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.

Time filter

Source Type

News Article | May 4, 2017

BOSTON--(BUSINESS WIRE)--Red Hat, Inc. (NYSE:RHT), the world's leading provider of open source solutions, today announced Avni Khatri, president of Kids on Computers, and Jigyasa Grover, a student at Delhi Technological University, as the 2017 Women in Open Source Award winners. Both will be recognized today at Red Hat Summit, which has been taking place in Boston this week. In its third year, the Women in Open Source Awards were created and sponsored by Red Hat to honor women who make important contributions to open source projects and communities, or those making innovative use of open source methodology. Nominations for this year's awards were accepted for two categories: academic (those currently enrolled in a college or university) and community (those working or volunteering on projects related to open source). Finalists were determined based on nomination criteria, with the public voting to determine the winners. Khatri, who was recognized in the community category, is the president of Kids on Computers, a non-profit that sets up computer labs installed with free and open source software (FOSS) in underserved communities. An open source contributor for more than 16 years, she is passionate about helping kids and parents receive unlimited access to education to give them more autonomy over their lives and improve their communities. She sees FOSS as instrumental to realizing this vision, and has worked to bring technology to communities around the world with Kids on Computers. As a volunteer since 2010 and the organization’s president since 2012, Khatri has traveled to remote communities in Mexico, India, and Morocco to install school labs with Linux computers, FOSS applications, and open content such as offline Wikipedia and Khan Academy, and she has enabled local volunteers to support these labs. Khatri recently helped start For a Living, an open source platform designed to help students to learn about different careers by interviewing professionals based on careers, interests, and skill sets. She also served as a co-chair of the Open Source Track at Grace Hopper Celebration of Women in Computing in 2010, and co-chair of the conference’s Open Source Day in 2011 and 2012. Grover, who was recognized in the academic category, is a student at Delhi Technological University (formerly known as Delhi College of Engineering), pursuing a bachelor’s of technology in computer engineering. An open source contributor for three years, her journey in open source began through work in competitive algorithmic C/C++ program source. As her skills progressed, she eventually became one of the top contributors to Pharo 4.0, which was released in 2015. Grover was a participant in Google Summer of Code in 2015 and 2016, and is now a mentor. She has been awarded research opportunities by the National Research Council of Canada and the European Smalltalk Users Group (ESUG) at Institut de recherche pour le développement (IRD) France. She is the director of Women Who Code Delhi, and she participates in Google Developers Group, Google Women Techmakers, Women in Science and Engineering, Systers, and Indian Women in Computing. In addition, Grover is a platform developer, organizer, and mentor for Learn IT, Girl!, and has conducted Android app development workshops for teenagers in Singapore. The winners will each receive a $2,500 stipend with suggested use to support open source projects or efforts. In addition, they will be featured on and given the opportunity to speak at a future Red Hat Women’s Leadership Community event. DeLisa Alexander, executive vice president and chief people officer, Red Hat “Congratulations to Avni and Jigyasa on being recognized as 2017 Women in Open Source Award winners. Their passion and dedication is making an impact in open source communities and the world, and they are an inspiration to future generations. In addition to their technical contributions, I commend their efforts to mentor and advocate for others. Diversity and inclusion are priorities for Red Hat, and we are proud to recognize women who are making a difference in the industry.” Avni Khatri, president of Kids on Computers “I am honored that my work with free and open source software and Kids on Computers has been recognized with the Women in Open Source Award. Open source has made it possible for us to provide technology and educational content to communities at scale so that we can improve the lives of kids who don’t have access to technology. Through this award, I will be able to continue my work to bring technology to kids in underserved communities around the world, with the hope that it will allow them to better their lives as well as their communities. Thank you, Red Hat, for recognizing and supporting this work.” Jigyasa Grover, student, Delhi Technological University "I am thrilled, encouraged, and humbled to be presented with the prestigious Women in Open Source Academic Award. As a proud member of the open source community, I aspire to continue my work and exploration of new open source technologies, and to keep connecting people with the range of opportunities open source offers. Thank you, Red Hat, for this recognition and honor.” Red Hat is the world's leading provider of open source software solutions, using a community-powered approach to provide reliable and high-performing cloud, Linux, middleware, storage and virtualization technologies. Red Hat also offers award-winning support, training, and consulting services. As a connective hub in a global network of enterprises, partners, and open source communities, Red Hat helps create relevant, innovative technologies that liberate resources for growth and prepare customers for the future of IT. Learn more at Certain statements contained in this press release may constitute "forward-looking statements" within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements as a result of various important factors, including: risks related to the ability of the Company to compete effectively; the ability to deliver and stimulate demand for new products and technological innovations on a timely basis; delays or reductions in information technology spending; the integration of acquisitions and the ability to market successfully acquired technologies and products; fluctuations in exchange rates; the effects of industry consolidation; uncertainty and adverse results in litigation and related settlements; the inability to adequately protect Company intellectual property and the potential for infringement or breach of license claims of or relating to third party intellectual property; risks related to data and information security vulnerabilities; changes in and a dependence on key personnel; the ability to meet financial and operational challenges encountered in our international operations; and ineffective management of, and control over, the Company's growth and international operations, as well as other factors contained in our most recent Annual Report on Form 10-K (copies of which may be accessed through the Securities and Exchange Commission's website at, including those found therein under the captions "Risk Factors" and "Management's Discussion and Analysis of Financial Condition and Results of Operations". In addition to these factors, actual future performance, outcomes, and results may differ materially because of more general factors including (without limitation) general industry and market conditions and growth rates, economic and political conditions, governmental and public policy changes and the impact of natural disasters such as earthquakes and floods. The forward-looking statements included in this press release represent the Company's views as of the date of this press release and these views could change. However, while the Company may elect to update these forward-looking statements at some point in the future, the Company specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing the Company's views as of any date subsequent to the date of this press release. Red Hat and the Shadowman logo are trademarks or registered trademarks of Red Hat, Inc. or its subsidiaries in the U.S. and other countries. Linux® is the registered trademark of Linus Torvalds in the U.S. and other countries.

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.

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.

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.

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.

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.

Gupta M.,Delhi Technological University | Kumar N.,Delhi Technological University
Renewable and Sustainable Energy Reviews | Year: 2012

Biodiesel is a promising fuel for diesel engines in wake of its renewable nature and environmental benefits. Biodiesel can be produced by different pathways; however, glycerol (or glycerin, glycerin) is a valuable by-product which is formed during this process. As mandates are being enforced by different government worlds over, the demand of biodiesel is likely to go up. With increased demand and production of biodiesel, significant quantity of glycerol shall be available. There is an urgent need to find alternative application area of glycerol so that viability of biodiesel industry can be sustained. In the present study, the focus has been made on the various application areas of using surplus glycerol from biodiesel industries to make them more financially attractive. Amongst the different pathways of using glycerol as a source of energy; direct combustion, mixing with agricultural solid wastes and then burning, blending directly or indirectly with other fuels, hydrogen and hydrocarbon production from glycerol, etherification, etc. are prominent one. The requirement, advantages and limitations of each approach have also been evaluated in the study. Combustion of glycerol if not done properly would result in formation of acrolien which is highly toxic in nature and efforts should be made to use glycerol indirectly to produce energy (i.e. all the pathways expect the direct combustion and the solid fuel method). The production of hydrogen from glycerol via APR appears to be the best solution to the disposal problem since the hydrogen yield via APR is highest. Moreover the process occurs at lower pressure and temperature when compared to steam reforming, and it is a single step process. Etherification, tri-acetylisation, and blending have been found to be useful for improving the performance of automotives by facilitating proper and smooth combustion of fuel. © 2012 Elsevier Ltd. All rights reserved.

Malhotra R.,Delhi Technological University
Applied Soft Computing Journal | Year: 2015

Background: Software fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults. Method: In this study we perform a systematic review of studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the statistical techniques and other machine learning techniques. Further the strengths and weaknesses of machine learning techniques are summarized. Results: In this paper we have identified 64 primary studies and seven categories of the machine learning techniques. The results prove the prediction capability of the machine learning techniques for classifying module/class as fault prone or not fault prone. The models using the machine learning techniques for estimating software fault proneness outperform the traditional statistical models. Conclusion: Based on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers. However, the application of the machine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work. © 2014 Elsevier B.V. All rights reserved.

Singh C.P.,Delhi Technological University
International Journal of Theoretical Physics | Year: 2014

In this paper we consider a spatially homogenous and anisotropic Bianchi type-V space-time model to investigate the effects of a magnetic field in string cosmology. We assume that the string's direction and magnetic field are along x-axis. The field equations are solved by using the equation of state for a cloud of strings and variable magnetic permeability. We derive exact solutions for three types of strings: (i) Nambu strings, (ii) string model where the sum of energy density and string tension density is zero and (iii) Takabayasi strings. We examine the behaviour of scale factors and other physical parameters with and without magnetic field and it is found that the magnetic field effects the dynamics of the universe at early time. During late time the universe becomes isotropic even in the presence of magnetic field. The universe expands with decelerated rate during early stages of the evolution of the universe but it goes to marginal inflation at late times. © 2013 Springer Science+Business Media New York.

Kumar S.,Delhi Technological University
Monthly Notices of the Royal Astronomical Society | Year: 2012

In this paper, we show that the expansion history of the Universe in power-law cosmology essentially depends on two crucial parameters, namely the Hubble constant H 0 and deceleration parameter q. We find the constraints on these parameters from the latest H(z) and SNe Ia data. At 1σ level the constraints from H(z) data are obtained as and kms -1Mpc -1, while the constraints from the Type Ia supernovae (SNe Ia) data are and kms -1Mpc -1. We also perform the joint test using H(z) and SNe Ia data, which yields the constraints and kms -1Mpc -1. The estimates of H 0 are found to be in close agreement with some recent probes carried out in the literature. The analysis reveals that the observational data successfully describe the cosmic acceleration within the framework of power-law cosmology. We find that the power-law cosmology accommodates well the H(z) and SNe Ia data. We also test the power-law cosmology using the primordial nucleosynthesis, which yields the constraints q≳ 0.72 and H 0≲ 41.49kms -1Mpc -1. These constraints are found to be inconsistent with the ones derived from the H(z) and SNe Ia data. We carry out the statefinder analysis, and find that the power-law cosmological models approach the standard Λ cold dark matter (ΛCDM) model as q→-1. Finally, we conclude that despite having several good features power-law cosmology is not a complete package for the cosmological purposes. © 2012 The Author Monthly Notices of the Royal Astronomical Society © 2012 RAS.

Loading Delhi Technological University collaborators
Loading Delhi Technological University collaborators