Aiming at the Intravascular Ultrasound(IVUS) image with strong background noise and pseudo shadow interference,a novel method of automatic vessel wall edge detection for IVUS images is proposed.In the process of detecting edge,the noise can be reduced by using the spatial and temporal correlation among the sequences.The new regulatory factor and adaptive normal external force are introduced in the GVF-Snake model which not only enlarges the edge capture range and makes the extraction effect of the image more accuracy,but also improves the robustness of the active contour to noise.It uses cubic B-spline to enhance the edge smoothness,and speeds up the convergence.Experimental results demonstrate that the method has higher accuracy and shorter running time.
We describe our solution for the NTIRE–2021 High Dynamic Range Challenge with Single Frame Track where we achieved the 3 rd place in terms of muPSNR and the 1 st place in terms of PSNR. Aiming at this challenge we introduce the Task-specific Network based on Channel Adaptive RDN(TCRDN) that achieves good performance on the similarity with the ground truth. The network is divided into three subnets: Image Reconstruction(IR), Detail Restoration(DR) and Local Contrast Enhancement(LCE). Each subnet processes its own task, and results are fused to produce the HDR output. We carefully design these subnets so that they are properly trained for their intended purpose: detail restoration in the IR subnet and contrast enhancement in the LCE subnet. The Channel Adaptive RDN is a novel network working as the subnet backbone that combines the classic Residual Dense Network(RDN) and the Gate Channel Transformation layer. The L1 loss is used for training the network and the final model can balance the trade–off between PSNR and muPSNR for high performance in the competition's task.
Since beamspace MUSIC (BM-MUSIC) method cannot resolve coherent sources, and the use of spatial smoothing pre-process leads to array aperture loss and reduction in detectable source numbers, a new beamspace MUSIC (BMC-MUSIC) method is presented for non-circular sources to this end. The proposed method exploits the characteristics of non-circular sources to construct conjugate symmetric Toeplitz matrix from received data vectors to form pseudo covariance matrix, then the source directions of arrival (DOAs) are estimated by techniques similar to BM-MUSIC method. Simulation results show that the proposed method can resolve coherent sources using a single or few snapshots without spatial smoothing preprocessing and its performance is better than the BM-MUSIC method with spatial smoothing.
Abstract Intrusion detection is a crucial technology in the communication network security field. In this paper, a dynamic evolutionary sparse neural network (DESNN) is proposed for intrusion detection, named as DESNN algorithm. Firstly, an ensemble neural network model is constructed, which is processed by a dynamic pruning rule and further divided into advantage subnetworks and disadvantage subnetworks. The dynamic pruning rule can effectively reduce the subnetworks weight parameters, thereby increasing the speed of the subnetworks intrusion detection. Then considering the subnetworks performance loss caused by the dynamic pruning rule, a novel evolutionary mechanism is proposed to optimize the training process of the disadvantage subnetworks. The weight of the disadvantage subnetworks approach the weight of the advantage subnetworks by the evolutionary mechanism, such that the performance of the ensemble neural network can be improved. Finally, an optimal subnetwork is selected from the ensemble neural network, which is used to detect multiple types of intrusion. Experiments show that the proposed DESNN algorithm improves intrusion detection speed without causing significant performance loss compare with other fully-connected neural network models.
In the face of an infectious disease, a key epidemiological measure is the basic reproduction number, which quantifies the average secondary infections caused by a single case in a susceptible population. In practice, the effective reproduction number, denoted as $$R_t$$ , is widely used to assess the transmissibility of the disease at a given time t. Real-time estimating this metric is vital for understanding and managing disease outbreaks. Traditional statistical inference often relies on two assumptions. One is that samples are assumed to be drawn from a homogeneous population distribution, neglecting significant variations in individual transmission rates. The other is the ideal case reporting assumption, disregarding time delays between infection and reporting. In this paper, we thoroughly investigate these critical factors and assess their impact on estimating $$R_t$$ . We first introduce negative binomial and Weibull distributions to characterize transmission rates and reporting delays, respectively, based on which observation and state equations are formulated. Then, we employ a Bayesian filtering for estimating $$R_t$$ . Finally, validation using synthetic and empirical data demonstrates a significant improvement in estimation accuracy compared to conventional methods that ignore these factors.
Tiny-YOLOv3 is a simplified YOLO algorithm and has the characteristics of simple network model and small computational cost, which is very suitable for real-time target detection applications. Aiming at low accuracy of Tiny-YOLOv3 used in detecting small target objects, Tiny-YOLOv3 algorithm is improved by changing two-scale detection to three-scale detection and calculating the parallelism ratio of the loss function with CIoU (Complete-IoU) in this paper. Meanwhile, the BN (Batch Normalization) layer is merged into the convolutional layer, which speeds up the forward inference of Tiny-YOLOv3. The improved Tiny-YOLOv3 is realized in an FPGA (Field programmable gate array) to detect targets. The experimental results show that detection accuracy of the improved Tiny-YOLOv3 is increased by 48.6% and the detection speed of the improved Tiny-YOLOv3 is decreased by only 5% compared with Tiny- YOLOv3. It is suitable for realization on FPGA.
This letter reports a new nanocrossbar array ESD protection design. The unique nanocrossbar array structures ensure uniform ESD discharging and achieve fast ESD response speed and >; 8A ESD protection capability in prototypes. The nanoswitching ESD protection effect eliminates large leakage current inherent to traditional p-n-junction-type ESD protection devices.
Artificial searching swarm algorithm (ASSA) is a novel intelligent optimization algorithm. In this work, an improved ASSA (IASSA) with global inertia weight is proposed. IASSA and ASSA are used for optimizing multivariable functions and the performances have been compared. The results proved that the IASSA possesses a better performance than that of ASSA in both searching precision and convergent speed.
Slice is one of the major operations in on-line analysis processing which has played an important role in the application of decision support. Based on data cube, by mining the maximum singular value of the slices, a method was proposed in this paper to extract the inner rules of movement. Algebraic theories proved that it is feasible. And the numerical experiment also demonstrated that it is efficient.
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