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Near-field Beamforming Technique Based on 3-D Cylindrical Array: Enlarging Region and Improving Spectral Efficiency 12

Near-field Beamforming Technique Based on 3-D Cylindrical Ar...

引用

12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Gao, Shuai Zhou, Kai Wang, Ying Xiang, Jianhong Zhong, Yu Harbin Engineering University College of Information and Communication Engineering Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9798350351019

For large incidence/emergence angles in uniform linear arrays, the near-field region shrinks sharply. In this paper, we present Uniform 3D Cylindrical Array (UCA-3D), which can enlarge the near-field region and improve the spectral efficiency. Specifically, using the rotational symmetry of UCA-3D, we can obtain near-field regions that are uniformly enlarged at all angles. As UCA-3D can generate orthogonal near-field beams along the same direction, we can improve the spectral efficiency. The simulation results prove that the theory is valid, and the UCA-3D is feasible. © 2024 IEEE.

关键词： Beamforming

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A Parallel GMRES Method for Large Linear System 3

A Parallel GMRES Method for Large Linear System

引用

3rd International Symposium on Computer Technology and Information Science, ISCTIS 2023

作者： He, Kangxin Xi, Guojiang Chen, Ying Southwest China Institute of Electronic Technology Agile and Intelligent Computing Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9798350325386

Solving large linear system is of fundamental importance in engineering and scientific computing. Based on a multi-core CPU cluster, we propose a parallel GMRES (Generalized Minimal Residual) method to solve a large linear system Ax=b, where A is an n × n matrix, and in practice, n can be 105 or even 106. We validate the effectiveness of the algorithm with the test problem coming from the finite element discretization of Poisson equation. © 2023 IEEE.

关键词： Linear systems

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An Encoded Double Auxiliary Pilot Channel Estimation Method for Intrinsic Interference Cancellation in FBMC/OQAM Systems 6

An Encoded Double Auxiliary Pilot Channel Estimation Method ...

引用

6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Xing, Sixuan Xiang, Jianhong Wang, Ying Qi, Liangang Zhong, Yu College of Information and Communication Engineering Harbin Engineering University Harbin China Harbin Engineering University Key Laboratory of Advanced Ship Communication and Information Technology Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9798350389692

Filter bank multicarrier technique (FBMC) is considered as a promising modulation technique suitable for future communications. However, the presence of intrinsic interference prevents the efficient implementation of techniques such as channel estimation. This paper presents a encode double auxiliary pilot channel estimation method (EDAP) for filter bank multicarrier offset quadrature amplitude modulation (FBMC/OQAM) systems. In order to eliminate the intrinsic interference, we add auxiliary symbols around the pilot, and at the same time perform a precoding process to minimize the auxiliary pilot energy loss and computational complexity. In order to validate the above method, the OQAM/FBMC system is modeled. Theoretical analysis and numerical results show that the method improves the channel estimation accuracy by 2.15-3.22 dB in double selective channels while maintaining low power consumption and low complexity. © 2024 IEEE.

关键词： Quadrature amplitude modulation

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JOINT CLASSIFICATION OF HYPERSPECTRAL AND SAR DATA USING ASYMMETRIC RESIDUAL CROSS TRANSFORMER

JOINT CLASSIFICATION OF HYPERSPECTRAL AND SAR DATA USING ASY...

引用

IET International Radar Conference 2023, IRC 2023

作者： Sun, Yaxiu Wang, Minhui Sun, Rui Hao, Lihong Xiang, Jianhong National Key Laboratory of Communication Anti-Jamming Technology College of Information and Communication Engineering Harbin Engineering University Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9781839539954

With the increasing amount of remote sensing (RS) data obtained from satellites or airborne platforms, The combined use of multimodal remote sensing data to improve classification performance has been widely concerned. Therefore, a new transformer-based hyperspectral and synthetic aperture radar (HS-SAR) data joint classification network ACCT was proposed in the paper. Combining the strengths of convolutional neural networks (CNN) and transformer, focus on extracting specific and common features from HS and SAR data. In addition, adding layer scale to the output of each residual block can learn diagonal matrix and improve the dynamics of training. Then, combining the advantages of residual join and asymmetric convolution kernel, the parameters are reduced while the feature information is fully learned. Experiments in this paper are carried out on two famous multimodal fusion datasets: Augsburg (AU), and Berlin (BL) datasets. Results show that ACCT achieves better classification accuracy than several existing models. OA reaches 97.20%, and 85.02% on the two datasets. © The Institution of Engineering & Technology 2023.

关键词： Convolutional neural networks

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Noise Detection and Removal for Underground Communication Systems Based on L2 N onnal Inner Product 6

Noise Detection and Removal for Underground Communication Sy...

引用

6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Yu, Bangwei Wang, Yong Qi, Liangang Liu, Yang College of Information and Communication Engineering Harbin Engineering University Harbin China Harbin Engineering University Key Laboratory of Advanced Ship Communication and Information Technology Harbin China Sichuan Key Laboratory of Agile Intelligent Computing Chengdu China

ISBN: (纸本)9798350389692

In underground communication systems, continuous mud pulse signals are susceptible to pump noise during transmission, resulting in a high bit error rate(BER). In this paper, a Normal Inner Product Orthogonal Matching Pursuit(NIPOMP) algorithm is proposed for the transmission characteristics of continuous waves in the underground. First, a cyclic prefix(CP) is added in front of the sent data. Then the observation vector is obtained by making a difference between the CP of the latter set of data and the CP of the former set of data. Then, the columns of the sensing matrix that are most correlated with the observation vectors are selected as candidate support sets by calculating the L2 norm. The least squares method was used to solve for the previous set of pump noise estimates. Finally, the pump noise is reconstructed using the correspondence between the time and frequency domains. This paper establishes a complete underground communication system. We discuss the noise characteristics of pump noise and analyze the denoising performance of the NIPOMP algorithm in depth. Simulation results show that the algorithm significantly reduces the system BER in complex channel environments with more drastic fading, including gas-phase, liquid-phase, gas-liquid two-phase, and liquid-solid two-channel environments. © 2024 IEEE.

关键词： Bit error rate

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Global Mean Square Error Separation Loss 4

Global Mean Square Error Separation Loss

引用

2022 4th International Conference on Artificial Intelligence and Computer Science, AICS 2022

作者： Hou, Yun Key Laboratory of Agile Intelligent Computing Southwest China Institute of Electronic Technology Sichuan Chengdu China

Class imbalance problem greatly affects the accuracy of deep-learning-based object detectors. To weaken its influence, this paper proposes a global mean square error separation loss (GMSES Loss). GMSES Loss assigns a factor to each example, and then enhances the learning of hard-to-learn examples by reducing the factors of easy-to-learn examples. Meanwhile, the global mean square error separation method is introduced to enhance the learning of foreground classes. To verify the effectiveness of our algorithm, experiments are conducted with YOLOv4 and RetinaNet as the baselines, respectively. The results have shown that our loss function can improve the performance of one-stage object detectors without adding any extra hyperparameters. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Mean square error

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K-MEANS CLUSTERING BASED SPARSE CROSS-ENTROPY MINIMIZATION ALGORITHM FOR DOA ESTIMATION

K-MEANS CLUSTERING BASED SPARSE CROSS-ENTROPY MINIMIZATION A...

引用

IET International Radar Conference 2023, IRC 2023

作者： Guo, Qiang Jiang, Hanyu Xiang, Jianhong Zhong, Yu College of Information and Communication Harbin Engineering University Harbin China Key Laboratory of Advanced Ship Communication and Information Technology Harbin Engineering University Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9781839539954

Aiming at the problem that traditional greedy class compressed sensing algorithms are influenced by noise in the estimation of direction of arrival (DOA), this paper proposes the K-Means Clustering based Sparse Cross-Entropy Minimization (KMCSCEM) algorithm. First the algorithm selects atoms by minimizing the sparse cross-entropy to improve the accuracy of atom selection at the low signal-to-noise ratio (SNR). Subsequently, K-mean clustering is added in each iteration to reduce the range of atom selection, which further improves the estimation accuracy. Finally, the speed of convergence is improved by introducing an orthogonal least squares (OLS) criterion. Simulation experiments show that the proposed algorithm has an exact estimation ratio close to 90% when the SNR is less than -5 dB. © The Institution of Engineering & Technology 2023.

关键词： Signal to noise ratio

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Channel estimation for massive MIMO systems based on adaptive angular domain compensation 5

Channel estimation for massive MIMO systems based on adaptiv...

引用

5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Liu, Ting Wang, Yong Xiang, Jianhong Wang, Ying Liu, Yang College Of Information And Communication Engineering Harbin Engineering University Harbin150001 China Sichuan Key Laboratory Of Agile Intelligent Computing Southwest China Institute Of Electronics Technology Chengdu610036 China

ISBN: (纸本)9798400709272

Millimeter-wave (mmWave) and massive Multiple Input Multiple Output (MIMO) technologies rely heavily on accurate channel state information (CSI) to significantly increase system capacity. However, the large size of the antenna array makes it more difficult to obtain accurate channel state information. We propose to utilize beam domain channels to improve the channel estimation accuracy. Specifically, we first construct the angular domain compensation. By utilizing this angular domain compensation, we further propose an angular domain compensated alternating direction multiplier method (ADC-ADMM) algorithm. The scheme models the channel estimation problem as a bi-objective convex optimization problem with joint angular domain compensation, which can capture channel state information more comprehensively. In addition, the angular domain compensation can provide a priori information and improve the robustness in noisy environments. The simulation results confirm the feasibility of the proposed ADC-ADMM scheme. © 2023 ACM.

关键词： Millimeter waves

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Asymmetric Residual Transformer for Hyperspectral Image Classification Using Limited Training Samples 5

Asymmetric Residual Transformer for Hyperspectral Image Clas...

引用

5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Sun, Yaxiu Wang, Minhui Xiang, Jianhong Hao, Lihong Sun, Rui Wu, Yaruo College Of Information And Communication Engineering Harbin Engineering University Harbin150001 China National Key Laboratory Of Communication Anti-Jamming Technology College Of Information And Communication Engineering Harbin Engineering University Harbin150001 China Agile And Intelligent Computing Key Laboratory Chengdu610000 China

ISBN: (纸本)9798400709272

In recent years, the combination of convolutional neural network (CNN) and transformer has become a trend. CNN is good at capturing local information, while transformer is good at capturing global features of images. Hyperspectral images (HSI) have rich spatial feature information, and hundreds of continuous spectral bands contain a lot of deep sequence semantic information. Therefore, combining CNN and transformer can fully learn data feature information, which CNN can extract local spatial information, and transformer can extract band sequence information. In this paper, a new model ARFormer is proposed, which combines asymmetric residual connection (AR) block and improved vision transformer. First, the AR block contains the three-dimensional (3D) asymmetrical convolution and residual connection, which could extract abundant features information with fewer convolutional layers and parameters of model. Then the improved vision transformer introduces Re-attention, which could increase the diversity of the attention graph at different levels. Experiments in this paper are carried out on three famous hyperspectral datasets, including Indian Pines (IP), Trento (TR), and Pavia University (UP) datasets. The experimental results of HSI classification show that ARFormer achieves better classification accuracy than several existing models. Especially when the training samples are limited. © 2023 ACM.

关键词： Convolution

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Asymmetric Residual Transformer for Hyperspectral Image Classification Using Limited Training Samples 23

Asymmetric Residual Transformer for Hyperspectral Image Clas...

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Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Yaxiu Sun Minhui Wang Jianhong Xiang Lihong Hao Rui Sun Yaruo Wu National Key Laboratory of Communication Anti-Jamming Technology College of Information and Communication Engineering Harbin Engineering University China Agile and Intelligent Computing Key Laboratory China

ISBN: (纸本)9798400709272

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