AIUB Journal of Science and Engineering (AJSE)

A Microstrip Antenna Model for Clinical Monitoring Applications

2025-01-22T01:57:19+06:00

The use of microstrip antenna in microwave imaging for clinical assessment assistance has grown to a great extent. In this paper, an inset feed defected ground structure rectangular patch antenna with Rogers RT/duroid 5880 as the substrate material is designed in the frequency range of 4.80 GHz to 5.80 GHz for clinical diagnosis assistance. A four layered human head phantom model is used for evaluation in Ansys HFSS electromagnetic simulation. The suggested antenna is simulated over the head phantom model in three different positions i.e., over the head phantom model free of tumor, over the head phantom model with a superficial tumor region of 6mm radius and over the head phantom model with deep-seated tumor. The quantitative assessment of the proposed technique is conducted using different antenna parameters, such as: radiation efficiency, reflection factor, co-polarization, cross-polarization, surface current in comparison to the cutting-edge techniques. Graphical representation of the parameters is also presented in order to conclude the presence of tumor inside the brain.

The Distribution Network Loss Minimization by Incorporating DG Using Particle Swarm Optimization (PSO) Technique

2025-01-22T01:57:02+06:00

This research is based on the power system optimal planning and operation segment. This study presents a methodology for reducing power losses in distribution power systems through the incorporation of both fixed and uncertain photovoltaic (PV) generation, utilizing the Particle Swarm Optimization (PSO) technique. The proposed approach aims to address the unpredictability of PV generation and optimize the placement and size of Distributed Generation (DG) units to minimize I²R loss in the distribution network. The main focus is on how to minimize the existing losses occurring in the distribution system. In the distribution system the I²R loss amount is higher compared to the transmission system. The reason behind the high amount of I²R loss in the distribution system is due to the high ratio, high current and low voltage. To reduce these losses, several researchers provide solutions, such as network reconfiguration, capacitor allocation, DG allocation, and DSTATCOM allocation. In this research I have focused on DG allocation solution for reducing the I²R loss. I have determined the most suitable photovoltaic generation capacity and location to minimize power loss in the system using the PSO algorithm. The PSO techniques can accurately determine the optimal locations for PV generation in the IEEE-33 bus and IEEE-69 bus radial feeder, which preserves power quality for consumers and reduces energy loss during distribution. To conduct this study, three simulation scenarios were employed, namely: a) the base case, b) without accounting for the uncertainty of solar irradiance and load demand, and c) considering the uncertainty of solar irradiance and load demand. The comparison between the three scenario results will also be shown in the results section for both the IEEE-33 and IEEE-69 bus systems. MATLAB R2021a was utilized to evaluate the algorithm's effectiveness in the IEEE-33 and IEEE-69 bus systems

Temperature Characteristics of Nano-Dimensional FinFET with P Type GaP Semiconductor Channel Based on ION/IOFF and Subthreshold Swing (SS)

2025-01-22T01:56:45+06:00

This study discusses the effects of working temperature on the GaP-FinFET structure. Using the Multi-Gate Field Effect Transistors (MuGFET) simulation tool, the properties of FinFET have been generated over a temperature range of T=0 °C to T=125 °C. First, the structure of P-channel GaP-FinFET with such temperature vary and constant channel Fin parameters are simulated to find their current-voltage characteristics. The higher ΔI at the applied voltage inside the -0.7 to -1 V gate voltage range of the P-channel GaP-FinFET work as a temperature sensor in nano dimensions. The highest ∆I and sensitivity of temperature for P-channel GaP-FinFETs happen at Vg=-1 V, although the SS and ION/IOFF deteriorate as the working temperature rises.

In Bangladesh, A Techno-Economic Evaluation of Rice Straw Based Power Generation

2025-01-22T01:56:28+06:00

The Availability of sufficient electrical energy is essential for a nation’s economic growth to be steady. Bangladesh should, therefore, have enough electricity infrastructure to maintain its economic growth. Several kinds of agricultural waste are available in Bangladesh as an agricultural country. Therefore, Bangladesh may generate energy (such as electricity) from this enormous agricultural waste. One of the potential agricultural wastes for use as a source of energy is rice straw, but only if it is properly and methodically processed. This research investigates the technical potential of using filtered rice straw to produce energy in Bangladesh. Environmental risks are posed by the estimated 4 million tonnes annual burn of rice straw in an open field. Rice straws can produce a net annual electricity output of 217.21 GWh, corresponding to an input of 8640 tonnes of rice straw, according to simulation and full-scale experiments. But our country’s available rice straw is almost 119.3 million tonnes/year. Notice that we used 17-tonne rice straw in the aspen simulator. The plant can reduce CO₂ emission of 0.03%. Total cost is $4,082,005 per year, and the per unit cost is $0.019. This thesis work aims to present a concept for producing electricity in Bangladesh’s rural areas using rice straw. Small and medium-sized power plants based on rice straw are very helpful for producing and distributing electricity in rural areas. A comprehensive process model for biomass gasification in a twin-fire fixed bed gasifier is developed using the ASPEN PLUS simulator. The chemical process industries primarily use the process modeling tool Aspen Plus for process monitoring, optimization, and conceptual design. This simple course on the Aspen Plus Simulation engine will teach you how to model the most common chemical plant unit operations.

Optimal Sizing of an Islanded Microgrid System: A case study in Manpura Island, Bangladesh

2025-01-22T01:56:11+06:00

Renewable energy sources (RESs) perform a crucial role in addressing energy crisis in remote rural areas where it is uneconomical to expand electrical distribution systems. A possible solution to this issue includes the development of microgrids that utilize RESs, such as solar. This study proposed an approach of optimal sizing of an islanded microgrid at Manpura Island, Bangladesh, consisting of several configurations including photovoltaic (PV) systems, diesel generator (DG), and three distinct battery technologies, lead acid (LA), lithium-ion (Li-ion), and nickel-iron (Ni-Fe) are intended to meet the island's electrical load demand. Grey wolf optimization (GWO) is used to reduce the life cycle cost (LCC) and cost of energy (COE) by taking operational constraints into account. Further, indicators of the loss of power supply probability (LPSP) assess the reliability and effectiveness of the island microgrid system. The results demonstrate that the GWO outperforms both the genetic algorithm (GA) and particle swarm optimization (PSO) method in term of optimal systems performance with LPSP of 0%, LCC of $202748 and COE of 0.3048$/KWh.

Exploring Silicon Nitride Gate Materials for Enhanced Performance of Silicon-Based MOSFET

2025-01-22T01:55:54+06:00

This research paper investigates the potential of silicon nitride gate materials for enhancing the performance of silicon- based Metal-Oxide -Semiconductor Field-Effect Transistors (MOSFETs). Through simulations conducted using COMSOL Multiphysics, we analyzed the impact of using silicon nitride gate materials on MOSFET performance. Our results demonstrate that silicon nitride gate materials offer improved device characteristics, including reduced gate leakage currents, enhanced carrier mobility, and reduced threshold voltage variability. These findings underscore the potential of silicon nitride as a key material for advancing the performance of MOSFETs, paving the way for more efficient and reliable semiconductor devices in the future.

Structural, Optical, and Electronic Properties Analysis of Praseodymium Doped ZnO: Insights from Density Functional Theory with GGA+U Approach

2025-01-22T01:55:37+06:00

ZnO is widely used as a semiconductor material due to its wide bandgap, high exciton binding energy, and excellent transparency in the visible range, which make it suitable for optoelectronic applications. Doping in ZnO is important because it allows for controlling its electrical properties, enables the tuning of conductivity, and enhances its functionality for specific applications. Doping can introduce new energy levels within the bandgap. Moreover, it improves the performance of ZnO-based devices. This study explored the structural, optical, and electronic properties of pure and Praseodymium ion (Pr3+) doped ZnO using GGA+U based DFT. Results agreed with prior research, showing compatible lattice parameters and band gap for pure ZnO. Increasing Pr concentration expanded lattice parameters and volumes while reducing the energy band gap. Pr doping shifted the Fermi level to the upper conduction band, causing an overlap between the conduction and valence bands. This indicated a transition from a semiconductor to an n-type degenerate semiconductor with metal-like characteristics. Higher doping concentrations led to a shift in density of states towards lower energies. Computed optical properties exhibited red shifts in absorption peaks and increased absorption in the near and far ultraviolet regions following Pr doping. Similar red shifts were observed in the reflectivity spectrum and other optical properties. The real dielectric constant (ε1 (ω)) displayed negative values, signifying metallic behavior at specific photon energies, consistent with band structure optimization.

A Novel Framework for Enhancing Data Collection Efficiency Through Multiple Mobile Sink Nodes in Wireless Sensor Networks

2025-01-22T01:55:20+06:00

The transmission of oceanographic data and the existence of impurities in Submerged Wireless Sensor Networks (SWSN) have been the subject of a significant amount of research in recent years. Maintaining link stability, latency in establishing connections, data loss in real-time broadcasts, and limited transmission ranges are some of the difficulties these networks face. Several routing solutions have been proposed to address these issues, but none of them have been able to provide effective transmission. Supported by both simulation and experimental results, this research presents a framework for an all-inclusive data-collecting system. Our system uses intelligent cluster sensor nodes to successfully relay temperature, pH, and turbidity data in the Indus River; turbidity values range from 6.5 to 31 NTU. Our suggested approach considerably enhances data transmission performance in submerged wireless sensor networks, according to the experimental results. When combined with Zigbee technology, a Modified Pseudo Orthogonal M-Sequence Data Acquisition System can greatly improve the performance of Routing Protocols in Submerged Wireless Sensor Networks.

A Performance Analysis on Matrix Factorisations in Solving Musical Instrument Source Separation Problem

2025-01-22T01:55:03+06:00

Audio signal decomposition breaks a mixture of musical instrument audio signals into its fundamental musical instrument components. Machine learning is one of the methods widely used in audio signal decomposition. However, the limitation of computer hardware and the complexity of the algorithm may cause the computational speed of machine learning to deteriorate. This paper aims to use the contemporary matrix factorisation to extract the fundamental musical instrument audio signal component from the mixture of musical instrument audio signals. We choose nine contemporary matrix factorisation techniques and compare their performance in separating the mixture of musical instrument audio signals. We create five scenarios with different melody complexity to test the matrix factorisation techniques. Based on the Signal-to-Noise Ratio, Nonnegative Matrix Factorisation with Kullback-Leibler Divergence (NMF-KL) is the best separation performance when the monotonic noise is not added to the mixture of musical instrument audio signals. Initially, NMF-KL has good separation when monotonic noise is added to the simple recurring mixture of musical instrument audio signals, but as the melody complexity increases the NMF-KL separation performance starts to deteriorate. Lastly, the matrix factorisation techniques do not work well when the white noise is added to the mixture of musical instrument audio signals.

Automatic Detection and Classification of Diabetic Retinopathy from Optical Coherence Tomography Angiography Images using Deep Learning - A Review

2025-01-22T01:54:45+06:00

Diabetes-related retinopathy (DR) is a microvascular complication of diabetes that affects many people (DR). Both the prevalence of DR cases and the number of diabetics worldwide have risen considerably. DR weakens vision and can lead to full or partial blindness if ignored. Ocular visual serves as a crucial instrument in detecting, assessing, handling, and recording DR. Over the last few years, advances in technological imaging have enabled the creation of images with improved readability and comparison while requiring significantly less time, effort, and disruption. The choriocapillaris and retinal microvasculature can be studied using optical coherence imaging the procedure is based on differences in floating in blood cells (OCTA). Additionally, OCTA can find preclinical microvascular anomalies that appear before clinically evident DR symptoms. To identify and categorize diabetic retinopathy in OCTAimages, deep learning algorithms may be developed. By using DL techniques, such as CNNs, the images can be analyzed and features extracted for accurate diagnosis.In this paper, we want to offer readers a review of Diabetic retinopathy classification techniques utilizing machine learning and deep learning from OCTAimages, as well as a summary of the various OCTA datasets currently in use for this purpose. Also, we confirmed that the proposed model could work by comparing its performance to that of anML-based categorization tool that uses manually created features extracted from OCTA images. This review gave an overview of recent studies on DL-based image analysis models for OCTA images. It also addressed possible problems with clinical deployment and future directions for research.