Companies co-founded: HealthParametrics Inc.
Research group: Group for Instrumentation and Processing of Biosignals (GRIP)
Areas of research:
5. Neuromodulation *
Companies co-founded: Scandent LLC
Research group: RFID Research Group (Identigo)
Research group: Computer Architecture Research Group (CARG)
Areas of research:
(Conference papers can be found in Bolic’s CV)
(Active projects are labeled with ‘*’)
Dr. Bolic developed signal processing algorithms to remove noise, estimate parameter and classify different features from a number of physiological signals including electrocardiography (ECG), blood pressure, photoplethismography, bioimpedance, breathing and so on.
There is increasing recognition that the methods used by physicians to measure blood pressure give inconsistent readings, and in a home-care setting, the possibilities for measurement errors are exasperated by the lack of clinical supervision. Dr. Bolic’s current research on blood pressure estimation is focused on developing a mathematical model for arterial and oscillometric blood pressure to use the model to develop more accurate algorithms for blood pressure estimation. The work on blood pressure and ECG resulted in following journal publications:
· M. Forouzanfar, S. Rajan, I. Batkin, H. R. Dajani, M. Bolic, and V. Groza, “Oscillometric Blood Pressure Estimation: Past, Present, and Future,” accepted for publication in Reviews in Biomedical Engineering, 2015.
· M. Forouzanfar, S. Ahmad, I. Batkin, H. R. Dajani, M. Bolic, and V. Groza, “Model-Based Mean Arterial Pressure Estimation Using Simultaneous Electrocardiogram and Oscillometric Blood Pressure Measurements,” IEEE Transactions on Instrumentation and Measurements, Issue 99, 2015.
· M. Forouzanfar, I. Batkin, H. R. Dajani, M. Bolic, V. Groza, S. Rajan, “Ratio-Independent Blood Pressure Estimation by Modeling the Oscillometric Waveform Envelope,” IEEE Transactions on Instrumentation and Measurements, short paper, Vol. 63, Issue 10, pp. 2501-2503, 2014.
· Batkin, S. Ahmad, M. Bolic, V. Groza, H. Dajani, M. Forouzanfar, “Apparatus and Method for ECG Assisted Blood Pressure Measurement,” Canada patent, Application number: 2,276,204, 2014.
· M. Forouzanfar, S. Ahmad, I. Batkin, H. R. Dajani, M. Bolic, and V. Groza, “Coefficient-Free Blood Pressure Estimation Based on Pulse Transit Time-Cuff Pressure Dependence,” IEEE Transactions on Biomedical Engineering, Vol. 60, Issue 7, 1814 – 1824, 2013.
· S. Ahmad, I. Batkin, O. Kelly, H. R. Dajani, M. Bolic, and V. Groza, “Multi-Parameter Physiological Analysis in Obstructive Sleep Apnea Simulated With Mueller Maneuver,” IEEE Transactions of Instrumentation and Measurements, Vol. 62, Issue 10, pp. 2751 – 2762, 2013.
· K. Soueidan, S. Chen, H. R. Dajani, M. Bolic and V. Groza, “Augmented blood pressure measurement through the noninvasive estimation of physiological arterial pressure variability,” Physiological Measurement, Vol 33, Issue 6, pp. 881-899, 2012.
· S. Ahmad, S. Chen, K. Soueidan, I. Batkin, M. Bolic, H. Dajani, V. Groza, “Electrocardiogram-Assisted Blood Pressure Estimation,” IEEE Transactions on Biomedical Engineering, Vol. 59, No. 3, pp. 608-618, 2012.
· M. Forouzanfar, H. R. Dajani, V. Z. Groza, M. Bolic, S. Rajan, “Feature-based Neural Network Approach for Oscillometric Blood Pressure Estimation,” IEEE Transactions on Instrumentation and Measurements, vol 60, no. 8, pp. 2786 - 2796, 2011.
· S. Chen, M. Bolic, V. Z. Groza, H. R. Dajani, I. Batkin, S. Rajan, “Extraction of Breathing Signal and Suppression of its Effects in Oscillometric Blood Pressure Measurement,” IEEE Transactions on Instrumentation and Measurements, vol 60, no. 5, pp. 1741 - 1750, 2011.
· S. Ahmad, M. Bolic, H. Dajani, V. Groza, I. Batkin, “Measurement of Heart Rate Variability Using an Oscillometric Blood Pressure Monitor,” IEEE Transactions on Instrumentation and Measurements, vol. 59, no. 10, pp. 2575-2590, 2010.
The final goal of this project is to develop a robust system for detection of attempted suicide events in prisons in time to allow for life-saving interventions. This will be done by processing signals extracted from the radars. The objectives of the signal processing algorithms are to reliably estimate breathing and heartbeat signals and then to produce an alarm when significant changes in physiological conditions are observed.
· M. Mabrouk, S. Rajan, M. Bolic, M. Forouzanfar, H. R. Dajani and I. Batkin, “Human breathing rate estimation from radar returns using harmonically related filters,” accepted with major corrections in Journal of Sensors, Hindawi Publishing Corporation, 2016.
· M. Mabrouk at al., Remote sensing of human breathing at a distance, US provisional patent application, 2014.
In hearing aids, background noise and cocktail effect need to be removed while preserving naturalness and intelligibility of the processed speech signal. This is very difficult to do using traditional signal processing algorithms. Dr. Bolic’s group developed novel statistical signal processing algorithms called particle filters for speech enhancement problems with the goal of achieving high intelligibility of enhanced speech. Related publications include: and 2 Ph.D. and 1 M.Sc. theses and in the following journal publications:
· F. Mustiere, M. Bolic, M. Bouchard, “All-Pole Modelling of Discrete Spectral Powers: A unified approach,” IEEE Transactions on speech and audio processing, vol. 20, issue 2, pp. 705-708, 2011.
· B. Laska, M. Bolic and R. Goubran, “Multiple Model Spectral Amplitude Particle Filter Speech Enhancement,” IEEE Transactions on speech and audio processing, vol 18, no. 8, pp. 2155 – 2167, 2010.
· B. Laska, M. Bolic, R. Goubran, “Discrete Cosine Transform Particle Filter Speech Enhancement Speech Communication,” Speech Communications, Elsevier, vol 52, no. 9, pp. 762-775, 2010.
· F. Mustiere, M. Bolic, M. Bouchard, “Speech enhancement based on nonlinear models using particle filters,” IEEE Transaction on Neural Networks, vol. 20, Issue 12, pp. 1923-1937, 2009.
· F. Mustiere, M. Bolic, M. Bouchard, “Low-cost improvements for speech denoising using Rao-Blackwellised particle filters,” Signal Processing, Elsevier, Vol. 88, pp. 2678– 2692, 2008.
· P. Longa, A. Miri, M. Bolic, “A Modified Distributed Arithmetic Based Area-Efficient Architecture for Discrete Wavelet Transforms,” IET Electronic Letters, Vol. 44, Issue 4, pp. 270 – 271, 2008.
· B. Laska, R. Goubran, M. Bolic, “Subband improved proportionate LMS for acoustic echo cancellation in changing environments,” IEEE Signal Processing Letters, Vol. 15, pp. 337 – 340, 2008.
Bioimpedance data has not been widely used for diagnostic purposes despite of its great potential because the measurements are not reproducible from one session to another. Dr. Bolic proposed a number of new methods for accurate classification of features using bioimpedance data. This should open a whole set of new possibilities for using bioimpedance measurements in diagnostics. This resulted in the following publications:
· Sh. Gholami-Boroujeny, M. Bolic, “Extraction of Cole parameters from the electrical bioimpedance spectrum using bacterial foraging optimization algorithm,” conditionally accepted in Medical & Biological Engineering & Computing, Springer, 2015.
· Nejadgholi, M. Bolic, “A Comparative Study of PCA, SIMCA and Cole model for Classification of Bioimpedance Spectroscopy Measurements,” Computers in Biology and Medicine, Elsevier, Vol. 63, pp 42 – 51, 2015.
· Nejadgholi, H. Caytak, M. Bolic, I. Batkin, S. Shirmohammadi, “Preprocessing and Parameterizing of Bioimpedance Spectroscopy Measurements By Singular Value Decomposition,” Physiological Measurement, Vol. 36, No. 5, pp. 983-999, May 2015.
Dr. Bolic’s current research on stimulation is related to improving transcranial direct current stimulation (tDCS) and personalizing the stimulation based on the feedback information. In addition, he worked on designing a tongue stimulation unit that can be used to control mouse on the computer using tongue for disabled people. The work on electrical stimulation resulted in following publications:
· H. Caytak, D. Shapiro, A. Borisenko and M. Bolic, “Advances in tDCS could provide a mainstream clinical tool for noninvasive neuromodulation,” IEEE Pulse, Vol 6, Issue 2, pp. 21-24, 2015.
· F. Tremblay, A. Remaud, A. Mekonnen, S. Gholami-Boroujeny, K.É. Racine, M. Bolic, “Lasting Depression in Corticomotor Excitability associated with Local Scalp Cooling,” conditionally accepted in Neuroscience Letters, Elsevier, 2015.
· Sh. Gholami-Boroujeny, A. Mekonnen, I. Batkin, M. Bolic, “Theoretical Analysis of the Effect of Temperature on Current Delivery to the Brain during tDCS,” Brain Stimulation, Elsevier, http://dx.doi.org/10.1016/j.brs.2014.12.006, 2015.
· O. Dragichi, I. Batkin, I.S. Chapman, M. Bolic, et al, “Neurostimulation System, Device, and Method,” US 8,874,220 B2, 2014.
Dr. Bolic’s research is currently focused on the development of new systems for localization and proximity detection of passive RFID tags and wireless charging. His vision is that automated and ubiquitous localization and proximity detection will be of great use in future Internet of Things as well as in every application in which it is important to detect social interactions and proximity among people and objects. The work resulted in following publications:
· M. Bolic, M. Rostamian, P.M. Djuric, “A Novel UHF RFID System for Proximity Detection in the Internet of Things,” IEEE Pervasive Computing, Issue 2, pp. 70-76, 2015.
· J. Wang, M. Bolic, “Reducing the Phase Cancellation Effect in Augmented RFID System,” accepted in International Journal of Parallel, Emergent and Distributed Systems, Taylor & Francis Group, 2015.
· P.K. Mishra, R.F. Stewart, M. Bolic, M. Yagoub, “RFID in Underground-Mining Service Applications,” IEEE Pervasive Computing, Volume 13, Issue 1, pp.72-79, January 2014.
· Borisenko, M. Bolic, M. Rostamian, “Intercepting UHF RFID signals through synchronous detection,” EURASIP Journal on Wireless Communications and Networking 2013 (1), 1-10, http://jwcn.eurasipjournals.com/content/pdf/1687-1499-2013-214.pdf.
· Athalye, V. Savic, M. Bolic and P. M. Djuric, “Novel Semi-passive RFID System for Indoor Localization,” IEEE Sensors Journal, vol. 12, no. 2, pp. 528-537, 2013.
· M. Bolic, A. Borisenko, P. Seguin, "Automating Evidence Collection at the Crime Scene using RFID Technology for CBRNE Events," An International Journal of Forensic Science Policy & Management, Taylor & Francis, Vol 3, Issue 1, pp. 3-11, 2012.
· P.K. Mishra, R.F. Stewart, M. Bolic, M. Yagoub, “RFID Technology for Tracking and Tracing Explosives and Detonators in Mining Services Applications,” accepted for publication in Journal of Applied Geophysics, Elsevier, 2011.
· H. Guo, V. C.M. Leung, M. Bolic, “M-ary RFID Tags Splitting With Small Size Of Idle Slots,” IEEE Transactions on Automation Science and Engineering, Vol. 9, Issue 1, pp. 177-181, 2011.
· N. Irfan, M. Bolic, M. Yagoub, and V. Narashiman, “Localization of Sensors in Indoor Environment with Neural Network Method,” Telecommunication Systems Journal, Springer, vol. 44, issue 1, pp. 149-158, 2010.
· M. Bolic, A. Athalye, T. Li, “Performance of passive UHF RFID systems in practice,” in M. Bolic, D. Simplot-Ryl, I. Stojmenovic, RFID Systems: Research trends and challenges, edited book, Wiley, 2010.
· H. Liu, M. Bolic, A. Nayak, I. Stojmenović, Integrating of RFID and Wireless Sensor Network, Book chapter, Encyclopedia on Ad Hoc and Ubiquitous Computing, World Scientific Press, Singapore, Editor Dharma P. Agrawal, 2009.
· H. Liu, M. Bolic, A. Nayak, I. Stojmenović, “Taxonomy and Challenges of Integration of RFID and Wireless Sensor Networks,” IEEE Network, vol. 22, no. 6, pp. 26-32, 2008.
Dr. Bolic’s group has developed algorithms and software tools for acceleration of software on a variety of hardware platforms including FPGA, GPUs and configurable processors. His research goal is to develop methodology for automated acceleration of software for heterogeneous embedded hardware platforms. Main part of his research includes real time implementation of complex signal processing algorithms such as particle filters. The work resulted in following publications:
· J. Parri, D. Shapiro, M. Bolic and V. Groza, “Returning Control to the Programmer: SIMD Intrinsics for Virtual Machines,”ACM Queue, Vol. 9 Issue 2, February 2011. The same paper was published in ACM Communications.
· B. Bandali, E. Gad and M. Bolic, “Accelerated Harmonic-Balance Analysis using a Graphical Processing Unit Platform,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol 33, Issue 7, pp. 1017-1030, 2014.
· W. Wang, J. Parri and M. Bolic, Virtualization of hardware accelerator, PCT Patent application, 2013.
· J. Parri, J.-M. Desmarais, D. Shapiro, M. Bolic and V. Groza, “A Case Study on Hardware/Software Codesign in Embedded Artificial Neural Networks,” to appear in “Applied Computational Intelligence in Engineering and Information Technology,” Editors: Radu-Emil Precup, Szilveszter Kovacs, Stefan Preitl and Emil M. Petriu, Springer-Verlag, 2012.
· T. Li, M. Bolic and P.M. Djuric, “Resampling Methods for Particle Filtering,” IEEE Signal Processing Magazine, Vol. 32, Issue 3, pp. 70-86, 2015.
· T. Li, S. Sun, M. Bolić and J. M. Corchado, “Algorithm Design for Parallel-Processing Implementation of the SMC-PHD Filter,” accepted conditionally in Signal Processing, Elsevier, 2015.
· M. Bolic, A. Athalye, P. M. Djuric, S. Hong, “Study of Algorithmic and Architectural Characteristics of Gaussian Particle Filters,” The Journal of Signal Processing Systems, Springer, vol. 61, no. 2, pp. 205-218, 2010.
· S. Hong, S.-S. Chin, P. M. Djurić, M. Bolić, “Design and Implementation of Flexible Resampling Mechanism for High-Speed Parallel Particle Filters,” The Journal of VLSI Signal Processing, Springer, vol. 44, Issue 1-2, pp: 47 – 62, August 2006.
· Athalye, M. Bolic, S. Hong and P. M. Djuric, “Generic Hardware Architectures for Sampling and Resampling in Particle Filters,” EURASIP Journal of Applied Signal Processing, Issue 17, pp. 2888-2902, 2005.
· S. Hong, P. M. Djuric, M. Bolic, ”Simplifying Physical Realization of Gaussian Particle Filters with Block Level Pipeline Control,” EURASIP Journal of Applied Signal Processing, No. 4, pp. 575-587, 2005.
· S. Hong, M. Bolic, and P. M. Djuric, “An Efficient Fixed-Point Implementation of Residual Systematic Resampling Scheme for High-Speed Particle Filters,” IEEE Signal Processing Letters, vol. 11, no. 5, 2004.
· M. Bolic, V. Drndarevic, W. Gueaieb, “Signal processing for High Count Rate Spectrometry with NaI(Tl) Detector,” IEEE Transaction on Instrumentation and Measurements, vol. 59, no. 1, pp. 122-130, 2010.
· M. Bolic, V. Drndarevic, “Digital Gamma-Ray Spectroscopy Based on FPGA Technology,” Nuclear Instruments and Methods in Physics Research Section A, Elsevier Science, vol. 842, pp. 761-766, 2002.