IJEER Vol no. 14, Issue 2


An Investigation on the Effectiveness of a Novel Bowtie Antenna for Biomedical Applications

Microwave breast imaging uses longer-wavelength, low-power waves to deliver comprehensive breast tissue information for safer and more accurate breast cancer diagnosis. This is an alternative to other more traditional approaches such as ultrasound, Positron Emission Tomography (PET) and X-ray mammography. This paper aims at discussing performance summary of bowtie antenna with slits loaded to detect breast cancer. The antenna that can be found in the present paper has a frequency band of operation of 4 GHz to 6 GHz and consists of a triangular patch fed by a rectangular feed line. The FR4 substrate with dielectric constant is 4.3 is exploited to make phantom models with and without a tumor and antenna models with or without slits.

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Design and Improvement of a Modified Patch Antenna of Microstrip with an Integrated Filter and L-Shaped Slot for ISM-Band Applications

The microstrip antennas are often used in the wireless communications due to their size and the ease with which they can be made. Nonetheless, their performance is greatly affected by modifications in engineering and physical properties. So, the simple microstrip antenna was designed in this research and the analysis of the most important variables was conducted. This involves modification of the substrate thickness, feed width, type of insulating material and ground level parameters. The experiment involved the study on its influence on bandwidth, antenna gain, and impedance matching within the 2.4 GHz ISM frequency range. The simulation was done via CST software along with the FR-4 insulating material was 1.6 mm thick.

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Intelligent Fault-Tolerant Control for 4-DOF Robotic Manipulator Using Sliding Mode Control and RBFNN Against Lumped Uncertainties

This paper presents an intelligent fault-tolerant controller to eliminate factors affecting the robot of precise working ability. First, the dynamic mathematical model of a 4 degrees of freedom (DOF) robotic manipulator with uncertainty factors will be presented. Next, the proposed controller is based on a sliding mode controller (SMC) to accurately control the trajectory, and radial basis function neuron networks (RBFNN) estimate the lumped uncertainties occurring in the system. Additionally, some simulations will be performed to validate the performance of the designed controller on MATLAB Simulink. Finally, the effectiveness of the proposed method is quantitatively assessed based on the root mean square error (RMSE).

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Power Quality Enhancement Through Harmonic Filters Using Novel Bat Algorithm in RDS with Nonlinear Distributed Generation

The improvement of power quality (PQ) is the main aim of this paper. Here, the enhancement of PQ through harmonic filters (HFs) is simulated on the IEEE-69 bus radial distribution system (RDS) with nonlinear distributed generation (NLDG). The aim of minimizing harmonic distortion within standard limits is achieved using proper placement of HFs and an optimization algorithm. The optimization problem in this study is characterized by nonlinear constraints. The placement of HFs is accomplished using a newly published method. It is a nonlinear load position-based current injection (NLPCI). To determine an appropriate rating of HFs for reducing total harmonic distortion of voltage (THDv) and meeting the standards set by the IEEE, a novel bat algorithm (NBA) is employed. The NBA is compared with the Bat Algorithm (BA), Particle Swarm Optimization (PSO), Gray Wolf optimization (GWO) and the Firefly Algorithm (FA) in terms of efficacy. The comparative analysis of results shows that in terms of computational efficiency, the NBA performs better than the BA, PSO, GWO and FA.

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Comprehensive PSCAD-Based Analysis of Sheath Overvoltage in 33 kV XLPE Cables Considering Grounding and Segmentation Effects

The performance reliability of underground medium voltage (MV) networks is largely dependent on how metallic sheaths behave both in a normal operating mode as well as during transients. One critical form of stress applied to the insulation is that of excessive sheath voltage which can rapidly degrade the insulation material used on underground conductors and endanger operators. This paper details a thorough examination of overvoltage mechanisms affecting sheaths of 33kV XLPE single-core underground cable through a simulation approach. By establishing a PSCAD/EMTDC model, it was analyzed how the variables of sheath grounding resistance, cable length, load current, and grounding location affect the segmented cable systems.

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Optimal Active Power Rescheduling for Transmission Congestion Management Using Competition of Tribes and Cooperation of Members Algorithm

Congestion management in transmission systems is one of the key challenges in deregulated electricity markets for independent system operators. This issue can be solved by optimal rescheduling of active power generation in congested transmission systems. This paper introduces a metaheuristic-based methodology for solving the congestion problem in the transmission lines. The proposed approach employs the competition of tribes and cooperation of members (CTCM) algorithm for effective optimal rescheduling of active output power generation in congested transmission systems.

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Active and Reactive Power Control of DFIG for Wind Energy Generation using MRAC

The Doubly Fed Induction Generator (DFIG) utilized in wind energy conversion systems (WECS) requires regulation of both active and reactive power to ensure stability and proper functioning. Model Reference Adaptive Control (MRAC) scheme augmented with flux-oriented vector control strategy is proposed for ensuring effective power management without extra sensors. RSC (machine side converter) and GSC (grid side converter) are to be controlled using Lyapunov-based adaptive control for improved power extraction and stability of the grid.

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Fuzzy Logic-Based DSTATCOM for an Optimal Reactive Power Dispatch Solution in a Grid-Connected System

In recent decades, there has been a substantial and dramatic implementation of renewable energy sources globally. Electrical systems should fulfill consumer load requirements while transmitting electricity with reduced loss, increased power quality, and dependability. However, the accessibility of steady and dependable electricity in emerging nations raises concerns about the depletion of energy production and the detrimental effects it has on the ecosystem. The most effective real-world and operational method to meet consumers' growing electricity requirements while upholding uncompromising ecological standards for power generation is to integrate a wind-solar-based microgrid into the distribution system.

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Design and Optimization of a High-Performance WSNs Architecture for Advanced Audio-Based Sensing Applications

This article describes an audio-based wireless sensor network (WSN) node. Audio-based applications require the WSN node to capture, process, and transmit audio over radio frequency (RF). In contrast to WSNs, which usually serve only a few bytes of data, WSNs for audio signals must handle raw audio data at a high data rate using high-performance WSN nodes to capture and process audio accurately. The purpose of this paper is to describe how to build high-performance WSN nodes using a high-performance DSP chip and comprehensive audio processing algorithms. The key challenge to implementing DSP chips at WSN nodes is that DSP chips consume an inordinate amount of power.

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T-Truncated Fractal Slot UWB Antenna for Wide Band Scanning in RF Sensor Network

The emergence of wireless communication networks (WCNs) introduces new opportunities for efficient spectrum utilization through wide scanning of the network. Leveraging Software Defined Radios (SDRs), users can conduct wide spectrum sensing and adjust transmission properties dynamically. The concept of opportunistic use of available spectrum requires adaptable antennas with ultra-wide band scanning capabilities. Dynamic Spectrum Access (DSA) emerges as a promising solution to congestion within densely populated networks. In this study, we introduce an innovative compact antenna specifically crafted for wide spectrum sensing.

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A Geometrically Reconfigurable Patch Antenna with Resonant-Mode Control for CubeSat Communications

Compact and effective antenna designs are needed to meet the radiation stability, lightweight, and compactness criteria of CubeSat communication systems. This study presents a CST Studio Suite-optimized tiny single-layer microstrip patch antenna for X-band CubeSat applications. In order to improve radiation performance and impedance matching without adding more structural complexity, a novel method incorporating resonant-mode control via geometric reshaping is suggested. With a directivity of 5.24 dBi and 6.38 dBi, respectively, and a reflection coefficient (S₁₁) of −20.06 dB at 11.347 GHz and −54.92 dB at 12.809 GHz, the first antenna design exhibits dual-band performance.

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Optimizing Cubic Spline Control Points via Tabu Search for Enhanced ECG Classification Using DNN

The quality of electrocardiogram (ECG) signals plays a crucial role in the performance of deep neural network (DNN) models on cardiac arrhythmia classification. The performance of deep neural network (DNN) models on cardiac arrhythmia classification is highly influenced by the quality of electrocardiogram (ECG) signals. This paper presents a novel optimization-based preprocessing framework CS-TS-DNN that combines Cubic Spline interpolation with Tabu Search (TS) metaheuristic for the automatic selection of optimal spline control points to represent the ECG signal. The proposed model can be used to optimize the data adaptively for improved classification accuracy and signal morphology preservation without employing heuristic approaches to pre-processing.

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Acute Myocardial Infarction (AMI) Detection Using Multimodal Dataset, Optimized Variational Stacked Autoencoder (OVSAE) and Self-Attention Long Short-Term Memory (SALSTM) Classifier

Acute Myocardial Infarction (AMI) is a vital public health concern, because it is the primary factor of death globally. Therefore, timely identification is crucial, especially in resource-constrained situations without centralized testing. (1) Background: Assessment, risk assessment, and treatment all depend on electrocardiograms (ECGs). ECG segments are artificially corrupted with various noise types (e.g., Gaussian noise, baseline wander) to create noisy training data.; (2) Methods: In this paper, signal denoising with an Optimized Variational Stacked Autoencoder (OVSAE) model which involves training a Neural Network (NN) to reconstruct clean ECGs from noisy versions, effectively learning to separate signal from noises and then decompressing the noise removed signals. OVSAE is introduced to adaptively remove noisy signals from ECG signals.

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CXR-CapsNet: CNN-RNN-RL based Caption Generation Model

Automated reporting of chest radiographs is an emerging task at the intersection of medical image analysis and natural language generation. In this problem, a model receives a chest X-ray and produces a clinically meaningful textual description, including the presence or absence of respiratory diseases. Conventional systems rely on an encoder–decoder pipeline in which a convolutional neural network (CNN) encodes the image and a recurrent neural network (RNN) decodes the representation into a report word by word. Recent work has shown that reinforcement learning can further align generated reports with sequence-level objectives.

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Design and Realization of Circular Polarized Dual Band Planar Antenna using Metamaterial Techniques for Wireless Communications

This work introduces a novel dual-band circular polarized (CP) microstrip patch antenna for wireless communications. The antenna features a square patch with corner truncated and an etched rectangular slot for circular polarization. The geometric modifications are mainly to control resonating frequencies, bandwidth and to optimize the axial ratio (AR) at the resonating frequencies. The proposed dual-band CP metamaterial antenna is designed to function within the wireless frequency ranges i.e., 2.28-2.48 GHz and 4.48-4.64 GHz with a measured gains of 2.62 dBi and 2.8 dBi at 2.4 as well as 4.5 GHz respectively.

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Hybrid CNN–ML Framework for Soil Classification and Crop Recommendation

The analysis of soils and proper prediction of crops are very important to help grow more productive agriculture and sustainable food production. A Hybrid CNN–Machine Learning (CNN-ML) Framework for Soil Classification and Crop Prediction is proposed in this paper to combine deep learning and machine learning approaches for intelligent farming decision-making. The proposed framework uses Convolutional Neural Networks (CNNs) for automatic soil classification and machine learning algorithms for crop recommendation. There are five types of soil which are described by their image, such as: Black Soil, Cinder Soil, Laterite Soil, Peat Soil and Yellow Soil. CNN, MobileNetV2 and ResNet50 were implemented and compared to assess the effectiveness of different deep learning architectures.

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Performance Evaluation of Kaiser Windowing in Time, Frequency, and Hybrid Domains for F-OFDM-Based 5G Systems

Filtered orthogonal frequency division multiplexing (F-OFDM) has emerged as a promising waveform candidate for 5G systems due to its enhanced spectral containment and flexibility. However, conventional F-OFDM implementations rely on single-domain windowing or filtering, which limits the achievable trade-off between spectral efficiency and error-rate performance. This paper proposes a unified hybrid-domain Kaiser windowing framework that jointly applies time-domain and frequency-domain shaping within a single analytical formulation. A weighted hybrid shaping parameter is derived to balance time localization and spectral confinement in a transparent and reproducible manner.

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Modified Direct Power Control for the Grid-Side Converter of a Wind-Driven PMSG

This paper presents an improved Direct Power Control (DPC) approach applied to the Grid-Side Converter (GSC) of a wind-driven Permanent Magnet Synchronous Generator (PMSG). The proposed method modifies the conventional switching table by incorporating the errors of the direct and quadrature current components along with the filtered grid voltage vector. The objective is to enhance steady-state behavior and reduce power fluctuations. A comprehensive simulation model is developed in MATLAB/Simulink to assess system performance under different loading conditions and varying wind speeds.

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Drone Localization Estimation Using Wireless Sensor Network and Deep-Learning Techniques

This paper presents a powerful hybrid localization model of drones to be used in GPS-denied areas through the combination of Long Short-Term Memory (LSTM) networks with an Extended Kalman Filter (EKF). The system network is based on a Wireless Sensor Network (WSN) in which sensors are arranged in equilateral triangle triples to enable the combination of the Angle-of-Arrival (AoA) and Time-Difference-of-Arrival (TDoA) measurements. The traditional EKF only model may not be good with complex residual dynamics as well as motion uncertainties but the LSTM component is specifically used to model these nonlinearities which gives much better state estimation compared to what conventional kinematic models can offer.

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PQ-Constrained OLTC Optimization in EV-Integrated Distribution Systems Using Secretary Bird Optimization Algorithm

The adoption of electric vehicles is increasing rapidly; this EV charging is most uncertain thing. The distribution system losses are high due to uncertain usage of power supply issues and it increases due to EV charging stations integration in the distribution side. This paper proposes the Secretary Bird Optimization Algorithm based optimal OLTC tap positions to minimize the power losses and improvement of voltage profile in the distribution system. In general, constant power loading is considered in distribution system but here in this proposed approach voltage dependent load modelling is adapted and integrated the various DG systems (both PV and wind), capacitor placement, to compensate the power losses before going to apply the OLTC as much as possible to reduce the power loss.

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Intelligent Capacitor Selection and Analysis for Self-Excited Reluctance Generator under Variable Conditions

This manuscript provides an analysis of a wind-driven self-excited reluctance generator (WDSERG) performance running under variable load conditions while maintaining a regular output voltage, and designs an Artificial Neural Network (ANN) model to forecast the value of the excitation capacitance required to maintain a WDSERG's generated voltage within desired bounds. The self-excited reluctance generator (SERG) has advantages over the induction generator (IG), which include steady frequency regardless of load or capacitance variation with proper performance. The analysis of steady-state for the reluctance generator (RG) is conducted according to d-q axes transformation.

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A Diagnostic Method for Induction Motor Inter-Turn Faults Based on Wavelet Transform and Neural Networks

Conventional protection systems for induction motors often fail to detect inter-turn short circuit (ITSC) faults accurately. These faults are usually misclassified as overload or phase imbalance, which may lead to unnecessary tripping and downtime. Early detection of ITSC faults is important, as fault severity increases rapidly due to insulation degradation. This paper presents a diagnostic method based on wavelet transform and artificial neural networks (WTANN). The method uses stator current signals for fault detection without requiring additional sensors. The extracted features are used to classify motor conditions as ITSC, overload (OL), or short circuit fault (SCF).

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A Learned Three Operator Splitting Network for Accelerated MRI Reconstruction Using Complex-Valued Deep Unrolling

Magnetic Resonance Imaging (MRI) is the cornerstone of modern medical diagnosis and research, providing high spatial resolution visualization of the anatomical and functional information of the body’s internal structure in a non-ionizing, non-carcinogenic and non-invasive manner. Despite these superior properties, the MRI data acquisition process is inherently slow, limiting this technique in scenarios where time is crucial. Consequently, accelerating MRI through k-space under-sampling at sub-Nyquist rates and reconstructing high-quality images from incomplete measurements has emerged as an active area of research in the past few decades.

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Symmetrical Sine-Carrier PWM-Based SVM for Performance Enhancement of Three-Phase Two-Level Wye Rectifier

This paper proposes a symmetrical sine-carrier pulse width modulation (SSCPWM)-based space vector modulation (SVM) strategy for enhancing the performance of a three-phase two-level Wye rectifier. Unlike conventional triangular-carrier PWM (TCPWM) and inverted sine-carrier PWM (ISCPWM), the proposed method employs a symmetrical sinusoidal carrier waveform to reshape the pulse distribution while preserving the simplicity of comparator-based carrier PWM implementation. The modulation signals are generated from a current-sector-based SVM framework. These signals are then directly compared with the proposed carrier to generate the switching signals. Analytical expressions for the switching instants and duty ratios are derived to clarify the nonlinear carrier-crossing characteristics introduced by the sinusoidal carrier.

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A New Optimally Designed FOPID Controller for the 3-Area Deregulated Environment Using MWHOA Approach

This work proposes a metaheuristic optimization approach for AGC in deregulated power systems. The analysis is carried out on a 3-AMS comprising gas turbine power plants, hydroelectric, thermal power stations and wind energy units. The primary objective is to reduce ACE, which includes tie-line power deviations and frequency fluctuations, under different operating conditions. To achieve optimal performance, a MWHOA is employed to determine the optimal gain parameters of FOPID. The proposed strategy evaluates the dynamic performance of generators in a 3-AMS under a deregulated environment.

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Evaluation of Random Forest Algorithm Performance in Predicting the Flashover Voltage of Polluted Insulators

This study aims to evaluate the capability of the Random Forest model to predict the flashover voltage of polluted insulators, with particular emphasis on the effect of hyperparameter tuning strategies on model accuracy and stability. A two-stage methodology was adopted. In the first stage, Grid Search and Particle Swarm Optimization were compared for tuning the model hyperparameters using a published dataset of cap-and-pin insulators. The results showed close agreement between the two methods in terms of the mean root mean square error, with a slight accuracy advantage for Particle Swarm Optimization, whereas Grid Search provided higher stability and greater computational simplicity.

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Hybrid PSO–GWO Based Multi-Objective Economic Emission Dispatch for Interconnected Power Systems with Renewable Energy Integration

Economic emission dispatch (EED) is an important optimization problem in today's power systems with significant renewable energy integration. This work presents a hybrid particle swarm optimization and grey wolf optimization (PSO-GWO) approach for the multi-objective economic emission dispatch (EED) problem with solar and wind integration. The hybrid algorithm improves the exploration-exploitation trade-off by incorporating the social learning behaviour of particle swarm optimization and the hierarchical hunting behavior of grey wolf optimization. The uncertainty of renewable energy is modeled using probability distributions to enhance dispatch reliability. A weighted multi-objective approach is adopted to optimize fuel cost and emissions.

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Energy Storage System Optimal Allocation for Improving Microgrid Operation

The rapid expansion of microgrids (MGs) is driven by the need to meet growing electricity demand sustainably through high penetration of renewable energy resources (RERs). However, the intermittency of RERs introduces significant operational uncertainty, making energy storage systems (ESSs) essential for reliable MG operation. The ESS planning problem is formulated as a constrained optimization model that incorporates power balance, battery capacity limits, and technical and operational constraints of MGs. Because it offers a viable solution, this study investigates the optimal method to allocate ESSs (batteries) using metaheuristic optimization techniques. In this work, the newly proposed Walrus Optimizer (WO) approach, which shows promising results, is used. A modified IEEE 33-bus distribution test system is employed as the benchmark. The performance of the WO is compared with five established metaheuristics: Detective Behavior Algorithm (DBA), Stochastic Social Learning Optimization (SSLO), Improved Grey Wolf Optimizer (I-GWO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). All of these algorithms aspire to reduce power losses, boost voltage profiles, and enhance overall efficiency, reliability, stability, and operational cost reduction. Simulation results show that the WO-based framework yields a 53.56% reduction in total system power losses. Moreover, it strengthens the weakest bus voltage to 0.9803 p.u., improves the voltage stability index (VSI) to 1.0201 p.u., and accomplish a cost reduction of 10.74%, outperforming other competitive algorithms. The results demonstrate the originality and effectiveness of the WO for optimal ESS integration in RER-dominated MGs, providing an encouraging tool for enhancing voltage stability, reducing losses, and supporting reliable, sustainable, and cost-effective MG operation.

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Model-Driven Deep Learning OAMP Detection Based on LDPC for Scalable Massive MIMO Systems

The paper analyzes the performance of Massive MIMO system with LDPC code. Utilizing the benefit of the power of model driven deep learning, which is a deep learning technique whose main characteristic is to maintain the mathematical structure of the model, converting the iterative detector into a neural network by making the parameters learnable rather than fixed. This method requires less data and has a faster training rate. This learning technique is used in order to enhance the output of the detector and thereby enhance the output of the LDPC decoder. The simulation outcomes reveal the strength of integrating both OAMP-NET detector with LDPC to scalable massive MIMO systems with different antenna configurations ranging from 4×4 to 64×64. The proposed system OAMP-NET+LDPC achieves a significant improvement in detection performance in both perfect and imperfect CSI scenarios. In addition, we conducted a comprehensive analysis of scalability and computational complexity to evaluate its practical feasibility for different MIMO configurations. In addition, we conducted generalization experiments under unseen SNR conditions and variable channel correlations, in addition to ablation studies at the depth of the unfolded network to prove the robustness and effectiveness of the proposed system.

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A Novel DC-DC Buck Converter for Portable Applications

This paper presents a simple pulse-generator-based DC–DC buck converter capable of adaptive ripple reduction and automatic CCM/DCM mode transition over a wide load-current range. The proposed converter consists of a pulse generator, a buffer and dead-time circuit, and complementary PMOS/NMOS power switches. Unlike conventional hysteretic buck converters, the proposed pulse generator dynamically adjusts the switching operation according to the load condition by monitoring the PMOS conduction current. Consequently, reverse inductor current is suppressed under light-load conditions, enabling automatic transition between continuous-conduction mode (CCM) and discontinuous-conduction mode (DCM) without requiring additional mode-control circuitry. Simulation results based on a 0.18-μm CMOS process demonstrate that the proposed converter operates over a load-current range from 0 mA to 500 mA with a switching frequency ranging from 310 kHz to 3.50 MHz. At a load current of 500 mA, the converter achieves an output-voltage ripple of 372.4 μVp-p and an output-current ripple of 4.95 mAp-p. In addition, transient recovery times of 6.9 μs and 4.5 μs are achieved for load-current transitions from 0 mA to 500 mA and from 500 mA to 0 mA, respectively. The proposed converter achieves a peak power efficiency of 85% while providing low ripple characteristics, automatic operating-mode transition, reverse-current suppression, and a simple control architecture.

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Design of Subtractive Fuzzy Clustering-Based PI Controller for Level Control of Quadruple Tank System with Dead Time

The performances of the Proportional-Integral controller, Fuzzy Logic controller, and Subtractive Fuzzy Clustering-based PI controller (SFC-PI) have been investigated for regulating the level in a Quadruple Tank System with Dead Time (QTSWDT). The QTSWDT is an ideal benchmark to test various control approaches since it has nonlinear dynamics and complicated interactions between tanks. While classic PI controllers are successful in controlling linear systems, they face difficulties in regulating the nonlinearities and cross-couplings inherent in QTSWDT. Fuzzy logic controllers offer extended adaptation to nonlinearities but require substantial tuning. The SFC-PI controller, which offers subtractive fuzzy clustering to instinctively generate fuzzy rules, surpasses the other techniques by significantly reducing ISE, IAE, settling time, and peak overshoot. Simulation outputs disclose that the SFC-PI controller has the best overall performance, making it a competent choice for complex nonlinear control applications.

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IOSS Criterion for Lipschitz Nonlinear Fixed Point Discrete -Time Systems Employing External Interference and Saturation Overflow

This paper introduces a novel criterion for assessing the Input-Output-to-State Stability of Lipschitz nonlinear discrete-time systems subject to external interference and saturation arithmetic. By using the Lipschitz condition along with the 'passivity property' of saturation arithmetic and the Lyapunov stability concept, the suggested criterion ensures the suppression of the effects of external interference while guaranteeing asymptotic stability without considering such interference. Two examples are presented to illustrate the effectiveness of the suggested results.

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SRR Metamaterial-aided PET-Based Flexible Inset-Feed Patch Antenna for X-Band Applications

This paper introduces an adjustable inset-feed rectangular metamaterial-based patch antenna with a Polyethylene Terephthalate (PET) substrate (relative permittivity εr = 2.8; height h = 0.16 mm) use within the X-band range at about 10 GHz (the resonance frequency is: f r ≈ 9.9525 GHz) as part of wearable radio-frequency (RF) device systems. The SRR-unit-cell used as a single square split ring resonator on the ground-plane has a μ-negative (μ < 0) effect to improve the properties of the antenna. To optimize the properties of the proposed antennas, the parametric optimization performed overall design parameters including: substrate-thickness, notch-gap, and feed-width. The proposed SRR-assisted configuration exhibits a return loss of −41.85 dB, directivity of 7.65 dBi, bandwidth of 97.8 MHz, and a VSWR of 1.01 at the resonant frequency of 9.9525 GHz. Compared with the conventional inset-feed patch antenna, the proposed design demonstrates approximately 12.9% improvement in impedance matching and nearly 30% enhancement in directivity. The compact geometry, low-profile PET substrate, and improved radiation characteristics make the antenna suitable for wearable sensing, RFID, radar, and X-band wireless communication systems.

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