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Articles 151 - 180 of 234
Full-Text Articles in Electrical and Electronics
Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks, Charles Strickland
Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks, Charles Strickland
College of Graduate Studies: Theses & Dissertations
This work proposes a neural network architecture that was designed to predict and reverse engineer frequency hopping jamming systems. The neural network was initially optimized for use with a 12th order linear shift feedback register maximum length sequence utilizing a minimal polynomial as the characteristic polynomial. This neural network was then scaled to accommodate 7 different sequences, of orders 6 through 12. The neural network was trained for these sequences using training data that is 10 times the length of the sequence. This information is then used to generate a hopping sequence that reduces the jamming interference to 0 with …
Topologically Optimized Electrodes For Electroosmotic Actuation, Jianwen Sun, Jianyu Zhang, Ce Guan, Teng Zhou, Shizhi Qian, Yongbo Deng
Topologically Optimized Electrodes For Electroosmotic Actuation, Jianwen Sun, Jianyu Zhang, Ce Guan, Teng Zhou, Shizhi Qian, Yongbo Deng
Mechanical & Aerospace Engineering Faculty Publications
Electroosmosis is one of the most used actuation mechanisms for the microfluidics in the current active lab-on-chip devices. It is generated on the induced charged microchannel walls in contact with an electrolyte solution. Electrode distribution plays the key role on providing the external electric field for electroosmosis, and determines the performance of electroosmotic microfluidics. Therefore, this paper proposes a topology optimization approach for the electrodes of electroosmotic microfluidics, where the electrode layout on the microchannel wall can be determined to achieve designer desired microfluidic performance. This topology optimization is carried out by implementing the interpolation of electric insulation and electric …
A Study On Peer-To-Peer Energy Trading Market In Low-Voltage Distribution Systems By Considering System Operation Criteria, Pikkanate Angaphiwatchawal
A Study On Peer-To-Peer Energy Trading Market In Low-Voltage Distribution Systems By Considering System Operation Criteria, Pikkanate Angaphiwatchawal
Chulalongkorn University Theses and Dissertations (Chula ETD)
This dissertation investigates the challenges and proposes solutions for enhancing the efficiency and resilience of power systems through peer-to-peer (P2P) energy trading markets, focusing on the context of Thailand. It emphasizes assessing P2P energy transactions to prevent overloading of distribution lines and voltage fluctuations. The study proposes a method to determine the allowable maximum trading power (MTP) for sellers and buyers, considering the operating conditions of the distribution system and forecast error data of photovoltaic (PV) generation and load consumption and leveraging real-time data and algorithms to adjust trading power based on network constraints, ensuring power exchange does not affect …
Design And Development Of Industrial Internet Of Things For Multi-Cloud Factory Vehicle Monitoring System, Patchapong Kulthumrongkul
Design And Development Of Industrial Internet Of Things For Multi-Cloud Factory Vehicle Monitoring System, Patchapong Kulthumrongkul
Chulalongkorn University Theses and Dissertations (Chula ETD)
Material handling equipment, such as forklifts, is broadly used in many industries to increase production efficiency. However, if the equipment unexpectedly breaks down, it can potentially interrupt manufacturing. It's beneficial for the industry to monitor equipment usage data in real-time so that engineers can identify faults before they occur. In this thesis, a prototype for a factory vehicle monitoring system has been designed and implemented. The proposed solution is to acquire real-time usage data from the motor drive of the factory vehicle and send the data to the cloud via narrowband IoT. The received real-time data streams can be stored …
Small Objects Detection In Aerial Imagery For System-On-Chip Fpgas, Wimonthip Saenprasert
Small Objects Detection In Aerial Imagery For System-On-Chip Fpgas, Wimonthip Saenprasert
Chulalongkorn University Theses and Dissertations (Chula ETD)
Unmanned aerial vehicles (UAVs) offer significant advantages in accessing remote or high-risk locations. Yet, they also present challenges concerning privacy and safety. Effective air traffic management is crucial for ensuring compliance with security regulations. The primary objective is to monitor the flight trajectories of target objects in the airspace, necessitating efficient object detection, especially for distant objects, to facilitate surveillance and preparedness for addressing potential threats. However, the current performance of existing models in detecting small objects is inadequate for real-time surveillance applications. This study advocates prioritizing object detection over precise prediction of object bounding box sizes. We propose a …
Endoscopic Image Super-Resolution Algorithm Using Edge And Disparity Awareness, Mansoor Hayat
Endoscopic Image Super-Resolution Algorithm Using Edge And Disparity Awareness, Mansoor Hayat
Chulalongkorn University Theses and Dissertations (Chula ETD)
Integrating Stereo Imaging technology into endoscopic procedures marks a significant leap forward in medical imaging. This technology equips surgeons with enhanced depth perception and intricate views of internal structures, improving diagnostic accuracy and surgical precision. However, implementing stereo imaging in endoscopy presents certain challenges, such as low- resolution and blurred images that can compromise the quality of medical diagnoses and interventions. To address these issues, our research has developed a cutting-edge endoscopic image super-resolution model. This model incorporates a feature extraction module and a sophisticated cross-view feature interaction module, specifically designed to tackle the complexities of endoscopic images. It was …
Energy-Aware Ai-Driven Framework For Edge-Computing-Based Iot Applications, Muhammad Zawish, Nouman Ashraf, Rafay Iqbal Ansari, Steven Davy
Energy-Aware Ai-Driven Framework For Edge-Computing-Based Iot Applications, Muhammad Zawish, Nouman Ashraf, Rafay Iqbal Ansari, Steven Davy
Conference papers
The significant growth in the number of Internet of Things (IoT) devices has given impetus to the idea of edge computing for several applications. In addition, energy harvestable or wireless-powered wearable devices are envisioned to empower the edge intelligence in IoT applications. However, the intermittent energy supply and network connectivity of such devices in scenarios including remote areas and hard-to-reach regions such as in-body applications can limit the performance of edge computing-based IoT applications. Hence, deploying state-of-the-art convolutional neural networks (CNNs) on such energy-constrained devices is not feasible due to their computational cost. Existing model compression methods, such as network …
Optical Properties Of Periodically And Aperiodically Nanostructured P-N Junctions, Z. Taliashvili, E Łusakowska E Łusakowska, S. Chusnutdinow, A. Tavkhelidze, L. Jangidze, S. Sikharulidze, Nima Ghadiri, Z. Chubinidze, R. Melkadze
Optical Properties Of Periodically And Aperiodically Nanostructured P-N Junctions, Z. Taliashvili, E Łusakowska E Łusakowska, S. Chusnutdinow, A. Tavkhelidze, L. Jangidze, S. Sikharulidze, Nima Ghadiri, Z. Chubinidze, R. Melkadze
Articles
Recently, semiconductor nanograting layers have been introduced and their optical properties have been studied. Spectroscopic ellipsometry has shown that nanograting significantly modifies the dielectric function of c-Si layers. Photoluminescence spectroscopy reveals the emergence of an emission band with a remarkable peak structure. It has been observed that nanograting also alters the electronic and magnetic properties. In this study, we investigate the quantum efficiency and spectral response of Si p-n junctions fabricated using subwavelength grating layers and aperiodically nanostructured layers.
Exploiting Multimode Antennas For Mimo And Aoa Estimation In Size-Constrained Iot Devices, Abel Zandamela, Alessandro Chiumento Alessandro Chiumento, Alessandro Chiumento, Nicola Marchetti, Max Ammann, Adam Narbudowicz
Exploiting Multimode Antennas For Mimo And Aoa Estimation In Size-Constrained Iot Devices, Abel Zandamela, Alessandro Chiumento Alessandro Chiumento, Alessandro Chiumento, Nicola Marchetti, Max Ammann, Adam Narbudowicz
Articles
This work proposes compact multimode Multiple-Input–Multiple-Output (MIMO) antennas for Angle of Arrival (AoA) estimation in miniaturized Internet of Things (IoT) systems. The method excites different orthogonal radiating modes (TM 21 , TM 02 , and TM 31 modes) for beamforming capabilities, and the AoA performance is investigated using the Multiple Signal Classification (MUSIC) algorithm, executed using numerical and experimental data. The technique is tested at 2.238GHz , while using an antenna diameter
Swarm Electrification: A Comprehensive Literature Review, Stephen Sheridan, Keith Sunderland, Jane Courtney
Swarm Electrification: A Comprehensive Literature Review, Stephen Sheridan, Keith Sunderland, Jane Courtney
Articles
In the global North, the need to decarbonize power generation is well documented and the challenges faced are endemic to the design of the electrical grids. With networks relying on centralized generation, it can be difficult to replace fossil-fuel power plants with renewable energy sources as generation may be intermittent causing grid instability when there is no ‘spinning reserve’ [1]. In parts of the global south, however, many under-electrified nations have high levels of solar irradiance. This, combined with falling prices for solar panels, is allowing for alternative paths to electrification from costly grid extensions and has resulted in grids …
Adaptive Fuzzy Supplementary Controller For Ssr Damping In A Series-Compensated Dfig-Based Wind Farm, Mohamed Abdeen, Sayed Hosny Ahmed El-Banna, Sara Elgohary, Hend Mostafa, Nora Ghaly, Nourhan Adel, Zeinab Elkhwas, Mohamed Alahmady, Hossam Zawbaa, Salah Kamel
Adaptive Fuzzy Supplementary Controller For Ssr Damping In A Series-Compensated Dfig-Based Wind Farm, Mohamed Abdeen, Sayed Hosny Ahmed El-Banna, Sara Elgohary, Hend Mostafa, Nora Ghaly, Nourhan Adel, Zeinab Elkhwas, Mohamed Alahmady, Hossam Zawbaa, Salah Kamel
Articles
Although using a series compensation technique in a long transmission line effectively increases the transmittable power; it may cause a sub-synchronous resonance (SSR) phenomenon. Gate-controlled series capacitor (GCSC) is an effective method for SSR damping by controlling the turn-off angle. In the previous studies, a constant supplementary damping controller (SDC) was used for controlling the turn-off angle, which can mitigate the SSR phenomenon. However, these methods can not capture the maximum transmittable power at different operating points. In this paper, a fuzzy logic controller (FLC) is proposed to compute the gain of SDC based on the wind speed and the …
An Adaptive Vector Control Method For Inverter-Based Stand-Alone Microgrids Considering Voltage Reduction And Load Shedding Schemes, Sumit Kumar Jha, Deepak Kumar, Prabhat Ranjan Tripathi, Bhargav Appasani, Hossam Zawbaa, Salah Kamel
An Adaptive Vector Control Method For Inverter-Based Stand-Alone Microgrids Considering Voltage Reduction And Load Shedding Schemes, Sumit Kumar Jha, Deepak Kumar, Prabhat Ranjan Tripathi, Bhargav Appasani, Hossam Zawbaa, Salah Kamel
Articles
The droop mechanism is widely utilized in a stand-alone microgrid (MG) to regulate power-sharing among distributed generators (DG). However, over the years, the droop phenomena are continually modified to lessen the deviation in voltage and frequency parameters caused due to classical droop. This study suggests computing the droop coefficient for voltage–current (V–I) droop to take into account for proportional power distribution among DGs. In grid utility networks, the conservation voltage reduction (CVR) strategy is widely used to curtail the use of energy. Hence, this paper investigates the CVR's performance for stand-alone MG by performing the adaptive vector control scheme in …
Wind Energy Harvesting And Conversion Systems: A Technical Review, Sinhara M. H. D. Perera, Ghanim Putrus, Michael Conlon, Mahinsasa Narayana, Keith Sunderland
Wind Energy Harvesting And Conversion Systems: A Technical Review, Sinhara M. H. D. Perera, Ghanim Putrus, Michael Conlon, Mahinsasa Narayana, Keith Sunderland
Articles
Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results …
Embedded Ai For Wheat Yellow Rust Infection Type Classification, Uferah Shafi, Rafia Mumtaz, Muhammad Deedahwar Mazhar Qureshi, Zahid Mahmood, Sikander Khan Tanveer, Ihsan Ul Haq, Syed Mohammad Hassan Zaidi
Embedded Ai For Wheat Yellow Rust Infection Type Classification, Uferah Shafi, Rafia Mumtaz, Muhammad Deedahwar Mazhar Qureshi, Zahid Mahmood, Sikander Khan Tanveer, Ihsan Ul Haq, Syed Mohammad Hassan Zaidi
Articles
Wheat is the most important and dominating crop in Pakistan in terms of production and acreage, which is grown on 37% of the cultivated area, accounting for 70% of the total production. However, wheat yield is highly affected by stripe rust, which is considered the most devastating fungal disease, causing 5.5 million tonnes of loss per year globally. In order to minimize this loss, the accurate and timely detection of rust disease is crucial instead of manual inspection. Towards this end, we propose a system to detect wheat rust disease and classify its infection types into four classes, including healthy, …
Design And Implementation Of A New Adaptive Mppt Controller For Solar Pv Systems, Saibal Manna, Deepak Kumar Singh, Ashok Kumar Akella, Hossam Kotb, Kareem M. Aboras, Hossam Zawbaa, Salah Kamel
Design And Implementation Of A New Adaptive Mppt Controller For Solar Pv Systems, Saibal Manna, Deepak Kumar Singh, Ashok Kumar Akella, Hossam Kotb, Kareem M. Aboras, Hossam Zawbaa, Salah Kamel
Articles
This research provides an adaptive control design in a photovoltaic system (PV) for maximum power point tracking (MPPT). In the PV system, MPPT strategies are used to deliver the maximum available power to the load under solar radiation and atmospheric temperature changes. This article presents a new adaptive control framework to enhance the performance of MPPT, which will minimize the complexity in system control and efficiently manage uncertainties and disruptions in the environment and PV system. Here, the MPPT algorithm is decoupled with model reference adaptive control (MRAC) techniques, and the system gains MPPT with overall system stability. The simulation …
Distributed Intermittent Fault Diagnosis In Wireless Sensor Network Using Likelihood Ratio Test, Bhabani Sankar Gouda, Meenakshi Panda, Trilochan Panigrahi, Sudhakar Das, Bhargav Appasani, Omprakash Acharya, Hossam Zawbaa, Salah Kamel
Distributed Intermittent Fault Diagnosis In Wireless Sensor Network Using Likelihood Ratio Test, Bhabani Sankar Gouda, Meenakshi Panda, Trilochan Panigrahi, Sudhakar Das, Bhargav Appasani, Omprakash Acharya, Hossam Zawbaa, Salah Kamel
Articles
In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects in the network if they are not diagnosed and inform the fault status to other nodes. Fault diagnosis is difficult when the nodes behave faulty some times and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated …
Metaheuristic Optimization Techniques Used In Controlling Of An Active Magnetic Bearing System For High-Speed Machining Application, Suraj Suraj, Pabitra Kumar Biswas Pabitra Kumar Biswas, Sukanta Debnath, Anumoy Ghosh, Thanikanti Sudhakar Babu, Hossam Zawbaa, Salah Kemal
Metaheuristic Optimization Techniques Used In Controlling Of An Active Magnetic Bearing System For High-Speed Machining Application, Suraj Suraj, Pabitra Kumar Biswas Pabitra Kumar Biswas, Sukanta Debnath, Anumoy Ghosh, Thanikanti Sudhakar Babu, Hossam Zawbaa, Salah Kemal
Articles
Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain parameters of PID controller for the proposed closed-loop active magnetic bearing (AMB) system. Performance indices or, objective functions on which these optimization techniques are executed are integral absolute error (IAE), integral square error (ISE), integral …
Performance Improvement Of Hybrid System Based Dfig-Wind/Pv/Batteries Connected To Dc And Ac Grid By Applying Intelligent Control, Younes Sahri, Salah Tamalouzt, Sofia Lalouni Belaid, Mohit Bajaj, Sherif S.M. Ghoneim, Hossam Zawbaa, Salah Kamel
Performance Improvement Of Hybrid System Based Dfig-Wind/Pv/Batteries Connected To Dc And Ac Grid By Applying Intelligent Control, Younes Sahri, Salah Tamalouzt, Sofia Lalouni Belaid, Mohit Bajaj, Sherif S.M. Ghoneim, Hossam Zawbaa, Salah Kamel
Articles
One of the main causes of CO2 emissions is the production of electrical energy. Therefore, many researchers goal’s is to develop renewable power systems. This paper proposes a new intelligent control development of hybrid PV–Wind-Batteries. Neuro-Fuzzy Direct Power Control (NF-DPC) is invested in order to enhance system performance and generated currents quality. An improved MPPT algorithm based on Fuzzy Controller (FC) is invested for PV power optimization. In addition, a new Modified Fuzzy Direct Power Control (MF-DPC) is developed and applied to the grid side converter to control the active and reactive power by monitoring the involved active power flow …
Local Electricity Market Operation In Presence Of Residential Energy Storage In Low Voltage Distribution Network: Role Of Retail Market Pricing, Aziz Saif, Shafi K. Khadem Shafi K. Khadem, Michael Conlon, Brian Norton
Local Electricity Market Operation In Presence Of Residential Energy Storage In Low Voltage Distribution Network: Role Of Retail Market Pricing, Aziz Saif, Shafi K. Khadem Shafi K. Khadem, Michael Conlon, Brian Norton
Articles
Local Electricity Market (LEM) appears as a promising consumer-centric market-based approach that extends the self-consumption method, widely implemented in residential households, to collective self-consumption in the local energy communities, enabled through peer-to-peer (P2P) transactions. To facilitate the integration of LEM in the wholesale electricity market (WEM), it is paramount to comprehend the synergy of retail electricity pricing on the LEM operation hosted in the low-voltage distribution network (LVDN). The paper presents a co-simulation framework consisting of a local electricity market model coupled with a three-phase distribution network simulator to perform a holistic case study for a smart energy community in …
Nipuna: A Novel Optimizer Activation Function For Deep Neural Networks, Golla Madhu, Sandeep Kautish, Khalid Abdulaziz Alnowibet, Hossam Zawbaa, Ali Wagdy Mohamed
Nipuna: A Novel Optimizer Activation Function For Deep Neural Networks, Golla Madhu, Sandeep Kautish, Khalid Abdulaziz Alnowibet, Hossam Zawbaa, Ali Wagdy Mohamed
Articles
In recent years, various deep neural networks with different learning paradigms have been widely employed in various applications, including medical diagnosis, image analysis, self-driving vehicles and others. The activation functions employed in deep neural networks have a huge impact on the training model and the reliability of the model. The Rectified Linear Unit (ReLU) has recently emerged as the most popular and extensively utilized activation function. ReLU has some flaws, such as the fact that it is only active when the units are positive during back-propagation and zero otherwise. This causes neurons to die (dying ReLU) and a shift in …
Interpreting Energy Utilisation With Shapley Additive Explanations By Defining A Synthetic Data Generator For Plausible Charging Sessions Of Electric Vehicles, Prasant Kumar Mohanty, Gayadhar Panda, Malabika Basu, Diptendu Sinha Roy
Interpreting Energy Utilisation With Shapley Additive Explanations By Defining A Synthetic Data Generator For Plausible Charging Sessions Of Electric Vehicles, Prasant Kumar Mohanty, Gayadhar Panda, Malabika Basu, Diptendu Sinha Roy
Articles
Electric vehicles (EVs) are an effective solution for reducing reliance on non-renewable energy sources. However, the lack of charging infrastructure and concerns over their range are some of the biggest hurdles to adopting EVs. Charging infrastructure for EVs is, however, on the rise. Proper planning of charging stations vis-`a-vis road networks and related points of interest such as transportation hubs, schools, shopping centres, etc., alongside such roads become vital to laying out a plan for such infrastructure, particularly for developing countries like India where EV adoption is relatively in a nascent stage. Synthetic datasets can help overcome these hurdles and …
Lightnet: A Novel Lightweight Convolutional Network For Brain Tumor Segmentation In Healthcare, Dongyuan Wu, Junyi Tao, Zhen Qin, Rao Asad Mumtaz, Jin Qin, Linfang Yu, Jane Courtney
Lightnet: A Novel Lightweight Convolutional Network For Brain Tumor Segmentation In Healthcare, Dongyuan Wu, Junyi Tao, Zhen Qin, Rao Asad Mumtaz, Jin Qin, Linfang Yu, Jane Courtney
Articles
Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the …
H2020 Auto-Dan Project: Enhance The Participation Of The Community To Demand Response By Providing The State-Of-The-Art Technological And Policy Solution, Rene Peeren, Dharmesh Dabhi, John Dalton
H2020 Auto-Dan Project: Enhance The Participation Of The Community To Demand Response By Providing The State-Of-The-Art Technological And Policy Solution, Rene Peeren, Dharmesh Dabhi, John Dalton
Conference papers
The growing demand for electricity in Europe has increased the need for a more flexible and sustainable power system. In recent years, Demand Response (DR) has emerged as a promising solution to meet this need, by providing an opportunity for residential and smaller commercial consumers to actively participate in the electricity market. This research paper investigates the potential for DR among the residential community and small commercial electricity consumers in Europe and identifies the technological barriers and drivers that impact consumer engagement with DR programs in Europe. The different DR opportunities are identified and validated at the six different demo …
Quantum Classifiers For Video Quality Delivery, Tautvydas Lisas, Ruairí De Fréin
Quantum Classifiers For Video Quality Delivery, Tautvydas Lisas, Ruairí De Fréin
Conference papers
Classical classifiers such as the Support Vector Classifier (SVC) struggle to accurately classify video Quality of Delivery (QoD) time-series due to the challenge in constructing suitable decision boundaries using small amounts of training data. We develop a technique that takes advantage of a quantum-classical hybrid infrastructure called Quantum-Enhanced Codecs (QEC). We evaluate a (1) purely classical, (2) hybrid kernel, and (3) purely quantum classifier for video QoD congestion classification, where congestion is either low, medium or high, using QoD measurements from a real networking test-bed. Findings show that the SVC performs the classification task 4% better in the low congestion …
Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutua Mutiso, Ruairí De Fréin, Ali Malik, Eliel Kibanza, Marco Sahbane, Maxime Pantel
Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutua Mutiso, Ruairí De Fréin, Ali Malik, Eliel Kibanza, Marco Sahbane, Maxime Pantel
Conference papers
Range anxiety poses a hurdle to the adoption of Electric Vehicles (EVs), as drivers worry about running out of charge without timely access to a Charging Point (CP). We present novel methods for optimising the distribution of CPs, namely, EV portacharge and GEECharge. These solutions distribute CPs in Dublin, in this paper, by considering the population density and Points Of Interest (POIs) or road traffic. The object of this paper is to (1) develop and evaluate methods to distribute CPs in Dublin city; (2) optimise CP allocation; (3) visualise paths in the graph network to show the most used roads …
Architecting Future Multi-Modal Networks Coexistence, Generalization & Testbeds, Maqsood Ahamed Abdul Careem
Architecting Future Multi-Modal Networks Coexistence, Generalization & Testbeds, Maqsood Ahamed Abdul Careem
Legacy Theses & Dissertations (2009 - 2024)
Next Generation (xG) wireless networks are poised to revolutionize the way people, devices, data and processes sense, communicate, interact, and collectively enable a wide range of emerging applications, ranging from smart cities, connected healthcare, and advanced vehicular communication to extended reality. To facilitate this seamless interoperability, these networks need to evolve to accommodate and integrate multiple modalities in communication/ sensing technologies and spectrum, heterogeneous networks, trends in signal-processing (statistical, AI-driven, and distributed systems), centralized and distributed architectures, and device/ network hardware resources. However, to cater to the high-target metrics and wide-range of applications, these multi-modal networks must efficiently address multi-faceted …
Optimal Battery Sizing For An Industrial Factory: A Hybrid Approach Using Particle Swarm Optimization And Load Estimation, Muhammad Hammad Hassan
Optimal Battery Sizing For An Industrial Factory: A Hybrid Approach Using Particle Swarm Optimization And Load Estimation, Muhammad Hammad Hassan
Chulalongkorn University Theses and Dissertations (Chula ETD)
Solar photovoltaic (PV) systems are crucial in addressing global energy demands with clean and renewable electricity. However, the intermittent nature of solar energy requires integrating batteries to ensure a reliable power supply. This study focuses on optimizing battery size for solar PV systems to balance energy storage during high irradiance periods and discharge during low sunlight, thereby enhancing system efficiency and cost-effectiveness. Accurate load forecasting and load estimation, essential for effective energy storage management, is achieved using long short-term memory (LSTM) networks, which excel in handling time series data. Particle Swarm Optimization (PSO) is then employed to determine the optimal …
Blockchain Framework For Secured On-Demand Patient Health Records Sharing, Meryem Abouali
Blockchain Framework For Secured On-Demand Patient Health Records Sharing, Meryem Abouali
Dissertations and Theses
As the healthcare industry continues to digitize and share patient data for a better understanding of patient health history, cybersecurity must remain a top priority. However, patient health record (PHR) data is extremely sensitive and faces significant challenges due to its distributed nature across various healthcare facilities and providers, which creates a lack of interoperability among healthcare systems. Most patient health record systems adopt a centralized management structure and deploy PHRs to the cloud, which raises privacy concerns when sharing patient information over a network. Thus, there is a need for a framework that considers patient privacy and data security …
Single Image Super Resolution Using Deep Attention Network, Jagrati Talreja
Single Image Super Resolution Using Deep Attention Network, Jagrati Talreja
Chulalongkorn University Theses and Dissertations (Chula ETD)
This thesis introduces two innovative frameworks for Single Image Super Resolution (SISR) that enhances image quality while optimizing computational efficiency. The motivation for this research is driven by the need to overcome the limitations of current SISR techniques like capturing long-range dependencies, enhancing multi-scale feature learning, mitigating over-smoothing, and managing computational demands. The choice to incorporate attention mechanisms in both frameworks is motivated by their proven ability to allow the model to focus on the most relevant parts of the image, thereby improving the reconstruction of high-resolution details from low-resolution inputs. By integrating attention mechanisms with inception blocks, the proposed …
A Review Of Piezoelectric Footwear Energy Harvesters: Principles, Methods, And Applications, Bingqi Zhao, Feng Qian, Alexander Hatfield, Lei Zuo, Tian-Bing Xu
A Review Of Piezoelectric Footwear Energy Harvesters: Principles, Methods, And Applications, Bingqi Zhao, Feng Qian, Alexander Hatfield, Lei Zuo, Tian-Bing Xu
Mechanical & Aerospace Engineering Faculty Publications
Over the last couple of decades, numerous piezoelectric footwear energy harvesters (PFEHs) have been reported in the literature. This paper reviews the principles, methods, and applications of PFEH technologies. First, the popular piezoelectric materials used and their properties for PEEHs are summarized. Then, the force interaction with the ground and dynamic energy distribution on the footprint as well as accelerations are analyzed and summarized to provide the baseline, constraints, potential, and limitations for PFEH design. Furthermore, the energy flow from human walking to the usable energy by the PFEHs and the methods to improve the energy conversion efficiency are presented. …