Morphing Switched-Capacitor Converters with Variable Conversion Ratio

High-voltage-gain and wide-input-range dc-dc converters are widely used in various electronics and industrial products such as portable devices, telecommunication, automotive, and aerospace systems. The two-stage converter is a widely adopted architecture for such applications, and it is proven to have a higher efficiency as compared with that of the single-stage converter. This paper presents a modular-cell-based morphing switched-capacitor (SC) converter for application as a front-end converter of the two-stage converter. The conversion ratio of this converter is flexible and variable and can be freely extended by increasing more SC modules. The varying conversion ratio is achieved through the morphing of the converter's structure corresponding to the amplitude of the input voltage. This converter is light and compact, and is highly efficient over a very wide range of input voltage and load conditions. Experimental work on a 25-W, 6-30-V input, 3.5-8.5-V output prototype, is performed. For a single SC module, the efficiency over the entire input voltage range is higher than 98%. Applied into the two-stage converter, the overall efficiency achievable over the entire operating range is 80% including the driver's loss

Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates

The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive thresholding is proposed. It is shown that this algorithm is both structurally consistent and risk consistent and the error probability of structure learning decays faster than any polynomial in the number of samples under fixed model size. For the high-dimensional scenario where the size of the model d and the number of edges k scale with the number of samples n, sufficient conditions on (n,d,k) are given for the algorithm to satisfy structural and risk consistencies. In addition, the extremal structures for learning are identified; we prove that the independent (resp. tree) model is the hardest (resp. easiest) to learn using the proposed algorithm in terms of error rates for structure learning.Comment: Accepted to the Journal of Machine Learning Research (Feb 2011

High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion

We consider the problem of high-dimensional Gaussian graphical model selection. We identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is based on thresholding of empirical conditional covariances. Under a set of transparent conditions, we establish structural consistency (or sparsistency) for the proposed algorithm, when the number of samples n=omega(J_{min}^{-2} log p), where p is the number of variables and J_{min} is the minimum (absolute) edge potential of the graphical model. The sufficient conditions for sparsistency are based on the notion of walk-summability of the model and the presence of sparse local vertex separators in the underlying graph. We also derive novel non-asymptotic necessary conditions on the number of samples required for sparsistency

A Comparison of the Classification of Vegetation Characteristics by Spectral Mixture Analysis and Standard Classifiers on Remotely Sensed Imagery within the Siberia Region

As an alternative to the traditional method of inferring vegetation cover characteristics from satellite data by classifying each pixel into a specific land cover type based on predefined classification schemes, the Spectral Mixture Analysis (SMA) method is applied to images of the Siberia region. A linear mixture model was applied to determine proportional estimates of land cover for, (a) agriculture and floodplain soils, (b) broadleaf, and (c) conifer classes, in pixels of 30 m resolution Landsat data. In order to evaluate the areal estimates, results were compared with ground truth data, as well as those estimates derived from more sophisticated method of image classification, providing improved estimates of endmember values and subpixel areal estimates of vegetation cover classes than the traditional approach of using predefined classification schemes with discrete numbers of cover types. This technique enables the estimation of proportional land cover type in a single pixel and could potentially serve as a tool for deriving improved estimates of vegetation parameters that are necessary for modeling carbon processes

Bulk superconductivity in Bi4O4S3 revealed by specific heat measurement

Specific heat experiments on a well-characterized polycrystalline sample of the BiS2 based superconductor Bi4O4S3 revealed that it shows a crear specific heat anomaly at about Tc = 4.4 K, consistent with Tc from the resistivity and dc susceptibility. This observation indicates the superconductivity of Bi4O4S3 to be bulk in nature

The effect of manganese oxide on the sinterability of hydroxyapatite

The sinterability of manganese oxide (MnO2) doped hydroxyapatite (HA) ranging from 0.05 to 1 wt% was investigated. Green samples were prepared and sintered in air at temperatures ranging from 1000 to 1400 °C. Sintered bodies were characterized to determine the phase stability, grain size, bulk density, hardness, fracture toughness and Young's modulus. XRD analysis revealed that the HA phase stability was not disrupted throughout the sintering regime employed. In general, samples containing less than 0.5 wt% MnO2 and when sintered at lower temperatures exhibited higher mechanical properties than the undoped HA. The study revealed that all the MnO2-doped HA achieved >99% relative density when sintered at 1100–1250 °C as compared to the undoped HA which could only attained highest value of 98.9% at 1150 °C. The addition of 0.05 wt% MnO2 was found to be most beneficial as the samples exhibited the highest hardness of 7.58 GPa and fracture toughness of 1.65 MPam1/2 as compared to 5.72 GPa and 1.22 MPam1/2 for the undoped HA when sintered at 1000 °C. Additionally, it was found that the MnO2-doped samples attained E values above 110 GPa when sintered at temperature as low as 1000 °C if compared to 1050 °C for the undoped HA