Signal Processing and Renewable Energy (SPRE)
SPRE as a peer-reviewed open access journal which publishes Original papers, Theory-based Papers, Review Papers, Case Study Reports, Developed Conference Papers, and Technology Reports, in all aspects of Electrical, Electronics, power, control, telecommunications, bio-electrical medical engineering, Computer, Artificial Intelligence and robotics. SPRE was a Print & Online from 2017-2021 and it is an online journal from 2022-now. Publication Process SPRE publishes open-access peer-reviewed papers. Editor assigns each submitted manuscript to anonymous reviewers to decide on the acceptance/revision/rejection of the paper. The Author is responsible for checking the galley proofs prior to publication. The review process may take approximately two to three months to be completed. If we cannot find a reviewer for the submitted papers within 3 months after submission, the authors have the right to inform us and withdraw their paper from our journals. Publication Frequency The Journal publishes quarterly in March, June, September, and December. Owner: Islamic Azad University Fully Open Access: The journal provides readers with immediate free access to all published content. Publication Fees: There is no publication fee associated with the SPRE.
Classification of Meditation EEG Signals Using Empirical Wavelet Transform and Atom Search Optimization-Based Feature Selection
Analysis of the electroencephalogram (EEG) is a significant technique for deciphering brain activity during meditation; nonetheless, the proper classification of EEG signals is challenging due to the existence of noise, high dimensionality, and overlapping components. In order to alleviate the above pitfalls, the current work presents a novel framework consisting of Empirical Wavelet Transform (EWT) for adaptive sub-band decomposition and Atom Search Optimization (ASO) for optimal selection of features. EEG signals were decomposed into five standard frequency bands using the application of wavelets from the EWT,and eight statistical features were extracted from each band. ASO was also utilized for the selection of the most discriminative features and the compression of dimensionality while preserving the important information. The extracted features underwent classification with various machine learning models consisting of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Random Forest. Experimental results validated that SVM in combination with the compression of features produced the greatest accuracy of classification (95%), higher than baseline methods lacking a selection of features and state-of-the-art methods tied in performance. The outcomes indicate that the above technique increases both the accuracy of classification and the speed of computation and is thus appropriate for practical applications such as brain interfaces and state monitoring during meditation. The innovation of the work consists of the combination of adaptive signal decomposition and optimal selection of features to advance the performance of classification according to EEG.
Copyright (c) 2026 Elham Simakani, Iman Ahanian, Mahdi Eslami, Amir Amirabadi (Author) https://creativecommons.org/licenses/by/4.0
Improved PDP of the Open-Loop Phase-FrequencyDetector to Reduce the Output Jitter of Phase-Locked Loop Circuits in CMOS Technology
A new phase-locked loop (PLL) with an improved open-loop phase-frequency detector block (PFD) with a dead zone close to zero and a compatible charge pump (CP) to reduce the power consumption and the dead zone and output jitter is proposed. Since in all phase-locked loop systems, phase-frequency detector and charge pump elements are designed and used separately, inherently, detecting the phase difference by the phase-frequency detector and applying it to the charge pump circuit requires time. This time creates a dead zone in the entire phase-locked loop system and causes a jitter in the phase-locked loop. This paper solves this challenge by combining PFD and CP blocks. This structure's proposed design and simulation results show that the overall phase-locked loop system has a very low output jitter due to the significant reduction of the dead zone and the integration of the phase-frequency detector blocks and the charge pump. The simulations carried out in 90 nm CMOS technology indicate also a reduction in power consumption to 85 microwatts, a dead zone close to zero, and an operating frequency of 4.2 GHz, phase noise is -123dBc/Dec and the results of which have been compared with previous works. Monte-Carlo simulation from post-layout simulation is carried out and the result is that the average power consumption ranged from a minimum of 61.4 microwatts to a maximum of 101.6 microwatts, and the delay and PDP also changed from 36.6ps to 59.1ps and 3.22 to 4.23, respectively.
Copyright (c) 2026 MohammadSadegh Zarbakhsh, Behzad Ghanavati, Pejman Shaabani, Sajad Tabatabaee Tabatabaee (Author) https://creativecommons.org/licenses/by/4.0
High-Accuracy and Low-Latency ECG-Based Heart Rate Detection Using High-Level Synthesis on Zynq-7000
Heart rate is one of the most critical vital signs for assessing an individual’s health. Therefore, researchers are trying to find a precise and fast method of calculating and monitoring the heart rate. One of the common methods for calculating the heart rate is using electrocardiogram signals (ECG). So far, many designs have been created for heart rate calculation using these signals, but most of these methods involve lengthy and complex pre-processing steps. Therefore, this study aims to employ a new simple structure for heart rate calculation using electrocardiogram signals, unlike the previous complex methods (wavelet transform, Tompkins, Pan). In addition, the proposed design was implemented on the Zynq series 7 chip by a high-level synthesis method to evaluate its performance. This design, besides simplicity, has a relatively high accuracy of about 99.45%.
Copyright (c) 2026 Maryam Sadat Jalali Shahri, Farzan Khatib, Seyyed Javad Seyyed Mahdavi, Alireza Heidari Bakavoli, Mahdi Kahrom (Author) https://creativecommons.org/licenses/by/4.0
Physics-Guided Temporal Transformer for CO2 Corrosion with SHAP Case study: Asaluyeh SPGC Complex, Iran
The structural integrity of the oil and gas industry is at high risk due to CO₂-induced corrosion; consequently, practical, explainable mechanisms are required to accurately predict this phenomenon. The available mechanistic models, like the de Waard–Milliams equation, though entrapping fundamental thermodynamics, do not withstand dynamic dosing and time-varying conditions. Although the conventional machine learning and deep learning models improve accuracy, though often lack physics consistency and interpretability. A Physics-Guided Temporal Transformer (PGTT), which integrates a Temporal Fusion Transformer backbone with a physics-informed loss (of de Waard–Milliams), and SHapley Additive exPlanations (SHAP) for feature attribution, is proposed here. By applying a comprehensive sequential dataset from 22 inhibitor dosing experiments (15,400 samples during 60+ hours), this PGTT achieves higher performance with an average <!-- [if gte msEquation 12]><m:oMath><m:r>MSE</m:r><m:r> </m:r><m:r>of</m:r><m:r> 0.067 ± 0.005</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 16.5pt; mso-text-raise: -16.5pt; mso-ansi-language: EN-GB; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> , <!-- [if gte msEquation 12]><m:oMath><m:r>MAE</m:r><m:r> </m:r><m:r>of</m:r><m:r> 0.145 ± 0.004</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 16.5pt; mso-text-raise: -16.5pt; mso-ansi-language: EN-GB; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> , and <!-- [if gte msEquation 12]><m:oMath><m:r>R</m:r><m:r>²</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 16.5pt; mso-text-raise: -16.5pt; mso-ansi-language: EN-GB; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> of <!-- [if gte msEquation 12]><m:oMath><m:r>0.997 ± 0.001</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 16.5pt; mso-text-raise: -16.5pt; mso-ansi-language: EN-GB; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> over five independent runs, and outperforms Random Forest <!-- [if gte msEquation 12]><m:oMath><m:r>(</m:r><m:r>MSE</m:r><m:r>=0.0838, </m:r><m:r>R</m:r><m:r>²=0.9968)</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 16.5pt; mso-text-raise: -16.5pt; mso-ansi-language: EN-GB; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> , <!-- [if gte msEquation 12]><m:oMath><m:r>SVR</m:r><m:r> (</m:r><m:r>MSE</m:r><m:r>=8.683, </m:r><m:r>R</m:r><m:r>²=0.6675)</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 16.5pt; mso-text-raise: -16.5pt; mso-ansi-language: EN-GB; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> , and MLP <!-- [if gte msEquation 12]><m:oMath><m:r>(</m:r><m:r>MSE</m:r><m:r>=0.1081, </m:r><m:r>R</m:r><m:r>²=0.9959)</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 16.5pt; mso-text-raise: -16.5pt; mso-ansi-language: EN-GB; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> baselines. According to the SHAP, Temperature (26.5%) and CO₂ pressure (17.7%) are identified as the dominant stimulants, consistent with corrosion science. This case study reveals a reduction in prediction error of less than 5%, supporting proactive inhibitor dosing and pipeline integrity management.
Copyright (c) 2026 Afshin Mohammadi, Ebrahim Akbari, Homayun Motameni, Faraein Aeini (Author) https://creativecommons.org/licenses/by/4.0
Integrated Optimization of Distribution System in The Presence of DGs and EVs: Reconfiguration, Charging Management, and Reactive Power Coordination Using MINLP
This study presents an integrated framework for optimizing distribution systems in the presence of distributed generations (DGs) and electric vehicles (EVs). The proposed model investigates several key scenarios: distribution system reconfiguration (DSR), optimal siting of DGs, simultaneous DSR and DGs siting, and finally, the optimization of active power (P) from the EVs charging process coordinated with the reactive power (Q) support from these vehicles. These elements are formulated within a mixed-integer nonlinear programming (MINLP) framework and are executed in two distinct steps for the scenario involving EVs integration. In this work, trigonometric functions and linearization approximations are eliminated from the power flow equations. They are replaced by a matrix representation based on the real and imaginary components of voltage and current. This approach reduces computational complexity and enables the attainment of a global optimal solution using the branch-and-bound algorithm in the GAMS environment. The model's objectives are threefold: minimizing active power loss (APL), reducing voltage deviation (VD), and improving the voltage profile. Simulation results on the standard 33-bus test system demonstrate that the proposed two-step framework achieves a reduction in APL of up to 96.21%, improves VD by 99.55%, and enhances the voltage profile. Consequently, the system performance indices are significantly enhanced compared to the conventional particle swarm optimization (PSO) method.
Copyright (c) 2025 Azadeh Barani, Majid Moazzami, Ghazanfar Shahgholian, Fariborz Haghighatdar-Fesharaki (Author) https://creativecommons.org/licenses/by/4.0
A Low-Power CNTFET-Based Optical Communication Receiver for 40 Gbps Applications
This paper presents an optical receiver system for 40 Gbps communication applications in 32 nm carbon nanotube field-effect transistor (CNTFET) technology. The architecture consists of a modified regulated cascode (MRGC)-based transimpedance amplifier (TIA), followed by limiting amplifiers (LAs). The TIA is designed using the <!-- [if gte msEquation 12]><m:oMath><m:f><m:fPr><m:type m:val="lin"/><m:ctrlPr></m:ctrlPr></m:fPr><m:num><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>g</m:r></m:e><m:sub><m:r>m</m:r></m:sub></m:sSub></m:num><m:den><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>I</m:r></m:e><m:sub><m:r>D</m:r></m:sub></m:sSub></m:den></m:f></m:oMath><![endif]--> methodology to optimize performance and power efficiency. Simulation results indicate that the modified TIA achieves a transimpedance gain of 56.6 dBΩ, a −3 dB bandwidth of 28 GHz, an input-referred noise current of 14 pA/√Hz, and a power consumption of only 177 µW. The TIA is fully transistor-based and occupies a compact 2240 nm × 1216 nm chip area. The LA stage provides a voltage gain of 10.3 dB, a bandwidth of −3 dB at 32 GHz, and power consumption of 382 µW, in an area of 11.14 µm². The optical receiver system demonstrates a transimpedance gain of 86.9 dBΩ, a −3 dB bandwidth of 34.2 GHz, total power consumption of 1387 µW, and occupies a chip area of 48.40 µm². All simulations are performed at a supply voltage of ±0.45 V, with the photodiode and load capacitors set to 10 fF. The gain, bandwidth, and input-referred noise characteristics of the TIA are derived analytically and discussed using the <!-- [if gte msEquation 12]><m:oMath><m:f><m:fPr><m:type m:val="lin"/><m:ctrlPr></m:ctrlPr></m:fPr><m:num><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>g</m:r></m:e><m:sub><m:r>m</m:r></m:sub></m:sSub></m:num><m:den><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>I</m:r></m:e><m:sub><m:r>D</m:r></m:sub></m:sSub></m:den></m:f></m:oMath><![endif]--> design methodology. Both analytical and simulation results confirm the suitability of the topology as a low-power optical receiver for next-generation communication systems.
Copyright (c) 2026 Nazanin Zamani, Mehdi Amoon, Zahra Alaie, Mahmoud Daneshvar Farzanegan (Author) https://creativecommons.org/licenses/by/4.0
PiGT-2F: Physics-Informed Graph Transformer for Robust Short-Term Distribution Forecasting
High-resolution node-level forecasts of net load and distributed photovoltaic (PV) injection are increasingly essential for modern distribution system operations and planning. Yet most feeders remain sparsely metered and subject to intermittent telemetry loss, degrading purely statistical forecasting pipelines. We present PiGT-2F, a physics-informed graph Transformer that (i) learns jointly across electrically coupled feeders, (ii) modulates spatial attention by branch admittance, and (iii) embeds soft penalties that encourage compliance with Kirchhoff’s Current Law and ampacity limits. Evaluated on a multi-feeder benchmark constructed from public feeder models, national load profiles, PV telemetry, and realistic random-plus-structured telemetry gaps, PiGT-2F lowers system-average nRMSE by 15–34% across 5- to 60-minute horizons and cuts physics-violation metrics by up to 80% relative to strong deep learning baselines. The archite­cture’s near-linear temporal attention cost enables multi-day history windows, and its physics regular-ization provides resilience when the grid is partially blind. Evaluation follows a feeder-level holdout to assess cross-topology generalization, and includes an anonymized real-telemetry micro-validation; code, configuration files, a benchmark generator, and a containerized reproduction package accompany the paper as described in the “Code and Data Availability” section.
Copyright (c) 2025 Mohammad Shahbazi, Hamid Tohidi, Majid Nojavan (Author) https://creativecommons.org/licenses/by/4.0
Data Augmentation for Attack Detection in IoT Networks Using Contrastive Learning
With the rapid expansion of the Internet of Things (IoT) in industrial, medical, and smart city applications, ensuring network security has become a fundamental challenge. One of the primary issues in Intrusion Detection Systems (IDS) is the imbalance in training data and the scarcity of rare attack data, both of which reduce the accuracy of learning models. In this study, a two-layer method based on data augmentation and improved attack classification was proposed to enhance the accuracy of detecting rare threats in IoT. In the first layer, a contrastive learning-based encoder was designed to generate new samples for the minority class using partial differential equations. This approach extracts the shared latent features of minority class data and reflects them with maximum possible accuracy while maintaining the highest level of distinction from other classes. In the second layer, a Bidirectional Long Short-Term Memory (BLSTM) model with a propagation-based classifier was employed to process the augmented data and improve attack detection accuracy. Evaluation results on the NSL-KDD dataset demonstrated that the proposed method outperformed existing models by achieving a 2.22% improvement in accuracy, a 0.68% improvement in precision, a 5.09% improvement in recall, a 2.9% improvement in F1-score, and a 12.34% reduction in false alarms. These findings indicated an increase in the reliability of the proposed approach for detecting rare attacks and enhancing the performance of intrusion detection systems in IoT networks.
Copyright (c) 2025 Hadi Mahdavinia, Mohammadreza Soltanaghaei, Mahdi Esmaeili (Author) https://creativecommons.org/licenses/by/4.0
A Comprehensive Approach to Intelligent Cardiac Patient Screening Based on Electrocardiography Using Deep Neural Networks
Cardiovascular diseases remain a leading cause of global mortality, making early detection in pre-hospital settings critical. This paper explores the need for developing intelligent screening tools for cardiac patients, leveraging electrocardiography and artificial intelligence to support emergency medical technicians and general practitioners in underserved areas. The objective is to design a mobile application that rapidly analyzes electrocardiogram (ECG) data to classify patients into three categories (normal, suspicious, critical) and, in necessary cases, identify the specific cardiac condition. The study utilized a dataset of 704 ECG samples collected from medical centers in Arak, Markazi Province, Iran. The proposed methodology encompasses three approaches: (1) manual extraction of nine key ECG features combined with a multilayer perceptron (MLP) neural network, (2) ECG image analysis using convolutional neural network (CNN), and (3) processing of raw 10-second ECG signals (5,000 data points) with a hybrid CNN and Bidirectional Long Short-Term Memory (BiLSTM) model. The models achieved accuracies of 95% for the manual feature extraction approach, 97% for the ECG image analysis, and 98% for the raw signal processing. The study’s innovations include the development of a high-accuracy multi-task model optimized for mobile execution and a user-friendly interface tailored for non-specialist users. By incorporating automated preprocessing, noise filtering, and actionable next-step recommendations, this tool enables rapid and accurate screening in emergency settings, reducing mortality and enhancing pre-hospital care.
Copyright (c) 2025 Majid Mehrad, Majid Nojavan, Dr. Sadigh Raissi, Dr. Mehrdad Javadi (Author) https://creativecommons.org/licenses/by/4.0
A Squarer Circuit's Topological Innovations for Signal Processing Transcendence
In this study, we embark on an exploration of the versatile applications of a CMOS squaring circuit, unveiling its potential as an invaluable asset in the realm of analog signal processing. The reconfigurable nature of this circuit bestows upon it a remarkable malleability, rendering it a fundamental analog cell, a veritable canvas upon which a myriad of building blocks can be meticulously crafted. These building blocks encompass a diverse array of functionalities, including multipliers, logarithmic modules, dividers, and exponential function generators, each a vital component in the symphony of analog signal manipulation. Moreover, this innovative circuit presents an ingenious solution for the implementation of analog logarithmic and exponential functions, characteristics that have long eluded seamless integration into CMOS technology. The proposed circuits have been meticulously simulated using the industry-standard HSPICE, with the BSIM3v3 model tailored for a 0.35μm MOS process. Furthermore, the resulting simulations have been validated through rigorous analysis and cross-referencing with MATLAB-derived data, ensuring the utmost accuracy and reliability of our findings.
Copyright (c) 2025 Ahmed Hussen Mohammed (Author) https://creativecommons.org/licenses/by/4.0
Infrared and Visible Image Fusion Using a Generative Adversarial Network Based on Fuzzy Logic Trained with the Harris Hawks Optimization Algorithm
Infrared and visible image fusion integrates complementary data from multi-sensor imagery into a single, information-rich composite, enhancing scene understanding across diverse applications. Despite recent advances in machine learning-driven fusion, challenges such as artifacts, blurred features, and poorly enhanced critical regions persist. We propose a novel Fusion Generative Adversarial Network (FGAN) that synergistically combines a fuzzy logic- based generator with a support vector machine (SVM)-powered discriminator. The generator leverages a Mamdani-type fuzzy logic system, optimized via the Harris Hawks Optimization (HHO) algorithm, targeting entropy, PSNR, and SSIM metrics to refine fusion quality. Concurrently, the discriminator employs the Frechet Inception Distance (FID) to robustly distinguish real and synthetic images. Evaluated on the TNO dataset using MATLAB, our FGAN delivers superior subjective visual quality and objective performance, outperforming state-of-the-art methods and setting a new benchmark for image fusion.
Harris Hawks Optimization (HHO) Algorithm
Copyright (c) 2025 Mahvash Zarimeidani, Amir Amirabadi, Nasrin Amiri, Iman Ahanian, Siavash Es'haghi (Author) https://creativecommons.org/licenses/by/4.0
Optimized Reversible Parity-Preserving Multiplier Circuits in Nanotechnology
Reversible logic, as one of the new technologies, plays a significant role in designing quantum computers due to the absence of energy loss, as well as using reversible circuits in low-power CMOS circuits, quantum calculations, and DNA computing. Multipliers are among the most critical circuits in computer calculations. This study proposes an optimized unsigned and signed reversible parity-preserving multiplier circuit. In addition, this study seeks to present a reversible block, as well as reversible signed and unsigned multiplier circuits using the presented block and the proposed design. The presented circuits reduce the quantum cost and are considered parity-preserving. In order to utilize the designs presented in variable sizes, two circuits in n×n sizes are provided. Further, a number of equations are presented to calculate the quantum costs, along with the number of constant inputs and garbage outputs in the proposed circuits.
Copyright (c) 2025 Meysam Mappar, Ehsan PourAliAkbar, Amin MehranZadeh, Mohammad Mosleh (Author) https://creativecommons.org/licenses/by-nd/4.0
An Interval Type 2 Fuzzy Extreme Learning Machine-Based Brain Emotional Learning
Over the past three decades, Brain Emotional Learning (BEL) models, inspired by the neural architecture of the mammalian limbic system, have been established as effective machine intelligence frameworks capable of integrating artificial neural networks and neuro-fuzzy systems. This study introduces a novel neuro-fuzzy prediction model, BELIT2FELM, which combines BEL with an Interval Type 2 Fuzzy Extreme Learning Machine (IT2FELM) for chaotic time series prediction. The proposed model employs an Online Interactive Recurrent Sequential learning method (OIRS-IT2FELM) to ensure rapid training with minimal parameter tuning and learning parameters. Specifically, the antecedent parameters of the type 2 membership functions in the BELIT2FELM model are initialized randomly, while the consequent type 1 parameters are derived analytically. The model's effectiveness is validated through experiments on two benchmark chaotic time series and a real-world application in electricity price forecasting for New York City. Results demonstrate that BELIT2FELM achieves lower root mean squared error (RMSE) compared to traditional BELT1FELM and other advanced models, particularly in the presence of noise.
Brain emotional learning, Interval type 2 fuzzy extreme learning machine, noisy time series prediction.
Copyright (c) 2025 Dr. Mehdi Golshan, Prof. Mohammad Teshnehlab, Dr. Arash Sharifi (Author) https://creativecommons.org/licenses/by-nd/4.0
Deep Learning Based Cancer Classification Using Selected Gene Expression Data
Cancer incidence has yearly surged by 23.6 million in 2019, with 10 million deaths from cancer reported in the same year, signifying a 26% and 21% upsurge in cases and deaths over the last decade. Gene expression (GE) analysis is a robust technique for the early diagnosis and classification of cancers by identifying unique molecular characteristics in diverse organs. Unlike other methods, identifying cancer-related genes from GE profiles results in more effective and tailored therapies. This technique, however, suffers from shortcomings such as huge amounts of GE data available, making data extraction and analysis a challenging task, a dearth of inclusive databases, and poor access to GE data extraction technology in some regions. This research proposes a deep learning (DL)-based minimum-redundancy-maximum-relevance (mRMR) technique for the precise identification of six cancer types using GE data, where DL-mRMR merges DL and feature selection (FS) to pick effective features and reduce dataset dimensions. Entrenched in FS, DL-mRMR downsizes the volume of input features reflecting similarities between cancers and within samples of a given tumor, and eventually balances the samples in the database, thereby diagnosing cancer types using a DL classifier. TCGA database simulations reveal a 99% accuracy (ACC) of DL-mRMR in differentiating breast, colorectal, kidney, and lung cancers based on GE data. In addition, due to the reduction in the size of the input sample vector, the complexity of the classifier algorithm and the number of samples required for its training are lower than similar approaches.
Copyright (c) 2025 Setare Tabasi, Iman Ahanian, Nasrin Amiri, Nader Jafarnia Dabanloo, Hamide Barghamadi (Author) https://creativecommons.org/licenses/by-nd/4.0
Robust Combined Control of Proton Exchange Membrane Fuel Cell Equipped by Boost Converter
Given the critical role of energy in modern life, this article introduces a novel approach to enhance and optimize the power output of PEM fuel cells. The study focuses on a PEM fuel cell system equipped with a boost converter and proposes a robust hybrid control strategy for the boost converter to maximize power extraction from the PEM fuel cell source. The proposed hybrid robust method combines two well-known techniques. The first is the reset control technique, which, despite its simplicity, excels in eliminating regulatory errors, improving transient and steady-state responses, and overcoming the limitations of linear controllers. The second is the sliding mode control (SMC) technique, specifically the Adaptive Super-Twisting Sliding Mode Control. This method ensures robustness and fast controller performance against uncertainties and disturbances. The proposed control strategy enhances the boost converter's efficiency, thereby increasing the power extracted from the PEM fuel cell. Additionally, it stabilizes the output voltage of the PEM fuel cell, achieving higher power output compared to alternative methods. Implementation results in the MATLAB environment, along with comparisons to the adaptive sliding mode-PI method, confirm the effectiveness and superiority of the proposed robust hybrid approach.
Adaptive super twisting sliding mode control
Copyright (c) 2026 Hassan Khosh Kholgh Sima, Alireza Sahab, Abdolreza Tawakli, Hussein Mahdi Nia (Author) https://creativecommons.org/licenses/by/4.0
A Hybrid Defense Framework for Multi-Area AGC Systems Integrating RBPF-UKF, Noise-Adaptive DRL, and Lightweight Blockchain with Post-Quantum Cryptography
Modern smart grids are facing increasing cyber threats, including False Data Injection (FDI), delays, and combined attacks, which can destabilize multi-area Automatic Generation Control (AGC) systems and lead to cascading blackouts. This study proposes a hybrid defense framework that integrates three advanced components: a Rao-Blackwellized Particle Filter with Unscented Kalman Filter (RBPF-UKF) for precise nonlinear state estimation under non-Gaussian noise, reducing frequency deviation to below 15 MHz within 2.5–4 seconds; a noise-adaptive Deep Reinforcement Learning (DRL) module utilizing a lightweight two-layer MLP, achieving 99% accuracy in detecting FDI, 97% for delays, and 98.5% for combined attacks; and a lightweight Practical Byzantine Fault Tolerance (PBFT) blockchain secured with CRYSTALS-Kyber/Dilithium post-quantum cryptography, ensuring data integrity with a 0.3-second consensus latency. Tested on a five-area IEEE 39-bus system, the framework surpasses traditional methods like LSTMs, DDPG, and Hyperledger Fabric, reducing estimation errors by 60% and meeting standards such as IEC 62443-4-2 and NIST SP 800-208. Its modular design allows seamless integration with legacy SCADA systems. Additionally, FPGA-based acceleration and energy-efficient variants of McEliece cryptography enhance scalability and sustainability in IoT and distributed grid environments. While effective under extreme conditions (0.5–4 seconds latency, 20% packet loss), scaling to ultra-large networks with over 100 nodes remains challenging, which encourages future research into alternative consensus mechanisms, such as DPoS and ABFT.
Noise-Adaptive Deep Reinforcement Learning
Copyright (c) 2025 Navid Rashtian, Alireza Yari, Nahid Ardalani, Payam Rabbanifar, Seyed Javad Mirabedini (Author) https://creativecommons.org/licenses/by-nd/4.0
Error diversity in Signal Direction of Arrival, impact on signal Localization and Mitigation Techniques
Extensive research has been conducted on the signal direction of arrival using a variety of techniques. One of the most important applications of signal processing is the determination of the direction of surrounding signals. A common application in signal direction of arrival is obtaining the location of the radiating source as a subsequent step in the processing chain. Signal localization methods are broadly categorized into two approaches: those relying on angle-of-arrival estimation and those independent of angle-of-arrival estimation. Within the diverse range of localization methods, especially those dependent on angle-of-arrival, signal direction is determined from two or more stations, ultimately enabling signal location. This paper provides a review of the various errors inherent in signal localization using both triangulation and time difference of arrival techniques and compares their performance. Utilizing localization models and understanding the errors within them, both with and without the need for angle-of-arrival estimation, and mastering the types of errors present, allows for the implementation of appropriate solutions to achieve an optimal estimate of the signal source location. This article aims to equip the user with the ability to identify signal locations with minimal error. Finally, by presenting the types of errors involved, the optimal scenario is presented to determine the signal location with the least error to improve target Localization.
Copyright (c) 2025 Mohammad Najafzadeh, Leila Ghabeli (Author) https://creativecommons.org/licenses/by-nd/4.0
Enhanced Video Super-Resolution via a Dual-Stage Framework with VDSR and HMRF-DCNN
Video super-resolution (VSR) is a critical task in video processing, aiming to enhance the resolution of consecutive frames while maintaining visual quality. This paper presents a comprehensive approach to video super-resolution, integrating deep learning networks with Hidden Markov Random Field (HMRF) techniques. The proposed method consists of two stages: In the first stage, the Very Deep Super-Resolution (VDSR) technique is employed to sharpen frame edges and enhance resolution, prioritizing perceptually significant details by focusing on luminance components. Additionally, random patching optimizes VDSR performance by enhancing relevant image details while mitigating computational burdens. In the second stage, a parallel network integrates the output of the first phase, HMRF-based inputs, and chronological inputs to capture spatial and temporal dependencies for final resolution enhancement. This multi-faceted approach ensures superior resolution and visual quality in the final output frames. Experimental evaluation demonstrates significant improvement over existing methods, with a peak signal-to-noise ratio (PSNR) of 37.0295 and a structural similarity index (SSIM) of 0.94683. The proposed method presents a promising solution for high-quality video super-resolution, addressing the complex interplay of resolution enhancement and visual fidelity in video processing.
Copyright (c) 2025 Mahnaz Mahdizadeh, Ali Akbar Khazaei, Seyyed Javad Seyyed Mahdavi Chabok, Farzan Khatib (Author) https://creativecommons.org/licenses/by-nd/4.0
A 12 bit Non-Uniform Sampling SAR ADC Using Switchable Sampling Frequency
In this study, a frequency-division based successive approximation register analog-to-digital converter (SAR ADC) with two tone input signals and 12-bit resolution is developed in 0.18μm CMOS technology. The sampling frequency is 24 KHz. The base of this concept is non-uniform sampling achieved by phase/frequency detectors, frequency divider blocks, and up/down thermometer counter. In this design, the power consumption of the data converter is significantly lowered without sacrificing the linearity of the system by decreasing the sample frequency when the input signal will be slow down, such as biological signals that do not change frequently. At a 1.8 V supply, a factor of merit (FOM) of fJ/conversion 6.95 is attained.
Analog-to-digital converter (ADC), Successive approximation register (SAR), non-uniform sampling, reduced frequency, and low power
Copyright (c) 2025 Bahareh Shojaeirad, Amir Amirabadi, Iman Ahanian (Author) https://creativecommons.org/licenses/by-nd/4.0
A density functional theory study of electronic and optical properties of armchair and zigzag silicon carbide nanosheets
Using the density functional theory (DFT) based on first principles calculations, the two-dimensional (2D) nanostructure of silicon carbide (SiC) is investigated in this article. The band structure, density of states, and optical properties of 2D armchair (n,n) and zigzag (n,0) nanostructures (n=3,4,5) are calculated. Calculations show that the introduced nanostructures have a direct energy gap of 2.82 electron volts (eV), a valence band edge of nearly -1.38 eV, and a conduction band edge of almost 1.44 eV. Also, the calculations show that in armchair silicon carbide nanosheets (ASiCNSs) (3,3) at the point of 3.55 electron volts, the absorption coefficient is 432466cm-1, which is the highest absorption coefficient among all studied structures in this paper.
Copyright (c) 2025 ghobad Mohammad karimi, Mohammad azim Karami, Hassan Ghalami Bavil Olyaee, Javad Karamdel (Author) https://creativecommons.org/licenses/by-nd/4.0
An Optimal Method for Fog Resource Allocation in the New Generation of Smart Grid
The evolution of smart grids, driven by renewable energy integration, advanced metering infrastructure, and the proliferation of the Internet of Things (IoT), requires low-latency, scalable computing paradigms. Traditional cloud computing struggles with latency (200–500ms) and bandwidth limitations, making fog computing a promising alternative for real-time applications like demand response 200ms and distributed energy resource management 500ms. This study proposes a semi-decentralized fog computing framework that leverages the Monte Carlo Tree Search algorithm for optimized task scheduling across heterogeneous fog nodes (3000–9000 MIPS, 512 MB–2 GB memory). The framework integrates lightweight security (AES-128, OAuth 2.0, differential privacy with ε = 0.1), robust fault tolerance (97% task completion under 20% node failure), and Software-Defined Networking for dynamic orchestration. Evaluated using iFogSim and scenarios inspired by real-world environments e.g., California microgrids, the framework achieves a 5% higher task acceptance rate (AR: 0.80–0.86 vs. 0.32 for SPA), doubles delay utility (DU: 0.71–0.73 vs. 0.26), and improves the objective function (OBJ) by 50% (0.72–0.86 vs. 0.26–0.32) compared to benchmarks such as the Service Provider Algorithm (SPA) and Reinforcement Learning (RL). Energy consumption is reduced by 12% (0.48 watts/task), and deployment costs drop by 20%, $450/node, ensuring scalability and resilience in dynamic smart grid environments.
A New Approach for Modeling Energy Information and Investigation of the Energy Consumption of Furnaces with Various Steel Grades
Accurate prediction of furnace energy consumption in the steel industry by considering different grades has received less attention, despite its importance in energy optimization. In this study, the modeling of steel furnace energy consumption information flow using an artificial neural network (ANN) was investigated for four steel-grade furnaces (A283, KA37, S235, S355). Experimental data, including furnace volume, heating time, average slab temperature, average furnace temperature, and fuel energy consumption, were collected from an industrial unit. Independent variables (furnace volume, heating time, slab and furnace temperatures) and dependent variable (fuel energy consumption) were modeled using a feedforward multilayer neural network (MLP) and presented as a function. The results showed that the performance of the ANN was poor for regression coefficients (R) less than 0.5, acceptable in the range of 0.5 to 0.7, and very accurate for R > 0.8. The agreement between the numerical and experimental results confirms the validity of this method and demonstrates its application in optimizing fuel consumption and reducing waste.
Copyright (c) 2025 Mohammad Parvaneh, Farivar Fazelpour, Ahmad Khoshgard (Author) https://creativecommons.org/licenses/by-nd/4.0
A Tunable Plasmonic Graphene-Based Optical Gate for Advanced Photonic Applications
This paper presents the design and analysis of a plasmonic graphene-based optical gate with a tunable wavelength selection mechanism, targeting applications in advanced photonic integrated circuits. Graphene, known for its exceptional electrical, thermal, and optical properties, is utilized to develop a compact and efficient optical structure. The proposed mechanism employs thin-layer graphene, dielectric substrates, and an air cavity to achieve tunable resonance at specific wavelengths. Finite-difference time-domain (FDTD) simulations reveal two primary resonance wavelengths at 11000 nm and 7300 nm, demonstrating high transmission efficiency and quality factors of 365 and 400, respectively. The tunability of the mechanism is explored by varying the chemical potential of graphene and the refractive index of the dielectric medium, allowing precise control over the resonance wavelengths. Building on this mechanism, we design a plasmonic graphene-based AND logic gate, which is analyzed for different input states to validate its logical operation. This study underscores the potential of plasmonic graphene-based devices for high-performance, miniaturized photonic systems, with implications for next-generation optical computing and communication technologies.
Copyright (c) 2025 Mohammad Mohsen Khodayari, Siavash Es'haghi (Author) https://creativecommons.org/licenses/by-nd/4.0
Stock Price Prediction using Multi-Faceted Information based on Deep Recurrent Neural Networks
Accurate prediction of stock market trends is crucial for informed investment decisions and effective portfolio management, ultimately leading to enhanced wealth creation and risk mitigation. This study proposes a novel approach for predicting stock prices in the stock market by integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, using sentiment analysis of social network data and candlestick data (price). The proposed methodology consists of two primary components: sentiment analysis of social networks and candlestick data. By amalgamating candlestick data with insights gleaned from Twitter, this approach facilitates a more detailed and accurate examination of market trends and patterns, ultimately leading to more effective stock price predictions. Additionally, a Random Forest algorithm is used to classify tweets as either positive or negative, allowing for a more subtle and informed assessment of market sentiment. This study uses CNN and LSTM networks to predict stock prices. The CNN extracts short-term features, while the LSTM models long-term dependencies. The integration of both networks enables a more comprehensive analysis of market trends and patterns, leading to more accurate stock price predictions.
Stock Price Prediction, Deep Learning, Sentiment Analysis, Long Short-Term Memory, Convolutional Neural Network
Copyright (c) 2025 Lida Shahbandari, Elahe Moradi, Mohammad Manthouri (Author) https://creativecommons.org/licenses/by-nd/4.0
Actuator Fault Event-Triggered Compensation for Multiagent Fractional-Order Nonlinear Systems
This paper proposes a novel distributed adaptive dynamic event-triggered control (DADETC) scheme for uncertainties multi-agent fractional-order nonlinear systems affected by actuator faults. The proposed controller can compensate for a wide range of actuator failures without the necessity for explicit fault detection. To address the problem of the unnecessary waste of communication resources, a distributed dynamic event-triggered control (DETC) is designed. In the design process, it is assumed that only the information of the first state of the followers is available. A state observer is used to measure non-measurable states. Neural networks are used to approximate uncertainties. Then, an adaptive fault strategy is applied for compensate the loss of effectiveness and stuck actuator faults. A detailed analytical proof is presented in the following to show that the proposed strategy successfully avoids the Zeno phenomenon. Finally, the Lyapunov fractional-order stability method is employed to guarantee the boundedness of all closed-loop signals and the convergence of the tracking error to a small neighborhood of the origin.
Copyright (c) 2025 Fatemeh Gholami, Mahnaz Hashemi, Ghazanfar Shahgholian (Author) https://creativecommons.org/licenses/by-nd/4.0
Designing a fuzzy controller by optimizing the particle swarm method to track the maximum power point in solar systems
While the solar system offers considerable benefits, it also presents certain drawbacks, including a substantial initial investment and relatively low efficiency. Consequently, this article aims to develop a controller utilizing an intelligent approach grounded in fuzzy logic. We will compare this method with various alternatives, including chaos and observation techniques, the incremental guidance method, the relative extremum extraction method, the guidance algorithm method, the P&O perturbation and observation method, and the sliding mode control method featuring two DISMCO integrals. The proposed fuzzy logic controller, enhanced by particle swarm optimization (PSO), seeks to optimize the maximum power point in solar cells. One of the challenges associated with the fuzzy method is that the parameters of the membership function are determined through a trial-and-error approach. To address this issue, the particle swarm optimization algorithm is employed to establish the parameters of the membership function in fuzzy logic. The effectiveness of the proposed method has been validated through comparisons with computer simulations conducted using MATLAB/Simulink software.
Copyright (c) -1 Masoud Kadkhoda, Ahmad Moradi, Davoud Rahmati (Author) https://creativecommons.org/licenses/by-nd/4.0
Detection of ADHD from Electroencephalogram Using Independent Component Analysis
Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders affecting children. Delayed diagnosis and treatment of ADHD might result in poor school performance. In this paper, an intelligent system is proposed for detecting ADHD from electroencephalograms. The system first preprocesses the electroencephalograms to remove the powerline and blinking interferences. Then, it applies independent component analysis to separate the signals of the sources that generate the preprocessed electroencephalograms. Later, 3 features of Shannon entropy, kurtosis, and skewness are extracted from the independent components. A total of 57 features are obtained for 19 electroencephalogram channels. Finally, two classifiers (i.e., the k-nearest neighbors with k=3, and an artificial neural network) were used to detect ADHD from the extracted features. We used a public database containing signals from 61 children with ADHD and 60 healthy children to evaluate the performance of the proposed system. The accuracy, sensitivity, and specificity of the artificial neural network were 99.92%, 99.89%, and 99.95%, respectively, while these metrics were all 100% for the k nearest neighbors. The results imply that the features extracted from independent components of electroencephalograms are more powerful than other linear/nonlinear features directly extracted from the signals.
Attention deficit hyperactivity disorder
ADHD, electroencephalogram, independent component analysis, signal processing, feature ex-traction, artificial neural network, k-nearest neighbors.
Copyright (c) 2025 Fereshteh Ghanavati, Mohammad Adeli (Author) https://creativecommons.org/licenses/by-nd/4.0
A Fuzzy-based Reset Control Technique for Enhanced Multi-Agent Microgrid Performance
Microgrid systems play a critical role in energy management and the development of diverse communities. However, these systems' islanded setup and operation within a multi-agent structure present significant challenges for secondary frequency and voltage control. This paper proposes a robust and intelligent hybrid approach for frequency control in multi-agent islanded microgrid systems to address these challenges. The proposed control technique combines robust reset control with a fuzzy intelligent approach. The reset control technique effectively eliminates frequency tracking errors and, due to its integral structure, overcomes the limitations of traditional linear controllers. The fuzzy intelligent technique is employed to dynamically adjust and tune the coefficients of the reset controller. The integration of these two approaches ensures effective frequency control in multi-agent islanded microgrids, even under the presence of disturbances and uncertainties caused by renewable energy sources and variable loads. The performance of the proposed controller is evaluated through simulations in the MATLAB environment under various scenarios. Comparative analyses with PI and PID controllers demonstrate that the proposed controller achieves faster frequency regulation and superior performance in overcoming adversities compared to conventional methods.
Copyright (c) 2025 Shahram Fallah Faal , Alireza Sahab, Behnam Alizadeh (Author) https://creativecommons.org/licenses/by-nd/4.0
Non Blind Image Restoration Using Hidden Markov Modeling and Minimum Entropy Approach
In this paper a new method based on HMM (Hidden Markov Models) is presented for image restoration. We assume the image is treated by a finite impulse response (FIR) channel where the image is modelled as a Markov process. The superior property of this new method is the detection of the most likely gray level for each pixel based on the probabilities defined for both the noisy blurred observations and the original image. We also propose a new method to find the more structured region of the image based on the minimum entropy approach. Performance of the proposed algorithm is illustrated through simulations employing images blurred with different point spread functions. Comparison between the introduced method and the other proposed methods shows the superiority of HMM method specially for large spreading blurs.
Copyright (c) 2025 Leila Ghabeli, Hamidreza Amindavar (Author) https://creativecommons.org/licenses/by-nd/4.0
Quantum cascade laser modelling in the terahertz range and investigating the effect of temperature on its characteristics
Quantum cascade lasers (QCLs) provide the most intriguing design for quantum engineering and show how artificial materials with tailored properties can be produced through quantum design. Their inherent quantum nature conceptually affects their core concepts. QCLs actually exhibit intrinsic linewidths that approach the quantum limit, and are locked into their own states. Minimized metrology also works in the boundary frequency domains as far. In this paper, the effect of temperature on the characteristics of GaN/AlGaN and GaAs/AlGaAs quantum cascade lasers in the terahertz range is done based on rate equations. The temperature dependence of the phonon sputtering rate, changes in line width and lifetime of thermoion emission have been investigated, and also the effect of temperature on the photon lifetime in two materials, GaAs and GaN, has also been investigated and compared, and the effect of temperature on the output photon density, power and gain The unsaturated mode and its dependence on temperature show that GaN material can work at high temperatures.
Copyright (c) 2025 Vahed Servati Khajeh, Ali Naderi Saatlo (Author) https://creativecommons.org/licenses/by-nd/4.0