The 7th International Conference on Digital Arts, Media and Technology (DAMT) and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (NCON)
Keynote 1: Professor (distinguished) Pasuk Mahakkanukrauh, MD
Biography: Professor (distinguished) Pasuk Mahakkanukrauh received the Doctor of Medicine in 1989 and received a certificate of Medical Education from Dundee University, Scotland in 1995. Her research interest fields are Neuroanatomy, Forensic Osteology, Clinical Anatomy, and Rehabilitation. She has received many honors and awards from the Society of Anatomy of Thailand, Chiangmai University, Asia-Pacific International Congress of Anatomy, The Medical Council of Thailand, and the National Research Council of Thailand. From 2003 to the present, she has served as the vice president of Cadaveric Surgical Training Center, Chairman of Forensic Osteology Research Center, Chairman of excellence center of osteology and research and training Center, and Head of the Department of Anatomy, Faculty of Medicine, Chiang Mai University, Thailand. She has become an assistant professor, an associate professor, and professor in 1995, 1998, and 2005 respectively. In addition, she has got a distinguished professor position in 2013 for the research aspects of her academic career.
Forensic Osteology Transformed by Artificial Intelligence: Forensic osteology is a discipline that examines human skeletal remains in medicolegal contexts. Biological identification is a key research interest in forensic osteology. Copious studies in this field have been conducted internationally, but their findings do not apply to a Thai population as their data is specific to their studied populations. Therefore, the Forensic Osteology Research Center (FORC) has been a leading force in conducting osteological studies that have forensic applications for Thai victims, which prompts a speedy investigation process. By considering the impact of population-specific studies, our current research at FORC explores the application of artificial intelligence in biological identification to improve the usability and accuracy of the published methods, which will be significant in the practice of forensic medicine in Thailand as well as the rest of the world.
Keynote 2: Professor Piya Kovintavewat
Biography: Professor Piya Kovintavewat received the B.Eng. summa cum laude from Thammasat University, Thailand (1994), the M.S. degree from Chalmers University of Technology, Sweden (1998), and the Ph.D. degree from Georgia Institute of Technology (2004), all in Electrical Engineering.
Prior to working at NPRU, he worked as an engineer at Thai Telephone and Telecommunication company (1994-1997), and as a research assistant at National Electronics and Computer Technology Center (1999), both in Thailand. He also had work experiences with Seagate Technology, Pennsylvania, USA (summers 2001, 2002, and 2004).
He is currently a Professor in Electrical Engineering Program, Faculty of Science and Technology, Nakhon Pathom Rajabhat University (NPRU), Nakhon Pathom, Thailand. His main research interests include coding and signal processing as applied to digital data storage systems. He is now a member of IEEE (USA), IEICE (Japan), and ECTI (Thailand) Association.
Signal Detection Evolution in High-Density Magnetic Recording Systems: Due to a super-paramagnetic phenomenon, the perpendicular magnetic recording (PMR) technology utilized in today's hard disk drives is reaching its storage capacity limit of roughly 1 Terabit per square inch (Tb/in2). This talk will focus on one of the promising recording technologies, bit-patterned magnetic recording (BPMR), which can achieve an areal density (AD) of up to 4 Tb/in2. Because BPMR experiences several disturbances such as two-dimensional interference, electronics noise, media noise, track-misregistration, and skew angel, which can affect system performance if precautions are not taken care of. In this talk, we will discuss the evolution of signal detection in a BPMR system, including the deep learning approach, that can improve overall system performance. In practice, the more the sophisticated detection techniques, the better the system performance.