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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

Computer Modeling in Engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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Infrared small target detection based on spatial attention density peaks searching

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Optoelectronics Letters 2025年第2期21卷 90-96页

作者： RAO Junmin LU Siyu LIU Shijian LI Fanming CAS Key Laboratory of Infrared System Detection and Imaging Technology Shanghai Institute of Technical Physics University of Chinese Academy of Sciences Shanghai institute of Technical Physics Chinese Academy of Sciences School of information Science and Technology Shanghai Tech University

The detection of small targets poses a significant challenge for infrared search and tracking (IRST) systems,as they must strike a delicate balance between accuracy and *** this paper,we propose a detection algorithm based on spatial attention density peaks searching (SADPS) and an adaptive window selection ***,the difference-of-Gaussians (DoG) filter is introduced for preprocessing raw infrared ***,the image is processed by ***,an adaptive window selection scheme is applied to obtain window templates for the target scale ***,the small target feature is used to enhance the target and suppress the ***,the true targets are segmented through a *** experimental results show that compared with the seven state-of-the-art small targets detection baseline algorithms,the proposed method not only has better detection accuracy,but also has reasonable time consumption.

关键词： Infrared imaging

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Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation

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Chinese Journal of Aeronautics 2014年第5期27卷 1242-1250页

作者： Liu Yu Dong Kai Wang Haipeng Liu Jun He You Pan Lina institute of information fusion Naval Aeronautical and Astronautical University

The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.

关键词： Adaptive split-merge scheme Gaussian sum filter Nonlinear non-Gaussian State estimation Squared-root cubature Kalman filter

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Persymmetric adaptive detection of range-spread targets in subspace interference plus Gaussian clutter

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Science China(information Sciences) 2023年第5期66卷 271-282页

作者： Tao JIAN Jia HE Yu LIU You HE Congan XU Zikeng XIE Research institute of information fusion Naval Aviation University

In this paper, we consider the adaptive detection problem of range-spread targets embedded in subspace interference plus structured Gaussian clutter. The target signal and interference are assumed to lie in two linearly independent subspaces with unknown coordinates. The clutter component is modeled as a complex Gaussian vector with an unknown persymmetric covariance matrix. We leverage the persymmetric structure to design a two-step detector according to the Rao test criterion. The theoretical results show that the proposed detector possesses the constant false alarm rate property with respect to the clutter covariance matrix. Furthermore, the numerical results show that the proposed detector exhibits better detection performance than the existing unstructured subspace detectors, particularly under a limited training data size. In addition, the proposed detector outperforms the existing persymmetric subspace detectors.

关键词： adaptive detection persymmetry structured interference constant false alarm rate Rao test

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A novel RHT-TBD approach for weak targets in HPRF radar

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Science China Earth Sciences 2016年第12期59卷 59-72页

作者： Hongbo YU Guohong WANG Wei WU Shuncheng TAN institute of information fusion Naval Aeronautical and Astronautical University

A novel approach, which can handle ambiguous data from weak targets, is proposed within the randomized Hough transform track-before-detect(RHT-TBD) framework. The main idea is that, without the pre-detection and ambiguity resolution step at each time step, the ambiguous measurements are mapped by the multiple hypothesis ranging(MHR) procedure. In this way, all the information, based on the relativity in time and pulse repetition frequency(PRF) domains, can be gathered among different PRFs and integrated over time via a batch procedure. The final step is to perform the RHT with all the extended measurements, and the ambiguous data is unfolded while the detection decision is confirmed at the end of the processing *** classic methods, the new approach resolves the problem of range ambiguity and detects the true track for targets. Finally, its application is illustrated to analyze and compare the performance between the proposed approach and the existing approach. Simulation results exhibit the effectiveness of this approach.

关键词： high pulse repetition frequency weak targets track-before-detect randomized Hough transform multiple hypothesis ranging range ambiguity resolution

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Radial acceleration estimation of multiple high maneuvering targets

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Journal of Systems Engineering and Electronics 2014年第2期25卷 183-193页

作者： Shuyi Jia Guohong Wang Shuncheng Tan institute of information fusion Naval Aeronautical and Astronautical University

The acceleration of a high maneuvering target in signal processing is helpful to enhance the performance of the tracker and facilitate the classification of targets. At present, most of the research on acceleration estimation is carried out in cases of a single target with time-frequency analysis methods such as fractional Fourier transform （FRFT）, Hough-ambiguity transform （HAT）, and Wigner-Vil e distribution （WVD）, which need to satisfy enough time duration and sampling theorem. Only one reference proposed a method of acceleration estimation for multiple targets based on modified polynomial phase transform （MPPT） in the lin-ear frequency modulation （LFM） continuous-wave （CW） radar. The method of acceleration estimation for multiple targets in the pulse Doppler （PD） radar has not been reported so far. Compressive sensing （CS） has the advantage of sampling at a low rate and short duration without sacrificing estimation performance. There-fore, this paper proposes a new method of acceleration estimation for multiple maneuvering targets with the unknown number based on CS with pulse Doppler signals. Simulation results validate the effectiveness of the proposed method under several conditions with different duration, measurement numbers, signal to noise ra-tios （SNR）, and regularization parameters, respectively. Simulation results also show that the performance of the proposed method is superior to that of FRFT and HAT in the condition of multiple targets.

关键词： radial acceleration compressive sensing (CS) highmaneuvering targets fractional Fourier transform (FRFT) Hough-ambiguity transform (HAT).

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A coordinated planning strategy of energy storage allocation and line upgrading to increase photovoltaic consumption with lowest planning cost

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Science China Technological Sciences 2025年

作者： Bo ZHANG Yuqi JI Lu ZHANG Wei TANG Xiaohui ZHANG Liang WANG Haoran DING Hanchen MAO College of information and Electrical Engineering China Agricultural University State Grid Heilongjiang Electric Power Research institute State Grid Heilongjiang Electric Power Company Limited School of Electrical and information Engineering Zhengzhou University of Light Industry

Random integration of massive distributed photovoltaic (PV) generation poses serious challenges to distribution networks. Voltage violations, line overloads, increased peak–valley differences, and power-flow reversals can occur at different locations, times, and severities. Traditional planning methods such as energy storage (ES) allocation and upgrading of lines may result in poor economics and low equipment utilization. This study proposes a distribution-network planning strategy that coordinates three planning mechanisms:ES allocation to substations and to feeders, and line upgrading. The proposed strategy achieved the lowest annual cost, considering the distribution network problems at different locations, times, and severities. A net load curve description method was first proposed considering economic planning and the uncertain distribution of loads and PVs. Second, a multistage optimization model for ES allocation and line upgrading was established based on the net load curves. Finally, an improved IEEE33 node system was used in the case study. The planning results show that the proposed coordinated planning strategy can address the problems caused by the random integration of massive distributed PVs, improve equipment utilization, and obtain the lowest total annual planning cost.

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Distributed fiber optic sensing system for vibration monitoring of 3D printed bridges

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Optoelectronics Letters 2025年第1期21卷 28-34页

作者： ZHAO Shuai ZHOU Ran LUO Mingming LIU Jianfei LIU Xiongfei ZHOU Tao School of Electronic information Engineering Hebei University of Technology Tianjin Key Laboratory of Electronic Materials and Devices School of Civil Engineering and Transportation Hebei University of Technology Tianjin Jinhang institute of Technical Physics

The fiber optic sensing technology provides data support in structural health monitoring of the macro facilities, including design, construction, and maintenance of bridges, tunnels, ports and other infrastructures. In this paper, a distributed vibration sensing system is proved to be responsive to a single touch over a 1.8-m-long equivalent fiber segment, covering a vibration frequency from 5 Hz to 25 kHz. The sensing fiber was arranged as an S type layout on the bridge to recognize the standing state, windblown disturbance, and walking vibration. Moreover, the knocking and climbing events are recognized fiber laying spinning lines and hanging on the fences, respectively. The demonstration shows an accurate positioning and sensitive vibration monitoring applied on the automated three-dimensional(3D) printed bridge, which is applicable to all kinds of 3D printed facilities as intelligent sensory neuro-networks.

关键词： Spinning (fibers)

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A robust statistical prediction model for late-summer heavy precipitation days in North China

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Science China Earth Sciences 2025年第1期68卷 158-171页

作者： Shunli JIANG Tingting HAN Xin ZHOU Huijun WANG Zhicong YIN Xiaolei SONG Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education Nanjing University for Information Science and Technology Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

Recently, heavy precipitation(HP) events have occurred frequently in North China(NC), causing devastating economic losses and human fatalities. However, the short-term climate prediction of HP is quite limited. Combining year-to-year increment(DY) method and sliding correlations, we developed a robust seasonal prediction model for late-summer HP days(HPDs) in NC during 1982–2022, utilizing three independent predictors—February sea surface temperature(SST) in the Indian Ocean(SST＿IO), February snow depth over North Asia(SDE＿NA), and May melted snow depth in NC(MSDE＿NC). The SST＿IO anomalies affect NC's precipitation through the Pacific-Japan pattern. The SDE＿NA anomalies are associated with East Asian anomalous anticyclone by southeastern propagation of Rossby wave at Eurasia. The MSDE＿NC anomalies are followed by vertical motion and moisture anomalies in situ and thereby cause precipitation anomalies. This prediction model can well simulate the variations of the HPDs, with a correlation coefficient(CC) of 0.81(0.65) between the observed and predicted HPDs＿DY(HPDs＿anomaly). The percentage with the same sign for 15 extreme HPDs＿anomaly years(PSSE) is 100%.Moreover, in the cross-validation test during 1982–2022, the PSSE for HPDs＿anomaly is as high as 100%, along with a low rootmean-square error of 1.14. For independent hindcasts during 2013–2022, the CC between the observed and predicted HPDs＿DY(HPDs＿anomaly) is 0.93(0.83), together with high Nash-Sutcliffe efficiency(0.82) and agreement index(0.89). Specifically, the predictions are broadly consistent with the observations for 2015, 2016, 2017, 2021, and 2022, reflecting excellent performance of this prediction model of HPDs in NC.

关键词： Heavy precipitation at North China Year-to-year increment approach Robust seasonal prediction Sea surface temperature Snow depth

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Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random feld modeling

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Chinese Journal of Aeronautics 2013年第6期26卷 1534-1543页

作者： Yuan Zhan He You Cai Fuqing Research institute of information fusion Technology Naval Aeronautical and Astronautical University

Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge *** this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF）and maximum a posteriori（MAP）estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly *** the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent *** normalized ratios constructed in different directions are computed,based on which two edge strength maps（ESMs）are *** fnal edge detection result is achieved by fusing the results of two thresholded *** experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt＇s fgure of merit and visual evaluation in most cases.

关键词： Discontinuity-adaptive Markov random feld(DAMRF) Edge detection Local mean reflectivity Maximum a posteriori(MAP) estimation Synthetic aperture radar(SAR

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