Henry Leung


Department of Electrical and Computer Engineering


McMaster University, 1991

Contact information


Information and Communications Technology Building: ICT 451



Neuro-Fuzzy and Soft Computing (ENEL 525)

Data Mining and Knowledge Discovery (ENEL 645)



Signals and Transforms (ENEL 327)

Preferred method of communication

Please contact me by email.


Research areas

  • Machine learning
  • Data analytics
  • Information fusion
  • Robotics
  • Sensor networks and IoT
  • Signal and image processing

Research activities

Machine learning and IoT

Noise pollution is a persistent problem for residents in an urban environment. In some cities, studies have estimated that 9 in 10 people are exposed daily to noise levels exceeding international guidelines. The negative health effects of excess noise include disruptions to sleep & relaxation, hearing loss, psychological & cognitive disorders, and high blood pressure. Bylaws aim to reduce the amount of noise pollution in a city, but noise assessments and monitoring are performed infrequently and are complaint-driven. In collaboration with the City of Calgary, we are developing a network of low-cost acoustic sensors to enable continuous monitoring of noise in the urban environment. Our contributions to this project are two-fold. The first contribution is the hardware design of the sensor nodes. We are using low-power wide area radio transceivers to enable data transmission between the sensor nodes and the network server. In addition, the sensors are battery operated (for ease of deployment), low-power (to limit network maintenance), robust (for continuous operation in Canadian weather) and possess a limited amount of in-situ data processing. The second contribution is the development of machine learning algorithms that will allow sensors to autonomously detect and classify acoustic events. We are using unsupervised machine learning techniques to distinguish between noise sources such as construction, traffic, gunshots, and music. 

Anomaly detection and information fusion

The recent focus on big data analytics to improve situational awareness and decision making have sparked an interest in autonomous processing of sensory data. The ability to detect anomalies and identify abnormal situations can empower organizations to make informed decisions and ultimately reduce costs. However, current approaches constrain anomalies to pre-defined situations and cannot generalize to learn new anomalies. With sensor big data, it is possible to develop an autonomous anomaly detection system capable of understanding situations for surveillance. Not only do anomalies change with time, but early detection requires a system to learn dynamic patterns at various scales. We propose an information fusion based anomaly detection approach that will be capable of autonomously processing multiple, continuous sensing information

and performing situation assessment in a dynamic environment. We will develop and test a software-based implementation on two applications: airborne sensing and pipeline monitoring. The anticipated research results on anomalous patterns will provide insight to situational anomalous events. This will enable organizations to make informed decisions in an evolving world of larger data. Another direct benefit is the potential for the results to be integrated in existing products or patents with our supporting partners. This project will be of great impact for next generation anomaly detection technologies to be deployed in the industry, which the high demand for faster, real-time monitoring and analysis is of major importance. 

Big data analytic for radar and multi-sensor data

Intelligence, Surveillance, and Reconnaissance (ISR) systems comprise of several sensors, such as radar, sonar, electro-optical, hyperspectral, and infrared. Improved sensor technologies generate sensors with enhanced capabilities such as increased resolution and sensitivity that can achieve the improved surveillance capability stated above. However, they also result in increasing amount of sensor data at a rate beyond using traditional tools to analyze. This project proposes to develop signal and image processing for multi-sensor and radar big data. Big data has the potential to stretch the horizon of traditional monitoring and surveillance systems by

fully exploiting the data available. Conventional signal processing techniques are mostly developed on optimality theory which are not practical for big data. There are two general approaches in big data analytic that can be adopted signal and image processing to process massive amount of data. The first one uses distributed processing to decompose a task on distributed computing units to reduce the load of individual computing units and to enhance computing speed. The Hadoop-based MapReduce framework is a widely used approach for big data storage and analytics. The second one uses the concept of approximation. It uses random projection usually based on hashing to reduce the dimensionality and tries to obtain results close to those based on the complete data set. In this project, new signal processing and machine learning techniques will be developed

based on approximation, sparse representation, distributed processing and ensemble approach that can fully exploit the information in the sensory big data to identify change of patterns for surveillance purpose. These techniques will be applied to the airborne radar, electro-optical (EO) and infrared (IR) data provided by

Defence Research and Development Canada (DRDC) and the industrial partners.                                                                        

Autonomous robots for elderly assistance 

With a substantial number of senior population opting for aging-in-place over retirement homes, there is a necessity to increase assistance in home environment. One of the major reasons for many seniors in urban environment to move to a retirement home is the pressure they face concerning health safety, among many others.  In these regards, robotic assistance for the aged seniors is a viable option to be explored. With aging, even the simplest of situation may turn out to be difficult. For instance, waking up from bed every morning to reaching out to the walker or wheel chair is very difficult for a senior with osteoarthritis. Having a mobile robotic assist may help a great deal. The robot being equipped with RGB-D sensor cameras, would navigate through the house determining the posture of the senior and reach out to help at every instance.  It has been a regular citing where, seniors often get physically injured slipping down on the bathroom floor or stairs. Most often than not, these injuries become fatal when not reported to the emergency helpline immediately due to absence of regular assistance in aging-in-place scenario. The robot being always connected to the home network and thus the internet can easily call for emergency assistance on determining any undesirable body posture of the senior which may be caused due to slipping down on a wet floor or twisting an ankle and so on.

Besides health related assistance, having a robotic assist around all the time, may also make their life a little comfortable by providing assistance in housekeeping and as a companion. Besides assistance, the mobile robot will help a great deal in monitoring and collecting data on to a remote server regarding food pattern, sleep pattern, and help in timely drug administration by creating steady reminder. This data may be of vital importance to maintain a healthy routine of the senior. 


Before joining the University of Calgary, Dr. Henry Leung was with the Department of National Defence (DND) of Canada as a defence scientist to conduct research and development of decision support systems, radar signal processing and data fusion. He was appointed as the national leader to represent Canada for the TTCP cooperative program on radar data processing and sensor fusion. His current research includes data mining, information fusion, machine learning, nonlinear dynamics, decision support, robotics, sensor networks and intern of things. He has published extensively in the open literature on these topics. He has over 240 journal papers and over 200 refereed conference papers. He also holds more than 15 patents. He is also a visiting professor of the Shanghau JiaoTong University, PR China (2017-2020) and a HaiTien academic visiting professor of the Dalian University of Technology, PR China (20180-2020). Dr. Leung is the Topical Editor of the International Journal of Advanced Robotic Systems on Robotic Sensors. He is the editor of the Springer book series on “Information Fusion and Data Science”. He is currently the associate editor of various journals such as IEEE Trams. Aerospace and Electronic Systems, IEEE Circuits and System Magazine, IEICE Trans. on Nonlinear Theory and Applications. He was the chair of the Nonlinear Circuits and Systems of the IEEE Circuit and System Society and has served on the program committee, organizing committee, track chairs for various conferences. He has also served as guest editors for various journals such as “Intelligent Transportation Systems” for the International Journal on Information Fusion, “Cognitive Sensor Networks” and “Deep Learning for Multi-sensor Multi-source Information Fusion” for the IEEE Sensor Journal. He is a Fellow of IEEE and SPIE. 


Journal papers in the last six years


P.Dong, Z.Jing, H.Leung, K.Shen and M.Li, “The labeled multi-Bernoulli filter for multi-target tracking with glint noise,” accepted for publication in the IEEE Trans. Aerospace and Electronic Systems

K.Wang, G.Zhang, Y.Leng and H.Leung, “Synthetic aperture radar image generation with deep generative models,” accepted for publication in the IEEE Geoscience and Remote Sensing Letters

P.Wang, G.Zhang and H.Leung, “Improving super-resolution flood inundation mapping for multispectral remote sensing image by supplying more spectral information,” accepted for publication in the IEEE Geoscience and Remote Sensing Letters

Q.Liu and H.Leung, “Variable augmented neural network for decolorization and multi-exposure fusion,” Information Fusion, vol.46, pp.114-127, March 2019

P.Wang, G.Zhang, Y.Y.Kong and H.Leung, “Superresolution mapping based on hybrid interpolation by parallel paths,” Remote Sensing Letters, vol.10, NO.2, pp.149-157, Feb 2019

P.Wang, G.Zhang and H.Leung, “Utilizing parallel networks to produce sub-pixel shifted images with multiscale spatio-spectral information for soft-then-hard sub-pixel mapping,” IEEE Access, vol.6, no.1, pp.57485-57496, Dec 2018

Z.Jing, M.Li and H.Leung, “Multi-target joint detection , tracking and classification based on random finite set for aerospace applications,” Aerospace Systems, https://doi.org/10.1007/s42401-018-0003-2 Nov 2018

P.Dong, Z.Jing, H.Leung, K.Shen and M.Li, “Robust consensus nonlinear information filter for distributed sensor networks with measurement outliners.” IEEE Trans Cybernetics, pp.1-13, 2018

X.Guang, Y.Gao, H.Leung, P.Liu and G.Li, “An autonomous vehicle navigation system based on inertial and visual sensors,” Sensors, vol.8, no.9, 2952 2018

J.Yi, X.Wan, D.Li and H.Leung, “Robust clutter rejection in passive radar via generalized subband cancellation,” IEEE Trans. Aerospace and Electronic Systems, vol.54, no.4, pp.1931-1946, Aug 2018

Z.Han, J.Zhao, H.Leung and W.Wang, “Construction of prediction interval for gas flow systems in steel industry based on granular computing,” Control Engineering Practice, vol.78, pp.79-88, 2018

P.Wang, L.Wang, Y.Wu and H.Leung, “Utilizing pansharpening technique to produce sub-pixel resolution thematic map from coarse remote sensing image,” Remote Sensing, vol.10, no.884, pp.1-15, 2018

P.Dong, Z.Jing,  H.Leung, K.Shen and J.Wang, “Student-t mixture labeled multi-Bernoulli filter for multi-target tracking with heavy-tailed noise,” Signal Processing, vol.152, pp.331-339, 2018

S.Mulhopadhyay and H.Leung, “Blind system identification using symbolic dynamics,” IEEE Access, vol.6, pp.24888-24903, May 2018

R.Wang, J. Guo and H.Leung, “Orthogonal circulant structure and chaotic phase modulation based analog to information conversion,” Signal Processing, vol.144, pp.104-117, Mar 2018

H.Jiang, S.Yi, L.Wu, H.Leung, Y.Wang, X.Zhou, Y.Chen and L.Yang, “Data-driven cell zooming for large-scale mobile networks,” IEEE Trans. on Network and Service Management, vol.15, no.1, pp.156-168, March 2018


K.Ma, H.Leung, E.Jalilian and D.Huang, “Fiber-optic acoustic-based disturbance prediction in pipelines using deep learning,” IEEE Sensors Letters, vol.1, no.6, article sequence number 6001404, Dec 2017

H.A.Hasting, J.Davidsen and H.Leung, “Challenges in the analysis of complex systems: introduction and overview,” European Physical Journal Special Topics. Vol.226, no.15, pp. 3185-3197, 2017

C.Seneviratne and H.Leung, “Mixing chaos modulations for secure communications in OFDM systems,” The European Physical Journal Special Topics, vol.226, no.15, pp.3287-3801, Dec 2017

Q.Liu, G.Shao, Y.Wang, J.Gao and H.Leung, “Log-Euclidean metrics for contrast preserving decolorization,” IEEE Trans. Image Processing, vol.26, no.12,pp.5772-5783, Dec2017

P.Dong, Z.Jing, H.Leung, and K.Shen, “Variational Bayesian adaptive cubature information filter based on Wishart distribution,” IEEE Trans. Automatic Control, vol.62, no.11, pp.6051-6057, Nov 2017

H.Zhu, K.Yuen, L.Mihaylova and H.Leung, “Overview of environmental perceptron for intelligent vehicles,” IEEE Trans. Intelligent Transportation Systems, vol.18, no.10, pp.2584-2601, Oct 2017

Q.Liu, P.X.Liu, Y.Wang and H.Leung, “Semi-parametric decolorization with Laplacian-based perceptual quality metric,” IEEE Trans. Circuits and Systems for Video Technology, vol.27, no.9, pp.1856-1868, Sept 2017

H.Zhu, M.Wang, K.Yuen and H.Leung, “Track-to-track association by coherent point drift,” IEEE Signal Processing Letters, vol.24, no.5, pp.643-647, May 2017

Z.Jiang, W.Yuan, H.Leung, X.You and Q.Zheng, “Coalition formation and spectrum sharing of cooperative spectrum sensing participants,” IEEE Trans. Cybernetics, vol.47, no.5, pp.1133-1146, May 2017

Y.Wang, K.Chen, J.Yu, N.Xiong, H.Leung, H.Zhou and L.Zhu, “Dynamic propagation characteristics estimation and tracking based on an EM-EKF algorithm in time-variant MIMO channel,” Information Sciences, vol.408, pp.70-83, April 2017

B.Wu, X.Zhou, Q.Jin, F.Lin and H.Leung, “Analyzing social roles based on a hierarchical model and data mining for collective decision making support,” IEEE Systems Journal, vol.11, no.1, pp.356-365, March 2017

J.Yi, X,Wan, H.Leung and M.Lu, “Joint placement of transmitters and receivers for distributed MIMO radars,” IEEE Trans. Aerospace and Electronic Systems, vol.53, no.1, pp.122-134, Feb 2017

B.Kong, Y.Wang, H.Leung, X.Deng, H.Zhou, and F.Zhou, “Sparse representation based range-doppler processing for integrated OFDM radar-communication networks," International Journal of Antennas and Propagation, vol. 2017, Article ID 6528956, 12 pages, 2017. doi:10.1155/2017/6528956

X.Jiang, H.Zhao and H.Leung, “Fault detection and diagnosis in chemical processes using sparse principal component selection,” Journal of Chemical Engineering of Japan, vol.50, No.1, pp.31-44, Jan 2017


S.Deng, Y.Wang, J.Yu, H.Leung and Y.Wang, “An efficient response distribution function for 3D MIMO channel modeling from a scatterer view”  International Journal of Future Generation Communication and Networking, Vol.9 No.10, pp.83-104, 2016

H.Zhu, J.Hu, H.Leung and B.Zhang “Recursive variational Bayesian inference to simultaneous registration and fusion,” International Journal of Advanced Robotic Systems, vol.13, no.124, doi: 10.5772/64012, pp.1-9, 2016

X.Wang, Y.Wang, H.Leung, S.Mukhopdyay, M.Tian and J.Zhou, “Inorganic material detection based on electrode sensor,” IEEE Sensor Journal, vol.16, no.11, pp.4147-4148, Nov2016

Y.Xia, H.Leung and M.Kamel, “A discrete-time learning algorithm for image restoration using a novel L2 norm noise constrained estimation,” Journal of Neurocomputing, vol.198, pp.155-170 July 2016

J.Yi, X.Wan, H.Leung, M.Lu and F.Cheng, “Non-cooperative registration for multistatic passive radars,” IEEE Trans. Aerospace and Electronic Systems, vol.52, no.2, pp.563-575, April 2016

Q.Xue, H.Leung, R.Wang, B.Liu, L.Huang and S.Guo,”The chaotic dynamics of drilling,” Nonlinear Dynamics, vol.83, No.4, pp.2003-2018, March 2016

L.Wu, H.Leung, H.Jiang, H.Zheng and L.Ma, “Incorporating human movement behavior into the analysis of spatially distributed infrastructure,” PLOS One, DOI10.1371/ Journal.pone. 0147216, pp.1-18, Jan2016

Q.Xue, H.Leung, R,Wang, B.Lin, G.Li and Y.Wu, “Continuous real-time measurement of drilling trajectory with new state space models of Kalman filter,” IEEE Trans. IM, vol.65, no.1, pp.144-154, Jan 2016

X.Gu, M.He, H.Leung and X.Gu, “Fast colorization for single-band thermal video sequences,” Neurocomputing, vol.171, pp.1146-1157, Jan2016

W.Yuan, X.You, J.Xu, H.Leung, T.Zhang and C.L.P.Chen, “Multi-objective optimization of cooperative spectrum sensing: Paretal solutions,” IEEE Trans. Cybernetic, vol.46, no.1, pp.96-106, Jan 2016


J.Yi, X.Wan, H.Leung and F.Cheng, “MIMO passive radar tracking under a single frequency network,” IEEE Selected Topics in Signal Processing, vol.9, no.8, pp.1161-1171, Dec 2015

H.Zhu and H.Leung, “A joint data association, registration and fusion approach for distributed tracking,” Information Sciences, vol.324, pp.186-196, Dec 2015

L.Hu, H.Leung, S.Xu and H.Zhang, “The controller combining positive velocity feedback with negative angle feedback for two-wheeled robot,” Cyber. and Inf. Technologies, vol.15, no.2, pp.159-170, Sept 2015

A.Maddumabandara, H.Leung and X.Liu, “Experimental evaluation of indoor localization using wireless sensor networks,” IEEE Sensor Journal, vol.15, no.9, pp.5228-5284, Sept 2015

Y.Nijsure, G.Kaddoum and H.Leung, “Cognitive chaotic UWB-MIMO radar based on non-parametric Bayesian technique,” IEEE Trans. Aerospace and Elec. Systems, vol.51, no.3, pp.2360-2378, July 2015

H.Leung, C.K.W.Seneviratne and M.Xu, “A statistical model for universal decentralized estimation in wireless sensor networks,” IEEE Trans. Signal Processing, vol.62, no.12, pp.3154-3164, June 2015

S.Sun, H.Leung and Z.Zhen, “Colored three dimensional reconstruction of vehicle thermal infrared images” Optical Engineering, vol.54, no.6, pp.063102-1 to 063102-8, doi:10.1117/1.OE.54.6.063102, June 2015

S,Wei and H.Leung, “Compromise rank genetic programming for automated nonlinear design of disaster management,” Mathematical Problems in Engineering, vol.2015, article ID 873794, 14 pages, 2015 doi:10,1155/2015/873794

X.Wang, Y.Wang, H.Leung, S.Mukhopadyay, M.Tian and J.Zhou. “Mechanism and experiment of planar electrode sensors in water pollutant measurement,” IEEE Trans. Instrumentation and Measurement, vol.64, no.2, pp.516-523, Feb 2015

L.Zhou, H.Leung, P.Xu, G.Ru, Q.Zhao and D.Xa, “The Kalman filtering blind adaptive multi-user detector based on tracking algorithm of signal subspace,” Information, vol.6, pp.3-13, doi:10.3390/info6010003, 2015


Z.Zhu, X.Huang and H.Leung, “Compensation of delay mismatch in a direct conversion transmitter,” IEEE Trans. Circuits and Systems – II, vol.61, no.12, pp.927-931, Dec 2014

L.Ma, H.Leung and D.Li, “Hybrid TDMA/CDMA MAC protocol for wireless sensor networks,” Journal of Networks, vol.9, no.10, pp.2665-2673, Oct 2014

Y.Xia and H.Leung, “A performance analysis of statistical optimal data fusion algorithms,” Information Sciences, vol.277, pp.808-824, Sept 2014

H.Zhu, Y.Li, J.Yu, H.Leung and Y.Li, “Ensemble registration of multisensor images by a variational Bayesian approach,” IEEE Sensor Journal, vol.14, no.8, pp.2698-2705, Aug 2014

Q.Tan, H.Leung, Y.Song and T.Wang, “Multipath ghost suppression for through-the-wall radar,” IEEE Trans. Aerospace and Electronic Systems, vol.50, no.3, pp.2284-2292, July 2014

S.Yan and H.Leung, “A carrier removal method for fringe projection profilometry based on radial basis function interpolation,” Optical Engineering, vol.53, no.7, pp.074113-1to074113-9, July 2014

X.Xu, J.Guo and H.Leung, “Blind equalization of power line communications using chaos,” IEEE Trans. Power Delivery, vol.29, no.3, pp.1103-1110, June 2014

Z.Zhu, X.Huang, M.Caron and H.Leung, “Blind self-calibration techniques for I/Q imbalance and DC-offsets,” IEEE Trans. Circuits and Systems-I, vol.61, no.6, pp.1849-1859, June 2014

X.Chen, H.Leung and M.Tien, “Multitarget detection and tracking for through-the-wall radars,” IEEE Trans Aerospace and Electronic Systems, vol.50, no.2, pp.1403-1415, April 2014

Y.Xia and H.Leung, “A fast learning algorithm for blind data fusion using a novel L_2 norm estimation,” IEEE Sensor Journal, vol.14, no.3, pp.666-672 March 2014

M.Rawat, S.Bhattacharjee, K.Rawat, F.Ghannouchi and H.Leung, “Generalized rational functions for reduced complexity behavioral modeling and digital predistortion of broadband wireless transmitters,” IEEE Trans. Instrumentation and Measurement, vol.63, no.2, pp.485-498 Feb 2014


B.C.Bao, Z.Liu and H.Leung, “Is memristor a dynamic element?” Electronic Letters, vol.49, no.24, pp.1523-1525, Nov 2013

Z.Zhu, X.Huang, M.Caron and H.Leung, “A blind AM/PM estimation method for power amplifier linearization,” IEEE Signal Processing Letters, vol.20, no.11, pp.1042-1045, Nov 2013

Y.Wang, Y.Liu, H.Leung, and R.Chen, “A segment collision inversion method for RFID tag reading,” IEEE Communication Letters, vol.17, no.10, pp.2008-2011, Oct 2013

H.Zhu, H.Leung and Z.He, “Joint state and parameter estimation in unknown non-Gaussian measurement noise using variational Bayesian techniques,” IEEE Trans. Aerospace and Electronic Systems, vol.49, no.4, pp.2601-2614, Oct 2013

Y.Wang, Y.Liu, H.Leung, R.Chen and A.Li “A multi-bit identification protocol for RFID tag reading,” IEEE Sensor Journal, vol.13, no.10, pp.3527-3536, Oct 2013

X.Gu, H.Leung and X.Gu, “Bayesian sparse estimation using double Lomax priors,” Mathematical Problems in Engineering, Special issue on Multimedia Data Fusion, vol.2013, article ID176249, pp.1-17, July 2013

M.Liu, Y.Wang, H.Leung and J.Yu, “A novel feature level data fusion method for indoor autonomous localization,” Mathematical Problems in Engineering, Special issue on Multimedia Data Fusion, vol.2013, article ID 382619, pp.1-12, July 2013

C.Yang, J.Zhao, W.Wang and H.Leung, “Prediction intervals for noisy nonlinear time series based on bootstrapping reservoir computing network ensemble,” IEEE Trans. Neural Networks and Learning Systems, vol.24, no.7, pp.1036-1048, July 2013

S.J.Hasaneini, C.J.B.Macnab, J.E.A.Bertram and H.Leung, “The dynamic optimization approach to locomotion dynamics: human-like gaits from a minimally constrained biped model,” Advanced Robotics, vol.27, no.11, pp.845-859, July 2013

Z.Zhu, X.Huang and H.Leung, “Joint I/Q imbalance and distortion calibration in direct conversion transmitter,” IEEE Trans. Wireless Communications, vol.12, no.6, pp.2941-2951, June 2013

C.T.Cheng, H.Leung and P.Maupin, “A delay-aware network structure for wireless sensor networks with partially correlated data,” IEEE Sensor Journal, vol.13, no.5, pp.1622-1631, May 2013

Z.Zhu, X.Huang and H.Leung, “Blind compensation of frequency dependent I/Q imbalance in direct conversion OFDM receivers,” IEEE Communications Letters, vol.17, no.2, pp.-297-300, Feb 2013

X.Chen, W.Yuan, W.Cheng, W.Liu and H.Leung, “Access point selection under QoS requirements in variable channel-width WLANs,” IEEE Wireless Communications Letters, vol.2, no.1, pp.114-117, Feb 2013

H.Zhu, H.Leung and Z.He, “A variational Bayesian approach to robust sensor fusion based on student-t distribution,” Information Sciences, vol.221, pp.201-214, Feb 2013

Z.Zhu, H.Leung and X.Huang, “Challenges in reconfigurable radio transceivers and application of nonlinear signal processing for RF impairment mitigation,” IEEE Circuits and Systems Magazine, vol.13, no.1, pp.44-65, Jan 2013