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IEEE Transactions on Green Communications and networking 2025年

作者： Liu, Fulai Huang, Hai Liu, Ruxin Yang, Jinwei Suo, Luyao Du, Ruiyan Northeastern University at Qinhuangdao Lab of Electromagnetic Environment Cognition and Control Utilization Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

In the case of interference perturbation, the wideband adaptive beamforming (WAB) weight vector may be mismatched, which leads to the decrease of interference suppression ability. To improve communication quality under the interference position perturbation, this paper presents a multi-head self-attention conventional neural network (MHSA-CNN)-based WAB algorithm with null broadening. In the presented approach, a MHSA-CNN structure is proposed to improve the prediction accuracy of beamforming weight vector in the case of interference perturbation. Specifically, by processing multiple attention heads in parallel to obtain the information of different signal subspaces, MHSA mechanism enables the network to dynamically adjust the attention distribution of signal features and effectively extract the global features of the covariance matrix. Then, based on focused reconstruction and null broadening, an effective neural network training label is used to enhance the ability of suppressing interferences. Finally, the well-trained MHSA-CNN can accurately output the weight vector suitable for WAB with null broadening in real time. Simulation results demonstrate that the proposed algorithm can suppress interferences accurately within the interference perturbation range and enhance the output signal-to-interference-plus-noise ratio while ensuring real-time communication performance. © 2025 IEEE.

关键词： Signal to noise ratio

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Application of Improved Canny Algorithm in Image Edge Detection

Application of Improved Canny Algorithm in Image Edge Detect...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Siyang Xu Peng Zhang Xin Song Runfeng Zhang Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

To solve the problems of poor denoising effect and limited threshold accuracy in traditional Canny algorithm, an improved Canny algorithm is proposed in this paper. Firstly, an image preprocessing method based on hybrid filtering is developed. On the basis of Gaussian filtering, the adaptive median filtering is adopted to denoise the image, and homomorphic filtering is used to enhance the high frequency information of image in the frequency domain. Besides, Sobel operator is used instead of Roberts operator to calculate the gradient amplitude and direction of the image. Finally, an iterative threshold selection method combining MEM and Otsu is proposed. After obtaining the high and low thresholds by MEM and Otsu respectively, the optimal threshold is calculated using iterative algorithm, which can improve the accuracy and adaptability of threshold. It is proved that the proposed algorithm can preserve the image details while ensuring the denoising effect, and the detected edges have better connectivity and smoothness.

关键词： Micromechanical devices Accuracy Filtering Image edge detection Noise reduction Diversity reception Noise

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A Highly Selective Wideband Filtering Stacked DDPA

A Highly Selective Wideband Filtering Stacked DDPA

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Information Technology,Big data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Kang Tian JiaSheng Zhang Dongsheng La Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China An Hui Ming Bian Electronic Science & Technology Co. Ltd.

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In this paper, a compact and highly selective stacked filtering dense dielectric patch (DDP) antenna (DDPA) is proposed. By placing a pair of thin DDP with high dielectric constant along the y axis on the DDP with low dielectric constant, the resonant frequency of the TE 12 mode is adjusted to approach low frequency and form a passband with the TE 11 mode. By loading a U-shaped microstrip feeding line on the microstrip feeding line, the bandwidth can be further broadened and the TE 11 , TE 11 + δ and TE 12 modes can be successfully excited in the passband. The radiation nulls are generated by the U-shaped microstrip line and non-radiative TE 12 mode, respectively. By adjusting the length of the U-shaped microstrip line and the size of DDP with high dielectric constant, the independent adjustment of radiation nulls can be achieved. Finally, the proposed filtering antenna achieves a relative bandwidth of 11.66 % (4.04-4.54 GHz). The average realized gain and peak gain are 6.89 dBi and 7.1 dBi, respectively. The realized gain in the passband is very flat, and the fluctuation is less than 0.7 dBi. The proposed antenna has the advantages of compactness, high selectivity and flat gain, and it can be applied to modern satellite communication and wireless communication systems.

关键词： Wireless communication Microstrip filters Dielectric constant Filtering Resonant frequency Microstrip antennas Microstrip Passband Wideband Satellite communications

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A Chinese multimodal depression dataset with personality labeling for older adults with underlying medical conditions

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IEEE Transactions on Affective Computing 2025年

作者： Zhao, Yuliang Zhang, Huawei Li, Jian Song, Siyang Lian, Chao Liu, Yinghao Wang, Yulin Fu, Changzeng School of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Northeastern University SSTC NEU Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China Osaka University Japan University of Cambridge AFAR Lab Department of Computer Science and Technology CambridgeCB3 0FT United Kingdom First Hospital of Qinhuangdao Qinhuangdao066004 China

Elderly individuals often suffer from underlying medical conditions, resulting in a significant decline in quality of life and a heightened susceptibility to depression. Presently, AI screening tools based on behavioral indicators offer an objective and effective approach to diagnosing depression. However, current AI depression screening tools are primarily tailored to adolescents and adults, exhibiting shortcomings in their applicability and accuracy for elderly individuals with underlying medical conditions. To address the above issues, firstly, this paper constructs a depression dataset for elderly people with underlying diseases by using semi-structured interviews. Secondly, based on cognitive science insights, it is recognized that personality factors significantly influence behavioral expressions and also determine the attitudes of elderly individuals toward current life circumstances/health issues. Therefore, besides annotating depression severity, the Big Five-10 personality scale was utilized to annotate participant personalities. Finally, a late fusion-based multi-task learning framework was proposed, and the effects of introducing gait information and personality annotation on the performance of depression assessment were investigated. The experimental findings affirm the importance of integrating gait information and personality assessment in improving depression detection effectiveness. This study provides valuable foundational resources, as well as beneficial references and insights, for the research on depression in the elderly. © 2010-2012 IEEE.

关键词： Depression Multimodal dataset Older adults Personality Underlying diseases

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An RTDNN-Based Hybrid Precoding and Combining Joint Optimization Algorithm for MUPC-MIMO Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2025年

作者： Liu, Fulai Liu, Yubiao Yang, Xianghuan Duan, Zhuoyao Shi, Baozhu Ji, Li Du, Ruiyan Northeastern University at Qinhuangdao Lab of Key Technology of Millimeter-wave Large-scale MIMO System Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Hebei University College of Mathematics and Information Science Baoding071002 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Qinhuangdao Vocational and Technical College Qinhuangdao066100 China School of Computer Science and Engineering Northeastern University Shenyang110004 China

To achieve a satisfactory balance between energy efficiency (EE) and spectral efficiency (SE) for hybrid precoding and combining (HPC), this paper presents a two-stage joint optimization HPC algorithm based on residual deep neural network (DNN) for multi-user partially connected (MUPC) structure, namely RTDNN algorithm. In the proposed algorithm, a two-stage framework is designed to optimize analog and digital coders at the transmitter and receiver simultaneously, which can effectively preserve coupling relationship of coders between the transmitter and receiver. Specially, the RTDNN architecture is mainly divided into two stage networks, which includes analog stage and digital stage networks. The analog stage network is developed to obtain the optimal analog precoder and combiner simultaneously. On this basis, the digital stage network is constructed to solve the digital precoder and combiner concurrently. To further improve the prediction accuracy of RTDNN-based HPC, residual modules are adopted into the developed two stage networks. Moreover, the proposed RTDNN is trained with unsupervised learning. Simulation results show that the EE of the proposed algorithm is at least enhanced by 0.6 bits/Hz/J with satisfactory SE than other methods. © 2017 IEEE.

关键词： Deep neural networks

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DeepMerge: Deep-learning-based Region-Merging for Image Segmentation

arXiv

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arXiv 2023年

作者： Lv, Xianwei Persello, Claudio Li, Wangbin Huang, Xiao Ming, Dongping Stein, Alfred School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Hebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University Qinhuangdao066004 China Dept. of Earth Observation Science Faculty ITC University of Twente Enschede7500AE Netherlands State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China The Department of Geosciences University of Arkansas FayettevilleAR United States Beijing100083 China

Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land objects with diverse shapes and sizes. Additionally, the determination of segmentation scale parameters frequently adheres to a static and empirical doctrine, posing limitations on the segmentation of large-scale remote sensing images and yielding algorithms with limited interpretability. To address the above challenges, we propose a deep-learning-based region merging method dubbed DeepMerge to handle the segmentation of complete objects in large VHR images by integrating deep learning and region adjacency graph (RAG). This is the first method to use deep learning to learn the similarity and merge similar adjacent super-pixels in RAG. We propose a modified binary tree sampling method to generate shift-scale data, serving as inputs for transformer-based deep learning networks, a shift-scale attention with 3-Dimension relative position embedding to learn features across scales, and an embedding to fuse learned features with hand-crafted features. DeepMerge can achieve high segmentation accuracy in a supervised manner from large-scale remotely sensed images and provides an interpretable optimal scale parameter, which is validated using a remote sensing image of 0.55 m resolution covering an area of 5,660 km2. The experimental results show that DeepMerge achieves the highest F value (0.9550) and the lowest total error TE (0.0895), correctly segmenting objects of different sizes and outperforming all competing segmentation methods. © 2023, CC BY.

关键词： Image segmentation

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