Mechanisms of Auger-induced chemistry derived from wave packet dynamics

To understand how core ionization and subsequent Auger decay lead to bond breaking in large systems, we simulate the wave packet dynamics of electrons in the hydrogenated diamond nanoparticle C_(197)H_(112). We find that surface core ionizations cause emission of carbon fragments and protons through a direct Auger mechanism, whereas deeper core ionizations cause hydrides to be emitted from the surface via remote heating, consistent with results from photon-stimulated desorption experiments [Hoffman A, Laikhtman A, (2006) J Phys Condens Mater 18:S1517–S1546]. This demonstrates that it is feasible to study the chemistry of highly excited large-scale systems using simulation and analysis tools comparable in simplicity to those used for classical molecular dynamics

Multi-learner based recursive supervised training

In this paper, we propose the Multi-Learner Based Recursive Supervised Training (MLRT) algorithm which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of the dataset, and utilize it to train the data accurately and efficiently. We observed that empirically, MLRT performs considerably well as compared to RPHP and other systems on benchmark data with 11% improvement in accuracy on the SPAM dataset and comparable performances on the VOWEL and the TWO-SPIRAL problems. In addition, for most datasets, the time taken by MLRT is considerably lower than the other systems with comparable accuracy. Two heuristic versions, MLRT-2 and MLRT-3 are also introduced to improve the efficiency in the system, and to make it more scalable for future updates. The performance in these versions is similar to the original MLRT system

Integration of knowledge-based system, artificial neural networks and multimedia for gear design

Design is a complicated area consisting of a combination of rules, technical information and personal judgement. The quality of design depends highly on the designer's knowledge and experience. This system attempts to simulate the design process and to capture design expertise by combining artificial neural networks (ANNs) and knowledge based system (KBS) together with multi-media (MM). It has been applied to the design of gears. Within the system the knowledge based system handles clearly defined design knowledge, the artificial neural networks capture knowledge which is difficult to quantify and multi-media provides a user-friendly interface prompting the user to input information and to retrieve results during design process. The finished system illustrates how features of different Artificial Intelligence techniques, KBS, ANNs and MM, are combined in a hybrid manner to conduct complicated design tasks

Yield Enhancement of Digital Microfluidics-Based Biochips Using Space Redundancy and Local Reconfiguration

As microfluidics-based biochips become more complex, manufacturing yield will have significant influence on production volume and product cost. We propose an interstitial redundancy approach to enhance the yield of biochips that are based on droplet-based microfluidics. In this design method, spare cells are placed in the interstitial sites within the microfluidic array, and they replace neighboring faulty cells via local reconfiguration. The proposed design method is evaluated using a set of concurrent real-life bioassays.Comment: Submitted on behalf of EDAA (http://www.edaa.com/

K Means Segmentation of Alzheimers Disease in PET scan datasets: An implementation

The Positron Emission Tomography (PET) scan image requires expertise in the segmentation where clustering algorithm plays an important role in the automation process. The algorithm optimization is concluded based on the performance, quality and number of clusters extracted. This paper is proposed to study the commonly used K Means clustering algorithm and to discuss a brief list of toolboxes for reproducing and extending works presented in medical image analysis. This work is compiled using AForge .NET framework in windows environment and MATrix LABoratory (MATLAB 7.0.1)Comment: International Joint Conference on Advances in Signal Processing and Information Technology, SPIT201