Showing 456 results for Ha
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.
Sahbasadat Rajamand, Abdulhamid Zahedi,
Volume 22, Issue 3 (September 2026)
Abstract
Noise parameters in many target tracking projects are assumed as known factors which is a main challenge because of uncertainty in measurement and state-model noise. Thus, many papers are focused on the accurate estimation of noise statistics. This paper is concentrated on this subject where it is tried to present three simple efficient methods in this regard. Estimation using n-step prediction, applying Kalman filter covariance and using Gamma distribution for noise parameters are the main concepts of the three proposed methods. Simulation results show the efficiency of all methods compared to other methods in the literature where the Gamma-distribution-based method is the most efficient work among other suggested ones in term of estimation error.
Lambe Adesina, Ahmed Olaoye, Olalekan Ogunbiyi, Khalil D. Dalegan,
Volume 22, Issue 3 (September 2026)
Abstract
Analyzing power systems for network planning and operation requires a thorough understanding of network parameters to evaluate system performance. Effective monitoring and management depend on power flow analysis and contingency assessment. This paper examines an electric power system with a focus on these critical aspects to enhance planning and operational efficiency. A regional transmission network is used as a case study, with simulations conducted using Power System Simulation (PSS) software. The network’s performance under various N-1 contingency cases was evaluated, emphasizing voltage stability and line loading violations. Vulnerable buses were identified through separate voltage and line loading violation analyses. This study also examines the interaction between STATCOM and the network during N-1 contingencies, demonstrating its effectiveness in improving voltage stability and reducing overloads. To assess voltage collapse proximity and outage effects on reactive power margin, QV curves were plotted for the most affected buses. STATCOM was placed at each affected bus to determine optimal performance, mitigating N-1 contingency effects. By effectively compensating for reactive power, the network’s power handling capacity was enhanced. Consequently, applying STATCOM significantly improved voltage profiles and increased the power handling capacity of affected buses before and after contingency scenarios, ensuring a more stable and resilient transmission network.
Kausar Ahmed, Bibhor Regan Gomes, S.m. Jobair Hossain, Emran Khan Musa, Fuyad Hasan Bhoyan, Md Humaion Kabir Mehedi, Jia Uddin,
Volume 22, Issue 3 (September 2026)
Abstract
Fires in indoor spaces such as residential and office buildings pose significant threats to human lives and property, causing substantial damage each year. Early and accurate fire detection plays a critical role in mitigating these risks and ensuring timely responses. However, conventional methods such as smoke sensors, temperature indicators, and standalone computer vision models suffer from limitations like false alarms, delayed detection, and high hardware demands. To address these challenges, we propose a novel three-layer verification framework for indoor fire detection to reduce false alarms, integrating smoke sensors, computer vision, and temperature monitoring into a multi-modal validation framework. The process begins with smoke sensors detecting potential fire incidents. The custom-trained YOLOv11n computer vision model verifies the detection using predefined thresholds, allowing immediate response without waiting for temperature escalation. If the computer vision model does not confirm the fire, the system initiates a temperature check as a final validation layer. Experimental evaluation of our model demonstrates a significantly high precision of 0.979 and a recall of 0.971. This layered approach ensures comprehensive detection, balancing reliability and resource efficiency. Our proposed hybrid AI-physical systematic framework demonstrates significant potential in reducing false alarms, improving detection accuracy, and prioritizing methodological scalability over industrial hardware. It lays the foundation for more reliable and energy-efficient fire safety solutions in smart buildings and industrial safety applications.
Usman Masud, Abdul Razzaq, Faraz Akram, Ahmad Zeeshan,
Volume 22, Issue 3 (September 2026)
Abstract
Long-haul optical communication systems face challenges from nonlinear impairments, chromatic dispersion, and signal attenuation, which can degrade performance over long distances. To address these limitations and enhance transmission quality, this study introduces a 64-channel DenseWavelength Division Multiplexing (DWDM) system. This system integrates Raman Fiber Amplifiers (RFA) and Dispersion Compensating Fibers (DCF) and achieves significant signal improvements. Specifically, a 15% increase in Q-factor and a 30% reduction in Bit Error Rate (BER) are observed. At 600 km and 15 Gbps, the Q-factor rises from 5.9 to 6.5, and the BER falls from 6.1 × 10?7 to 2.3 × 10?7. Channel 64 demonstrates exceptional performance, reaching a peak Q-factor of 26.0374, exceeding all other channels. The efficacy of this hybrid RFA + DCF system is evident in mitigating nonlinear effects such as Self-Phase Modulation (SPM) and Four-Wave Mixing (FWM), and in improving Optical Signal-to-Noise Ratio (OSNR). These advancements pave the way for high-performance, long-distance optical communication, with potential for further optimization through Raman-EDFA hybrid amplification and channel spacing adjustments.
Elahe Parham, Mohamad Feshki, Alireza Fallahi, Hamid Soltanian-Zadeh,
Volume 22, Issue 3 (September 2026)
Abstract
The discovery of relationships between brain connectivity and human intelligence is of great interest. In this study, we identify structural connections correlated with human intelligence and investigate the predictability of intelligence from brain structural connectivity. The study uses data from 137 healthy subjects from the Human Connectome Project (HCP, 1200 Subjects Release). Structural connectivity was estimated using tractography derived from diffusion tensor imaging (DTI) data. A connectivity matrix was constructed using the mean fractional anisotropy (FA) of white-matter pathways between 116 regions defined by the AAL atlas. Global graph measures and correlation analysis were applied to identify connections relevant to predicting fluid intelligence (Gf) and crystallized intelligence (Gc). For prediction, three regression models of linear regression, support vector regression (SVR), and multi-layer perceptron (MLP) were employed. Most connections associated with Gf or Gc were located in the right hemisphere. Connections originating from prefrontal, right temporal, limbic, and right occipital regions were related to Gf, whereas connections originating from prefrontal, temporal, and left parietal regions were related to Gc. Among the models, SVR showed superior performance, achieving R² values of 0.45 and 0.52 for Gf and Gc, respectively. No significant relationships were observed between global graph measures and Gf or Gc scores. These findings demonstrate that DTI-based structural connectivity can be used to predict both fluid and crystallized intelligence, with fine-grained regional definitions enabling more specific connectivity patterns than in previous studies.
E Vijaya Babu, G Shanthi, Ayesha Thabassum Ara, K V Balaramakrishna, Madhu Nakirekanti, K Narsimha Reddy,
Volume 22, Issue 3 (September 2026)
Abstract
Memristors are a viable future semiconductor memory substitute because of its nanoscale size, quick switching, low power consumption, and CMOS compatibility. CMOS flip-flops face drawbacks like large size, high power use, and charge loss at smaller scales. However, memristors provide a novel approach to the construction of FFs that improves outcomes. In the previous work, the execution of a four-bit PIPO shift register design was demonstrated using a D flip-flop. D Flip-flops are designed with NAND Gates. In this paper, we will improve the performance of flip-flops by using memristors, followed by the performance of D flip-flops and PIPO shift register using the Power Gating Technique. As the Results Session displays the power usage of the NAND Gate. The power consumption of a D flip-flop using the memristor design is 6.182 µW, while using the power gating technique, the power usage of D flip-flop is 5.827 µW. For DFF Power reduced by 86.1%, Delay reduced by 47.1% and PDP improved by 99.86% compared to conventional design. The power consumption of a PIPO using the memristor design is 22.52 µW, while using the power gating technique, the power usage of PIPO is 21.28 µW. The power consumption of PIPO circuit is reduced by 98.3% compared with conventional design.
Maryam Moazedi, Mohammad Reza Mosavi, Diego Martín de Andrés,
Volume 22, Issue 3 (September 2026)
Abstract
The Receiver Autonomous Integrity Monitoring (RAIM) method uses additional information to detect and remove spoofing signals by analyzing pseudo-range measurements. Therefore, assuming that spoofing signals are errors for the valid signal, RAIM can be a practical method that does not impose expensive hardware to the receiver. Typically, RAIM operates under the assumption that simultaneous multi-satellite errors are highly unlikely. For example, GPS satellite errors occur no more than three times per year. Some enhanced RAIM methods have been proposed in recent years that employ additional measurements, such as Doppler shift measurements, time-differential carrier phase measurements, and so on. Since simultaneous multiple fake satellites are common in spoofing cases, basic RAIM cannot counter these types of signals, and for eliminating more than one spoofing or error signal requires additional information, such as measurements on other frequencies or satellite systems, which increases the complexity of execution. In this paper, an anti-spoofing method based on Advanced RAIM (ARAIM) has been proposed with a novel slope-based RAIM availability assessment method. Simulation results on several spoofing data sets indicate the definitive success of the proposed methods in detecting and mitigating spoofing error, with a detection success rate of over 79% using the statistical method and over 87% using the Kalman filter method.
Amir M. Zaker, Mohammad Bod, Hamid G. Bafghi,
Volume 22, Issue 4 (December 2026)
Abstract
This paper proposes a novel wideband circularly polarized L/S feed system for portable satellite communications. The feed system integrates a multi-stage septum polarizer, a sloped waveguide transition, and an axial choke conical horn to ensure uniform reflector illumination of a 3-meter reflector antenna. Unlike existing septum-based feed designs, the proposed structure achieves an ultra-wide 47% fractional bandwidth (1.6–2.6 GHz) with an axial ratio below 2 dB. It is explicitly optimized for use as a reflector feed rather than as a standalone antenna. A foldable multi-piece strut mechanism is introduced to enhance portability and facilitate field deployment. In addition, a convex plate at the reflector vertex is employed to reduce feed blockage and improve gain, particularly at higher frequencies. Simulation and measurement results are compared, demonstrating superior performance of the proposed antenna to existing solutions, including high isolation (>15 dB) between ports and sidelobe levels better than 26 dB. The combination of wideband circular polarization, reflector-level optimization, and mechanical portability distinguishes this work from prior septum-horn and portable reflector-antenna solutions.
