Prof. Yun Lin
Harbin Institute of Technology, Harbin China
Title: ALAMEDA: AI-Powered Solutions for Early Diagnosis and Treatment of Brain Diseases
Bio:
Prof. Yun Lin received the Ph.D. degree from Harbin Engineering University, Harbin, in 2010. He was a Research Scholar with Wright State University, USA, from 2014 to 2015. He is currently a full professor at the College of Information and Communication Engineering, Harbin Engineering University. His current research interests include machine learning and data analytics over wireless networks, signal processing and analysis, cognitive radio and software defined radio, artificial intelligence, and pattern recognition. He has published more than 200 international peer-reviewed journals/conference papers, such as IEEE TSP/TII/ToC/TVT/TCCN/TITS/Tre, and INFOCOM, et al. He is serving as the Editor-in-Chief for EAI Endorsed Trans MCA, Editor for IEEE T Re, IEEE IoTJ, DCN, WiNet, et al. He had successfully organized several international workshops and symposia on platforms including INFOCOM, GLOBECOM, DSP, and ICNC.
Abstract:
At present, the scarcity of spectrum resources stands as the primary factor impeding the progress of satellite-terrestrial networks. Cell-free technology, which is capable of achieving user-centered service, is rapidly gaining momentum in terrestrial networks, driven by its notable advantages, including high spectral efficiency and low latency. It is anticipated to emerge as a viable solution to address the expansive coverage demands. PD-NOMA demonstrates the ability to serve multiple users with varying power in the same frequency band, facilitating the efficient utilization of spectrum resources. Nevertheless, the application of PD-NOMA in cell-free satellite-terrestrial networks encounters several challenges. The co-channel interference arising from spectrum sharing between networks, coupled with the complexities of interference within cell-free networks, poses a significant threat to signal transmission quality, undermining network spectrum efficiency. Aiming to enhance network transmission reliability, frequency efficiency, and energy efficiency, to tailor to the diverse needs of satellite-terrestrial networks, the joint resource management of both satellite and terrestrial is coordinated to improve the reliability, spectrum efficiency and energy efficiency of satellite-terrestrial networks, with the employment of PD-NOMA and cell-free techniques.
Dr. Muhammad Sajjad
Department of Computer Science, Islamia College Peshawar, Pakistan
Title: ALAMEDA: AI-Powered Solutions for Early Diagnosis and Treatment of Brain Diseases
Bio:
Muhammad Sajjad received his master’s degree from the Department of Computer Science, College of Signals, National University of Sciences and Technology, Rawalpindi, Pakistan, in 2012, and his Ph.D. in Digital Contents from Sejong University, Seoul, South Korea, in 2015. He has worked as an ERCIM Postdoctoral Research Fellow and a as a leading researcher of the ALAMEDA project at NTNU, Norway.
Currently, he is an Associate Professor in the Department of Computer Science at Islamia College University Peshawar, Pakistan, and heads the Digital Image Processing Laboratory. His supervision spans over various research projects, including big data analytics, medical image analysis, multi-modal data mining, image/video prioritization and ranking, fog computing, the Internet of Things, autonomous navigation, and video analytics.
His primary research interests are data analytics using ML/DL, image understanding, pattern recognition, robotic vision, and multimedia applications, with a current focus on economical hardware and deep learning, video scene understanding, activity analysis, fog computing, the Internet of Things, and real-time tracking. He has published over 90 papers in peer-reviewed international journals and conferences and serves as a professional reviewer for various esteemed journals and conferences.
Abstract:
This talk will introduce the international project ALAMEDA, which addresses the complexities of caring for patients with brain disorders such as Parkinson’s, Multiple Sclerosis, and Stroke. These conditions can severely impact patients’ quality of life and place a heavy burden on caregivers. By integrating AI healthcare support systems, ALAMEDA aims to revolutionize personalized rehabilitation assessments, ensuring timely and effective medical interventions while predicting and preventing aggravating conditions. The project’s approach leverages advanced AI methods and Big Data management, utilizing technologies like Big Data Analytics, Machine Learning, and Deep Learning to detect hidden patterns, identify anomalies, and understand relationships between patients, conditions, and treatments. Early detection of brain diseases is crucial, as symptoms often become irreversible at later stages. Additionally, ALAMEDA addresses the broader impact on health systems and societies, emphasizing the urgent need for digital transformation due to a projected shortage of 4.1 million skilled health professionals in the EU by 2030. AI-powered tools promise significant cost savings and improvements in healthcare delivery. The mission of ALAMEDA is to create a patient-centric AI system that benefits patients, healthcare providers, and caregivers. By focusing on personalized, predictive, and preventive healthcare, ALAMEDA strives to bridge gaps in early diagnosis and treatment, ultimately enhancing care and quality of life for those affected by brain diseases.