Scalable semantic brokering over dynamic heterogeneous data sources in InfoSleuth/spl trade/

InfoSleuth is an agent-based system for information discovery and retrieval in a dynamic, open environment. Brokering in InfoSleuth is a matchmaking process, recommending agents that provide services to agents requesting services. This paper discusses InfoSleuth's distributed multibroker design and implementation. InfoSleuth's brokering function combines reasoning over both the syntax and semantics of agents in the domain. This means the broker must reason over explicitly advertised information about agent capabilities to determine which agent can best provide the requested services. Robustness and scalability issues dictate that brokering must be distributable across collaborating agents. Our multibroker design is a peer-to-peer system that requires brokers to advertise to and receive advertisements from other brokers. Brokers collaborate during matchmaking to give a collective response to requests initiated by nonbroker agents. This results in a robust, scalable brokering system.     

Nucleation of copper on an assembly of carbon microelectrodes

The initial stage of copper electrodeposition on the electrochemically activated assembly of carbon microelectrodes from an acid solution of copper sulfate was investigated using cyclic voltammetry, potentiostatic pulse technique and electrochemical impedance spectroscopy. Analysis of the experimental current transients has been carried out using a non-linear fitting procedure according to the model that takes into account spherical diffusion towards a disc-shaped microelectrode. The higher values of diffusion coefficient in comparison with those observed on planar electrodes were explained with an increased diffusion caused by the electrode geometry. Impedance spectra showed two time constants, the high-frequency related to the charge transfer process and the low-frequency corresponded to the deposit morphology.     

Scalable and systolic Montgomery multiplier over GF(2m) generated by trinomials

A Montgomery's algorithm in GF(2^m) based on the Hankel matrix-vector representation is proposed. The hardware architecture obtained from this algorithm indicates low-complexity bit-parallel systolic multipliers with irreducible trinomials. The results reveal that the proposed multiplier saves approximately 36% of space complexity as compared to an existing systolic Montgomery multiplier for trinomials. A scalable and systolic Montgomery multiplier is also developed by applying the block-Hankel matrix-vector representation. The proposed scalable systolic architecture is demonstrated to have significantly less time-area product complexity than existing digit-serial systolic architectures. Furthermore, the proposed architectures have regularity, modularity and local interconnectability, making the.m highly appropriate for VLSI implementation.     

A novel fuzzy clustering algorithm for the analysis of axillary lymph node tissue sections

Recently Fourier Transform Infrared (FTIR) spectroscopic imaging has been used as a tool to detect the changes in cellular composition that may reflect the onset of a disease. This approach has been investigated as a mean of monitoring the change of the biochemical composition of cells and providing a diagnostic tool for various human cancers and other diseases. The discrimination between different types of tissue based upon spectroscopic data is often achieved using various multivariate clustering techniques. However, the number of clusters is a common unknown feature for the clustering methods, such as hierarchical cluster analysis, k-means and fuzzy c-means. In this study, we apply a FCM based clustering algorithm to obtain the best number of clusters as given by the minimum validity index value. This often results in an excessive number of clusters being created due to the complexity of this biochemical system. A novel method to automatically merge clusters was developed to try to address this problem. Three lymph node tissue sections were examined to evaluate our new method. These results showed that this approach can merge the clusters which have similar biochemistry. Consequently, the overall algorithm automatically identifies clusters that accurately match the main tissue types that are independently determined by the clinician.     

Visual insights into high-resolution earthquake Simulations.

This study focuses on the visualization of a series of large earthquake simulations collectively called TeraShake. The simulation series aims to assess the impact of San Andreas Fault earthquake scenarios in Southern California. We discuss the role of visualization in gaining scientific insight and aiding unexpected discoveries.     

Optical Splitting Trees for High-Precision Monocular Imaging

In this article, we consider the design of monocular multiview optical systems that form optical splitting trees, where the optical path topology takes the shape of a tree because of recursive beam splitting. Designing optical splitting trees is challenging when it requires many views with specific spectral properties. We introduce a manual design paradigm for optical splitting trees and a computer-assisted design tool to create efficient splitting-tree cameras. The tool accepts as input a specification for each view and a set of weights describing the user's relative affinity for efficiency, measurement accuracy, and economy. An optimizer then searches for a design that maximizes these weighted priorities. Our tool's output is a splitting-tree design that implements the input specification and an analysis of the efficiency of each root-to-leaf path. Automatically designed trees appear comparable to those designed by hand; we even show some cases where they are superior. With the help of the optimizer, the system demonstrates high dynamic range, focusing, matting, and hybrid imaging implemented on a single, reconfigurable camera containing eight sensors