Showing 443 results for Ha
Suhail Mahmoud Abdullah, Thamir Hassan Atyia,
Volume 21, Issue 4 (December 2025)
Abstract
Optimal control of DC motors remains a critical research area in modern control systems, given their wide industrial applications and the need for accurate performance under variable conditions. This paper explores the application of genetic algorithms (GAs) to optimize the control parameters of DC motors, particularly PID controllers, with the goal of improving the dynamic response and robustness of DC motor systems. Compared to traditional constraint-based tuning methods, GAs, inspired by natural selection and evolution, offer comprehensive search capabilities that significantly improve parameter optimization, providing better speed regulation, reduced overshoot, and minimal steady-state error. This review highlights the key challenges faced when using GAs. Comparative results from various studies demonstrate that GA-based controllers consistently outperform traditional tuning methods in terms of stability, efficiency, and adaptability. Key findings related to energy consumption and stability are highlighted. It is essential to analyze the system performance in terms of rise time (tr), settling time (ts), overshoot ratio (Mp%), and steady-state error (Ess). A proportional-integral-differential (PID) controller provides a stable response by tuning its parameters according to a specific methodology using a genetic algorithm. This paper concludes by emphasizing the potential of genetic generators as a powerful and flexible optimization tool for intelligent control of DC motors.
Omar S Abdulwahid, Saad G Muttlak, James Sexton, Michael J Kelly, Mohamed Missous,
Volume 21, Issue 4 (December 2025)
Abstract
An analysis of 6×6 µm2 GaAs/AlAs Asymmetric Spacer Layer Tunnel diode has been conducted to evaluate the DC and RF characteristics at different bias conditions. At zero voltage operation, the diode exhibited a measured curvature coefficient of 22 V-1, corresponding to a junction resistance of 27 kΩ. The measured and simulated S11 reflection coefficient of the integrated detector including the diode, matching circuit, and output capacitance achieved to be less than -10 dB at the desired frequency. The extracted low series resistance and junction capacitance of the tunnel diode resulted a high voltage sensitivity of 3650 V/W and low noise equivalent power of 5.5 pW/
at 11 GHz resonant frequency and -27 dBm input power. The developed detector model can be extended to implement RF detectors operating at frequencies reaching mm-wave regime applications. This is with consideration of the requirements for sub-micrometer scale mesa devices, eliminating the effects of associated parasitic elements and improved matching network performance.
Hamid Reza Sezavar, Saeed Hasanzadeh,
Volume 21, Issue 4 (December 2025)
Abstract
Marx generators that produce output pulses in the range of a few kilovolts (kV) with energies of a few millijoules (mJ) and rise times of a few nanoseconds (ns) have a variety of applications, including enhancing hydrogen production through electrolysis. In these generators, bipolar junction transistors (BJTs) operating in avalanche breakdown mode are employed as switches. This study explores the use of transistors specifically designed for avalanche breakdown to improve hydrogen generation efficiency from renewable energy sources. For this purpose, the FMMT415 transistor was implemented in the generator. The designed circuit was simulated with the transistors in avalanche breakdown mode, and the effects of various parameters on the output voltage were examined, particularly in the context of optimizing electrolysis performance. Based on the simulation results, the circuit was constructed and tested, and the differences in transistor parameters were evaluated. The simulation outcomes were then compared with the actual results. From these investigations, criteria were developed to determine the parameters that ensure suitable output voltage for Marx generator applications in hydrogen production. The optimal number of stages for the Marx generator was estimated based on the findings, highlighting its potential role in advancing sustainable hydrogen energy systems.
Mohammad Reza Eesazadeh, Zahra Nasiri-Gheidari,
Volume 21, Issue 4 (December 2025)
Abstract
This research focuses on electromagnetic position sensors, particularly synchros, which play a crucial role in the closed-loop control systems of permanent magnet synchronous machines (PMSMs). Compared to two-phase resolvers, three-phase synchros provide enhanced reliability by ensuring continued operation even in the event of an open-circuit fault. One of the key challenges in designing such sensors lies in selecting optimal windings and configurations while also developing efficient modeling techniques to minimize computational complexity. To address this issue, the study introduces a matrix-based method for designing wound rotor (WR) synchros. This approach allows for flexible configurations depending on the number of pole pairs and stator tooth counts. The proposed design methodology ensures adaptability and precision, making it a valuable tool for engineers working on electromagnetic sensor development. To validate the effectiveness of the proposed method, the Field Reconstruction Method (FRM) is employed, providing a fast and accurate modeling technique that can be implemented using MATLAB. Additionally, a comparative analysis is conducted with finite element analysis (FEA) to confirm the accuracy and reliability of the approach. Results demonstrate that the matrix-based method is an efficient and effective solution for optimizing WR synchro designs, significantly improving performance and computational efficiency.
Sudipta Chatterjee, Angshuman Majumdar, Arighna Basak, Amitesh Das, Vertika Rai,
Volume 22, Issue 1 (March 2026)
Abstract
This paper offers a comprehensive examination of smart robotic wheelchairs and their role in enhancing the mobility and independence of individuals with disabilities. Conventional wheelchairs often restrict users, leading to limited movement and accessibility. The emergence of smart robotic wheelchairs presents a promising solution to these issues. The study provides an overview of wheelchair technology, highlights challenges faced by individuals with disabilities, and assesses the benefits and drawbacks of smart robotic wheelchairs through a review of previous research. It delves into the features and functionalities of these wheelchairs, such as navigation and obstacle avoidance, autonomous and semi-autonomous modes, and customizable control options. Additionally, it analyses user experience, performance evaluation, and the impact on mobility and independence. The paper concludes by outlining future research directions and recommendations to further empower individuals with disabilities and enhance their quality of life.
Arun Pratap Singh Rathod, Pawan Kumar Mishra, Abhilasha Mishra,
Volume 22, Issue 1 (March 2026)
Abstract
In recent years, organic field effect transistors, also known as OFETs, have witnessed a substantial demand, mainly due to their expanding applications in the display and sensor industries, owing to simple fabrication techniques and cost-effective raw materials. But due to limited charge mobility, its applications are mostly focused on non-computing applications. Since OFETs are fundamental elements employed in an electronic circuit, the performance of the whole electronic device is correlated with its performance. The development of high performance OFET is particularly beneficial for establishing non-silicon-based chip manufacturing in developing countries worldwide. In an attempt to develop a high performance OTFT, double channel bottom gate organic field effect transistor (DCBG OFET) is proposed in this research article. DCBG OFET or OTFT is a single gate device comparable to a bottom gate bottom contact (BGBC) OTFT in structure, but it generates 4 times higher drain current in its conduction channel with identical material composition and structural dimensions compared to its analogous. A comprehensive comparative study has been presented here investigating performance parameters like transconductance, threshold voltage, subthreshold slope, linear and saturation mobility, etc., to determine the functional superiority of the DCBG OFET over other single gate OTFT structures like BGBC, top gate bottom contact (TGBC), and bottom gate top contact (BGTC) OTFTs. It has been observed that DCBG OTFT exhibits a four-fold improvement in the drain current with respect to conventional single gate OTFTs, and staggering 300% enhancements in parameters like transconductance, linear and saturation mobility are also observed in DCBG OFET over other OTFT architectures with matching material configuration and structural dimensions, operational under the identical voltage conditions.
Ilhem Boutana, Mohamed Rachid Mekideche,
Volume 22, Issue 1 (March 2026)
Abstract
Electromagnetic Tube Expansion (EMTE) is a high-velocity forming process that utilizes transient magnetic fields to plastically deform tubular workpieces without physical contact. The process requires the generation of large currents via a capacitor bank, producing intense magnetic pressures to achieve deformation. While EMTE offers significant advantages in precision and efficiency, a comprehensive understanding of the interplay between key working conditions and deformation mechanisms remains crucial for optimizing its performance. This paper presents a numerical investigation into the effects of critical working conditions on the electromagnetic tube expansion process. Using a coupled finite element model, the transient magnetic field and resultant tube deformation are analyzed under varying conditions. The results provide insights into the relationship between process parameters and deformation outcomes, highlighting the potential for optimizing EMTE systems for enhanced efficiency and uniformity. This study contributes to advancing the theoretical and practical understanding of EMTE, by offering guidance for the design of more effective forming strategies and equipment.
Manh-Hung Ha, Duc-Chinh Nguyen, Thai-Kim Dinh, Tran Tien-Tam, Do Tien Thanh , Oscal Tzyh-Chiang Chen,
Volume 22, Issue 1 (March 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.
Davood Maleki, Abolfazl Halvaei Niasar,
Volume 22, Issue 1 (March 2026)
Abstract
In electric propulsion systems for high-power applications, multi-phase Permanent Magnet Synchronous Motors (PMSMs) are highly advantageous due to their fast dynamic response and high reliability. This study investigates a twelve-phase PMSM with double stator windings, where each winding is powered by a single-phase H-bridge inverter. The control of both H-bridge inverters for each phase is managed by a dedicated microcontroller. Given the independence of the control systems (microcontrollers) and the absence of data exchange between them, the modeling is conducted in the 12-phase stationary reference frame. To address non-sinusoidal back-EMF phase voltages and mitigate torque ripple, a harmonic current injection method is independently applied to each phase. A model-free predictive current and speed controller (MFPCSC), based on an ultra-local model, is employed, replacing conventional PI or hysteresis current controllers. Additionally, extended state observers (ESOs) are designed to estimate uncertainties and parameter mismatches. Under fault conditions, a fault-tolerant control strategy is implemented, where the current angle of healthy windings is adjusted to suppress the second harmonic in the remaining healthy windings, thereby reducing torque ripple. The effectiveness of the proposed control methods is validated through simulations, both under normal operating conditions and various fault scenarios.
Mohammad Ali Razavi, Farid Tootoonchian, Zahra Nasiri Gheidari,
Volume 22, Issue 1 (March 2026)
Abstract
Synchros are electromagnetic sensors utilized to determine the angular position of a rotating shaft. This paper examines the impact of leakage flux from the Rotary Transformer (RT) on the induced voltages and the position detection accuracy of the Wound-Rotor (WR) synchro. Various methods are proposed to mitigate the negative effects of leakage flux from the RT. The leakage flux paths, which couple with the signal winding, are identified. Based on this analysis, the optimal distance between the sensor and the RT is calculated to minimize the adverse effects of leakage flux on the synchro's accuracy. Additionally, the RT structure is modified to reduce the leakage flux. Another effective approach involves the use of Electromagnetic Interference (EMI) shielding. In this context, a shield frame is designed for the RT, and the impact of different shield materials on reducing leakage flux is investigated. The results show that a copper-based shield significantly reduces the adverse effects of leakage flux and improves the sensor’s accuracy. To evaluate the effectiveness of the proposed methods, they are assessed through 3-D Time-Stepping Finite Element Analysis (3-D TSFEA) and experimental measurements on a prototype sensor. The experimental results show close agreement with the 3-D TSFEA, confirming the accuracy of the findings.
Hamid Ebrahimi, Hossein Torkaman, Alireza Sohrabzadeh, Hamid Javadi,
Volume 22, Issue 1 (March 2026)
Abstract
Mohammad Negintaji, Aghil Ghaheri, Ebrahim Afjei,
Volume 22, Issue 1 (March 2026)
Abstract
In the rapidly advancing domain of wireless power transfer systems, particularly for electric vehicle charging, the design of the magnetic coupler plays a crucial role in determining both system efficiency and practical implementation. Variations in coupler system designs lead to differences in self-inductance, mutual inductance, and AC resistance, directly impacting the energy transfer efficiency and power delivery capability of the system. This paper proposes a novel coil design for wireless power transfer systems, incorporating Double-DZ (DDZ) and Quadrature (Q) coils to improve lateral and yaw misalignment tolerance. The proposed design integrates the advantageous features of three structures—SDDP, DDQP and TTP—to introduce a novel configuration, DDZ-DDQZ, which enhances system stability and performance. By increasing misalignment tolerance, this method substantially enhances the robustness and real-world feasibility of wireless power transfer for electric vehicle charging.
Ali Esmaeilvandi, Mohammad Hamed Samimi, Amir Abbas Shayegani Akmal,
Volume 22, Issue 1 (March 2026)
Abstract
This paper introduces an improved multi-conductor transmission line (MTL) model for transformers' high-frequency transient and frequency response analysis, overcoming limitations in traditional models that fail to capture complex electromagnetic interactions during high-frequency events, such as lightning strikes and switching operations. The model accurately reflects real-world transformer behaviors under transient conditions by integrating particle swarm optimization (PSO) for efficient parameter estimation and incorporating frequency-dependent losses. The combined use of PSCAD and Python minimizes computational overhead, enabling high-fidelity simulations closely aligned with experimental transformer data. Validation against real transformer measurements demonstrates the model’s reliability in capturing high-frequency responses, essential for transformer diagnostics. This novel approach offers a practical tool for studying transformer frequency response analysis, which is an important tool in transformer diagnosis.
Phd Mohammad Abshari, Mansour Rafiee,
Volume 22, Issue 2 (June 2026)
Abstract
The present study aims to design, analyze, and simulate the synchronous reluctance motor (SynRM) based on the IEC90L frame and IE4 efficiency class. Initially, the permissible losses are calculated for the SynRM considering the given efficiency class. The SynRM is then designed using the calculated losses to generate the highest possible output power. In order to achieve optimal performance in terms of output power and power factor (PF), a parametric per-unit system is utilized for SynRM analysis, and the dimensions of various parts of the motor are determined based on design inputs (copper losses and magnetic loading). Subsequently, given this parametric model and the changing range of per-unit parameters, the characteristics of the available motors are thoroughly monitored with respect to output parameters, and the motor model is selected. To validate the analytical model, the finite element analysis (FEA) is conducted for the selected model, and the simulation results are compared with those of the analysis method and design inputs. Ultimately, to enhance overall motor performance, an optimization process was conducted, followed by a comprehensive evaluation of the optimized model to assess efficiency and torque improvements.
Duaa A. Kareem, Zaineb M. Alhakeem, Nawar Hayder Tawfeeq, Batool Dahham Al-Ali, Heba Hakim,
Volume 22, Issue 2 (June 2026)
Abstract
Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. According to this application, robust forecasting is required to obtain a signal identical to the original signal. This study proposes a forecasting technique that obtains a robust signal that can be used in different applications. A long short-term memory neural network (LSTM-NN) was used to predict future samples from present and past samples. An Electroencephalography (EEG) dataset was used to test this technique. Four channels were used as input examples, one of which was the predicted output. All four channel samples were fed into the four networks to predict the future samples. To decrease complexity, only one hidden layer is used for this purpose. The statistical results are promising for applications that require an almost perfectly predicted signal. The number of hidden cells is first very low (five cells only), which gives a Root Mean Square Error of less than 20, whereas when the number of hidden cells is increased to 100, the Root Mean Square Error (RMSE) is approximately 7.5 for all four channels.
Mina Baghani, Reza Bahri,
Volume 22, Issue 2 (June 2026)
Abstract
In this paper, the decoding order error of successive interference cancellation (SIC) of multicarrier nonorthogonal multiple access (NOMA) due to the random walk of the users and position estimation deviation is considered in resource allocation. This factor extremely degrades the performance of NOMA in terms of sum rate and outage probability. Therefore, two optimal power allocation strategies for users are derived that maximize the sum rate and minimize the outage probability. The simulation results show that by considering the decoding order error in resource allocation, better performance can be achieved compared to the previous power allocation algorithms without considering this fact, which are a well-known water filling algorithm and a power allocation that maximizes the rate with minimum rate constraint.
Zead Mohammed Yosif , Basil Shukr Mahmood, Saad Z. Alkhayat, Aws Hazim Saber ,
Volume 22, Issue 2 (June 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%.
Sowmya M, Sumi M, Harikrishnan A I,
Volume 22, Issue 2 (June 2026)
Abstract
This article presents the design and optimization of a Minkowski fractal slot-integrated antipodal Vivaldi antenna (MFS-AVA) for brain stroke detection. The antenna is proposed on a 65 × 65 × 1.6 mm³ FR-4 substrate and integrates a tapered slot radiator with a microstrip feed. Key parameters are optimized through parametric analysis. The exponential curve of the radiator arms and edge conductor is fine-tuned for improved bandwidth and impedance matching, while Minkowski fractal slots enhance the reflection coefficient, gain, and directivity. Simulated using CST Studio Suite 2016, the antenna attains an extensive bandwidth spanning from 1.23 GHz to 12 GHz, a maximum gain of 9 dBi, and a radiation efficiency of 87%. The radiation pattern exhibits a directional beam with minimal side lobes, making it suitable for focused microwave imaging. Compared to a conventional design, the MFS-AVA shows improved S11, VSWR, and surface current performance. Its effectiveness is validated using a four-layered tissue-mimicking cylindrical human head model, confirming adequate field penetration and compliance with safety standards. These results demonstrate the proposed antenna’s suitability for UWB microwave imaging in brain stroke detection.
Ayoub Khodaparast, Hassan Ghiti Sarand,
Volume 22, Issue 2 (June 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.
Balamanikandan A, Venkataramanaiah N, Sukanya M, Sudhakar Reddy N, Gomathy G, Venkatachalam K,
Volume 22, Issue 2 (June 2026)
Abstract
Physics-informed neural networks (PINNs) offer a promising route to bridge device-level simulations and compact circuit models. In this work, we present a hybrid modeling framework that integrates TCAD datasets with a baseline compact model and applies a PINN correction to capture stress-condition effects with high fidelity. The proposed approach achieves ≤ 2% route mean square error (RMSE) across more than 2,000 bias points, maintaining stable predictions under temperature (273–373 K) and radiation (0–100 krad) variations. Extracted Berkeley Short-channel IGFET Model (BSIM) parameters enable direct SPICE simulation, ensuring compatibility with standard circuit design workflows. For deployment, the trained PINN is exported as a quantized ONNX model, achieving sub-millisecond inference and ultra-low energy consumption (0.25 pJ/op) on a Cortex-M55 platform. This dual pathway supports both high-accuracy circuit simulation and real-time edge inference, making it suitable for embedded applications under constrained conditions. Comparative analysis with recent ANN-based models confirms that our physics-informed approach offers superior interpretability, SPICE readiness, and deployment efficiency. All datasets, code, and models are released to support reproducibility, benchmarking, and further research in compact modeling and edge-AI integration.
