University of Tasmania, Australia

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Aruneema Das

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Dr Aruneema Das

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Post-doctoral Researcher

Aruneema Das

Contact Details

Contact Campus Hobart CBD Campuses
Building HOBART Medical Science 1
Telephone +61 3 6226 2915
Email aruneema.das@utas.edu.au

General Responsibilities

MATLAB based GUI design for Lung function measurement instrumentation (ultrasound sensor based Spirometer), respiratory parameters estimation and statistical analysis for patient condition monitoring and diagnostics

Publications

  1. D. Howard, L. Kenney, C. Smith and A. Das, (2011), Hand & arm rehabilitation using functional electrical stimulation and rehabilitation robotics (Published in UoS RISE health supplement Journal).
  2. A. Griffiths, A. Das, B. Fernandes and P. Gaydecki (2007), A portable system for acquiring and removing motion artefact from ECG signals, IoP Journal of Physics: Conf. Series, 76 (1), 012038.
  3. A. Das, R. Folland, N. G. Stocks and E. L. Hines (2006), Stimulus reconstruction from neural spike trains: Are conventional filters suitable for both periodic and aperiodic stimuli?, Elsevier Signal Processing, 86, 1720 – 1727. ]
  4. R. Dutta, A. Das, N.G. Stocks, E.L. Hines, J.W. Gardner (2006), Stochastic resonance-based electronic nose: A novel way to classify bacteria, Elsevier Sensors and Actuators B-Chemical, 115 (1), 17 – 27.
  5. A. Das (2005), Performance enhancement of cochlear implant devices using stochastic resonance effect, Poster Presentation in Reception for UK's Best Younger Engineers, The House of Commons, London, UK.
  6. A. Das, N.G. Stocks, A. Nikitin and E.L. Hines (2004), Quantifying stochastic resonance in a single threshold detector for random aperiodic signals, Fluctuation Noise Letters, 4, L247–L265.
  7. A. Das (2004), Stochastic resonance in auditory models, Poster Presentation in International Workshop on Stochastic Resonance: New Horizons in Physics and Engineering, Max Planck Institute for the Physics of Complex Systems, Dresden, Germany.

Additional Information

Research Interests:

Biomedical Signal Processing and Sensor Informatics - “Stochastic Resonance” in non linear signal processing - Functional electrical stimulation - Statistical and Intelligent Signal Processing for real world multivariate signals - Blind Source Separation techniques like Independent component analysis - Noise reduction techniques like Adaptive filters and Wavelet transforms - Artificial neural network for pattern recognition/classification