Electrical Engineering Technical Journal

Miniaturizing Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) to the nanoscale results in specific structure and performance effects known as Short Channel Effects (SCEs). Performance degradation due to SCEs occurs due to increased leakage currents (IOFF), decreased threshold voltage (VTH) stability and breakdown voltage. Various solutions for these problems have been developed through the application of structural and material engineering techniques. One particular engineering technique that shows promise as a potential solution is to incorporate a Selective Buried Oxide Layer (SELBOX). In this article, we investigate how the introduction of a SELBOX impacts the electrical characteristics of nano-scale n-MOSFETs through device simulation (TCAD). For the purposes of this study, we developed a 20 nm n-MOSFET with a high-k HfO₂ gate dielectric using the TCAD Silvaco ATLAS software package. Three different types of SiO₂, Al₂O₃, and HfO₂ dielectric materials were used in conjunction with the SELBOX layer which is located at a depth of 30 nm from the drain. In addition, the location of the SELBOX as related to the drain was also varied from its original position to the direct vicinity of the channel to assess the overall effect of the SELBOX position. Finally, we compare the characteristics of a conventional-type MOSFET (no SELBOX) to those of devices modified by inclusion of the SELBOX at varying positions/depths from the drain. The findings indicate that the dielectric constant and band gap of the implanted material, as well as its closeness to the drain and channel region, substantially influence device performance. In the instance of SiO₂ as SELBOX material, the IOFF decreased by 33%, and the breakdown voltage significantly increased from 85.09 V to 491.4 V. The utilization of Al₂O₃ resulted in a 27% reduction in IOFF and an increase in breakdown voltage from 85.09 V to 275.7 V. Notably, the application of HfO₂ as SELBOX material resulted in a divergent effect: IOFF rose by 21%, but the breakdown voltage increased to 172.3 V.

A pixar image is not merely a visual representation of something; it carries within it a wealth of memories and im-portant information. Image processing has been, and continues to be, a primary focus for many researchers seeking to improve image quality, given its importance in numerous vital areas of life that demand high precision for realistic assessment. Due to the heavy reliance on sensitive materials in many fields, and because of ambient noise, poor light-ing, or compression distortion, image quality is compromised. This, in turn, limits the reliability of automated systems that depend on images for analysis and to provide dependable results. This paper aims to develop a Python-based program capable of increasing efficiency and improving the perceptual and analytical quality of digital images by applying and comparing different noise reduction and interpolation algorithms. Python was chosen for its high flexi-bility and open-source nature, allowing for the desired balance between computational efficiency and image clarity.

This paper presents a compact, tri-band Planar Inverted-F Antenna (PIFA) for 5G sub-6 GHz mobile handsets. The proposed design integrates a slotted G-shaped radiating patch and a defected ground structure (DGS) to achieve multiband operation within a low-profile, four-layer printed structure. The antenna resonates at 2.4 GHz, 3.7 GHz, and 5.7 GHz, effectively covering the 5G NR bands n41 and n78, alongside LTE and WLAN applications. Simulation results demonstrate wide bandwidths of 770 MHz, 400 MHz, and 600 MHz at the respective resonant frequencies, with a peak realized gain of 4.4 dBi. The antenna exhibits high radiation efficiency, a low specific absorption rate (SAR), and an omnidirectional pattern. With its compact dimensions and performance metrics that surpass existing designs, the proposed antenna is a compelling solution for modern 5G-enabled mobile phones.

This paper presents a structured hybrid control framework for mobile robot navigation and path tracking in uncertain and dynamic environments. The proposed architecture integrates a fuzzy logic controller as the primary decision-making layer with a multilayer perception (MLP) neural network used for nonlinear compensation. The fuzzy module processes real-time sensory inputs from front, left, and right distance measurements and generates the initial steering command using a Mamdani inference system. To enhance tracking accuracy and robustness, the neural network is trained in a supervised manner using the mean squared error (MSE) loss function to learn adaptive correction signals. The final steering command is obtained by combining the fuzzy output with the neural compensation term, forming a hybrid control strategy that preserves interpretability while improving adaptability. The system is implemented in a simulation environment and evaluated under obstacle-rich scenarios. Quantitative performance metrics, including MSE and root mean square error (RMSE), are used to assess tracking accuracy. Experimental results demonstrate improved trajectory precision, reduced tracking error, and enhanced stability compared to standalone fuzzy and neural approaches. The proposed framework maintains clear functional separation between decision-making and compensa-tion layers, improving modularity, transparency, and reproducibility. The results confirm that the integration of fuzzy reasoning with neural compensation provides an effective and computationally efficient solution for autonomous mo-bile robot navigation in nonlinear and uncertain environments.