Studies on jute–asphalt composites

The tensile properties of jute make it a suitable raw material for asphalt overlay (A/O) fabric. In this study, the thermal stability of jute under conditions of asphalt overlaying process was investigated and the compatibility of jute with asphalt was assessed through experimentation on jute–asphalt composites under mechanical and hygral loads. Fourier transform infra red (FTIR) spectroscopic study revealed probable chemical bonding between jute and asphalt. The test for ascertaining the capability of asphalt encasement for protecting jute against biodegradation under enzymatic attack was found positive. A 6‐month hygral treatment, of the jute–asphalt composite, showed significant increase in modulus of the material. The results indicate that although the strength of jute deteriorates by about 10% under asphalt overlaying condition, the overall tensile behavior of jute fabric–asphalt composite material is considerably superior to that of the pure jute fabric, even under biological and extended hygral loading conditions. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci 2008     

A translator system for the MATLAB language

A whole‐program MATLAB to C translation system is presented. The paper outlines the motivation for the problem, discusses the system's architecture, its features and limitations. The translator's operation is explained using an example input program. Details are given on how the system implements and specializes some of the language's built‐in primitives. Finally, the paper reports measurements evaluating the execution time and memory usage of the translated sources, and the compilation time required for the translations. Copyright © 2006 John Wiley & Sons, Ltd.     

Deformation structure in a Ti-24Al-11Nb alloy

A Ti-24Al-11Nb alloy has been heat-treated so as to obtain a microstructure of coarse α₂ particles (D0₁₉ structure based on Ti₃Al) in a matrix of the ordered β_o phase (B2 structure based on Ti₂AlNb). Dislocation structures generated by tensile strains of ∼2 pct at room temperature have been analyzed by transmission electron microscopy The β_o phase is shown to deform inhomogeneously on {110}, {112}, and {123} planes by α/〈211〉 slip. The slipband structure is complex, consisting of segments of heavily pinned edge dislocations with periodic cross slip of screw components on to secondary slip planes. Incompatibility stresses at α₂/β_o interfaces can generate fine α[100] slip as well. The α₂ phase deforms independently by α dislocation slip. Slipbands in the β_o phase can shear the α₂ phase by activatingc +a/2 slip on and slip planes, as well asa slip on higher order pyramidal planes, where the parallelism of the specific slip system is permitted by the Burgers relationship between the two phases.     

Comparing ANN and GMM in a voice conversion framework

In this paper, we present a comparative analysis of artificial neural networks (ANNs) and Gaussian mixture models (GMMs) for design of voice conversion system using line spectral frequencies (LSFs) as feature vectors. Both the ANN and GMM based models are explored to capture nonlinear mapping functions for modifying the vocal tract characteristics of a source speaker according to a desired target speaker. The LSFs are used to represent the vocal tract transfer function of a particular speaker. Mapping of the intonation patterns (pitch contour) is carried out using a codebook based model at segmental level. The energy profile of the signal is modified using a fixed scaling factor defined between the source and target speakers at the segmental level. Two different methods for residual modification such as residual copying and residual selection methods are used to generate the target residual signal. The performance of ANN and GMM based voice conversion (VC) system are conducted using subjective and objective measures. The results indicate that the proposed ANN-based model using LSFs feature set may be used as an alternative to state-of-the-art GMM-based models used to design a voice conversion system.     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.     

Aggregation dynamics of particles in a microchannel due to an applied magnetic field

Functionalized magnetic microspheres have promising applications in different microfluidic devices including MEMS-scale biosensors. These particles exhibit magnetic field-induced aggregation, which can be harnessed to achieve several practical tasks in microfluidic devices. For this, the particle aggregation needs to be well characterized. Herein, a numerical simulation and experimental validation of particle-chaining is presented. Simulations show that the particle aggregation time scales linearly with a group parameter. The predicted growth of one- two- and three-particle chains with time shows a similar trend as that found in the experiments. The results of the study could help predicting the performance of magnetic aggregate-based lab-on-a-chip devices.