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Survey on zone-based speed controller of vehicle using IoT

Nowadays the drivers drive vehicles at high speed even in speed limited areas without considering the safety of the public. Most of the road accidents occur due to over speeding and traffic violation. The traffic police are not able to control them with full effect. Also, it is not practical to monitor these areas throughout. Even though we have signboards at all the riskier zones like schools and hospitals but most of the drivers won’t look into them. Our project paves way for controlling the speed of the vehicles within certain limits in restricted zones without interruption of the drivers. Here, we make a survey on the model based on wireless technology by which we will be able to control the speed of the vehicle. This project composed of two separate units namely the zone status transmitter unit and the receiver unit. The RFID reader is attached along with the vehicle and the RFID Tag with these Zones. These tags are programmed to send a coded signal when the reader comes in proximity. A controlling module in the vehicle then takes the decision and control the speed accordingly by making use of the pulse width modulation concept Whenever the vehicles enter into these zones their receivers will receive this code and the speed of the vehicles is controlled automatically with the help of the speed controller attached to microcontroller unit present inside the vehicle. The tags are placed at the beginning and the end of the regions for which the speed should be reduced.

Published by: Druthi Raghuram Shanbhog, Arpitha M. P., Chandrakala, Komal Kumari, Shruthi B. M.

Author: Druthi Raghuram Shanbhog

Paper ID: V6I3-1231

Paper Status: published

Published: May 16, 2020

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Research Paper

Horizontal and vertical surface cleaning robot

In this present era, people lead a very busy life. People in cities have irregular and long working times. In such situation person always find a ways of saving time. Thus, manual work is taken over a by robotics now a days. This paper presents an autonomous robot for both floor and wall cleaning application. Since anything can run easily on horizontal surface, the main purpose of the present study is to develop a vertical cleaning robot for a single large windowpane such as a show window. It works on the principle, if we draw air in the suction cup using vacuum pump then it can stick to the surface.

Published by: Rakesh Pravinkumar Chavan, Devkanya Balbhim Kapre, Sonu Kumar, Swamini Pande, Ruchi Jain

Author: Rakesh Pravinkumar Chavan

Paper ID: V6I3-1226

Paper Status: published

Published: May 16, 2020

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Research Paper

A biochemical approach towards sealing viral replication pathway and also simultaneous approach to dampen immune response

Apart from all three domains of life [namely: archaea, bacteria, and eukaryotes], we might have come across a suspected molecular organization, whose existence as a living organism is still in question. This is living as well as could be said to be non-living is still a dilemma. It was really a fascinating thing for a student having an interest in searching for scientific causes and loves to study about organism there behavior and the way they interact environment, during our days of high school when we had just a glimpse of introduction about an organism that could be considered as living [i.e.; the reproduction is observed] only when it’s inside a living cell or so-called host.

Published by: Anandita Jha

Author: Anandita Jha

Paper ID: V6I3-1209

Paper Status: published

Published: May 16, 2020

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Review Paper

Review on vision based fire flame detection

The break-out of a fire should be determined rapidly in order to prevent material damage and human casualties. Traditional low-level feature-based methods have a high rate of false alarms and have low detection accuracy. To overcome these issues, deep learning models for fire detection at early stages during surveillance are used. Smaller convolutional kernels and fully connected layers of the deep learning models help to do classification effectively by reducing the computational requirements.

Published by: Prasobh John, Nisha C. A.

Author: Prasobh John

Paper ID: V6I3-1198

Paper Status: published

Published: May 15, 2020

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Review Paper

A review – To treat the solid waste or organic waste by using anaerobic digestion process

Abstract Biogas is very essential and beneficial gas. Biogas is to be produced from raw material like vegetable waste, fruits waste by break down of organic waste in the absence of oxygen to generate methane and carbon dioxide. Biogas is a mixture of methane, carbon dioxide (CO2), H2S, and other gases. For the generation of biogas active digestion process is to be used. Energy or biofuel generation from organic waste is economical and easy. Generally in rural areas biogas is to be generated. The generation of vegetable waste is increasing day by day from every household. Vegetable waste can be used for the bio methanation process which is anaerobic digestion using biogas plant. The biogas plant is a simple technique to produce methane. The biogas is a nonconventional source called gobar gas. In rural India, the daily waste obtained from animals is still used as raw material for biogas plants called “Gobar Gas” daily. Anaerobic treatment systems are divided into 'high-rate' systems with biomass retention and 'low-rate' systems without biomass retention.

Published by: Adarsh Kudkelwar, Hitesh Urkude, Akash Parate, Kaveri Raut, Sonu Chichghare, Nilesh Pal

Author: Adarsh Kudkelwar

Paper ID: V6I3-1164

Paper Status: published

Published: May 15, 2020

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Research Paper

Detecting breast cancer using Neural Networks

The goal of this paper is to detect the breast cancer using neural networks. Image processing techniques play an important role in the diagnostics and detection of diseases and monitoring the patients having these diseases. Breast Cancer detection of medical images is one of the most important elements of this field. Because of low contrast and ambiguous the structure of the tumor cells in breast images, it is still a challenging task to automatically segment the breast tumors. Our method presents an innovative approach to the diagnosis of breast tumor incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using balance contrast enhancement techniques (BCET). The results of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method and Thresholding method to segment the out boundaries of the breast and to locate the Breast Tumor boundaries (shape, area, spatial sizes, etc.) in the images. The third stage feature extraction using Discrete Wavelet Transform (DWT). Finally the artificial neural network will be used to classify the stage of Breast Tumor that is benign, malignant or normal. The early detection of Breast tumor will improves the chances of survival for the patient. Probabilistic Neural Network (PNN) with radial basis function will be employed to implement an automated breast tumor classification.

Published by: Gourav Chakraborty, Suman Das, Tannistha Sarkar, Souvik Kar, Sangita Roy, Dr. Saradindu Panda

Author: Gourav Chakraborty

Paper ID: V6I3-1215

Paper Status: withdrawn

Submitted: May 15, 2020

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