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2nd International Conference on Communication networks and Machine Learning, CNML 2024

作者： Wu, Bin Zhang, Wanqing Zhang, Jun Yu, Jing Huangfu, Taoyu Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Hebei Qinhuangdao China Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400711688

Submarines are vital for military defense and national security in strategic communication systems. Power Spectral Density (PSD) characterizes signal spectrum frequency dependence and is crucial for analyzing broadband random signals. This study proposes a simplified PSD estimation method using the Welch algorithm for detecting weak signals in noisy environments, particularly in submarine communications. MATLAB simulations demonstrate that the Welch algorithm with a Blackman window function and 512-point overlap improves variance performance and resolution, especially in low signal-to-noise ratio conditions. The results underscore the algorithm’s effectiveness in enhancing signal detection in submarines. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Environment-Tolerant Trust Opportunity Routing Based on Reinforcement Learning for Internet of Underwater Things

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6348-6360页

作者： He, Yu Han, Guangjie Hou, Yun Lin, Chuan Hohai University College of Information Science and Engineering Changzhou213200 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Changzhou213200 China Hohai University College of Computer Science and Software Engineering Nanjing211100 China Northeastern University Software College Shenyang110819 China

The Internet of Underwater Things (IoUT) has garnered significant interest due to its potential applications in monitoring underwater environments. However, the unique characteristics of acoustic communication, such as long propagation delays and high attenuation, present considerable obstacles for achieving efficient and dependable data transmission. Opportunistic routing is a crucial technique for enhancing packet delivery ratios by selecting a set of forwarding nodes and utilizing their cooperative forwarding to boost network throughput. Nevertheless, choosing an excessive number of forwarding nodes can lead to wasteful energy usage and extended communication delays. Moreover, the overlooked trustworthiness of forwarded nodes in most research works can undermine the effectiveness of opportunistic routing. Therefore, this study presents a novel trust opportunistic routing scheme that employs reinforcement learning to achieve resilience in constantly changing underwater settings. The combination of reinforcement learning and trust management enables the proposed opportunistic routing scheme to adapt to the unstable underwater environment and unknown malicious attacks. Initially, a method is introduced for measuring environmental fitness by considering multiple trust factors, including communication success rate, data reliability, and location dynamics. The proposed scheme then uses reinforcement learning to develop a reliable opportunistic routing method based on quantified state information. This component employs the obtained state to formulate action strategies and obtains reward values from environmental inputs. The reward update equation integrates these qualities to optimize the deployment of superior action strategies, finally achieving trust opportunistic routing for underwater data collection. Fundamental experimental results demonstrate that the proposed protocol performs exceptionally well in demanding underwater conditions, outperforming existing method

关键词： Reinforcement learning

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Evaluation of Distance Estimation Using Phase Information of OFDM Signal 100

Evaluation of Distance Estimation Using Phase Information of...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Liu, Jingwei Kikuchi, Noriyasu Kubo, Nobuaki Tang, Suhua The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Kanagawa Institute of Technology Department of Mechanical Engineering Kanagawa Japan Tokyo University of Marine Science and Technology Information and Communication Engineering Laboratory Tokyo Japan

ISBN: (纸本)9798331517786

In urban areas, due to the obstruction/reflection of GNSS signals by roadside buildings, position accuracy is greatly degraded, or even outage occurs when no enough satellites are available. To address this problem, vehicles and roadside units have been used as positioning anchors to estimate pedestrian position. Although previous methods exploited channel state information, which is superior to received signal strength for distance estimation, they rely on signal attenuation characteristics, and the accuracy is limited by temporal resolution and multipath propagation. This work focuses on using the phase information of V2X OFDM signals to estimate distance differences more robustly. We investigated the impact of the sampling rate, a crucial parameter, and conducted simulation evaluations using 3D maps and ray-tracing. Additionally, we built a testbed and performed evaluations with real-world OFDM signal data, which demonstrates that phase information can significantly improve the accuracy of distance measurements. © 2024 IEEE.

关键词： Frequency division multiplexing

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A Novel Teaching Strategy Based on EduCoder Platform for C Programming Language 5th

A Novel Teaching Strategy Based on EduCoder Platform for C P...

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5th International Conference on computer Science and Educational Informatization, CSEI 2023

作者： Li, Wei Hei, Xinhong Wang, Lei Wang, Yichuan School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China School of Mathematics and Computer Science Shaanxi University of Technology Hanzhong China

ISBN: (纸本)9789819994915

Experimental teaching is an important part of the C programming language course, which aims to cultivate students’ Computational thinking ability, problem solving ability and programming ability. However, the existing experimental teaching mode of the C programming language course has been criticized mainly for their: 1) it is difficult for teachers to check all students’ programs within a limited time;2) Limited computer room capacity, fixed time, and other factors make it difficult to meet the personalized requirements of students. In this paper, we put forward the construction scheme of experimental teaching of the C programming language course based on the EduCoder platform, which alleviates all the above two problems. The scheme follows the fundamental law of engineering practice ability training and constructs a LDEI practice teaching closed loop of "learning (L), doing (D), evaluation (E) and improvement (I)". Through specific cases, this paper elaborates the implementation process of this scheme in the experimental teaching of the C programming language course. Finally, a survey are conducted on 64 students from computer science and technology majors. The survey results show that the designed scheme effectively enhances the students’ Computational thinking ability and programming ability. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Voiceprint recognition model based on Ecapa-Tdnn and ResNet 5

Voiceprint recognition model based on Ecapa-Tdnn and ResNet

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5th International Conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Jia, Chengjie Fei, Rong Li, Xiaomeng School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710004 China Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510691667

In recent years, with the continuous progress of deep learning neural networks and the construction of large data sets, new breakthroughs in voiceprint verification technology for voice calls have been rapidly achieved. Deep learning-based neural networks, such as X-Vectors, ResNets, Ecapa-Tdnn, etc., are able to utilize their own network characteristics to extract rich voiceprint features, thus further improving the accuracy of voiceprint recognition, but there is still room for improvement in the performance and parameter count of the traditional models. In this paper, we propose the Ecapa2Tdnn model based on EcapaTdnn and ResNet, which has the advantage of possessing a deeper network architecture while performing better in terms of temporal modeling capability. This indicates that the model can effectively handle long time series of speech data, thus improving the performance of the voiceprint recognition system. The experimental results show that under the same experimental environment, compared with the equal error rate (EER) of 11.31% of the EcapaTdnn model, the Ecapa2Tdnn model proposed in this paper has only an EER of 10.27%, which is 1.04% lower than the former EER, and the number of parameters is still one-fourth of the original model. The model improves accuracy while reducing the total number of parameters, which is expected to be widely used in the field of voiceprint recognition. © 2025 SPIE.

关键词： Deep neural networks

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Adaptive Group Personalization for Federated Mutual Transfer Learning 41

Adaptive Group Personalization for Federated Mutual Transfer...

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41st International Conference on Machine Learning, ICML 2024

作者： Xu, Haoqing Shen, Dian Wang, Meng Wang, Beilun School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China College of Design and Innovation Tongji University Shanghai China

Mutual transfer learning aims to improve prediction with knowledge from related domains. Recently, federated learning is applied in this field to address the communication and privacy concerns. However, previous clustered federated learning (CFL) solutions lack theoretical guarantee of learnability recovery and require time-consuming hyper-parameter tuning, while centralized mutual transfer learning methods lack adaptability to concept drifts. In this paper, we propose the Adaptive Group Personalization method (AdaGrP) to overcome these challenges. We adaptively decide the recovery threshold with a nonparametric method, adaptive threshold correction, for tuning-free solution with relaxed condition. Theoretical results guarantee the perfect learnability recovery with the corrected threshold. Empirical results show AdaGrP achieves 16.9% average improvement in learnability structure recovery compared with state-of-the-art CFL baselines. Copyright 2024 by the author(s)

关键词： Federated learning

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Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

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computer Modeling in engineering & Sciences 2023年第11期137卷 2025-2047页

作者： Huabin Wang Ruichao Mo Yuping Chen Weiwei Lin Minxian Xu Bo Liu School of Computer Science and Engineering Huizhou UniversityHuizhou516007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science and Technology South China Normal UniversityGuangzhou510631China Department of New Network Technologies Peng Cheng LaboratoryShenzhen518066China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen518055China

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile ***,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy *** addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge ***,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model *** results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher ***,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

关键词： Pedestrian and vehicle detection YOLOv4 channel pruning cloud-edge collaboration

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Spatial-Temporal Semantic Feature Interaction network for Semantic Change Detection in Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12090-12102页

作者： Zhang, Yuhang Zhang, Wuxia Ding, Songtao Wu, Siyuan Lu, Xiaoqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an710121 China Xi'an University of Technology College of Computer Science and Engineering Shaanxi Xi'an710048 China Fuzhou University College of Physics and Information Engineering Fuzhou350002 China

Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU). The "from-to" information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multilevel features, leading to challenges in extracting LCLU features in complex scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multilevel Feature Interaction module (TML-FI), and a Multilevel Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multilevel features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multiscale features, thus improving the model's capability to interpret complex scenes. Finally, ML-FFD integrates the multilevel features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods. © 2008-2012 IEEE.

关键词： Change detection

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 IEEE.

关键词： Image segmentation

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Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

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computers, Materials & Continua 2022年第9期72卷 5939-5954页

作者： Shaher Alshammrei Sahbi Boubaker Lioua Kolsi Department of Mechanical Engineering College of EngineeringUniversity of Ha’ilHa’il2440Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Laboratory of Metrology and Energy Systems National Engineering SchoolEnergy Engineering DepartmentUniversity of MonastirMonastir5000Tunisia

Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and *** this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra *** achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and ***,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target *** its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following *** this aim,the MR was equipped with an ultrasonic sensor used as obstacle *** an obstacle is found,the MR updates its diagraph by excluding the corresponding ***,Dijkstra algorithm runs on the modified *** procedure is repeated until reaching the target *** verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 *** obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking *** study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.

关键词： Mobile robot(MR) STEAM path planning obstacle avoidance improved dijkstra algorithm

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