-Generation Embedded Systems with Synergies VLSI with De...

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Kalarani, G. Mohanapriya, M. Pattunnarajam, P. Sasikala, G. Sivakumar, J. Suresh, M. Jaya Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Ece Tamil Nadu Sriperumbudur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Mathematics Tamil Nadu Thiruvallur India

ISBN: (纸本)9798331529833

New generation of embedded systems with superior intelligence, energy efficiency, and performance have emerged as a result of the merging of deep learning with Very-Large-Scale Integration (VLSI) technology. Methodologies for design, optimisation strategies, and practical uses of next-generation embedded systems are the foci of this study, which investigates the ways in which VLSI and deep learning might work together. These systems have the potential to transform several industries, such as transportation, medicine, robotics, and the IoT, by harnessing the processing power of deep neural networks with the improvements in semiconductor fabrication. Prior to delving into the advantages of bespoke hardware design for deep learning inference and training, we trace the history of very large scale integration (VLSI) technology and its incorporation with deep learning algorithms. Investigated here are the design techniques that, when applied to very large scale integration (VLSI) architectures like FPGAs and ASICs, allow for the efficient mapping of deep learning models onto these devices. We show case studies that show how these methods work and talk about the trade-offs between performance, power consumption, and adaptability. The development of next-generation embedded systems relies heavily on optimisation approaches. Model compression, quantisation, and pruning are some of the optimisation strategies that we examine;they lessen the memory and computational demands of deep learning models without drastically altering their accuracy. For embedded devices with limited resources, these methods are crucial for implementing deep learning models. Additionally, we explore the practical uses of embedded systems augmented with VLSI and deep learning. By capitalising on the complementary strengths of VLSI and deep learning, applications like autonomous driving, medical imaging, and smart home automation are revolutionising entire industries. In this paper, we examine the design

关键词： Integrated circuit design

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Advising and Scheduling Tool Based on Electrical Power engineering Concepts

Advising and Scheduling Tool Based on Electrical Power Engin...

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2025 international conference on Control, automation, and Instrumentation, IC2AI 2025

作者： Nehme, Bechara Al Ahmar, Elie Zalaket, Joseph Electrical Telecommunication and Computer Engineering Department Jounieh Lebanon University Furn El Chebbak Faculty of Engineering Sagesse Baabda Lebanon

ISBN: (纸本)9798331529604

University administrations are always in need for tools to optimize their operation decreasing operational costs and ensuring a good learning experience for students. Advising and course offering are two main tasks that require knowledge in the field and expertise. The tasks need also data and its related analytics. This paper presents an innovative advising tool based on data mining leveraging from electrical power concepts. The tool uses Python and Excel to automate advising sheets and electrical power engineering by applying simultaneity and the factor of utilization to students records and courses to predict future course enrollment with a RMSE=0.75. The research proposed is unique since it helps administration and advisors. With the reduction of resources and disruption of the classical educational system new tools should be elaborated to reduce costs and secure on time graduation of students. © 2025 ieee.

关键词： Students

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