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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Khan, Awais Aziz, Ayesha Tariq, Muhamad Kamal, Mohsin National Univeristy of Computer and Emerging Sciences Department of Electrical Engineering Islamabad Pakistan School of Computer Science National Univeristy of Computer and Emerging Sciences Islamabad Pakistan School of Electrical Engineering and Computer Science National Univeristy Science and Technology Islamabad Pakistan

ISBN: (纸本)9798350355703

Forest plays a vital role in environmental change and the protection of forests is the utmost important thing. The study attempts to suggest a solution for a smart forest (Internet of Forest Things). As nowadays, the Internet of Things is emerging very vastly and a large number of researchers are working towards the field of Internet of Forest Things (loFT). The protection of smart forests is of utmost importance because the infrastructure is physically unprotected and they generate huge data about the forest. Due to scalability, smart forest controllers are small-sized and low-cost devices with less computational capabilities. Keeping it in mind, the designed algorithms should be lightweight so that they can meet the requirements. To address this issue, this paper proposes a machine learning approach to develop a lightweight security solution to address data altering and man-in-the-middle attacks. The algorithm generates a link fingerprint of the received signal strength indicator (RSSI) value of the communicating IoFT node. The packet is sent to the server unit and it is decoded and matched with the table to identify the path. A trained machine learning model at the server gets the RSSI value from the packet and successfully classify about the presence of an attack. © 2024 IEEE.

关键词： k-nearest neighbors

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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YOLO-LFD: A Lightweight and Fast Model for Forest Fire Detection

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computers, Materials & Continua 2025年第2期82卷 3399-3417页

作者： Honglin Wang Yangyang Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.

关键词： Forest fire detection YOLOv5 lightweight small object detection

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DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s

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computers, Materials & Continua 2025年第5期83卷 1929-1949页

作者： Honglin Wang Yaolong Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border ***,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV *** address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object *** leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small *** the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere *** components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference ***,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object *** results on the VisDrone 2019 dataset demonstrate the effectiveness *** to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter *** results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.

关键词： YOLOv8 UAV-based object detection AFPN small-object detection head SPPELAN DualConv loss function

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Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

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computer Modeling in engineering & sciences 2023年第9期136卷 2405-2424页

作者： Feisha Hu Qi Wang Haijian Shao Shang Gao Hualong Yu School of Computer Jiangsu University of Science and TechnologyZhenjiang212100China Department of Electrical and Computer Engineering University of NevadaLas VegasNV89154USA

Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian *** the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being *** address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone *** deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone *** default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of *** improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV *** on the above improvements,we create a novel anomaly detection strategy *** method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)*** experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.

关键词： UAV safety kernel extreme learning machine triangular global alignment kernel fast independent component analysis

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Engaging Undergraduate engineering Students in a Research and Design Experience 47

Engaging Undergraduate Engineering Students in a Research an...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Habash, Riadh University Of Ottawa School Of Electrical Engineering And Computer Science Canada

ISBN: (纸本)9798350382495

In today's constantly evolving knowledge-driven world, conducting research in all fields of study is crucial. To apply the above successfully in undergraduate engineering education, students should understand the importance of research for knowledge and design as a combined strategic skill. This practice paper emphasizes the significance of integrating the above skills in teaching power systems engineering. The fourth-year engineering students at the University of Ottawa, Canada worked collaboratively in teams under the guidance of their instructor to acquire essential research and design skills, including literature review, analysis, design, simulation, and presentation. An evaluation of the course and student progress revealed notable psychological and knowledge gains after completing the course. The students' survey and feedback demonstrated marked improvements in their skills and competencies. The paper highlights the importance of the early development of research skills as a strategic competence in undergraduate engineering education to promote the togetherness of design thinking and to prepare students for graduate studies and professional careers. © 2024 IEEE.

关键词： Students

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Predicting the Power Spectrum of Amplified OFDM Signals Using Higher-Order Intercept Points

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Chinese Journal of Electronics 2022年第2期31卷 213-219页

作者： YAN Siyuan YANG Xianzhen WANG Xiaoru LI Fu Department of Electrical and Computer Engineering Portland State University School of Computer Science and Technology Beijing University of Posts and Telecommunications

Orthogonal frequency-division multiplexing(OFDM) has been developed into a popular modulation scheme for wireless communication systems, used in applications such as LTE and 5 G. In wireless communication systems, nonlinearity caused by radio frequency(RF)amplifiers will generate distortions to both passband and adjacent channels such that the transmission quality is degraded. The study of this article aims to predict the power spectrum for OFDM based signals at the output of an RF amplifier due to the nonlinearity. In this article,based on Taylor polynomial coefficients, a power spectrum expression for amplified OFDM signals in terms of intercept points(up to nth-order) is derived. This model is useful to RF engineers in choosing and testing RF amplifiers with appropriate specifications, such as intercept points and gain, to meet the requirements of wireless standards. Measurements are carried out to confirm the results of the proposed model.

关键词： adjacent channels modulation scheme radio frequency amplifiers power spectrum expression Taylor polynomial coefficients RF amplifier higher-order intercept points passband channels amplified OFDM signals OFDM modulation wireless communication systems radiofrequency amplifiers wireless standards radio networks polynomials orthogonal frequency-division multiplexing OFDM based signals

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Supervised deep learning for content-aware image retargeting with Fourier Convolutions

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Multimedia Tools and Applications 2024年第36期83卷 83611-83627页

作者： Givkashi, MohammadHossein Naderi, MohammadReza Karimi, Nader Shirani, Shahram Samavi, Shadrokh Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Electrical and Computer Engineering McMaster University HamiltonONL8S 4L8 Canada Computer Science Department Seattle University SeattleWA98122 United States

Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, we present a new supervised approach for training deep learning models. We use the original images as ground truth and create inputs for the model by resizing and cropping the original images. A second challenge is generating different image sizes in inference time. However, normal convolutional neural networks cannot generate images of different sizes than the input image. To address this issue, we introduced a new method for supervised learning. In our approach, a mask is generated to show the desired size and location of the object. Then the mask and the input image are fed to the network. Comparing image retargeting methods and our proposed method demonstrates the model’s ability to produce high-quality retargeted images. Afterward, we compute the image quality assessment score for each output image based on different techniques and illustrate the effectiveness of our approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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Swapped logit distillation via bi-level teacher alignment

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Multimedia Systems 2025年第3期31卷 1-13页

作者： Limantoro, Stephen Ekaputra Lin, Jhe-Hao Wang, Chih-Yu Tsai, Yi-Lung Shuai, Hong-Han Huang, Ching-Chun Cheng, Wen-Huang Department of Electrical Engineering and Computer Science National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Cyberlink New Taipei City231 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan

Knowledge distillation (KD) compresses the network capacity by transferring knowledge from a large (teacher) network to a smaller one (student). It has been mainstream that the teacher directly transfers knowledge to the student with its original distribution, which can possibly lead to incorrect predictions. In this article, we propose a logit-based distillation via swapped logit processing, namely Swapped Logit Distillation (SLD). SLD is proposed under two assumptions: (1) the wrong prediction occurs when the prediction label confidence is not the maximum;(2) the "natural" limit of probability remains uncertain as the best value addition to the target cannot be determined. To address these issues, we propose a swapped logit processing scheme. Through this approach, we find that the swap method can be effectively extended to teacher and student outputs, transforming into two teachers. We further introduce loss scheduling to boost the performance of two teachers’ alignment. Extensive experiments on image classification tasks demonstrate that SLD consistently performs best among previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Students

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Explainable Depression Detection Using Large Language Models on Social Media Data 9

Explainable Depression Detection Using Large Language Models...

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9th Workshop on Computational Linguistics and Clinical Psychology, CLPsych 2024

作者： Wang, Yuxi Inkpen, Diana Buddhitha, Prasadith School of Electrical Engineering and Computer Science University of Ottawa Canada

ISBN: (纸本)9798891760806

Due to the rapid growth of user interaction on different social media platforms, publicly available social media data has increased substantially. The sheer amount of data and level of personal information being shared on such platforms has made analyzing textual information to predict mental disorders such as depression a reliable preliminary step when it comes to psychometrics. In this study, we first proposed a system to search for texts that are related to depression symptoms from the Beck’s Depression Inventory (BDI) questionnaire, and provide a ranking for further investigation in a second step. Then, in this second step, we address the even more challenging task of automatic depression level detection, using writings and voluntary answers provided by users on Reddit. Several Large Language Models (LLMs) were applied in experiments. Our proposed system based on LLMs can generate both predictions and explanations for each question. By combining two LLMs for different questions, we achieved better performance on three of four metrics compared to the state-of-the-art and remained competitive on the one remaining metric. In addition, our system is explainable on two levels: first, knowing the answers to the BDI questions provides clues about the possible symptoms that could lead to a clinical diagnosis of depression;second, our system can explain the predicted answer for each question. ©2024 Association for Computational Linguistics.

关键词： Social networking (online)

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