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Enhanced Localization in Wireless Sensor Networks Using a Bat-Optimized Malicious Anchor Node Prediction Algorithm.
Sensors (Basel)
December 2024
Power Electronics, Machines and Control (PEMC) Research Institute, University of Nottingham, 15 Triumph Rd, Lenton, Nottingham NG7 2GT, UK.
The accuracy of node localization plays a crucial role in the performance and reliability of wireless sensor networks (WSNs), which are widely utilized in fields like security systems and environmental monitoring. The integrity of these networks is often threatened by the presence of malicious nodes that can disrupt the localization process, leading to erroneous positioning and degraded network functionality. To address this challenge, we propose the security-aware localization using bat-optimized malicious anchor prediction (BO-MAP) algorithm.
View Article and Find Full Text PDFRobust and Fast Point Cloud Registration for Robot Localization Based on DBSCAN Clustering and Adaptive Segmentation.
Sensors (Basel)
December 2024
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning.
View Article and Find Full Text PDFModification of Erroneous and Correct Digital Texts.
Entropy (Basel)
November 2024
Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary.
The end-user paradox and the illusion of digital prosperity reveal the contradictory situation in which both non-professional and professional computer scientists and engineers seem satisfied with digital development but unaware of the magnitude of waste generated by end-users and their digital artifacts. To measure this waste and to reveal end-users' problem-solving strategies, our research group set up an objective measuring system that can calculate the entropy of digital texts (EDT). To calculate EDT, a testing process of 53 participants was launched where erroneous and correct natural language digital texts were modified according to the requirements of the tasks.
View Article and Find Full Text PDFDeveloping a Robust Fuzzy Inference Algorithm in a Dog Disease Pre-Diagnosis System for Casual Owners.
Animals (Basel)
December 2024
Division of Artificial Intelligence Engineering, National Korea Maritime & Ocean University, Busan 49112, Republic of Korea.
While the pet market is continuously rapidly increasing in Korea, pet dog owners feel uncomfortable in coping with pet dog's health problems in time. In this paper, we propose a pre-diagnosis system based on neuro-fuzzy learning, enabling non-expert users to monitor their pets' health by inputting observed symptoms. To develop such a system, we form a disease-symptom database based on several textbooks with veterinarians' guidance and filtering.
View Article and Find Full Text PDFEvaluation of Gamma Index Analysis for Detecting Errors in Patient-specific Quality Assurance in Intensity Modulated Radiotherapy.
Health Phys
January 2025
Department of Radiation Oncology, Medicine Faculty of Van Yüzüncü Yıl University, Van, Turkey.
Quality assurance practices performed before treatment are believed to identify various potential errors. In this study, 2-dimensional (2D) dosimetric results were analyzed by making some intentional mistakes in six different treatment plans. In this way, the detectability of errors was investigated.
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