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A Lightweight Object Detection Algorithm for Remote Sensing Images

engineering Letters 2025年第3期33卷 704-711页

作者： Hou, Donghao Zhang, Yujun Ren, Jia School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

With the continuous advancement of satellite technology, remote sensing images has been increasingly applied in fields such as urban planning, environmental monitoring, and disaster response. However, remote sensing images often feature small target sizes and complex backgrounds, posing significant computational challenges for object detection tasks. To address this issue, this paper proposes a lightweight remote sensing images object detection algorithm based on YOLOv9. The proposed algorithm incorporates the SimRMB module, which effectively reduces computational complexity while improving the efficiency and accuracy of feature extraction. Through a dynamic attention mechanism, SimRMB is capable of focusing on important regions while minimizing background interference, and by integrating residual learning and skip connections, it ensures the stability of deep networks. To further enhance detection performance, the FasterRepNCSPELAN4 module is introduced, which employs PConv operations to reduce computational load and memory usage. It also utilizes dilated convolutions and DFC attention mechanisms to strengthen feature extraction, thereby increasing the efficiency and accuracy of object detection. Additionally, this study integrates the GhostModuleV2 module, which generates core feature maps and employs lightweight operations to create redundant features, greatly reducing the computational complexity of *** results show that on the SIMD dataset, the improved YOLOv9 model has a parameter size of 167.88 MB and GFLOPs of 208.6. Compared to the baseline YOLOv9 model (parameter size: 194.57 MB, GFLOPs: 239.0), the parameter size is reduced by 13.71%, GFLOPs are reduced by 12.72%, and detection accuracy is improved by 1.4%. These results demonstrate that the proposed lightweight YOLOv9 model effectively reduces computational overhead while maintaining excellent detection performance, providing an efficient solution for object detection tasks in resou

关键词： Satellite imagery

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

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computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan Department of Electrical Engineering Balochistan University of Information Technology Engineeringand Management SciencesQuetta87300Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

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Security sharing of smart city communication data based on blockchain technology 10

Security sharing of smart city communication data based on b...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Jun, Guo Liming, Zhong Yuxia, Dong Junjie, Wu QuanZhou University of Information Engineering Software College College of software engineering QuanZhou China

ISBN: (纸本)9798350312461

When the smart city communication data is safely shared, the sharing process is mainly realized through single authorization attribute base encryption, and the key and ciphertext cannot be updated in time, which makes the encryption and decryption process in the data security sharing process take long. Therefore, relying on the blockchain attribute encryption algorithm, a new smart city communication data security sharing method is designed. Relying on the principle of attribute base encryption, the data encryption algorithm is designed to realize the encryption and decryption of smart city communication data. In chronological order, all encrypted data are stored on the block sharing chain, and the corresponding keys and ciphertext are quickly generated by the request of multiple nodes participating in joint computing. Finally, through the OBDD (orderly binary decision chart) access structure, the data on the sharing chain is efficiently accessed to realize the secure sharing of communication data. The experimental results show that when the proposed method is applied to share the data in smart city communication, the average time of data encryption and decryption is 1. 9s,0.2s, which greatly reduces the cost of data security sharing. © 2023 IEEE.

关键词： Cryptography

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AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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Design and Research of a computer Network Experimental Teaching Systembased on SDN 14

Design and Research of a Computer Network Experimental Teach...

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14th International Conference on Educational and information Technology, ICEIT 2025

作者： Tao, Jun Yu, Wangfeng Han, Huihui Wang, Xinyu School of computer and software engineering Anhui Institute of information engineering WuHu China

ISBN: (纸本)9798331540883

The importance of computer network experiments in teaching is elaborated, while traditional computer network systems have low efficiency and chaotic management, these shortcomings affect the effectiveness of related teaching heavily. To overcome the shortcomings of traditional experimental systems, a computer network experiment system based on SDN has been constructed, which relies on SDN and AAA to manage network equipment, network simulators, and student experiments. Laboratory managers rely on SDN for device authorization, students rely on SDN for fast and secure computer network experiments, and teachers rely on SDN to obtain corresponding experimental evaluations. Two typical experiments in the experimental system are introduced, and teaching has achieved good results through the experimental system. © 2025 IEEE.

关键词： Teaching

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Hierarchical spatial-spectral enhancement network for hyperspectral image and light detection and ranging data classification

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Yang, Zhichao Hao, Siyuan Li, Enyu Zhao, Kun Qingdao University of Technology College of Information and Control Engineering Qingdao China Beijing Jiaotong University College of Software Engineering Beijing China

The effective utilization of multimodal data is currently a prominent research focus in remote sensing. Hyperspectral image (HSI) and light detection and ranging (LiDAR) image can provide unique and complementary information. HSI can capture the spectral reflection characteristics of ground objects across hundreds of contiguous bands, whereas LiDAR can accurately measure the elevation of ground surfaces, providing precise information on topography and landforms. Combining them can yield more comprehensive and accurate information about ground objects. However, due to the underutilization of complementary information from multisource datasets, the extraction and integration of spatial and spectral information remain insufficient after fusion. We developed a hierarchical spatial-spectral enhancement network (HSSE-Net) for the classification of HSI and LiDAR data. Specifically, by employing convolutional operations to capture the spatial features of both HSI and LiDAR, we enhance the spatial feature expression capability of each modality. Next, we use a dual-branch network integrated with a transformer self-attention mechanism to process the fused data, which helps capture long-range dependencies and global information in the spectral data. This hierarchical enhancement of spatial and spectral information can further improve the cooperative classification performance. Extensive experiments conducted on two popular HSI and LiDAR datasets show that the HSSE-Net achieves average overall classification accuracies of 99.26% and 94.96% on the two datasets, respectively, fully demonstrating the effectiveness and superior performance of the proposed method. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Photointerpretation

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Classification of Fetal Cardiac Arrhythmia Using Heart Rate Variability and Machine Learning 5

Classification of Fetal Cardiac Arrhythmia Using Heart Rate ...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Shahid, Muhammad Arslan Shaukat, Arslan Akram, Muhammad Usman Butt, Wasi Haider College of Electrical and Mechanical Engineering Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350394764

Fetal arrhythmias can lead to cardiac failure or death;thus, early detection is crucial but challenged by noise and artifacts. This paper investigates fetal arrhythmia detection using time, frequency, and non-linear Heart Rate Variability (HRV) features extracted from fetal ECG signals in the NIFEEG dataset. Machine learning classifiers including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree, XGBoost, Naive Bayes, and Convolutional Neural Network (CNN) are evaluated. The HRV features provide insights into fetal heart rate patterns. While SVM, KNN, Decision Tree, and XGBoost show promise, CNN demonstrates the highest performance with 98.16% accuracy, 100% sensitivity, 96.15% specificity, and 98.11% F1-score. This highlights CNN's potential for early and reliable fetal arrhythmia detection. Statistical analysis supports the significance of HRV features, paving the way for improved prenatal monitoring. To further improve model generalizability, future work should focus on expanding the dataset size, exploring advanced deep neural network architectures such as LSTMs, and integrating supplementary physiological inputs like uterine contractions. Overall, this study demonstrates the capabilities of HRV-based machine learning approaches, especially CNNs, for robust fetal arrhythmia detection, offering valuable clinical implications for early diagnosis and management. © 2024 IEEE.

关键词： Feature extraction

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Hybrid DF and SIR Forwarding Strategy in Conventional and Distributed Alamouti Space-Time Coded Cooperative Networks

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computer Modeling in engineering & Sciences 2025年第2期142卷 1933-1954页

作者： Slim Chaoui Omar Alruwaili Faeiz Alserhani Haifa Harrouch Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Computer Science Department Applied CollegeUniversity of HailHail55424Saudi Arabia

In this paper,we propose a hybrid decode-and-forward and soft information relaying(HDFSIR)strategy to mitigate error propagation in coded cooperative *** the HDFSIR approach,the relay operates in decode-and-forward(DF)mode when it successfully decodes the received message;otherwise,it switches to soft information relaying(SIR)*** benefits of the DF and SIR forwarding strategies are combined to achieve better performance than deploying the DF or SIR strategy ***-form expressions for the outage probability and symbol error rate(SER)are derived for coded cooperative communication with HDFSIR and energy-harvesting ***,we introduce a novel normalized log-likelihood-ratio based soft estimation symbol(NL-SES)mapping technique,which enhances soft symbol accuracy for higher-order modulation,and propose a model characterizing the relationship between the estimated complex soft symbol and the actual high-order modulated ***-more,the hybrid DF-SIR strategy is extended to a distributed Alamouti space-time-coded cooperative *** evaluate the~performance of the proposed HDFSIR strategy,we implement extensive Monte Carlo simulations under varying channel *** demonstrate significant improvements with the hybrid technique outperforming individual DF and SIR strategies in both conventional and distributed Alamouti space-time coded cooperative ***,at a SER of 10^(-3),the proposed NL-SES mapping demonstrated a 3.5 dB performance gain over the conventional averaging one,highlighting its superior accuracy in estimating soft symbols for quadrature phase-shift keying modulation.

关键词： Cooperative communication soft information relaying soft symbols modeling cooperative diversity gain distributed Alamouti space-time code

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Spatiotemporal crowds features extraction of infrared images using neural network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2543-2556页

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Drieieva, Hanna Meleshko, Yelyzaveta AlRawashdeh, Hazim Al-Oraiqat, Karim A. Hasan, Yassin M. Y. Maricar, Noor Khan, Sheroz Department of Computer Science College of Engineering and Information Technology Onaizah Colleges Qassim Saudi Arabia Department of Cybersecurity and Software Central Ukrainian National Technical University Kropyvnytskyi Ukraine Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering College of Engineering and Information Technology Onaizah Colleges Qassim Saudi Arabia

Crowds can lead up to severe disasterous consequences resulting in fatalities. Videos obtained through public cameras or captured by drones flying overhead can be processed with artificial intelligence-based crowd analysis systems. Being a hot area of research over the past few years, the goal is not only to identify the presence of crowds but also to predict the probability of crowd-formation in order to issue timely warnings and preventive measures. Such systems will significantly reduce the probablity of the potential disasters. Developing effective systems is a challenging task, especially due to factors such as naturally occuring diverse conditions, variations in people or background pixel areas, noise, behaviors of individuals, relative amounts/distributions/directions of crowd movements, and crowd building reasons. This paper proposes an infrared video processing system based on U-Net convolutional neural network for crowd monitoring in infrared video frames to help estimate the people crowd with normal or abnormal trends. The proposed U-Net architecture aims to efficiently extract crowd features, achieve sufficient people marking-up accuracy, competitively with optimal network configurations in terms of the depth and number of filters to consequently minimise the number of coefficients. For further faster processing, hardware resources/implementation area savings, and lower power, the optimized network coefficients measured are represented in Canonic-Signed Digit with minimal number of nonzero (± 1) digits, minimizing the number of underlying shift-add/subtract operations of all multipliers. The achieved significantly reduced computational cost makes the proposed U-Net effectively suitable for resource-constrained and low power applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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