A fuzzy-driven genetic algorithm for sequence segmentation applied to genomic sequences

The fuzzy-driven genetic algorithm for sequence segmentation consists of a genetic algorithm whose objective function is driven by a fuzzy fitness finder. The genetic algorithm starts with an initial population of alternate solutions where each solution is a different partitioning of the sequence into segments. The algorithm uses adaptations of the standard genetic operators to reallocate the partitions so as to achieve optimal segmentations. A fuzzy fitness finding mechanism evaluates the fitness values of the evolving segmentations, taking into consideration the combined effect of multiple heterogeneous features that have been identified as governing factors for the formation of the segments. The relationships between segment elements can also be modeled by this novel approach of applying soft computing paradigms to the segmentation of multi-dimensional sequences. The algorithm developed in this work has been successfully implemented for gene sequence segmentation to predict groups of functionally related genes that lie adjacent on the genome sequences of bacterial genomes.     

Permutation entropy based real-time chatter detection using audio signal in turning process

Machine tool chatter is an unfavorable phenomenon during metal cutting, which results in heavy vibration of cutting tool. With increase in depth of cut, the cutting regime changes from chatter-free cutting to one with chatter. In this paper, we propose the use of permutation entropy (PE), a conceptually simple and computationally fast measurement to detect the onset of chatter from the time series using sound signal recorded with a unidirectional microphone. PE can efficiently distinguish the regular and complex nature of any signal and extract information about the dynamics of the process by indicating sudden change in its value. Under situations where the data sets are huge and there is no time for preprocessing and fine-tuning, PE can effectively detect dynamical changes of the system. This makes PE an ideal choice for online detection of chatter, which is not possible with other conventional nonlinear methods. In the present study, the variation of PE under two cutting conditions is analyzed. Abrupt variation in the value of PE with increase in depth of cut indicates the onset of chatter vibrations. The results are verified using frequency spectra of the signals and the nonlinear measure, normalized coarse-grained information rate (NCIR).     

Characterization of multilayer pvd nanocoatings deposited on tungsten carbide cutting tools

The present trends in the coating technologies are gradient coatings, metastable coatings, multicomponaent coatings and multilayer or super lattice coatings. The physical vapour deposition (PVD) process is well-suited technology for these advanced coating technologies. The performance of the coated tools can be improved considerably using multi-layer micro and nanocoatings. The present paper discusses the deposition and characterization of multilayer TiN/Al₂O₃ coatings on cemented tungsten carbide cutting tools using reactive sputtering. The characterization of the coatings was carried out using X-ray diffraction (XRD) for phase analysis, chemical composition using EDAX, adhesion and toughness evaluation using Rockwell indentation test and surface roughness. It was observed that with decrease in thickness of each alumina layer to nanolevel in multilayer coating system results considerable improvement in final surface finish, adhesion and toughness of the coating. The experimental results are presented and analyzed in this paper.     

Separation of C2–C4 hydrocarbons from methane by zeolite MFI hollow fiber membranes fabricated from 2D nanosheets

Separation of higher hydrocarbons from methane is an important and energy-intensive operation in natural gas processing. We present a detailed investigation of thin and oriented MFI zeolite membranes fabricated from 2D MFI nanosheets on inexpensive α-alumina hollow fiber supports, particularly for separation of n-butane, propane, and ethane (“natural gas liquids”) from methane. These membranes display high permeances and selectivities for C₂–C₄ hydrocarbons over methane, driven primarily by stronger adsorption of C₂–C₄ hydrocarbons. We study the separation characteristics under unary, binary, ternary, and quaternary mixture conditions at 298 K and 100–900 kPa feed pressures. The membranes are highly effective in quaternary mixture separation at elevated feed pressures, for example allowing n-butane/methane separation factors of 170–280 and n-butane permeances of 710–2,700 GPU over the feed pressure range. We parametrize and apply multicomponent Maxwell–Stefan transport equations to predict the main trends in separation behavior over a range of operating conditions.     

The architecture of virtual machines

A virtual machine can support individual processes or a complete system depending on the abstraction level where virtualization occurs. Some VMs support flexible hardware usage and software isolation, while others translate from one instruction set to another. Virtualizing a system or component -such as a processor, memory, or an I/O device - at a given abstraction level maps its interface and visible resources onto the interface and resources of an underlying, possibly different, real system. Consequently, the real system appears as a different virtual system or even as multiple virtual systems. Interjecting virtualizing software between abstraction layers near the HW/SW interface forms a virtual machine that allows otherwise incompatible subsystems to work together. Further, replication by virtualization enables more flexible and efficient and efficient use of hardware resources.     

Offshore Outsourcing: A Swot Analysis of a State in India

Global trends indicate that IT spending will increasingly go to offshore solution providers. This article demonstrates the use of a SWOT analysis technique for identifying the strengths, weaknesses, threats, and opportunities associated with a potential IT outsourcing location. The factors assessed include business conditions, physical infrastructure, IT infrastructure, financial institution and government support, and various labor characteristics specific to a region: the state of Kerala in India.     

An affordable modular mobile robotic platform with fuzzy logic control and evolutionary artificial neural networks

Autonomous robotics projects encompass the rich nature of integrated systems that includes mechanical, electrical, and computational software components. The availability of smaller and cheaper hardware components has helped make possible a new dimension in operational autonomy. This paper describes a mobile robotic platform consisting of several integrated modules including a laptop computer that serves as the main control module, microcontroller‐based motion control module, a vision processing module, a sensor interface module, and a navigation module. The laptop computer module contains the main software development environment with a user interface to access and control all other modules. Programming language independence is achieved by using standard input/output computer interfaces including RS‐232 serial port, USB, networking, audio input and output, and parallel port devices. However, with the same hardware technology available to all, the distinguishing factor in most cases for intelligent systems becomes the software design. The software for autonomous robots must intelligently control the hardware so that it functions in unstructured, dynamic, and uncertain environments while maintaining an autonomous adaptability. This paper describes how we introduced fuzzy logic control to one robot platform in order to solve the 2003 Intelligent Ground Vehicle Competition (IGVC) Autonomous Challenge problem. This paper also describes the introduction of hybrid software design that utilizes Fuzzy Evolutionary Artificial Neural Network techniques. In this design, rather than using a control program that is directly coded, the robot's artificial neural net is first trained with a training data set using evolutionary optimization techniques to adjust weight values between neurons. The trained neural network with a weight average defuzzification method was able to make correct decisions to unseen vision patterns for the IGVC Autonomous Challenge. A comparison of the Lawrence Technological University robot designs and the design of the other competing schools shows that our platforms were the most affordable robot systems to use as tools for computer science and engineering education. © 2004 Wiley Periodicals, Inc.