Journal of Modeling & Simulation in Electrical & Electronics Engineering
Year:2024 | Volume:4 | Issue:4|Papers Count:6

This manuscript introduces a highly sensitive refractive index sensor that utilizes a disk-shaped graphene absorber. This design takes advantage of graphene's exceptional properties and integrates pyramid-shaped air holes within the dielectric layer. The sensor operates in the terahertz (THz), with a specific focus on improving sensitivity and overall performance. Graphene's high electrical conductivity and tunable properties make it an ideal material for THz absorption, enabling precise detection of refractive index changes the proposed structure comprises a graphene pattern on the top layer, a SiO2 as dielectric in the middle layer, and a gold reflective in the bottom layer. Through full-wave simulation and transmission line modeling, the sensor's performance is validated, showing a remarkable rate of absorption of 99.99% at 4.26 THz. Further enhancement is achieved by introducing pyramidal air holes in the dielectric layer, significantly improving the sensor's quality factor and sensitivity. The quality factor of the sensor is improved from 14 to 23 by adding pyramidal air holes in the substrate layer. The structure was full-wave simulated using software CST with the FDTD solution method, and the transmission line method was done with the Matlab software. The results suggest that the planned sensor serves as a highly suitable candidate for early disease detection, including cancer and influenza, with potential applications in the medical industry.

In steganography, a text is placed in a digital image in a secure, imperceptible and retrievable way. The three main methods of digital image steganography are spatial methods, transformation and neural network. Spatial methods change the pixel values of an image to embed information, while transform methods embed information hidden in the frequency of the image. Neural networks are use to perform the hiding process  and it is the main part of this research. This research examines the use of LSTM[1] deep neural networks in digital image text steganography. This work extends an existing implementation that uses a two-dimensional LSTM to perform the preparation, hiding, and extraction steps of the steganography process. The proposed method modified the structure of  LSTM and used a gain function based on several image similarity measures to maximize the indiscernibility between an overlay and a steganographic image. Genetic algorithm helps in improving the structure of LSTM networks in the textual information within hidden images, with optimizations (number of layers, neurons, evaluations) and selection of appropriate features, increasing the accuracy, improving image quality and preventing overfitting. This method helps to find the optimal architecture for the LSTM network and improves the efficiency of the steganography. The proposed method demonstrates superior performance based on three evaluation metrics Peak Signal-to-Noise Ratio (PSNR[2]) in decibels, Mean Squared Error (MSE[3]), and accuracy rate in percentage compared to three other benchmark images (lena.png, peppers.png, mandril.png, and monkey.png), achieving values of 93.665275 dB, 0.6945 MSE, and 97.23% accuracy, respectively. The proposed method modified the structure of  LSTM and used a gain function.

Wireless Sensor Networks (WSNs) play a critical role in diverse applications, ranging from environmental monitoring to military surveillance. Clustering techniques are essential for optimizing energy consumption and improving data transmission efficiency in WSNs. This paper introduces the Intra-Cluster Election (ICE) technique, which enhances centralized clustering protocols within the LEACH-C (Low-Energy Adaptive Clustering Hierarchy-Centralized) framework. ICE improves cluster head selection by optimizing the cost function value and reducing convergence iterations. Although the improvements in energy efficiency and data delivery rates are modest compared to LEACH-C, primarily because its cost function does not account for the distance between cluster heads and the base station, ICE demonstrates significant advantages in optimization. The simulation results show that ICE substantially improves cluster head selection, leading to more efficient network operations across various WSN scenarios. It achieves approximately 12–20% improvement in the cost function compared to LEACH-C and reduces the number of required iterations by a factor ranging from 3 to 100, depending on the network conditions.

In the present study, an eight-channel photonic crystal wavelength demultiplexer is designed for separating eight telecommunication wavelengths: λ₁=1.5552 nm, λ₂=1.5563 nm, λ₃ =1.5579 nm, λ₄=1.5593 nm, λ₅ =1.5608 nm, λ₆ =1.5623 nm, λ₇ =1.5638 nm, and λ₈ =1.5653 nm. To this end, a two-dimensional photonic crystal structure composed of circular silicon rods arranged in an air background and fabricated on a silica (SiO₂) substrate is employed.The design and analysis are performed using the Plane Wave Expansion (PWE) method to compute the photonic band structure and guided modes, while the Finite-Difference Time-Domain (FDTD) method is used to calculate transmission efficiency and analyze propagation characteristics during wavelength-selective separation. Ultimately, by optimizing the resonator placement and the radius of point defects, a channel spacing of 1.44 nm and a transmission efficiency (TE) of 93% were achieved.

Chnology scaling becomes increasingly aggressive, lifetime reliability has emerged as a critical challenge for modern digital circuits, exacerbated by manufacturing process variations and aging effects. This paper introduces GenSO, a Genetic algorithm-based multi-objective Sequential circuit Optimization framework designed to enhance the lifetime reliability of sequential circuits modeled as Finite State Machines (FSMs), while simultaneously addressing initial delay and power consumption. The framework leverages a cross-layer approach, utilizing a gate-level delay degradation model that accounts for process variations and aging to estimate circuit lifetime reliability. A novel metric, termed Guardband-Aware Reliability (GAR), is proposed to provide a fair assessment of FSM lifetime reliability in relation to the guardband and timing yield specified by the designer. A multi-objective genetic algorithm is then employed to optimize delay, power consumption, and lifetime reliability in FSM-based sequential circuits. Experimental results demonstrate that GenSO successfully identifies non-dominated solutions for sequential circuit designs, achieving simultaneous optimization of initial delay, power consumption, and lifetime reliability. With a 15% delay overhead for a 6-year lifetime and a 10% variation ratio, GenSO improves circuit reliability by an average of 64.34%, significantly outperforming state-of-the-art reliability optimization frameworks, which typically achieve less than 30% improvement in lifetime reliability.

The utilization of orbital angular momentum (OAM) presents an effective approach that enables the simultaneous use of multiple channels on a single frequency. Compared with conventional OAM carrier waves—whose three-dimensional radiation pattern resembles a cone with a hollow region at its center—the planar-spiral orbital angular momentum (PSOAM) carrier propagates transversely, making it more suitable for various practical applications. For the first time, the generation of PSOAM waves using a uniform circular array of microstrip antennas based on electromagnetic metamaterials has resulted in a phase pattern distinct from the conventional OAM theory. This study demonstrates that incorporating metamaterials into the antenna array structure facilitates the generation of higher-order OAM modes with fewer elements than conventional antennas. Simulation results indicate that the proposed array achieves approximately a 27% reduction in the number of antenna elements. Furthermore, the introduced antenna exhibits a wide relative bandwidth of 1.93.