Combining Stream Mining and Neural Networks for Short Term Delay Prediction

The systems monitoring the location of public transport vehicles rely on wireless transmission. The location readings from GPS-based devices are received with some latency caused by periodical data transmission and temporal problems preventing data transmission. This negatively affects identification of delayed vehicles. The primary objective of the work is to propose short term hybrid delay prediction method. The method relies on adaptive selection of Hoeffding trees, being stream classification technique and multilayer perceptrons. In this way, the hybrid method proposed in this study provides anytime predictions and eliminates the need to collect extensive training data before any predictions can be made. Moreover, the use of neural networks increases the accuracy of the predictions compared with the use of Hoeffding trees only

Enhancement of sp(3)-bonding in high-bias-voltage grown diamond-like carbon thin films studied by x-ray absorption and photoemission spectroscopy

[[abstract]]X-ray absorption near-edge structure (XANES) and valence-band photoemission spectroscopy (VB-PES) were used to elucidate the electronic and mechanical properties of diamond-like carbon (DLC) thin films deposited by the plasma-enhanced chemical vapour deposition method at various bias voltages (Vb) using a C2H2 vapour precursor in an Ar+ atmosphere. The increase of Vb is found to increase and decrease the contents of sp3- and sp2-bonded carbon atoms, respectively, i.e. the films become more diamond-like. The Young's modulus measurements show increases with the increase of the presence of sp3-bonded carbon atoms in the structure of the DLC films.[[notice]]補正完

Performance improvement and validation of a new MAP reconstruction algorithm

We previously proposed a fast maximum a posteriori (MAP) algorithm, limited-memory Broyden-Fletcher-Goldfarb- Shanno with boundary constrains (LBFGS-B-PC), combining LBFGS-B with diagonal preconditioning. Previous results have shown in simulations that it converges using around 40 projections independent of many factors. The aim of this study is to improve the algorithm further by using a better initial image and a modified preconditioner that is less sensitive to noise and data scale. By initializing the algorithm with the best initial image (one full iteration of OSEM with 35 subsets), ROI values can converge almost twice as fast for the same computation time. Moreover, the new preconditioner makes the performance more consistent between high and low count data sets. In addition, we have found a means to choose the stopping criteria to reach a desired level of quantitative accuracy in the reconstructed image. Based on the results with patient data, the optimized LBFGS-B-PC shows promise for clinical imaging

Penalized PET/CT Reconstruction Algorithms With Automatic Realignment for Anatomical Priors

Two algorithms for solving misalignment issues in penalized PET/CT reconstruction using anatomical priors are proposed. Both approaches are based on a recently published joint motion estimation and image reconstruction method. The first approach deforms the anatomical image to align it with the functional one while the second approach deforms both images to align them with the measured data. Our current implementation alternates between alignment estimation and image reconstruction. We have chosen parallel level sets (PLSs) as a representative anatomical penalty, incorporating a spatially variant penalty strength. The performance was evaluated using simulated nontime-of-flight data generated with an XCAT phantom in the thorax region. We used the attenuation map in the anatomical prior. The results demonstrated that both methods can estimate the misalignment and deform the anatomical image accordingly. However, the performance of the first approach depends highly on the workflow of the alternating process. The second approach shows a faster convergence rate to the correct alignment and is less sensitive to the workflow. The presence of anatomical information improves the convergence rate of misalignment estimation for the second approach but slow it down for the first approach. Both approaches show improved performance in misalignment estimation as the data noise level decreases

Algorithms for Solving Misalignment Issues in Penalized PET/CT Reconstruction Using Anatomical Priors

In dual-modality systems, using anatomical priors has been shown to improve image quality and quantification in emission tomography. However, alignment between the functional and anatomical images is crucial. In this study, we propose two algorithms for solving misalignment issues. Both approaches are based on a recently published joint motion estimation and image reconstruction method. The first approach deforms the anatomical image to align it with the functional one while the second approach deforms both images to align them with the measured data. Our current implementation uses alternates between image reconstruction and alignment estimation. To evaluate the potential of these approaches, we have chosen Parallel Level Sets (PLS) as a representative anatomical penalty since it has shown promising results in literature, incorporating a spatially-variant penalty strength to achieve uniform local contrast and fast convergence rate. The performance evaluation was achieved by using simulated non-TOF data generated with an XCAT phantom in the thorax region. We used the attenuation image in the anatomical prior. The results demonstrated that both methods are able to estimate the misalignment and deform the anatomical image accordingly when a proper workflow for the alternating optimization is applied. However, the performance of the first approach depends highly on the workflow of the alternating process. In contrast, the second approach shows the ability to converge to the correct alignment faster than the first approach does, independent of the workflow. Our results indicate that it is possible to align functional and anatomical information, enabling the use of anatomical priors in practice

Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning

OAPA This paper reports on the feasibility of using a quasi-Newton optimization algorithm, limited-memory Broyden- Fletcher-Goldfarb-Shanno with boundary constraints (L-BFGSB), for penalized image reconstruction problems in emission tomography (ET). For further acceleration, an additional preconditioning technique based on a diagonal approximation of the Hessian was introduced. The convergence rate of L-BFGSB and the proposed preconditioned algorithm (L-BFGS-B-PC) was evaluated with simulated data with various factors, such as the noise level, penalty type, penalty strength and background level. Data of three 18F-FDG patient acquisitions were also reconstructed. Results showed that the proposed L-BFGS-B-PC outperforms L-BFGS-B in convergence rate for all simulated conditions and the patient data. Based on these results, L-BFGSB- PC shows promise for clinical application

Inactivation of myosin binding protein C homolog in zebrafish as a model for human cardiac hypertrophy and diastolic dysfunction

[[incitationindex]]SC

Electronic structure and bonding properties of Si-doped hydrogenated amorphous carbon films

[[abstract]]This work investigates the C K-edge x-ray absorption near-edge structure (XANES), valence-band photoelectron spectroscopy (PES), and Fourier transform infrared (FTIR) spectra of Si-doped hydrogenated amorphous carbon films. The C K-edge XANES and valence-band PES spectra indicate that the sp2/sp3 population ratio decreases as the amount of tetramethylsilane vapor precursor increases during deposition, which suggest that Si doping% enhances sp3 and reduces sp2-bonding configurations. FTIR spectra show the formation of a polymeric sp3 C–Hn structure and Si–Hn bonds, which causes the Young’s modulus and hardness of the films to decrease with the increase of the Si content.[[incitationindex]]SCI[[booktype]]紙

Electronic properties of a-CNx thin films: An x-ray-absorption and photoemission spectroscopy study

[[notice]]補正完畢[[booktype]]紙本[[booktype]]電子

Efficient simulation of the spatial transmission dynamics of influenza

Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we present a significant improvement to the efficiency of an individual based stochastic disease simulation framework commonly used in multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations. The ability to perform efficient simulation allows us to run a batch of simulations and take account of their average in real time. The averaged data are stable and can be used to differentiate spreading patterns that are not readily seen by only conducting a few runs. © 2010 Tsai et al.published_or_final_versio