Implementation of Impulse Noise Reduction Method to Color Images using Fuzzy Logic

Image Processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications. Impulse noise reduction method is one of the critical techniques to reduce the noise in color images. In this paper the impulse noise reduction method for color images by using Fuzzy Logic is implemented. Generally Grayscale algorithm is used to filter the impulse noise in corrupted color images by separate the each color component or using a vector-based approach where each pixel is considered as a single vector. In this paper the concepts of Fuzzy logic has been used in order to distinguish between noise and image characters and filter only the corrupted pixels while preserving the color and the edge sharpness. Due to this a good noise reduction performance is achieved. The main difference between this method and other classical noise reduction methods is that the color information is taken into account to develop a better impulse noise detection a noise reduction that filters only the corrupted pixels while preserving the color and the edge sharpness. The Fuzzy based impulse noise reduction method is implemented on set of selected images and the obtained results are presented

A Review on Present State-of-the-Art of Self Adaptive Dynamic Software Architecture

Enterprises across the world are increasingly depending on software to drive their businesses. It is more so with distributing computing technologies in place that pave way for realization of seamless business integration. On the other hand those complex software systems are expected to adapt changes dynamically without causing administrative overhead. Moreover software systems should exhibit fault tolerance, location transparency, availability, scalability self-adaptive capabilities to fit into present enterprise business use cases. To cope with such expectations software systems are to be built with a dynamic and self-adaptive software architecture which drives home quality of services perfectly. The point made here is that software systems are facing unprecedented level of complexity and aware of self-adaptation. Therefore it is essential to have technical knowhow pertaining to self adaptive dynamic software architecture. Towards this end, we explore present state-of-the-art of this area in software engineering domain. It throws light into dynamic software architectures, distributed component technologies for realizing such architectures, besides dynamic software composition and metrics to evaluate the quality of dynamic adaptation

A Grey Wolf Intelligence based Recognition of Human-Action in Low Resolution Videos with Minimal Processing Time

The usage of video cameras for security purposes has grown in recent years. The time for recognition of human plays an important role in solving many real time problems. In this paper, the process for identifying human action is done by separating the background using local binary pattern (LBP) and features extracted using faster histogram of gradients (FHOG) and Eigen values based on power method. The features are combined and optimized using grey wolf optimization (GWO) and finally classified using support vector machine (SVM). The experimental results are compared with existing methods in identifying the human action. The time factor is evaluated and compared with different optimization techniques like particle swarm optimization (PSO), Firefly algorithm (FA) and grey wolf optimization. The entire process is performed on three well known datasets like VIRAT dataset, KTH dataset and Soccer dataset. The comparison results prove that the recognition is done in quick time i.e. 10.28sec with improved rate of accuracy 93.35% for soccer dataset using proposed method

A RAPID RP-HPLC METHOD DEVELOPMENT AND VALIDATION FOR THE QUANTITATIVE ESTIMATION RIBAVIRIN IN TABLETS

Objective: To develop an accurate, precise and linear Reverse Phase High Performance Liquid Chromatography (RP-HPLC) method and validate as per ICH guidelines for the quantitative estimation of Ribavirin (200mg) in tablets.Methods: The optimized method uses a reverse phase column, Enable Make KromasilC18 (250 X 4.6 mm; 5μ), a mobile phase of phosphate buffer (pH 4.2): acetonitrile in the proportion of 85:15 v/v, flow rate of 1.0 ml/min and a detection wavelength of 215 nm using a PDA detector.Results: The developed method resulted in Ribavirin eluting at 2.606 min. Ribavirin exhibited linear in the range 25-150μg/ml. The precision is exemplified by the relative standard deviation of 0.4%. Percentage Mean recovery was found to be in the range of 98â€102, during accuracy studies. The limit of detection (LOD) and limit of quantitiation (LOQ) was found to be 0.24ng/ml and 0.73ng/ml respectively.Conclusion: An accurate, precise and linear RP-HPLC method was developed and validated for the quantitative estimation of Ribavirin in VIRAZIDE (200mg) tablets as per ICH guidelines and hence it can be used for the routine analysis in various pharmaceutical industries.Â

A comparative study of the consistent and simplified finite element analyses of Eigenvalue, problems

Classical displacement method of the finite element analysis of eigenvalue problems requires the use of consistent and conforming elements. However, simpler approaches based on relaxing the condition of consistency of the element descriptions, such as lumped inertia force method and others are also found to yield satisfactory results. In this paper we make a comparative study of the consistent and simplified approaches with reference to four representative problems. In the simplified approach studied in this paper, the contribution of straining modes in the derivation of the mass and geometric stiffness matrices is neglected and this simplifies their derivation substantially. The results indicate that this simplification introduces only small errors in the eigenvalues

Assessment of accuracies of finite eigenvalues

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Leveraging Self-Adaptive Dynamic Software Architecture

Software systems are growing complex due to the technological innovations and integration of businesses. There is ever increasing need for changes in the software systems. However, incorporating changes is time consuming and costly. Self-adaptation is therefore is the desirable feature of any software that can have ability to adapt to changes without the need for manual reengineering and software update. To state it differently robust, self adaptive dynamic software architecture is the need of the hour. Unfortunately, the existing solutions available for self-adaptation need human intervention and have limitations. The architecture like Rainbow achieved self-adaptation. However, it needs to be improves in terms of quality of service analysis and mining knowledge and reusing it for making well informed decisions in choosing adaptation strategies. In this paper we proposed and implemented Enhanced Self-Adaptive Dynamic Software Architecture (ESADSA) which provides automatic self-adaptation based on the runtime requirements of the system. It decouples self-adaptation from target system with loosely coupled approach while preserves cohesion of the target system. We built a prototype application that runs in distributed environment for proof of concept. The empirical results reveal significance leap forward in improving dynamic self-adaptive software architecture