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An Experimental Study on Synergic Effect of Sugar Cane Baggase ash and Fly Ash in Concrete
Yashwanth M K ; Nagarjuna P
Civil Engineering
Year: 2016, Volume:3, Issue : 4
Pages: 174 - 178
This paper aims to ascertain the synergic effect of utilizing bagasse ash and fly ash in concrete. In this study, bagasse ash & fly ash were physically and chemically characterized and partially replaced in the ratio of 0%, 5%, 10%, 15% and 20% by weight of cement to produce concrete. The fresh properties of concrete like slump test and hardened properties like compressive strength were carried out. From the results, it was observed that fresh properties of the bagasse ash and fly ash concrete like workability increases as the percentage of replacement of bagasse ash and fly ash increases and compressive strength of bagasse ash and flyash based concrete increases up to 15% ash replacement level as compared to normal concrete.
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Effect of Fire on RC Slab
Poorna. S
Civil Engineering
Year: 2016, Volume:3, Issue : 4
Pages: 179 - 183
Concrete is a construction material used for buildings, bridge decks and also used for other special purposes. Concrete has now become a very common construction material. The main advantage of this building material is that it can be designed as per the requirements. In the modern industry, grade of concrete used varies from M20 to M70. Even higher grades are designed if needed. When the concrete is used in special purposes the chance of fire exposure also increases. In case of unexpected fire, the properties of concrete changes and hence it is important to know the deflection rate of the concrete structures and the various effects of fire on concrete. Numerous studies were conducted on concrete both experimental and analytically to understand its behaviour under fire. In this study the thermal behavior of reinforced concrete slab exposed to fire is studied and presented. The paper mainly focuses on the percentage deflection of RC slab when exposed to elevated temperature. The RCC slabs were modeled using ANSYS14.5, to show the behavior of slab at elevated temperature with M25, M70 and M100 grade of concrete and with cover of 30mm,40mm and 50mm. Analysis was also carried out to study the load-deflection pattern and percentage of deflection with and without heat with a pressure of 0.1N/mm2. In stage one, 9 specimens were modeled to show the effect of different grade of concrete with different cover. The heat is applied on the basis of ISO 834 curve. The result showed that the deflection of a slab decreases as the cover provided increases. It was also found that the deflection of the slab decreases as the grade of concrete increases. From the result incurred it is seen that the analysis showed minimum deflection for M100 with cover 50mm. Hence it can be observed that, it is very essential to consider the effect of fire while designing special structures such as blast furnaces etc.
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Steganography using Reversible Texture Synthesis based on Error Histogram Shift
Anumol Antony ; Dr. Arun Kumar M N
Computer Science and Engineering
Year: 2016, Volume:3, Issue : 4
Pages: 184 - 191
Steganography is the method for concealing data inside another file, message, image, or video. The purpose of steganography is to hide data in a manner that existence of communication is unknown by an attacker. This proposed work presents stegnography in texture images utilizing reversible texture synthesis based on error histogram shift. Texture synthesis process synthesizes a large texture image from a smaller texture image, which has same local appearance. The texture synthesis procedure is fabricated into steganography concealing secret messages and in addition the source texture. The algorithm conceals the source texture image and embeds the secret messages through the procedure of texture synthesis and error histogram shift. This permits us to extract the secret messages and the source texture from a stego synthetic texture.
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Discriminative Robust LBP for Leukemia Detection
Nimi T P ; Divya T V
Computer Science and Engineering
Year: 2016, Volume:3, Issue : 4
Pages: 192 - 196
Leukemia is a group of cancers that usually begins in the marrow and results in high numbers of abnormal white blood cells. The bone marrow produces the cellular elements of the blood, including platelets, red blood cells and white blood cells. The differences between these groups lie on the texture, color, size and morphology of nucleus and cytoplasm. These white blood cells are not fully developed and are called blasts or leukemia cells. Image processing technique involved five basic components which are image acquisition, image pre-processing, image segmentation, feature extraction and classification. The proposed system firstly individuates in the blood image the leucocytes from the others blood cells, then it selects the lymphocyte cells, it evaluates morphological indexes from those cells and finally it classifies the presence of the leukemia. Discriminative Robust LBP is used for feature extraction. They alleviate the intensity reversal problem of object and background. Our proposed feature is tested on ALL IDB dataset and obtained 89 % accuracy.
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Automatic Meter Reading using LabVIEW
Himanshu Goyal ; Dr. Dharam Buddhi; Prof. Hari Kumar Singh
Mechanical Engineering
Year: 2016, Volume:3, Issue : 4
Pages: 197 - 202
The Designing and implementing a system based on wireless communication. This paper presents an implementation methodology for a wireless automatic meter reading system using LabVIEW platform and ZigBee network technology. A system design using LabVIEW which is low cost, high performance, highest data rate, highest coverage area and most appropriate to deal with disadvantages of traditional meter reading such as errors in reading, inaccuracy, external conditions affecting readings, delayed work we have implemented meter reading system based on latest ZigBee technology. In this project we have designed and implemented wireless system network for measuring utilities reading such as electricity. The system is designed using electric meter, ModBUS protocol, National Instruments software and hardware, and ZigBee module in close communication with GPRS for distant communication. This system performs tasks such as taking meter reading, distribution of bills, sending notice, cutting and reconnection of flow automatically. This model can lead to great deal of costs saving in electricity metering.
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Real Time Fire and Smoke Detection using Multi-Expert System for Video-Surveillance Applications
Jesny Antony ; Prasad J. C.
Computer Science and Engineering
Year: 2016, Volume:3, Issue : 4
Pages: 203 - 212
Fire and smoke Detection system plays an important role in surveillance and security systems. These systems are primarily designed to warn the occupants of fire so that they may safely evacuate the premises. The detection of smoke will help for the early detection of fire and helps to reduce the loss that caused from this kinds of accidents. In this paper, we propose a method, which detects fire and smoke areas by analyzing the videos that are acquired by surveillance cameras in real-time. The purpose is achieved by using a Multi-Expert System, which takes the evaluation of multiple experts such as color, movement and shape features separately and evaluated. The combined result shows a high performance compared with other existing systems. The method which is tested using a large number of fire and smoke videos, which is taken from different datasets as well as web and it gives a high accuracy with other systems.
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Efficient Optic Disc Segmentation and Peripappilary Atropy Detection in Digital Fundus Images
Ajeesha A. A ; Arun Kumar M N
Computer Engineering
Year: 2016, Volume:3, Issue : 4
Pages: 213 - 222
The basis of study and analysis of various eye diseases includes Optic disc(OD) nerve head region and OD center coordinates. The early detection of various eye pathologies like glaucoma and Diabetic Retinopathy helps to prevent the vision loss. So, there is a need to develop a fast and efficient algorithm for disease prediction. For that, reliable and efficient OD localization and segmentation are the important tasks. Therefore, this method aims for the efficient and automatic localization and segmentation of Optic Disc from digital fundus images and its peripappilary atropy detection to predict whether the optic disc is affected by any diseases like glaucoma. The proposed technique is divided into three subsections which deal with OD localization, OD boundary detection and peripappilary atropy Detection (PPA). OD localization makes use of the unique circular brightness structure associated with the OD, that is, the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. OD boundary detection includes accurate blood vessels inpainting for the removal of vascular structure in the optic disc region which is followed by intensity adjustment and region growing considering OD center as a seed point for reliable segmentation of fundus images. The presence of PPA indicates whether the eye is affected by the diseases like glaucoma. So from the segmented optic disc, PPA is detected using a threshold so that we can predict whether the person is affected by disease or not. This is done by calculating the Red by Green ratio for each pixel in the Region of Interest (ROI). The process is implemented in a MATLAB 2014 prototype and tested with images in the DRIVE Dataset. The results show that the suggested method has 82.14% of accuracy, 75% of precision and 81.82% of recall for segmentation and PPA prediction.
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Automatic Detection and Recognition of Text in Traffic Sign Boards Based on Word Recognizer
Seles Xavier ; Reshmi R
Computer Science and Engineering
Year: 2016, Volume:3, Issue : 4
Pages: 223 - 231
While driving one passes so many text based- traffic signs that sometimes even important information such as speed limits or no overtaking traffic signs can get lost. The driver can also be sometimes distracted by traffic and does not notice some of the text based traffic signs. The automatic text detection and recognition of traffic sign boards eliminates this issue by automatically detecting and recognizing the text in traffic signs boards. The text- based traffic sign boards should be detected by making use of HSV color thresholding. RANSAC algorithm and homography are used to vertically align the text characters and reduce distortion. This is followed by the recognition phase, which interprets the text in the detected traffic boards. Each letter of the text in the traffic sign board is identified and fed into the Optical character recognition (OCR) for interpretation. The recognition phase is further revised using a dictionary of words. Text recognition is also applied on images with noise. Vectorization is performed so that computational time can be reduced.
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Study of Noise Pollution Levels During the Festival Days of Simhastha Kumbh 2016 in Ujjain City, MP (India)
Ajay Kumar Mishra ; Prof. M K Koshta
Environment Engineering
Year: 2016, Volume:3, Issue : 4
Pages: 232 - 236
The primary aim of the study was to assess the noise level at two different places of Ujjain during Simhastha Kumbh Mela-2016 Mahaparv during Shahi Snaans and other holy dip days. Due to mass gathering during kumbh mela population density and number of automobiles increased and noise level compared to normal days also increased in the city. During the present study the noise levels were measured with the help of noise level meter at two different locations viz. Ramghat and Mangalnath ghat both are the most ancient ghats of ujjain near the bank of Kshipra river. Considering the increase in crowd of visitors and pilgrim near ghats, there is a need to study noise pollution in ujjain city during Simhastha kumbh. The study revealed the fact that in the festival days of shahi snaan noise level had reached beyond tolerable limits. The maximum noise levels during all three shahi snaan’s days were 82.3 dB(A), 83.6 dB(A) and 88.2 dB(A) respectively. It was also observed that Mangalnath ghat was noisier than Ramghat due to narrow roads, lack of space and improper traffic management. Such high noise levels may induce headache, sleeplessness and other varies effect which can reduce human working efficiency and produce negative impact on human health.
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Retinal Area Detector for Classifying Retinal Disorders from SLO Images
Shobha Rani Baben ; Paul P Mathai
Computer Science and Engineering
Year: 2016, Volume:3, Issue : 4
Pages: 237 - 245
The retina is a thin layer of tissue that lines the back of the eye on the inside. It is located near the optic nerve. The purpose of the retina is to receive light that the lens has focused, convert the light into neural signals, and send these signals on to the brain for visual recognition. The retina processes a picture from the focused light, and the brain is left to decide what the picture is. Since the retina plays vital role in vision, damage to it can also cause problems such as permanent blindness. So we need to find out whether a retina is healthy or not for the early detection of retinal diseases. Scanning laser ophthalmoscopes (SLOs) can be used for this purpose. The latest screening technology provides the advantage of using SLO with its wide ï¬eld of view, which can image a large part of the retina for better diagnosis of the retinal diseases. On the other side, the artefacts such as eyelashes and eyelids are also imaged along with the retinal area, during the imaging process. This brings forth a big challenge on how to exclude these artefacts. In this paper we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To provide a convenient primitive image pattern, and reduce the image processing tasks complexity we have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then extracts and calculates image based features which include textural and structural information and classiï¬es between retinal area and artefacts. And further the rectinal area is used to classify the retinal disorder based on machine learning approaches. The experimental evaluation results have shown good performance with an overall accuracy of 92%.
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Abstract
