Mobile-cloud Cross Development (McX)

There is a multitude of Mobile Operating Systems (MOSs) with iOS, Android, Windows Phone and, BlackBerry leading the space. New players continue to enter the market. Without a de-facto leader in this space, it has become necessary for businesses & developers to target multiple devices & MOSs in order to establish a relevant presence within their target audience. Cross-platform Mobile Development Tools (XMTs) were born out of this need to reduce developer effort in creating mobile applications by providing “write once run anywhere” (WORA) functionality. However, most of these tools sacrifice performance, features or maintainability in order to provide WORA functionality. Furthermore, these tools only attempt to manage the user interface and related client-side functionality. Most mobile applications need to follow the same principals that guide development of non-mobile web or desktop apps. Typical apps are deployed using an n-tier, cloud-based strategy with substantial functionality delegated to cloud resources. Given the above, there are two parts of an application’s anatomy that don’t get much attention – the cloud middleware functionality, and the database/model management features. In this paper I address these problems through creation of a Mobile-cloud Cross Development (McX) tool-chain that includes a type-safe meta-programming language, an integrated cloud node and, an active compiler. In order to effectively understand the problem with the current state of the art, I use 3 of the leading XMTs alongside the developed McX tool-chain and compare the effectiveness of each. The paper further introduces the language; it’s grammar and semantic structure, and provides discussions on how this approach fits the future of cross-platform, cloud-integrated mobile application development along with the associated issues and areas for further research

A Review on Prediction of Academic Performance of Students at-Risk Using Data Mining Techniques

Educational data mining is the procedure of converting raw data collected from educational databases into some useful information. It can be helpful in designing and answering research questions like performance prediction of students in academics, factors that affect the students’ performance, help the teachers in understanding the problems faced by the students to understand the course content and complexity of the subject taken so that the teachers can take timely action to control the dropout rate. This also includes improving the teaching learning process so that the interventions can be taken at the right time to improve the performance of the student. This paper is the review of the research work done in the field of educational data mining for the prediction of students’ performance. The factors that influence the performance of the students i.e. the type of classrooms they attend such as traditional or on-line, socio-economic, educational background of the family, attitude toward studies and challenges faced by the students during course progress. These factors leads to the categorization of the students into three groups “Low-Risk”: who have High probability of succeeding, “Medium-Risk”: who may succeed in their examination, “High-Risk”: who have High probability of failing or drop-out. It elaborates the different ways to improve the teaching learning process by providing the students personal assistance, notes, class-assignments and special class tests. The most efficient techniques that are used in educational data mining are also reviewed such as; classification, regression, clustering and and prediction

Analysis of Student's Data using Rapid Miner

Data mining offers a new advance to data analysis using techniques based on machine learning, together with the conventional methods collectively known as educational data mining (EDM). Educational Data Mining has turned up as an interesting and useful research area for finding methods to improve quality of education and to identify various patterns in educational settings. It is useful in extracting information of students, teachers, courses, administrators from educational institutes such as schools/ colleges/universities and helps to suggest interesting learning experiences to various stakeholders. This paper focuses on the applications of data mining in the field of education and implementation of three widely used data mining techniques using Rapid Miner on the data collected through a survey

Control Implementation of Squirrel Cage Induction Generator based Wind Energy Conversion System

306-311Trends towards PC-based computing platforms have given a path to innovate advanced, model-based control algorithms for the control of wind energy conversion systems. These advanced models allow us to gain increased reliability and improved efficiency. This work is focused on the digital signal processor based implementation aspects of a variable speed, grid connected; squirrel cage induction generator based wind energy conversion systems. Control design using a voltage source converter at the machine side have been described, designed, modeled and analyzed. A comprehensive analysis of the designed system is performed in terms of the active power harnessed, power quality and reactive power control. Optimal power point operation has been implemented so that highest available power is harnessed for varying wind velocities. A 2.2kW prototype with back to back connected voltage source converter has been implemented in hardware using TMS320F2812 digital signal processor. The experimental results illustrate the excellent performance of the system

Self-Ligating Brackets: A Review

Self- ligating brackets are ligature less bracket systems that have a mechanical device into the bracket to close off the edgewise slot. These brackets secure passive or active ligation mechanism that ensures consistent full bracket engagement. Reduced friction between archwire and bracket allows more rapid tooth movement. This results in good control of tooth position through an adequately dimensioned bracket. A review of self-ligation in general has been highlighted in this article

Control Implementation of Squirrel Cage Induction Generator based Wind Energy Conversion System

Trends towards PC-based computing platforms have given a path to innovate advanced, model-based control algorithms for the control of wind energy conversion systems. These advanced models allow us to gain increased reliability and improved efficiency. This work is focused on the digital signal processor based implementation aspects of a variable speed, grid connected; squirrel cage induction generator based wind energy conversion systems. Control design using a voltage source converter at the machine side have been described, designed, modeled and analyzed. A comprehensive analysis of the designed system is performed in terms of the active power harnessed, power quality and reactive power control. Optimal power point operation has been implemented so that highest available power is harnessed for varying wind velocities. A 2.2kW prototype with back to back connected voltage source converter has been implemented in hardware using TMS320F2812 digital signal processor. The experimental results illustrate the excellent performance of the system