Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD1, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.
Gaussian mixture models(GMM) is a widely used approach for background modeling. However, computational barriers have limited its usage in real-time video processing applications. In this paper, we discussed a new update algorithm to achieve the goal of fast detection. Dirichlet prior are introduced to avoid redundant Gaussian components, reducing the computation time of each pixel. Most of the existing GMM based techniques use background/foreground data proportion, which is highly sensitive to the environment, to detect object. To avoid its possible negative effects on segmentation, we use sigmoid function to approximate the probability of Gaussian component belongs to the background and set a threshold for it to segment. Experimental results show this method leads to a faster and a better segmentation than traditional GMM.
The study addresses the sensor control problem for multi-target tracking via delta generalised labelled multi-Bernoulli (δ-GLMB) filter, and proposes two novel single-sensor control schemes. One is that the Rényi divergence is used as the objective function to measure the information gain between the predicted and posterior densities of the δ-GLMB filter, and it is superior for the overall performance of the system. Since most of the sensor control schemes, including the scheme the authors proposed, are faced the curse of computation, thus the other novel scheme is proposed. This scheme, in which the sum of the statistical distances between the predicted states of targets and sensor is used as the objective function, evades the updated step of the multi-target filter, when computing the objective function for each admissible action. Moreover, these two sensor control schemes are applied to a distributed multi-sensor system, in which the proposed schemes are used for each sensor node and the generalised covariance intersection method is used to compute the fused multi-target posterior density. Finally, they adopt the sequential Monte-Carlo method in bearing and range multi-target tracking scenarios to illustrate the effectiveness of the proposed methods.
A novel direct torque control (DTC) method with copper loss minimization is proposed for the brushless dc motor (BLDCM) in electric vehicle (EV) application. In order to realize high efficiency and high torque control precision at the same time, a special stator flux linkage trajectory is designed based on pseudo-dq transformation for maximum torque per ampere (MTPA) control of BLDCM. The threephase conduction voltage vectors switch table is used to realize the flux linkage tracking control for high dynamic performance of torque control. The torque ripple caused by the non-ideality of the back EMF and the commutation in traditional two-phase conduction voltage vector based DTC method can be eliminated, which improves the torque control precision. The validity and effectiveness of the proposed DTC scheme are verified through experimental results.INDEX TERMS Brushless dc motor (BLDCM), direct torque control (DTC), maximum torque per ampere (MTPA), minimum copper loss, stator flux linkage control.
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