Atmospheric Reentry Dispersion Correction Ascent Phase Guidance for a Generic Reentry Vehicle

Launch vehicle explicit guidance mechanism depends on the estimation of the desired burnout conditions and driving the vehicle to achieve these conditions. The accuracy of the vehicle at the target point depends on how tightly these conditions are achieved and what is the strategy used to define the trajectory. It has been observed inthe literature that most of the guidance mechanisms during reentry use vacuum guidance equations that is durin greentry the atmospheric effects are not considered. In order to achieve minimum miss distance at the target point theat mospheric effects are to be considered during the guided phase and appropriate corrections should be executed,otherwise depending on the reentry flight path angle and ballistic coefficient the errors can be as high as tens of nautical miles. In this paper, the authors develop a novel approach to these vacuum guided launch vehicle problems.The paper elaborates how to calculate a prior the reentry dispersion during the ascent phase guidance and provide guidance corrections such that the terminal conditions are achieved with higher accuracy.Defence Science Journal, 2013, 63(3), pp.233-241, DOI:http://dx.doi.org/10.14429/dsj.63.373

Real Time Mid-course Maneuver and Guidance of a Generic Reentry Vehicle

The aim of any mission is to accomplish the final objective with desired accuracy and the same is valid for a generic launch vehicle. In many missions it is necessary to execute mid-course maneuvers with an intentional diversion trajectory to create a counter measure or to avoid certain specific known geographical locations. The current work elaborates a novel and practically implementable mid-course maneuver and an ascent phase guidance of a reentry vehicle executing an in-flight determined mid-course maneuver (trajectory reshaping) without compromising the accuracy of the final achieved target position. The robustness of the algorithm is validated with 6DoF simulation results by considering the dispersion of the burnout state vector conditions which arises due to variations in thrust profile, aerodynamics characteristics of the vehicle, atmosphere, etc.Defence Science Journal, 2013, 63(4), pp.346-354, DOI:http://dx.doi.org/10.14429/dsj.63.420

Noise reduction in ECG signals for bio-telemetry

In Biotelemetry, Biomedical signal such as ECG is extremely important in the diagnosis of patients in remote location and is recorded commonly with noise. Considered attention is required for analysis of ECG signal to find the patho-physiology and status of patient. In this paper, LMS and RLS algorithm are implemented on adaptive FIR filter for reducing power line interference (50Hz) and (AWGN) noise on ECG signals .The ECG signals are randomly chosen from MIT_BIH data base and de-noising using algorithms. The peaks and heart rate of the ECG signal are estimated. The measurements are taken in terms of Signal Power, Noise Power and   Mean Square Error

Comparison of Accuracy Measures for RS Image Classification using SVM and ANN Classifiers

The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multi-spectral image of Hyderabad and its surroundings area and also compare its performance with Artificial Neural Network (ANN) classifier. In this paper, a hybrid technique which we refer to as Fuzzy Incorporated Hierarchical clustering has been proposed for clustering the multispectral satellite images into LULC sectors. The experimental results show that overall accuracies of LULC classification of the Hyderabad and its surroundings area are approximately 93.159% for SVM and 89.925% for ANN. The corresponding kappa coefficient values are 0.893 and 0.843. The classified results show that the SVM yields a very promising performance than the ANN in LULC classification of high resolution Landsat-8 satellite images

Isolation, speciation and antifungal susceptibility patterns of candida isolated from cases of chronic balanoposthitis

Background:Balanoposthitis is a common condition affecting 11% of male genitourinary clinic attendees and it can be a recurrent or persistent condition. Various predisposing factors like diabetes mellitus, sexual intercourse and usage of oral antibiotics can cause chronic balanoposthitis. The Objective of the study was to isolation and speciation of candida and their antifungal susceptibility patterns from the cases of chronic balanoposthitis.Methods: The study group comprised of swabs collected from 62 male patients with chronic balanoposthitis attending sexually transmitted diseases (STD) outpatient department (OPD), King George Hospital (KGH), Visakhapatnam. Standard mycological tests for the candida isolation, speciation and antifungal susceptibility were done.Results: Out of 62 samples, (85%) were culture positive for candida. The most common species isolated was C. parapsilosis (37.7%), followed by C. glabrata (28.3%), C. albicans (15.09%), C. dubliniensis (9.4%), C. krusei (7.5%) and C. tropicalis (1.88%). Most of the candida species showed sensitivity to amphotericin B, Nystatin, clotrimazole and ketoconazole. A relative resistance to fluconazole and itraconazole was observed.Conclusions: Chronic balanoposthitis is the most common infection in men attending STD, OPD. In the present study, diabetes is main predisposing factor than sexual intercourse and candida non albicans predominated over C. albicans. Resistance of candida species to azoles is on rise. This establishes the importance of determination of antifungal susceptibility patterns to prevent the emergence of drug resistance, prior to initiation of therapy.

A CROSS CLOUD METHOD FOR PROTECTED APPROVED DEDUPLICATION

Previous systems cannot support differential authorization duplicate check, in a number of applications. Within the recent occasions, structural design was offered that comprised of dual clouds for effective outsourcing of understanding additionally to arbitrary computations towards an untrustworthy commodity cloud. With the development of cloud computing, efficient secure data deduplication has attracted much concentration in recent occasions from research community. Data deduplication may well be a committed data compression technique that's generally introduced for eliminating duplicate copies of repeating storage data. Dissimilar to established systems, private cloud is provided just like a proxy towards permitting data owner to safely execute duplicate check by differential legal rights and therefore this architecture is helpful while offering attracted much consideration from researchers.   Within our work we solve impracticality of deduplication by differential legal rights within cloud computing, we produce a hybrid cloud structural design comprised of everyone cloud and cloud

Exploratory Boosted Feature Selection and Neural Network Framework for Depression Classification

Depression is a burdensome psychiatric disease common in low and middle income countries causing disability, morbidity and mortality in late life. In this study, we demonstrate a novel approach for detection of depression using clinical data obtained from the on-going Mysore Studies of Natal effects on Ageing and Health (MYNAH), in South India where the members have undergone a comprehensive assessment for cognitive function, mental health and cardiometabolic disorders. The proposed model is developed using machine learning approach for classification of depression using Meta-Cognitive Neural Network (McNN) classifier with Projection-based learning (PBL) to address the self-regulating principles like how, what and when to learn. XGBoost is used for feature selection on the available data of assessments with improved confidence. To improve the efficiency of McNN-PBL classifier the best parameters are found using Particle Swarm Optimization (PSO) algorithm. The results indicate that the McNNPBL classifier selects appropriate records to learn and remove repetitive records which improve the generalization performance. The study helps the clinician to identify the best parameters to analyze the patient