MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

School of Mechanical Engineering

World University root node

3rd International Conference on Recent Trends in Engineering \& Technology

Performance Enhancement of a Centrifugal Blower using Experimental and Computational Techniques

International Journal of Innovative Research in Science, Engineering and Technology

Performance Evaluation of Intermittent Solid Adsorption Refrigeration System Running On Solar Energy

International Journal of Multidisciplinary and Current Research

Determination of the Coupled Field Stresses on Piston by Using Finite Element Analysis in Four Stroke Engine A Review

School of Computer Engineering & Technology

International Journal of Research in Computer Science and Management

Comparative Study of Spatial Color and Shape Features for Low Level Content Based Image Retrieval System

School of Civil Engineering

International Journal For Research In Technological Studies (ISSN 2348-1439 {\ldots}

“ Implementation of barcode technique for logistic management and its comparison with traditional logistic Management ”

INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH \& TECHNOLOGY (IJERT)

Recognition of Handwritten Marathi Vowels using Combination of Topological and Statistical

International Journal of Science and Research (IJSR)

Survey of various open source network simulators

The depletion of crude reserves and turbulent global and local economy leads to ever increasing prices of petroleum-based fuels. Refineries all across the globe are forced to process heavy crude and high sulfur crude which in effect embraces pollution problems on combustion front unless advanced technologies are implemented. All of these factors point toward the search of sustainable substitutes for conventional energy sources. In this scenario, the prospect of using biodiesel as an environment-friendly and sustainable energy source is very promising. Several countries are opting for a blend of 5-20% of biodiesel with conventional petro-based diesel. In this study, a generalized model is proposed for the prediction of important fuel properties of a biodiesel-diesel mixture. The model utilizes properties of each component along with its fractional composition in the mixture. Performance of the model has been evaluated for various biodiesel-diesel blends, where biodiesel were synthesized from diversified sources namely soybean, sunflower, ricebran, cottonseed oil etc. It has been clearly observed that the developed model is able to predict density, viscosity, cetane number, and pour point quite accurately irrespective of the source of biodiesel. The simple model can be utilized to predict the blending proportions of biodiesel with petro-based diesel in order to meet ASTM specification of the blended fuel. © 2014 American Chemical Society.

School of Chemical Engineering

Industrial and Engineering Chemistry Research

Synthesis of biodiesel from low-cost vegetable oil and prediction of the fuel properties of a biodiesel-diesel mixture

To achieve a panoptic machine vision, recognition of images from disparate classes like person, car, building, et cetera is of primal importance. The Locally-connected Neural Pyramid (LCNP) was proposed earlier to achieve a robust and a time efficient training of large datasets of images from these disparate classes. The objective of this paper is to propose a technique for fast training of th e LCNP. YIQ co ding is used to ex tract the color based information of the images as it separates the color information from the luminance information. As R GB to YIQ conversion is an embarrassingly parallel situation, this recoding can give a tremendous speed-up over the previous approach- where PCA de-correlation of RGB channels was carried out. Also, the use of YIQ coding has entailed a reduction in the complexity of the LCNP, thu s, reducing the computations considerably. This will further boost the time performance of the training. Despite a considerable reduction in the complexity of the LCNP and the use of YIQ coding, the recognition performance achieved by this approach is similar to the previous approach. A recognition rate of 85.62% is achieved for the testing samples of the LabelMe-12-50K dataset. We p ropose that if the previous method of de-correlating RGB ch annels using PCA is rep laced with YIQ coding, tremendous speed-up will be achieved. © 2014 IEEE.

School of Electronics & Communication Engineering

Souvenir of the 2014 IEEE International Advance Computing Conference, IACC 2014

CUD a-accelerated fast training of Locally connected Neural Pyramid using YIQ color coding

Classification of hand gesture is crucial for the development of hand gesture based system for human machine interaction. Gesture recognition system consists of hand gesture acquisition, segmentation, morphological filtering, contour extraction, and classification. This paper aims at classification of hand gesture as a similarity measure using Dynamic Time Warping and Piecewise Dynamic Time Warping. Experiments and evaluation on a subset of American Sign Language (ASL) hand gesture show that, by using Dynamic Time Warping hand gesture can be classified. Additionally, it is also estimated that Piecewise DTW can be efficiently used to speed up the computations of DTW. © 2014 IEEE.

2014 International Conference on Electronics and Communication Systems, ICECS 2014

Gesture recognition using DTW &amp; piecewise DTW