Daejin Park

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Kyungpook National University

South Korea

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Professor Daejin Park is a faculty member at Kyungpook National University, South Korea. His research focuses on advanced topics such as micro-accelerators, metaheuristic algorithms for antenna selection, and edge-AI software architectures. Recent publications include works on autonomous mobile robots, hardware acceleration for self-supervised learning, and scalable implementations of image-based systems. His expertise contributes significantly to the fields of intelligent transportation systems and embedded software solutions.

Recent Grants

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Cloud-connected Software Execution Architecture of Smart IoT Systms

Open Date: 2014-11-01

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Collaborators (2)

Wentong Cai

Nanyang Technological University (NTU)

SINGAPORE

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Seungmin Lee

Research Prof.

Kyungpook National University

SOUTH KOREA

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Daejin Park

Research Interests

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Contact this professor

About

Recent Grants

Articles (20)

MAIL: Micro-Accelerator-in-the-Loop Framework for MCU Integrated Accelerator Peripheral Fast Prototyping

Metaheuristic Algorithm‐Based Antenna Selection Collaborative Beamforming for Remote Sensors

Flexible Edge-AI Software Execution Architecture Based on Cloud-Connected Incremental Learning

ML-Based Fast and Precise Target Docking of Autonomous Mobile Robots for Intelligent Transportation Systems Using 2-D LiDAR

S 3A-NPU: A High-Performance Hardware Accelerator for Spiking Self-Supervised Learning With Dynamic Adaptive Memory Optimization

Ultralight Situation-Aware Hypervisor With Adaptive Starvation-Free Feedback Control for Embedded System

ML-Based Fast and Precise Embedded Rack Detection Software for Docking and Transport of Autonomous Mobile Robots Using 2-D LiDAR

Differential Image-Based Scalable YOLOv7-Tiny Implementation for Clustered Embedded Systems

Robust Embedded PID Control Software Execution Based on Automatic Malfunction Profile Feedback

A novel multi-level evaluation approach for human-coupled IoT applications

Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems

Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification

Digital-Twin Consistency Checking Based on Observed Timed Events With Unobservable Transitions in Smart Manufacturing

Runtime Tracking-Based Replication of On-Chip Embedded Software Using Transfer Function Learning for Dust Particle Sensing Systems

Efficient Object Detection Based on Masking Semantic Segmentation Region for Lightweight Embedded Processors

Abnormal beat detection from unreconstructed compressed signals based on linear approximation in ECG signals suitable for embedded IoT devices

Semantic Depth Data Transmission Reduction Techniques Based on Interpolated 3D Plane Reconstruction for Light-Weighted LiDAR Signal Processing Platform

Runtime ML-DL Hybrid Inference Platform Based on Multiplexing Adaptive Space-Time Resolution for Fast Car Incident Prevention in Low-Power Embedded Systems

Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices

Efficient Sensor Processing Technique Using Kalman Filter-Based Velocity Prediction in Large-Scale Vehicle IoT Application

Collaborators (2)

Wentong Cai

Seungmin Lee

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