查看更多>>摘要：As a safe and convenient imaging technology in clinical routine diagnosis,ultrasound imaging can pro-vide real-time 2D images of internal tissues and organs.To realize real-time 3D image reconstruction,pixel nearest neighbor interpolation(PNN)reconstruction algorithm and Bezier interpolation algorithm are combined into a hy-brid reconstruction algorithm.On this basis,a real-time interactive 3D ultrasound imaging system is developed.Through temporal calibration and spatial calibration,the six degrees of freedom poses of 2D ultrasound images can be accurately collected.The 3D volume reconstructed by the proposed 3D reconstruction algorithm is visualized by volume rendering.A multi-thread software system allows parallel operation of data acquisition,3D reconstruction,volume visualization and other functions.3D imaging experiments on a 3D printing femur model,a neck phantom and the neck of human volunteers were performed for systematic evaluation.When the reconstruction voxel size was set to be(0.53 mm3,1.03 mm3,1.53 mm3),the reconstruction errors of the femur and trachea model were respective-ly(0.23 mm,0.31 mm,0.56 mm)and(0.62 mm,0.88 mm,1.41 mm).Clinical feasibility was demonstrated by appli-cation of the 3D ultrasound imaging on the neck of human volunteers.
查看更多>>摘要：Predicting RNA binding protein(RBP)binding sites on circular RNAs(circRNAs)is a fundamental step to understand their interaction mechanism.Numerous computational methods are developed to solve this prob-lem,but they cannot fully learn the features.Therefore,we propose circ-CNNED,a convolutional neural network(CNN)-based encoding and decoding framework.We first adopt two encoding methods to obtain two original matri-ces.We preprocess them using CNN before fusion.To capture the feature dependencies,we utilize temporal convolu-tional network(TCN)and CNN to construct encoding and decoding blocks,respectively.Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED.We perform circ-CNNED across 37 datasets to evaluate its effect.The comparison and ablation experiments demonstrate that our method is superior.In addition,motif enrichment analysis on four datasets helps us to explore the reason for performance im-provement of circ-CNNED.
查看更多>>摘要：With the rapid development of industrial parks,its load model research has become a hot spot.In or-der to study the load of power system in industrial park,based on the characteristics of the industrial park load,a comprehensive load admittance static model with full voltage range adaptability is considered,and a comprehensive load model of distribution network of the industrial park is established.A complete parameter identification of the model is carried out through chaos particle swarm optimization.The simulation results show that the model can ef-fectively describe the load characteristics of the distribution network in industrial parks.
查看更多>>摘要：The pressure data of the train air braking system is of great significance to accurately evaluate its op-eration state.In order to overcome the influence of sensor fault on the pressure data of train air braking system,it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault,the system can accurately identify and locate the position of the faulty sensor,and estimate the fault data ac-cording to other normal data.A fault-tolerant mechanism based on multi-classification support vector machine(SVM)and adaptive network-based fuzzy inference system(ANFIS)is introduced.Multi-classification SVM is used to identify and locate the system fault state,and ANFIS is used to estimate the real data of the fault sensor.After estimation,the system will compare the real data of the fault sensor with the ANFIS estimated data.If it is similar,the system will recognize that there is a false alarm and record it.Then the paper tests the whole mechanism based on the real data.The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.
查看更多>>摘要：The adaptive fusion estimation problem was studied for the multi-sensor nonlinear under-observed systems with multiplicative noise.A one-step predictor with state update equations was designed for the virtual state with virtual noise first of all.An extended incremental Kalman filter(EIKF)was then proposed for the nonlinear un-der-observed systems.Furthermore,an adaptive filtering method was given for optimization.The fusion adaptive in-cremental Kalman filter weighted by scalar was finally proposed.The comparison analysis was made to verify the op-timization of the state estimation using adaptive filtering method in the filtering process.
查看更多>>摘要：Image quality assessment(IQA)model is designed to measure the image quality in consistent with subjective ratings by computational models.In this research,a valid no reference IQA(NR-IQA)model for color im-age sharpness assessment is proposed based on local color difference map in a color space.In the proposed model,the absolute color difference variation and relative color difference variation are combined to evaluate sharpness in YIQ color space(a color coordinate system for the development of the United States color television system).The differ-ence between sharpest and blurriest spot of an image is represented by the absolute color difference variation,and relative color difference variation expresses the variation in the image content.Extensive experiments are performed on five publicly available benchmark synthetic blur databases and two real blur databases,and the results prove that the proposed model work better than the other state-of-the-art and latest NR-IQA models for the prediction accura-cy on blurry images.Besides,the model maintains the lowest computational complexity.
查看更多>>摘要：Denoising(DN)and demosaicing(DM)are the first crucial stages in the image signal processing pipeline.Recently,researches pay more attention to solve DN and DM in a joint manner,which is an extremely un-determined inverse problem.Existing deep learning methods learn the desired prior on synthetic dataset,which lim-its the generalization of learned network to the real world data.Moreover,existing methods mainly focus on the raw data property of high green information sampling rate for DM,but occasionally exploit the high intensity and signal-to-noise(SNR)of green channel.In this work,a deep guided attention network(DGAN)is presented for real image joint DN and DM(JDD),which considers both high SNR and high sampling rate of green information for DN and DM,respectively.To ease the training and fully exploit the data property of green channel,we first train DN and DM sub-networks sequentially and then learn them jointly,which can alleviate the error accumulation.Besides,in order to support the real image JDD,we collect paired raw clean RGB and noisy mosaic images to conduct a realis-tic dataset.The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods,in terms of both quantitative metrics and qualitative visualization.
查看更多>>摘要：The modern university computer lab and kindergarden through 12th grade classrooms require a cen-tralized solution to efficiently manage a large number of desktops.The existing solutions either bring virtualization overhead in runtime or requires loading a large image over 30 GB leading to an unacceptable network latency.In this work,we propose Troy which takes advantage of the differencing virtual hard disk techniques in Windows systems.As such,Troy only loads the modifications made on one machine to all other machines.Troy consists of two mod-ules that are responsible to generate an initial image and merge a differencing image with its parent image,respec-tively.Specifically,we identify the key fields in the virtual hard disk image that links the differencing image and the parent image and find the modified blocks in the differencing images that should be used to replace the blocks in the parent image.We further design a lazy copy solution to reduce the I/O burden in image merging.We have imple-mented Troy on bare metal machines.The evaluation results show that the performance of Troy is comparable to the native implementation in Windows,without requiring the Windows environment.
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