Data Cleansing using Fast Fourier Transform:
Data quality has been a huge challenge in most organization. This project aims at creating a Discrete Fast Fourier transformation to convert the time-domain data into a frequency domain to identify the data anomalies.

The FFT can easily identify and eliminate data anomalies without the application of business rules. From Fourier we know that periodic waveforms can be modeled as the sum of harmonically-related sine waves. The Fourier Transform aims to decompose a cycle of an arbitrary waveform into its sine components; the Inverse Fourier Transform goes the other way—

it converts a series of sine components into the resulting waveform. These are often referred to as the “forward” (time domain to frequency domain) and “inverse” (frequency domain to time domain) transform.

Mobile Decision support systems:

Organizations are using decision support systems to enable fast decision making to be competitive in market place. With the advent of mobile technology the decision support systems could be mobile enabled. This can help top CEO’s to carry with them a decision support system that can help them make decisions on the fly. Most negotiations done by CEO’s require making quick decisions based on facts. This new research will enable them to make decisions faster.


ARC research team has helped the creation and advancement of databases, algorithms, computational and statistical techniques and theory to solve formal and practical problems arising from the management and analysis of biological data. Our team can create efficient software for studying interactions among proteins, ligands and peptides.