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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

Computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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6DFLRNet: 6D rotation representation for head pose estimation based on facial landmarks and regression

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Multimedia Tools and Applications 2024年第26期83卷 68605-68624页

作者： Zhao, Na Ma, Yaofei Li, Xiaopeng Lee, Shin-Jye Wang, Jian Engineering Research Center of Cyberspace School of Software Yunnan University Yunnan Kunming650504 China Key Laboratory in Software Engineering of Yunnan Province Yunnan University Kunming650091 China Institute of Management of Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China

Head pose estimation methods can be generally classified into two categories: model-based and appearance-based methods. The model-based approach relies on facial landmarks for three-dimensional reconstruction, aiming to achieve high-precision results. However, this method is heavily dependent on the accuracy of these landmarks. The appearance-based approach utilizes images as input and employs feature extraction and calculations to generate outcomes. While the appearance-based method boasts greater robustness, its accuracy falls short of the former. In this paper, a new and effective hybrid method is proposed. This hybrid approach combines the strengths of both methods. Unlike the conventional model-based methods, the proposed method regards the facial landmarks in 2D images as a sequence of neural network inputs and then obtains the head pose estimation results for users by neural network regression. The proposed method solves the fuzzy rotation labeling problem by using a rotation matrix representation, introducing a 6D rotation matrix representation as an intermediate state of the rotation matrix to achieve effective direct regression. Introducing face processing enhances the robustness of the model in cross-dataset scenarios. The proposed method achieves remarkable results based on imprecise face recognition and a simplistic model. The proposed method can be divided into three parts. First, the proposed method applies face processing on the input image;second, the method detects facial landmarks;and third, it converts these facial landmarks into sequences and obtains the 6D rotation representation of the head pose by regression. Extensive experiments on the publicly available BIWI, PRIMA, and DrivFace datasets show that this method is functional and performs better than other state-of-the-art methods. Compared to other methods, this approach demonstrates an average performance improvement of at least 10% across the dataset. © The Author(s), under exclusive licen

关键词： Rotation

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Representation Alignment and Adversarial Networks for Cross-lingual Dependency Parsing

Representation Alignment and Adversarial Networks for Cross-...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Ying Liu, Jianjian Yu, Zhengtao Gao, Shengxiang Huang, Yuxin Mao, Cunli Yunnan Provincial Key Laboratory of Artificial Intelligence Faculty of Information Engineering and Automation Kunming University of Science and Technology China

ISBN: (纸本)9798891761681

With the strong representational capabilities of pre-trained language models, dependency parsing in resource-rich languages has seen significant ***, the parsing accuracy drops sharply when the model is transferred to low-resource language due to distribution *** alleviate this issue, we propose a representation alignment and adversarial model to filter out useful knowledge from rich-resource language and ignore useless *** proposed model consists of two components, i.e., an alignment network in the input layer for selecting useful language-specific features and an adversarial network in the encoder layer for augmenting the language-invariant contextualized *** on the benchmark datasets show that our proposed model outperforms RoBERTa-enhanced strong baseline models by 1.37 LAS and 1.34 *** analysis shows that both alignment and adversarial networks are equally important in alleviating the distribution shifts problem and can complement each *** addition, the comparative experiments demonstrate that both the alignment and adversarial networks can substantially facilitate extracting and utilizing relevant target language features, thereby increasing the adaptation capability of our proposed model. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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EM2-YOLO: Lightweight Remote Sensing Image Detection 5th

EM2-YOLO: Lightweight Remote Sensing Image Detection

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5th International Conference on 3D Imaging Technologies—Multidimensional Signal Processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Liu, Xinyi Song, Yaolian Wang, Can School of Information Engineering and Automation Kunming University of Science and Technology Yunnan650500 China

ISBN: (纸本)9789819751808

Remote sensing photographs have a wealth of information, and object detection methods are crucial in this. On mobile and embedded platforms, deep learning-based target identification algorithms are challenging to implement. Additionally, the issues of delayed target detection and poor accuracy of small target detection for remote sensing photos must be resolved immediately. To address these issues, we provide EM2-YOLOv4, a simple method for recognizing remote sensing images. MobileNetv2 acts as the backbone network to reduce model complexity, while depth-separable convolution enhances detection speed. Additionally, we introduce a feature enhancement module and an Efficient Channel Attention (ECA) module to improve model accuracy. To address the issue of unequal assignment of positive and negative samples, we incorporate Focal Loss into the loss function. Experimental demonstrate that the model can perform detection at 45 frames per second with an average accuracy of 94.11%. The model has been significantly reduced, as seen by the fact that it is only 48 MB in size, but it still maintains good detection accuracy and accelerates detection. The model is ideal for usage on embedded devices because of this feature. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical remote sensing

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Informer-LSTM-EWMA for Panax Notoginseng with Prediction of Early Intervention Time 13

Informer-LSTM-EWMA for Panax Notoginseng with Prediction of ...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Ye, Jiahui Yang, Chunxi Zhang, Xiufeng Zhang, Jin Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650504 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650504 China

ISBN: (纸本)9798350361674

Panax notoginseng is one of economic crop with significant medicinal value in southwest China. However, traditional extensive fertilization and irrigation method usually damages the soil, making it difficult to achieve sustainable cultivation of Panax notoginseng. In order to assess the growth status of Panax notoginseng and take timely action, this paper proposed an Informer-LSTM-EWMA model to predict the growth trend of Panax notoginseng in cultivation fields. In the experimental greenhouse, we set up two experimental fields A and B to verify the feasibility of the proposed method. Firstly, wavelet denoising is utilized on the date collected by the sensor. Then, Informer and Long short-term Memory (LSTM) are used to train the denoised data to obtain data prediction. Further process the predicted data through Exponential Weighted Moving Average (EWMA) for trend prediction. Finally, the early intervention time is obtained by searching for the maximum value. The results indicate that the model can predict early intervention time and its predictive performance is far better than that of a single model. © 2024 IEEE.

关键词： Prediction models

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Enhancing Physiological Consistency in Dynamic PET/CT for Hepatocellular Carcinoma through Prior information and NSGA-III 7

Enhancing Physiological Consistency in Dynamic PET/CT for He...

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7th International Conference on information and Computer Technologies, ICICT 2024

作者： Xiong, Yiwei Li, Siming He, Jianfeng Wang, Shaobo Kunming University Of Science And Technology Faculty Of Information Engineering And Automation Kunming650500 China Hospital Of Kunming University Of Science And Technology First People's Hospital Of Yunnan PET/CT Center Kunming650031 China

ISBN: (纸本)9798350385625

Dynamic positron emission tomography/computed tomography (PET/CT) is a promising medical imaging technique that allows the visualization of biological information through the activity of tracers. Analyzing the time-activity curves (TACs) of PET using pharmacokinetic models enables the extraction of quantifiable physiological process information relevant to hepatocellular carcinoma (HCC). The difficulty in the research lies in ensuring that estimated modeling parameters translate into effective diagnostic outcomes. In this study, the nondominated sorting genetic algorithm III (NSGA-III) is employed for multi-objective optimization, enhancing physiological consistency in parameter estimation by introducing supplementary physiological objectives alongside the fitting error objective. Utilizing prior information on kinetic parameter in each subpopulation, the prior-based multi-population NSGA-III (p-MPNSGA-III) algorithm was proposed for parameter estimation in pharmacokinetic models. Parameters estimated from PET TACs of 24 real HCC patients undergo statistical difference and receiver operating characteristic (ROC) analyses, using P-values and areas under the curve (AUC) values to evaluate diagnostic performance between normal and tumor groups. Compared to the standard Genetic Algorithm (GA), the p-MPNSGA-III exhibits statistically significant differences in K1 (P=0.006), k2 (P=0.002), k3 (P=0.024), fa (P © 2024 IEEE.

关键词： Parameter estimation

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Efficient 3D Hilbert Curve Encoding and Decoding Algorithms

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Chinese Journal of Electronics 2022年第2期31卷 277-284页

作者： JIA Lianyin LIANG Binbin LI Mengjuan LIU Yong CHEN Yinong DING Jiaman Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Library Yunnan Normal University School of Information Science and Engineering Guangxi University for Nationalities School of Computing Informatics and Decision Systems Arizona State University

Hilbert curve describes a one-to-one mapping between multidimensional space and 1 D *** traditional 3D Hilbert encoding and decoding algorithms work on order-wise manner and are not aware of the difference between different input data and spend equivalent computing costs on them, thus resulting in a low efficiency. To solve this problem, in this paper we design efficient 3D state views for fast encoding and decoding. Based on the state views designed, a new encoding algorithm(JFK-3HE) and a new decoding algorithm(JFK-3HD) are proposed. JFK-3HE and JFK-3HD can avoid executing iteratively encoding or decoding each order by skipping the first 0 s in input data, thus decreasing the complexity and improving the efficiency. Experimental results show that JFK-3HE and JFK-3HD outperform the state-of-the-arts algorithms for both uniform and skew-distributed data.

关键词： order-wise manner multidimensional space encoding state-of-the-arts algorithms encoding algorithm parallel algorithms JFK-3HE statistical distributions decoding solid modelling design efficient 3D JFK-3HD traditional 3D Hilbert encoding equivalent computing costs different input data decoding algorithm Hilbert curve state views iterative methods

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Energy-Efficient Routing Algorithm That Avoids Empty and Redundant Forwarding

Energy-Efficient Routing Algorithm That Avoids Empty and Red...

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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Sun, Jia Hao Liu, Zeng Li School of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (纸本)9798350316728

Underwater information is mainly acquired through hydroacoustic networks, and routing protocols, as an important part of hydroacoustic networks, are related to the reliable transmission of data and the lifetime of the overall network. Depth-based routing protocols are more widely used because they do not need to obtain the 3D position information of nodes. However, relying only on depth information for routing results in null phenomenon and redundant forwarding during transmission. For the above two phenomena, the ENDOR protocol is proposed by improving the depth-based routing protocol, which is improved for the route selection and coordination phases by adding the node degree of the nodes to the selection criteria, and preventing the generation of the null phenomenon by selecting the nodes with high node degree. For redundant forwarding phenomenon is by optimizing the waiting time to inhibit nodes with low priority from forwarding the same packet, it is verified by simulation that the protocol can effectively reduce the occurrence of the nulling phenomenon and redundant forwarding phenomenon, save the node's energy, and prolong the survival cycle of the network. © 2023 IEEE.

关键词： Routing protocols

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ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data

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IEEE Access 2025年 13卷 100920-100933页

作者： Xianguang Jia Weijie Fang Yingying Lyu Jinke Zhang Mengyi Guo Dong Li Jie Qu Fengxiang Guo Faculty of Transportation Engineering Kunming University of Science and Technology Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

Aiming at the challenges of spatio-temporal heterogeneity and dynamic correlation in the prediction of travel origins and destinations for urban residents, this study invokes a spatio-temporal graph attention network (ST-GAT) OD prediction model based on cell phone signaling data. This model innovatively introduces the graph attention mechanism into the spatio-temporal graph network (ST-GNN), in the spatial dimension, the attention layer (GAL) dynamically learns the attention weights among nodes to adaptively capture the dynamic spatial dependencies in the transportation network, and in the temporal dimension, the temporal convolutional layer extracts the multiscale temporal patterns, which efficiently captures the complex spatiotemporal dependencies in the OD data. The data cleaning innovatively uses the DTW data cleaning model, and MSE, MAE, RMSE are used as evaluation indexes. In this paper, this model is compared with the existing ST-GCN, DMS, GMM, PSAM-CNN, ST-Transformer, and COMD models, and the experimental results show that the predicted values of the ST-GAT model have a significant improvement in the prediction accuracy compared to other models. The research in this paper provides an effective solution for the application of Intelligent Transportation System (ITS) and demonstrates the potential of Deep Learning (DL) in urban transportation planning.

关键词： Predictive models Data models Computational modeling Cellular phones Adaptation models Feature extraction Transportation Attention mechanisms Deep learning Data mining

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A low-complex one-bit DOA estimation algorithm based on DTFT 16

A low-complex one-bit DOA estimation algorithm based on DTFT

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhu, Guanghao Zhao, Xuanzhi Liu, Zengli Zeng, Sai Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Shanghai Marine Electronic Equipment Research Institute Shanghai201108 China

ISBN: (纸本)9781510689251

The existing one-bit direction of arrival (DOA) estimation methods based on sparse restoration or subspace have problems such as high computational cost, high estimation accuracy depending on the mesh size or the number of snapshots, etc., when used in massive uniform linear arrays (MULA). This paper considers the low-complexity one-bit DOA estimation problem of MULA with a single snapshot. Firstly, the initial DOA estimation is carried out by discrete Fourier transform (DFT). Then, the initial estimation of the angle is searched for the left and right intervals according to a certain method, and then the frequency refinement of the interval is carried out by discrete time Fourier transform (DTFT) to obtain accurate estimates. The resulting method is called a one-bit DTFT. The method is straightforward and easy to implement. The high-precision DOA estimation of MULA can be obtained through one snapshot, and the computational complexity is significantly lower than that of the existing one-bit DOA estimation methods. In addition, the proposed method can be extended not only to multiple snapshot scenarios, but also to other array structures such as nested arrays. The simulation results show the effectiveness of the one-bit DTFT method. © 2025 SPIE.

关键词： Discrete Fourier transforms

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