Profile

Thaweesak Yingthawornsuk

Thaweesak Yingthawornsuk

King Mongkut's University of Technology Thonburi

Dr. Thaweesak Yingthawornsuk, Ph.D. Educations: 2007, Ph.D. in Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA. 2003, M.Sc. in Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA. 1995, B.Sc.Ind. in Electrical Engineering, King Mongkut's University of Technology Thonburi (KMUTT), Thailand. Work Experiences: o HOD, Head of Department of Media Technology, KMUTT, Bangkok, Thailand (2008 – present) o Head of Division of Biomedical Media Technology, Dept. of Media Technology, KMUTT (2010 – present) o Knowledge Management Committee Board, Department of Media Technology, KMUTT University (2009 – present) o Research and Development Committee Board, Faculty of Industrial Education and Technology, KMUTT Univ. (2007 – present) o Joint Executive Administration Committee Board of Media Technology and Art Curriculums, KMUTT (2008 – present) o Executive Administration Committee Board of Human Resource, Dept. of Media Technology, KMUTT (2008 – present) o Research and Development Committee Board, Faculty of Ind. Ed.Tech., KMUTT(2007 – present) o Thesis and Dissertation Defense Committees, Faculty of Ind. Ed.Tech., KMUTT(2007 – present) o Research Assistant, Speech Research lab., Department of Electrical Engineering & Computer Research Area: Speech Processing - Automatic Speech Recognition, Energy Based Voice and Unvoiced Detection, Acoustic Feature Extraction, Applied Statistical Analysis on Emotional Speech, Spectral Analysis and Modeling of Vocal-Tract Characteristics for Emotional Speech Classifications Biomedical Signal Processing – Parametric and Nonparametric Estimation on Spectral Characteristics of ECG, Blood Pressure, and Microneuography Signals, Physiological System Identification and Modeling – Open-Loop Transfer-Function Estimation under Closed-Loop Baroreflex System in Human. Classification – Supervised and Unsupervised Classification, Traditional Classification, Gaussian Mixture Modeling Based Classification, Maximum Likelihood Approach, Bayesian Pdf-Based Classification, Support Vector Machine, Cross- Validation Approach.>