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

Representing an Object by Interchanging What with Where

Exploring representations is a fundamental step towards understanding vision. The visual system carries two types of information along separate pathways: One is about what it is and the other is about where it is. Initially, the what is represented by a pattern of activity that is distributed across millions of photoreceptors, whereas the where is 'implicitly' given as their retinotopic positions. Many computational theories of object recognition rely on such pixel-based representations, but they are insufficient to learn spatial information such as position and size due to the implicit encoding of the where information. 
Here we try transforming a retinal image of an object into its internal image via interchanging the what with the where, which means that patterns of intensity in internal image describe the spatial information rather than the object information. To be concrete, the retinal image of an object is deformed and turned over into a negative image, in which light areas appear dark and vice versa, and the object's spatial information is quantified with levels of intensity on borders of that image. 
Interestingly, the inner part excluding the borders of the internal image shows the position and scale invariance. In order to further understand how the internal image associates the what and where, we examined the internal image of a face which moves or is scaled on the retina. As a result, we found that the internal images form a linear vector space under the object translation and scaling. 
In conclusion, these results show that the what-where interchangeability might play an important role for organizing those two into internal representation of brain

Multidimensional integration through Markovian sampling under steered function morphing: a physical guise from statistical mechanics

We present a computational strategy for the evaluation of multidimensional integrals on hyper-rectangles based on Markovian stochastic exploration of the integration domain while the integrand is being morphed by starting from an initial appropriate profile. Thanks to an abstract reformulation of Jarzynski's equality applied in stochastic thermodynamics to evaluate the free-energy profiles along selected reaction coordinates via non-equilibrium transformations, it is possible to cast the original integral into the exponential average of the distribution of the pseudo-work (that we may term "computational work") involved in doing the function morphing, which is straightforwardly solved. Several tests illustrate the basic implementation of the idea, and show its performance in terms of computational time, accuracy and precision. The formulation for integrand functions with zeros and possible sign changes is also presented. It will be stressed that our usage of Jarzynski's equality shares similarities with a practice already known in statistics as Annealed Importance Sampling (AIS), when applied to computation of the normalizing constants of distributions. In a sense, here we dress the AIS with its "physical" counterpart borrowed from statistical mechanics.Comment: 3 figures Supplementary Material (pdf file named "JEMDI_SI.pdf"

Sketching-out virtual humans: A smart interface for human modelling and animation

In this paper, we present a fast and intuitive interface for sketching out 3D virtual humans and animation. The user draws stick figure key frames first and chooses one for “fleshing-out” with freehand body contours. The system automatically constructs a plausible 3D skin surface from the rendered figure, and maps it onto the posed stick figures to produce the 3D character animation. A “creative model-based method” is developed, which performs a human perception process to generate 3D human bodies of various body sizes, shapes and fat distributions. In this approach, an anatomical 3D generic model has been created with three distinct layers: skeleton, fat tissue, and skin. It can be transformed sequentially through rigid morphing, fatness morphing, and surface fitting to match the original 2D sketch. An auto-beautification function is also offered to regularise the 3D asymmetrical bodies from users’ imperfect figure sketches. Our current system delivers character animation in various forms, including articulated figure animation, 3D mesh model animation, 2D contour figure animation, and even 2D NPR animation with personalised drawing styles. The system has been formally tested by various users on Tablet PC. After minimal training, even a beginner can create vivid virtual humans and animate them within minutes