Conference Papers
Recent Submissions
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"Fiction to Function" Shaping Renewable Energy Education with MATLAB and ChatGPT-Driven EnvironmentsAddressing the gap in effective and engaging renewable energy education, this study, 'Fiction to Function,' transitions imaginative frameworks into practical educational tools, focusing on solar and wind energy in line with Sustainable Development Goals (SDGs). The goal is to empower users with deep insights into renewable energy through the novel integration of MATLAB and ChatGPT-driven environments, enhancing the analysis and exploration of these energy sources. The study employs a structured approach using MATLAB App Designer to develop a user-friendly GUI, aligning instructional content with the SDGs and integrating ChatGPT via the OpenAI API for dynamic, interactive learning experiences. This integration provides a multifaceted platform that not only educates but also engages users in practical scenarios, bridging the gap between theoretical knowledge and real-world application. The results demonstrate an enhanced understanding of renewable energy concepts among users, showcasing the efficacy of combining MATLAB's analytical capabilities with ChatGPT's AI-driven guidance. Ultimately, the study highlights the transformative potential of innovative technology in sustainable energy education, setting a precedent for future educational tools in this domain.
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Prediction of Adsorption and Desorption Isotherms for Atmospheric Water HarvestingThis study improves the mathematical modeling for the potential of atmospheric water harvesting (AWH) using desiccant materials. This research is crucial in highlighting the sustainable water management strategies of AWH systems in converting atmospheric moisture into a vital water source. The primary focus of our research methodology was the analytical derivation of sorption isotherms, essential for the hygrothermal simulation of desiccant materials. This was accomplished through the application of two established models, namely, the Guggenheim-Anderson-de Boer (GAB) and Van Genuchten (VG). Experimental data on various anhydrous salts from existing literature have been used. An in-depth comparative analysis of these models reveals that the VG model aligns more closely with the experimental data, thus asserting its superiority in enhancing the selection and efficiency of desiccant materials in AWH systems. By confirming the VG model’s superiority in accurately modeling sorption isotherms, our research not only improves the model of AWH systems but also, importantly, contributes to the development of advanced water harvesting technologies.
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Integrating Human-Centricity, Sustainability, and Resilience in Digital Twin Models for Industry 5.0 : A Multi-Objective Optimization ApproachThis paper presents the InduDesc framework, an innovative digital twin model within the CupCarbon software, designed for the advanced needs of Industry 5.0. It integrates human-centred ergonomics, sustainability and resilience into the Flexible Job Shop Scheduling Problem (FJSP), traditionally an NP-hard challenge. By minimising operating times and balancing machine utilisation with ergonomic and sustainability considerations, the framework provides a dynamic workload management tool based on real-time 'fatigue' metrics. Using a tabu search algorithm, InduDesc generates a Pareto frontier to help decision makers identify strategies that efficiently align with the integrated goals of Industry 5.0.
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Battery Management System for Enhancing the Performance and Safety of Lithium-Ion BatteriesBattery packs integrated into the grid offer a promising solution for energy storage, but their efficient operation requires precise monitoring and control, which is achieved through Battery Management Systems (BMS). This paper proposes a temperature-dependent second-order RC equivalent circuit model to reflect the battery’s dynamic characteristics accurately. Then, a novel BMS design, incorporating Extended Kalman Filtering (EKF), a constant current-constant voltage (CCCV) charging, and a passive balancing algorithm to estimate the battery state of charge (SOC), balance voltage levels, and monitor thermal characteristics. The research also includes a comprehensive simulation study conducted in SIMULINK with the Simscape toolbox to assess the effectiveness of the analyzed BMS. The simulation results demonstrate the effictiveness of the proposed BMS design in monitoring the battery pack’s state, maintains cell balancing, estimates SOC, and keeps the temperature and current levels within safe limits. This model can help guide a more efficient and accurate BMS design for future studies.
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Bayesian Optimization Algorithm for ConvLSTM-AE model To Forecast Solar IrradiationAs much as the development of smart grid systems, accurate and reliable solar irradiance is crucial. In smart grid systems, forecasting solar irradiance remains a vital solution to mitigate the best management of energy.This study presents a new approach using a hybrid deep learning model, Conv-LSTM-AE, enhanced by Bayesian algorithm optimization. In four seasons of the years, the model was evaluated based on the accuracy using different metrices errors, and time computing in training. The model exhibits exceptional adeptness as compared to other deep learning models, showing that the Conv-LSTM-AE with Bayesian optimization is a standout performer with remarkable accuracy. This development represents a substantial step towards utilizing state-of-the-art technology for the efficient use of solar power within intelligent grid systems.
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Rechargeable Battery State Estimation Based on Adaptive-Rate Processing and Machine LearningThe generalization of the use of electronic systems and their integration in industrial systems and different aspects of modern life (internet of things, electric vehicles, robotics, smart grids), give rise to new challenges related to the storage and optimized management of energy. Lithium-on batteries perfectly meet this objective due to their good qualities such as high energy density, small installation size, low self-discharge and high supply capacity. However, their wide application requires further research on battery failure prediction and health management. Intelligent “battery management systems” (BMSs) employ the real-time estimation and control algorithms to improve the battery safety while enhancing its performance. Nevertheless, BMS are complex and require increased processing power which could lead to more power consumption. In this context, the present article provides a new approach for efficient prediction of the “Lithiumion” (Li-ion) battery cells capacities by analysing and exploiting the battery parameters, acquired by an event-driven module. It acquires the intended cells voltages, currents and temperature values during the charge-discharge cycles. The solution is based on the machine learning algorithms and event-based segmentation. The “National Aeronautics and Space Administration” (NASA) has provided a high-power Li-Ion cells dataset for the purpose of research and innovation. This dataset is used to test and evaluate the suggested approach. The evaluation of the overall performance of the chain has shown encouraging results of the proposed approach.
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Design of receiver RF front end for mm-Wave 5G applicationsRecently, the research emphasis has shifted towards 5G due to its potential to accommodate the increasing demand for data traffic, extensive interconnectivity of devices, and the emergence of numerous novel applications. Given the inadequacy of speed provided by spectrum resources in the lower frequency bands of 5G, a novel sub-generation utilizing millimeter wave frequencies has been introduced. The new sub-generation is called mm-wave 5G and provides higher bandwidth for faster speed and higher capacity. RF system architecture, circuits, and antenna innovation will be required to provide the requisite speed and capacity. In this paper the design challenges and trade-offs in RF front-end circuits and receiver sub-systems are discussed. Moreover, the massive multiple-input multiple-output (MIMO) techniques are examined. In addition, design, and simulation of key components in 5G mm-wave receiver, including the design of linear phased array antenna receiver and an analog-to-digital converter (ADC) is presented. Sigma-Delta (ΣΔM) Converters are a type of ADC with a technique that involves oversampling and shaping quantization noise to achieve a higher resolution which make it a best choice to be used in the design of the mm wave 5G communication applications. The proposed ΣΔM uses multistage architecture to provide high-order noise shaping and high resolution. The simulation results shows that the designed Cascade 2-2 MASH ΣΔ achieve a signal to noise ratio of 110(resolution of 16 bits).
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Accurate Classification of Cervical Cancer Based on Multi-layer Perceptron Hunger Games search Optimization techniqueCancer is distinguished by the presence of abnormal cellular proliferation and growth, both of which serve as signs and symptoms for this kind of illness. Computer vision, deep learning, and metaheuristics optimization techniques are increasingly important for solving complex medical Artificial Intelligence (AI) problems such as cancer detection. This paper introduces a new methodology for training the Multi-Layer Perceptron (MLP) using the optimization algorithm known as Hunger Games search Optimization technique (HGO) and apply this method to classify the cervical cancer. The main goal of this method is to reduce the error and enhance the classification rate of cervical cancer. The outcomes show that the MLP with HGO algorithm performed better than other algorithms in terms of classification efficacy and accuracy rate. Simulation outcomes indicate that the proposed strategy performs better than previously published research in terms of effectiveness for the classification optimization methods.
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Deep Learning for Fault Detection of Digital VLSI CircuitsWith the increasing complexity and scale of digital VLSI designs, ensuring reliability in IC design necessitates effective fault detection processes during the pre-silicon stage. Many fault detection algorithms lead to significant computational time due to the problem of search space explosion. To handle the ever-growing volume of data, deep learning algorithms, a subset of machine learning techniques, can be employed. In this paper, we propose two novel approaches for fault detection (FD) of digital VLSI circuits, specifically targeting stuck-at faults. The first proposed model is semi-supervised FD model that aims to mitigate the search space explosion issue by leveraging both unsupervised and supervised learning processes. The second presented model is based on an optimizer for finding the appropriate configurations for detecting stuck-at faults in digital circuits. The initial proposed model achieves maximum validation accuracy of approximately 98% applied to circuits from ISCAS’85. This model yields a higher accuracy compared to the second approach that achieves maximum accuracy of around 95% when applied to the same circuits from the ISCAS'85 benchmark.
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Reconfigurable Multiband Matching Circuit Design for a Non-resonant Antenna in Wireless Point-of-Care BiosensorsPoint-of-care (PoC) test is a portable test that allows patients and healthcare practitioners to perform ondemand real-time tests outside the laboratory and hospital environments. Its widespread adoption in healthcare systems enhanced the quality of care by reducing errors and providing rapid results which allow for faster treatment decisions. Adding wireless connectivity to PoC systems provides secure and instant wireless access to patients’ vital signs by healthcare professionals regardless of their location, as they can provide real-time remote monitoring, diagnostic and emergency services for patients. This paper presents a design of a reconfigurable impedance matching network (RIMN) for a non-resonant antenna chip to be implemented in wearable biosensors utilized in PoC tests. The purpose of the RIMN is to match the antenna chip’s impedance to the Industrial, Scientific and Medical (ISM) frequencies, viz: 900 MHz and 2.4 GHz. Return losses at below -10 dB with high bandwidths (of 200 MHz) and radiated power efficiencies (of over 95 %) have been achieved. The proposed mathematically modeled integrated hybrid wireless RFPerovskite energy harvester for multipurpose 5G/6G/Wi-Fi PoC biosensors promises superior performance metrics (including over 78% energy-harvesting efficiency) than the conventional approaches.
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Maximum Power Point Tracking Controller with Neural Gas Network PID of Boost ConverterEach photovoltaic (PV) system demonstrates a specific point on the current-voltage (IV) curve characteristics where the power generated reaches its maximum value which is referred to as the maximum power point (MPP). The maximum power point tracking (MPPT) system is designed to sample the output of solar cells and adjust the load in order to achieve maximum power output under various environmental conditions. In this paper, a design of a boost converter with MPPT based on a PID controller is proposed. The goal is to optimize the extraction of the highest attainable power from photovoltaic systems. The extracted power is subsequently directed to the load through a boost converter, which elevates the voltage to the necessary level. The PID controller is calibrated by Neural Gas Network (NGN) and utilized to achieve a consistent output voltage irrespective of fluctuations in the power source or the connected load. Moreover, an NGN technique is applied to generate the optimal PID parameters before using the PID. The comprehensive system design depicted has been simulated using MATLAB Simulink to verify the functionality of the system.
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Fault Detection in Analog/Digtal VLSI Circuits based on Artificial IntelligenceDue to the explosive demand and rapid development of the electronics industry, the integration and complexity of electronic devices have significantly grown. Fault detection has played a vital role in identification of faults in electronic circuits, ensuring normal operation and reliability of systems. Given traditional methods of fault detection are often inaccurate and time-consuming, artificial intelligence techniques are a growing interest for researchers of this field. In this paper, various artificial intelligence-based fault detection techniques in analog and digital VLSI circuits are described. In addition, two distinct models for fault detection of digital circuits based on deep learning are proposed. The primary goal of the first approach is utilization of a stacked sparse autoencoder to avoid the search space explosion problem. The second proposed method utilizes an optimization method for detecting the best model configuration. The proposed models deliver maximum validation accuracy of 97.7% and 95.7% respectively, implemented on digital circuit from ISCAS’85.
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VVoIP Quality Improvement Over IMS Network based on background subtractionThe dependence on making Voice and Video over Internet Protocol (VVoIP) calls has increased dramatically in recent times. The use of VVoIP applications is not limited to social applications only but extended to business applications. The improvement of VVoIP call quality is one of the most important factors that enhance the spread of VVoIP services. In many cases, the high network traffic prevents the possibility of sending high data rate live video streams as sending high data rate live stream over high-traffic networks causes an increase in the data loss rate.In this paper, the codec switching techniques used to improve the quality under live networks with high traffic are discussed. In addition, A technique based on background subtraction to improve the quality of VVoIP calls using Python code to detect the moving pixels (foreground) and subtract them from the frame to discard the background is proposed.The proposed technique was tested on large scale live network. The tested parameters impacting the Quality of Service (QoS) were packet loss and delay variation (Jitter). The study based on the comparison of packet loss and Jitter for the three common resolutions 480P, 720P and, 1080P over high traffic network without using background subtraction vs. using background subtraction. The results showed a significant improvement in different rates for each resolution and network condition.
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High-Performance and Energy-Efficient FIR Filter Architecture Using Parallel Prefix Adder-Based Triangular Common Subexpression Elimination Algorithm for IoT Enabled Wireless Sensor NetworkWireless sensor networks (WSN) generate an enormous quantity of data, which necessitates preprocessing at the source to lower the entire amount of data collected for transmission and storage. The finite impulse response (FIR) filter is extensively utilized as a signal preprocessing phase in WSNs. The triangular common subexpressions elimination framework is suggested in which the number of logical operators (LO) and logical depths (LD) in FIR filter implementations has been significantly reduced. In the presented Triangular CSE approach, the occurrences of common Triangular subexpressions are first examined across the complete set of filter coefficients. The proposed method considerably reduces the computational burden, which is again reduced by using Vertical common expressions and Horizontal common expressions. The paper includes a detailed illustration of the algorithm and compares existing algorithms. The proposed architecture minimizes the delay units, structural adders, and adder circuits in the FIR filter's multiplier blocks (MBAs) to decrease the overall complexity of the hardware. Furthermore, the design is improved using Parallel Prefix Adder Based on Kogge-Stone Tree. The LD is not increased during the reduction in LO. Energy consumption is also investigated, along with hardware expenses. Compared to other methods, the proposed solution decreases the number of structural adders while slightly increasing the number of delay elements.
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Fire Detection and Seeing the Invisible Through Smoke Using Hyperspectral and Multi-Spectral ImagesThe global warming has serious impact on our climate. Due to this, the frequency and the intensity of forest fires is increasing. It has shown serious challenges such as the protection of resources, human and wild life, health, and property. This study focuses on developing an artificial intelligence assistive innovative solution for active fire detection in the context of smart cities and vicinities. This paper addresses spectral analysis, detection and classification of active fires and seeing the invisible through smoke and thin clouds. The appealing applications are in urban surveillance, smart cities, future industries, forests and earth observation. The idea is realizable by using an intelligent hybridization of machine/deep learning models and using multi-sensor images (aerial, satellite). For this purpose, we use hyperspectral images (Visible, Near Infra-red (NIR) and Short-Wave Infrared (SWIR)) from AVIRIS aerial and Multi-Spectral Sentinel-2 satellite images. AVIRIS images are 224 spectral bands of wavelengths with a spatial resolution of 15 meters, which varies from 366nm (nanometers) up to 2500nm. However, AVIRIS image studied for their spectral richness of wavelengths not yet completely exploited by machine and deep learning and in SWIR to detect active fires. While, Sentinel-2 image has 13 spectral bands (Visible, NIR and SWIR) with three spatial resolutions (10, 20 and 60 meters). First, we explain and describe the preparation phase of hyperspectral and multispectral image databases of forest fires. These databases contain hyperspectral and multispectral endmembers data of different sites for forest fires. Then, we conduct a spectral analysis from these endmembers to characterize the hyperspectral/multispectral reflectance of active fires to identify the distinct wavelengths for fire detection. We identify the wavelengths that can be used for an effective identification of fire and to see through fires smoke and thin clouds. Onward, the selected feature set is processed by robust machine/deep learning algorithms and their performance is compared for automated identification of fire and invisible vision amelioration. The proposed machine/deep learning method secured an overall test accuracy of 99.1%.
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Exploring the Maze: A Comparative Study of Path Finding Algorithms for PAC-Man GameArtificial Intelligence (AI) has become an integral part of our lives, finding applications across various industries. Search algorithms play a crucial role in AI. This paper focuses on the comparison of different search algorithms within the context of path-planning in the UC Berkeley’s PAC-Man’s game. The algorithms under consideration include Depth-First Search (DFS), Breadth-First Search (BFS), Uniform Cost Search (UCS), Iterative Deepening Depth First Search (IDDFS), and A⇤ Search. The objective is to identify the most effective algorithm in terms of path-finding performance. The study’s findings reveal that the A⇤ search algorithm outperforms the others in terms of score, cost, and node expansion, making it the most suitable choice for finding the shortest path in the PAC-Man’s game.
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Supply Chain 5.0: Vision, Challenges, and PerspectivesThe recent technological advancements have transformed the modern supply chains in complex networks. Consequently, modern supply chain systems are facing several challenges, including limited visibility in both upstream and downstream supply chains, lack of trust among the different stakeholders, and transparency plus traceability. Current supply chain systems do not present the needed framework to overcome the existing challenges. On the other hand, the new paradigm of the Supply Chain 5.0 has the potential to effectively address these obstacles and incorporates the foresight of future disruptions. This paper aims to explore the emerging paradigm of supply chain 5.0 and conduct a systematic analysis of recent and relevant works related to this supply chain version. Additionally, it aims to examine its visionary aspects, analyze associated challenges, and provide insights to the potential future directions of supply chain management. We have systematically analyzed the recent and relevant works addressing this new vision of supply chains.
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Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain OptimizationThis paper explores the transition to Industry 5.0, highlighting its focus on sustainable, human-centred and resilient industrial progress. In this new era, the integration of advanced technology with human expertise is crucial, emphasising the importance of balancing efficiency, cost, quality, and sustainability. At the heart of this research is Multi-Objective Optimisation (MOO), which is used to address the complex challenges of modern manufacturing systems. We propose an innovative approach that combines mathematical modelling with swarm intelligence to tackle complex optimisation problems. A detailed Multi-Objective Mixed Integer Linear Programming (MILP) model is developed and its effectiveness is demonstrated through the application of Multi-Objective Particle Swarm Optimisation (MOPSO). The study compares the performance of MOPSO with traditional optimisation methods using synthetic data analysis. The results not only demonstrate the potential of MOPSO in modern manufacturing, but also set the stage for future research to integrate human ergonomics into the optimization framework, thereby contributing to the holistic advancement of Industry 5.0.
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Optimal placement and sizing of distributed generation for power factor improvementThe present investigation utilizes the Forward-Backward Sweep (FBS) technique in combination with the Sea Horse Optimization (SHO) algorithm to maximize the distribution network's capacity and arrange Distributed Generators (DGs) according to their intended use case. Through the use of Torrit software and the integration of MATPOWER toolbox in MATLAB, the network is methodically assessed under four scenarios (Case A to Case D), featuring four DG types (type 1, type 2, type 3, and type 4). The most promising of them is Case C, which uses type 3 DGs and shows impressive reductions in reactive and active power loss along with a significant increase in power factor and voltage profile. The conclusions provide utility operators looking for the best DG deployment tactics with insightful information.
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PWM based Software defined radio ModelingModern wireless networks are allocated in different frequency bands and have different specifications. However, many wireless devices are required to support different standards which is the case for mobile devices and IoT. For example, a modern mobile device usually supports 5G or 4G besides older generations back to 2G, Wi-Fi standards 802.11 a/b/g/n/ac and Bluetooth with its different versions. To support all these standards, an increasing complexity is added to the design of RF front-end that should be more flexible and more programmable.Software-defined-radio SDR aims to achieve a flexible front-end that is fully programmable and flexible in order to support more standards in different frequency bands and with different specifications. It takes advantage of the increasing enhancements in IC fabrication processes which enables performing signal processing in digital domain with high speed and accuracy more than what analog signal processing can achieve.In this work, a review of traditional receivers as well as multistandard receivers and SDRs are performed, then an SDR based on Pulse-width-modulation PWM RF-to-digital receiver is demonstrated. The chosen PWM based SDR is modeled using MATLAB Simulink.