IJEER Vol no. 14, Issue 1


Miniaturized Wearable Antenna Design for Wireless Body Area Networks at 5.8 GHz

Microstrip patch antennas are extensively utilized due to their strong mechanical properties, lightweight design, ease of manufacturing, and adaptability to both flat and curved surfaces. Additionally, they can support dual and triple frequency operations. For wearable applications, it is crucial to ensure that the antenna poses no harm to the human body while still performing effectively at higher frequencies. In this paper, a microstrip patch antenna was developed to operate in the 5.8 GHz ISM band, specifically targeting wearable use cases. To suit wearable environments, Teflon was chosen as the substrate material due to its low dielectric constant (εr = 2.1), which contributes to better antenna performance.

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Bandwidth-Enhanced Microstrip Patch Antenna Configurations for Sub-6 GHz and mmWave 5G Applications

The evolution from 4G to 5G introduces several design challenges, including spectrum sharing, significantly wider operating bandwidths, and advanced antenna design. To meet the demands of 5G communication, antennas must exhibit compact geometry, an efficient and well-matched feeding mechanism, and compatibility with large-scale manufacturing. This paper presents the design and optimization of a microstrip antenna operating at the 28 GHz band.

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Design and Analysis of Vivaldi Antenna for UWB Applications

This paper is about the design and analysis of an ultra-wideband Vivaldi antenna. The antenna is fabricated on FR4 of size 90x53 mm² and thickness of 1.4 mm. The slot profile of the antenna is exponentially tapered and is designed to support ultra-wideband (UWB) communication. Two identical slots were made on the radiator of the antennas to enhance the efficiency of radiation and reduce surface current. Measurement results show that it has a wide operating frequency of 3 GHz to 12 GHz with good impedance of (S11 < -10 dB) across all the band. The gain of the antenna is measured to be 8.37 dBi which is classified as high gain antenna and may be utilized in high frequency and other high gain applications.

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Cyber-Physical AI-IoT Robotic Solution for Automated Lavatory Hygiene and Resource Management Optimization

Automated lavatory cleaning has improved with a new specialized robot. This system has three main goals; building an autonomous robot, using water efficiently, and setting up a daily cleaning schedule with set intervals. The robot has cleaning tools on an adjustable arm and uses sensors to move around freely. Its arm can reach tough spots like the toilet bowl, sink, and floor. Users can program the cleaning schedule with a timer. The toilet-cleaning robot has been put through its paces across a range of restroom settings, including public, business, and private restrooms. The water-saving cleaning system uses a high-pressure water pump and nozzles that function as bathroom cleaning equipment to clean designated toilet sections.

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Adaptive Fuzzy Network Denoising for Enhanced Thin Ice Visualization in Cross-Polarized Sentinel-1 SAR

Detecting thin (baby) ice in HV-polarized Sentinel-1 extra-wide (EW) sea-ice SAR images is challenging because thermal noise and the scalloping effect can mask weak backscatter signals. This paper proposes an adaptive denoising and thresholding approach using a self-designed fuzzy logic controller network (FLCN) to enhance baby-ice visualization. The approach automatically selects “no object” and “minimum object” regions and applies a data-driven correction factor to improve noise suppression without relying on external parameters. The FLCN generates input–output membership functions autonomously, reducing the need for manual tuning

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Speed Control of a Battery-Less Isolated PV–Diesel Hybrid System Using a Modified MRAC–Fuzzy Controller

This article presents a generic model of the PV- diesel generator system and simulation of speed control of an alternator in isolated network. A diesel generator is a machine that converts diesel fuel into electrical energy. It is typically used as a backup power source or in areas where access to the electrical grid is unavailable. In an insulated network system, a photovoltaic (PV) generator with an inverter interface is employed in parallel using a PI regulator without a battery. The distribution system has one basic purpose: to ensure, a continuous power supply to customers while ensuring that the voltage remains within acceptable limits. The main challenge in maintaining system stability during load operation lies in controlling everything variables. A sudden droplet in solar irradiance will result in an immediate reduction in the PV power output.

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A Simplified Continuous PWM Using Nearest Four Vectors Based on 2L-SVM for Common-Mode Voltage Reduction in Vienna T-Type Rectifier

Vienna rectifiers are widely employed in high-performance DC power conversion systems, including electric vehicle (EV) charging infrastructure, renewable energy systems, telecommunication infrastructure, and uninterruptible power supplies (UPS). This paper presents a simplified continuous pulse-width modulation (PWM) strategy for the Vienna T-type rectifier, developed using the nearest four vectors derived from the two-level space vector modulation (2L-SVM) principle. The proposed method restructures the switching-state sequence across twelve updated sectors and incorporates a unified zero-sequence injection scheme to satisfy the inherent operating constraint (OC) while ensuring fully continuous modulation.

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Stochastic AI-Driven Resilience Framework for Power Grids Considering Communication-Link Outages and Operator Reliability

Modern power systems no longer fail only because of line or generator outages; they also drift into insecure states when the SCADA/EMS links slow down or when the operator cannot react at the required pace. To study this joint effect, we build an AI-supported stochastic resilience framework that treats the communication layer and the human layer as first-class, time-varying elements of the grid. Communication delay and packet–drop behaviour are captured through a multi-level Markov description, while operator performance is estimated through a small cognitive–reliability block that changes with workload and stress. On top of these two sources of uncertainty, a reinforcement-learning controller updates the stabilizing actions so that the system can return to acceptable voltage and frequency bands after faults.

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Four-Port Multi-Function Bidirectional Converter for V2V, V2H, V2G, and G2G Applications

Portable charging devices for electric vehicle owners are among the key solutions for efficient, safe, continuous, and emergency energy use, enabling electric vehicles to function as both continuous power sources and loads. This paper discusses a four-port converter that performs energy conversion for loads, distributed power sources for EVs, and AC and DC microgrids for household distribution. The converter is designed with a structure aimed at connecting energy between main EV devices and distributed sources in the distribution grid using bidirectional energy conversion principles and voltage step-up/step-down for each independent operating mode.

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Hybrid Approach to IBC Solar Cell Simulation: Coupling of Deep Neural Network and Advanced Electrical Analysis

This study introduces a sophisticated computational model developed in MATLAB to simulate the performance of interdigitated back contact (IBC) solar cells under various environmental conditions. The main objective of this work is to develop and validate a neural network that can predict Iph and Io of an IBC solar cell under varying environmental conditions (temperature and irradiance). The model is based on a deep feedforward neural network comprising two hidden layers with 20 and 10 neurons, respectively. This architecture enables accurate prediction of key electrical parameters, such as photocurrent (Iph) and reverse saturation current (Io), influenced by factors like temperature and irradiance. Trained on synthetic data representing realistic fluctuations in these variables, the neural network significantly outperforms conventional experimental models in predictive accuracy.

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A Reconfigurable Single-Electron Threshold-Logic Reversible Module for Ultra-Low-Power Nanoelectronics

As CMOS technology approaches its physical scaling limits, the semiconductor industry is actively seeking alternative technologies to sustain Moore's Law. This paper proposes a novel Reversible Logic Module (RLM) based on Single-Electron Tunneling (SET) technology and Threshold-Logic Gates (TLGs) for ultra-low power and high-density nanoelectronics circuits. The RLM is reconfigurable and capable of implementing fundamental reversible gates such as Toffoli, Feynman, Fredkin, and Peres gates, which are essential for reversible computing. The design is validated through extensive simulations using single-electronics simulators, demonstrating 0.25 aJ per operation and a delay of 10 ps.

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An Enhanced Loss Model–Based Online Flux Optimization Technique for Vector-Controlled Induction Motor Drives Including Leakage Inductance

A fresh and original approach is presented in this research as a means of controlling the amount of flux that is produced by an induction motor drive. Some of the algorithms that are used to decrease losses in induction motors include a loss-model based technique, which is one of several algorithms. Two advantages of this technique are its quick response and accurate conclusions. On the other hand, precise motor drive and loss modelling is necessary for the success of this strategy. During the process of developing the loss model, one of the ongoing challenges is to achieve accuracy while simultaneously controlling complexity.

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Intelligent Security Surveillance System for Communities in Areas Adjacent to Abandoned or Vacant Lots with Image Processing Technology

This work presents the development and testing of an intelligent security surveillance system capable of automatically detecting people and flames, with an alert system sending notifications via Telegram Bot. The objective is to increase the efficiency of security in the communities by testing under various environmental conditions at detection ranges from 5 to 30 meters during daylight, low light, and night times, including testing gaits such as slow walking and running. Next, the system was tested in the Rung Sawang Village 1 community, Bang Khen District, Bangkok, which has an area adjacent to a large abandoned or vacant lot. The test results showed that the system was able to detect people in daylight with a maximum similarity percentage of 98.45% at a distance of 5 meters and could detect at a distance of as far as 30 meters in cases involving slow walking.

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Real-Time Implementation of dSPACE DS1104 to a Full-Bridge DC-DC Converter with EMI Mitigation

A conventional isolated full-bridge (FB) DC–DC converter for electric vehicle (EV) battery charger is implemented and experimentally validated using a real-time digital control platform based on the dSPACE DS1104 controller presented in this paper. The proposed charger is designed to deliver a regulated output of 56 V at 15 A for charging a 48 V lithium-ion battery pack. This work focuses on practical power-stage design with appropriate semiconductor device selection, high-frequency transformer design, output filter design considering switching stress mitigation. To address electromagnetic interference (EMI) in full-bridge converters due to high dv/dt and di/dt switching transitions, a passive mitigation approach with an input capacitor and RC snubber network is presented.

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Application of the JAYA Algorithm for Optimal Power Flow and RES uncertainty with Distributed Generation on the IEEE 30-Bus System

Because they are intermittent and stochastic, Renewable Energy Sources (RES) like wind and solar add a great deal of uncertainty to power systems when integrated. Particularly in large-scale systems like the 220kV IEEE 30-Bus network, these uncertainties make grid stability maintenance challenging. This paper introduces a framework that uses JAYA optimization to reduce the effects of RES uncertainty on grid performance. Power flow, voltage stability, and reactive power support are optimized in variable RES generation situations using the JAYA algorithm, which is renowned for its robust convergence qualities and its simplicity. To keep the system stable, the suggested method makes real-time adjustments to control parameters such reactive power compensators, tap-changing transformers, and generator outputs.

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Optimal Reactive Power Flow in Electrical Networks Using AI-Based Optimization Techniques

This paper shows a new method to enhance the performance of loop electrical systems by finding optimal reactive power. The Bees Algorithm and the Jaya Algorithm are used to determine the optimal settings of control variables such as generator voltage, tap positions of transformers, and reactive power output of shunt compensators. These methods are tested on IEEE 14 and IEEE 30 bus systems. The results show that hybridizing the Bees Algorithm with the Newton Raphson method has a more favorable effect than hybridizing the Jaya Algorithm with the Newton Raphson method in terms of minimizing active power losses and achieving faster convergence to the optimal solution.

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Model Predictive Control for Trajectory Tracking in Multi Input Multi Output Systems

Trajectory tracking in Multi Input Multi-Output (MIMO) systems remains a fundamental challenge in advanced control engineering due to strong coupling effects, nonlinear dynamics, and stringent operational constraints. Traditional controllers like PID and LQR frequently fail to ensure resilience, optimality, and constraint compliance. In this paper, we look at the use of Model Predictive Control (MPC) as a trajectory tracking framework for MIMO systems. The suggested methodology, which incorporates system modelling, horizon-based optimization, and constraint handling, is assessed using simulated benchmarks versus PID and LQR controllers The findings show that despite closely adhering to input and state constraints, MPC achieves higher tracking accuracy, up to 52% lower RMSE, and shorter settling times.

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Photovoltaic-Based Eleven-Level Cascaded H-Bridge Inverter with Bidirectional Switches to Minimum Harmonic Distortion

In this article, a single-phase modified cascaded H-Bridge eleven-level inverter that produces a precise sinusoidal output voltage while significantly reducing THD. The proposed topology enables a reduction in the number of switches relative to the traditional cascaded H-bridge inverters but with a THD of about 3. The designed inverter construction consists of four bidirectional switches, including a conservative H-Bridge that allows adequate voltage control and enhances overall structure performance. The primary goal of this effort is to decrease the number of devices without sacrificing power quality and to provide an affordable solution that is suitably tailored for various applications that demand high-quality AC voltage. By minimizing harmonics and guaranteeing that the production voltage waveform intentionally closes a sinusoidal waveform, a suitable switching policy contributes to the development of the system's performance.

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CNN-Guided Dual-Chaotic Encryption and Wavelet Domain Embedding for Robust and Adaptive Image Watermarking

With the rise of digital content creation and sharing, protecting multimedia assets from illegal use, decoding, and piracy, without a doubt, is becoming more important as time passes. As such, this work proposes a novel digital image watermarking framework that combines Convolutional Neural Networks (CNNS), Discrete Wavelet Transform (DWT), and a dual chaotic encryption technique based on Logistic Map and Tent Map fusion. An adaptive encryption and watermark embedding in the frequency domain of the host image ensures imperceptibility, robustness, and security. Feature statistics are extracted from a normalized watermark using a two-layer CNN, dynamically creating initial conditions for chaotic maps. The high-entropy resultant mask is fused with the generated sequences and used to encrypt the watermark with modular arithmetic. The encrypted watermark is embedded in the LL sub-band of a DWT-transformed host image using alpha blending. The final watermarked image is then reconstructed using inverse DWT. CNN features are used to regenerate identical chaotic sequences to decrypt and retrieve the watermark during extraction.

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