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39th International Symposium on Automation and Robotics in Construction, ISARC 2022

作者： Yin, H. Liew, J.M. Lee, W.L. Ang, M.H. Yeoh, J.K.W. College of Design and Engineering National University of Singapore Singapore Singapore Department of Electronic and Computer Engineering Hong Kong University of Science and Technology HKSAR Hong Kong

ISBN: (纸本)9789526952420

Conventional mobile robots rely on pre-built point cloud maps for online localization. These map points are generally built using specialized mapping techniques, which involve high labor and computational costs. While in the architectural, engineering and construction (AEC) industry, asplanned building information modelings (BIM) are available for management and operation. In this paper, we consider the use of the digital representations of BIM for robot localization in built environments. First, we convert BIM data into localization-oriented point clouds, which is easy to implement and operate compared to relatively complex SLAM systems. Then, we perform iterative closest point (ICP)-based localization on the metric map using a laser scanner. The experiments are tested using collected laser data and BIM in the real world. The results show that ICP-based localization can track the robot pose with low errors ( © 2022 International Association on Automation and Robotics in Construction.

关键词： Iterative methods

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Protecting Vulnerable Road users using IoT-CNN for Safety Measures

Protecting Vulnerable Road users using IoT-CNN for Safety Me...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： R C Karpagalakshmi M Maria Sampoornam Viyyapu Lokeshwari Vinya Monikapreethi S K Yuvaraj S C. Srinivasan Alliance College of Engineering and Design Alliance University Bengaluru Karnataka India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Computer Science and Engineering Vardhaman College of Engineering Ranga Reddy Telangana India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The increasing number of road accidents involving Vulnerable Road Users (VRUs) highlights the urgent need for innovative safety solutions. This study proposes a novel approach that leverages IoT and Convolutional Neural Network (CNN) technologies to enhance VRU safety. By deploying IoT sensors on vehicles and infrastructure, the proposed model can accurately track VRU locations and environmental conditions in real-time. A CNN-based model is developed to robustly detect and classify VRUs under various traffic scenarios, with continuous improvement through learning. The integration of IoT and CNN enables real-time communication and timely warnings, facilitating proactive interventions to prevent accidents. Experimental evaluations demonstrate the effectiveness of the proposed system in improving VRU safety. This research offers a promising solution for reducing road accidents and creating safer urban environments.

关键词： Technological innovation Road accidents Urban areas Transportation Road safety Real-time systems Sensors Convolutional neural networks Stakeholders Protection

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design and Study of Circuits Using Reversible Logic 2

Design and Study of Circuits Using Reversible Logic

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2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021

作者： Sooriamala, A.P. Thomas, Aby K. Korah, Reeba Hindustan Institute of Technology and Science Department of ECE Chennai India Alliance College of Engineering and Design Alliance University Department of ECE Bangalore India

ISBN: (纸本)9781665428675

In VLSI systems design, reversible Logic has gained importance for Reducing power. This paper focuses on designing of sequential circuits and reducing the number of constant inputs - CI and the garbage outputs - GO. Basic reversible gates are used to build sequential circuits. D Flip flop, Latch and RAM cell are developed. All circuits are VHDL coded on Xilinx tool, simulated and verified. Reduction in CI of more than 33 percent and GO of more than 80 percent is achieved. This is achieved by properly reusing the outputs for configuring the constant input of other gates. © 2021 IEEE.

关键词： Flip flop circuits

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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Lung Cancer Classification Using Machine Learning

Lung Cancer Classification Using Machine Learning

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： Ravi Kumar Sachdeva Priyanka Bathla Pooja Rani Rohit Lamba G S Pradeep Ghantasala Rajesh Sharma R Department of Computer Science & Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science & Engineering Chandigarh University Mohali Punjab India MMICTBM Maharishi Markandeshwar (Deemed to be University) Ambala Haryana India MMEC Maharishi Markandeshwar (Deemed to be University) Ambala Haryana India Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Lung cancer is leading among nineteen different types of cancer deaths across the world. Later the lung cancer is detected, the lesser the chances of survival for an individual. The authors have proposed a model to predict lung cancer in the initial stages. To build the model, the authors applied seven different classifiers: Naive Bayes (NB), K Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Adaboost, Logistic Regression (LR), and Xgboost, to the lung cancer dataset accessible on Kaggle. The authors further applied class balancing using the SMOTE technique and observed the effectiveness of classifiers. The authors found that the class balancing improved the performance. LR has achieved the highest accuracy (96.30%) among all applied classifiers after applying class balancing.

关键词： Radio frequency Technological innovation Logistic regression Accuracy Lung cancer Predictive models Nearest neighbor methods Feature extraction Random forests Logistics

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Comparative Analysis of SCM Muscle Fatigue in Office Workers with Hunched Posture: A Study on Chronic Lower Back Pain versus Non-Affected Individuals

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Biotechnology and Applied Biochemistry 2025年第0期 2025 Feb 3页

作者： Guduru, Rama Krishna Reddy Ramineni, Joshna Ramakrishna, M.M. Sridhar, D. Bharathi, Hema Department of Bioengineering SIMATS Engineering SIMATS University Chennai India SIMATS Engineering SIMATS University Chennai India Alliance College of Engineering and Design Alliance University - Central Campus Bengaluru India Department of Physical Science and Information Technology AEC&RI Tamil Nadu Agricultural University Coimbatore India

This study examines the disparities between people suffering from chronic lower back pain (LBP) and those who do not, with a particular focus on how extended periods of sitting with poor posture affect fatigue and discomfort in the sternocleidomastoid (SCM) muscles among office employees. Eighty university staff and students were enrolled in the study by matching age, BMI, and type of job. They were split into two groups later: a control group (n = 40) with no back pain and a pain group (n = 40) with a history of lower back pain (LBP). Pain intensity was measured using a Visual Analog Scale (VAS), while SCM muscle activity was measured via surface electromyography (sEMG) during both normal and hunched postures. Participants maintained each posture for a specified duration of 30 min. The study revealed that Pain_Hunched group exhibited significantly reduced SCM muscle activity compared to the Control_Hunched group (mean difference = −9.728, p © 2025 International Union of Biochemistry and Molecular Biology, Inc.

关键词： Electromyography

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A Comparative Analysis of Image Processing and Deep Learning Techniques for License Plate Recognition in Difficult Environments

A Comparative Analysis of Image Processing and Deep Learning...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Tejaswi S Tina Babu Tejaswi K Department of Computer Science Seshadripuram Degree College Mysuru India Department of Computer Science and Engineering School of Engineering Alliance University Bengaluru India Department of Computer Applications JSS Science and Technology University Mysuru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions.

关键词： Deep learning Support vector machines YOLO Accuracy Lighting computer architecture Real-time systems Vectors License plate recognition Meteorology

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AI-Powered NLP Systems for Differential Diagnosis: Improving COPD Exacerbation Detection

AI-Powered NLP Systems for Differential Diagnosis: Improving...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： K. Vishalatchi N. Thenmoezhi Shobica K Rajan. S P.T. Sivagurunathan M. Udhayamoorthi Department of Electronics and Communication Engineering AAA College of Engineering and Technology Sivakasi Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Department of Electronics and Communication Engineering Velalar College of Engineering and Technology Erode Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering Karur Department of Computer Science and Design Karpagam College of Engineering Coimbatore

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Chronic Obstructive Pulmonary Disease (COPD) exacerbation is a life-threatening condition which manifests with overlapping symptoms that require early and correct differential diagnosis to ensure effective treatment. To this end, in this study, we propose a Natural Language Processing (NLP) enabled AI system to enhance COPD exacerbation identification and classification from EHRs and clinical notes. Based on large language models (LLMs) and AI based multi agent architectures, we automate data extraction and analysis of patients' data such as symptoms, patient's medical background and lifestyle factors. We perform unstructured clinical text processing for the cases of COPD exacerbation vs other respiratory conditions using multiple machine learning classifiers and NLP pipelines. It combines structured and unstructured data sources to increase the precision of conjunctive diagnostics and thus shorten the time needed for medical professionals to assess critical cases. In the study, different AI models are evaluated by random forest, logistic regression and Naïve Bayes classifiers, and results indicate that the random forest model is able to detect COPD exacerbation with the highest accuracy (80%) and F1-score (73%). This AI powered NLP system can automate the Medical History analysis and differential diagnosis process to be more efficient in diagnosis, reduce misclassification and improve patient outcomes. The model will be developed to include real-time patient monitoring and has been integrated with hospital information systems for clinical decision supports.

关键词： Accuracy Large language models Feature extraction Natural language processing Chronic obstructive pulmonary disease Differential diagnosis Real-time systems Artificial intelligence Medical diagnostic imaging Random forests

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I-Card: A Generative AI-Supported Intelligent design Method Card Deck 25

I-Card: A Generative AI-Supported Intelligent Design Method ...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Chen, Liuqing Cheang, Wengteng Jiang, Zhaojun Xu, Yuan Cai, Zebin Sun, Lingyun Childs, Peter Han, Ji Hansen, Preben Zuo, Haoyu College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China School of Computer Science and Engineering Central South University Hunan Changsha China International Design Institute Zhejiang University Zhejiang Hangzhou China Dyson School of Design Engineering Imperial College London London United Kingdom Department of Innovation Technology and Entrepreneurship University of Exeter Exeter United Kingdom Dept of Computer and Systems Sciences Stockholm University Kista Sweden

ISBN: (纸本)9798400713941

A design method card deck helps designers understand and provoke thinking by presenting each method in a simple format and allow designers to switch between methods seamlessly by maintaining the same simple format across the deck. However, recent observations have shown designers hesitate to use a card deck due to the lack of support, while other tools have provided identified support with generative AI. Through a formative study, we identified the specific support designers need when applying the design method cards and intentions in integrating generative AI. Accordingly, we developed the intelligent design method card deck, I-Card, which integrates generative AI to provide applicable design methods, design knowledge and data support, and interactive and dynamic support. A user study demonstrates that I-Card improved the design efficiency and applicability by offering personalized guidance, enhanced decision-making with comprehensive data generation and provided more design inspiration via interactive support. © 2025 Copyright held by the owner/author(s).

关键词： Product design

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DIGITAL TWINS FOR REAL-TIME MONITORING AND OPERATION OF COFFEE VALUE CHAIN AND SUPPLY CHAIN

International Journal of Mechatronics and Applied Mechanics

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International Journal of Mechatronics and Applied Mechanics 2024年第17期2024卷 114-124页

作者： Le, Chi An Le, Chi Hieu Nguyen, Van Duy Zlatov, Nikolay Le, Tan Hung Nguyen, Tien Anh Chu, Anh My Mahmud, Jamaluddin Le, Van Dang Nguyen, Ho Quang Ramesh, Dharavath Mengistu, Samueal Behera, Amar Packianather, Michael S. Faculty of Science and Engineering University of Greenwich Kent United Kingdom Institute of Biotechnology and Environment Nha Trang University Nha Trang Viet Nam Institute of Mechanics Bulgarian Academy of Sciences Sofia Bulgaria SOICT Hanoi University of Science and Technology Hanoi Viet Nam Le Quy Don Technical University Hanoi Viet Nam College of Engineering Universiti Teknologi MARA Selangor Malaysia ARTC Agency for Science Technology and Research Singapore Institute of Engineering and Technology Thu Dau Mot University Binh Duong Viet Nam Computer Science and Engineering Indian Institute of Technology Dhanbad India Department of Design Indian Institute of Technology Kanpur India School of Engineering Cardiff University Cardiff United Kingdom

There has been significant effort and a growing need to develop innovative and cost-effective solutions for real-time monitoring and operation of value chains and supply chains, especially to enhance the predictability and optimisation of complex production systems for a better adaptation to disruptions and market fluctuations as well as improved sustainability. This is particularly important when taking into account the impacts and emerging advancements of smart agriculture, smart manufacturing, Digital Twins, and Industry 5.0, where data-driven solutions and AI-enabled decision-making play an important role for improving real-time monitoring, quality control and management, and operational efficiency. This study presents a conceptual framework for integrating Digital Twins into a smart agriculture platform, focusing on the real-time monitoring and operation of the coffee value chain and supply chain, to demonstrate the potential of Digital Twins in advancing smart agriculture and digital supply chains. © 2024, Cefin Publishing House. All rights reserved.

关键词： Smart manufacturing

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