Invited Speakers
ICSSA 2026 features invited speakers from leading institutions and industry experts who will share insights on emerging research, technologies, and applications in smart sensors, intelligent systems, wireless networks, sensing, and AI-driven innovation.
Prof. Dr. Hangguan Shan
Zhejiang University
Prof. Ir. Ts. Dr. Ahmad ’Athif Mohd Faudzi
Universiti Teknologi Malaysia
Prof. Ir. Ts. Dr. Sharifah Hafizah Syed Ariffin
Universiti Teknologi Malaysia
Assoc. Prof. Dr. Ahmad Shahrafidz Khalid
Universiti Kuala Lumpur
Assoc. Prof. Ts. Dr. Wahidah Hashim
Universiti Tenaga Nasional
Ir. Dr. Ahmad Nizar Harun
MIMOS Berhad
Ir. Dr. Mohd Nabil Muhtazaruddin
Universiti Teknologi Malaysia
Assoc. Prof. Ts. Dr. Azian Azamimi Abdullah
Universiti Malaysia Perlis
Prof. Dr. Hangguan Shan
College of Information Science and Electronic Engineering,
Zhejiang University
Talk Title: Perceptive Mobile Networks for UAV Surveillance: From the Perspective of Cooperative Sensing
Date & Time: 22 September 2026, Tuesday, 2.30 PM
Short Biography
Hangguan Shan received the Ph.D. degree in electrical engineering from Fudan University in 2009. He is currently a Professor with the College of Information Science and Electronic Engineering, Zhejiang University.
His current research interests include resource management for wireless networks, edge computing and edge intelligence, integrated sensing and communication, and cooperative perception.
He received the Best Industry Paper Award from the IEEE WCNC 2011 and the Best Paper Awards from IEEE WCSP 2023 and IEEE/CIC ICCC 2024. He is currently on the editorial boards of IEEE Transactions on Vehicular Technology and IET Communications. He was also an Editor of IEEE Transactions on Green Communications and Networking. He is a Senior Member of IEEE.
Talk Abstract
Unmanned aerial vehicles (UAVs) are expected to be widely deployed across various applications owing to their low cost and high mobility. However, unregulated UAV operations may pose significant threats to public safety. Enabled by Integrated Sensing and Communication (ISAC) technology, future wireless networks are envisioned to integrate communication and sensing functionalities by sharing the same hardware infrastructure and spectrum resources, gradually evolving into Perceptive Mobile Networks (PMNs) with network-wide sensing capabilities.
Through cooperative sensing among multiple base stations, PMNs can obtain diverse spatial observations from different perspectives, thereby enabling comprehensive and cost-effective UAV surveillance.
In this talk, several key challenges in PMN design will be discussed, including sensing coverage assurance, network clutter suppression, and the tradeoff between sensing and communication performance. The talk will also examine the establishment of a theoretical framework for analysing the dual-functional performance of cooperative sensing-enabled PMNs, which can facilitate the optimisation of network configurations such as base station deployment and resource allocation.
Finally, the talk will discuss the design of cooperative transmit-receive beamforming for multi-cell anti-UAV ISAC systems to enhance the reliability of point-target UAV monitoring.
Assoc. Prof. Ts. Dr. Azian Azamimi Abdullah
Faculty of Electronic Engineering & Technology,
Universiti Malaysia Perlis
Talk Title: AI-Based Automated Classification of Mosquito Eggs from Ovitrap Images for Dengue Vector Surveillance
Date & Time: TBC
Short Biography
Dr. Azian Azamimi Abdullah obtained her bachelor’s and master’s degrees in Electrical and Electronic Engineering from the University of Tokushima, Japan, in 2006 and 2009, respectively.
She further advanced her academic journey by completing her Ph.D. in Information Science at the Nara Institute of Science and Technology (NAIST), Japan, in 2017. Before transitioning to academia, Dr. Azian gained valuable industry experience as an engineer at Toshiba Electronics from 2006 to 2007.
She is currently an Associate Professor at the Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP). Dr. Azian’s research interests span bioinformatics, artificial intelligence, big data, and machine learning.
She has authored numerous books and journal articles and actively shares her work at both national and international conferences. Dr. Azian is also a Senior Member of IEEE, reflecting her active involvement in the global engineering community.
Talk Abstract
Dengue vector surveillance requires rapid and reliable estimation of Aedes mosquito breeding activity, yet conventional ovitrap analysis still depends on manual microscopic egg identification and counting. This work presents an AI-based system for automated mosquito egg classification, localisation, segmentation, and counting from ovitrap images.
The proposed workflow combines RGB-to-HSV image preprocessing and morphological refinement with EfficientNet binary classification, YOLOv8 object detection, and YOLOv8-seg instance segmentation. The dataset comprised 193 ovitrap images with 10,095 annotated mosquito egg instances for detection and segmentation.
For the classification stage, class imbalance was addressed by augmenting the Non-Egg class to form a balanced set of 376 images. Experimental results show that fine-tuned EfficientNet-B0, B3, and B4 achieved 97.37% accuracy, 95.00% precision, 100.00% recall, and a 97.44% F1-score, while EfficientNet-B3 provided the best practical balance between accuracy and computational cost.
YOLOv8-seg achieved 0.89 bounding-box precision and 0.88 mAP@0.50 for object localisation. Counting tests demonstrated close agreement with ground truth under normal and low-light conditions, including 205 detected eggs from 206 ground-truth eggs at a low confidence threshold.
The developed graphical user interface supports automated counting, risk categorisation, and surveillance-site visualisation for more scalable dengue vector monitoring.
