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Recent Papers

Uncertainty in Improving Durability Aspects and Mechanical Properties of Bamboo Reinforced Concrete

The construction industry uses the most of clean energy and also produces most of the carbon dioxide in the present global world. The production of one ton of steel and concrete emits more than two tonne and one tonne respectively of carbon dioxide whereas production of one tonne of bamboo consumes more than one tonne of carbon dioxide from the atmosphere. In SAARC countries like India where about two-third of the population live below poverty line, there is a great need of some other suitable construction material in terms of cost, availability and environment friendly. Towards this, many researchers in the past has emphasized on the use of Bamboo as reinforcement. Bamboo offers competitive strength to mass ratio, compared to re-bars. Since it is a natural material,the necessity to understand their long term behaviour regarding its acceptance as a suitable construction material in general is important. Due to the advantageous characteristics of bamboo, efforts should be made to popularize the use of bamboo. In this study, a review of the existing research work on Bamboo Reinforced Concrete with the emphasis on the various factors, which are considered as demerits in making bamboo reinforced concrete construction, as a tool is presented. This uncertainty study may serve the goal of making the dream of common people, to have their own home, come true. Keywords: Bamboo Reinforced Concrete, Durability, Mechanical Properties, Economic and Environmental Friendly Reinforcement.

Published by: Dhanendra Kumar, Dr. S. Mandal

Author: Dhanendra Kumar

Paper ID: V1I1-1136

Paper Status: published

Published: May 4, 2015

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A Beam Technology on the Pen

Today we see projector everywhere which run on the different techniques. This has made life of the user to costlier. So, we present a work on beam or a halo technique on the pen. This technology gives projecting information on the walls. The main implementation behind is applying a beam or a halo technology to the pen, and then we can use that pen to projecting an information on the walls. The projector used is LCD projector.

Published by: Pradeep M, B.C Arjun

Author: Pradeep M

Paper ID: V1I1-1138

Paper Status: published

Published: May 4, 2015

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Human Factor Considerations in Sewing Machine for Workstation Design

There is a need of ergonomically fit sewing machine workstation so that sewing machine operators can perform their tasks comfortably. The present study was initiated where worker’s perceptions were measured by preparing questionnaire to find out difficulties during sewing machine work. Questionnaires were filled up by 100 sewing machine operators from different parts of the region. Questionnaire included the different modules which are Perceived posture, localized postural discomfort and estimated endurance time. Worker’s perceptions were analyzed by finding their critical values. Criticality index of questionnaire was calculated for concluding the most critical areas. Based on the criticality index, recommended parameters were performed on selected operators. Nine sewing machine operators performed experiments at twelve different combinations of table height, desk slope and pedal position. ANOVA technique was used to analyze the data obtained from nine selected sewing machine operators after performing experiments and the dimensional adjustment of sewing machine workstation was made, in relation to desk height, desk slope and pedal position. The recommendations were formulated in order to minimize the load on the musculoskeletal system during operation in terms of anthropometric dimensions with respect to sewing desk height, desk slope and pedal position. Statistical methods were used to find out the significance of the parameters considered in the study. So sewing machine workstation is said to be best fitted; only if it will have desk height +10 cms (above elbow height), desk slope 10° and pedal position -4 cms.

Published by: Gurbhej Singh, Hitesh Vasudev

Author: Gurbhej Singh

Paper ID: V1I1-1137

Paper Status: published

Published: May 3, 2015

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Local Feature based Descriptors and their Applications

This paper presents a study on SIFT (Scale Invariant Feature transform) which is a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection and SURF (Speeded-up Robust features) which is speeded up the SIFT’s detection process without scarifying the quality of the detected points. SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.

Published by: Ridhi Jindal

Author: Ridhi Jindal

Paper ID: M1P3-1137

Paper Status: published

Published: January 3, 2015

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A Beam Technology on the Pen

Today we see projector everywhere which run on the different techniques. This has made life of the user to costlier. So, we present a work on beam or a halo technique on the pen. This technology gives projecting information on the walls. The main implementation behind is applying a beam or a halo technology to the pen, and then we can use that pen to projecting an information on the walls. The projector used is LCD projector.

Published by: Pradeep M, B.C Arjun

Author: Pradeep M

Paper ID: M1P2-1145

Paper Status: published

Published: November 20, 2014

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Classifications & Misclassifications of EEG Signals using Linear and Ada Boost Support Vector Machines

Epilepsy is one of the frequent brain disorder due to transient and unexpected electrical interruptions of brain. Electroencephalography (EEG) is one of the most clinically and scientifically exploited signals recorded from humans and very complex signal. EEG signals are non-stationary as it changes over time. So, discrete wavelet transform (DWT) technique is used for feature extraction. Classifications and misclassifications of EEG signals of linearly separable support vector machines are shown using training and testing datasets. Then AdaBoost support vector machine is used to get strong classifier.

Published by: Neelam Rout

Author: Neelam Rout

Paper ID: M1P2-1150

Paper Status: published

Published: November 20, 2014

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