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Adaption of Sensors and Microcontrollers for Train Safety Mission - Zero tolerance for accidents
(2012-09-17T06:29:42Z) Rekha, C; SHRIDHAR, BHARADHWAJ .K; SURAJ, M.D; PAVAN., N
Railways are the most important mode of transport and which has moved to the leisure and luxurious levels in developing nations. Modernization of railways in many countries has graduated it to the realm of smart transportation, and sensors accompanying with software have increasingly been playing a key role. In the rapidly flourishing country like India, accidents in the unmanned level crossings are increasing day by day. No fruitful steps have been taken so far in these areas. A significant challenge which arises in this context is travel comfort, safety and high operational efficiency. Realizing the importance of this area, the paper focuses on the “TRAIN SAFETY MISSION - ZERO TOLERANCE FOR ACCIDENTS” - that is making railway operations free of accidents, be it derailment, collision or fire on trains using sensors and microcontrollers. To bring out the convergence of sensing, communicating, computing and control over the accidents, following measures are discussed in the paper.
Automatic Detection of Defects on Periodically Patterned Textures
(2012-09-15T09:11:49Z) Asha, V; Bhajantri, N; Nagabhushan, P
Defect detection is a major concern in quality control of various products in industries. This paper presents two different machine-vision based methods for detecting defects on periodically patterned textures. In the first method, input defective image is split into several blocks of size same as the size of the periodic unit of the image and chi-square histogram distances of each periodic block with respect to itself and all other periodic blocks are calculated to get a dissimilarity matrix. This dissimilarity matrix is subjected to Ward’s hierarchical clustering to automatically identify defective and defect-free blocks. The second method of defect detection is based on Universal Quality Index which is a measure of loss of correlation, luminance distortion and contrast distortion between any two signals. Quality indices of a periodic block with respect to itself and all other periodic blocks are calculated to get a similarity matrix containing quality indices. Specific variances of the periodic blocks are derived from the quality index matrix through orthogonal factor model based on eigen decomposition. These variances are subjected to Ward’s hierarchical clustering to automatically identify defective and defect-free blocks. Results of experiments on real fabric images with defects show that the defect detection methods based on chi-square histogram distance and universal quality index yield a success rate of 98.6% and 97.8% respectively.
Automatic Detection of texture Defacts Using Texture Periodicity and Chi - Square Histogram Distance
(2012-09-15T09:23:36Z) Asha, V; Bhajantri, N; Nagabhushan, P
Cloud Computing issues at design and implementation level : A Survey
(2012-06-28T05:36:16Z) Nirmala
A Descriptive Study of ICT in Education
(2012-09-17T06:37:28Z) Krishnachandra, M; Arjya, Bhuiyan; Lakshmi, J.Divya; Ashraf, Ali M K
Tremendous growth in information communication technology evolves rapid rate of creating modern classroom education, which brings the stakeholders to learn effectively. It shows more visual than the normal teaching methods. An opportunity of generating ICT services in urban is very huge because the technology spreads fast. But in terms of rural remained relatively untouched. Higher education in the country is experiencing a major transformation in terms of access, equity and quality. This change over is highly influenced by the swift developments in ICTs all over the world. At the same time the introduction of ICTs in the higher education has profound implications for the whole education process ranging from investment to use of technologies in dealing with key issues of admission, management, efficiency and quality. ICT could be used in education system for many reasons like learning, teaching, learning through entertainment and for instructional purposes. In this regard the computer can play a major role in enhancing the teaching and learning process. For making this process we need to frame some rules and, regulations which could be determined by the policy makers, educationalist and stake holders. Ultimately This paper highlights recent methods of ICTE ie..OLPC,EDUCOM and M-Learning to utilize it optimum, and its enhancements, pros and cons, etc.
Fabric Inspection using Gradient Space and its Energy
(2015-11-27T06:59:40Z) Asha, V; Bhajantri, N U; Nagabhushan, P
In this paper, we propose a machine vision algorithm for fabric inspection in patterned textures with the help of gradient space obtained using Newton’s forward difference scheme and its energy. Gradient space image is obtained from the input defective image and is split into several blocks of size same as that of the periodic unit of the input defective image. Energy of the gradient space image is used as input feature space for identifying defective and non defective periodic blocks using Ward’s hierarchical clustering. Experiments on real fabric images with defects show that the proposed method can be used for automatic detection of fabric defects in textile industries
FIS : Fabric Inspection System- An overview
(2012-09-17T06:32:59Z) REKHA, C; GOUTAMI, DEGALA; VIPULA, SHREE.D; ASHWINI, G.B
In developing countries like India mostly defects arising on the fabrics are still detected manually which consumes more time. To establish good company image and to be more competitive in the marketplace, inspection system can ensure the optimum quality of product on hand and help to improve the product quality. Therefore, a lot of factories pay attention to improve the inspection system, especially in textile industry. The automated inspection by machine, on the other hand, can solve this shortcoming. Secondly, it eliminates high inspection error due to human frailty. Thirdly ,it can save the labor cost, and reduce the demand for highly skilled inspectors. Various approaches for fabric defect detection have been proposed in past. In the present paper the comprehensive list of references to some recent works and techniques are discussed and explains how image processing and ATMEL Microcontroller helps to identify the defects on the fabrics. The recognizer identifies any faults through ATMEL microcontroller by the circuit operation. For programming the controller Atmel program is used. In addition to that a stepper motor is connected in the output. This motor operates if there is no fault and fails if the fault is identified. The recognizer is suitable for developing countries identifies the fabric defects within economical cost and produces less error prone inspection system in real time.
owards Performance Improvement of Cloud Applications under Virtualized Environment using PSO based K-means++ Algorithm
(2015-11-27T06:06:50Z) Nirmala, A.P.; Sridaran, R
Virtualization generally provides performance isolation between virtual machines running simultaneously on a single physical machine. But isolation does not happen to its fullest extent which may lead to performance interference. Due to this, the performance of applications running on one VM may get affected by running applications of co-existing VMs. This further leads to delay in execution time. As a result of this, throughput of cloud applications will be degraded. In order to deal with this situation, the proposed work has been presented which uses Particle Swarm Optimization based k-means++ algorithm for its implementation. The proposed work has been compared with some of the existing approaches and found to have better throughput and reduction in run time and cost.
Similarity Measures for Automatic Defect Detection on Patterned Textures
(2012-12-26T06:26:22Z) Asha, V; Nagabhushan, P; Bhajantri, N. U.
Similarity measures are widely used in various applications such as information retrieval, image and object recognition, text retrieval, and web data search. In this paper, we propose similarity-based methods for defect detection on patterned textures using five different similarity measures, viz., Normalized Histogram Intersection Coefficient, Bhattacharyya Coefficient, Pearson Product-moment Correlation Coefficient, Jaccard Coefficient and Cosine-angle Coefficient. Periodic blocks are extracted from each input defective image and similarity matrix is obtained based on the similarity coefficient of histogram of each periodic block with respect to itself and other all periodic blocks. Each similarity matrix is transformed into dissimilarity matrix containing true-distance metrics and Ward’s hierarchical clustering is performed to discern between defective and defect-free blocks. Performance of the proposed method is evaluated for each similarity measure based on precision, recall and accuracy for various real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple, knot, and missing pick.
Unsupervised Detection of Texture Defects Using Texture Periodicity and Universal Quality Index
(2012-09-15T09:28:24Z) Asha, v; Bhajantri, N; Nagabhushan, P

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