Sunday, March 27, 2011

A secure message board: an online transaction based project.

SYNOPSIS

A secure message board

Computerization has made the world in to a small global village. In this fast generation, where in all the work is done in jet speed people cannot afford to waste even the single second. After all “Time is money”. By virtue of today’s socioeconomic environment and continuing advancements in information technologies, companies are offering more computer related services and becoming more sophisticated computers having high capacity of storage and fast method of retrieving real time information have become boon for corporate

Statement about the problem:

A secure message board is an online transaction based project. Its prime concern is to provide a messaging system which will help users to share their ideas and feelings, suggestions etc to other users. This enables the users to present his/her doubts and other matters online.

Objective:

• It offers the users to express their ideas, suggestions etc...
• Enables the user to present their doubts and other related matters through online.
• Provides a facility for quick information retrieval
• Decreases response time.



MODULES:
The different modules that will make the whole system are as follows:

 User
 Administrator
 Message System

MODULE DESCRIPTIONS:

1. User Module
Two types of users are there paid and non-paid. All users must be registered to access the site. After the registration users can post their doubts, questions, suggestions, arguments etc…

 Online Registration (Personal Details)
 Login Name and Password for a registered users
 Edit Details
2. Administrator Module
Administrator has the overall control of the site. The registered users, posted questions, answers, their suggestions etc…, and everything will be monitored by the Administrator and after verification all these messages will be uploaded to the site.

 Posting works
 Editing works
 Validations
 Deletions
 Overall control of the site

3. Message System
This module offers the users to post their doubts and they can discuss with other users and experts.

 Post Doubts, suggestions, ideas etc…
 Post Answers
 Verification of Messages
 Email

TOOLS USED:

ASP.Net
SQLSERVER 2000

contact
info@empirics.co.cc

Sunday, August 1, 2010

Alarm Pattern Analysis Using Computational Intelligence Approach

In today’s high value manufacturing, machines are often equipped with sensors to detect disturbances in
the system, which may trigger the corresponding alarm(s) when limits are exceeded. Apart from displaying on a
terminal for human intervention, these alarm values are also logged to a database. Machine faults or breakdown
may be characterized by a set of alarm patterns. The ability to detect these patterns early can help to alert and
prevent impending machine failure, which is extremely useful for mission critical machines. This project involves
the development of pattern identification concepts and algorithms based on computational intelligence approaches
to predict machine failures. Some of these approaches include Ant Colony System, Genetic Algorithm, and
Statistical methods.
Students with keen interest to learn computational intelligence for pattern recognition and those with knowledge
in manufacture equipment management will have an added advantage for this project

Saturday, July 31, 2010

Investigation of the Sequential Accelerator on the Perceptron for Pattern Recognition

In machine learning methods, when the input data becomes extremely large, the current direct methods
require too large learning times and memory. This project investigates one sequential method to overcome this
problem. It is quite a simple method to implement and is tested using the Perceptron as the base classifier. The
perceptron converges very slowly. So it will be interesting to find out if the proposed accelerator can improve
significantly the computational times of this simple classifier. The student will try various investigations on
different very large data sets and to measure their computational complexities. It has been shown that this
sequential method is very fast and only need a small subset of the large data set to complete the learning.