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Tasks-oriented channel optimization and resource allocation in vehicular networks: a hierarchical reinforcement learning based approach

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 7624-7636页

作者： Lin, Runtao Guo, Caili Zhang, Biling Chen, Jiujiu Li, Hanyang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Nanchang Hangkong University Key Laboratory of Processing and Pattern Recognition of Jiangxi Province School of Information Engineering Nanchang330063 China China Mobile Communication Co. Ltd Wireless and Terminal Technology Research Institute Beijing100053 China

The number of devices, data volume, and pictures and videos in the vehicular networks have all increased significantly as a result of the continued development of 6G technology, putting an enormous burden on the edge communication resources. To solve this problem, a good resource allocation scheme is necessary. Existing resource allocation algorithms often pursue accurate transmission and ignore time-varying channels. In order to further obtain real and reasonable resource allocation results, this paper introduces semantic communication. It then decomposes that problem to further establish a hierarchical framework. The problem is addressed via a task-oriented channel optimization and resource allocation scheme based on a hierarchical reinforcement learning (TCRS-HRL) algorithm. First, a channel optimization and resource allocation problem under an imperfect channel state is developed, and it is divided into two separate sub problems for optimization. Second, the resource allocation problem at the edge is addressed with the introduction of hierarchical reinforcement learning and the TCRSHRL algorithm. The ideal channel state and resource allocation approach are found by constructing the state space, action space, and environmental feedback for each aspect individually. Finally, simulations have demonstrated the superiority and performance benefits of the proposed algorithm compared to current methods, showing that the implementation of TCRS-HRL can result in an accuracy improvement of roughly 8%. © 1967-2012 IEEE.

关键词： Reinforcement learning

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An image compression-encryption algorithm based on 2-D compressive sensing

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Journal of Computational Information Systems 2013年第24期9卷 10057-10064页

作者： Mo, Yan Zhang, Aidi Zheng, Fen Zhou, Nanrun Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang 330063 China Department of Electronic Information Engineering Nanchang University Nanchang 330031 China

An image compression-encryption algorithm based on 2-D compressive sensing is proposed, which can accomplish encryption and compression simultaneously. The measurements are performed in two directi-ons and the measurement matrices are constructed as partial Hadamard matrices, which are controlled by chaos map. The proposed algorithm is sensitive to the keys and it can resist various attacks. Simulation results verify the validity and reliability of the proposed algorithm. Copyright © 2013 Binary Information Press.

关键词： Compressed sensing

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Fre-Yolo: Feature Refinement Extraction Network with Yolo for Blade Tip Small Point Light Detection

SSRN

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SSRN 2024年

作者： Zheng, Wenhao Xiong, Bangshu Yi, Hui Au, Qiaofeng Chen, Jiujiu Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang330063 China China Helicopter Research and Development Institute China

Detecting blade tip point light sources based on airborne computer vision is a critical step in measuring blade tip distance for coaxial unmanned helicopters. However, detecting blade tip point light sources quickly and highly accurately in a helicopter airborne environment is a challenge due to the extremely tiny imaging size of the blade tip point light sources. To this end, we propose a FRE-YOLO for detecting blade tip point light sources based on the YOLO network structure. In this work, we focus on designing a Feature Refinement Extraction network (FRE), which can greatly enrich the feature information of small targets during Backbone downsampling. Secondly, we also design a new paradigm Smooth Gaussian Distance (SmoothGD) which measures the degree of target bounding box matching. SmoothGD is mainly applied to regression loss calculation and label assigner, which can effectively improve the small target detection accuracy. Meanwhile, we construct the Blade Tip Point Light (BTPL) dataset for the network training. In addition, in order to evaluate the effectiveness of the proposed method more fairly, this paper conducts the same experiments on BTPL and the small target dataset VisDrone on MMYOLO. A large amount of experimental data shows that the proposed method has a positive impact on the detection of tiny point sources at the tip of the blade. It is also at the advanced level in the field of generalized small object detection. © 2024, The Authors. All rights reserved.

关键词： Extraction

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A New Method for recognition Partially Occluded Curved Objects under Affine Transformation

A New Method for Recognition Partially Occluded Curved Objec...

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International Conference on Intelligent System and Knowledge Engineering, ISKE

作者： GuiMei Zhang JiYuan Xu JianXin Liu Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Province China School of Mechanical Engineering and Automation Xihua University Chengdu Sichuan China

A new method dealing with recognition of partially occluded and affine distortion objects is presented. The method is designed for objects with smooth curved boundary. It divides an object into affine-invariant parts based on the feature point. And a new approach for matching each part is presented in this paper. Robust Hausdorff distance (RHD) is introduced to measure the similarity between feature points set of model and that of target. In terms of the new RHD, the optimal affine transform can be estimated. And then the sub-curve match pairs are calculated based on the optimal affine transformation. The experimental results show proposed algorithm are capable of coping with partial occlusion and affine transformation.

关键词： Transforms Shape Robustness pattern recognition image recognition Mathematical model Distortion

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Motion estimation and spatial-temporal filter-based infrared small target detection algorithm

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International Journal of Wireless and Mobile Computing 2015年第3期8卷 256-261页

作者： Wang, Zhonghua Liao, Yuan Liu, Qingping Li, Chunyong School of Information and Engineering Nanchang Hangkong University Nanchang Jiangxi Province330063 China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang Jiangxi Province330063 China Department of Information Engineering Nanchang Hangkong University Nanchang330063 China

Under the effect of solar variation, atmospheric attenuation and thermal radiation distribution, the grey value of interference source is close to or equal to the target grey value. With the distance between the imaging system and the target being very long, the target is dotted or mottled in the image. In addition, the target motion is usually unknown and abrupt. All these factors cause the negative effect of small target detection. In this paper, we present a motion estimation and spatial-temporal filter-based algorithm to detect infrared small targets under complex background. The algorithm is divided into three steps: (1) researching the image transformation technology from greyscale pattern to entropy pattern;(2) studying the anisotropic smoothing and local constraint criterion of entropy flow;and (3) introducing spatial-temporal filter for detecting small targets through energy accumulation and trial search of targets. Compared to other methods, experimental results indicate that the proposed method can robustly detect small targets under complicated background. Copyright © 2015 Inderscience Enterprises Ltd.

关键词： Entropy

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Research on Multi-level Attention-based Human Pose Estimation

Research on Multi-level Attention-based Human Pose Estimatio...

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Jun Gao Hua Huang QiShen Li ShaoQi Xia School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China

Detecting human key points from a single image is very challenging due to occlusion, blurring, illumination and scale changes. In this paper, this problem is addressed by designing an effective network structure. Since global and local information plays an important role in reasoning about human body structure and invisible keypoints, Multi-level Attention Network (MAN) is proposed. First, compared with traditional multi-resolution networks, it enables multi-resolution feature maps with greater information variance by generating them directly from the highest resolution feature map, which in turn increases the abundance of feature information after final fusion. Secondly, it effectively integrates global and local information in different resolution feature maps through the Feature Alignment Attention Block(FAAB), and intensifies them in a targeted manner. On the COCO dataset, with HRNet (Sun K. et al [1]) as the baseline network, HRNet of inserted MAN improves 1.1-2.3 AP points over the baseline network.

关键词： Computational modeling High performance computing Pose estimation Lighting Feature extraction Cognition Sun

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An image fusion method of multi-scale image based on HMM and genetic algorithms

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Journal of Computational Information Systems 2015年第12期11卷 4367-4374页

作者： Jiang, Zetao Wu, Donghui The Institute of Computer Science and Engineering Guilin University of Electronic Technology Guilin China The Key Lab of Image Processing and Pattern Recognition of Jiangxi Province Nanchang Hangkong University Nanchang China

At present, in the field of pixel-level image fusion, researchers tend to treat each pixel independently, which destroys the relationship between the images to be fused. In view of this defects, this paper aims to propose a method for multi-scale image fusion based on Genetic Algorithm (GA) under the guidance of Hidden Markov Model (HMM). This fusion method is set on the following principles. First of all, the wavelet transformation (WT) of the two images to be fused will be conducted. And then establish HMM for the fusion coefficients from high to low in descending hierarchical order. Finally, guided by HMM, the author applies GA to searching hierarchically for the optimal fusion coefficient. With this method, the best fusion effect will be achieved. This method take good advantage of the correlation information between the images to be fused so as to improve the fidelity and accuracy of the fusion. Given this, the author makes an empirical analysis on this fusion method, which proves to be feasible and effective. ©, 2015, Binary Information Press. All right reserved.

关键词： Genetic algorithms

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Research on Monocular Depth Estimation Method based on Multi-Level Attention and Feature Fusion

Research on Monocular Depth Estimation Method based on Multi...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Zhongyu Wu Hua Huang Qishen Li Penghui Chen School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China

Monocular depth estimation is a fundamental task in computer vision and has drawn increasing attention. Recently, attention-based models and encoder-decoder architectures have led to great improvements in monocular depth estimation. Typically, most of the previous methods used repeated simple up-sampling operations during decoding, which may not make full use of the potential properties of the features extracted by the encoder, and there are problems of inaccurate prediction of the edge and depth maximum region. We propose an attention-based feature fusion module for encoder and decoder. We treat the monocular depth estimation as a pixel-level optimization problem, where the coarsest encoder feature is used to initialize the pixel-level optimization, which is then refined to higher resolution by the proposed attentional feature fusion (AFF). We formulate the prediction problem as ordinal regression over the bin centers that discretize the continuous depth range. It predicts a correspondingly different distribution of bins based on different pictures and we predict bins at the coarsest level using global pooling and MLP layers. In the NYUV2 dataset, the proposed architecture improving original model by 2.5.% and 1.1%, in terms of Log10 and Absolute relative error, respectively.

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Novel Regularization Method for Reduced Biquaternion Neural Network

SSRN

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SSRN 2023年

作者： Gai, Shan Huang, Xiang School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Nanchang330063 China

A reduced biquaternion neural network (RQNN) is a new type of neural network framework that has achieved significant success in machine learning. However, as the reduced biquaternion algebra system contains infinite zero divisors, the RQNN is easily trapped in a local minimum and overfitted. We propose a new regularization scheme for the RQNN to address these issues. First, we propose a new operation in the reduced biquaternion domain—the reduced biquaternion complex modulus (RQCM). Second, we analyse the algebraic properties and obtain the geometric meaning of the RQCM. Finally, we design an improved weight decay method using the RQCM. Unlike conventional weight decay methods with matrix norm constraints, the proposed method can better project the reduced biquaternion in terms of the scale and phase. Colour image classification and denoising tasks are used to demonstrate the superior performance of the RQCM over state-of-the-art weight decay methods. The source code can be found at https://***/tasteimage/Reg4RQNN . © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Research on attention-based multiscale information fusion with the real-time instance segmentation method

Research on attention-based multiscale information fusion wi...

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Yifan He Hua Huang Qishen Li Guiwen Zhang School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China

Instance segmentation is a comprehensive computer vision task that involves a wide range of other tasks. Recently, the study of real-time instance segmentation methods has received more attention for the development of autonomous driving. Although existing real-time instance segmentation methods are fast, their accuracy does not meet practical needs. Most methods go for segmentation based on object detection, and their effectiveness is overly dependent on the effectiveness of detection. This paper proposes a new attention-based multiscale information fusion method based on Cheng, T. et al. [1]. Firstly, the PPM module of the baseline network is replaced with the module Multiscale Context Attention (MSCA) designed in this paper based on the baseline network, which uses atrous convolution with different ratios to obtain information of four scales, and then uses non-local attention to enhance the information of features. It can effectively suppress the interference of redundant information on the instance segmentation results. Secondly, a new feature fusion approach is designed, which no longer uses bilinear interpolation, but sub-pixel up sampling combined with attention. We did experiments related to this module on the coco dataset and demonstrated its effectiveness, with a 0.5% improvement over the baseline network.

关键词： Interpolation Computer vision Convolution High performance computing Interference Object detection Real-time systems

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