Model-based X-ray CT Image and Light Field Reconstruction Using Variable Splitting Methods.

Model-based image reconstruction (MBIR) is a powerful technique for solving ill-posed inverse problems. Compared with direct methods, it can provide better estimates from noisy measurements and from incomplete data, at the cost of much longer computation time. In this work, we focus on accelerating and applying MBIR for solving reconstruction problems, including X-ray computed tomography (CT) image reconstruction and light field reconstruction, using variable splitting based on the augmented Lagrangian (AL) methods. For X-ray CT image reconstruction, we combine the AL method and ordered subsets (OS), a well-known technique in the medical imaging literature for accelerating tomographic reconstruction, by considering a linearized variant of the AL method and propose a fast splitting-based ordered-subset algorithm, OS-LALM, for solving X-ray CT image reconstruction problems with penalized weighted least-squares (PWLS) criterion. Practical issues such as the non-trivial parameter selection of AL methods and remarkable memory overhead when considering the finite difference image variable splitting are carefully studied, and several variants of the proposed algorithm are investigated for solving practical model-based X-ray CT image reconstruction problems. Experimental results show that the proposed algorithm significantly accelerates the convergence of X-ray CT image reconstruction with negligible overhead and greatly reduces the noise-like OS artifacts in the reconstructed image when using many subsets for OS acceleration. For light field reconstruction, considering decomposing the camera imaging process into a linear convolution and a non-linear slicing operations for faster forward projection, we propose to reconstruct light field from a sequence of photos taken with different focus settings, i.e., a focal stack, using an alternating direction method of multipliers (ADMM). To improve the quality of the reconstructed light field, we also propose a signal-independent sparsifying transform by considering the elongated structure of light fields. Flatland simulation results show that our proposed sparse light field prior produces high resolution light field with fine details compared with other existing sparse priors for natural images.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108981/1/hungnien_1.pd

A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud

Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for power-aware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.Comment: 10 page

Web Usage Mining to Extract Knowledge for Modelling Users of Taiwan Travel Recommendation Mobile APP

This work presents the design of a web mining system to understand the navigational behavior of passengers in developed Taiwan travel recommendation mobile app that provides four main functions including recommend by location , hot topic , nearby scenic spots information , my favorite and 2650 scenic spots. To understand passenger navigational patterns, log data from actual cases of app were collected and analysed by web mining system. This system analysed 58981 sessions of 1326 users for the month of June, 2014. Sequential profiles for passenger navigational patterns were captured by applying sequence-based representation schemes in association with Markov models and enhanced K-mean clustering algorithms for sequence behavior mining cluster patterns. The navigational cycle, time, function numbers, and the depth and extent (range) of app were statistically analysed. The analysis results can be used improved the passengers\u27 acceptance of app and help generate potential personalization recommendations for achieving an intelligent travel recommendation service

Extracorporeal membrane oxygenation for neonatal congenital diaphragmatic hernia: The initial single-center experience in Taiwan

Background/Purpose Extracorporeal membrane oxygenation (ECMO) is a treatment option for stabilizing neonates with congenital diaphragmatic hernia (CDH) in a critical condition when standard therapy fails. However, the use of this approach in Taiwan has not been previously reported. Methods The charts of all neonates with CDH treated in our institute during the period 2007–2014 were reviewed. After 2010, patients who could not be stabilized with conventional treatment were candidates for ECMO. We compared the demographic data of patients with and without ECMO support. The clinical course and complications of ECMO were also reviewed. Results We identified 39 neonates with CDH with a median birth weight of 2696 g (range, 1526–3280 g). Seven (18%) of these patients required ECMO support. The APGAR score at 5 minutes differed significantly between the ECMO and non-ECMO groups. The survival rate was 84.6% (33/39) for all CDH patients and 57.1% (4/7) for the ECMO group. The total ECMO bypass times in the survivors was in the range of 5–36 days, whereas all nonsurvivors received ECMO for at least 36 days (mean duration, 68 days). Surgical bleeding occurred in four of seven patients in the ECMO group. Conclusion The introduction of ECMO rescued some CDH patients who could not have survived by conventional management. Prolonged (i.e., > 36 days) ECMO support had no benefit for survival

Non-Typhoidal Salmonella and the Risk of Kawasaki Disease: A Nationwide Population-Based Cohort Study

ObjectiveThe aim of this study was to investigate the relationship between non-typhoidal Salmonella (NTS) infection and the risk of Kawasaki disease (KD) by using a nationwide population-based data set in Taiwan.MethodsIn this retrospective cohort study, we enrolled 69,116 patients under 18 years of age, with NTS from January 1st, 2000, to December 31st, 2013, using the population-based National Health Insurance Research Database of Taiwan. A comparison group without NTS was matched (at a 1:4 ratio) by propensity score. The two cohorts were followed from the initial diagnosis of NTS until the date of KD development or December 31st, 2013. Cox proportional hazard regression analysis was conducted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) after adjusting for covariates. Also, we conducted sensitivity analyses to examine our findings.ResultsAfter adjusting for covariates, the risk of KD for the children with NTS was significantly higher than that of the comparison group (hazard ratio = 1.31; 95% confidence interval = 1.03-1.66; p < 0.01). Stratified analysis showed that the associated risk of the investigated outcome was significant in children aged ≤2 years (aHR= 1.31, 95% C.I. 1.02-1.69), in female patients (aHR= 1.46, 95% C.I. 1.03-2.08), and in those without allergic diseases.ConclusionsNTS is associated with an increased risk of KD in Taiwanese children

Effects of Lutein on Hyperosmoticity-Induced Upregulation of IL-6 in Cultured Corneal Epithelial Cells and Its Relevant Signal Pathways

Dry eye is a common disorder characterized by deficiency of tear. Hyperosmoticity of tear stimulates inflammation and damage of ocular surface tissues and plays an essential role in the pathogenesis of dry eye. Cultured human corneal epithelial (CE) cells were used for the study of effects of lutein and hyperosmoticity on the secretion of IL-6 by CE cells. Cell viability of CE cells was not affected by lutein at 1–10 μM as determined by MTT assay. Hyperosmoticity significantly elevated the secretion of IL-6 by CE cells as measured by ELISA analysis. The constitutive secretion of IL-6 was not affected by lutein. Lutein significantly and dose-dependently inhibited hyperosmoticity-induced secretion of IL-6. Phosphorylated- (p)- p38 MAPK, p-JNK levels in cell lysates and NF-κB levels in cell nuclear extracts were increased by being exposed to hyperosmotic medium. JNK, p38, and NF-κB inhibitors decreased hyperosmoticity-induced secretion of IL-6. Lutein significantly inhibited hyperosmoticity-induced elevation of NF-κB, p38, and p-JNK levels. We demonstrated that lutein inhibited hyperosmoticity-induced secretion of IL-6 in CE cells through the deactivation of p38, JNK, and NF-κB pathways. Lutein may be a promising agent to be explored for the treatment of dry eye