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1st international conference on advances in science, engineering and robotics technology, ICASERT 2019

ISBN: (纸本)9781728134451

The proceedings contain 275 papers. The topics discussed include: applying model-free reinforcement learning algorithm in network slicing for 5G;subject oriented data partitioning - a proposed data warehousing schema;large magnetic remanence property based a new material used in the linear generator for wave energy extraction;smart water system at house using Arduino Uno and C# desktop application to reduce water wastage and energy loss;design and development of question answering system in Bangla language from multiple documents;question bank similarity searching system (QB3S) using NLP and information retrieval technique;and Q-learning based particle swarm optimization algorithm for optimal path planning of swarm of mobile robots.

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Feedback Control Based on Trust for Varied Automation Transparency 1

Feedback Control Based on Trust for Varied Automation Transp...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Bandyopadhyay, Krishnan Banerji, Nandan Mishra, Ishani Kandpal, Ankit St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Karnataka Bengaluru India Sikkim Manipal Institute of Technology Sikkim Manipal University Department of Computer Science and Engineering Sikkim India New Horizon college of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560103 India Bipin Tripathi Kumaon Institute of Technology Department of ECE Almora Dwarahat India

ISBN: (纸本)9798331518578

Interactions between people and automation depend heavily on human confidence in the latter. Lack of trust can cause people to not use automation, while too much trust can cause them to put their faith in a flawed autonomous system, which could have detrimental effects on them. Transparency and trust in automation are vital for the usefulness and security of human-robotization frameworks. The majority of the research that has been done on the trust that human operators have in robotics and automation has been an ongoing variable that has received little attention. paid to how trust changes over time. In order to fill this research void, we offer a study that examines how trust is dynamic. Using real-time trust measurements, we first discovered options to quantify trust of entirety, which we described as a metric that considers trust throughout the entire human-automation interaction experience. In this study, a feedback control strategy for powerfully changing mechanization straightforwardness and transparency concurring on client trust is analysed. The proposed approach tweaks mechanical way of behaving and correspondence by incorporating an input control component with a trust assessment model. The adequacy of the feedback control framework in improving errand execution and trust adjustment under different levels of computerization straightforwardness and transparency is checked utilizing MATLAB reenactments. © 2025 IEEE.

关键词： Human robot interaction

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A Machine Learning Approach Using MATLAB on robotics Control and Navigation for Autonomous Systems 1

A Machine Learning Approach Using MATLAB on Robotics Control...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kunchakuri, Naveen Pachigolla, Yesu Ratnam Kandari, Chandana Clarksburg MD United States Pflugerville TX United States Germantown MD United States

ISBN: (纸本)9798331518578

Smart autonomous robotics must be able to navigate in complicated surroundings. Developing complex navigational platforms to transport autonomous robotics from one location to another has taken years of engineering and study. Notwithstanding their general effectiveness, a new area of study is focused on creating machine learning (ML) methods to deal with the identical issue. Nevertheless, there hasn't been any direct evaluation of the developing and traditional approaches to this issue up to this point. Since the optimum management issue has gained popularity as a study topic, reinforcement learning (RL) methods are preferred for supervised learning in the ML field due to the impossibility of selecting every possible solution for continuous-state issues and the lack of a specified trainer. This work proposes an approach to tackle autonomous robotic navigation using the 2 most widely used RL methods, Sarsa (λ) and Q (λ). To boost efficiency, the suggested framework, built using MATLAB, utilizes condition and action collections that are constructed uniquely. Both in simulation and real-world settings, the platform has an elevated performance percentage in navigating autonomous robotics to a specified destination by overcoming barriers. Furthermore, it is feasible to compare the results of the Sarsa and Q techniques and evaluate how the starting variables employed by the RL techniques, such as λ, affect learning. © 2025 IEEE.

关键词： Adversarial machine learning

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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THz-BA: Terahertz Antenna Design for Biological Applications with a Focus on Riboflavin Detection 4

THz-BA: Terahertz Antenna Design for Biological Applications...

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4th international conference on robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Hasan, Md. Fahad Hasan, Mahbub Hossain, Arafat Faisal, Fahim Heart, Mahadi Hasan Rahman, Mahmudur American International University-Bangladesh Electrical and Electronics Engineering Dhaka Bangladesh New Mexico Institute of Mining and Technology Electrical Engineering SocorroNM United States Bangladesh Army University of Engineering and Technology Qadirabad Cantonment Information and Communication Engineering Natore Bangladesh Computer Science Brac University - Bangladesh Dhaka Bangladesh Daffodil International University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530938

In this work, we have designed a terahertz antenna for riboflavin detection. Three layers make up the structure of the microstrip antenna: the bottom layer models the ground plane, the middle layer models the substrate, and the top layer models the rectangular patch. The antenna design employed a FR4 substrate with a thickness of 1.7 μm and a dielectric constant of 4.3. The copper material used for the ground plane, feed line, and patch has a thickness of 0.00035μm. The suggested configuration offers an omnidirectional radiation pattern with a gain of 5.662 dBi. The antenna operates in two frequency bands. Two frequencies have been identified: the first is 2.3361 THz to 2.4622 THz with a bandwidth of 0.12605 THz, and the second is 4.4986 THz to 4.8711 THz with a bandwidth of 0.3725 THz and projected antenna return loss of -21.088dB and-21.427dB. At 2.40 THz and 4.75THz, the antenna resonates. Thus, the aforementioned antenna can be used for biological purposes, such as riboflavin detection. © 2025 IEEE.

关键词： Microstrip antennas

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Motion Planning in Automated Vehicles Based on Hierarchical MPC 1

Motion Planning in Automated Vehicles Based on Hierarchical ...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kapila, Pooja Kumar, Pankaj Negi, Rahul Bharti, Bhupender Kumar Punhani, Akash Bisht, Suraj Singh IIMT College of Engineering Department of Artifical Intelligence & Data Science India Amity University Department of Statistics Kolkata India Tula's Institute Department of ECE Dehradun248197 India Department of Mechanical Engineering VMSB UTU Campus Pithoragarh India KIET Group of Institutions Department of Computer Science Noida India Bipin Tripathi Kumaon Institute of Technology Department of ECE Dwarahat Almora India

ISBN: (纸本)9798331518578

In this study, a thorough hierarchical control structure that supports autonomous decision-making that arises in autonomous systems and robots is proposed. Distinct state and decision/control sets are frequently used to describe high-level decision making in a conventional hierarchical control architecture. To keep up with proficiency, wellbeing, and solace, robotized vehicles generally depend serious areas of strength for on powerful movement arranging calculations. The capacity of Model Prescient Control (MPC) to oversee imperatives and upgrade directions progressively has made it a strong system for movement arranging. To approve the proposed strategy, this exploration utilizes MATLAB recreations to look at various levelled MPC for movement arranging in robotized vehicles. With the low-level regulator guaranteeing exact direction observing and the undeniable level organizer computing a worldwide direction, a two-layer progressive MPC structure is fabricated. The discoveries show how well the progressive MPC handles street ebb and flow, dynamic hindrances, and continuous computational impediments. It is demonstrated that the suggested autonomous decision-making scheme's recursive practicality and stability are ensured by carefully crafting its essential components. An intelligent vehicle's autonomous lane-changing system is developed using the suggested framework. Simulation demonstrates that it has a potential capacity to manage a variety of behaviours in complex and dynamic contexts. It is anticipated that the suggested framework will be useful for autonomous decision-making in a variety of robotics along with autonomous technologies where safety as well as optimality are important factors. © 2025 IEEE.

关键词： Motion planning

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Design and Implementation of Hand Exoskeleton for Rheumatoid Arthritis Rehabilitation

Design and Implementation of Hand Exoskeleton for Rheumatoid...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Balaji, Sudharshan Jaba Deva Krupa, A. Bharanidivya, Madhavan Dhanalakshmi, Samiappan Srm Institute of Science and Technology College of Engineering and Technology Department of Electronics and Communication Engineering Chennai603203 India

ISBN: (纸本)9798331529833

Inflammation of the joints is a hallmark of rheumatoid arthritis (RA), a long-term inflammatory disease that can cause significant discomfort, limited range of motion, and impaired hand function. Developing a tailored hand exoskeleton to help RA patients carry out routine activities and regain some dexterity in their hands is the primary goal of this study. This exoskeleton is designed specifically for RA sufferers. It uses precise actuation technology and soft, adaptable materials to help in gripping and finger extension without making the patient uncomfortable or requiring them to move against their will. With its built-in sensors, the exoskeleton keeps tabs on the user's every motion, analyzing their joint locations and patterns to provide real-time feedback and tailor adjustments to their specific mobility and pain. RA sufferers will be able to modify the support levels of the exoskeleton based on the type of activity they are performing and the amount of pain they are experiencing. Control will be achieved through an interface that is easy to use. This project aims to produce an assistive device that improves the quality of life for individuals with rheumatoid arthritis (RA) by boosting freedom in daily activities while minimizing joint stress. This will be accomplished by merging several biomedical principles with modern robotics. © 2025 IEEE.

关键词： Exoskeleton (robotics)

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Blockchain for Cybersecurity_ Securing Data Transactions and Enhancing Privacy in Digital Systems 1

Blockchain for Cybersecurity_ Securing Data Transactions and...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rajababu, Durgam Surya, S. Padhiary, Mrutyunjay Modi, Harshadkumar School of Engineering SR University Department of EEE Telangana Warangal India Porur Chennai600116 India Triguna Sen School of Technology Assam University Department of Agricultural Engineering Assam Silchar788011 India Government Polytechnic Gandhinagar India

ISBN: (纸本)9798331518578

The feasibility of using blockchain technology as a method to improve cybersecurity through data security transactions and users' anonymity is discussed in this paper. It scans network traffic, sings out abnormities, and uses the clustering approach to find cybersecurity risks. The identified evidence points to the prospects of blockchain to disrupt threat management and enhance the resilience of digital landscapes against cyber threats. © 2025 IEEE.

关键词： Cybersecurity

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SciTexC: A Framework for Scientific Text Classification in Bengali using Transformer-Based Techniques 4

SciTexC: A Framework for Scientific Text Classification in B...

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4th international conference on robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Nahiyan, Fahmida Hoque, Mohammed Moshiul Chittagong University of Engineering and Technology Department of Computer Science & Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798331530938

Scientific text classification (STC) in NLP automatically categorizes texts into predefined scien-tific domains. Although there is enormous progress in STC regarding high-resource languages, it must be explored in low-resource languages, specifically Ben-gali. This study introduces a framework, SciTexC (Scientific Text Classifier), which uses a transformer-based technique that classifies Bengali texts into six scientific domains: Physics, Chemistry, Biology, Information and Communication technology (ICT), Mathematics, and Others. A new corpus, STexD (scientific text dataset), containing 6000 Bengali texts, was also developed for classification tasks. Five transformer-based models were used to perform the task, including mBERT, dmBERT, Bangla BERT, Indic BERT, and XLM-RoBERTa. The evaluation results demonstrate that XLM-RoBERTa outperformed the other exploited models, achieving the highest F1 score (0.9158). © 2025 IEEE.

关键词： Classification (of information)

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Predictive Analysis of Diabetes Data in Healthcare System Using Machine Learning Algorithms 4

Predictive Analysis of Diabetes Data in Healthcare System Us...

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4th international conference on robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Al Arafat, Kazi Abdullah Al Amin Rahman, Md Siddique, Sumaya Roni, Mahmudur Rahman Parvez, Imtiaz Atish Dipankar University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh American International University Department of Computer Science and Engineering Dhaka Bangladesh Utah Valley University Department of Computer Science 800 W University Pkwy OremUT84058 United States

ISBN: (纸本)9798331530938

Diabetes, influenced by factors like high blood pressure, aging, obesity, and poor lifestyle choices, has become a significant health issue, increasing the risk of heart disease, kidney disease, stroke, and other serious conditions. Traditional diagnostic methods in hospitals can sometimes be misleading, underscoring the need for big data analysis in healthcare. This approach can uncover hidden patterns in diabetes-related data, enabling more accurate predictions of outcomes. This paper aims to classify large diabetes datasets to predict the disease's behavior using various attributes. A machine learning model with sixteen classifiers, fine-tuned through k-fold cross-validation, was developed to ensure optimal results, with accuracy measured by F1 score, precision, and recall. © 2025 IEEE.

关键词： Adversarial machine learning

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