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Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

Multimedia Tools and Applications 2024年 1-25页

作者： Polo-Rodríguez, A. Valera, J.C. Peral, J. Gil, D. Medina-Quero, J. Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings. © The Author(s) 2024.

关键词： Ultra-wideband (UWB)

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Detection of Alzheimer's Disease using Deep Learning: An Optimized Approach 6

Detection of Alzheimer's Disease using Deep Learning: An Opt...

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6th International Conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Ahmed, Gulnaz Joo Er, Meng Zikria, Shahid Sadiq Fareed, Muhammad Institute of Artificial Intelligence and Marine Robotics Dalian Maritime University Dalian China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou China

ISBN: (纸本)9798350371253

This paper suggests a new CNN that requires just a few parameters to diagnose AD and is perfect for training on smaller datasets. Compared to existing state-of-the-art models, the proposed model uses a more optimized technique to identify the early stages of Alzheimer's disease. The image is sharpened and normalized as part of the preparation phase to help the model learn more quickly and succinctly. We used a bespoke CNN model that makes precise predictions with minimum parameters. Five Convolutional layers and three fully connected layers, which have equally learned samples from each class, make up the proposed model. The approach was compared using a variety of accuracy metrics, such as accuracy, AUC, precision, recall, and Fl-Score, as well as categorical cross-entropy loss, self, and other comparisons. To accurately forecast the method, several trials were carried out. The values for the evaluation measures are as follows: accuracy, AUC, precision, recall, fl-score, and loss are 99.95%, 99.99%, 99.97%, 99.95%, and 0.0016 respectively. According to simulation results, the proposed model performs better than existing cutting-edge models in all evaluation metrics. © 2023 IEEE.

关键词： Medical imaging

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Support vector machine with discriminative low-rank embedding

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1249-1262页

作者： Guangfei Liang Zhihui Lai Heng Kong Computer Vision Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Breast and Thyroid Surgery BaoAn Central Hospital of ShenzhenShenzhenChina

Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.

关键词： iterative methods machine leaning support vector machunes

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Reward-Based Energy-Aware Routing Protocols in Wireless Sensor Networks

Reward-Based Energy-Aware Routing Protocols in Wireless Sens...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Smart cities, industrial automation, healthcare, and environmental monitoring are just a few of the industries that have seen revolutionary changes as a result of Wireless Sensor Networks’ (WSNs) explosive growth. WSNs are significantly hampered by the limited energy resources of their sensor nodes, which are usually battery-powered and placed in inaccessible locations, despite their wide-ranging applications. As a result, energy efficiency is now a crucial component of these networks’ operation and design. Though these protocols frequently fail to adjust to the dynamic and unpredictable nature of WSNs, traditional energy-aware routing methods have been created to decrease energy consumption by choosing the best paths for data transfer. By allowing sensor nodes to learn from their surroundings and make wise routing decisions, the machine learning field of reinforcement learning (RL) provides a viable answer to these problems. In contrast to conventional techniques, RL-based protocols provide continuous energy optimization by dynamically modifying their routing strategies in response to real-time feedback. This paper investigates how to include RL methods—specifically, Q-learning and SARSA—into WSN energy-aware routing protocols. The suggested method creates a more distributed and scalable solution by enabling each node to independently learn and modify its routing choices. The effectiveness of RL-based routing protocols is compared to traditional methods through comprehensive simulations. The findings show that by slowing down the rate at which energy is being used up by each node, RL not only increases energy efficiency but also lengthens the network's operational life. In particular, Q-learning demonstrates a notable improvement in sustaining data delivery rates and network connectivity under various circumstances. Although a little more computationally demanding, the SARSA algorithm offers greater flexibility in situations where network topology changes often. Th

关键词： Multi agent systems

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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Using Gamification to Increase Healthcare Professionals’ Cybersecurity Awareness

Using Gamification to Increase Healthcare Professionals’ Cy...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Cybersecurity knowledge is essential in today’s increasingly digital healthcare environment to protect sensitive patient data and guarantee the integrity of healthcare systems. But conventional cybersecurity training approaches frequently fail to hold healthcare professionals’ attention, which results in inadequate application and retention of the material. In order to increase cybersecurity awareness among healthcare professionals, this study investigates the use of gamification—a technological technique that incorporates game design aspects into non-gaming contexts—as an innovative method. A mixed-methods research methodology is used in the study to assess the effectiveness of a cybersecurity training program that is gamified. To encourage user involvement and motivation, the program incorporates aspects like leaderboards, interactive scenarios, badges, and points. The study included a total of 100 healthcare professionals from various departments, offering a thorough understanding of the program’s effects across a range of positions and experience levels. Pre- and post-training surveys were used to gather quantitative data on participant changes in knowledge, attitudes, and behaviors pertaining to cybersecurity procedures. Furthermore, semi-structured interviews yielded qualitative insights that further elucidated the participants’ perspectives and experiences with the gamified training method. Comparing gamification to conventional training approaches, the results show that gamification dramatically improves engagement and knowledge retention. The willingness of the participants to acquire and use cybersecurity techniques in their daily lives has reportedly increased. Additionally, the gamified components strengthened the training objectives by fostering a competitive yet cooperative learning environment. The potential of gamification as a game-changing instrument for cybersecurity education in healthcare settings is highlighted by this study. Healthcare busines

关键词： Cybersecurity

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Collaborating for Success: Optimizing System Efficiency and Resilience Under Agile Industrial Settings 23

Collaborating for Success: Optimizing System Efficiency and ...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Katyara, Sunny Sharma, Suchita Damacharla, Praveen Garcia, Carlos O'Farrell, Francis Long, Philip Irish Manufacturing Research Company Ltd Department of Robotics & Automation Ireland Software Engineering Division Shutterfly United States KineticAI Inc WoodlandsTX United States

ISBN: (纸本)9798350373578

Designing an efficient and resilient human-robot collaboration strategy that not only upholds the safety and ergonomics of shared workspace but also enhances the performance and agility of collaborative setup presents significant challenges concerning environment perception and robot control. In this research, we introduce a novel approach for collaborative environment monitoring and robot motion regulation to address this multifaceted problem. Our study proposes novel computation and division of safety monitoring zones, adhering to ISO 13855 and TS 15066 standards, utilizing 2D lasers information. These zones are not only configured in the standard three-layer arrangement but are also expanded into two adjacent planes, thereby enhancing system uptime and preventing unnecessary deadlocks. Moreover, we also leverage 3D visual information to track dynamic human articulations and extended intrusions. Drawing upon the fused sensory data from 2D and 3D perceptual spaces, our proposed hierarchical controller stably regulates robot velocity, validated using Lasalle in-variance principle. Empirical evaluations demonstrate that our approach significantly reduces task execution time and system response delay, resulting in improved efficiency and resilience within collaborative settings. © 2024 IEEE.

关键词： Efficiency

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DGMiniNet:Dynamic Graph Convolution Network for LiDAR Point Cloud Semantic Segmentation on Spherical coordinates 23

DGMiniNet:Dynamic Graph Convolution Network for LiDAR Point ...

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7th International Conference on Big Data and Internet of Things, BDIOT 2023

作者： Wu, Rui Liu, Zhen Department of Software Engineering North China University of Techology Beijing100144 China Department of Artificial Intelligence and Robotics Xian Jiaotong University Xian710049 China

ISBN: (纸本)9798400708015

Semantic segmentation of LiDAR data is a vital activity aiming at giving a semantic category to each point in the LiDAR point cloud, allowing accurate recognition and segmentation of objects and structures in the *** can obtain detailed information about semantic categories like roads, buildings, vehicles, pedestrians, etc. by classifying the points in the point cloud. This information can be used to support decision-making in the areas of autonomous driving, intelligent transportation systems, environment sensing, and scene *** this research, we propose DGMiniNet, a spherical projection-based approach for semantic segmentation of LiDAR. First, a 2D representation is learned after spherically projecting the 3D LiDAR point ***, DGMiniNet receives the 2D *** feature maps and using 2D fully convolutional neural networks (FCNN) to generate semantic labels in 2D *** labels that have been given in two-dimensional space are then reprojected into three-dimensional space via the post-processing *** point cloud contour extraction is accomplished by enhancing the association of dynamic feature maps with labels while simultaneously adding information from semantic segmentation, resulting in more semantically intelligible and geometrically accurate *** semantickitti dataset was used to train our method, which has a 93% accuracy rate. © 2023 ACM.

关键词： Semantic Segmentation

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Potholes Detection Using Yolo Algorithms and Their Integration Into Gazebo Simulation

Potholes Detection Using Yolo Algorithms and Their Integrati...

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9th International Conference on Control, robotics and Cybernetics, CRC 2024

作者： Hassan Tanveer, M. Fatima, Zainab Rehman, Tanazzah Siddique, Moaaz Shah, Arbaz Voicu, Razvan Cristian Farrukh Shahid, M. Kennesaw State University Department of Robotics and Mechatronics Engineering Marietta30060 United States NED University of Engineering and Technology Department of Software Engineering Karachi Pakistan FAST University of computing and Emerging Science Department of AI and Data Science Karachi Pakistan

ISBN: (纸本)9798331517489

Road cavities, often called potholes, pose a major problem for our transportation infrastructure, causing accidents, costly repairs, and traffic jamming. This study shows using a computer vision technique called You Only Look Once (YOLO) to find potholes on roads. YOLO is proficient and efficient for realtime object detection. The researchers want to see which version of YOLO is more effective in object analysis in pictures and videos. We'll be comparing YOLOv5, YOLOv7, and YOLOv8, thereby also setting up the system in Gazebo simulated environment. By carefully analyzing the results, we aim to understand the advantages and disadvantages of each YOLO version. This will help us see the most useful ones for finding potholes when managing and maintaining roads. © 2024 IEEE.

关键词： Object detection

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A novel extended Portuguese of Interactive and Multi-Criteria Decision Making and Archimedean Bonferroni mean operators based on prospect theory to select green supplier with complex q-rung orthopair fuzzy information

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 177-191页

作者： Zeeshan Ali Tahir Mahmood Jeonghwan Gwak Naeem Jan Department of Mathematics&Statistics International Islamic University IslamabadIslamabadPakistan Department of Software Korea National University of TransportationChungjuKorea Department of Biomedical Engineering Korea National University of TransportationChungjuKorea Department of AI Robotics Engineering Korea National University of TransportationChungjuKorea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungjuKorea

The word sustainable or green supply chain refers to the concept of incorporating sustainable environmental procedures into the traditional supply *** supply chain management gives a chance to revise procedures,materials and operational *** the fuzziness of assessing data and the spiritual situations of experts in the decision-making procedure are two important *** main contribution of this analysis is to derive the theory of Archimedean Bonferroni mean operator for complex qrung orthopair fuzzy(CQROF)information,called the CQROF Archimedean Bonferroni mean and CQROF weighted Archimedean Bonferroni mean operators which are very valuable,dominant and classical type of aggregation operators used for examining the interrelationship among the finite number of attributes in modern data fusion *** and well-used properties of the initiated theories are also diagnosed with some special ***,the theory of extended TODIM tool using the prospect theory based on CQROF information was discovered,which play an essential and critical role in the environment of fuzzy set ***,a real life problem by computing a green supply chain management based on the initiated CQROF operators was evaluated and fully illustrating the feasibility and efficiency of the diagnosed work with the help of a comparison between existing and prevailing theories.

关键词： fuzzy set fuzzy sets

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