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Exploring 2D representation and transfer learning techniques for indoor localization

Multimedia Tools and Applications 2025年 1-13页

作者： Kerdjidj, O. Himeur, Y. Atalla, S. Copiaco, A. Sohail, S.S. Amira, A. Fadli, F. Mansoor, W. Gawanmeh, A. Centre of Development of Advanced Techniques Algiers Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom School of Computing Science and Engineering VIT Bhopal University Kothri Kalan India Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

Indoor localization (IL) plays a critical role in various applications, including asset tracking, emergency response, and indoor navigation. Traditional methods relying on one-dimensional (1D) representations of received signal strength indicator (RSSI) data often suffer from limitations such as low accuracy, environmental sensitivity, and susceptibility to interference. To overcome these challenges, this paper proposes Scalogram-Based Transfer Learning for Indoor Localization (STL-IL), a novel framework that significantly enhances localization performance. The proposed approach transforms 1D RSSI signals into two-dimensional (2D) representations using scalogram-based time-frequency analysis, coupled with advanced transfer learning (TL) techniques. By leveraging the scalogram method to generate image representations of the data and employing diverse wavelet types. Furthermore, pre-trained deep learning models, including GoogleNet and SqueezeNet, are utilized to achieve state-of-the-art performance. Experimental results demonstrate that the STL-IL system achieves remarkable accuracy levels of 95% and 96% for SqueezeNet and GoogleNet, respectively. The proposed framework not only improves localization accuracy but also enhances robustness in dynamic and challenging environments, making it a promising solution for real-world indoor localization applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Towards Automated BIM and BEM Model Generation using a B-Rep-based Method with Topological Map

Towards Automated BIM and BEM Model Generation using a B-Rep...

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2024 Mid-term Symposium on Spatial Information to Empower the Metaverse

作者： Roman, Oscar Mazzacca, Gabriele Farella, Elisa Mariarosaria Remondino, Fabio Bassier, Maarten Agugiaro, Giorgio Trento Italy University of Trento Trento Italy Department of Mathematics Computer Science and Physics University of Udine Italy Department of Civil Engineering TC Construction - Geomatics KU Leuven Faculty of Engineering Technology Ghent Belgium School of Architecture and Built Environment Delft University of Technology Netherlands

In many countries, recent boosts in the construction and renovation sectors and energy efficiency directives are driving a growing interest in the built environment among designers and maintainers. In this context, customized software solutions tailored for Building Information Modelling (BIM) and Building Energy Modelling (BEM) are proving to be indispensable for optimizing operational efficiency within the architecture, Engineering, Construction, Owner, and Operator (AECOO) sector and for facilitating the generation of buildings' Digital Twins (DTs). These DTs rely on accurate geometry and ancillary information (semantics, sensors, etc.) to define properties of single elements, enabling crucial simulations in structural conditions or energy needs. However, BIM and BEM model creation and their enrichment with semantic information are highly time-consuming and prone to manual errors. Hence, there is an increasing demand for automatic methods featuring a high level of geometric accuracy to reconstruct building elements, such as walls, floors, and openings captured via 3D reality-based surveying. This paper introduces an automated method for creating Boundary Representation (B-Rep) models from 3D surveying data for the generation of digital building replicas. The method is based on the detection and computation of topological elements from 3D reality-based point clouds. It proves valuable for architectural or design workflows and for conducting energy or quality system simulations. © Author(s) 2024.

关键词： Architectural design

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AN ACTIVE LEARNING PROCEDURE FOR THE INTERAURAL TIME DIFFERENCE DISCRIMINATION THRESHOLD 26

AN ACTIVE LEARNING PROCEDURE FOR THE INTERAURAL TIME DIFFERE...

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26th International Conference on Digital Audio Effects, DAFx 2023

作者： Gulli, Andrea Fontana, Federico Serafin, Stefania Geronazzo, Michele Department of Mathematics Computer Science and Physics Università di Udine Udine Italy Department of Architecture Design and Media Technology Aalborg University Copenhagen Denmark Department of Engineering and Management Università di Padova Padua Italy

Measuring the auditory lateralization elicited by interaural time difference (ITD) cues involves the estimation of a psychometric function (PF). The shape of this function usually follows from the analysis of the subjective data and models the probability of correctly localizing the angular position of a sound source. The present study describes and evaluates a procedure for progressively fitting a PF, using Gaussian process classification of the subjective responses produced during a binary decision experiment. The process refines adaptively an approximated PF, following Bayesian inference. At each trial, it suggests the most informative auditory stimulus for function refinement according to Bayesian active learning by disagreement (BALD) mutual information. In this paper, the procedure was modified to accommodate two-alternative forced choice (2AFC) experimental methods and then was compared with a standard adaptive "three-down, one-up" staircase procedure. Our process approximates the average threshold ITD 79.4% correct level of lateralization with a mean accuracy increase of 8.9% over the Weibull function fitted on the data of the same test. The final accuracy for the Just Noticeable Difference (JND) in ITD is achieved with only 37.6% of the trials needed by a standard lateralization test. © 2023 Andrea Gulli, Federico Fontana et al.

关键词： Inference engines

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SiamCircle: Trajectory Representation Learning in Free Settings 23rd

SiamCircle: Trajectory Representation Learning in Free Sett...

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23rd International Symposium on Intelligent Data Analysis, IDA 2025

作者： Nasri, Maedeh Baratchi, Mitra Koutamanis, Alexander Rieffe, Carolien Department of Developmental Psychology Leiden University Leiden Netherlands Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Faculty of Architecture and the Built Environment Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of Psychology and Human Development Institute of Education UCL London United Kingdom

ISBN: (纸本)9783031913976

Trajectory representation learning (TRL) is an intermediate step in handling trajectory data to realize various downstream machine-learning tasks. While most previous TRL research focuses on modeling structured movements in large-scale urban spaces (e.g., cars or pedestrians on streets), this paper focuses on a more challenging scenario of modeling free movement in small-scale social spaces (e.g., children playing in a schoolyard). We present a TRL model, SiamCircle, to process raw trajectories without additional feature extraction to prevent information loss. SiamCircle adopts a Siamese network with Circle Loss to learn trajectory embeddings. Furthermore, SiamCircle employs a data augmentation process to enable self-supervised learning and enrich the input data to address the limited access to high-quality data and ground truth. We evaluate the performance of SiamCircle in downstream tasks using trajectory ranking and clustering performance via seven evaluation metrics collectively. Using an ablation study, we explored the impact of different loss functions on the model’s performance. Accordingly, we selected a 2-D convolutional design with Circle Loss as the best-performing model. In a comparative study, we compared our model against three other baselines. We observed up to 19% improvements in trajectory ranking tasks and achieved the highest average rank in supervised clustering tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sorting

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Landscape of sign language research based on smartphone apps: coherent literature analysis, motivations, open challenges, recommendations and future directions for app assessment

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Universal Access in the Information Society 2024年第2期23卷 687-702页

作者： David, Dianes Alamoodi, A.H. Albahri, O.S. Zaidan, B.B. Zaidan, A.A. Garfan, Salem Ismail, Amelia Ritahani Albahri, A.S. Alsinglawi, Belal Malik, R.Q. Perak Malaysia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates International Islamic University Malaysia Kuala Lumpur Malaysia Baghdad Iraq School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia Medical Intrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq

Numerous nations have prioritised the inclusion of citizens with disabilities, such as hearing loss, in all aspects of social life. Sign language is used by this population, yet they still have trouble communicating with others. Many sign language apps are being created to help bridge the communication gap as a result of technology advances enabled by the widespread use of smartphones. These apps are widely used because they are accessible and inexpensive. The services and capabilities they offer and the quality of their content, however, differ greatly. Evaluation of the quality of the content provided by these applications is necessary if they are to have any kind of real effect. A thorough evaluation like this will inspire developers to work hard on new apps, which will lead to improved software development and experience overall. This research used a systematic literature review (SLR) method, which is recognised in gaining a broad understanding of the study whilst offering additional information for future investigations. SLR was adopted in this research for smartphone-based sign language apps to understand the area and main discussion aspects utilised in the assessment. These studies were reviewed on the basis of related work analysis, main issues, discussions and methodological aspects. Results revealed that the evaluation of sign language mobile apps is scarce. Thus, we proposed a future direction for the quality assessment of these apps. The findings will benefit normal-hearing and hearing-impaired users and open up a new area where researchers and developers could work together on sign language mobile apps. The results will help hearing and non-hearing users and will pave the way for future collaboration between academicians and app developers in the field of sign language technology. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Smartphones

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Exploiting autoencoder’s weakness to generate pseudo anomalies

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Neural Computing and Applications 2024年第23期36卷 14075-14091页

作者： Astrid, Marcella Zaheer, Muhammad Zaigham Aouada, Djamila Lee, Seung-Ik Department of Artificial Intelligence University of Science and Technology Daejeon34113 Korea Republic of Field Robotics Research Section Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg1855 Luxembourg Department of Computer Vision Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE is expected to well reconstruct normal but to poorly reconstruct anomalous data. However, contrary to the expectation, anomalous data are often well reconstructed as well. In order to further separate the reconstruction quality between normal and anomalous data, we propose creating pseudo anomalies from learned adaptive noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. The generated noise is added to the normal data to create pseudo anomalies. Extensive experiments on Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the effectiveness and generic applicability of our approach in improving the discriminative capability of AEs for anomaly detection. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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An Efficient Intrusion Detection System using Advanced Machine Learning Techniques in SDN for Healthcare System

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Asif, Muhammad Waseem Aqdus, Aqsa Amin, Rashid Chaudhry, Shehzad Ashraf Alsubaei, Faisal S. Iqbal, Sajid Department of Computer Science and IT The University of Lahore Lahore54000 Pakistan Department of Computer Science University of Chakwal Chakwal48800 Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey The Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges, such as a high false positive rate, poor accuracy of detection, slow response to threats, and inability to scale well. This paper proposes an efficient and real-time intrusion detection system (IDS) using advanced machine learning techniques within a software-defined networking (SDN) framework specifically tailored for healthcare systems. The proposed architecture implements a Machine Learning (ML) model that combines the SVM and KNN to better identify malicious activities. Full sets of detection and mitigation capabilities are implemented to address different types of traffic in the network with the least interference. Through the different evaluation measures, the efficiency of the proposed model is assured. Network performance is determined by success rate queries, packet losses in each domain path, and the CPU being used by the system. Responsiveness is measured through delay metrics grounded on end-to-end delay, hop-to-hop packet delay, latency rate, and propagation delay. Moreover, model accuracy fidelity is reviewed via precision assessment, alpha (α) affecting the accuracy of the model, and confusion matrix with different techniques with the proposed hybrid SVM-KNN model. Last of all, a comparison of the security of the models in question strengthens the argument in favor of the proposed model. More specifically, flow and network topology diagrams are included to show how integration may be accomplished in linkage or merger with existing healthcare networks. The results also present a 30% overall advancement in detection and mitigation by presenting the hybrid SVM-KNN model to overcome other traditional models. This proposed model shows significant improvements not less than 20-30% improvement in CPU use, 30-50% reduction in end-to-end delay,

关键词： Decision trees

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A novel Cross-Layer Protocols for Resource Allocation in Mobile Networks 15

A novel Cross-Layer Protocols for Resource Allocation in Mob...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Giri, Rakesh Kumar Vittapu, Mani Sarma Dev, Anoop Shewale, Chaitali R. Majid, Mohd Aliff Abdul Kulkarni, Nandini Sunrise University Alwar Department of Computer Science and Engineering India Mallareddy Engineering College and Management Sciences Department of Computer Science and Engineering Kistapur Village Medchal Telangana Hyderabad India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura India Vishwakarma Institute of Technology Department of Information Technology Pune India University Teknology MARA Faculty of Hotel and Tourism Management Department of Foodservice Management Malaysia Symbiosis International University Department of Symbiosis School of Planning Architecture and Design Nagpur Nagpur India

ISBN: (纸本)9798350370249

Cross-layer Protocols for proper resource Allocation in Mobile Networks are a singular technique to enhance the efficiency of wireless networks. The method utilizes multiple layers of the radio protocol stack better to coordinate cell node scheduling and resource allocation. Using combining communication, routing, and scheduling functionalities in an unmarried protocol, pass-Layer Protocols for proper resource Allocation can offer a sizable will increase in the number of facts that may be transferred and the overall latency and throughput of the community. This technique can also enhance the radio interface's operation by lowering interference and encouraging collaborative communications. Eventually, by using facts and managing records from the communique and routing layers, go-Layer Protocols for proper resource Allocation can reduce the complexity of the community, even offering greater scalability. This paper affords unique cross-Layer Protocols for resource Allocation in Cellular Networks. The proposed protocol permits integrated resource allocation in the cell network environment, considering communication and computation layers. © 2024 IEEE.

关键词： Network layers

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A New Belief-Based Incomplete Pattern Unsupervised Classification Method : Extended Abstract 39

A New Belief-Based Incomplete Pattern Unsupervised Classific...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Zhang, Zuowei Liu, Zhe Ma, Zongfang Zhang, Yiru Wang, Hao University of Rennes 1 Institut de Recherche en Informatique et Systèmes Aléatoires France Xi'an University of Architecture and Technology School of Information and Control Engineering China Norwegian University of Science and Technology Department of Computer Science Norway

ISBN: (纸本)9798350322279

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets. © 2023 IEEE.

关键词： Uncertainty analysis

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