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Saini S.C.,Riga Technical University | Sharma Y.,Riga Technical University | Bhandari M.,Riga Technical University | Satija U.,LNMIIT
Proceedings - International Conference on Communication Systems and Network Technologies, CSNT 2012 | Year: 2012

This work presents a comparative study of two different control strategies for a flexible single-link manipulator. The dynamic model of the flexible manipulator involves modeling the rotational base and the flexible link as rigid bodies using the Euler Lagrange's method. The resulting system has one Degree-Of-Freedom (one DOF) and it provide freedom to increase the degree as well. Two types of regulators are studied, the State-Regulator using Pole Placement, and the Linear-Quadratic regulator (LQR). The LQR is obtained by resolving the Ricatti equation, in this work, we apply and compare two strategies to control the tip of the flexible link: state-feedback and linear quadratic regulator. These regulators are designed to reduce tip vibrations and increase system stability due to the flexibility of the arm. © 2012 IEEE. Source

Vaishnav M.,LNMIIT
Data Compression Conference Proceedings | Year: 2014

In this paper, a novel method for lossless compression of video is proposed. Almost all the prediction based methods reported in literature are of two pass. In the first pass, motion compensated frame is obtained and in the second, some sophisticated method is used to predict the pixels of the current frame. The proposed method is an efficient replacement for the first method that predicts current pixel using an estimate of deviation from the pixel at same temporal location in the previous frame. In this scheme, causal pixels are divided into bins based on the distance between the current and causal pixels. The novelty of the work is in finding out the fixed coefficients of the bins for a particular type of video sequence. The overall performance of the proposed method is same with much lower computational complexity. © 2014 IEEE. Source

Sureka A.,Indian Institute of Technology Delhi | Lal S.,Indian Institute of Technology Delhi | Agarwal L.,LNMIIT
Proceedings - Asia-Pacific Software Engineering Conference, APSEC | Year: 2011

Defect tracking systems such as Bugzilla and JIRA and source code version control systems such as CVS and SVN are widely used applications to support software development and maintenance activities. Previous studies show that bug databases and version databases are often used as standalone and separate repositories without explicit linkages between issue reports and corresponding commit transactions. This is because developers often do not explicitly mention or tag commit transactions with the relevant bug report IDs. The lack of explicit links between these two databases has been identified as a serious process data quality issue (incomplete and biased data) having implications in predictive model building (such as defect density and error proneness computation) and hypothesis-testing based on the dataset. Researchers have proposed solutions to link the two databases and performed experiments on open source projects such as FireFox Mozilla. We review previous approaches and propose a novel technique (based on Fellegi-Sunter (FS) Model for record linkages) to automatically integrate the two databases that overcomes some of the drawbacks of traditional methods. We validate the proposed approach by performing experiments on publicly available bug and version dataset obtained from two open-source projects (Apache HTTP Server and WikiMedia). The results of our experiments demonstrate that the proposed solution is effective in recovering traceability links (missing links) between bug-fixing commits and corresponding bug reports. © 2011 IEEE. Source

Jadhav T.,IIT IndoreMadhya Pradesh | Misra R.,IIT IndoreMadhya Pradesh | Biswas S.,LNMIIT | Sharma G.D.,R and nter for Science and Engineering
Physical Chemistry Chemical Physics | Year: 2015

In this study, we have used three D-A type carbazole substituted BODIPY (carbazole connected to the meso position of BODIPY) small molecules as donors along with PC71BM as an electron acceptor for the fabrication of solution processed bulk heterojunction organic solar cells. The devices based on the as cast active layer showed power conversion efficiency in the range of 2.20-2.70%, with high open circuit voltage (Voc) in the range of 0.94-1.08 V. The high Voc is related to the deeper highest occupied molecular orbital energy level of these small molecules. The power conversion efficiency (PCE) of devices based on thermally annealed and solvent vapor annealed (TSVA) 3a:PC71BM and 3c:PC71BM processed active layers improved up to 5.05% and 4.80%, respectively, attributed to the improved light harvesting ability of active layers, better phase separation for exciton dissociation and balanced charge transport, induced by the TA and TSVA treatment. This journal is © the Owner Societies. Source

17th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2015 - Proceedings | Year: 2015

Memory-based collaborative filtering (CF) techniques have been widely implemented for predicting ratings to unseen items by aggregating ratings of similar users or items in recommender systems (RS). Usually, sufficient ratings from similar users or similar items are not available in the rating matrix, due to the data sparsity problem. Further, these techniques suffer from correlation based problems inherent in used similarity measures. Consequently, higher prediction accuracy cannot be achieved. In this paper, we propose the use of fuzzy Naïve Bayesian (FNB) classifier for user based CF and item based CF for implicitly computing similarity between users as well as items on the basis of conditional probabilities and develop fuzzy Naïve Bayesian classifier to user based CF (FNB-UB-CF) and item based CF (FNB-IB-CF). We further develop a hybrid RS (FNB-UB-IB-CF) by combining the proposed FNB-UB-CF and FNB-IB-CF. Their combinations would be helpful in alleviating the sparsity because both user ratings and item ratings are employed. Experimental results demonstrate that the proposed methods are indeed more robust against data sparsity and give better recommendation quality using a popular MovieLens dataset. © 2015 ACM. Source

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