Nakorn "ICE" Kumchaiseemak

About ME(s) :

Hello, I am Mr. Nakorn (Ice) Kumchaiseemak, and I am thrilled to introduce myself. I am a Ph.D. student and also kitesurfer. In 2014, I received my B.Sc. degree from King Mongkut's University of Technology North Bangkok (KMUTNB) in Thailand, and in 2017, I completed my M.S. degree from Kasetsart University in Bangkok. During my M.S. studies, I worked as a research assistant at Kasetsart University in the Excitable Media Lab, supervised by Assoc. Prof. Chaiya Luengviriya (Dr.rer.nat.). In this lab, I focused on computer simulations for reaction-diffusion systems and conducted research on electronic devices for agriculture, such as FDR soil moisture sensors and long-term thermometers. In 2018, I have decided to pursue a Ph.D. degree at the School of Information Science at VISTEC and involved in research at both the Vision & Learning Lab and the Brain Lab, under the supervision of Supasorn Suwajanakorn (Ph.D.) and Assoc. Prof. Theerawit Wilaiprasitporn, (D.Eng., SMIEEE). Currently, in 2023, I'm working at the Microwave, Signals and Systems (MS3) group in Delft University of Technology, The Netherlands as a guest Ph.D. researcher, under the supervision of Assoc. Prof. Francesco Fioranelli (Ph.D., SMIEEE). My research interests lie at the intersection of RADAR, Human-Computer Interaction (HCI), and Deep Learning, where I am actively exploring cutting-edge advancements in these areas.


Academic Education(s) & Experience(s)

Ph.D. Information science and Technology (International program), VISTEC, Thailand

Guest Ph.D. Researcher,  MS3 group, Delft University of Technology, The Netherlands.

M.S. Physic, Kasetsart University, Thailand

B.Sc Industrial Physics and Medical Instrumentation, KMUTNB, Thailand

Award(s) & Scholarship(s)

Selected Project(s)

Capturing Head Poses Using FMCW Radar and Deep Neural Networks (1).mp4

Capturing Head Pose  Using FMCW Radar and Deep Neural Networks (IEEE Transactions on Aerospace and Electronic Systems 2025)

This paper presents the first subject-specific head pose estimation approach using only one Frequency Modulated Continuous Wave (FMCW) radar data frame. Specifically, the proposed method incorporates a deep learning (DL) framework to estimate head pose rotation and orientation frame-by-frame by combining a Convolutional Neural Network operating on RangeAngle radar plots and a PeakConv network. The proposed method is validated with an in-house collected dataset, including annotated head movements that varied in roll, pitch, and yaw, and these were recorded in two different indoor environments. It is shown that the proposed model can estimate head poses with a relatively small error of approximately 6.7-14.4 degrees for all rotational axes and is capable of generalizing to unseen, new environments when trained in one scenario (e.g., lab) and tested in another (e.g., office), including in the cabin of a car.

Impulse-Radio UWB Self-Localization (IEEE Sensors  Journal 2024)

This study introduces a simple and cost-effective localization system using a moving ultra-wideband (UWB) radar sensor and passive reflectors at fixed points. We propose a hybrid pipeline that first predicts the ranges from the sensor to the reflectors, and then predicts the radar’s position from these ranges. Two key components of the hybrid pipeline are a neural network for range prediction and an optimization-based “association” step. The neural network solves the challenge of predicting individual ranges from the mixed radar signal, while the association step matches each predicted range to its corresponding reflector using a novel regularized trilateration formulation. 

DipSAR (IEEE Sensors Conference 2023)

We present a deep learning-based approach called DipSAR for reconstructing millimeter-wave synthetic aperture radar (SAR) images from sparse samples. The primary challenge lies in the requirement of a large training dataset for deep learning schemes. To overcome this issue, we employ the deep image prior (DIP) technique, which eliminates the need for a large dataset and instead utilizes only the sparse sample itself. 

RA-CNN (IEEE Transactions on Geoscience and Remote Sensing, 2022)

We propose a deep learning-based approach to localizing a small moving object with a single millimeter-wave frequency-modulated continuous-wave (FMCW) radar. This pilot study establishes a new baseline for small-object tracking using FMCW and can enable tracking of small animals, such as ants inside the colony for behavior studies. 

SleepPoseNet (IEEE Journal of Biomedical and Health Informatics, 2020)

This study investigates the performance of an off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet.

Publication(s)

Research interest(s)

Contact(s)

Wangchan Valley 555 Moo 1 Payupnai, Wangchan, Rayong 21210 Thailand 

Email :     Nakorn.k_s18@vistec.ac.th, 

N.Kumchaiseemak@tudelft.nl

Hobbie(s) 

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