Biosignals and Systems Analysis Lab at Bio-engineering Department, McGill University, Montreal, Canada. From November 2017 to December 2018, I was a postdoctoral researcher in Biomedical Signal Processing Lab at the School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada. From July 2016 to October 2017, I was a postdoctoral fellow at Electrical Engineering Department, Concordia University, Montreal, Canada. In April 2016, I graduated with a Ph.D. degree from Signal Processing Laboratory, at Concordia University, Montreal, Canada.I am currently a postdoctoral researcher in
McGill University, Montreal, Canada
School of Electrical Engineering and Computer Science
University of Ottawa, Ottawa, Canada
Electrical and Computer Engineering Department
Concordia University, Montreal, Canada
Ph.D. - Electrical Engineering
Concordia University, Montreal, Canada
Master of Science - Electrical Engineering
Shahid Beheshti University, Tehran, Iran
Bachelor of Science - Electrical Engineering
Khaje Nasir University, Tehran, Iran
As a postdoctoral fellow at McGill University, I am involved in a time-varying functional brain connectivity related project using artificial intelligence. More specifically, using multimodal neuroimaging data (simultaneous fMRI-EEG, MEG), I investigate the time-varying characteristics of resting-state networks over different time scales, as well as quantifying the effect of physiological fluctuations (pressure, heart rate, respiration) on fMRI-based resting-state connectivity.
As a postdoctoral fellow at University of Ottawa, I was involved in several projects at Biomedical Signal Processing Lab including: (i) I worked on vital sign monitoring using contactless ultra-wideband radar. More specifically, respiration and heartbeat rates of human subjects were estimated and the posture of targets were detected in a room environment using supervised learning. (ii) In an activity monitoring project, I worked on event recognition problem for home care purposes. More specifically, several algorithms were developed using time-frequency analysiss, time series analysis and deep learning approaches to build an automatic system for detetcion and prediction of fall incidents in senior houses and to assist the elderly living alone.
Organized The 7th IEEE Research Boost: to promote industry driven research.
Organized The 6th IEEE Research Boost: to foster collaborative research projects and networking among the industry, government and academia.
Organized The 5th IEEE Research Boost: to bring together IEEE Members, Academics, Industry, Public Sector and Students to network and enjoy an inspiring technical presentation and discussion.
Course instructor in ENCS6021-Engineering Analysis II.
Delivered lectures to 44 graduate students at Concordia University.
Organized The 4th IEEE Research Boost to foster collaborative research projects and networking among the industry, government and academia, as well as to present some of the top candidates in their field of expertise.
Organized IEEE Montreal Keynote Event: Cars of the Future, to bring together IEEE Members, Academics, Industry, Public Sector and Students to network and enjoy an inspiring technical presentation and discussion.
Organized The 3rd IEEE Research Boost to foster collaborative research projects and networking among the industry, government and academia, as well as to present some of the top candidates in their field of expertise.
As a postdoctoral fellow at Intelligent Signal & Information Processing Lab , I worked on analyzing large-scale data using graph-based signal processing. This work was partnered by Ericsson Montreal with a focus on intrusion detection in cloud systems by online auditing. I also worked on graph-based reachability assessment of the cloud entities in cloud networks. Graph signal processing framework has recently emerged to extend high-dimensional data analysis to networks and irregular domains, and thus, many existing signal/image/video processing, machine learning problems can be revisited from a new graph-based perspective. In view of this, I initiated my research on graph signal processing by investigating the capability of the graph-based filtering for edge-preserving smoothing of images, and accordingly its application in image abstraction and stylization. A unified iterative filtering was developed to perform detail manipulation and fine detail boosting in parallel. In addition, an alternative filtering approach was established in the graph Fourier domain, resulting in noticeable gain in image compression and restoration. Further, I expanded the applicability of the graph signal processing framework by developing an intrusion detection method for distributed sensor networks based on statistical properties of the graph signals. The probabilistic dependence graph was further used for fault detection and localization in smart grid.
As a Lab Instructor at Concordia University, I was responsible for conducting lab tutorials on DC/AC circuits, transformers and DC motor for a group of 20 students. Assisting students in using various hardware tools such as Oscilloscopes, Multimeter and Wattmeter. As a Teacher Assistant and Head TA, I was responsible for training, assisting and supervising a team of 21 teacher assistants, where I demonstrated strong leadership and organizational skills along with a solid work ethics. I coordinated TAs (21 sections), prepared material for tutorial sessions and solutions for assignments, organized midterm exam and prepared the midterm problems, and assigned and coordinated invigilators for the midterm test.
As a research assistant in multimedia signal processing research group at the department of electrical and computer engineering, I conducted research on the processing of image and video signals with special references to their space-time-frequency representations, statistical modeling, developing various estimation and detection techniques, experimentations, and performance studies. Specifically, 1) A unified probabilistic model for the contourlet coefficients of the image signals was developed using the alpha-stable family of distributions. An important motivation for using such distribution was the non-gaussian behavior of the contourlet coefficients of images as well as an appropriate number of parameters that could be used for a better modeling performance. 2) The performance of the probabilistic model was investigated in various types of contourlet representations such as real/complex, and shift-variant/invariant. In particular, the statistics of the data samples were analyzed in terms of the transformation matrices and mathematical operations of the transform. 3) The performance of the developed model was investigated in several estimation techniques such as denoising (reducing additive white Gaussian noise), despeckling (reducing multiplicative and correlated noise), deblurring (restoration from the degradation due to point spread function), and fusion (obtain an informative signal from multi-sensed data). Estimation performances were evaluated on several data sources that include natural images and video, medical images, and remote sensing images. 4) The performance of the developed model was also studied in the case of the detection of edge and textures and the detection of watermark for copyright protection.
IEEE Transactions on Image Processing
IEEE Transactions on Multimedia
IEEE Transactions on Information Forensics & Security
IEEE Transactions on Signal & Information Processing over Networks
Transactions on Circuits & Systems for Video Technology
Transactions on Circuits & Systems II: Express Briefs
Elsevier Signal Processing
IET Image Processing
Elsevier Signal Processing: Image Communication
Springer Journal on Circuits, Systems & Signal Processing
Wiley International Journal of Imaging Systems and Technology
International Symposium on Circuits and Systems (ISCAS)
International Conference on Current Research in Signal Processing & Communications.
Mid-West Symposium on Circuits and Systems (MWSCAS)
Canadian Conference on Electrical and Computer Engineering (CCECE)
Iranian Conference on Electrical Engineering (ICEE)
International Conference on Information Science, Signal Processing and their Applications (ISSPA)
International Conference on Industrial Electronics & Applications (ISIEA)
Subject: Pervasive Healthcare through Human-centered Sensing
Subject: Contactless Monitoring for Pervasive Healthcare
Subject: Contactless Monitoring for Pervasive Healthcare
In Biosignals and Systems Analysis Lab at McGill University, I was involved in a time-varying functional brain connectivity related project using artificial intelligence. More specifically, using multimodal neuroimaging data (simultaneous fMRI-EEG, MEG), I investigated the time-varying characteristics of resting-state networks over different time scales, as well as quantifying the effect of physiological fluctuations (pressure, heart rate, respiration) on fMRI-based resting-state connectivity.
In Biomedical Signal Processing Lab at University of Ottawa, I worked on several projects including: (i) I worked on vital sign monitoring using contactless bistatic ultra wide-band radar. More specifically, respiration and heartbeat rates are estimated and the posture of targets are detected using supervised learning methods. (ii) In a human activity monitoring project, I worked on event detection/recognition problem for home care purposes. More specifically, fall detection algorithms are developed using deep learning approaches in order to assist the elderly.
In Intelligent Signal & Information Processing Lab at Concordia University, I worked on several projects including: (i) analyzing large-scale data using graph-based signal processing. This work was partnered by Ericsson-Montreal with a focus on intrusion detection in cloud systems by online auditing. In addition, I worked on graph-based reachability assessment of the cloud entities in cloud networks. More specifically, unsupervised intrusion detection scheme in cloud networks was developed. In addition, a supervised graph-based anomaly detection scheme was developed using the statistical modeling in cyber physical systems with embedded sensors; (ii) Two classification algorithms for motor imagery-based brain computer interface were developed. First, graph-based spatio-temporal filters were devised to efficiently identify subject-specific features from brain activity signals and translate them into device commands. Second, a CSP-based deep convolutional neural network was trained for classification of left/right hand/foot MI-BCI signals; (iii) Using graph signal processing framework, I developed iterative graph-based filtering applied in both vertex and spectral domains for image abstraction, stylization and saliency detection purposes.
I enjoy a great professional collaboration with researchers across the world. We have a very professional, friendly and enthusiastic research members in a variety of research domains or academic positions. We also welcome whoever is willing to share and promote the knowledge by joining our research group.
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My office is located at the following address:
361 McConnell Engineering Building,
817 Sherbrooke Ave W
Montreal, QC, H3A 2A7, Canada.
Tel : +1-514-5570594
E-mail : hamidreza dot sadreazami at mail dot mcgill dot ca