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Rethinking the Encoder-decoder Structure in Medical Image Segmentation from Releasing Decoder Structure

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Journal of Bionic engineering 2024年第3期21卷 1511-1521页

作者： Jiajia Ni Wei Mu An Pan Zhengming Chen school of artificial Intelligence Anhui Polytechnic UniversityWuhu241000China school of information Science and engineering HoHai UniversityChangzhou213000China

Medical image segmentation has witnessed rapid advancements with the emergence of encoder-decoder based *** the encoder-decoder structure,the primary goal of the decoding phase is not only to restore feature map resolution,but also to mitigate the loss of feature information incurred during the encoding ***,this approach gives rise to a challenge:multiple up-sampling operations in the decoder segment result in the loss of feature *** address this challenge,we propose a novel network that removes the decoding structure to reduce feature information loss(CBL-Net).In particular,we introduce a Parallel Pooling Module(PPM)to counteract the feature information loss stemming from conventional and pooling operations during the encoding ***,we incorporate a Multiplexed Dilation Convolution(MDC)module to expand the network's receptive ***,although we have removed the decoding stage,we still need to recover the feature map ***,we introduced the Global Feature Recovery(GFR)*** uses attention mechanism for the image feature map resolution recovery,which can effectively reduce the loss of feature *** conduct extensive experimental evaluations on three publicly available medical image segmentation datasets:DRIVE,CHASEDB and MoNuSeg *** results show that our proposed network outperforms state-of-the-art methods in medical image *** addition,it achieves higher efficiency than the current network of coding and decoding structures by eliminating the decoding component.

关键词： Medical image segmentation Encoder-decoder architecture Attention mechanisms Releasing decoder architecture Neural network

design of a novel hybrid quantum deep neural network in INEQR images classification

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Chinese Physics B 2024年第6期33卷 230-238页

作者：王爽王柯涵程涛赵润盛马鸿洋郭帅 school of information and Control engineering Qingdao University of TechnologyQingdao 266033China school of Sciences Qingdao University of TechnologyQingdao 266033China

We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.

关键词： quantum computing image classification quantum–classical hybrid neural network quantum image representation interpolation

Analysis of learnability of a novel hybrid quantum-classical convolutional neural network in image classification

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Chinese Physics B 2024年第4期33卷 275-283页

作者：程涛赵润盛王爽王睿马鸿洋 school of Sciences Qingdao University of TechnologyQingdao 266033China school of information and Control engineering Qingdao University of TechnologyQingdao 266033China

We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum *** this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from *** the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of ***,we use the MNIST dataset to test the potential of the *** results indicate that the HQCCNN has good performance in solving classification *** binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.

关键词： parameterized quantum circuits quantum machine learning hybrid quantum-classical convolutional neural network

Masked Autoencoders as Single Object Tracking Learners

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Computers, Materials & Continua 2024年第7期80卷 1105-1122页

作者： Chunjuan Bo XinChen Junxing Zhang school of information and Communication engineering Dalian Minzu UniversityDalian116600China school of information and Communication engineering Dalian University of TechnologyDalian116024China

Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking *** address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called *** pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,*** appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video ***,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel *** this way,ViT learns to understand both the appearance of images and the motion between video frames *** results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional ***,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for *** instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.

关键词： Visual object tracking vision transformer masked autoencoder visual representation learning

Probabilistic modeling of multifunction radars with autoregressive kernel mixture network

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Defence Technology（防务技术） 2024年第5期35卷 275-288页

作者： Hancong Feng Kaili.Jiang Zhixing Zhou Yuxin Zhao Kailun Tian Haixin Yan Bin Tang school of information and Communication engineering University of Electronic Science and Technology of ChinaChengdu 611731China

The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic *** methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative *** paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse *** results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.

关键词： Probabilistic forecasting Multifunction radar Unsupervised learning Change point detection Outlier detection

An adaptive 3D reconstruction method for asymmetric dual-angle multispectral stereo imaging system on UAV platform

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Science China(information Sciences) 2024年第8期67卷 285-299页

作者： Chen WANG Xian LI Yanfeng GU Zixu WANG school of Electronics and information engineering Harbin Institute of Technology Beijing Institute of Control and Electronic Technology

A multispectral imaging system often cannot capture 3D spatial information owing to hardware limitations, which diminishes the effectiveness across various domains. To address this problem, we have developed a multispectral stereo imaging system along with an adaptive 3D reconstruction algorithm. Unlike existing unmanned aerial vehicle stereo imaging systems, our multispectral stereo imaging system uses two multispectral cameras with asymmetric spectral bands positioned at different angles. This design enables the acquisition of a higher number of bands and lateral spatial information while maintaining a lightweight structure. This system introduces challenges such as large geometric distortions and intensity differences between multiple bands. To accurately recover 3D spatial information, we propose an adaptive 3D reconstruction method. This method employs a position and orientation system-assisted projection transformation and a normalized threshold adjustment strategy. Finally, mutual information is used to reconstruct the multispectral images densely, effectively addressing nonlinear differences and generating a comprehensive multispectral point cloud. Our stereo system was used for two real data collections in different regions, and the efficacy of the proposed 3D reconstruction method was validated by comparing it with existing methods and commercial software.

关键词： 3D reconstruction feature extraction multispectral images stereo imaging system unmanned aerial vehicle

QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams

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Chinese Journal of Electronics 2024年第3期33卷 645-656页

作者： Zequn NIU Jingfeng XUE Yong WANG Tianwei LEI Weijie HAN Xianwei GAO school of Computer Science and Technology Beijing Institute of Technology school of Space information Space Engineering University

In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.

关键词： Training Streams Random forests

Blockchain-Enabled Federated Learning for Privacy-Preserving Non-IID Data Sharing in Industrial Internet

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Computers, Materials & Continua 2024年第8期80卷 1967-1983页

作者： Qiuyan Wang Haibing Dong Yongfei Huang Zenglei Liu Yundong Gou school of Electrical and information engineering Hunan Institute of TechnologyHengyang421002China

Sharing data while protecting privacy in the industrial Internet is a significant *** machine learning methods require a combination of all data for training;however,this approach can be limited by data availability and privacy *** learning(FL)has gained considerable attention because it allows for decentralized training on multiple local ***,the training data collected by data providers are often non-independent and identically distributed(non-IID),resulting in poor FL *** paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain *** overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local *** approach can significantly improve the accuracy of FL training when there is relatively large *** addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model ***,we evaluate the performance of the proposed scheme using commonly used open-source image *** simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet.

关键词： Federated learning data sharing non-IID data differential privacy blockchain

Secure state estimation for cyber-physical systems with unknown input sliding mode observer

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Control Theory and Technology 2024年第2期22卷 244-253页

作者： Panpan Zhang Yuwei Ren Yixian Fang Ang Liu school of information Science and engineering Shandong Normal UniversityJinan250358ShandongChina school of information engineering Shandong Management UniversityJinan250357ShandongChina

In recent years, cyber attacks have posed great challenges to the development of cyber-physical systems. It is of great significance to study secure state estimation methods to ensure the safe and stable operation of the system. This paper proposes a secure state estimation for multi-input and multi-output continuous-time linear cyber-physical systems with sparse actuator and sensor attacks. First, for sparse sensor attacks, we propose an adaptive switching mechanism to mitigate the impact of sparse sensor attacks by filtering out their attack modes. Second, an unknown input sliding mode observer is designed to not only observe the system states, sensor attack signals, and measurement noise present in the system but also counteract the effects of sparse actuator attacks through an unknown input matrix. Finally, for the design of an unknown input sliding mode state observer, the feasibility of the observing system is demonstrated by means of Lyapunov functions. Additionally, simulation experiments are conducted to show the effectiveness of this method.

关键词： Sparse attacks Cyber-physical systems Unknown input sliding mode Secure state estimation Adaptive switching mechanism