International Journal of Electrical and Electronic Engineering & Telecommunications
IJEETC invites original, previously unpublished, research, survey and tutorial papers, plus case studies and short research notes, on both applied and theoretical aspects of electrical and electronic engineering & telecommunications.
Hybrid CNN-LSTM Architecture with MFCC-Based Class-Balanced Augmentation for Arabic Speech Emotion Recognition
Arabic speech emotion recognition (SER) is also associated with serious challenges, including the variety of dialects, excessive unbalanced classes, and the lack of sufficient data. In order to resolve these concerns, the paper provides a comprehensive methodology, which integrates hybrid CNN-LSTM architecture with strategic data augmentation. Our method is evaluated with the help of two Arabic emotional speech data sets: EYASE (Eastern Youngsters Arabic Speech Emotions) which is the independent validation set and BAVED (Basic Arabic Vocal Emotions Dataset) which is the actual training data. Class-balanced augmentation techniques such as pitch shifting, time stretching, and noise injection are used to enhance the grotesquely unbalanced BAVED dataset with approximately 1,600 samples of each emotion class. Our hybrid architecture integrates both the bidirectional LSTM networks and attention mechanism of modelling a temporal sequence, and the convolutional neural network of extracting spatial features of MFCC representations. The experimental findings demonstrate that the performance of the POL2 is increased substantially, as the general accuracy on BAVED increases to 97.23% as compared to baseline (89%). High arousal emotion recognition is mainly enhanced with almost random performance (50% F1-score) to impeccable classification (100% F1-score). Systematic decomposition according to which architectural sophistication contributes 26 percent of the total performance gains and data augmentation contribute approximately 74 percent to the same. Cross-dataset testing of EYASE indicates that the generalization of EYASE is high across different speakers and recording conditions and that this accuracy is 87%. The findings demonstrate that balanced augmentation of data quality has a greater impact on performance than architectural complexity alone, providing useful guidance for the development of Arabic emotion recognition systems in environments with limited resources.
Speech Emotion Recognition, Arabic Language, CNN-LSTM, Attention Mechanism,
Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
Analysis of FIR Window Length and Phase Lead Effects on Repetitive Control Performance for Hip and Knee Joint Trajectory Tracking
This research examines a repetitive-control plug-in for a PID controller used in a two-degree-of-freedom lower-limb model involving the hip and knee joints. All experiments were performed in MATLAB using a periodic reference trajectory and discrete-time simulation of the robotic dynamics. The plug-in uses a finite-impulse-response time frame and a ring buffer to recycle prior control efforts, while a lead term adjusts the phase. Performance was evaluated using the root mean square tracking error during iterations and at steady state. An exhaustive examination of lead compensation values (rcLead 0, 2, 4, and 6) and time window lengths (winLen 3, 5, 7, and an additional steady-state case at winLen 9) was conducted. The controller rapidly reduced errors across several scenarios without observed divergence in simulation. A superior configuration was identified wherein rcLead 2 combined with winLen 5 yielded the lowest steady-state error for both joints, attaining 0.014510 degrees at the hip and 0.004741 degrees at the knee, while also ensuring the most rapid practical convergence. Elevated lead values resulted in increased residual error, while windows that were excessively short or long either amplified noise or caused response delays. The findings indicate that a moderate lead time combined with an intermediate window length provides an efficient and adaptable framework for periodic tracking. Future endeavors will concentrate on validating the methodology on hardware, broadening the analysis to encompass a more extensive array of motion patterns, and executing automated modifications to essential parameters.
Assoc. Prof. Dr. Phichitphon Chotikunnan
A Confidence-Weighted Memory-Augmented Multimodal Voice Agent for Real-Time Emotion-Aware Interaction
Conversational emotion-aware systems are critical in the development of human-centric artificial intelligence. Single-modality systems often fail to identify acoustic patterns and text meaning associated with emotional expression. The study proposes an emotion-aware conversational framework that integrates offline-trained speech emotion recognition with a multimodal chatbot pipeline. In the offline phase, three self-supervised speech models —Wav2Vec2, HuBERT, and Whisper—were fine-tuned on the RAVDESS emotional speech dataset for multi-class emotion classification. Among them, the fine-tuned Whisper model achieved superior performance and was selected as the primary acoustic emotion encoder. The trained Whisper model was then deployed within a real-time chatbot architecture, where speech input is transcribed using automatic speech recognition and combined with acoustic emotion predictions. A confidence-weighted multimodal fusion strategy integrates audio and text-based emotion cues, followed by an Adaptive Confidence-Weighted Temporal Memory (ACWTM) module to model short-term emotional continuity. The model attained 87.3 percent accuracy and outperforms the unimodal baselines. It demonstrates enhanced robustness and conversational coherence with stability, offering a scalable framework for real-time empathetic human-AI interaction.
Enhancing Cybersecurity in Internet of Things Networks Using Advanced Deep Learning Techniques for Intrusion Detection
The Internet of Things (IoT) is widely adopted in an increasing number of industries, such as healthcare, industrial automation, and smart infrastructure. They also reveal some critical security vulnerabilities in the systems that use IoT. These IoT networks are heterogeneous, resource-constricted, and faced with dynamic attack vectors, increasingly being targeted by threats such as distributed denial-of-service (DDoS) attacks, malware propagation, and data breaches. Following a static rule-based approach, traditional Intrusion Detection System (IDS) fail to scale appropriately with the diverse attack patterns presented to IoT ecosystems, and existing computational limitations. In this paper, we present a deep-learning-based IDS Framework that integrates Convolutional Neural Networks (CNNs) for spatial anomaly detection, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and a hybrid Attention-CNN-LSTM architecture for the holistic spatial-temporal dependency analysis. The framework is evaluated in benchmark datasets and achieves state-of-the-art performance attaining 98.9% accuracy, 98.3% F1-score, and 0.994 AUC-ROC on the IoT-23 dataset. It equally demonstrates tremendous effectiveness against stealthy attack detection, with low-and-slow DDoS (99.1%) and botnet (98.4%) attacks; additionally, the framework totally adapts to the heterogeneous environment of the IoT. The hybrid architecture uses significant computational resources (9.7 hours training time, 8.4 GB GPU memory); however, it manages detection accuracy against resource usage, thereby making it a good candidate for real-time implementation.
Internet of Things Security, Intrusion Detection System, Deep Learning IDS
Forecasting Generation in Motor Generators Using Neural Networks
optimization, energy planning, generation, fuel consumption, model validation
Lightweight Authenticated Encryption Scheme for Secure RSSI-Based Indoor Localization
Indoor localization is a key application of Wireless Sensor Networks (WSNs) for tracking objects, where secure communication is essential to protect sensitive location data. This paper proposes an authenticated encryption scheme optimized for lightweight WSN devices with low processing time. The scheme integrates multiple cryptographic algorithms: ASCON-AEAD for authenticated encryption, X25519 for key exchange, ED25519 for digital signatures, SHA-256 for data integrity, and timestamp-based replay protection. Raspberry Pi devices serve as anchor (AN) and unknown nodes (UN), where AN transmit encrypted data containing estimated position parameters, including RSSI and pathloss exponent (PLE) values. UN then decrypt data from the three nearest AN. Experimental results show the system requires only 17.82 ms processing time at AN and 6.48 ms at UN, with Wi-Fi FTP transmission averaging 3.12 ms per packet. This approach provides an efficient, privacy-aware, and low-power localization environment, outperforming traditional methods such as RSA, AES, and MD5 combination.
On the Design of Differential Difference Transconductance Amplifier (DDTA) and Its Applications
This paper presents the design and implementation of a novel active building block termed the differential difference transconductance amplifier (DDTA). The proposed DDTA integrates the characteristics of the differential difference amplifier (DDA) and the transconductance amplifier (TA) to enhance flexibility in analog signal processing. It comprises three TA stages—two forming a DDA structure and one functioning as a TA—allowing independent electronic tuning of transconductance gain (gm). The DDTA features a simple architecture, wide input capability, extremely high input impedance, and high output resistance at both intermediate and output current terminals, enabling accurate current-mode signal processing with minimal loading effects. To demonstrate its versatility, a voltage-mode universal biquadratic filter and dual-mode quadrature oscillator, both based on a single DDTA and utilizing only two capacitors, have been designed and simulated. PSPICE simulation results confirm wide linear voltage-to-current conversion, wideband transconductance performance, and robust impedance characteristics, all in excellent agreement with theoretical predictions. These features validate the effectiveness and practicability of the proposed approach for modern analog circuit designs.
Differential Difference Transconductance Amplifier (DDTA)
Deep Learning-Based Channel State Estimation Approach for OFDM System
This paper presents a channel estimation method that employs pilot symbols, which contain no data, and machine learning (ML) to estimate channel coefficients at data symbol locations. In the proposed method, pilot symbols are inserted among data symbols. Channel coefficients are first estimated at pilot symbol positions using the linear minimum mean squared error (LMMSE) and then fed to a feed-forward neural network (FFNN) to predict channel coefficients at data symbol positions. In some conventional pilot-aided channel estimation schemes, the cross-correlation matrix for channel coefficients at data and pilot positions is an essential parameter for estimating channel coefficients at data positions; however, this statistical information may not be available. In the proposed scheme, channel coefficients at data positions can be predicted without any statistical information related to channel coefficients at data positions. Also, the proposed model avoids the high computational complexity in statistical-based channel estimation methods. Computer simulations show that the proposed scheme can achieve performance comparable to classical systems, especially for low signal-to-noise ratio (SNR).
Dataset, Neural network , Deep Learning, FFNN, Pilot Symbols, Training
Mobility models based AODV Mobile Ad Hoc Networks Performance Evaluation using NetSim Simulation Program
Mobile Ad Hoc Networks (MANETs) are self-organizing, wireless networks devoid of any infrastructure in which mobile nodes can form connections and transmit messages cooperatively. Their adaptive nature and ability to self-configure makes them a viable solution in an Internet of Things (IoT) environment, while node mobility does impose some challenges in terms of route stability, packet loss, and energy wastage. This study examines how two different mobility models affect the performance of the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in the NetSim environment version 14.2. The effect of using two different speeds (10 m/s and 50 m/s) for both models and the AODV routing protocol were put to the test by observing five quality of service (QoS) metrics: Throughput, End-to-End Delay, Packet Delivery Ratio (PDR), Routing Overhead, and Energy Consumption. From the simulation results, it is concluded that node mobility will have a major affect routing behavior and energy use. Overall, the Random Waypoint mobility model provides the highest throughput and PDR along with moderate delay and energy consumption, as compared to Random Walk and No-Mobility solutions. All mobility models (including No-Mobility) exhibited degraded performance at 50 m/s due to excessive link breakage, however Random Waypoint provided the most overall balance in performance. In summary, these findings show that moderate, structured node mobility will enhance route stability, and the overall transport protocol performance for MANETs. These findings add value to potential designs for mobile network optimization in the future IoT.
Enhancing Power Quality in Grid-Connected Fuel Cell Systems Using the Gradient-Based Optimizer
Abstract—Recent studies on Energy Management Systems (EMS) have focused on enhancing fuel cell-integrated grid systems. Effective power electronics control strategies for such systems should be easy to implement, capable of minimizing harmonic distortion, and robust under weak grid conditions. However, weak grids present significant challenges, including elevated total harmonic distortion (THD), voltage fluctuations, harmonics from nonlinear loads, and frequency deviations. To address these issues, this paper proposes advanced EMS control strategies optimized using the Gradient-Based Optimizer (GBO) for fuel cell-integrated microgrid systems. The performance of the proposed GBO-PI controller is assessed through two analytical frameworks: THD, convergence analyses, and transient response of DC link voltage under both fixed and variable load conditions. This algorithm has optimized advanced control strategies for EMS in fuel cell-integrated grid systems to enhance the DC link voltage, improving transient response and power quality. The conventional method yields a THD of 6.61%, which is reduced to 0.60% using the proposed controller. The GBO-PI surpassed the settling time of PSO-PI, GJO-PI, and the no-controller scheme by margins of 61.4%, 60.0%, and 26.7%, respectively. Additionally, the proposed GBO-PI outperforms the compared methods—no controller, GJO-PI, and HHO-PI by 34.21%, 14.46%, and 11.67% in terms of overshoot, respectively. The findings demonstrate that Advanced control strategies enhance fuel cell microgrid integration by improving robustness and sustainability, while effectively managing dynamic load variations and ensuring stable power injection into the electrical grid.
Integrating Terrestrial and Non-Terrestrial Networks in 6G: A Review of Architectures, AI-Driven Techniques, and Sustainability Strategies
The integration of terrestrial networks (TNs) and non-terrestrial networks (NTNs) has become a cornerstone of sixth-generation (6G) communications, promising ubiquitous connectivity, global coverage, and resilient service delivery. While several existing reviews have examined this integration, they largely focus on isolated aspects such as architecture, control, virtualization, or artificial intelligence (AI), leaving sustainability underexplored. This paper provides a comprehensive review that systematically synthesizes the state-of-the-art across three interdependent dimensions: architectural approaches, AI-driven optimization, and sustainability strategies. We first classify architectural frameworks shaping TN–NTN integration, then examine how AI techniques drive intelligent resource management and orchestration, and finally position sustainability as a core design pillar through energy- and carbon-aware strategies. Unlike prior reviews, this work consolidates fragmented perspectives into a triadic taxonomy that illustrates how architectural design choices influence AI-driven orchestration and sustainability outcomes. The review also identifies open challenges, including interoperability across domains, standardization gaps, and balancing energy efficiency with performance guarantees. By linking architecture, intelligence, and sustainability, the paper offers a forward-looking roadmap for building resilient, intelligent, and green TN–NTN systems, thereby contributing to the realization of 6G as an enabler of equitable global connectivity and alignment with United Nation Sustainable Development Goals (UN SDGs).
Fuzzy Clustering-Based Temperature Control System for Energy Efficiency and Cost Optimization in Smart Homes
Temperature control systems are among the most demanding and critical uses of server rooms, industrial plants, and smart homes because they are nonlinear and dynamic. Although PID controllers are ubiquitous in climate control, their fixed linear gain parameters are not very adaptable to different operating conditions. Fuzzy control systems, especially soft clustering-based systems, offer more versatile methods by facilitating smooth and continuous adjustment of temperature and thus more precise and adaptive control. On the basis of both Fuzzy K-Means and Fuzzy C-Means (FCM) algorithms, in this research, a clustering-based PID system is developed and evaluated. The system performance is verified via simulation with emphasis placed on temperature tracking accuracy, precision, and cost of energy. The results illustrated that the FCM-PID controller achieves the highest stable as well as precise temperature response compared to conventional PID and Fuzzy K-Means PID controllers. In addition, it achieves around 10% of energy cost reduction and accuracy of 97.6% with a lower MAE of 0.48°C, reflecting its real-time adaptive control efficiency. These results suggest that the integration of FCM-PID has the potential to be a smart energy-saving technology for future generations of HVAC systems and other temperature-sensitive applications
Timer-Based Capacitance-to-Voltage Converter
This article presents the design and analysis of a capacitance-to-voltage converter based on 555-timer ICs as the core design element. The circuit structure consists of two primary active components: two 555-timer ICs connected in conjunction with a low-pass filter circuit. The first 555-timer IC operates in astable mode to generate a signal that controls the timing of the second IC. The target capacitor or capacitive sensor to be measured is connected to the second 555-timer IC, which operates in monostable mode. The output signal from the second 555-timer IC is then converted into a DC voltage output using a low-pass filter circuit. The circuit was tested using laboratory capacitors in the range of 52.72 pF to 807.61 pF, and the data were analyzed using Microsoft Excel. The results showed excellent agreement with the measurements obtained from the GW INSTEK LCR-819. The maximum uncalibrated error was found to be −3.32% of full scale. After applying linear curve fitting in Excel, the maximum error was reduced to 0.92% of full scale, confirming the effectiveness of the calibration approach. Furthermore, the circuit was applied to measure the moisture content of paddy rice using a cylindrical capacitive sensor within the moisture range of 12.1% to 23.2%. The experimental data were fitted to a derived equation, which was then used to predict rice moisture content, producing results that corresponded closely with those obtained from the KETT PM-450 grain moisture meter.
Capacitance-to-voltage converter (CVC), 555-timer, Capacitive sensor, Capacitive readout circuit
A Robust IDS System for Intelligent Phishing Website Detection
Phishing websites continue to pose significant threats to cybersecurity by deceiving users into revealing sensitive information. To address this, this paper proposes a machine learning-based phishing detection model using a hybrid feature selection method that combines the Dragonfly Algorithm (DA) and Whale Optimization Algorithm (WOA), referred to as DAuWOA. The model leverages the ISCX-URL2016 dataset for evaluation. The proposed feature selection technique improves detection accuracy by removing irrelevant attributes and enhancing model efficiency. Adaptive Boosting (AdaBoost) is used as the classifier, and its hyperparameters are fine-tuned using Grid Search. Experimental results demonstrate that the proposed DAuWOA-AdaBoost model achieves superior performance compared to existing methods, attaining 97.07% accuracy, 96.89% recall, 97.15% precision, 97.02% F1-score, and 94.14% MCC. The proposed model not only boosts accuracy but also enhances computational efficiency, showing promise for real-time phishing detection systems.
Code Generation by Large Language Models: A Comparative Analysis of ChatGPT, Claude, and DeepSeek
As generative AI models become increasingly integrated into software development workflows, understanding their efficiency and code quality is critical. This study offers a comprehensive comparison of three leading AI models—ChatGPT GPT-4-turbo, Claude Sonnet, and DeepSeek-V3—for automated code generation, focusing specifically on sorting algorithms. The models are evaluated across multiple metrics including execution time, memory usage, peak memory consumption, logical and physical file sizes, and code readability. Python implementations of Insertion Sort, Merge Sort, Quick Sort, and Heap Sort are generated by each model and benchmarked in a consistent Linux Docker environment. Results reveal that ChatGPT leads in overall efficiency, with the fastest average execution time, the lowest peak memory usage, and the highest readability scores. DeepSeek demonstrated competitive performance, especially in producing readable code, while Claude showed higher memory consumption and lower readability. This analysis provides practical insight into the trade-offs between code quality and system performance in AI-generated programming, offering valuable guidance for researchers and developers alike
Explainable AI-Based Mapping of Public Sentiment on the Impact of Environmental Policies: A Cross-Regional Social Media Analysis
It is essential to comprehend public opinion regarding environmental sustainability programs to develop successful policies and interventions. Conventional survey techniques, however, are inadequate to obtain current, large-scale opinion trends. This research uses machine learning and deep learning techniques, such as BERT and LSTM, to investigate tweets and social media conversations on environmental policy. Unstructured text is converted to structured sentiment scores, identifying opinions as positive, negative, or neutral. In addition, an Extended Policy Analytical Framework was utilized, which included region- and policy-resolved assessment, event-aligned sentiment effect analysis, emotion profiling using the NRC Emotion Lexicon, cross-region generalization, and Explainable AI methods like SHAP-based keyword interpretation. BERT proved to be the most accurate classifier with an accuracy of 0.97, and this was followed by Random Forest with an accuracy of 0.94. Results show substantial sentiment changes surrounding key policy events like COP26 in India (+0.14) and US carbon tax discussions (−0.15) and identify characteristic emotional patterns by policy topic, with hope and excitement leading clean energy debates, anger and fear dominating carbon pricing discussions, and sadness and anger defining deforestation control. Cross-region comparison revealed 5–7% decreases in macro-F1, pointing to the importance of region-based adjustment, and SHAP-based explanations detected framing effects impacting public support or opposition. Overall, this research illustrates the viability of explainable and AI-driven sentiment analysis to track and decode public debate on environmental policy. The results offer actionable, evidence-based information for policymakers, allowing more effective communication strategies, greater public engagement, and assistance in the implementation of effective sustainability practices in different regions.
AC/AC converter flexibly connects AC microgrid with integrated storage system
A more stable grid is achieved by storage systems, with AC microgrids providing more continuous grid operation by supplying energy to electrical loads from distributed energy sources and energy sources from the old distribution grid. Energy conversion between components in the microgrid system such as power sources, storage systems and AC microgrids is a requirement to contribute significantly to energy management. AC/AC converters use basic DC/DC conversion forms to convert energy such as Cuk, Sepic. The article provides specific application content of renewable energy sources supplied to microgrids integrated with storage systems using AC/AC converters with 3 ports that can continuously convert energy for loads in the grid and storage systems. The results of the work are performed by simulation software and experiments with power fluctuations in the range of 2-5.5kW. The converter has an average efficiency of each case together close to 97% when the proposed converter is switched with 3 main switches to connect to three different independent ports in a power system connected to an AC microgrid. The converter is simulated and tested in the laboratory with the results compared with the reference literature.
Evaluating Efficiency and Productivity Trends of Indian Electricity Distribution Utilities: DEA-Malmquist Analysis
This study evaluates the technical efficiency and total factor productivity (TFP) of 54 Indian electricity distribution utilities (DMUs) from 2020 to 2023 using Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI). The Charnes-Cooper-Rhodes (CCR) model revealed that only 35.18% of DMUs (19 out of 54) were technically efficient, with average efficiency improving marginally from 0.84 to 0.88. Slack-based inefficiencies resulted in an estimated input overutilization of Rs. 63,270.12 crore, highlighting significant operational inefficiencies. While pure efficiency improved by 9%, technological regress (TECHCH = 0.932) due to outdated infrastructure and regulatory delays led to a 6.76% decline in productivity. Persistent Aggregate Technical and Commercial (AT&C) losses (19.24%) further contributed to a 3.83% drop in TFP. Top-performing utilities like DMU46 (DGVCL) and DMU44 (Puducherry PD) demonstrated cost efficiency (Rs. 0.776 crores/MU) and reduced ACS-ARR gaps, whereas lagging DMUs such as DMU05 (NESCO) and DMU43 (TANGEDCO) suffered from high losses and technological stagnation. The findings underscore the urgent need for AI-driven smart grid interventions to enhance efficiency, reduce losses, and ensure sustainable electricity distribution in India by 2047
Electricity distribution utilities, Data Envelopment Analysis (DEA), Total Factor Productivity (TFP)
A Multi-Objective ESTO Framework for Efficient PV Siting and Sizing in Radial Distribution Systems
There is a clear increase in the growth of renewable energy sources, especially photovoltaics (PV), which requires selecting the appropriate method for connecting them to the distribution network in terms of siting and size to minimize losses as much as possible, improve system efficiency, and enhance voltage stability. This paper presents an Enhanced Search and Tuning Optimization (ESTO) algorithm for addressing the optimal PV siting and size problem. The ESTO algorithm utilizes an adaptive system for dynamically balancing exploration and exploitation stages and thus reducing convergence speed and overcoming local minimum points. The approach is implemented for IEEE 33-bus and IEEE 69-bus systems using backward/forward sweep load flow analyses. The simulation results showed significant improvements in voltage deviation, with the lowest voltage being 0.931pu at bus 18, and it became 0.968 pu, while losses were reduced by 64.7%. The ESTO algorithm was compared with previous algorithms such as PSO, GA and ALO and proved to be superior.
Copyright (c) 2025 International Journal of Electrical and Electronic Engineering & Telecommunications
Performance Evaluation and Latency Optimization of Wi‑Fi 7 in High-Density Wireless Environments
This work studies Wi-Fi 7 (IEEE 802.11be) performance under the context of high-density wireless network situations and particularly focuses on parameters like latency, throughputs, jitter, and packet loss. The intention is to evaluate the performance of Wi-Fi 7 in comparison to Wi-Fi 6 (IEEE 802.11ax) under various network load conditions and assess its ability to support latency-sensitive and bandwidth-intensive applications. A controlled series of experiments was conducted with the help of NS-3 simulations by utilizing 20 to 50 client nodes operating on dual-band access points with support for Multi-Link Operation (MLO). The model of traffic used was VoIP, video conferencing, AR/VR, and background TCP flows to reproduce application behavior as found in real scenarios. The result was such that Wi-Fi 7 achieves up to 40% higher throughputs and approximately 48% lower latency in higher-density situations compared to Wi-Fi 6. Additionally, Wi-Fi 7 exhibited stable jitter and minimal packet loss rates even as the user load increased. These benefits are the result of architectural advancements, including wider channel bandwidths (up to 320 MHz), spatial multiplexing, and parallel use of links. The results suggest that Wi-Fi 7 can meet the growing demands of today's urban, industrial, and intelligent infrastructure installations by providing a solid and scalable quality of service in dense wireless environments.
Evaluation and Optimization of Graphene Nanosheet FET: Analog/RF Perspectives
Graphene Nanosheet Field Effect Transistor (GNSFET) is constructed for the first time by using Technology Computer-Aided Design (TCAD), marking advancement in nanosheet FET technology. The device’s analog /RF performance are evaluated with respect to the main geometric parameters of GNSFET, such as the gate-length, the channel-width, and the channel- thickness. Key analog/RF performance metrics have been analyzed targeting the optimization process, including the following: ION/IOFF ratio, subthreshold-swing (SS), transconductance (gm), transconductance efficiency (gm/Id), output conductance (gds), gate capacitance (Cgg), cut-off frequency (FT), and intrinsic gain (Av). Output results reveal that scaling-down (Tch) to 6 nm, (Wch) to 14 nm, and (Lg) to 16 nm enhances the ION/IOFF ratio to 5.2×1011 and reduces SS to 60.05 mV/dec, which creates better switching efficiency. Reducing (Tch) from 8 nm to 6 nm, and (Wch) from 18 nm to 12 nm boosts the ION/IOFF ratio by 34.48% and 33.19%, respectively, highlighting the main role of device’s small geometry in improving electrostatic control. Transconductance (gm) increases by 22.51% when (Lg) is scaled from 16 nm to 12 nm, while Cgg decreases with reductions in (Lg), (Wch), and (Tch), aligning with graphene’s inherent high carrier mobility. Notably, FT improves by 11.71% under (Tch) scaling, underscoring graphene’s potential for high-frequency RF applications. Although, intrinsic-gain (Av) benefits from (Wch) and (Tch) scaling, it exhibits a significant augmentation (38.88%) when (Lg) is increased from 12 nm to 16 nm, reflecting a trade-off between gain optimization and speed. This work could be considered as foundational framework for graphene-based nanosheet FETs in next-generation analog/RF circuits.
NSFET, graphene, analog/RF, performances optimization
Determination of the Modern Marine Gas Turbine Rotor Forced Vibration Parameters by Numerical and Experimental Methods
In this paper the problem of the gas turbine rotor forced vibration has been studied. The paper outlines a finite elements refined mathematical model of the gas turbine rotor durability, taking into account the influence of forced vibration, high temperature and centrifugal force, constructional damping parameters and can be used for the various types of marine gas turbine engines including even floating power plants. The developed mathematical model is based on the curvilinear finite elements of hexagonal type. Its verification has been done by the usage the modern highly automatized experimental equipment that is based on the holographic interferometry method application in real time. The first impeller cooled blades forced vibration parameters have been determined for several most dangerous forced vibration modes, temperatures up to 10000C and angular velocities of shaft rotation, corresponding with the gas turbine engine normal working regime. The calculated data together with the numerical and experimental investigation of this rotor fluid flow and thermal state will be used for the next stage of studies, concerning the rotor creep and fatigue crack problems.
gas turbine rotors; forced vibration; gas flow temperature; centrifugal force; constructional damping; durability
Multi-Objective Dynamic Self-Healing of Unbalanced and Harmonic-Rich Smart Distribution Networks Using Improved Whale Optimization Algorithm
The increasing penetration of intermittent renewable energy sources (RES), coupled with the decentralized architecture of modern power distribution networks, has introduced substantial challenges in maintaining system stability and ensuring power quality—particularly under fault conditions and nonlinear operating regimes. This paper proposes a dynamic self-healing model for smart distribution systems based on an Improved Whale Optimization Algorithm (IWOA), with the objective of simultaneously optimizing active power loss, voltage deviation, total harmonic distortion (THD), and phase voltage unbalance ratio (PVUR). The proposed IWOA incorporates a nonlinear shrinking mechanism, discrete solution mapping, and a normalized, equally weighted multi-objective function structure. These enhancements significantly improve convergence behavior, solution accuracy, and optimization performance in complex combinatorial search spaces. The proposed framework is validated on a modified IEEE 33-bus distribution system featuring unbalanced topologies, harmonic disturbances, and distributed RES integration. Simulation results demonstrate that IWOA outperforms conventional metaheuristics such as Particle Swarm Optimization (PSO), Differential Evolution (DE), and the original WOA in terms of convergence speed, energy efficiency, and power quality enhancement. This study highlights a promising direction for advanced automated optimization strategies in resilient and sustainable energy distribution infrastructures.
Improved Whale Optimization Algorithm (IWOA);
Safety Engineering in Adaptive Multimedia System Design for Emergency Accessibility Based on WCAG
People with disabilities often face critical challenges in accessing timely and comprehensive emergency alerts. This study presents an adaptive multimedia system that uses safety engineering principles using Web Content Accessibility Guidelines (WCAG). Based on WCAG, the system was created to meet visual, auditory, and tactile output requirements based on user profiles and environmental conditions. The system inputs are fed through the Internet of Things (IoT) and processed by a rule-based adaptation engine. The emergency adaptive system combines environmental data, user health, and manual input to generate appropriate multimedia alerts (audio, visual, or vibration). A prototype was built using React/Tailwind for the user interface and integrated with sensors for environmental monitoring. The system was evaluated in an emergency scenario for 30 participants across visual, auditory, and cognitive impairment groups. The evaluation results showed a high average System Usability Scale (SUS) score of 86.2, indicating excellent usability. The average Accessibility Coverage Metric (ACM) score was also obtained at 0.89, indicating broad and inclusive modality support. Based on the experiments, the Adaptive Decision Function (ADF) score exceeded 0.90 for combinations such as visual with vibration and audio output. This shows strong adaptive performance under various conditions. These results demonstrate the potential of the system to improve the inclusiveness of emergency response for people with disabilities.
Agent-Based Secure Route Switching and QoS Management in VANETs using the Wiedemann Car-Following Prediction Model
In Vehicular Ad-hoc Networks (VANETs), ensuring robust security alongside high Quality of Service (QoS) is essential due to the network’s dynamic topology and limited infrastructure, which can impact safety and comfort applications. This paper presents an agent-based, security-aware multi-path route switching protocol designed to enhance QoS in VANETs. The protocol integrates a secure route-connectivity prediction agent, leveraging the Wiedemann car-following model, with a flexible QoS agent that intelligently selects and switches among secure multi-path routes from source to destination. Unlike traditional methods, the QoS agent employs multiple adaptive switching strategies—including Least Path Switching (LPS), Least Path Switching with Minimum Delay (LPSMD), and Least Disconnection Delay (LDD)—to maintain route stability and optimize performance. To secure communication, vehicular nodes utilize a hybrid key agreement mechanism combining Diffie-Hellman and Short Authentication String (SAS) protocols executed over Wi-Fi Direct out-of-band channels. Extensive simulations in a heterogeneous VANET environment assess the protocol under varying conditions such as node density, average vehicle speed, data collection intervals, and the prevalence of man-in-the-middle attacks (MITMAs). The results show that secure protocol variants significantly improve packet delivery ratios, especially under adversarial and high-mobility conditions, while incurring only modest increases in end-to-end delay due to security overhead. Performance trends remain consistent across scenarios, with LDD-based strategies achieving the best results in both secure and non-secure modes. These outcomes confirm the proposed protocol’s effectiveness in enhancing both security and QoS in real-time VANET environments.
5G New Radio LDPC Codes Based Generalized Joint Subcarrier-Time Index Modulation DCSK System
LDPC (Low-Density Parity-Check) codes are essential error-correcting codes for 5G New Radio wireless communication, significantly improving the reliability of data transmission across noisy and unpredictable wireless channels. Since an evaluation and discussion of the performance with channel coding is significantly absent in two dimensional index modulation DCSK system. Therefore, in this paper, the 5G New Radio LDPC Codes Based Generalized Joint Subcarrier-Time Index Modulation DCSK System (NR-LDPC-GJSTIM-DCSK) is proposed, where the 5G-NR-LDPC code is used as channel coding. The aim is to improve the system's performance over AWGN and multipath Rayleigh fading channels compared to the uncoded GJSTIM-DCSK system. In the suggested system, the detected index bits at the receiver are transformed into soft bits and combined with the soft demodulated bits before the decoding process. The results indicate that the proposed coding techniques enhance the NR-LDPC-GJSTIM-DCSK system. Additionally, the performance of the 5G NR-LDPC-GJSTIM-DCSK system surpasses that of the NR-LDPC-GSIM-DCSK-II sys-tem and is nearly equivalent to the performance of the NR-LDPC-GFTIM-DCSK-II system at high SNR.
5G new radio LDPC, joint subcarrier-time index modulation, differential chaos shift keying, multipath Rayleigh fading channel Introduction
SD-LLN: a novel architecture using SDN and NFV to adapt RPL in dynamic Low-Power and Lossy network (LLN)
In low-power and lossy networks (LLNs), significant challenges arise due to the severe constraints on energy, memory, and processing capacities. These limitations hinder the efficiency of routing protocols like RPL (Routing Protocol for Low-Power and Lossy Networks), particularly in dynamic network topologies where frequent changes occur. We propose an innovative architecture integrating Software-Defined Networking (SDN) and Network Function Virtualization (NFV) with RPL protocol to address this issue. SDN and NFV technologies introduce greater flexibility, programmability, and centralized control, enabling the network to adapt more effectively to dynamic changes. Our proposed SD-LLN architecture leverages these technologies to enhance RPL's adaptability and resilience in varying network conditions. We improve network performance by integrating SDN/NFV with RPL, particularly in routing efficiency, stability, and resource optimization. Compared to traditional RPL implementations, our approach demonstrates significant advantages. The results highlight the potential of this method in managing LLNs, offering a robust framework to effectively overcome dynamic, resource-constrained environments' challenges.
Hidden Weapon Detection and Tracking in Thermal Video Using Auto-Labeling and Deep Learning Approaches
This study proposes an automated framework for detecting and tracking concealed weapons in thermal video, aimed at real-time surveillance in high-security public areas like airports and stadiums. Due to the lack of relevant public datasets, thermal videos were recorded using an infrared camera in scenarios simulating concealed weapons. Frames extracted from the videos were automatically annotated using the GroundedSAM model, which aligns textual prompts (" gun"," knife") with image content, eliminating manual labeling. A YOLOv11n model was trained on over 6,200 labeled thermal images, achieving 92% precision, 82% recall, and 88% mAP\@50. For tracking across frames, a Graph Neural Network (GNN) connected detected objects over time, yielding 0 ID switches and 0.83 consistency for guns, and 1 ID switch with 0.46 consistency for knives. The integration of smart annotation, thermal-aware detection, and GNN tracking demonstrates strong potential for real-time, robust weapon detection in crowded, security-sensitive environments.
GroundedSAM, Autodistill, YOLOv11n, thermal video dataset, GNN Tracking algorithm, and special evaluation metrics for tracking algorithm.
A Robust and Explainable Deepfake Detection Image Framework Using Transfer Learning with Attention Mechanism
The widespread emergence of deepfake technology has raised major concerns regarding the authenticity of digital media. This paper presents a robust and interpretable deepfake image detection framework that combines enhanced image preprocessing with advanced deep learning techniques. A dual-stage preprocessing pipeline, integrating Error Level Analysis (ELA) and Structured Forest Edge Detection (SFED), is proposed to amplify manipulation artifacts and edge inconsistencies. The refined images are processed through five convolutional neural networks (CNNs), each embedded with a Convolutional Block Attention Module (CBAM) to highlight tampering-sensitive features. To enhance explainability, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualize model decisions. Furthermore, a stacking ensemble strategy using Extreme Gradient Boosting (XGBoost) is applied to combine predictions from all models for improved accuracy and generalization. Experimental results on a balanced dataset of 140,000 real and fake facial images demonstrate superior performance, with ensemble accuracy reaching 97.25%. The proposed approach offers a reliable and explainable solution to address the challenges posed by synthetic facial image generation.
Deepfake detection, convolutional neural networks (CNN), Error Level Analysis (ELA), Structured Forest Edge Detection (SFED), Convolutional Block Attention Module (CBAM) attention, Grad-CAM, ensemble learning, and image forensics.
Detection and Classification of Rice Plant Diseases Using Fusion Deep and Texture Features
This thesis presents the development of an intelligent system for the early detection of plant diseases using deep learning and advanced image processing techniques. The primary objective is to support food security and enhance agricultural productivity. The proposed system applies Contrast-Limited Adaptive Histogram Equalization (CLAH) and U-shaped convolutional neural Network (U-Net) for image segmentation to improve image quality and accurately localize diseased regions. Feature extraction combines deep features from Convolutional Neural Networks (CNNs) and GramNet, a model based on the Residual Network-18 architecture, with texture features obtained using the gray-level co-occurrence matrix. All features are concatenated to create a comprehensive feature representation. A stacking ensemble approach is adopted for classification using support vector machine, k-nearest neighbors, and extreme gradient boosting as base classifiers. In contrast, the light gradient boosting machine is the final meta-classifier. Experimental evaluation on a balanced dataset shows the model achieves 99.99% accuracy on training data and 96.90% on testing data. This work demonstrates the effectiveness of integrating preprocessing, hybrid feature extraction, and ensemble learning in early plant disease detection.
Deep learning, ensemble classification, feature fusion, rice disease detection, smart agriculture, U-Net segmentation