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An Encode-and CRT-Based Scalability Scheme for Optimizing Transmission in Blockchain

Computer Modeling in engineering & sciences 2024年第5期139卷 1733-1754页

作者： Qianqi Sun Fenhua Bai Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China

Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal ***,scalability challenges,characterized by diminished broadcast efficiency,heightened communication overhead,and escalated storage costs,have significantly constrained the broad-scale application of *** paper introduces a novel Encode-and CRT-based Scalability Scheme(ECSS),meticulously refined to enhance both block broadcasting and ***,ECSS categorizes nodes into distinct domains,thereby reducing the network diameter and augmenting transmission ***,ECSS streamlines block transmission through a compact block protocol and robust RS coding,which not only reduces the size of broadcasted blocks but also ensures transmission ***,ECSS utilizes the Chinese remainder theorem,designating the block body as the compression target and mapping it to multiple modules to achieve efficient storage,thereby alleviating the storage burdens on *** evaluate ECSS’s performance,we established an experimental platformand conducted comprehensive *** results demonstrate that ECSS attains superior network scalability and stability,reducing communication overhead by an impressive 72% and total storage costs by a substantial 63.6%.

关键词： Blockchain network coding block compression transmission optimization

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Magnetic Resonance Imaging Reconstruction Based on Butterfly Dilated Geometric Distillation

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Journal of Shanghai Jiaotong University (science) 2025年第3期30卷 590-599页

作者： Duo, Lin Xu, Boyu Ren, Yong Yang, Xin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

In order to improve the reconstruction accuracy of magnetic resonance imaging (MRI), an accurate natural image compressed sensing (CS) reconstruction network is proposed, which combines the advantages of model-based and deep learning-based CS-MRI methods. In theory, enhancing geometric texture details in linear reconstruction is possible. First, the optimization problem is decomposed into two problems: linear approximation and geometric compensation. Aimed at the problem of image linear approximation, the data consistency module is used to deal with it. Since the processing process will lose texture details, a neural network layer that explicitly combines image and frequency feature representation is proposed, which is named butterfly dilated geometric distillation network. The network introduces the idea of butterfly operation, skillfully integrates the features of image domain and frequency domain, and avoids the loss of texture details when extracting features in a single domain. Finally, a channel feature fusion module is designed by combining channel attention mechanism and dilated convolution. The attention of the channel makes the final output feature map focus on the more important part, thus improving the feature representation ability. The dilated convolution enlarges the receptive field, thereby obtaining more dense image feature data. The experimental results show that the peak signal-to-noise ratio of the network is 5.43 dB, 5.24 dB and 3.89 dB higher than that of ISTA-Net+, FISTA and DGDN networks on the brain data set with a Cartesian sampling mask CS ratio of 10%. © Shanghai Jiao Tong University 2024.

关键词： Convolution

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Fast Parallel Magnetic Resonance Imaging Reconstruction Based on Sparsifying Transform Learning and Structured Low-Rank Model

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Journal of Shanghai Jiaotong University (science) 2025年第3期30卷 499-509页

作者： Duan, Jizhong Xu, Yuhan Huang, Huan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

The structured low-rank model for parallel magnetic resonance (MR) imaging can efficiently reconstruct MR images with limited auto-calibration signals. To improve the reconstruction quality of MR images, we integrate the joint sparsity and sparsifying transform learning (JTL) into the simultaneous auto-calibrating and k-space estimation (SAKE) structured low-rank model named JTLSAKE. The alternate direction method of multipliers is exploited to solve the resulting optimization problem, and the optimized gradient method is used to improve the convergence speed. In addition, a graphics processing unit is used to accelerate the proposed algorithm. The experimental results on four in vivo human datasets demonstrate that the reconstruction quality of the proposed algorithm is comparable to that of JTL-based low-rank modeling of local k-space neighborhoods with parallel imaging (JTL-PLORAKS), and the proposed algorithm is 46 times faster than the JTL-PLORAKS, requiring only 4 s to reconstruct a 200 × 200 pixels MR image with 8 channels. © Shanghai Jiao Tong University 2023.

关键词： Magnetic resonance imaging

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Spatial-Frequency Complementary Transformer and Multi-Scale Attention Refinement for Image Inpainting

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Journal of Shanghai Jiaotong University (science) 2025年 1-15页

作者： Ren, Shuangde Li, Huafeng Zhang, Yafei Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

The ability to capture long-range contextual information is crucial for image inpainting networks when dealing with complex semantic scenes and large areas of damage. Therefore, Transformers and frequency-based learning methods have gained wide attention due to their powerful global modeling capabilities. However, these methods only learn global features within a single domain, ignoring the complementarity of modeling global dependencies in both spatial and frequency domains. To address this issue, a spatial-frequency complementary Transformer is proposed, which combines the advantages of spatial global learning for global structure generation and frequency global learning for repetitive texture inpainting. This solution enhances the network’s performance in complex semantic scenes and large areas of damage. For spatial global learning, deformable convolution v4 (DCNv4) is introduced to image inpainting for the first time. By utilizing its sparse and efficient global modeling capabilities, a more lightweight inpainting is realized, and the occurrence of blurring and artifacts in the inpainting results is minimized. Additionally, a multi-scale attention refinement module is proposed to enhance the network’s ability to inpaint local detail. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms existing advanced inpainting models while maintaining lower parameter counts and a faster inference speed. © Shanghai Jiao Tong University 2025.

关键词： Contrastive Learning

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Improved Sensitivity Encoding Parallel Magnetic Resonance Imaging Reconstruction Algorithm Based on Efficient Sum of Outer Products Dictionary Learning

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Journal of Shanghai Jiaotong University (science) 2025年第3期30卷 561-571页

作者： Duan, Jizhong Su, Yan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

Sensitivity encoding (SENSE) is a parallel magnetic resonance imaging (MRI) reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction. The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms, resulting in a limited reconstruction accuracy. Therefore, we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning (SOUPDIL) with the SENSE model, called SOUPDIL-SENSE. The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques. The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity, eliminates the noise and artifacts of the reconstructed images, and improves the reconstruction accuracy. © Shanghai Jiao Tong University 2023.

关键词： Magnetic resonance imaging

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Research on a vehicle and pedestrian detection algorithm based on improved attention and feature fusion

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Mathematical Biosciences and engineering 2024年第4期21卷 5782-5802页

作者： Liang, Wenjie Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

With the widespread integration of deep learning in intelligent transportation and various industrial sectors, target detection technology is gradually becoming one of the key research areas. Accurately detecting road vehicles and pedestrians is of great significance for the development of autonomous driving technology. Road object detection faces problems such as complex backgrounds, significant scale changes, and occlusion. To accurately identify traffic targets in complex environments, this paper proposes a road target detection algorithm based on the enhanced YOLOv5s. This algorithm introduces the weighted enhanced polarization self attention (WEPSA) self-attention mechanism, which uses spatial attention and channel attention to strengthen the important features extracted by the feature extraction network and suppress insignificant background information. In the neck network, we designed a weighted feature fusion network (CBiFPN) to enhance neck feature representation and enrich semantic information. This strategic feature fusion not only boosts the algorithm's adaptability to intricate scenes, but also contributes to its robust performance. Then, the bounding box regression loss function uses EIoU to accelerate model convergence and reduce losses. Finally, a large number of experiments have shown that the improved YOLOv5s algorithm achieves mAP@0.5 scores of 92.8% and 53.5% on the open-source datasets KITTI and Cityscapes. On the self-built dataset, the mAP@0.5 reaches 88.7%, which is 1.7%, 3.8%, and 3.3% higher than YOLOv5s, respectively, ensuring real-time performance while improving detection accuracy. In addition, compared to the latest YOLOv7 and YOLOv8, the improved YOLOv5 shows good overall performance on the open-source datasets. ©2024 the Author(s)

关键词： Object detection

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CT-MFENet: Context Transformer and Multi-Scale Feature Extraction Network via Global-Local Features Fusion for Retinal Vessels Segmentation

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Journal of Shanghai Jiaotong University (science) 2024年 1-15页

作者： Shao, Dangguo Yang, Yuanbiao Ma, Lei Yi, Sanli Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases. Retinal vessel images often suffer from category imbalance and large scale variations. This ultimately results in incomplete vessel segmentation and poor continuity. In this study, we propose CT-MFENet to address the aforementioned issues. First, the use of context transformer (CT) allows for the integration of contextual feature information, which helps establish the connection between pixels and solve the problem of incomplete vessel continuity. Second, multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales. In the decoding stage, we introduce a local-global fusion module. It enhances the localization of vascular information and reduces the semantic gap between high- and low-level features. To address the class imbalance in retinal images, we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures. We conducted experiments on the publicly available DRIVE, CHASEDB1, STARE, and IOSTAR datasets. The experimental results show that our CT-MFENet performs better than most existing methods, including the baseline U-Net. (Figure presented.) © Shanghai Jiao Tong University 2024.

关键词： Feature extraction

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GNNPPOR: A Proximal Policy Optimization Multi-Factor Joint Routing Approach Based on Graph Neural Networks in FANETs

IEEE Networking Letters

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IEEE Networking Letters 2025年第2期7卷 130-134页

作者： Song, Jian Li, Jing Wang, Qingwang Gu, Yebo Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China

Given the significant challenges of low resource utilization, load imbalance, and difficulties in meeting quality of service requirements in Flying Ad Hoc Networks (FANETs) routing protocols, this letter proposes a Graph Neural Network (GNN)-based approach for proximal policy optimization routing (GNNPPOR). The approach aims to integrate traffic engineering into FANETs to effectively distribute network load and meet quality of service requirements. In GNNPPOR, we design a GNN model that first aggregates multi-dimensional network state information efficiently through a message-passing mechanism. Subsequently, the network state is updated in real-time using a gated recurrent unit to adapt to dynamic changes in the FANETs network state. Finally, a multi-factor joint decision-making approach is proposed to identify suitable routes for each traffic based on the current network state. Simulation results demonstrate that GNNPPOR outperforms existing methods in several key metrics. Specifically, packet delivery rate increased by 25.3%, while energy consumption and network jitter decreased by 12.8% and 24.9%, respectively. © 2019 IEEE.

关键词： Graph neural networks

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DSFNet: Deep Fusion Parallel MRI Reconstruction Based on SPIRiT and Complex Convolution

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Journal of Shanghai Jiaotong University (science) 2024年 1-13页

作者： Duan, Jizhong Ren, Xinmin Chen, Shengyi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

Long data acquisition time is an inherent disadvantage of magnetic resonance imaging (MRI). To accelerate the data acquisition speed of MRI, undersampling is required, which results in low imaging quality. Based on an iterative self-consistent parallel imaging reconstruction (SPIRiT) and improved complex convolutional neural networks, a deep SPIRiT fusion network (DSFNet) is proposed to improve the quality of reconstructed images. The DSFNet model first uses the SPIRiT model for the reconstruction of under-sampled k-space data. Subsequently, a cascaded complex convolutional neural network with dense connections is utilized for further reconstruction of the calibrated k-space data. Besides, a data consistency layer ensures the fidelity of the reconstructed image in both k-space and image domains. The final magnitude image is fused from the two parts of the reconstruction magnitude images by the sequentialized model-based Bayesian optimization fusion module at a certain ratio. Experimental results on different knee datasets show that DSFNet can bring about a substantial improvement in the visualization quality, peak signal-to-noise ratio and structural similarity of the reconstructed images compared to the SPIRiT, Deepcomplex and DONet models. (Figure presented.) © Shanghai Jiao Tong University 2024.

关键词： Signal to noise ratio

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Regional Constraint Module-Based Multi-Agent Path Planning Approach for Car-like Agents

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Journal of Shanghai Jiaotong University (science) 2024年 1-11页

作者： Fang, Chengyuan Mao, Jianlin Li, Dayan Wang, Ning Wang, Niya Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

Multi-agent path finding (MAPF) is a challenging problem widely employed in automated docks and warehouse systems. However, when the above scenarios require car-like agents to perform the tasks, due to the complexity of the environment and the specificity of the shape of the agents, numerous conflicts between agents may occur in the process of path planning, which seriously affects the efficiency of the system and leads to a long runtime. To address these above problems, we propose a regional constraint module-based car-like conflict-based search (RCM-CL-CBS), which sets up the safe region to detect the conflicts between agents, maximizing the selection of paths with larger spatial resources under the same cost, and specifies the safe-exclusive region for colliding agents, reducing the probability of agents’ collisions within a certain region. We conduct experiments under four scenario types including factory and warehousing instances. Compared with the baseline algorithms, the experimental results denote that our method reduces the computational burden in terms of resolving agent conflicts, and improves the efficiency of problem-solving. In particular, in the warehouse scenario, compared to the car-like conflict-based search (CL-CBS), CL-CBS in the sequential framework (CL-CBS-SE), and improved CL-CBS (ICL-CBS), our method in the sequential framework minimizes the runtime by 89.6%, 53.4%, and 46.6%, respectively. (Figure presented.). © Shanghai Jiao Tong University 2024.

关键词： Motion planning

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