Color Cherenkov imaging of clinical radiation therapy

Color vision is used throughout medicine to interpret the health and status of tissue. Ionizing radiation used in radiation therapy produces broadband white lig...     

Aesthetic 3D model evolution

A new research frontier for evolutionary 2D image generation is the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming. Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey was conducted, and survey takers showed a statistically significant preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic evolution of 3D structures is a promising new research area for evolutionary design.     

European research policy and bibliometric indicators, 1990�C2005

This paper describes and analyses the role played in the development of bibliometric indicators??and the use made of bibliometric indicators for policy purposes??by the European Commission??s Directorate-General Research in the period 1990?C2005.     

A multilinear constraint on dichromatic planes for illumination estimation.

A new multilinear constraint on the color of the scene illuminant based on the dichromatic reflection model is proposed. The formulation avoids the problem, common to previous dichromatic methods, of having to first identify pixels corresponding to the same surface material. Once pixels from two or more materials have been identified, their corresponding dichromatic planes can be intersected to yield the illuminant color. However, it is not always easy to determine which pixels from an arbitrary region of an image belong to which dichromatic plane. The image region may cover an area of the scene encompassing several different materials and, hence, pixels from several different dichromatic planes. The new multilinear constraint accounts for this multiplicity of materials and provides a mechanism for choosing the most plausible illuminant from a finite set of candidate illuminants. The performance of this new method is tested on a database of real images.     

利用结构化ASIC实现信号处理应用

FPGA所具有的设计灵活性和大吞吐量特性使其成为传统数字信号处理(DSP)器件可靠的芯片解决方案,例如无线基站、医学成像和图像记录等高性能DSP应用.在很多情况下,FPGA和高密度ASIC、DSP一起布置在同一块电路板上.通常由ASIC和FPGA分担的硬件功能现在主要由FPGA来实现,这是因为FPGA能够为DSP提供具有成本效益的方案,广泛应用于各种领域.     

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.