PSR: Unified Framework of Parameter-Learning-Based MR Image Superresolution

Magnetic resonance imaging has significant applications for disease diagnosis. Due to the particularity of its imaging mechanism, hardware imaging suffers from resolution and reaches its limit, and higher radiation intensity and longer radiation time will cause damage to the human body. The problem is expected to be solved by a superresolution algorithm, especially the image superresolution based on sparse reconstruction has good performance. Dictionary generation is a key issue that affects the performance of superresolution algorithms, and dictionary performance is affected by dictionary construction parameters: balance parameters, dictionary size, overlapping block size, and a number of training sample blocks. In response to this problem, we propose an optimal dictionary construction parameter search method through the experiment to find the optimal dictionary construction parameters on the MR image and compare them with the dictionary obtained by multiple sets of random dictionary construction parameters. The dictionary we searched for the optimal parameters of the dictionary construction training has more powerful feature expressions, which can improve the superresolution effect of MR images.

Comparison of Poisson and Quasi-Poisson Regression: A Simulation study

Poisson regression is often used to model count data. However, it requires the assumption of equidispersion which not always met in the real application data. Quasi-Poisson can be considered as an alternative to handle this problem. The objective of this essay is to explain about the Quasi-Poisson regression, the likelihood construction, parameter estimation, and its implementation in real life data. The numerical method used in this study is Newton-Raphson which is equivalent to Iterative Weighted Least Square (IWLS) at the end of calculation. The simulation results for the data with the above problem showed that, in case of overdispersion, Quasi-Poisson regression with Maximum Quasi-Likelihood method provided a good fit to the data compared to Poisson regression.

Comparison of Poisson and Quasi-Poisson Regression: A Simulation study

Poisson regression is often used to model count data. However, it requires the assumption of equidispersion which not always met in the real application data. Quasi-Poisson can be considered as an alternative to handle this problem. The objective of this essay is to explain about the Quasi-Poisson regression, the likelihood construction, parameter estimation, and its implementation in real life data. The numerical method used in this study is Newton-Raphson which is equivalent to Iterative Weighted Least Square (IWLS) at the end of calculation. The simulation results for the data with the above problem showed that, in case of overdispersion, Quasi-Poisson regression with Maximum Quasi-Likelihood method provided a good fit to the data compared to Poisson regression.

THE DESIGN OF HYBRID ELECTROMECHANICAL VALVE ACTUATOR: EFFECTS OF CONSTRUCTION PARAMETERS

In this paper, a novel modified cylindrical core, axis symmetric, low-inductance, hybrid permanent magnet (PM)/electromagnet (EM) magnetomotive force (MMF) actuator is proposed for variable valve timing camless engine. The new design provides large magnetic force with low energy consumption (startup and holding force), permanent magnet demagnetization isolation and improved transient response. The design and construction parameter sensitivity simulation results confirm the force of approximately 200 N (in the presence of coil current) and 500 N (in the absence of coil current) at equilibrium position and armature seat, respectively. An improvement in transition time due to low coil inductance, flexible control with parallel coils and 41.96% low energy dissipation is observed.