Application of Fuzzy Logic on Image Edge Detection

In this paper a novel method for an application of digital image processing, Edge Detection is developed. The contemporary Fuzzy logic, a key concept of artificial intelligence helps to implement the fuzzy relative pixel value algorithms and helps to find and highlight all the edges associated with an image by checking the relative pixel values and thus provides an algorithm to abridge the concepts of digital image processing and artificial intelligence. Exhaustive scanning of an image using the windowing technique takes place which is subjected to a set of fuzzy conditions for the comparison of pixel values with adjacent pixels to check the pixel magnitude gradient in the window. After the testing of fuzzy conditions the appropriate values are allocated to the pixels in the window under testing to provide an image highlighted with all the associated edges

Usability Determination Using Multistage Fuzzy System

AbstractThe evaluation of software is important for enhancing the modification and improvement in a software development process. There are many factors to evaluate a software process. One of the factors is the Quality of software, which cannot be calculated with ease; as Quality of software is dependent on other factors. Software Usability is one of the significant aspects on which quality of software depends. A number of software usability models have been proposed by a number of researchers, each model considers a set of factors. In real world, we are facing many obstacles in implementing any of these proposed usability models as there is a lack in its precise definition and the concept of globally accepted usability. This paper aims to define the term ‘usability’ using a detailed taxonomy which includes all the aspects of usability and is globally accepted. Generalized Usability Model (GUM) with taxonomy has been proposed in this paper. This paper also shows how to determine the usability of a software application using a fuzzy based system which has been implemented using multistage fuzzy logic toolbox

Designing an Energy Efficient Network Using Integration of KSOM, ANN and Data Fusion Techniques

Energy in a wireless sensor network (WSN) is rendered as the major constraint that affects the overall feasibility and performance of a network. With the dynamic and demanding requirements of diverse applications, the need for an energy efficient network persists. Therefore, this paper proposes a mechanism for optimizing the energy consumption in WSN through the integration of artificial neural networks (ANN) and Kohonen self-organizing map (KSOM) techniques. The clusters are formed and re-located after iteration for effective distribution of energy and reduction of energy depletion at individual nodes. Furthermore, back propagation algorithm is used as a supervised learning method for optimizing the approach and reducing the loss function. The simulation results show the effectiveness of the proposed energy efficient network

Offline Handwriting Recognition Using Genetic Algorithm

In this paper, a new method for offline handwriting recognition is presented. A robust algorithm for handwriting segmentation has been described here with the help of which individual characters can be segmented from a word selected from a paragraph of handwritten text image which is given as input to the module. Then each of the segmented characters are converted into column vectors of 625 values that are later fed into the advanced neural network setup that has been designed in the form of text files. The networks has been designed with quadruple layered neural network with 625 input and 26 output neurons each corresponding to a character from a-z, the outputs of all the four networks is fed into the genetic algorithm which has been developed using the concepts of correlation, with the help of this the overall network is optimized with the help of genetic algorithm thus providing us with recognized outputs with great efficiency of 71%

A Review on Modeling of AlGaN/GaN MODFET based on Artificial Neural Networks

High electron mobility transistors (HEMTs) based on GaN have gained attention mainly due to its high quality performance especially in high-frequency as well as high-power devices. Significant developments have been donein terms of fabrication and performance of HEMT through several modeling techniques. This review article focuses on artificial neural networks for modeling of HEMT devices with enhanced performance.The focus of this article is further extended to the discussion of different models of AlGaN/GaN HEMT devices

Microwave Analysis for Two-Dimensional C-V and Noise Model of AlGaN/GaN MODFET

A new two-dimensional analytical model for the capacitance-voltage and noise characteristics of a AlGaN/GaN MODFET is developed. The two-dimensional electron gas density is calculated as a function of device dimensions. The model includes the spontaneous and polarization effects. The contribution of various capacitances to the performance of the device is shown. The model further predicts the transconductance, drain conductance, and frequency of operation. A high transconductance of 160 mS/mm and a cut-off frequency of 11.6 GHz are obtained for a device of 50 nm gate length. The effect of gate length on the gate length behaviour of the noise coefficients P, R, and C is also studied. The effect of parasitic source and gate resistance has also been studied to evaluate the minimum noise figure. The excellent agreement with the previously simulated results confirms the validity of the proposed model to optimize the device performance at high frequencies

Mining of diverse short non-coding RNAs from transcriptome of milk somatic cells of Murrah buffalo

The non-coding RNAs (ncRNA) are known to regulate expression of genes at the transcription, translation and processing levels. The present study was conducted to identify diverse short ncRNAs from milk somatic cells of lactating Murrah buffaloes. Elucidating the molecular drivers of lactation in dairy animals will help understand the process of lactation, eventually leading to improvement in milk production and quality. In order to discover the ncRNA, the transcriptome data of 12 samples of somatic cells from buffalo milk were analyzed. A web based pipeline, exceRpt was used to perform the analysis. The most abundant short ncRNA molecules discovered in buffalo milk were the miRNAs, followed by snRNAs. Least number of rRNAs was discovered in the investigated samples. The total number of rRNAs, tRNAs, snRNAs, snoRNAs and miRNAs were 12, 23, 72, 51 and 229 respectively, in the entire dataset. On matching with miRBase v22.1, a total of 1724, 897, 211 and 4 miRNAs were observed to be common to human, bovine, caprine and ovine genomes. The results provide information on the bioavailability of short ncRNAs in buffalo milk somatic cells, most of which are largely uncharacterized. The generated information is a step towards developing a database for ncRNAs in buffalo species

Mycobacterium tuberculosis ClpP Proteases Are Co-transcribed but Exhibit Different Substrate Specificities

PMCID: PMC3613350This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited