Low Dose CT Image Reconstruction With Learned Sparsifying Transform

A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images. We adopt an alternating algorithm to optimize the PWLS-ST cost function that alternates between a CT image update step and a sparse coding step. We adopt a relaxed linearized augmented Lagrangian method with ordered-subsets (relaxed OS-LALM) to accelerate the CT image update step by reducing the number of forward and backward projections. Numerical experiments on the XCAT phantom show that for low dose levels, the proposed PWLS-ST method dramatically improves the quality of reconstructed images compared to PWLS reconstruction with a nonadaptive edge-preserving regularizer (PWLS-EP).Comment: This is a revised and corrected version of the IEEE IVMSP Workshop paper DOI: 10.1109/IVMSPW.2016.752821

Face recognition system in deep learning

This article mainly introduces the application of deep learning in face recognition system, which makes face recognition system quicker, safer and higher recognition rate

Automatic Modulation Classification Using Compressive Convolutional Neural Network

The deep convolutional neural network has strong representative ability, which can learn latent information repeatedly from signal samples and improve the accuracy of automatic modulation classification (AMC). In this paper, a novel compressive convolutional neural network (CCNN) is proposed for AMC, where different constellation images, i.e., regular constellation images (RCs) and contrast enhanced grid constellation images (CGCs), are generated as network inputs from received signals. Moreover, a compressive loss constraint is proposed to train the CCNN, which aims at capturing high-dimensional features for modulation classification. Additionally, CCNN utilizes intra-class compactness and inter-class separability to enhance the classification and robustness performance for the different orders of modulations. The simulation results demonstrate that CCNN displays superior classification and robustness performance than existing AMC methods

Numerical simulation of combustion characteristics in a 660 MW tangentially fired pulverized coal boiler subjected to peak-load regulation

Coal power generation will be subjected to stringent peak-load regulation as renewable energy will become the mainstream option for the electrical power system in the future. Therefore, it is imperative to explore the combustion characteristics of coal-fired boilers in peak-load regulation. This paper aims to reveal the combustion characteristics of a 660 MW tangentially fired pulverized coal boiler under various loads in conjunction with different burner arrangements. The combustion characteristics of the furnace are analyzed under the context of boiler maximum continue rate, turbine heat acceptance and 75% turbine heat acceptance, respectively. The analytical results show that the middle tangential circle has a large diameter and that the flame tends to adhere to the wall. The overall combustion is relatively consistent when the same group of burners operate. It is basically similar to the change of component concentration in different cases. In comparison with other loads, the temperature reduction in the furnace under 75% turbine heat acceptance reduces NO by around 20%. In addition, by comparing the bottom burner group (ABCDE) with the upper burner group (BCDEF) under the same loads, it can be discovered that NO is reduced by roughly 70–80 ppm

Dynamic functional network connectivity associated with post-traumatic stress symptoms in COVID-19 survivors

Accumulating evidence shows that Coronavirus Disease 19 (COVID-19) survivors may encounter prolonged mental issues, especially post-traumatic stress symptoms (PTSS). Despite manifesting a plethora of behavioral or mental issues in COVID-19 survivors, previous studies illustrated that static brain functional networks of these survivors remain intact. The insignificant results could be due to the conventional statistic network analysis was unable to reveal information that can vary considerably in different temporal scales. In contrast, time-varying characteristics of the dynamic functional networks may help reveal important brain abnormalities in COVID19 survivors. To test this hypothesis, we assessed PTSS and collected functional magnetic resonance imaging (fMRI) with COVID-19 survivors discharged from hospitals and matched controls. Results showed that COVID-19 survivors self-reported a significantly higher PTSS than controls. Tapping into the moment-to-moment variations of the fMRI data, we captured the dynamic functional network connectivity (dFNC) states, and three discriminative reoccurring brain dFNC states were identified. First of all, COVID-19 survivors showed an increased occurrence of a dFNC state with heterogeneous patterns between sensorimotor and visual networks. More importantly, the occurrence rate of this state was significantly correlated with the severity of PTSS. Finally, COVID-19 survivors demonstrated decreased topological organizations in this dFNC state than controls, including the node strength, degree, and local efficiency of the supplementary motor area. To conclude, our findings revealed the altered temporal characteristics of functional networks and their associations with PTSS due to COVID-19. The current results highlight the importance of evaluating dynamic functional network changes with COVID-19 survivors.</p