IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING

Noise Covariance Matrix Estimation in Target Tracking in Three Approaches: n-step Prediction, Kalman Gain Covariance and Gamma-distribution of Noise Statistics

Abdulhamid Zahedi

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.

Improving Voltage Stability using Static Synchronous Compensator: A Contingency Assessment of Nigeria Transmission Network

Lambe Adesina

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.

Reducing False Alarms in Fire Detection Systems with YOLOv11 and Multi-Sensor Validation

Jia Uddin

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.

Enhancing Signal Quality in Long-Haul Optical Networks: Techniques for Amplification, Dispersion, and Nonlinear Impairment

Ahmad Zeeshan

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.

Structural Connectivity of Brain Regions May Predict Human Intelligence

Hamid Soltanian-Zadeh

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.

Analysis of A Novel Quasi-Reflectionless Microstrip Bandpass Filter Using A New Simple Approach

Soolmaz Abbasalizadeh

This paper proposes a new one-cell single-port microstrip bandpass filter (BPF) named the main cell, which consists of a band-pass section connected with one band-stop section at the input port. Considering the main cell, this paper provides a new two-cell two-port BPF with quasi-reflectionless behavior at its input and output ports. This two-cell two-port BPF is constructed with three sections of a series bandpass filter at the center and two shunt-connected bandstop filters at the input and output ports. Moreover, this paper analyzes the BPF’s S-parameters and, to the best of the authors’ knowledge, derives its formulas in a closed form, for the first time. Furthermore, to the best of the authors’ knowledge, this paper presents a novel approach for determining the filter’s cut-off frequency by utilizing the derived S-parameters. This paper compares the calculated results with the simulated to evaluate the analysis. In addition, this paper proposes a three-cell two-port BPF to increase the out-of-band attenuation loss. Finally, this paper manufactures the two-cell and three-cell BPFs on an FR-4 substrate to evaluate the efficiency of the presented concepts. The comparisons between the electromagnetic simulation and the measurement results have good agreements with each other. The measurement results of the three-cell two-port BPF show a center frequency of 2.6 GHz, a bandwidth of 0.27 GHz, and a minimum in-band insertion loss of -3 dB. Also, the fabricated filter has an in-band return loss lower than -8 dB over the entire bandwidth and a stopband attenuation level better than 50 dB.

Power-Gated Memristor-Based Optimized PIPO Shift Register

G Shanthi

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.

An Anti-spoofing Algorithm using a Kalman Filter based ARAIM Algorithm

Maryam Moazedi

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.

An Improved Procedure for Analysis of Distribution Networks Under Direct and Indirect Lightning Discharges

Nabiollah Ramezani

This study investigates overvoltages transmitted through low-voltage (LV) networks, which pose a significant risk to sensitive electronic devices. High-frequency component models of the LV network are employed to analyze overvoltages propagating through LV distribution transformers. To efficiently assess these transients, the Monte Carlo method is applied, enabling a focused analysis within a constrained simulation domain while reducing computational time. Furthermore, a protective algorithm is proposed to safeguard LV networks and connected loads against lightning-induced overvoltages. The study also evaluates the influence of significant mitigation measures, including spark-gap-based protection and the installation of surge protective devices (SPDs), on overvoltage suppression in LV distribution systems. Finally, the effects of lightning strikes on the load-side lightning protection system (LPS) and the resulting induced overvoltages in the LV network are investigated. The proposed network and its components are simulated using both MATLAB and ATP-EMTP to ensure comprehensive analysis. In addition to the computational efficiency achieved through the enhanced Monte Carlo method, the proposed methodology offers a practical and effective approach for improving overvoltage protection in LV distribution networks.

Grid Restoration Optimization Under Dynamic Circuit Breaker Failures: A Structural Importance-Based Repair Prioritization Framework Modeling

Mahdi Arabsadegh

This article proposes an innovative framework for enhancing resilience in power grid restoration that integrates dynamic breaker failure modeling with structural topology analysis. Unlike conventional approaches focusing solely on breaker health metrics, our method introduces a novel Structural Importance Coefficient (SIC) quantifying each breaker’s criticality through graph-theoretic measures (betweenness/closeness centrality) and cascading failure impact. The hybrid probabilistic-physical failure model combines Weibull-Bayesian degradation analysis with environmental stressors (humidity, temperature) to estimate real-time malfunction probabilities. A hierarchical optimization algorithm then prioritizes repairs by jointly optimizing SIC and health status, achieving: (1) 28% faster critical load recovery, (2) 40% reduction in repair resource waste via strategic SIC-based allocation, and (3) adaptive microgrid formation under uncertainty. Validated on IEEE 39/118-bus systems, the framework demonstrates superior performance compared to Monte Carlo-based methods (e.g., 35% higher load restoration during storms) while requiring no historical data archives. Key innovations include the SIC metric for topology-aware decision-making and a two-stage optimization protocol balancing local breaker conditions with global network resilience. Practical implementation is highlighted through SCADA-compatible modules.

An Integrated Threat Model: Quantum Machine Learning Attacks on Satellite Communications and a Multi-Layered Defense Framework

Arash Kosari

Satellite communications are the invisible backbone of our connected world, supporting everything from daily internet access to critical military missions. Yet, beneath their importance lies a hidden vulnerability: the physical layer remains exposed to increasingly sophisticated cyber threats. In this paper, we explore how quantum technologies could be weaponized against these systems and how they might be defended. We present an integrated attack model that brings together Quantum Support Vector Machines (QSVM) for highly precise signal prediction and Quantum Random Number Generators (QRNG) for stealthy noise injection. Using realistic simulations on Qiskit, GNU Radio, and MATLAB, we show that such an attack can succeed 85% of the time, with only a 15% chance of being detected, while causing a 30% rise in bit errors. These results underline the disruptive potential of quantum-enhanced adversaries. To counter this, we propose a layered defense strategy combining post-quantum cryptography, machine learning–driven intrusion detection, adaptive signal processing, and hardware safeguards. Our findings not only reveal the scale of the challenge but also offer a roadmap toward securing future satellite networks in the quantum era.

Open EMS Based Design of a CSRR Bandpass Filter for 5G: An Accessible Simulation Approach

Gauravkumar Asari

Bandpass filters (BPFs) are critical components in 5G radio-frequency front-end systems, where wide bandwidth, low insertion loss, and compact size are simultaneously required. In this paper, a metamaterial-inspired wideband BPF based on Complementary Split Ring Resonators (CSRRs) loaded with a dumbbell-shaped Defected Ground Structure (DGS) is proposed for mid-band 5G applications centered at 4.7 GHz. Unlike conventional designs that rely on commercial electromagnetic solvers, the proposed filter is developed and analyzed using a fully open-source electromagnetic simulation framework based on Open EMS, enabling cost-effectiveness and design reproducibility. The design evolution from a single-ring CSRR to a triple-ring configuration is systematically presented to enhance magnetic coupling and bandwidth. The incorporation of a dumbbell-shaped DGS further modifies the ground current distribution, leading to improved selectivity and reduced insertion loss. The optimized filter achieves a fractional bandwidth of approximately 43%, a minimum insertion loss of 0.72 dB, and a return loss better than -35 dB using a low-cost FR-4 substrate. Comprehensive parametric analysis, metamaterial characterization, group delay response, and electromagnetic field distribution are provided to validate the proposed approach. The results demonstrate that the open-source Open EMS-based methodology can achieve performance comparable to commercial solvers, offering an accessible and reliable design pathway for next-generation microwave filter development.