Mr Ardalan Faezmehr, Mr Shahab Fatemi, Prof. Mohammad Reza Daliri ,
Volume 20, Issue 4 (11-2024)
Abstract
Manh-Hung Ha, Duc-Chinh Nguyen, Thai-Kim Dinh, Tran Tien-Tam, Do Tien Thanh , Oscal Tzyh-Chiang Chen,
Volume 22, Issue 1 (3-2026)
Abstract
This paper develops a robust and efficient method for the classification of Vietnamese Sign Language gestures. The study focuses on leveraging deep learning techniques, specifically a Graph Convolutional Network (GCN), to analyze hand skeletal points for gesture recognition. The Vietnamese Sign Language custom dataset (ViSL) of 33 characters and numbers, conducting experiments to validate the model's performance, and comparing it with existing architectures. The proposed approach integrates multiple streams of GCN, based on the lightweight MobileNet architecture. The custom dataset is preprocessed to extract key skeletal points using Mediapipe, forming the input for the multiple GCN. Experiments were conducted to evaluate the proposed model's accuracy, comparing its performance with traditional architectures such as VGG and ViT. The experimental results highlight the proposed model superior performance, achieving an accuracy of 99.94% test on the custom ViSL dataset, reach accuracy of 0.993% and 0.994% on American Sign Language (ASL) and ASL MINST dataset, respectivly. The multi-stream GCN approach significantly outperformed traditional architectures in terms of both accuracy and computational efficiency. This study demonstrates the effectiveness of using multi-stream GCNs based on MobileNet for ViSL recognition, showcasing their potential for real-world applications.
Ali Amini, Farshid Mahmouditabar, Nick Baker, Abolfazl Vahedi,
Volume 22, Issue 1 (3-2026)
Abstract
In recent years, due to the increase in electricity generation, the need for optimized Wound Rotor Synchronous Generators (WRSGs) has been felt more than ever. One of the important characteristics of a generator in a power system is its voltage harmonics. In addition to this, the amount of generated power and efficiency are also important. The goal of this research is multi-objective design using dampers, with improved number and shape. WRSGs have been selected as a case study. With the help of surrogate modeling and the PSO algorithm, which are more efficient and accurate than classical methods, the final design has been presented. In the end, the comparison of the initial and final designs shows the realization of all goals. Also, economic issues in terms of the selection of damper material have been investigated.
Zead Mohammed Yosif , Basil Shukr Mahmood, Saad Z. Alkhayat, Aws Hazim Saber ,
Volume 22, Issue 2 (3-2026)
Abstract
A mobile robot must be autonomous to avoid obstacles while traveling towards the target. Dynamic obstacle avoidance remains a significant challenge in mobile robotics. Although reactive navigation strategies have been applied to address this problem, relying on the single-stage module often results in limited efficiency and restricted overall performance. This paper proposes combining an adaptive neuro-fuzzy inference system (ANFIS) and a neural network (NN). The data for obstacle severity classification were used to train the Neural Network. The relative velocity and distance between the mobile robot and obstacles determine the zone. Zone 1 is dangerous, and Zone 5 is safe. This paper uses the ANFIS to avoid obstacles during the mobile robot's motion and to avoid collisions. Based on our empirical study, three essential features have been considered in this paper: the relative speed, distance, and angle between the robot and the obstacle as inputs to the obstacle avoidance system ANFIS. The output was a suggested steering angle and speed for the mobile robot. The simulation results for the tested cases show the capability of the proposed controller to avoid static and dynamic obstacles in a fully known environment. Our results show that the ANFIS System enhances the proposed controller's performance, reducing path length, processing time, and the number of iterations compared to state-of-the-art research papers. The proposed work demonstrated better performance in path length reduction (approximately 6%) and time taken reduction to reach the target, which is reduced by about 60%.
Ayoub Khodaparast, Hassan Ghiti Sarand,
Volume 22, Issue 2 (3-2026)
Abstract
Real-time control applications, crucial in robotics, industrial automation, and medical devices, demand precise and predictable timing for reliable operation. This paper presents an experimental investigation into the latency performance of various Linux kernels, including standard Linux, a low-latency kernel, Xenomai, and a real-time kernel patched with PREEMPT_RT. Our test setup utilizes a data acquisition card to measure the latency between sending and receiving a pulse signal through analog input-output channels, generated by a C++ code. This latency metric serves as an indicator of the responsiveness of the kernel and other control objects on a specific computer system. Our experiments were conducted under a wide range of conditions to comprehensively assess latency performance. This includes different versions of standard and real-time Linux kernels, varying numbers of CPU cores, program priority levels, data saving rates, a range of data acquisition cards, communication protocols, thread assignments to processor cores, and test durations. The results highlight the importance of long-term testing to accurately determine the maximum latency. Furthermore, the findings demonstrate significantly lower latency for the PREEMPT_RT patched kernel across various tests, indicating its suitability for demanding real-time control applications that require tight timing constraints.
