Miodrag Bolic

Miodrag Bolic


Computer Engineering Undergraduate Associate Director

Interim Applied AI ELG Program Graduate Director

University of Ottawa, Ottawa, Canada
School of Electrical Engineering and Computer Science

mbolic [AT] uottawa [DOT] ca
Office: 613 562-5800 ext. 6224

Room: CBY A-616, 161 Louis Pasteur, Ottawa, Ontario, Canada, K1N 6N5

Research Groups

Curriculum Vitae


Dr. Bolic's research is multidisciplinary and includes biomedical signal processing and instrumentation, computer architectures, UAV detection and radio frequency identification (RFID) systems. Dr. Bolic is currently interested in addressing real-life problems through signal processing and machine learning in biomedical and automotive applications. In biomedical applications, he works with patient data in close collaboration with physicians and other medical personnel while developing wearable as well as contactless solutions with radars and thermal, depth and RGB cameras. In automotive applications, he is focusing on counter-drone and inspection problems with UAVs, again focusing on working with experimental field data. In these applications, his approach is to model and simulate the problem in order to understand it better. He is currently developing machine learning solutions for:

  • Quantifying uncertainty in estimation and classification
  • Dealing with long-term time series with sparse labels
  • Dealing with computer vision and time series problems with domain shift
  • Performing sensor fusion with many sensors including multiple cameras and radars


His main recent research contributions can be classified as follows:

Biomedical signal processing and instrumentation

  • Blood pressure measurement: Developed methods for robust and reliable measurement of systolic and diastolic blood pressure using both traditional and continuous blood pressure monitoring techniques, see HDRG Research.
  • Contactless estimation of breathing rate and activities using radars and cameras: New methods for fall detection, posture detection and breathing estimation have been devised, see HDRG Research.
  • Heart failure monitoring: New methods for classifying breathing patterns and estimating central vein pressure have been recently developed.

UAV Detection

  • Signal processing and machine learning algorithms for detecting UAVs using radars and cameras: Development of hybrid physical and machine learning models for detecting UAVs, see CARG Research.

New Book:

Pervasive Cardiac and Respiratory Monitoring Devices: Model-Based Design is the first book to combine biomedical instrumentation and model-based design. As the scope is limited to cardiac and respiratory devices only, this book offers more depth of information on these devices; focusing in on signals used for home monitoring and offering additional analysis of these devices. The author offers an insight into new industry and research trends, including advances in contactless monitoring of breathing and heart rate.


Current teaching:

Teaching exoerience:

  • ELG6163 Digital Signal Processing Microprocessors, Software and Applications, winter 2006, 2007
  • ELG6158 Digital systems architectures, Fall 2007, Winter 2010, 2012
  • ELG7177 Topics in Communications: Radio Frequency Identification (RFID) Systems, Fall 2009, 2011, 2012
  • ELG7187 Topics in Computers: Multiprocessor Systems on Chip, Fall 2010, Winter 2012.
  • BIOM 5100 / BMG 5103 / SYSC5302 / ELG 6321 Bioinstrumentation/Principles & Design of Advanced Biomedical Instrumentation, Fall 2013, 2014, 2015, 2016, 2018, 2020.
  • ELG 7172B: Topics in Signal Processing: Principles of Data and Error Analysis in Engineering, 2017
  • ELG5218: Uncertainty Evaluation in Engineering Measurements and Machine Learning, 2019, 2021, 2022.

  • Research Associates, Ph.D. and M.Sc. Students


    Please see the list in Curriculum Vitae