Online De-Noising of Radar Data using Multi Resolution Analysis

Target Tracking is an active research area, which encompasses various applications in Defence as well as Commercial applications. For estimating state vectors of tracked objects, Kalman filtering techniques are widely used, and the performance of Kalman filter depends on priory assumptions like state transition models and measurement uncertainties. In practical real time applications, all these priory assumptions are not available always and existing models are not suitable for target dynamics, which have an impact on the tracking quality, and some times filter, may diverge also. Recently Wavelet based multi resolution analysis has become a powerful tool, for image compression and de-noising applications and does not require explicit priory knowledge like Kalman filter for noise suppression. However, It is found that during real time de-noising, wavelet analysis exhibits poor performance due to certain artifacts. In order to improve the performance, a method is proposed and implemented that utilizes variable moving window and symmetric extension techniques

Big Data Analytics Issues and Challenges: A Survey

In the digital world data accumulation is increasing more and more. Billions of devices are connected worldwide already and expected to connect 50 billons of devices by the year 2020. All devices produce Peta bytes of information by transforming and sharing with light speed on optical and wireless networks. The fast growth of such large data facing numerous issues and challenges in big data analytics, as the rapid growth of variety of data, hardware platforms, software, speed and security. Utilizing the data in decision making is one of the big challenges. Scaling the platforms and frameworks are other important challenges. This paper surveys the characteristics of big data including its characteristics, issues and challenges, right choices of platforms and models depending on their computational requirements and challenges

Artificial Intelligence an Essential Expected Computer World Surveillance

A position paper toward an important and urgent discussion on how best uses the potential of Artificial Intelligence in the context of Computer World surveillance. AI is often cited in papers on Computer World surveillance. But what is meant is using pre-existing AI techniques in Computer World surveillance. AI techniques are established around applications. Computer World surveillance has never been an area of deliberation in AI. In this paper we argue that Computer World surveillance calls for new and specific AI techniques developed with that kind of application in mind. In practice, this paper is based on a broad overview of different slants, which have the budding to be game changers in Computer World surveillance. This paper focuses on web solicitation security and supporters the use of Knowledge Based Systems, probabilistic reasoning and Bayesian apprising to control the probability of false positives and false denials

Empirical Analysis of Function Point Analysis – Research View

Software measurement [1], once an obscure and esoteric specialty, has become essential to good software engineering [2, 3, 4]. Many of the best software developers measure characteristics of the software to get some sense of whether the requirements are consistent and complete, whether the design is of high quality, and whether the code is ready to be tested. Effective project manager’s measure attributes of process and product to be able to tell when the software will be ready for delivery and whether the budget will be exceeded. Informed customers measure aspects of the final product to determine if it meets the requirements and is of sufficient quality. And maintainers must be able to assess the current product to see what should be upgraded and improved. Here, we present empirical research in two areas, Function Oriented Analysis to find function points and weightages of plan driven vs agile development based on type of project, validations will be carried out using train test procedure for FPA and Ideal Point Analysis for weightage

A Computational Approach to Predict the Severity of Breast Cancer through Machine Learning Algorithms

Breast cancer is one type of cancer which causes from breast tissue. A lump in the breast, skin dimpling, breast shape changes, fluid from the nipple, or a red scaly patch of skin are some of signs of breast cancer. In the world, cancer is one of the most leading causes of deaths among the women. Among the cancer diseases, breast cancer is especially a concern in women. Mammography is one of the methods for finding tumor in the breast. This method is utilized to detect the cancer which is helpful for the doctor or radiologists. Due to the inexperience�s in the field of cancer detection, the abnormality is missed by doctor or Radiologists. Segmentation is very expensive for doctor and radiologists to examine the data in the mammogram. In mammogram the accuracy rate is based on the image segmentation. The recent clustering techniques are presented in this paper for detection of breast cancer. These Classification algorithms have been mostly studied which is applied in a various application areas. To maximize the efficiency of the searching process various clustering techniques are recommended. In this paper, we have presented a survey of Classification techniques