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Recent Submissions
Hybrid Nature Inspired Algorithm for Capacity and Secrecy Optimization in MIMO
(Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-11-30) Rahman, Md. Samiur
In this dissertation, a novel hybrid optimization framework is proposed for maximiz ing the capacity and spectral efficiency of Multiple-Input-Multiple-Output (MIMO)
systems with spatially correlated antennas under Rayleigh fading channels. The sys tem model incorporates Doppler shift to account for mobility and applies a power
allocation scheme optimized using a novel Hybrid Particle Swarm Optimization and
Chameleon Swarm Algorithm (PSOCSA) that incorporates the combination of two
adaptive swarm intelligence meta-heuristics; Particle Swarm Optimization (PSO) with
global search proficiency and Chameleon Swarm Algorithm (CSA) which includes
the adaptive exploration-exploitation mechanism. The Proposed algorithm maintains
a population of particles that exploit the best-known solution while also escaping from
local optima which in turn helps to speed up the convergence for a global optimum so lution. Comparative analysis demonstrates that the proposed PSOCSA algorithm sig nificantly outperforms other state-of-the-art algorithms in terms of both computational
efficiency and capacity maximization across various MIMO configurations (4x4, 8x8,
16x16, 64x64). Subsequently, the problem is further extended to secrecy rate optimiza tion in MIMO wiretap systems by incorporating a MIMO-capable Eavesdropper in the
MIMO network, where the goal becomes maximizing the secrecy capacity so that the
legitimate receiver receives as much channel capacity as compared to the eavesdrop per. Simulation results show how the Hybrid PSOCSA consistently achieves supe rior performance compared to other standalone state-of-the-art algorithms for a variety
of MIMO configurations — traditional (4x4 and 8x8) and Massive MIMO (16x16,
32x32, 64x64), as well as a realistic 5G setting with 128x128 antennas and a range of
eavesdropper antenna arrays up to 64, providing maximized secrecy rates with reduced
computational complexity and smaller standard deviations indicating faster conver gence and robustness. Moreover, the developed system model is designed with several
practical factors, such as; antenna correlation, Doppler effect, interference power from
neighboring cells, and imperfect Channel State Information (CSI), which represent the
security challenges in real-world secure communication, and thus replicating compli cated and practical implications of modern wireless communication setups. Statistical
tests like the Wilcoxon Rank-Sum Test and T-Test were applied to validate the perfor mance of the proposed hybrid algorithm. The numerical results exhibit the generality
of the Hybrid PSOCSA to secure and enhance the diversity and robustness of next generation wireless systems in terms of security and overhead efficienc
Optimal Unit Commitment for Renewable Energy and Energy Storage Integrated Power System
(Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-11-30) Apon, Hasan Jamil
As the demand for electricity rises, effective management of generating units becomes essential for ensuring a reliable and sustainable energy supply. The Unit Commitment Problem (UCP) plays a critical role in scheduling generation units to meet fluctuating load demands while minimizing operational costs. Concurrently, the Combined Economic Emission Dispatch (CEED) problem addresses the dual objectives of reducing fuel costs and harmful emissions from power plants. This research focuses on optimizing these interconnected problems within the IEEE 39-bus system, featuring 10 generating units, while incorporating renewable energy sources (RESs) and energy storage systems (ESSs).
The study employs the African Vulture Optimization Algorithm (AVOA) for solving the UCP to achieve an operational cost of $559,791.23 and emissions totalling 26,608.40 tons per day without considering the associated transmission losses. Utilizing the Multi-Objective African Vulture Optimization Algorithm (MOAVOA) for the CEED problem resulted in a significant reduction of emissions to 22,748.63 tons per day, illustrating the trade-offs between economic efficiency and environmental impact. Additionally, the integration of PV systems at 10% and 20% penetration levels showed substantial emissions reductions, even as operational costs increased slightly. The research further explores the role of ESSs in managing variability in PV output, thereby enhancing grid reliability and reducing the risk of load shedding.
Overall, this study highlights the importance of developing balanced strategies that integrate cost considerations, emissions reduction, and renewable technologies. It advocates for continued investment in innovative energy solutions and supportive regulatory frameworks to facilitate the transition to cleaner, more resilient energy systems.
End-to-end Digitization of Handwritten Circuit Diagrams
(Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-10-30) Ahmed, Nadim
Handwritten engineering diagrams are still commonly used in many industries and academic settings, but the lack of digitization limits their utility in modern workflows. While significant effort has been made to digitize handwritten content in other engineering domains, the digitization of handwritten circuit diagrams remains underexplored. Despite the growing demand for automated digitization systems in electronics and electrical engineering, there remains a lack of comprehensive datasets and research efforts targeting this specific domain. This thesis addresses these challenges through contributions to both dataset preparation and model development for an end-to-end digitization system. A dataset titled Digitize-HCD was developed, comprising 1,277 handwritten circuit diagrams from over 150 volunteers. The dataset includes detailed annotations for component symbol recognition, text label recognition, and component port localization, featuring 18,602 component symbol annotations across 17 classes and 11,936 text labels comprising 44,443 characters. Additionally, a component port localization dataset was created using Gaussian heatmaps to represent port locations for each component type. Four baseline object detection models—RTMDet, YOLOv8, Faster R-CNN, and EfficientDet—were tested for component symbol detection, with YOLOv8 (CSPDarknet P5 backbone) achieving the highest overall mAP of 79.9%, and an mAP50 of 98.9%. YOLOv8 was selected as the final model due to its balance of high accuracy, low computational cost (FLOPs of 129G), and low latency (54.8 ms). Text detection was performed using the Differentiable Binarization Network (DBNet), while recognition employed the Show, Attend and Read (SAR) framework. A U-Netbased framework using Gaussian heatmap predictions was applied for component port localization. With this framework, simpler components like Resistors (MSE = 1.697, SDR = 98.89%) and Inductors (MSE = 1.586, SDR = 99.12%) showed high accuracy, while more complex components such as MOSFET (N-Channel) (MSE = 3.776, SDR = 89.19%) demonstrated higher prediction errors. The results from the component symbol detection, text detection and recognition, and port localization modules were integrated to reconstruct the circuit topology and generate a SPICE-compatible netlist, enabling seamless simulation of digitized handwritten circuits. This research contributes a publicly accessible dataset and a comprehensive end-to-end system for the end-to-end digitization of handwritten circuit diagrams, capable of transforming handwritten circuit diagrams into machine readable formats.
Performance Evaluation of Photonic Crystal Fiber for Sensing Applications
(Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-10-30) Mahbub, Sheikh Montasir
This research focuses on a novel approach of refractive index sensing by utilizing the nonlinear properties
of Hollow Core Photonic Crystal Fibers (HC-PCF) which help ultra-short pulses to get compressed as they
propagate through the fiber. The researchers have been working on optical fiber based sensors in recent
years but their sensing mechanisms rely on quite a few number of parameters.
In this work, a solution to this problem has been proposed which works quite efficiently. Hollow Core
Photonic Crystal Fiber (HC-PCF) filled with either gas or liquid having different refractive indices is
exposed to ultra-short pulses. Due to the variation in refractive indices, the fiber characteristic parameters
for each testing case appear to be unique. Hence for each case, the ultra-short pulse that has been sent
through the fiber changes its shape uniquely as it traverses through the fiber and the power of the output
pulse also appears to be non-identical. By analyzing this change in shape and power, the Material Under
Sensing (MUS) can be easily detected. In this work, successful sensing of gaseous materials and also liquid
materials have been demonstrated.
In terms of gas sensing, as much as 64% compression sensitivity has been achieved alongside 369.07 W of
Power Upsurge for CO2 as MUS. Whereas, if the MUS for hollow core photonic crystal fiber is chosen
with a higher refractive index within the range of 1.35 – 1.455, as much as 11.6% of compression sensitivity
has been achieved with 2313.918W of elevation in Power. This innovative approach holds promise for the
detection of a large variety of petrochemical elements, and biological elements with accuracy and
reliabilit
Thermo-economic Analysis and Multi-Objective Optimization of Advanced Three-Stage Cascaded Refrigeration Technologies
(Department of Mechanical and Production Engineering(MPE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-11-30) Nabil, Mahdi Hafiz
The rising global demand for refrigeration, driven by industrial, medical, and technological needs, necessitates advancing highly performing and environment-friendly cooling technologies. This study explores advanced refrigeration techniques to address the limitations of conventional vapor-compression refrigeration (VCR) systems, particularly in ultra-low temperature (ULT) applications. While widely used, conventional VCR systems suffer from significant performance degradation at temperatures lower than -40°C, primarily due to excessive compression ratios and high discharge temperatures. To overcome these challenges, cascade refrigeration systems (CRS) have emerged as a promising alternative, enabling ultra-low temperatures (ULT) by combining multiple refrigeration cycles.
This research presents the modelling and analysis of two novel advanced three-stage cascade refrigeration systems: an advanced triple cascade refrigeration system (ATCRS) and an ejector-enhanced advanced triple cascade refrigeration system (EATCRS). The ATCRS integrates a suction-line heat exchanger (SLHX) and flash tank (FLT) to enhance thermodynamic performance, achieving an 8.57% improvement in coefficient of performance (COP) and a 7.24% increase in second law efficiency over traditional cascade systems. The EATCRS incorporates an ejector system in the medium-temperature circuit (MTC), further improving system efficiency. Compared to the ATCRS, the EATCRS demonstrates a 32.82% increase in COP and a 27.34% boost in exergy efficiency, significantly outperforming conventional systems as well as the ATCRS. Moreover, the economic analysis indicates that despite an initial 9.78% increase in annual costs due to the ejector integration, the EATCRS achieves a 25.73% reduction in costs compared to other advanced systems.
This study fills a critical gap in the current research by providing a comprehensive analysis of three-stage cascade refrigeration systems equipped with advanced VCR modifications along with multi-objective optimization of both systems using ANN-based genetic algorithm identifying optimal operating points ensuring the maximum possible performance by keeping the system cost within acceptable limit. The results highlight the potential of these systems to accommodate the increasing demand for ultra-low temperature refrigeration while addressing critical environmental and economic concerns. This work lays the foundation for future research aimed at optimizing refrigeration systems to ensure energy sustainability and environmental protection.