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International Journal of Interactive Mobile technologies 2025年第2期19卷 126-142页

作者： Ouaissa, Mariya Ouaissa, Mariyam Computer Systems Engineering Laboratory Cadi Ayyad University Marrakech Morocco Laboratory of Information Technologies Chouaib Doukkali University El Jadida Morocco

The Internet of Things (IoT) is a network of interconnected devices that enables data exchange. It is widely used in areas such as healthcare, aviation, agriculture, energy, and home automation. Despite its rapid growth and the massive adoption of connected devices, IoT presents significant security risks. Traditional threat modeling approaches are insufficient to address these risks. Architecture-based modeling is recommended, as it considers the entire system and helps in understanding potential threats. Threat modeling is a systematic technique used to identify and evaluate potential threats that could compromise the security of a system. The main objec-tive is to understand the vulnerabilities of a system in order to design appropriate security measures to mitigate them. This paper aims to analyze and mitigate specific IoT smart home threats using the STRIDE threat modeling framework, which systematically identifies potential vulnerabilities at the development level. By applying STRIDE, which stands for Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege, we focused on addressing key security threats, including denial of service (DoS), phishing, and man-in-the-middle (MitM) attacks. Our findings demonstrate that the proposed mitigation strategies are effective in countering these threats, providing a robust security layer for IoT smart homes. Through this study, we highlight the importance of architecture-based threat modeling to enhance security within the IoT ecosystem and offer practical solutions that strengthen IoT smart home resilience. The outcomes of the STRIDE-based analysis and the effectiveness of the mitigation techniques are detailed, offering empirical evidence to support our approach. © 2025 by the authors of this article. Published under CC-BY.

关键词： Phishing

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Interaction through bionic signals. Overview 24

Interaction through bionic signals. Overview

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Ralev, Ivan Krastev, Georgi University of Ruse Department of Computer Systems and Technologies Ruse Bulgaria

ISBN: (纸本)9798331515799

The paper examines the possibilities of bionic technology as a means of communication with a computer system. A classification of the most commonly used signal detection technologies is carried out. A model of a system using this technology is presented. An overview of the possibilities of the application of such systems in the daily life of the mass consumer has been carried out. © 2025 IEEE.

关键词： Human computer interaction

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A Secure IoT Network for Healthcare systems Based on Quantum Key Distribution Protocol and AES Encryption 16th

A Secure IoT Network for Healthcare Systems Based on Quantum...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Win, Myat Su Wai, Khaing Khaing Faculty of Computer Systems and Technologies University of Computer Studies Mandalay Myanmar Department of Information Technology Support and Maintenance University of Computer Studies Yangon Myanmar

ISBN: (纸本)9789819615308

The primary function of communication through the Internet is to ensure data security. Internet of Things (IoT) devices have evolved to include embedded systems and sensors capable of connecting, gathering, and sending information to the Internet. The main goal is to secure the entire data path: sensor-to-gateway and gateway-to-server data communication. In conventional cryptosystems, the strength of the encryption key is crucial in preventing unauthorized access to sensitive data. However, its security is limited as discovering a quick way to determine the secret key can lead to a system security breach. Quantum cryptography is a unique type that utilizes a quantum characteristic in transmitting information known as photons to guarantee absolute security for the data being sent. This paper tested three Quantum Key Distribution protocols (BB84, B92, and BBM92) to determine which protocol suits IoT network security using the QuVis simulator. As a result, the B92 protocol is better than the other two protocols. This research proposes a solution to secure an IoT network using Quantum Key Distribution Protocol B92 and the Advanced Encryption Standard (AES). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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Modeling and Simulating more efficient and reliable fuel cell vehicles through enhancing the integration of the 6kW PEM fuel cell stack with DC/DC boost converter and BLDC motor 33

Modeling and Simulating more efficient and reliable fuel cel...

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33rd Southern African Universities Power Engineering Conference, SAUPEC 2025

作者： Mtumka, Olona Krishnamurthy, Senthil Cape Peninsula University of Technology Bellville Center for Intelligent Systems and Emerging Technologies Department of Electrical Electronic and Computer Engineering Cape Town7530 South Africa

ISBN: (纸本)9798331535162

This study focuses on enhancing the integration of the PEM fuel cell, DC/DC boost converter, and BLDC motor, which help to develop more efficient and dependable fuel cell vehicles. The development of renewable energy sources for power generation has received much attention due to the depletion of fossil fuel supplies worldwide and economic difficulties. Fuel cells are one of the most efficient and cleanest power generation methods. Polymer electrolyte membrane (PEM) fuel cell is the preferred type of fuel cell for vehicles because of its high energy density, fast start-up, and lack of pollution. This paper aims to model and simulate a PEM fuel cell stack to power a Brushless DC (BLDC) motor for a fuel cell vehicle to reduce emissions in the transportation sectors. A boost converter is implemented to boost the output voltage of the PEM fuel cell stack to 100 V. The paper utilizes the trapezoidal commutation of the BLDC motor to drive the inverter. The trapezoidal commutation uses the six-switching technique to control the inverter's phase currents. The variation of fuel flow rate and drive cycle are included to observe the effect on the operation of the fuel cell stack. The paper presents the modeling and simulation of the PEM Fuel cell stack to improve power conversion, energy management, and vehicle control, helping to make fuel cell vehicles more viable for widespread adoption in the future. © 2025 IEEE.

关键词： Brushless DC motors

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Estimation of orange tree production by regression from video segments under uncontrolled conditions

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Neural Computing and Applications 2025年第5期37卷 3703-3717页

作者： Mollineda, Ramón A. Sandoval, Javier Rodríguez, Christian D. Heredia, José A. Institute of New Imaging Technologies Universitat Jaume I Castelló de la Plana Spain Department of Computer Languages and Systems Universitat Jaume I Castelló de la Plana Spain Ambimetrics S.L. Castelló de la Plana Spain Department of Industrial Systems Engineering and Design Castelló de la Plana Spain

Precision agriculture covers a wide range of information and communications technologies aimed at supporting current challenges in crop estimation, productivity increase, and food security. In particular, crop yield estimation can provide very valuable information on crop distribution, with the aim of optimising logistics and harvest timing. This paper focuses on deep learning-based regression solutions for estimating the number of visible oranges on trees, from real-world crop row videos captured by a camera placed on a farm vehicle. Count predictions based on individual frames were compared with those based on variable size sequences of frames centred on each tree (video segments). The performance of three deep neural networks designed for regression was evaluated in terms of the regression error and the uncertainty of the estimates, and differences were analysed using nonparametric hypothesis testing. Experiments were conducted on a new dataset composed of annotated video segments of orange tree rows acquired under uncontrolled conditions, which has been made publicly available. Results statistically prove the value of considering multiple frames and the feasibility of yield estimation by regression in the wild. These findings are expected to contribute to optimising decision-making in crop resource management. Unlike most efforts so far, which involve counting fruits by detection in tree images usually captured manually, this work explores counting fruits by regression on trees from real-world video data. © The Author(s) 2024.

关键词： Citrus fruits

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A Comparative Study of Deep Learning and Modern Machine Learning Methods for Predicting Australia's Precipitation

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 943-966页

作者： Farman, Hira Mastoi, Qurat-Ul-Ain Abbas, Qaiser Ahmad, Saad Alshahrani, Abdulaziz Jan, Salman Syed, Toqeer Ali Department of Computer Science Iqra University Karachi Institute of Economics and Technology Karachi74600 Pakistan School of Computing and Creative Technologies University of the West of England Bristol United Kingdom Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Faculty of Computer Science IQRA University Karachi Pakistan Faculty of Computer Studies Arab Open University 512 United Kingdom Islamic University of Madinah CO 42351 Madinah Saudi Arabia

Floods are chaotic weather patterns that cause irreversible and devastating harm to people's lives, crops, and the socioeconomic system. It causes extensive property damage, animal mortality, and even human fatalities. To mitigate the risk of flooding, it is imperative to create an early warning system that can accurately forecast the amount of rain that will fall tomorrow. Rainfall forecasting is essential to the lives of people and is absolutely important everywhere in the world. The rainfall prediction model reduces risk and helps to prevent further human deaths. Statistics cannot reliably forecast rainfall since the atmosphere is dynamic. Due to the preceding factors, this study uses machine learning and deep learning techniques to estimate precipitation. The purpose of this study is to develop and evaluate a prediction model for forecasting rainfall of 5 cities of Australia (Darwin, Sydney, Perth airport, Melbourne, Brisbane). The Dataset was gathered from the national meteo-rological organization of Australia is the Australian Government Bureau of Meteorology, also known as the BOM. To monitor and forecast meteorological conditions, climatic trends, and natural calamities like cyclones, storms, floods, the Bureau of Meteorology is essential. The dataset includes 14, 5460 size, 23 features detailed city-specific monthly averages for Australia from 2008 to 2017(10 years). An effective rainfall forecasting was produced by integration of a number of Machine Learning and Deep Learning techniques, including Random Forest model (RF), Decision Tree (DT) and Gradient Boosting classifier (GBC), Artificial Neural Network (ANN), and Recurrent Neural Network (RNN). The models were trained to forecast rainfall, reducing the potential impact of floods. Results indicate that combining neural networks and Random Forests provides the most accurate predictions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Rain

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Interaction through bionic signals. Overview

Interaction through bionic signals. Overview

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Ivan Ralev Georgi Krastev Department of Computer Systems and Technologies University of Ruse Ruse Bulgaria

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

关键词： Human computer interaction Electrooculography Computational modeling Entertainment industry Electrocardiography Hardware Electromyography Electroencephalography Safety Signal detection

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Optimization of Micro-Object Identification with Factorization and Correction Mechanisms for Distorted Image Points

Optimization of Micro-Object Identification with Factorizati...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Isroil I. Jumanov Rustam A. Safarov Olimjon I. Djumanov Intelligent systems and computer technologies Samarkand state university Samarkand Uzbekistan

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The scientific and methodological foundations for the optimal identification of micro-objects with mechanisms for using information structural components, features, statistical and dynamic characteristics of images have been developed. Mechanisms for tracking, detecting and correcting distorted points, factorizing the structural components of the image, filtering, segmentation, tracking distorted points under conditions of a priori insufficiency, parametric uncertainty, and low reliability of information processing are proposed. Methods for converting the initial brightness of a point into homogeneous objects as a function of two variables have been studied and nonlinear optimization functionals have been used. Mechanisms for factorization of the structural components of the image in the form of an indicator of linear connectivity, “neighborhood relationship” of three separate algorithms have been implemented. An alternative image factorization algorithm has been developed. A comparative analysis of effectiveness was carried out. The effectiveness of the mechanisms for detecting and correcting distorted points was studied using the criterion of the probability of undetected errors, the root-mean-square error, according to Shannon's estimates, the average level of intensity of the points, and the total compactness parameter. Rank estimates of integral indicators of image factorization were obtained for 30 images with 100 tests. A software package for visualization, recognition, and classification of images of microobjects has been developed, the implementations of which have been tested using real medical diagnostic data.

关键词： Image segmentation Uncertainty Software packages Nonlinear distortion Brightness Noise Information processing Reliability Object recognition Optimization

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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