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Articles 961 - 990 of 14322
Full-Text Articles in Entire DC Network
Design And Optimization Of Matrix Core Transformer For High Power Bidirectional Isolated Dc/Dc Converter, Zhe Zhao
Graduate Theses and Dissertations
Thanks to the advancement of semiconductor technology, the silicon carbide (SiC) devices with high voltage and power capability are developed and widely used in several industry applications, e.g., gird-connected photovoltaic (PV) system, data center power supply, and electrical vehicle (EV) charging station. After the concept of solid-state transformer (SST) was proposed as an important solution to the medium voltage (MV) power conversion systems, this bidirectional isolated DC/DC converter has attracted much attention in academia and industry. One of the main advantages of SST is to improve both the power density and efficiency of the power conversion system by replacing the …
Low-Cost Vehicle Controller Testing System, Kevin R. Jung
Low-Cost Vehicle Controller Testing System, Kevin R. Jung
Electrical Engineering
This project aims to create a system to facilitate easier evaluation of PCBs designed for low-voltage vehicle applications with an emphasis on accessibility for student teams in collegiate design series' such as Formula SAE. Student teams or smaller vehicle electronics manufacturers often need to verify designs and validate functionality during both development and manufacturing. An inexpensive, small, and portable, yet capable, system for taking measurements and simulating inputs would allow designers to put their boards into vehicle-representative conditions and environments without needing to connect to actual vehicle hardware. Additionally, while systems exist off-the-shelf that could fulfill the requirements needed by …
Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing, Reski Noviania, Enny Itje Sela, Luther Alexander Latumakulita, Steven R. Sentinuwo
Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing, Reski Noviania, Enny Itje Sela, Luther Alexander Latumakulita, Steven R. Sentinuwo
Knowledge Engineering and Data Science
Predicting debtor eligibility is essential for effective risk management and minimizing lousy credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy. It integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non experts to …
A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan
A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan
Knowledge Engineering and Data Science
Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …
Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri
Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri
Knowledge Engineering and Data Science
Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …
Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani
Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani
Knowledge Engineering and Data Science
The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC …
Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen
Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen
Knowledge Engineering and Data Science
Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …
Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo
Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo
Knowledge Engineering and Data Science
This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …
Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred
Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred
Knowledge Engineering and Data Science
The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster …
Real-Time Predictive Analytics For Healthcare Monitoring And Terrain Classification Using Data Streaming And Online Learning, Ayemon Baraka
Real-Time Predictive Analytics For Healthcare Monitoring And Terrain Classification Using Data Streaming And Online Learning, Ayemon Baraka
UNLV Theses, Dissertations, Professional Papers, and Capstones
This thesis develops a scalable, real-time predictive framework aimed at improving outcomes in healthcare monitoring and gait classification. Specifically, it addresses two primary applications: early detection of cardiovascular events, myocardial infarction (MI) and arrhythmia, through continuous analysis of ECG data streams; and adaptive terrain classification from gait data. Utilizing data streaming technologies such as Apache Kafka and Apache Spark, coupled with machine learning models, this framework enables near real-time and precise predictions from high-throughput, real-time data. Furthermore, the integration of online learning frameworks, including the River API and TensorFlow I/O, allows for continuous model adaptation, enhancing the system’s responsiveness and …
Alteration Of Peripheral Cytokines In Alzheimer’S Disease, Zhengshi Yang
Alteration Of Peripheral Cytokines In Alzheimer’S Disease, Zhengshi Yang
UNLV Theses, Dissertations, Professional Papers, and Capstones
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with the clinical symptoms of gradual cognitive decline and memory loss. Biologically, abnormal accumulation of Aβ (Aβ) plaque and tau tangles in the brain are the core pathological hallmarks in AD. Although inflammatory response is not specific to AD, emerging evidence suggests that the disrupted innate immune system within the central nervous system, particularly microglial activation, might act as the bridge linking Aβ aggregation to pathological phosphorylation and aggregation of tau. The inflammatory response was suggested to initially have a neuroprotective role of attempting to clear Aβ plaques, and then become neurotoxic …
Delineating Genetic Influences On Neurodegenerative Disorders And Infectious Diseases Through Advanced Computational Methods, Xiaowei Zhuang
Delineating Genetic Influences On Neurodegenerative Disorders And Infectious Diseases Through Advanced Computational Methods, Xiaowei Zhuang
UNLV Theses, Dissertations, Professional Papers, and Capstones
Genetics plays a critical role in understanding the molecular mechanisms underlying neurodegenerative disorders and pathogen evolution in infectious diseases. For example, identifying genetic variants associated with a disease phenotype uncovers functional pathways that could lead to potential drug targets and therapeutic interventions. In addition, tracking the genetic evolution of pathogens enables early detection and warning of infectious disease outbreaks. In both applications, given the large amount of genetic data, advanced computational methods, including longitudinal and multivariate models, could significantly boost the statistical power and capture interrelationships among traits, environmental factors and genetic influences. This dissertation focuses on four applications of …
Clusteredlog: Optimizing Log Structures For Efficient Data Recovery And Integrity Management In Database Systems, Mariha Siddika Ahmad, Brajendra Panda
Clusteredlog: Optimizing Log Structures For Efficient Data Recovery And Integrity Management In Database Systems, Mariha Siddika Ahmad, Brajendra Panda
Electrical Engineering and Computer Science Faculty Publications and Presentations
In modern database systems, efficient log management is crucial for ensuring data integrity and facilitating swift recovery from potential data corruption or system failures. Traditional log structures, which store operations sequentially as they occur, often lead to significant delays in accessing and recovering specific data objects due to their scattered nature across the log. ClusteredLog addresses the limitations of traditional logging methods by implementing a novel logical organization of log entries. Instead of simply storing operations sequentially, it groups related operations for each data item into clusters. As a result, ClusteredLog enables faster identification and recovery of damaged data items …
Enhancing Webtas: From An Atam Simulator To A Developmental Environment, Elizabeth G. Schmidt
Enhancing Webtas: From An Atam Simulator To A Developmental Environment, Elizabeth G. Schmidt
Electrical Engineering and Computer Science Undergraduate Honors Theses
As interest in a niche research area, like the abstract Tile Assembly Model (aTAM), grows it is crucial to ensure that simulation tools are accessible and user-friendly for a broad audience. Existing resources can pose challenges for less experienced users, highlighting the need for enhancements that improve usability and functionality. To address these issues, key upgrades to the current online simulation platform, WebTAS, were implemented, including breakpoint functionality and nondeterminism checks, alongside features such as XML file integration, image capturing, and simulation view resets. These enhancements collectively elevate the platform from a basic learning tool to a comprehensive environment for …
Control Of X-Delta Arm And Self-Leveling Robot Systems For Uav Applications, Prathibha Sobha
Control Of X-Delta Arm And Self-Leveling Robot Systems For Uav Applications, Prathibha Sobha
UNLV Theses, Dissertations, Professional Papers, and Capstones
The water droplet penetration test (WDPT) is an environmental assessment method for analyzing soil properties by measuring the penetration of water droplets into soil. This test provides insights into soil moisture retention and resilience, especially valuable in fire-prone or arid regions where soil recovery and erosion control are essential. Although this project does not conduct an actual WDPT, it investigates two key robotic systems designed to support such applications: an X-delta robot arm, which simulates precise droplet delivery patterns, and a self-leveling platform that ensures stability on uneven terrain, where the X-delta robot is mounted. Together, these systems examine the …
Hardware Applications Of High-Speed Fault Detection Algorithms, Daniel Zintsmaster
Hardware Applications Of High-Speed Fault Detection Algorithms, Daniel Zintsmaster
All Theses
The growing need for a sustainable electric energy infrastructure has driven research into control, protection, and optimization of power systems. A key challenge is that the changing grid must operate at increasingly higher speeds, but many current hardware devices cannot meet these demands. This thesis focuses on power system protection, aiming to design a hardware solution that can detect and isolate faults in microseconds, ensuring faster, more reliable grid operations. The first hardware developed was for a low-voltage direct current (LVdc) microgrid, which faces challenges due to a lack of protection schemes and novel speed requirements. This thesis presents protection …
Higher Order Bessel Beams Integrated In Time (Hobbit) With Engineered Light Frequencies And Applications In Computational Imaging, Tyler Cramer
All Theses
This thesis presents an adaptive optical system which leverages the frequency shifts from an acousto-optic deflector (AOD) form an array of uniformly spaced frequency diverse beamlets. These beamlets are then spatially transformed (wrapped) into a circular array forming a non-diffracting ring with an unprecedented orbital angular momentum (OAM) mode switching rate of 50Mhz with an OAM range of ±64. Along with frequency diversity, the non-diffracting range of the beams are analyzed 6 Rayleigh ranges of an equivalently sized gaussian beam. Inherent in the frequencies superposed on the AOD are associated relative phases which can be controlled to form sets of …
Three-Dimensional Environmentally Sustainable Neuromorphic Computing System Based On Natural Organic Memristor, Mohammed Rafeeq Khan
Three-Dimensional Environmentally Sustainable Neuromorphic Computing System Based On Natural Organic Memristor, Mohammed Rafeeq Khan
Graduate Theses and Dissertations (2019 - present)
A three-dimensional neuromorphic (3D) computing architecture based on environmentally sustainable natural organic honey memristors is proposed in this thesis. A set of comprehensive and experimental results indicate that the proposed systems exhibit remarkable inference accuracy, consistently surpassing the 90% threshold, even with different challenges such as device variations and nonlinearity. This study also considers four different conductance drift situations, the effects of analog-to-digital converter (ADC) quantization, and multiple algorithms, such as VGG8 and DenseNet-40. The deliverable of this thesis will test the stability of the proposed systems and explore their potential applications and scalability in real-world situations.
Photophoretic Optical Particle Trapping Improvements Using Beam Reflection, Ian A. Garcia, Xiaomin Jin Phd., Kun Hua Phd.
Photophoretic Optical Particle Trapping Improvements Using Beam Reflection, Ian A. Garcia, Xiaomin Jin Phd., Kun Hua Phd.
Master's Theses
Current research has shown that photophoretic optical trapping (POT) is a promising method for creating true three-dimensional holograms. A landmark study by Smalley et. al. at BYU was able to generate a full hologram within a 2 cm edge cube with remarkable success. However, a key drawback to this method is its poor ability to hold trapped particles within the focal point of the laser for an extended period of time. An investigation from our group presented at SPIE Photonics West 2023 sought to improve these results by varying the focal length of the trapping and the wavelength of the …
Nanostructured Silicon Meta-Waveguides And Surfaces For Enhanced Light-Matter Interaction, Saddam Gafsi
Nanostructured Silicon Meta-Waveguides And Surfaces For Enhanced Light-Matter Interaction, Saddam Gafsi
All Dissertations
In this study, we tackle the challenge of light-matter interaction engineering and local field enhancement using various silicon photonic platforms based on three different approaches. (i) The first approach is based on boundary condition engineering where we present and show through simulation and experimental results, a silicon nanodisk “diabolo” configuration able to support significant local field enhancement levels in the range ~102 to 104 in the high index medium through proper structural modifications at the nanoscale. We show that the presented optical behavior is consistent with the anapole modes characterized by the observed near-field enhancement and coinciding with far-field suppression. …
Uncovering The Light Network Load Performance Penalty Of The Network Link Outlier Factor (Nlof), Michelle Lara
Uncovering The Light Network Load Performance Penalty Of The Network Link Outlier Factor (Nlof), Michelle Lara
Open Access Theses & Dissertations
This thesis evaluates the effectiveness of the Network Link Outlier Factor with Most Likely Link (NLOF: MLL) algorithm under varying network load conditions. Repeated simulation experiments using Mininet were conducted for four different network-wide load levels: 100 Mbps, 500 Mbps, 1 Gbps, and 5 Gbps. Using statistical inference, our experimental results indicate that NLOF: MLL is ineffective under light load conditions (i.e., 100Mbps load) due to the limited network flow data available for its learning process. This limitation highlights a key challenge in applying the algorithm to lightly loaded networks. A preliminary algorithm was proposed to address this light-load performance …
Dynamic Optimization Of Directed Energy Deposition Build Conditions Using Real-Time Monitoring Via Closed-Loop Control, Callan Herberger
Dynamic Optimization Of Directed Energy Deposition Build Conditions Using Real-Time Monitoring Via Closed-Loop Control, Callan Herberger
Open Access Theses & Dissertations
Directed Energy Deposition (DED) is an additive manufacturing process that is being rapidly adopted by industry and is well suited for the fabrication of complex components in various metal alloys. DED provides unique benefits such as design flexibility, the potential for in-situ alloying, and an open environment that allows for unobstructed monitoring within the build chamber. Despite these benefits, fully exploiting additive manufacturing's (AM) potential remains a complex task for designers. This dissertation presents a framework for controlling Directed Energy Deposition process variables through in-situ monitoring. An exploration into modifying AM build conditions through the development and implementation of a …
Chipless 3d Microfluidic Rf Sensing, Sheikh Dobir Hossain
Chipless 3d Microfluidic Rf Sensing, Sheikh Dobir Hossain
Open Access Theses & Dissertations
Effective management of the cold chain is essential to uphold the quality and safety of products vulnerability to physical factors like temperature, humidity, pressure, etc., including perishable foods, pharmaceuticals, and vaccines. Chipless Radio Frequency Identification (RFID) sensors have become increasingly favored as a viable technology for tracking, inventory, and sensing industries due to their wireless and non-line of sight (NLOS) situations, straightforward fabrication, cost efficiency, and adaptability to challenging environmental conditions. However, there is still a need for advancements in RFID sensors to make them promise for cold chain applications. Integration of flexibility and non-volatile memory into the existing RFID …
A Cmos Ldo Voltage Regulator For Low Power Applications, Nicolaus Vail
A Cmos Ldo Voltage Regulator For Low Power Applications, Nicolaus Vail
Graduate Theses and Dissertations
This thesis presents the design, simulation, layout, and testing of a low dropout linear voltage regulator (LDO) in a 180 nm CMOS process. The LDO is intended for use in low-power, battery-operated applications, and it has an adjustable output for a variety of different implementations. It can supply up to 100 mA of current, which is enough to power many small electronic circuits. It has a dropout voltage of 150 mV, and it consumes less than 200 μA of current during operation, which is on par with many commercial regulators. The line regulation is also comparable to commercially available LDOs, …
Virtual Control: A Comparison Of Methods For Hand-Tracking Implementation, Nathan Roberts
Virtual Control: A Comparison Of Methods For Hand-Tracking Implementation, Nathan Roberts
Honors Program: Senior Projects (Public)
This thesis examines the design philosophy of modern virtual reality applications that utilize hand-tracking as a primary form of user input. The analysis presented hopes to provide ideas for future implementations of this technology so that more immersive experiences are developed. This analysis starts with the discussion of a modern example of successful hand-tracking implementation, then comparing that implementation to a recent senior design project. This comparison is primarily based on each experience’s ability to create interactivity and immediacy. Interactivity is the degree to which the user can quickly and reliably make changes to their virtual environment, while immediacy is …
Synthesis Of Binary-Input Multi-Valued Output Optical Cascades For Reversible And Quantum Technologies, Ishani Agarwal, Miroslav Saraivanov, Marek Perkowski
Synthesis Of Binary-Input Multi-Valued Output Optical Cascades For Reversible And Quantum Technologies, Ishani Agarwal, Miroslav Saraivanov, Marek Perkowski
Electrical and Computer Engineering Faculty Publications and Presentations
This paper extends the decomposition from the group theory based methods of Sasao and Saraivanov to design binary input multivalued output quantum cascades realized with optical NOT, SWAP, and Fredkin Gates. We present this method for 3, 5, and 7 valued outputs, but in general it can be used for odd prime valued outputs. The method can be extended to realize hybrid functions with different valued outputs. A class of local transformations is presented that can simplify the final cascade circuits. Using these simplifying transformations, we present an upper bound on the maximum number of gates in an arbitrary -variable …
Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill
Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill
Theses and Dissertations
This research introduces a novel DL approach for SCA that combines power consumption and EM signals to enhance encryption key deduction by leveraging a dual-channel CNN architecture. A new dataset, consisting of simultaneous power and EM signal collections during 128-bitAES encryption, was developed to train and evaluate the model’s effectiveness. The combined approach achieved an 88% reduction in traces needed, from 50 traces to 6, for encryption key classification, outperforming traditional methods such as random forest, DPA, DEMA,and individual side channel CNN models. These findings highlight the potential of integrating multiple side channels in SCA to improve performance without the …
Extracting Permittivity And Permeability Using The Position-Insensitive And Calibration-Independent Method On A Rectangular Waveguide, James Conrad Denemark
Extracting Permittivity And Permeability Using The Position-Insensitive And Calibration-Independent Method On A Rectangular Waveguide, James Conrad Denemark
Theses and Dissertations
Classic methods for extracting material characteristics require known measurements to accurately calibrate the network analyzer. Previous work demonstrated a position-insensitive and calibration-independent (PiCi) transmission/reflection method to extract a material’s permittivity. This thesis proposes a method with the same function, manipulated to use one empty measurement and then two samples of different thicknesses. The PiCi method is first adopted for rectangular waveguide which resulted in inaccurate permittivity data when compared to the calibrated solution. Once detector mismatch corrections were applied, the PiCi method produced accurate results. Using a 2-D numerical root search, permittivity and permeability material characteristics are now successfully extracted …
Theoretical Advancements In Hawkes Processes And Their Practical Applications, Xi Zhang
Theoretical Advancements In Hawkes Processes And Their Practical Applications, Xi Zhang
Theses and Dissertations
Hawkes self-exciting point processes have been widely used in fields such as seismology, finance and social media analysis. Despite decades of research focusing on these processes, several key aspects remain under-explored. These include broadly applicable methods for model evaluation and selection, intuitive nonparametric inference approaches akin to kernel density estimation for probability density functions, and strategies to enhance the performance of generative point process models in predictive tasks. In this dissertation, first, we extend the time-rescaling theorem, which is traditionally limited to non-terminating processes with complete observations, to accommodate terminating processes and incomplete observations as well. This extension allows for …
Mathematical Model For Helically Propagating Spatially Multiplexed Mimo Channels Inside Single Core Optical Fibers, Ibrahim Barka
Mathematical Model For Helically Propagating Spatially Multiplexed Mimo Channels Inside Single Core Optical Fibers, Ibrahim Barka
Theses and Dissertations
It all started when our curiosity was sparked by observing light beams from a Multi-Channel Laser Source precisely coupled into a single-core optical fiber positioned on an optical bench in the lab. The multiplexed light beams, characterized by different spatial azimuthal angles, were guided by the fiber, tracing helical paths and producing a bright central spot with concentric rings depending on the launched angles, a phenomenon we came to call Spatial Domain Multiplexing (SDM). Conventional concepts of hybrid and LP modes couldn’t fully explain the behavior of SDM modes, leading us to the question of the types of field configurations …