Delhi Technological University

About: Delhi Technological University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 4427 authors who have published 6761 publications receiving 71035 citations. The organization is also known as: Delhi College of Engineering & DTU.

Host sensitized novel red phosphor CaZrSi2O7 : Eu3+ for near UV and blue LED-based white LEDs

Abstract: A series of red phosphors Ca1?xZrSi2O7?:?Eux (x = 0.5,1,5,10,12?mol%) were prepared by a solid-state reaction technique at various temperatures and their structural and optical properties were investigated. The x-ray diffraction profiles showed that all peaks could be attributed to the monoclinic phase CaZrSi2O7 doped with Eu3+. SEM, FTIR, TG and DTA profiles have also been characterized to explore their structural properties. The luminescence properties of these resulting phosphors have been characterized by photoluminescence spectra. The host matrix itself has shown a strong blue emission which has its maximum intensity at 470?nm. The excitation spectra of CaZrSi2O7?:?Eu3+ revealed two excitation bands at 395 and 464?nm which correspond to the sharp 7F0?5L6 and 7F0?5D2 transitions of Eu3+ and matches well with the two popular emissions from n-UV/blue GaN-based LEDs. The prominent red emission was obtained at 615?nm by the excitation transitions 5L6, 5D2 of Eu3+ through the non-radiative energy transfer process from the host to the Eu3+ ion. The effects of charge compensation by monovalent ions on the luminescence behaviour of a red emitting phosphor CaZrSi2O7?:?Eu3+ were investigated. The high colour saturation and the low thermal quenching effect of these phosphors make it a potential red component for white light emitting diodes (w-LEDs).

An effective web page recommender system with fuzzy c-mean clustering

Abstract: With the exponential development of the number of users browsing the internet, an important factor that now the developer community is focussing on is the user experience Recommender systems are the platforms that make personalized recommendations for a particular user by predicting the ratings for various items Recommender systems majorly ignore the sequential information and rather focus on content information, but sequential information also provides much information about the behavior of the user In this research work, we have presented a novel web-based recommender system which is based on sequential information of user’s navigation on web pages We received top-N clusters when Fuzzy C-mean (FCM) clustering is employed We determined the similar users for the target user and also evaluated the weight for each web page We have tried to solve that problem of recommender systems as we offered a system to forecast a user’s next Web page visit In our work, we proposed a system which generates recommendations to the users, by considering the sequential information that exists in their usage patterns of Web pages We employed fuzzy clustering to give recommender system a sequential approach We calculated weights for each page category considered in our system and predict top page recommendation for the target user The real-world dataset of MNSBC is used in the experiments The dataset consists of 5000 user entries with 6, entries per user When we performed a comparison between the existing model with our proposed model, then it clearly showed that the accuracy of the proposed model is almost three times better than some existing systems The accuracy of our proposed model is nearly 33 %

Multiwalled carbon nanotube modified microfluidic-based biosensor chip for nucleic acid detection

Abstract: An impedimetric microfluidic–based biosensor was fabricated and investigated for quantification of the DNA sequence specific to chronic myelogenous leukemia (CML). The sensor chip was constructed by electrophoretic deposition of carboxyl-modified multiwalled carbon nanotubes (MWCNT) on the patterned (via wet chemical etching method) indium–tin–oxide (ITO) coated glass substrate. The MWCNT surface was immobilized with CML specific deoxyribonucleic acid probe, followed by sealing of the biochip with poly (dimethylsiloxane) microchannel for fluid control. This integrated miniaturized system was used to monitor complementary target DNA concentration by measuring the interfacial charge transfer resistance via hybridization. The presence of complementary DNA in buffer solution resulted in significant decrease in electrical conductivity of the interface thereby presenting a barrier for transport of the redox probe ions. Under optimal conditions, this microfluidic biochip exhibited good calibration range from 1fM to 1 μM and a response time of 60 s.

Synthetic data augmentation for surface defect detection and classification using deep learning

Abstract: Deep learning techniques, especially Convolutional Neural Networks (CNN), dominate the benchmarks for most computer vision tasks. These state-of-the-art results are typically obtained through supervised learning, for which large annotated datasets are required. However, acquiring such datasets for manufacturing applications remains a challenging proposition due to the time and costs involved in their collection. To overcome this disadvantage, a novel framework is proposed for data augmentation by creating synthetic images using Generative Adversarial Networks (GANs). The generator synthesizes new surface defect images from random noise which is trained over time to get realistic fakes. These synthetic images can be used further for training of classification algorithms. Three GAN architectures are trained, and the entire data augmentation pipeline is implemented for the Northeastern University (China) Classification (NEU-CLS) dataset for hot-rolled steel strips from NEU Surface Defect Database. The classification accuracy of a simple CNN architecture is measured on synthetic augmented data and further it is compared with similar state-of-the-arts. It is observed that the proposed GANs-based augmentation scheme significantly improves the performance of CNN for classification of surface defects. The classically augmented CNN yields sensitivity and specificity of 90.28% and 98.06% respectively. In contrast, the synthetically augmented CNN yields better results, with sensitivity and specificity of 95.33% and 99.16% respectively. Also, the use of GANs is demonstrated to disentangle the representation space and to add additional domain knowledge through synthetic augmentation that can be difficult to replicate through classic augmentation. The proposed framework demonstrates high generalization capability. It may be applied to other supervised surface inspection tasks, and thus facilitate the development of advanced vision-based inspection instruments for manufacturing applications.

Ontologies for Software Engineering: Past, Present and Future

Abstract: Background/Objectives: Research in recent years has probed integration amongst research field of Software Engineering & Semantic Web technology, addressing the advantages of applying Semantic techniques to the field of Software Engineering. Prolifically published studies have further substantiated the benefits of ontologies to the field of Software Engineering, which clearly motivate us to explore further opportunities available in this collaborated field. This paper is a survey expounding such opportunities while discussing the role of ontologies as a Software Life-Cycle support technology. Method/Statistical Analysis: Survey centred on providing an overview of the state-of-art of all the ontologies available for Software Engineering followed by their categorization based on software life cycle phases and their application scope. Findings: Characterization of ontologies as a Software Life-cycle support technology, instigated by the increasing need to investigate the interplay between Semantic Web & Software Engineering with the ultimate goal of enabling & improving Software Engineering capabilities. Application/Improvements: This paper discusses the practical and potential applications of ontologies in the field of Software Engineering followed by the issues and challenges that will keep this field dynamic and lively for years to come.