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Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

IEEE ACCESS 2024年 12卷 46834-46850页

作者： Reitz, Philipp Kuenzle, Christian Franchi, Norman Luebke, Maximilian Friedrich Alexander Univ Erlangen Nurnberg Chair Elect Smart City Syst D-91058 Erlangen Germany

Frequency-modulated continuous-wave (FMCW) radars are becoming increasingly important in industrial applications due to their low cost, simple signal processing, and high resolution. The research is driven by the growing trend of radar applications in industrial settings, including tasks like autonomous robot navigation, radar-based monitoring of humans, and enhancing safety across various scenarios, such as smart factories, traffic control, or environmental monitoring. The network of distributed sensor nodes is a feature to be implemented by the sixth generation of wireless mobile networks. As a result, 6G will become an enabler technology that will develop even further use cases and advance the expansion of these technologies. Therefore, the mutual interference of these sensor systems is becoming highly relevant since they can be avoided by scheduling the individual resources of the sensor nodes by a network structure. This paper investigates the interference behavior of FMCW radar sensors in a dense indoor scenario. In these environments, in particular, the reliability of the sensor system plays an important role, and disruption can have critical consequences like not recognizing a target, triggering a false alarm, or the complete failure of the sensing. To correctly classify the expected interference in the overall context, the paper investigates the physical layer effects on the performance, such as multipath propagation, channel spread, delay, Doppler shift, and attenuation. By evaluating these effects using state-of-the-art signal processing, it was revealed that a high channel spread in particular and the dynamics of the radar channel can significantly reduce the detection quality, as the interference power can exceed the received power by up to 20 dB, depending on the scenario. The simulation results given in this paper show that a potential communication between the radars and compliance with a synchronization delay, the interference problems can be reliab

关键词： Interference Chirp Radar Sensors Delays Radar signal processing Time-frequency analysis Frequency modulation Continuous wavelet transforms Channel estimation Robot sensing systems Sensor systems Physical layer Wireless sensor networks Wireless communication Environmental monitoring Telecommunication traffic detection algorithms Performance evaluation Channel simulation CIR FMCW radar industrial radar interference interference mitigation mmWave multipath

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Community Deception in Attributed Networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第1期11卷 228-237页

作者： Fionda, Valeria Pirro, Giuseppe Univ Calabria DeMaCS I-87036 Arcavacata Di Rende Italy Sapienza Univ Rome DI I-00185 Rome Italy

Community detection algorithms that analyze networks to identify communities of nodes are an essential part of the network analysis toolkit used daily by different analysts (e.g., data scientists and law enforcement). However, there is not enough awareness that members of a community C (either revealed or not) inside a network $G$ could act strategically to evade such tools either for legitimate (e.g., activist groups in authoritarian regimes) or malicious (e.g., terrorists) purpose. Community deception offers this possibility. By identifying a certain number of C 's member connections to be rewired, community deception algorithms can successfully hide a community that wants to stay below the radar of detection techniques. However, the state-of-the-art deception approaches have focused on networks without attributes, although real-world networks (e.g., Facebook) include attributes (e.g., age and sex) that play a central role in detecting more accurate communities. This article faces three novel challenges introduced when designing deception techniques for networks with attributes. The first concerns how to model and encode attributes most flexibly. The second is about framing attribute-aware community deception as an optimization problem. Finally, the challenge of solving the optimization problem by leveraging network topology and attributes also arises. We leverage a simple way to model network attributes as edge weights, a novel optimization function called community diffusion, and Diffuser a greedy algorithm to optimize diffusion, to solve the above challenges. We evaluated Diffuser against several community detection algorithms and compared it with state-of-the-art deception approaches on various real-world networks. From the evaluation, we can draw two main observations. First, adopting attribute-oblivious deception techniques leads to unsatisfactory results. Second, community diffusion as an optimization function specific to attributed networks is preferred to

关键词： Image edge detection Optimization Detectors detection algorithms Social networking (online) Network analyzers Minimization Social network deception

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A Wormhole Attack detection Algorithm Integrated With the Node Trust Optimization Model in WSNs

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IEEE SENSORS JOURNAL 2022年第7期22卷 7361-7370页

作者： Teng, Zhijun Du, Chunqiu Li, Meng Zhang, Hengjia Zhu, Weihua Northeast Elect Power Univ Minist Educ Key Lab Modern Power Syst Simulat & Control & Ren Jilin 132000 Jilin Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132000 Jilin Peoples R China Jilin Univ Sch Comp Sci & Technol Changchun 130012 Peoples R China Jilin Elect Informat Vocat & Tech Coll Sch Comp Sci Jilin 132000 Jilin Peoples R China

The openness of Wireless Sensor Networks (WSNs) layout makes them vulnerable to attacks. One of the most threatening attacks is the wormhole attack. The wormhole attack is difficult to be detected since it causes false routing by private tunnels and damages to WSNs in data transmission. Therefore, in order to resist wormhole attacks and improve network performance, this paper proposes a detection algorithm integrated with the node trust optimization model (NTOM-DA) against wormholes in WSNs. First, add the node whose number of neighbors exceeds the threshold to the list of suspicious nodes, and then the exclusive neighbors of suspicious nodes communicate with each other. Mark the path whose hops exceed the wormhole threshold as the path to be tested. Establish a trust model to calculate the node trust and evaluate the path trust. The simulation results show that NTOM-DA is superior in detecting wormhole attacks. Even in the face of a network with high node density and high attack degree, it still has a high detection rate and a low false-positive rate, which effectively guarantees the safe and reliable operation of WSNs.

关键词： Wireless sensor networks detection algorithms Sensors Routing Data communication Optimization Layout Wireless sensor networks wormhole attack wormhole threshold trust model path trust evaluation

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A Performance-Optimized Deep Learning-Based Plant Disease detection Approach for Horticultural Crops of New Zealand

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IEEE ACCESS 2022年 10卷 89798-89822页

作者： Saleem, Muhammad Hammad Potgieter, Johan Arif, Khalid Mahmood Massey Univ Sch Food & Adv Technol Dept Mech & Elect Engn Auckland 0632 New Zealand Massey Univ Massey AgriFood Digital Lab Palmerston North 4472 New Zealand

Deep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only on leaves, but also on other parts of plants, including stems, canes, and fruits. Furthermore, the detection of multiple diseases in a single plant organ at a time has not been performed. Similarly, plant disease has not been identified in various crops in the complex horticultural environment with the same optimized/modified model. To address these research gaps, this research presents a dataset named NZDLPlantDisease-v1, consisting of diseases in five of the most important horticultural crops in New Zealand: kiwifruit, apple, pear, avocado, and grapevine. An optimized version of the best obtained deep learning (DL) model named region-based fully convolutional network (RFCN) has been proposed to detect plant disease using the newly generated dataset. After finding the most suitable DL model, the data augmentation techniques were successively evaluated. Subsequently, the effects of image resizers with interpolators, weight initializers, batch normalization, and DL optimizers were studied. Finally, performance was enhanced by empirical observation of position-sensitive score maps and anchor box specifications. Furthermore, the robustness/practicality of the proposed approach was demonstrated using a stratified k-fold cross-validation technique and testing on an external dataset. The final mean average precision of the RFCN model was found to be 93.80%, which was 19.33% better than the default settings. Therefore, this research could be a benchmark step for any follow-up research on automatic control of disease in several plant species.

关键词： Diseases Crops Deep learning detection algorithms Optimization Convolutional neural networks Plants Convolutional neural networks deep learning optimization algorithms cross-validation plant disease detection

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A Crack detection Algorithm for Concrete Pavement Based on Attention Mechanism and Multi-Features Fusion

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 11710-11719页

作者： Qu, Zhong Chen, Wen Wang, Shi-Yan Yi, Tu-Ming Liu, Ling Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China Southwest Comp Co Ltd Inst Informat Technol Chongqing 400060 Peoples R China Chongqing Coll Mobile Commun Chongqing 401520 Peoples R China

Crack detection for concrete pavement is an important and fundamental task to ensure road safety. However, automatic crack detection is a challenging topic due to the complicated concrete pavement background and the diversity of cracks. Inspired by the latest developments of deep learning in computer vision, we propose a novel crack detection algorithm of concrete pavement based on attention mechanism and multi-features fusion, and make it possible to deal with various cracks in different pavement backgrounds. The proposed network is constructed using the encoder-decoder structure. The architecture of the encoder part is consisted of Res2Net modules with attention mechanism to achieve fast focus of cracks. Cascade and parallel mode dilated convolutions are set as the center part to enlarge the receptive field of feature points without reducing the resolution of the feature maps. The decoder integrates multiple side output feature maps for pavement crack detection in the manner of feature pyramid. We use ODS, OIS and AP to evaluate the performance of our network. To demonstrate the validity and accuracy of the proposed method, we compare it with some existing methods. The experimental results in multiple crack datasets reveal that our method is superior to these methods.

关键词： Feature extraction Semantics Decoding Encoding Intelligent transportation systems detection algorithms Task analysis Concrete pavement crack detection dilated convolution attention mechanism multi-features fusion

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PW-YOLO-Pose: A Novel Algorithm for Pose Estimation of Power Workers

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IEEE ACCESS 2024年 12卷 116841-116860页

作者： Su, Qin Zhang, Juling Chen, Mingju Peng, Hongming Sichuan Univ Sci & Engn Sch Automat & Informat Engn Yibin 644000 Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov Yibin 644000 Peoples R China Power Internet Things Key Lab Sichuan Prov Chengdu 610095 Peoples R China

To address the detection challenges of keypoints, such as misdetections and omissions causedby backgrounds, occlusions, small targets, and extreme viewpoints in complex electrical power operationenvironments for power workers. This study proposes a 2D pose estimation algorithm for power workersbased on YOLOv5s6-Pose: PW-YOLO-Pose. In this study, the detection rate of occluded keypoints isimproved by embedding the Swin Transformer encoder in the top layer of the backbone network. Theproposed BiFPN (a weighted bi-directional feature pyramid network) structure with a small target detectionlayer improves the detection rate of small target characters and the precision of their keypoints'*** keypoint regression precision is improved overall by using CA (coordinate attention) in the model neckand improving the bounding box regression loss function to Wise-IoU. The algorithmic model in this studydemonstrates excellent detection and largely meets the real-time requirements on the proposed power workerpose estimation dataset in this study. ThemAP(0.5)(The mean average precision when the threshold for objectkeypoint similarity is set to 0.5.) andmAP(0.5:0.95)are 93.35% and 64.75% respectively, which are 5.22% and1.53% higher than the baseline model. The detection time of a single image is 21.3 ms, respectively. It canserve as a valuable theoretical foundation and reference for behavior recognition and state monitoring ofpower workers in intricate electrical power operation environments.

关键词： Pose estimation Prediction algorithms YOLO Feature extraction Inference algorithms detection algorithms Transformers Bidirectional control Power system reliability Personnel Encoding Electrical power operation pose estimation YOLO-Pose detection of keypoints

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Enhancing Relationship Link Prediction With Hierarchical Feature Enhancement

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IEEE ACCESS 2024年 12卷 174387-174398页

作者： Xu, Zhouying Wang, Huiyue Coll Econ & Management Nefu Harbin 150040 Peoples R China State Grid Hebei Elect Power Co Ltd Shijiazhuang 050000 Peoples R China

In this study, we endeavor to assess the potential for establishing friendships between users, a challenge that intersects with the domains of friend recommendation and link prediction. Notably, prevailing models in friend recommendation predominantly consider only the attributes of users, whereas link prediction models primarily emphasize network structure. In this paper, we propose a novel methodology by regarding users' attributes as node-level features, extracting them from tweet text using GPT-style models, and considering the structure of the constructed network as network-level features. The network-level features are obtained through a soft-partitioning overlapping community detection algorithm, namely BIGCLAM(Cluster Affiliation Model for Big Networks). We then integrate the node-level and network-level features to mutually enhance their predictive capabilities. Through rigorous experimental validation across two distinct dataset types-a homogeneous network and a heterogeneous network-our results demonstrate that the fusion of node-level and network-level features significantly improves performance.

关键词： Feature extraction Social networking (online) detection algorithms Semantics Neural networks Generative AI Deep learning Computational modeling Attention mechanisms Vectors Link prediction node-level feature network-level feature

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Persistent Wide-Area Maritime Surveillance Using Smallsats With MIMO Radar Beamforming

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2024年第3期39卷 16-27页

作者： Weissman, Isaac Independent Radar Consultant Washington DC USA

This article has presented an application, utilizing existing technologies, that would achieve uninterrupted radar surveillance of maritime traffic over wide ocean areas. The application entails the deployment in GEO of a large number of small satellites interconnected by MIMO processing in order to form large virtual arrays with acceptable angular resolution. The potential benefits of such an application are substantial. However, although there appear to be no fundamental obstacles to such an implementation, there are significant design challenges, including the minimizations of the aggregate launch mass and the data processing burden. A risk reduction program is suggested to address these challenges by implementing the application in distinct stages. For persistent maritime surveillance, there seems to be no better practical alternative to geostationary radar deployments.

关键词： Small satellites Surveillance Radar Marine vehicles Radar cross-sections Sea surface Radar antennas Maritime communications Array signal processing Radar applications Payloads MIMO radar detection algorithms GEO maritime surveillance MIMO persistence radar smallsats

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PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances in the Arctic

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1-1页

作者： Heidler, Konrad Nitze, Ingmar Grosse, Guido Zhu, Xiao Xiang Tech Univ Munich TUM Chair Data Sci Earth Observat SiPEO Sch Engn & Design Dept Aerosp & Geodesy D-80333 Munich Germany Helmholtz Ctr Polar & Marine Res Alfred Wegener Inst Permafrost Res Sect D-14473 Potsdam Germany Univ Potsdam Inst Geosci D-14469 Potsdam Germany Tech Univ Munich TUM Munich Ctr Machine Learning D-80333 Munich Germany

Arctic permafrost is facing significant changes due to global climate change. As these regions are largely inaccessible, remote sensing plays a crucial rule in better understanding the underlying processes across the Arctic. In this study, we focus on the remote detection of retrogressive thaw slumps (RTSs), a permafrost disturbance comparable to slow landslides. For such remote sensing tasks, deep learning has become an indispensable tool, but limited labeled training data remains a challenge for training accurate models. We present PixelDINO, a semi-supervised learning approach, to improve model generalization across the Arctic with a limited number of labels. PixelDINO leverages unlabeled data by training the model to define its own segmentation categories (pseudoclasses), promoting consistent structural learning across strong data augmentations. This allows the model to extract structural information from unlabeled data, supplementing the learning from labeled data. PixelDINO surpasses both supervised baselines and existing semi-supervised methods, achieving average intersection-over-union (IoU) of 30.2 and 39.5 on the two evaluation sets, representing significant improvements of 13% and 21%, respectively, over the strongest existing models. This highlights the potential for training robust models that generalize well to regions that were not included in the training data.

关键词： Permafrost Remote sensing Data models Training data Satellite images Arctic Semisupervised learning Semantic segmentation Climate change detection algorithms retrogressive thaw slumps (RTSs) self-distillation without labels semantic segmentation semi-supervised learning

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Network Security Perception System Integrating Improved CNN Algorithm and Improved GRU Algorithm

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IEEE ACCESS 2024年 12卷 163805-163818页

作者： Huang, Yu Zhao, Ziyi Shi, Canbin China Southern Power Grid Co Ltd EHV Transmiss Co Guangzhou 510663 Peoples R China Jinan Univ Coll Phys & Optoelect Engn Guangzhou 510632 Peoples R China Beijing Venustech Cybervis Co Ltd Beijing 100193 Peoples R China

To enhance the accuracy and real-time response of network security-aware systems, the convolutional neural network is optimized by introducing exponentially weighted dempster-shafer evidence theory. Additionally, the gated recurrent unit is improved using an adaptive boosting algorithm. A novel network security-aware model integrating both methods is constructed. Experimental results show that the model achieves a detection accuracy of up to 94%, with the lowest values for miss rate, false positive rate, and false alarm rate being 7.84%, 2.47%, and 3.11%, respectively. This indicates that the proposed model provides an efficient and accurate solution for network security defense.

关键词： Network security Convolutional neural networks Accuracy Mathematical models Hidden Markov models Adaptation models Real-time systems Heuristic algorithms detection algorithms Convolutional neural network gated recurrent unit network security perception detection

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