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Learning Objectives

  • LO1: To provide knowledge of virtual instrumentation
  • LO2: To enable understanding of virtual signal processing tools
  • LO3: To introduce biomedical applications of virtual instrumentation

Course Outcomes

  • CO1: Ability to understand programming concepts for virtual instrumentation
  • CO2: Ability to analyse bio-signal processing algorithms using virtual instrumentation
  • CO3: Ability to develop virtual codes for biomedical applications

Course Contents

Introduction to virtual instrumentation – Loops and structures – Arrays and clusters – Graphs and charts File and string handling – Basics of data acquisition – Common communication buses using DAQ assistant – Real world DAQ and issues – Network and distributed systems.

Data handling techniques – Signal acquisition and sampling theorem – Effect of undersampling – Convolution – Designing an FIR and IIR filters – FFT analysis of periodic and aperiodic signals – Designing of low pass filter – High pass filter – Bandpass filter – Band reject filter – Notch filter and Comb filter.

Processing of ECG, EMG and EOG signals – Adaptive signal processing – Data compression techniques – AZTEC – TP – CORTES and KL transform.


  1. Sanjay Gupta and Joseph John, Virtual Instrumentation Using Labview, Tata McGraw Hill Education Private Limited, 2010.
  2. Behzad Ehsani, Data Acquisition using LabVIEW, Packt Publishing, 2016.
  3. Kunal Mitra, Stephanie Miller, Short Pulse Laser Systems for Biomedical Applications, Springer Briefs in Applied Sciences and Technology, 2017.
  4. Leon Goldman, The Biomedical Laser Technology and Clinical Applications, Springer Verlag, 1981.

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