Research

We are committed to developing novel and efficient deep and machine learning models that enhance performance, interpretability and trustworthiness, and improve impact and accessibility. Our solutions are designed to contribute to the academic advancements, strategic growth and platform investments of Canadian industries. AiX Lab is dedicated to cultivating highly skilled scholars with cutting-edge AI and equipping them to contribute to various industry sectors.

Projects

Automotive Research

Autonomous Navigation in self-driving cars

ELMs and RL play fundamental roles in modern autonomous driving systems. This research introduces light-weight and efficient ELM and DRL models for end-to-end autonomous navigation in complex scenarios based on camera as the only sensor.

Visual SLAM (simultaneous localization & mapping)

This research focuses on the development and deployment of high-definition localization and adaptive mapping in real time for enhanced autonomous navigation in GPS-degraded and visually ambiguous environments. We incorporate semantic understanding into SLAM models for accurate mapping of highly dynamic environments.

Scene Perception models in self driving cars

This project aims to enhance real-time multi-object detection capabilities for self-driving cars, specifically operating effectively in various adverse weather conditions (e.g., rain, fog, and snow), which can significantly impair the accuracy and reliability of object detection systems in autonomous vehicles.

Driver Attention

This research focuses on driver's visual and manual distraction. Capturing driver’s video through camera, attention level is analyzed. Key objective is to design reliable and compact models to facilitate gaze estimation and action recognition in DMS.

Driver Impairment

This research focuses on addressing driver fatigue and the influence of alcohol and drugs on driving behavior. Through innovative solutions and advanced technologies, we aim to uncover key biomarkers and behavioral indicators for real-time impairment monitoring and contributing to a safer driving.

Driver State

This project is dedicated to understanding the dynamic assessment of driver's emotion, well-being and cognitive workload and enhance the overall driving experience. The objective is to shape the future of transportation with a focus on holistic driver well-being and mental resilience.

Adaptive Handover System

An adaptive handover system is a novel idea aimed at optimizing the control transfer between driver and automated system. This requires dynamically adjusting a TOR based on a real-time analysis of both driver's state and road/traffic conditions to perform the most appropriate action. This could mean altering a request's modality, timing, or location. Such an adaptive approach promises to enhance safety by mitigating the risks associated with a handover system.

Agriculture Research

Vision based growth monitoring

Leveraging AI and computer vision technologies, we are dedicated to transforming the assessment of the growth dynamics of crops and plants. Our vision-based approach enables precise and real-time monitoring of key indicators, contributing to enhanced crop management practices. From tracking plant development to identifying potential stress factors, our research aims to empower farmers with actionable insights for optimized yields and sustainable farming.

Clinical Research

AI-Based Musical Intervention to Improve Emotion

Mental disorders have a significant influence on the daily activities of Canadians. Musical intervention can provide a non-invasive treatment through changing emotional state and creating positive mood. The main objective of this project is a long-term solution for musical intervention through an optimized machine learning framework for a real-time emotion recognition and musical intervention system integrated in an empathetic speaker. During music play, the emotional influence will be detected from EEG and the music database will be customized.

AI-based classification of children’s speech-sound development

Speech and language sampling are key components of a Speech-Language Pathologist’s assessment of the presence/absence of a disorder. The main objective of this project is to develop a novel LLM-based framework to process and analyze children's speech data and recognize common developmental patterns and errors.

AI-based application for monitoring developmental outcomes in children who have hearing loss

Developing an AI-based digital application for early hearing detection and intervention that aims to address current PedAMP administration limitations by streamlining data collection, improving accuracy, and enhancing data accessibility for decision-making and program evaluation.

Selected Publications

Book Chapter

S. Nikan, M. Ahmadi, “Recognition of human faces under different degradation conditions,” in Trends in Digital Signal Processing, 1st ed. Singapore, Singapore: Pan Stanford Publishing Pte. Ltd., vol. 1, ch. 11, pp. 333-356, 2015.

Journal

H. Hassani*, S. Nikan, A. Shami, “Traffic navigation via reinforcement learning with episodic-guided prioritized experience replay,”Eng. Appl. Artif. Intell., vol.137, 2024.
H. Hassani*, S. Nikan, A. Shami, “Improved exploration-exploitation trade-off through adaptive prioritized experience replay,” Neurocomputing, vol. 614, 2024.
J. A. Miller*, S. Nikan, M. H. Zaki, “Navigating the handover: reviewing takeover requests in level 3 autonomous vehicles,” IEEE OJVT, vol. 5, pp. 1073-1087, 2024, doi: 10.1109/OJVT.2024.3443630.
M. Nabipour*, S. Nikan, “Action unit analysis for monitoring drivers’ emotional state,” IEEE Sensors Journal, vol. 24, no. 15, pp. 24758-24769, 2024.
I. Shaer, S. Nikan, A. Shami, “Efficient transformer-based hyper-parameter optimization for resource-constrained IoT environments,” IEEE IoT Magazine, In Press, 2024.
K. Aghamohammadesmaeilketabforoosh, S. Nikan, G. Antonini, J. M. Pearce, "Optimizing Strawberry Disease and Quality Detection with Vision Transformers and Attention-Based Convolutional Neural Networks," Foods, vol. 13, no. 12, pp. 1869, 2024.
R. Yahyaabadi*, S. Nikan, "An Explainable Attention Zone Estimation for Level 3 Autonomous Driving," IEEE Access, vol. 11, pp. 93098 - 93110, 2023.
Y. Ma, V. Sanchez, S. Nikan, D. Upadhyay, B. Atote, T. Guha, "Real-Time Driver Monitoring Systems through Modality and View Analysis," arXiv preprint arXiv:2210.09441, 2022.
S. Nikan, K. Van Osch, M. Bartling, D. G. Allen, S. A. Rohani, B. Connors, S. K. Agrawal, H. M. Ladak, PWD-3DNet: A deep learning-based fully automated segmentation of multiple structures on temporal bone CT scans, IEEE Transactions on Image Processing, 2020, vol. 30, pp. 739-753.
S. Nikan, K. Van Osch, M. Bartling, D. G. Allen, S. A. Rohani, B. Connors, S. K. Agrawal, H. M. Ladak, "PWD-3DNet: A deep learning-based fully automated segmentation of multiple structures on temporal bone CT scans," IEEE Transactions on Image Processing, 2020, vol. 30, pp. 739-753.
S. Nikan, M. Ahmadi, "A modified technique for face recognition under degraded conditions, Journal of Visual Communication and Image Representation," vol. 55, pp. 742-755, 2018.
F. Gwadry-Sridhar, S. Nikan, A. Hamou, SJ. Seung, T. Petrella, AM. Joshua, S. Ernst, N. Mittmann, "Resource utilization and costs of managing patients with advanced melanoma: a Canadian population-based study," Current Oncology, 2017, vol. 24, no. 3, pp. 168–175.
S. Nikan, M. Ahmadi, "Recognition of human faces in the presence of incomplete information, International Journal on Advances in Software," 2015, vol. 8, no. 3&4, pp. 450-456.
S. Nikan, M. Ahmadi, "Performance evaluation of different feature extractors and classifiers for recognition of human faces with low resolution images," International Journal of Intelligent Systemsand Applications in Engineering, 2014, vol. 3, no. 2, pp. 72-77.
S. Nikan, M. Ahmadi, "Local gradient-based illumination invariant face recognition using LPQ and multi-resolution LBP fusion," IET Image Processing, 2015, vol. 9, no. 1, pp. 12-21.

Conference

Dehrouyeh, I. Shaer, S. Nikan, F. B. Ajaei, A. Shami, “Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure,” Accepted in IEEE ICC'25 - CSM Symposium, Montreal, Canada, 2025.
R. Yahyaabadi*, S. Nikan, "Skeleton-based driver action recognition using ResGGCNN," in ISSCS, Iasi, Romania, pp. 1-4, 2023.
M. Nabipour*, S. Nikan, "A Deep Learning-Based Remote Plethysmography with the Application in Monitoring Drivers’ Wellness," in ISSCS, Iasi, Romania, pp. 1-4, 2023.
M. Samadi, H. Kharrati, M.A. Badamchizadeh, H. Hassani*, S. Nikan, “Diagnosing faults in smart grids using liquid time-constant network,” in ICEET, Turkey, 2023.
M. Mohseni*, S. Nikan, A. Shami. AI-based Traffic Forecasting in 5G network, in CCECE2022, September 2022.
S. Nikan, D. Upadhyay, "Appearance-Based Gaze Estimation for Driver Monitoring," in NeurIPS, pp. 127-139, 2023.
Y. Ma, V. Sanchez, S. Nikan, D. Upadhyay, B. Atote, T. Guha, "Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-Attention," in CVPR, pp. 2616-2624, 2023.
S. Nikan, S. K. Agrawal, H. M. Ladak, Fully Automated Segmentation of the Temporal Bone from Micro-CT using Deep Learning, in SPIE’20, Houston, TX, United States, Feb 2020.
S. Nikan, F. Gwadry-Sridhar, M. Bauer, Pattern recognition application in ECG arrhythmia classification, in HEALTINF’17, Porto, Portugal, Feb 2017, pp. 48-56.
S. Nikan, F. Gwadry-Sridhar, M. Bauer, Machine learning application to predict the risk of coronary artery atherosclerosis, in CSCI’16, Las Vegas, USA, Dec 2016, pp. 34-39.
S. Nikan, M. Ahmadi, Partial Face Recognition Based on Template Matching, in SITIS’15, Bangkok, Thailand, Nov 2015, pp. 160-163.
S. Nikan, M. Ahmadi, “Partial face recognition based on template matching,” in SITIS’15, Bangkok, Thailand, pp. 160-163, Nov 2015.

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