Abstract

Every year in the country innumerable number of life and property is getting lost in the disasters. Pre-disaster planning i.e., mitigation and preparedness can have a good impact on minimizing the post-disaster response i.e., emergency, rescue and rehabilitation. This also reduces tragedy and suffering to a great extent. Andhra Pradesh being 4th largest state in India by area of 2,75,608 km² and 5th largest in population with over 100 million and a density of 277 persons/km2 is spread over 23 districts. Although it lies in the central part of peninsular Indian shield it has prone to many disasters which caused large number of casualties and innumerable property loss. This paper discusses the disasters like cyclone, floods, drought, earthquakes, tsunami, fire, etc., with statistics relevant to each disaster. Finally, we recommend the effecient strategies for disaster risk mitigation.

Abstract

Water Reactors are nuclear power plants that use water to control and remove the heat from the nuclear fuel in order to convert heat to electricity. In this report, we have considered the number of nuclear reactors and their locations in our country. Among all those nuclear reactors, we selected the nearest kalpakkam reactor which is located in Chennai, India. All the faults, around 500 kms from the reactor are considered. From the past history of earthquake data (1812-2002), we considered the maximum magnitude of each fault and source parameters for some faults has been determined. The soil liquefaction characteristics of a site and the procedure for generating Synthetic accelerogram using the Stochastic Method Simulation of ground motion from earthquakes (SMSIM) have been discussed. For performance evaluation, a 3-dimensional model of the dome was taken and dynamic (Time History analysis) was performed to evaluate the displacements at each floors. A 3-dimensional model of the dome was modeled in computer program SAP2000 and Dynamic (time history) analysis was performed on the structure to evaluate the structure's response. The model was analyzed for the earthquake ground motion obtained from SMSIM.

Abstract

Earthquake performance of RC bare frame has been well documented in the past. Also, damage patterns in reinforced concrete frames during the past earthquakes have been extensively studied. However, more recently, several destructive earthquakes, including the 1999 Athens (Greece) earthquake, the 1999 İzmit and Düzce earthquakes (Turkey), 1999 Chi Chi (Taiwan) earthquake, 2001 Bhuj (India) earthquake, and the 2003 Boumerdes (Algeria) earthquake, have given more insights to performance of RC frame constructions. In order to understand the complete behavior of RC frame, it is required to study the performance from no loading condition till the complete collapse. In this paper, a set of four bare frames designed as per Indian Standards are considered. All the frames are subjected to Northridge earthquake ground motion. Newly developed numerical model called Applied Element Method (AEM) is considered for analysis. Initially, the linear analysis is carried out and the results were compared with commercially available software. Later, progressive collapse analysis is performed. Collapse process of each bare frame is discussed in detail.

Abstract

This paper aims to simulate the Tsunami wave propagation in Indian Ocean. The simulation is done using OPENGL (Open Graphics Library) in Linux. The Analysis area covers from 8.15 degrees (-ve North) to 26 degrees (+ve north) and 71.72 degrees (+ve East) to 104.78 degrees (+ ve East). The Bathymetry data and Grid map are derived from GEBCO (General Bathy-metric Chart Of Oceans). The Bathymetric Grid Map is divided in the form of Grids. The Grid Map using OPENGL is scaled, transformed and displaced to get the depth value corresponding to the point which is assumed as epicentre.

Abstract

Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Disease severity is assessed using a rotational asymmetry metric by examining the symmetry of macular region. The performance of the proposed methodology and features are evaluated against several publicly available datasets. The detection performance has a sensitivity of 100% with specificity between 74% and 90%. Cases needing immediate referral are detected with a sensitivity of 100% and specificity of 97%. The severity classification accuracy is 81% for the moderate case and 100% for severe cases. These results establish the effectiveness of the proposed solution.

Abstract

Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Glaucoma is one of the most common causes of blindness. The manual examination of optic disk (OD) is a standard procedure used for detecting glaucoma. In this paper, we present an automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images. A novel OD segmentation method is proposed which integrates the local image information around each point of interest in multidimensional feature space to provide robustness against variations found in and around the OD region. We also propose a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is employed to derive a reliable subset of vessel bends calledr-bends followed by a local spline fitting to derive the desired cup boundary. The method has been evaluated on 138 images comprising 33 normal and 105 glaucomatous images against three glaucoma experts. The obtained segmentation results show consistency in handling various geometric and photometric variations found across the dataset. The estimation error of the method or vertical cup-to-disk diameter ratio is 0.09/0.08 (mean/standard deviation) while for cup-to-disk area ratio it is 0.12/0.10. Overall,the obtained qualitative and quantitative results show effective-ness in both segmentation and subsequent OD parameterization for glaucoma assessment.

Abstract

The author first reviews the recently proposed Li-Hwang's biometric-based remote user authentication scheme using smart cards; then shows that the Li-Hwang's scheme has some design flaws in their scheme. In order to withstand those flaws in their scheme, an improvement of their scheme is further proposed. The author also shows that the improved scheme provides strong authentication with the use of verifying biometric, password as well as random nonces generated by the user and the server as compared to that for the Li-Hwang's scheme and other related schemes.

Abstract

Establishment of pairwise keys between sensor nodes in a sensor network is a difficult problem due to resource limitations of sensor nodes as well as vulnerability to physical captures of sensor nodes by the enemy. Public-key cryptosystems are not much suited for most resource-constrained sensor networks. Recently, elliptic curve cryptographic techniques show that public key cryptosystem is also feasible for resource-constrained sensor networks. However, most researchers accept that the symmetric key cryptosystems are viable options for resource-constrained sensor networks. In this paper, we first develop a basic principle to address the key pre-distribution problem in mobile sensor networks. Then, using this developed basic principle, we propose a scheme which takes the advantage of the post-deployment knowledge. Our scheme is a modified version of the key prioritization technique proposed by Liu and Ning. Our improved scheme provides reasonable network connectivity and security. Moreover, the proposed scheme works for any deployment topology.

Abstract

Pneumoconiosis, a lung disease caused by the inhalation of dust, is mainly diagnosed using chest radiographs. The effects of using contralateral symmetric (CS) information present in chest radiographs in the diagnosis of pneumoconiosis are studied using an eye tracking experimental study. The role of expertise and the influence of CS information on the performance of readers with different expertise level are also of interest. Experimental subjects ranging from novices & medical students to staff radiologists were presented with 17 double and 16 single lung images, and were asked to give profusion ratings for each lung zone. Eye movements and the time for their diagnosis were also recorded. Kruskal-Wallis test (χ2(6) = 13.38, p = .038), showed that the observer error (average sum of absolute differences) in double lung images differed significantly across the different expertise categories when considering all the participants. Wilcoxon-signed rank test indicated that the observer error was significantly higher for single-lung images (Z = 3.13, p < .001) than for the double-lung images for all the participants. Mann-Whitney test (U = 28, p = .038) showed that the differential error between single and double lung images is significantly higher in doctors [staff & residents] than in non-doctors [others]. Thus, Expertise & CS information plays a significant role in the diagnosis of pneumoconiosis. CS information helps in diagnosing pneumoconiosis by reducing the general tendency of giving less profusion ratings. Training and experience appear to play important roles in learning to use the CS information present in the chest radiographs.

Abstract

Segmentation in echo-cardiographic images is a difficult task due to the presence of speckle noise, low contrast and blurring. We present a novel method based on clustering performed in the feature space. A new feature-based image representation is proposed. It is obtained by computing a local feature descriptor at every pixel location. This descriptor is derived using the Radon-Transform to effectively characterise local image context. Next, an un-supervised clustering is performed in the feature space to segment regions in the image. The performance of the proposed method is tested on both synthetic and real images. A comparison against well established feature descriptors is carried out to demonstrate the strengths and applicability of the proposed representation. Overall, the results indicate promise in the strategy of doing segmentation of noisy data in image descriptor space.