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All Journal International Journal of Electrical and Computer Engineering
Chi-Ngon Nguyen
Can Tho University
Computer Science & IT Electrical & Electronics Engineering

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Early detection of slight bruises in apples by cost-efficient near-infrared imaging Chanh-Nghiem Nguyen; Van-Linh Lam; Phuc-Hau Le; Huy-Thanh Ho; Chi-Ngon Nguyen
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp349-357

Abstract

Near-infrared (NIR) spectroscopy has been widely reported for its useful applications in assessing internal fruit qualities. Motivated by apple consumption in the global market, this study aims to evaluate the possibility of applying NIR imaging to detect slight bruises in apple fruits. A simple optical setup was designed, and low-cost system components were used to promote the future development of practical and cost-efficient devices. To evaluate the effectiveness of the proposed approach, slight bruises were created by a mild impact with a comparably low impact energy of only 0.081 Joules. Experimental results showed that 100% of bruises in Jazz and Gala apples were accurately detected immediately after bruising and within 3 hours of storage. Thus, it is promising to develop customer devices to detect slight bruises for not only apple fruits but also other fruits with soft and thin skin at their early damage stages.
A multi-microcontroller-based hardware for deploying Tiny machine learning model Van-Khanh Nguyen; Vy-Khang Tran; Hai Pham; Van-Muot Nguyen; Hoang-Dung Nguyen; Chi-Ngon Nguyen
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5727-5736

Abstract

The tiny machine learning (TinyML) has been considered to applied on the edge devices where the resource-constrained micro-controller units (MCUs) were used. Finding a good platform to deploy the TinyML effectively is very crucial. The paper aims to propose a multiple micro-controller hardware platform for productively running the TinyML model. The proposed hardware consists of two dual-core MCUs. The first MCU is utilized for acquiring and processing input data, while the second is responsible for executing the trained TinyML network. Two MCUs communicate to each other using the universal asynchronous receiver-transmitter (UART) protocol. The multi-tasking programming technique is mainly applied on the first MCU to optimize the pre-processing new data. A three-phase motors faults classification TinyML model was deployed on the proposed system to evaluate the effectiveness. The experimental results prove that our proposed hardware platform was improved 34.8% the total inference time including pre-processing data of the proposed TinyML model in comparing with single micro-controller hardware platform.
Co-Authors Chanh-Nghiem Nguyen Hai Pham Hoang-Dung Nguyen Huy-Thanh Ho Phuc-Hau Le Van-Khanh Nguyen Van-Linh Lam Van-Muot Nguyen Vy-Khang Tran