We develop algorithms for image processing, computer vision, data analysis, etc for industrial applications, implemented mostly with Python. The projects are in the extraction of entities from images, algorithms for optimization, trend analysis, fingerprint identification, etc.

Big data analysis is mostly done with Python and Pandas. The projects are in bus passenger traffic analysis, NMR spectral peak identification, geospatial data analysis, etc.

The projects we take up include stochastic models for passenger traffic prediction, flood run-off, and stock prices. Models are developed with Kalman filters, particle filters, and quadratic Volterra filters. Many swarm models and wavelet-based methods are developed for image processing tasks.

The big data generated by business organizations are analyzed for statistical decision-making and trend analysis. Algorithms in signal processing are utilized for stock market data analysis and prediction. Python and its financial tools are employed for solving stochastic equations and financial modeling.

Our team work on a variety of machine learning and AI tools for intelligent traffic systems, stock price prediction, image recognition, etc. Research is being carried out in developing intelligent digital communication systems that will outperform the current adaptive systems.

Image recognition is a key task in all AI applications and we work on many research issues in image processing and computer vision. We develop quadratic filters and machine learning models for fingerprint enhancement and recognition. ML models for the identification of mechanical parts and extraction of entities from document images are being developed.