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Efficient 3D Tracking and Detection of Multi-Rotor UAVs Using mmWave Radar with Semi-Supervised Learning

IEEE Sensors Journal 2025年第12期25卷 22001-22014页

作者： Xi, Rui Wei, Wenjie Zhang, Malu University of Electronic Science and Technology of China Department of Computer Science and Engineering Chengdu611731 China

Small UAVs pose security risks to sensitive areas and individuals due to their rapid movement and wide coverage capabilities. Effective monitoring necessitates the deployment of lightweight and energy-efficient surveillance systems. This research introduces an efficient 3D tracking and detection approach for small UAVs, utilizing millimeter-wave radars and Spiking Neural Networks (SNNs). By capturing micro-Doppler characteristics of UAV movements, it effectively processes low signal-to-noise ratios and uncertain signals. An improved angle estimation algorithm, combining dynamic programming and particle filters, enables realtime 3D UAV tracking with reduced computational complexity. Then, a simple UAV detection model based on SNN architecture is developed by leveraging UAVs’ position and corresponding Doppler information. Furthermore, a bio-inspired semi-supervised method is proposed to facilitate the training of SNNs using a limited number of annotated samples. The effectiveness of the proposed methodology is evaluated under various environmental conditions. Results indicate a significant improvement in tracking computation time efficiency, with the recognition model size reduced to one-tenth of its original size, yet it maintains near-original system performance. © 2001-2012 IEEE.

关键词： Semi-supervised learning

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Image classification of lotus in Nong Han Chaloem Phrakiat Lotus Park using convolutional neural networks

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Artificial Intelligence in Agriculture 2024年第1期11卷 23-33页

作者： Thanawat Phattaraworamet Sawinee Sangsuriyun Phoempol Kutchomsri Susama Chokphoemphun Department of Electrical and Computer Engineering Faculty of Science and EngineeringKasetsart University Chalermphrakiat Sakonnakhon Province Campus SakonnakhonThailand Department of Computer and Information Science Faculty of Science and EngineeringKasetsart University Chalermphrakiat Sakonnakhon Province Campus SakonnakhonThailand

The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus ***,as a training area,it lacks appeal and learning motivation due to its conventional presentation of information regarding lotus *** current study introduced the concept of smart learning in this setting to increase interest and motivation for *** neural networks(CNNs)were used for the classification of lotus plant species,for use in the development of a mobile application to display details about each *** scope of the study was to classify 11 species of lotus plants using the proposed CNN model based on different techniques(augmentation,dropout,and L2)and hyper parameters(dropout and epoch number).The expected outcome was to obtain a high-performance CNN model with reduced total parameters compared to using three different pre-trained CNN models(Inception V3,VGG16,and VGG19)as *** performance of the model was presented in terms of accuracy,F1-score,precision,and recall *** results showed that the CNN model with the augmentation,dropout,and L2 techniques at a dropout value of 0.4 and an epoch number of 30 provided the highest testing accuracy of *** best proposed model was more accurate than the pre-trained CNN models,especially compared to Inception *** addition,the number of total parameters was reduced by approximately 1.80–2.19 *** findings demonstrated that the proposed model with a small number of total parameters had a satisfactory degree of classification accuracy.

关键词： Lotus Park Classification Smart learning park Convolutional neural network

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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A Novel Reliable and Trust Objective Function for RPL-Based IoT Routing Protocol

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computers, Materials & Continua 2025年第2期82卷 3467-3497页

作者： Mariam A.Alotaibi Sami S.Alwakeel Aasem N.Alyahya Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Information Technology Shaqra UniversityShaqra11961Saudi Arabia

The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the selection of appropriate routing protocols, which is crucial for maintaining high Quality of Service (QoS). The Internet engineering Task Force’s Routing Over Low Power and Lossy Networks (IETF ROLL) working group developed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) to meet these needs. While the initial RPL standard focused on single-metric route selection, ongoing research explores enhancing RPL by incorporating multiple routing metrics and developing new Objective Functions (OFs). This paper introduces a novel Objective Function (OF), the Reliable and Secure Objective Function (RSOF), designed to enhance the reliability and trustworthiness of parent selection at both the node and link levels within IoT and RPL routing protocols. The RSOF employs an adaptive parent node selection mechanism that incorporates multiple metrics, including Residual Energy (RE), Expected Transmission Count (ETX), Extended RPL Node Trustworthiness (ERNT), and a novel metric that measures node failure rate (NFR). In this mechanism, nodes with a high NFR are excluded from the parent selection process to improve network reliability and stability. The proposed RSOF was evaluated using random and grid topologies in the Cooja Simulator, with tests conducted across small, medium, and large-scale networks to examine the impact of varying node densities. The simulation results indicate a significant improvement in network performance, particularly in terms of average latency, packet acknowledgment ratio (PAR), packet delivery ratio (PDR), and Control Message Overhead (CMO), compared to the standard Minimum Rank with Hysteresis Objective Function (MRHOF).

关键词： IoT LLNs RPL objective function OF MRHOF OF0 routing metrics reliability trustworthiness

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Patterns of Widespread Structural Abnormalities in Autism Spectrum Disorder using Diffusion Tensor Imaging

Patterns of Widespread Structural Abnormalities in Autism Sp...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Bharti, Dilip Kumar Singh, Sanjay Kumar Kumar, Mukesh Kanaujia, Vishal Kumar Aeri, Manisha Abes Engineering College Computer Science And Engineering-Data Science Ghaziabad India Kashi Institute Of Technology Department Of Computer Science & Applications Mirzamurad U.P Varanasi India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun248002 India Abes Engineering College Computer Science And Engineering Data Science Department Ghaziabad India

ISBN: (纸本)9798350366846

Diffusion tensor imaging (DTI) is a neuroimaging approach that lets in for the visualization and quantification of the structural integrity of white depend fibers in the brain. In latest years, DTI has come to be an essential tool for investigating the underlying neurobiological mechanisms of autism spectrum sickness (ASD), a neurodevelopmental disorder characterized via problems in social communication and repetitive behaviors. Through the usage of DTI, researchers have identified good sized styles of structural abnormalities in the brains of individuals with ASD. these abnormalities encompass changes in the microstructure of white remember fibers, along with decreased fiber density, changes in fiber directionality, and decreased connectivity between mind areas. Additionally, there may be evidence of bizarre mind development in early formative years, with a few research finding that these structural variations are present even in very younger children with ASD. One constant finding in DTI studies of ASD is reduced white rely integrity inside the corpus callosum, a large fiber package deal that connects the two hemispheres of the mind. This abnormality has been connected to deficits in social communiqué and language in people with ASD, as well as difficulties in integrating records from distinctive brain areas. The results showed extensive structural abnormalities in people with ASD in comparison to TD individuals. © 2024 IEEE.

关键词： Diffusion tensor imaging

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Safety-certified Receding-horizon Motion Planning and Containment Control of Autonomous Surface Vehicles via Neurodynamic Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Lyu, Guanghao Peng, Zhouhua Wang, Dan Wang, Jun School of Marine Electrical Engineering Dalian Maritime University Dalian China Department of Computer Science and the School of Data Science City University of Hong Kong Hong Kong

This paper addresses the safety-certified motion planning and containment control of under-actuated autonomous surface vehicles subject to model uncertainties, external disturbances, and input constraints in the presence of stationary and moving obstacles. A three-level modular control architecture is proposed with a trajectory generation module at its planning level, an adaptive guidance module at its guidance level, and a kinetic control module at its control level. Specifically, at the planning level, a safety-certified containment trajectory generator is designed to generate safe trajectories over a rolling time window to achieve containment formation and collision avoidance with neighboring ASVs, stationary obstacles, and moving obstacles via dynamic control barrier functions and twotimescale neurodynamic optimization models. At the guidance level, an adaptive line-of-sight guidance law is developed based on a finite-time predictor to estimate unknown sideslip angles and generate guidance commands. At the control level, an optimal control law is designed based on finite-time neural predictors and control Lyapunov functions for the autonomous surface vehicle with input constraints to follow the desired guidance commands. The effectiveness and characteristics of the proposed method are demonstrated via simulations and hardware-in-theloop experiments for cooperative exploration. IEEE

关键词： Optimization

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AI-Driven Agronomy: Enhancing Crop Yield and Market Profits 4

AI-Driven Agronomy: Enhancing Crop Yield and Market Profits

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Mourya, Kona Chandra, Sai Reddy, N Abhinay Kumar, Aiswarya S Department of Computer Science and Engineering Department of Computer Science and Engineering

ISBN: (纸本)9798350307757

The agriculture sector plays an important role to the nation's economy, contributing significantly to GDP and employing a sizable section of the labour force. Nonetheless, precisely projecting food production and demand remains a difficult task. Traditional prediction approaches are time-consuming and prone to errors, resulting in inefficient resource allocation and financial losses. To solve these issues, we offer an innovative platform that uses machine learning algorithms to predict the best crops for production based on environmental conditions, as well as forecast market demand and pricing patterns for those *** comparing the effectiveness of various crop forecast classification models, the Voting Classifier, which incorporates numerous models, scored an amazing accuracy rate of 0.986. This predictive power can help farmers and stakeholders make more informed decisions, resulting to better profitability and resource efficiency. Furthermore, for price estimate, we used a Stacking Regressor model, which combines the top two models, the Random Forest Regressor and the XGBoosting Regressor. The Stacking Regressor performed better than the individual regression models, earning an optimised MSE of 277.58. This ensemble approach greatly improved price estimation accuracy, allowing stakeholders to make more informed decisions and increase profitability in the agriculture industry. Finally, the suggested intelligent platform offers a promising option for precise crop forecast and price estimation, benefiting farmers and stakeholders by optimising resource allocation, increasing profitability, and contributing to the agriculture sector's longterm growth. © 2024 IEEE.

关键词： Forecasting

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An efficient Quasi-Affine Transformation Evolutionary algorithm with fixed dimension updating and its application in UAV 3D path planning

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9755-9781页

作者： Sung, Tien-Wen Zhao, Baohua Zhang, Xin Lee, Chao-Yang Fang, Qingjun Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm's practicability. © 2024 - IOS Press. All rights reserved.

关键词： Optimization

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Intrumer:A Multi Module Distributed Explainable IDS/IPS for Securing Cloud Environment

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computers, Materials & Continua 2025年第1期82卷 579-607页

作者： Nazreen Banu A S.K.B.Sangeetha Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai600026Tamil NaduIndia

The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have been developed to tackle these ***,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.

关键词： Cloud computing intrusion detection system transformers and explainable artificial intelligence(XAI)

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