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Survey on Underwater Acoustic Wireless Sensor Networks of Routing Protocols

In the past few years wireless Sensor Network has been an emerging technology. As it is a permutation of computation, sensing and communication. In the 70% of the earth a huge amount of unexploited resources lies covered by oceans. To coordinate interact and share information among themselves to carry out sensing and monitoring function underwater sensor network consists number of various sensors and autonomous underwater vehicles deployed underwater. This paper is concerned about the underwater acoustic wireless sensor network of routing protocol applications and UW-ASNs deployments for monitoring and control of underwater domains.

Published by: Neha Jain, Hitesh Jangir

Author: Neha Jain

Paper ID: V3I2-1533

Paper Status: published

Published: April 26, 2017

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Smart Public Transport System Using Internet-Of-Things

Efficient transportation could be an important issue to be considered in public transport system [PTS]. To make a city smart and digitalised this is a small contribution. Smart public transportation system [SPTS] using IOT shows that how IOT can be applied to PTS and present the navigational facilities for urban bus passengers. SPTS provides three novel information services for bus passengers: 1) Micro-navigation [MN] 2) Crowd-aware route recommendation [CARR] and 3) Bus arrival time estimation [BATE]. MN gives fine grained information or guidance about passenger’s bus journey by recognizing and tracking the bus a person boarded. CARR collects the crowd levels of different routes and predicts and suggests the best and less crowded routes to passengers. BATE collects bus locations and predicts the estimated arrival time to passenger’s location with shared route details so that BATE will be more accurate. SPTS provides an efficient and reliable PTS which is simple, attractive and user friendly.

Published by: Apsara .S, Rashmi G. A, Mohankumari K .V, Anitha .L, Jyothi .B

Author: Apsara .S

Paper ID: V3I2-1540

Paper Status: published

Published: April 26, 2017

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Maximum Solar Power Tracker Mechanically By Using Dual Axis Tracker

To formulate a method for harvesting the maximum solar irradiance and thereby increase the output of system. To reduce the energy expenditure by solar tracking system and conserve energy without any extra hardware components. The solar power is tracked mechanically in both the axis by using dual axis tracker efficiently to obtain maximum irradiance available from the sun.

Published by: B. Balaji, A. Arulvizhi, J. Jayashree

Author: B. Balaji

Paper ID: V3I2-1587

Paper Status: published

Published: April 26, 2017

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Architecture in Water

This paper deals with the understanding of what is architecture in water. The main aim is to understand its types, study its evolution, its scope in the current scenario, a brief study of construction techniques, sustainable approaches, problems faced while and after construction. At last, the aim is to arrive at a conclusion that why architecture in water is important.

Published by: Manali Surana

Author: Manali Surana

Paper ID: V3I2-1577

Paper Status: published

Published: April 26, 2017

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Performance Evaluation of a Modified Method Based On Patch Based Image Modelling For Image Denoising

Digital images play a significant role both in daily life as well as in areas of research and technology. Data sets collected by image sensors are generally infected by noise. Imperfect instruments, problems with the data acquisition process, and interfering natural phenomena can all degrade the data of interest. Furthermore, noise can be introduced by transmission errors and compression. Thus, denoising process is often a necessary and the first step to being taken before the images data is being analyzed. It is necessary to apply an efficient denoising technique to compensate for such data corruption. Image denoising still remains a challenge for researchers because noise removal introduces artifacts and causes blurring of the images. The challenge of any image denoising algorithm is to suppress noise while producing sharp images without loss of finer details. A modified method based on patch-based image modeling is proposed in this research work. The main part of proposed method is the use of image nonlocal self-similarity (NSS) prior. NSS prior refers to the fact that a local patch often has many nonlocal similar patches to it across the image. This fact significantly enhances the denoising performance. Patch Groups are extracted from training images by putting nonlocal similar patches into groups. According to these Patch Groups, Gaussian Mixture Model learning algorithm is developed to learn the NSS prior. The whole process is repeated 4 times to make the system learn more and more. The iteration process regulates and optimized some of the variables. MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and Correlation coefficient has been taken as output parameters to evaluate the performance of proposed system. MATLAB R2013a has been taken as implementation platform using image processing toolbox.

Published by: Renu Sharma, Gaurav Kumar Sangal

Author: Renu Sharma

Paper ID: V3I2-1575

Paper Status: published

Published: April 26, 2017

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Survey of Various Methods for Image Denoising

An Image is a worth, a thousand words & in this digital age, images are everywhere. Most of the digital images contain some form of noise. The purpose of denoising is to reconstruct the original image from its noisy observation as accurately as possible. The important property of a good image denoising model is that it should completely remove noise as far as possible. Estimation of the noise level in an image is a very important parameter to improve the efficiency of denoising. This article presents different approaches used so far by the researchers for the estimation of blind noise level using the statistical and averaging method and denoising of an image. The paper also contains problems in different approaches identified by the survey. Image denoising has a very rich history beginning from the mid-70s. Patch based image modeling has achieved a great success in low-level vision such as image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across the image, has significantly enhanced the denoising performance. However, in most existing methods only the NSS of input degraded image is exploited, while how to utilize the NSS of clean natural images is still an open problem.

Published by: Renu Sharma, Gaurav Kumar Sangal

Author: Renu Sharma

Paper ID: V3I2-1574

Paper Status: published

Published: April 26, 2017

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