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All Journal Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science
Trong-Minh Hoang
Posts and Telecommunications Institute of Technology
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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RAC: a reusable adaptive convolution for CNN layer Nguyen Viet Hung; Phi Dinh Huynh; Pham Hong Thinh; Phuc Hau Nguyen; Trong-Minh Hoang
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp753-763

Abstract

This paper proposes reusable adaptive convolution (RAC), an efficient alternative to standard 3×3 convolutions for convolutional neural networks (CNNs). The main advantage of RAC lies in its simplicity and parameter efficiency, achieved by sharing horizontal and vertical 1×k/k×1 filter banks across blocks within a stage and recombining them through a lightweight 1×1 mixing layer. By operating at the operator design level, RAC avoids post-training compression steps and preserves the conventional Conv–BN–activation structure, enabling seamless integration into existing CNN backbones. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on CIFAR-10 using several architectures, including ResNet-18/50/101, DenseNet, WideResNet, and EfficientNet. Experimental results demonstrate that RAC significantly reduces parameters and memory usage while maintaining competitive accuracy. These results indicate that RAC offers a reasonable balance between accuracy and compression, and is suitable for deploying CNN networks on resource-constrained platforms.
A Cost-Effective Real-Time Human Activity Recognition System Using Supervised Learning Algorithms and Wearable Acceleration Sensors Nguyen Thi Thu; Phung Cong Phi Khanh; Trong-Minh Hoang; Duc-Tan Tran; Nguyen Ngoc Linh; Nguyen Canh Minh
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i2.6830

Abstract

Human activity recognition (HAR) plays a vital role in health monitoring by providing detailed insights into daily movements. This study aims to enhance HAR by developing a lightweight and efficient machine learning model that balances accuracy, real-time performance, and affordability. Using acceleration data from a wearable inertial sensor, we extracted a novel feature set optimized for computational efficiency. The proposed model was evaluated on a benchmark dataset, achieving an accuracy of 98.9%, in classifying six essential daily activities: walking, walking upstairs, walking downstairs, laying, sitting, and standing. These results demonstrate the model’s potential for real-time health monitoring applications, offering a cost-effective and deployable solution for wearable-based activity recognition.
Co-Authors Duc-Tan Tran Nguyen Canh Minh Nguyen Ngoc Linh Nguyen Thi Thu Nguyen Viet Hung Pham Hong Thinh Phi Dinh Huynh Phuc Hau Nguyen Phung Cong Phi Khanh