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Delhi, India

Delhi Technological University , formerly known as Delhi College of Engineering , is a premier government university located in New Delhi, India. It is one of the oldest Engineering Colleges of India & first Engineering College of Delhi. It was established in 1941 as Delhi Polytechnic, and was under the control of the Government of India.The college has been under the government of the National Capital Territory of Delhi since 1963 and was affiliated to the University of Delhi from 1952 to 2009.In 2009 the college was given a state university status thus changing its name to Delhi Technological University.Till the year 2009, DCE shared its admission procedure and syllabus for various B.E courses with Netaji Subhas Institute of Technology,formerly of which were prescribed by Faculty of Technology, University of Delhi.It offers courses towards Bachelor of Engineering , Bachelor of Technology , Master of Engineering , Master of Technology , Master of Science , Doctor of Philosophy and Master of Business Administration and contains 14 academic departments with a strong emphasis on scientific and technological education and research. Wikipedia.


Saini M.K.,D. C. R. University of Science and Technology | Kapoor R.,Delhi Technological University
International Journal of Electrical Power and Energy Systems | Year: 2012

Power quality (PQ) interest has increasingly evolved over the past decade. The paper surveys the application of signal processing, intelligent techniques and optimization techniques in PQ analysis. This paper carries out a comprehensive review of articles that involves a comprehensive study of signal processing techniques used for PQ analysis. Within this context intelligent techniques such as fuzzy logic, neural network and genetic algorithm as well as their fusion are reviewed. Tabular presentation (i.e. highlighting the important techniques) has also been provided for comprehensive study. Although this review cannot be collectively exhaustive, it may be considered as a valuable guide for researchers who are interested in the domain of PQ and wish to explore the opportunities offered by these techniques for further improvement in the field of PQ. © 2012 Elsevier Ltd. All rights reserved. Source


Manjunath K.,Delhi Technological University | Kaushik S.C.,Indian Institute of Technology Delhi
Renewable and Sustainable Energy Reviews | Year: 2014

Heat exchangers are thermal systems which are used extensively, have a major role in energy conservation aspect and preventing global warming. This paper is based on reviews of scientific work and provides a state-of-the-art review of second law of thermodynamic analysis of heat exchangers. Initially, the basics of heat exchangers are briefly provided along with second law analysis which also includes two-phase flow analysis and thermoeconomic analysis. Following this, detail literature survey based on performance parameters such as entropy generation, exergy analysis, production and manufacturing irreversibilities (cumulative exergy destruction associated with the production of material and manufacturing of component or assembly of components) and two phase fluid loss of heat exchangers is presented including constructal law applied to analyze heat exchangers. Constructal theory along with second law analysis can be used for the systematic design of heat exchangers. This review highlights the importance of first and second law investigations of heat exchangers leading to energy conservation. © 2014 Elsevier Ltd. Source


Mehata M.S.,Delhi Technological University
Applied Physics Letters | Year: 2012

Electroabsorption (E-A) and electrophotoluminescence (E-PL) responses of polymer films of CdSe quantum dots (QDs) incorporated sulfide-substituted poly(1,4-phenylene vinylene) derivative CdSe-S3PPV were measured. The observed Stark shift both in E-A and E-PL responses is likely to be caused by a substantial contribution of change in molecular polarizability (Δᾱ) and change in electric dipole moment (|Δμ|) following photoexcitation. Together with Stark shift, field-induced photoluminescence (PL) quenching and enhancement were observed depending on excitation energy. The quenching of PL of CdSe-S3PPV film is interpreted in terms of an exciton model-a breaking of electron-hole pairs in the presence of electric field. © 2012 American Institute of Physics. Source


Malhotra R.,Delhi Technological University
Applied Soft Computing Journal | Year: 2014

The demand for development of good quality software has seen rapid growth in the last few years. This is leading to increase in the use of the machine learning methods for analyzing and assessing public domain data sets. These methods can be used in developing models for estimating software quality attributes such as fault proneness, maintenance effort, testing effort. Software fault prediction in the early phases of software development can help and guide software practitioners to focus the available testing resources on the weaker areas during the software development. This paper analyses and compares the statistical and six machine learning methods for fault prediction. These methods (Decision Tree, Artificial Neural Network, Cascade Correlation Network, Support Vector Machine, Group Method of Data Handling Method, and Gene Expression Programming) are empirically validated to find the relationship between the static code metrics and the fault proneness of a module. In order to assess and compare the models predicted using the regression and the machine learning methods we used two publicly available data sets AR1 and AR6. We compared the predictive capability of the models using the Area Under the Curve (measured from the Receiver Operating Characteristic (ROC) analysis). The study confirms the predictive capability of the machine learning methods for software fault prediction. The results show that the Area Under the Curve of model predicted using the Decision Tree method is 0.8 and 0.9 (for AR1 and AR6 data sets, respectively) and is a better model than the model predicted using the logistic regression and other machine learning methods. © 2014 Published by Elsevier B.V. Source


Singh K.,Bharat Heavy Electricals Ltd. | Kapoor R.,Delhi Technological University
Pattern Recognition Letters | Year: 2014

This paper presents a novel Exposure based Sub-Image Histogram Equalization (ESIHE) method for contrast enhancement for low exposure gray scale image. Exposure thresholds are computed to divide the original image into sub-images of different intensity levels. The histogram is also clipped using a threshold value as an average number of gray level occurrences to control enhancement rate. The individual histogram of sub images is equalized independently and finally all sub images are integrated into one complete image for analysis. The simulation results show that ESIHE outperforms other conventional Histogram Equalization (HE) methods in terms of image visual quality, entropy preservation and better contrast enhancement. © 2013 Elsevier B.V. All rights reserved. Source

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