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Remote Interference Source Localization: A Multi-UAV-Based Cooperative Framework

Chinese Journal of Electronics 2022年第3期31卷 442-455页

作者： WU Guangyu GU Jiangchun Department of Computer Science and Technology University of Science and Technology of China School of Communication Engineering Army Engineering University of PLA

Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles(UAVs), exploiting UAVs to locate the interference source has attracted intensive research interests. The off-the-shelf UAV-based interference source localization schemes locate the interference sources by employing the UAV to keep searching until it arrives at the target. This obviously degrades time efficiency of localization. To balance the accuracy and the efficiency of searching and localization, this paper proposes a multi-UAV-based cooperative framework alone with its detailed scheme, where search and remote localization are iteratively performed with a swarm of UAVs. For searching, a low-complexity Q-learning algorithm is proposed to decide the direction of flight in every time interval for each UAV. In the following remote localization phase, a fast Fourier transformation based location prediction algorithm is proposed to estimate the location of the interference source by fusing the searching result of different UAVs in different time intervals. Numerical results reveal that in the proposed scheme outperforms the stateof-the-art schemes, in terms of the accuracy, the robustness and time efficiency of localization.

关键词： unmanned aerial vehicles low-complexity Q-learning algorithm path planning fast Fourier transforms remote interference source localization multi-robot systems UAV swarm mobile robots remote localization location prediction algorithm aerospace robotics autonomous aerial vehicles learning (artificial intelligence) fast Fourier transformation cooperative communication spectrum resources radiofrequency interference mobile radio flight direction multiUAV-based cooperative framework

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A Novel Three-Stage PolSAR Ship Detection Framework based on Deep Learning 2

A Novel Three-Stage PolSAR Ship Detection Framework based on...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Xue, Junfan Yin, Junjun Yang, Jian University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798331515669

Polarimetric Synthetic Aperture Radar (PolSAR) image ship detection is one of the crucial applications of PolSAR systems and holds significant research value. However, current deep learning-based polarimetric SAR image ship detection methods face challenges such as difficult post-processing and slow detection speed. To solve the above problems, we propose a novel Three-Stage Polarimetric SAR Ship Detection Framework based on deep learning. The main concept is to use a sliding window to extract image slice samples, then employ the Ship Coarse Location Module (SCLM) to identify slice samples containing ships. Next, the Polarization Feature Enhancement Module (PFEM) enhances the features of these slice samples to reduce the feature distance of pixels of the same category and increase the feature distance of pixels of different categories. Afterward, the enhanced slice samples are fed into the KAN Vision Transformer Detection Module (KVITDM) for patch-level detection. Finally, decision fusion is applied to combine the detection results generated from different initial sliding window positions. Experimental results show that the proposed method has good performance in different scenes and outperforms traditional and existing deep learning-based polarimetric SAR ship detection methods. © 2024 IEEE.

关键词： Deep learning

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High performance receiving and processing technology in satellite beam hopping communication

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Journal of Systems engineering and Electronics 2024年第4期35卷 815-828页

作者： ZHAI Shenghua HUI Tengfei GONG Xianfeng ZHANG Zehui GAO Xiaozheng YANG Kai School of Communication Engineering Xi’Dian UniversityXian 710126China CAST(China Academy of Space Technology)-Xi’an Institute of Space Radio Technology Xi’an 710010China School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China

Beam-hopping technology has become one of the major research hotspots for satellite communication in order to enhance their communication capacity and ***,beam hopping causes the traditional continuous time-division multiplexing signal in the forward downlink to become a burst signal,satellite terminal receivers need to solve multiple key issues such as burst signal rapid synchronization and high-per-formance ***,this paper analyzes the key issues of burst communication for traffic signals in beam hopping sys-tems,and then compares and studies typical carrier synchro-nization algorithms for burst ***,combining the requirements of beam-hopping communication systems for effi-cient burst and low signal-to-noise ratio reception of downlink signals in forward links,a decoding assisted bidirectional vari-able parameter iterative carrier synchronization technique is pro-posed,which introduces the idea of iterative processing into car-rier *** at the technical characteristics of communication signal carrier synchronization,a new technical approach of bidirectional variable parameter iteration is adopted,breaking through the traditional understanding that loop struc-tures cannot adapt to low signal-to-noise ratio burst ***,combining the DVB-S2X standard physical layer frame format used in high throughput satellite communication systems,the research and performance simulation are *** results show that the new technology proposed in this paper can significantly shorten the carrier synchronization time of burst signals,achieve fast synchronization of low signal-to-noise ratio burst signals,and have the unique advantage of flexible and adjustable parameters.

关键词： beam-hopping high throughput satellite high per-formance reception processing

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The Granger Causality Analysis between Acupoints in the Same Meridian Based on Skin Conductance 7

The Granger Causality Analysis between Acupoints in the Same...

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7th IEEE International Conference on Smart Internet of Things, SmartIoT 2023

作者： Shi, Feifei Li, Nannan Wu, Xiaodong Naouri, Abdenacer Ning, Huansheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350316575

The granger causality analysis is a widely used statistical method of interpreting how different units work together to realize certain functions. It indicates how one time series impacts another under a certain time lag order. For different Acupoints (APs) in Traditional Chinese Medicine (TCM), some researchers begin to regard them as a complex and unified dynamic system and try to understand the hidden mechanisms. In this work, we adopt the granger causality analysis on APs' skin conductance to explore whether there are interdependencies between them, especially for those located in the same meridian. The results show that the causal relations between APs vary from different meridians, in particular for APs in the Lung meridian, the causal relations are much more obvious. Additionally, the average time lag order of the granger causality analysis from APs with smaller IDs to larger ones are greater than vice versa, which may be consistent with the flowing directions of the meridians. Moreover, the closer the APs are in the same meridian, the smaller the time lag order. The work proves the granger causality analysis as a powerful method of underpinning the functions of APs in the same meridian. In the future, more explorations of the internal cooperation between different APs could be made based on skin conductance, to provide scientific basis for TCM APs and meridians. © 2023 IEEE.

关键词： Wearable technology

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Research and implementation of network impairment simulation system 23

Research and implementation of network impairment simulation...

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Zhang, Dongyan Xu, Yuanjun School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798400700705

Network impairment simulation is designed to simulate different Internet network environments on a local area network(LAN) and provide testing environments for different services under various network conditions (latency, bandwidth limitations, network conflicts, etc.). This verifies the application in a real network environment. In this paper, we implement a system based on the Linux platform which communicate with the Linux kernel through netlink to achieve network damage simulation using the traffic control mechanism in Linux. In this simulation system, it also implements simulation according to certain network loss policies in besides implementing network delay, packet loss, speed limit, repetition, chaos, jitter, etc. For network delay simulation, we proposed to use sparrow search algorithm-optimized long short-Term memory(SSA-LSTM) algorithm to predict future delays using past network delay data. And to perform accurate simulation in the network impairment simulation system based on the results obtained from the prediction, which makes the network impairment simulation about delay more consistent with the real WAN environment. Finally, the experiment verifies the function of the network impairment simulation system. © 2023 ACM.

关键词： Long short-term memory

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Federated Learning Scheme Based on Gradient Compression and Local Differential Privacy

Federated Learning Scheme Based on Gradient Compression and ...

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2023 IEEE International Conference on Control, Electronics and computer technology, ICCECT 2023

作者： Zhao, Lun Hu, Siquan Shi, Zhiguo University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350399028

As a deep learning framework for distributed deployment, federated learning allows users to complete model training while retaining the original data, which meets the local data privacy protection needs of each user. Differential privacy, as a classic data desensitization technology, is often used to improve privacy protection. Many studies have applied it to machine learning, but it is accompanied by the problem of model utility. In addition, in the traditional federated learning model training, there are frequent gradient exchanges between the user and the central server, which poses a huge challenge to the communication overhead of the model and affects the training efficiency of the model. In order to solve these problems, we propose a new federated learning scheme, firstly propose a StaComp compression algorithm to compress the gradient parameters before the user uploads them, use the local differential privacy mechanism to add noise to protect the user's local data. Finally, we evaluate our scheme on the MNIST dataset. Experiments show that the scheme of this article is close to the classic FedAvg algorithm in the aspect of model accuracy, and is better than other methods based on differential privacy. At the same time, it significantly reduces the training time, improves the convergence speed of the model, and saves communication costs. © 2023 IEEE.

关键词： Data privacy

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FMNet: Semantic Segmentation of Small Targets in Food Factories by Fusing Multi-Scale Features 2

FMNet: Semantic Segmentation of Small Targets in Food Factor...

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2nd International Conference on Artificial Intelligence, Human-computer Interaction and Robotics, AIHCIR 2023

作者： Li, Botao Zhang, Taohong Yang, Huguang Xia, Chenglong Ma, Yuejia School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350360363

How to better integrate features of different scales is an important research direction in the field of semantic segmentation. In the task of semantic segmentation of small objects, there is often a problem of incorrect object boundary segmentation, which is caused by the model's inability to effectively integrate multi-scale information and obtain sufficient features. To solve this problem, we designed a fusing multi-scale features *** is included by multi scale pyramid pooling(MSPP) module and atrous convolution fusion(ACF) *** module is designed to extract multi-scale features based on spatial pyramid pooling with different *** module has different rates of dilated convolution. These two modules together form an overall fusing multi-scale module. We created a dataset for online detection in a food workshop and our model outperforms other semantic segmentation models on this dataset, with an accuracy of up to 97%. In addition, the computational and parameter costs of our model only slightly increase. © 2023 IEEE.

关键词： Semantic Segmentation

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A DE-LGCA Personalized Recommendation Algorithm Study

A DE-LGCA Personalized Recommendation Algorithm Study

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2023 International Conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2023

作者： Zhang, Qingda Shi, Zhiguo School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781510666474

A graph convolutional neural network is a special type of neural network that GCN can use to extract features from graphs and use these features for classification or regression. The current lightweight graph convolutional neural network in the recommendation field only considers the case of fixed neighborhood weights, with the increase in the number of training layers of the network recommendation effect will rapidly decline resulting in the problem of oversmoothing and overfitting. In this paper, in order to solve the above problem, this paper will introduce a DE-LGCA personalized recommendation algorithm that uses a random wandering strategy for data enhancement based on a lightweight graph convolutional neural, while introducing an attention mechanism to increase the node neighborhood weights, and show how to use this algorithm to achieve personalized recommendations. Experiments were conducted on three publicly available datasets, and three performance metrics, Precision, Recall, and Normalized Discounted Cumulative Gain (NDCG), were used to evaluate the performance of DE-LGCA, which not only outperformed the comparison method in terms of performance metrics, but also improved the efficiency of the algorithm. © 2023 SPIE.

关键词： Convolutional neural networks

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Computational Experiments of Signal Detection Algorithms in VANETs based on Parallel Intelligence 3

Computational Experiments of Signal Detection Algorithms in ...

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Li, Yi Han, Shuangshuang Chen, Yueyun Du, Liping University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China

ISBN: (纸本)9798350318470

With the rapid growth of connected vehicles in Internet of Vehicle (IoV), ensuring reliable and secure communication is significant. This paper presents a parallel intelligence-based signal detection method for vehicular networks. The proposed method exploits the main idea of parallel intelligent to analyze wireless communication signal in real-time, achieving efficient and accurate algorithm options for difference IoV environment and different requirements. Experimental results demonstrate that the comparison and analysis of the proposed method based on the detection accuracy and computational complexity, while also contributing to enhanced security in vehicular networks. This approach offers a promising solution for timely, reliable and secure communication in the era of connected vehicles. © 2023 IEEE.

关键词： Signal detection

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>Student Expression Recognition Algorithm Based on TransConv 2

>Student Expression Recognition Algorithm Based on TransConv

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2nd International Conference on Machine Learning, Control, and Robotics, MLCR 2023

作者： Hu, Siquan Zhang, Xiaoxia Shi, Zhiguo University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350381245

Human facial expression is one of the expressions of inner emotion, which is an important means of emotional communication between people. With the development of artificial intelligence, human facial expression recognition has been widely studied as an important topic in the field of computer vision, especially in the context of teaching scenarios with important applications. Therefore, adopting more novel facial expression feature extraction methods and improving models with higher accuracy of expression recognition are crucial for the task of human facial expression recognition. In this paper, we carry out the research on student expression recognition algorithms in the context of teaching scenarios, in order to improve the accuracy of facial expression recognition, the experiments are based on the JAFFE dataset and the CK+ dataset to construct a caricatured expression dataset consisting solely of locally populated key points of facial expressions, and improve the expression recognition method based on the traditional convolutional neural network, and put forward an expression recognition method based on the TransConv algorithm. The expression recognition method is trained and compared under the same dataset. The experimental results show that the training accuracy of the method proposed in this paper is higher, with an accuracy of up to 90%, and the processing speed is faster. The method described in this paper is feasible and effective, and can play a good role in assisting teachers to grasp the teaching process. © 2023 IEEE.

关键词： Face recognition

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