Biomaterial Strategies for Immune System Enhancement and Tissue Healing
Qualitative and Quantitative Performance Optimization of Simple Gas Turbine Power Plant using Three Different Types of Fuel
Efficient Shopping: RFID-Powered Cart with Automated Billing System
Medical Drone System for Automated External Defibrillator Shock Delivery for Cardiac Arrest Patients
A Critical Review on Biodiesel Production, Process Parameters, Properties, Comparison and Challenges
Review on Deep Learning Based Image Segmentation for Brain Tumor Detection
Chemistry and Chemical Engineering: Approaches, Observations, and Outlooks
Integration of PMS Software and Decision Matrix Tool Based on Data Acquired from Latest IT Advanced Sensors and 3D CAD Models in Marine Operations Field
A Series of Tool-Life Studies on Aluminium Matrix Hybrid Composites
Dynamic Changes in Mangrove Forest and Lu/Lc Variation Analysis over Indian Sundarban Delta in West Bengal (India) Using Multi-Temporal Satellite Data
The Impacts of Climate Change on Water Resources in Hilly Areas of Nepal
An Analysis of Machining Forces On Graphite/Epoxy, Glass/Epoxy and Kevlar/Epoxy Composites Using a Neural Network Approach
Deformation Behaviour of Fe-0.8%C-1.0%Si-0.8%Cu Sintered P/M Steel during Powder Preform Forging
A Series of Tool-Life Studies on Aluminium Matrix Hybrid Composites
Achieving Manufacturing Excelence by Applying LSSF Model – A Lean Six Sigma Framework
Design and Analysis of Piezo- Driven Valve-Less Micropump
Despite recent advances in the areas of fingerprint identification, fingerprint matching continues to be a chanllenging pattern recognition problem. The first step to this problem is the extraction of landmarks known as minutiae points from a print. Once extracted, these points are then compared to all sets on file in search of a match. The accurate extraction of minutiae from an image is the basis for the entire matching process. Various minutiae extraction approaches have been proposed in the literature, each with its own merits and degree of sucess. The most common approach is to extract the ridges in the fingerprint image through skeletonization, apply ridge following, and use rule-based classification for minutiae detection. Our emphasis in this paper is on extracting the minutiae from the original gray-scale images, without any image preprocessing. In particular, we have implemented and compared three methods based on elgenspace representations and neural network classifiers. Moreover, we present preliminary results of an attempt to fuse the outputs of these three methods using a clustering algorithm unique to this type of problem.
Wireless Technology has advanced tremendously over the past decade, introducing a wide array of devices with networking abilities. Adhoc networks are dynamically created and maintained by a group of wireless enabled devices(gargets) without the assistance of pre-existing infrastructure like Base Station(BS) for communication purposes. In this paper, we consider the issue of performance enhancement of wireless networks in terms of ‘overhead messages per node and power consumption’. Minimizing power consumption is an important challenge in mobile networks. Wireless network interface is often a device’s single largest consumer of power. To fulfill the above requirements, we have designed a topology management scheme for adhoc wireless networks. In our topology management scheme, MARI(Mobile Agents with Routing Intelligence) nodes are selected in such a way that, the MARI nodes have maximum power level among their one hop neighbors and all non-MARI nodes are within the transmission range of MARI nodes. These MARI nodes have the routing intelligence i.e. they make all decisions relating to routing. The gateway nodes having sufficient power level are selected so that they can forward packets between MARI nodes. Gateway nodes do not have routing intelligence. These MARI and gateway nodes stay continuously awake to route the packets of other member nodes.
In this paper a model of a contemporary textbook for high level programming language learning has been proposed. The development of new computer technologies provide the opportunity of combining the classical method of teaching in classroom and study from printed textbooks, with new more contemporary methods, wherever a computer is available, with a CD-ROM-based textbook. This kind of textbook enables programming language learning, with constant reader interaction and tracking of simulation of the execution of each instruction of a program as well as all the changes in the execution of that program.
This study used a teaching pedagogy of problem based learning (PBL) to compare any significant change in knowledge, skills and attitude in comparison to the traditional teaching of engineering graphics. The quasi-experimental study sample comprised forty eight (N=48) students in a Foundation of Graphics course at North Carolina State University and involved a pre-test / post-test control group, using single control and single treatment groups of 24 persons per group.
Staff risk analysis in the railway maintenance workplaces is a very complicated subject determined by numerous aspects including human error. Many risk assessment techniques currently used are comparatively mature tools. Howerver, in many circumstances, the application of these tools may not give satisfactory results due to the risk data incomplete or the high level of uncertainty involved in the risk data. This paper presents a methodology for staff risk assessment using fuzzy reasoning approaches (FRA), in particular, in the railway maintenance workplaces. The proposed method can evaluate qualitative and quantitative risk data associated with the railway maintenance workplaces in a uniform manner for staff risk analysis. Input parameters, probability of occurrence of hazardous events and their consequent severities in terms of membership functions, are used as inpurt information to analyse risks. The risk associated with the risk rules is then mapped back to the defined risk level expressions. The outcomes of the risk assessment are represented as the risk degrees and the defined risk categories of risk levels with a belief of percentage, which provide very useful risk information to risk analysts, project managers and site engineers. The proposed methodology permits risk analysts to assess the risks associated with the hazardous events directly using linguistic terms. This will provide railway risk analysts, managers and engineers with a method and tool to improve their safety management and set saftety standards. A case example on staff risk assessment at Victoria line depot is used to demonstrate the proposed approach. The result indicate that by using the proposed methodology risks associated with a railway maintenance workplace can be assessed effectively and efficiently. The proposed method could be transferred to other applications and used by other industrial companies who face to manage saftety and health on the construction sites.
By the methods of Auger-electron spectrometry (AES), scanning electron microscopy (SEM), fast electron diffraction (FED) and wavelength dispersive spectrometry (WDS), were carried out complex investigations on the structure and elemental distribution in Beilby layer formed during mechanical polishing of ground surface of Fe-44% Cr-4% Al-0.3% La alloy. It was established that, Beilby layer formed during mechanical polishing of ground surface of investigated alloy consists of two sub-zones with uniform thicknesses combined with each other organically. The first (outer)zone with thickness of ~250E is truly amorphous layer saturated by oxygen and carbon atoms; and the second, underlying (inner)zone of ~ 350E thickness, also has high concentration of oxygen and carbon, but has nanocrystallite(~20E) structure (in roentgen-amorphous condition). The ratio between thicknesses of the given zones, which componse the Beilby layer, to certain extent depends on the polishing duration and condition of initial surface preceding to mechanical polishing. On the basis of observable peculiarities of the character in thickness changes, of revealed zones, in the dependence of polishing duration and initial surface condition before mechanical polishing, there develop notions on the hierarchical consequence of stages of Beilby layer (and layers under it) formation as a product of combination of processes, of visco-plastic mass-transfer and growth of crystal structure defects, which are accompanied by plastic deformations. The stabile existence at room temperature the superficial layers with truly amorphous and “roentgen-amorphous” condition, which are so nonequilibrium against underlying crystalline structure, is explained by the dissolution of those layers by carbon and oxygen atoms and their interaction with the basic atoms which compose the investigated alloy.
The scope of this paper is to present a neural network approach towards sensor fault (deterioration) diagnosis in Linear Time Invariant(LTI) systems. The novelty of the approach lies in associating with each state feedback gain factor a scalar a, which is defined as the sensor healthiness factor. This scalar is made to vary from 1(no fault condition) to 0(full fault condition) in predetermined steps. The intermediate values of a portray the deterioration modes of the sensor. The Integral Absolute Error (IAE) criterion is employed for extracting the signature of the fault and the classification is done using Artificial Neural Network (ANN) classifier. The proposed diagnosis approach is applied to a dc motor system to validate the effectiveness of the technique.
The purpose of this study was to compare used tyre pyrolysis oil blends with conventional diesel fuel when fueling a diesel engine. The test fuel was characterized based on the density, viscosity, boiling point, calorific value, sulphur content, flash point, carbon residue, ash and water content. The thermo physical and chemical characteristics were also evaluated for raw oil, refined oil before and after distiliation. The fuel was then blended with diesel and used in an internal combustion engine. Blends of 20%, 40%, 60% and 75% by volume were investigated. This exhaust emissions and performance data were collected for study state operation at different load conditions. It has been found from the performance and emission analysis that the usage of oil as blends with diesel in direct injection Cl engines has shown similar performance and reduced emission as that of same Cl engine operated in pure diesel.
