Side Lobe Level Reduction And Array Thinning Of Concentric Circular Antenna Arrays,
2024
Electronics and Electrical Communications Engineering Dept., Faculty of Engineering, Tanta University, Tanta, Egypt.
Side Lobe Level Reduction And Array Thinning Of Concentric Circular Antenna Arrays, Alzahraa H. Nosier, Ahmed M. Elkhawaga, Mohamed E. Nasr, Nessim M. Mahmoud, Amr H. Hussein
Mansoura Engineering Journal
This paper presents a new beamforming technique based on the hybrid combination of the convolution algorithm (CA) and the genetic algorithm (GA) for reducing side lobe level (SLL) and array thinning of concentric circular antenna arrays (CCAA), which is denoted as C/GA technique. The CA determines the excitations of the elements, while the GA optimizes the radii of the circular arrays to adjust the half-power beamwidth (HPBW). For CCAA consisting of uniform feeding circular arrays, we assume that there are excitation coefficients that are distributed symmetrically around the array center and arranged in a vector. The excitation vector is convolved …
Dung Dkar Cloak: Exploring Soft Interfaces For Sonic Interactions,
2024
EJ Tech
Dung Dkar Cloak: Exploring Soft Interfaces For Sonic Interactions, Judit Eszter Kárpáti, Esteban De La Torre
Textile Society of America: Symposium Proceedings
The importance of crossmodal interaction within the contemporary cultural, technological and scientific panorama has evidently gained significant attention due to its remarkable advantages in creating a meaningful, interwoven, and integrated experience. The use and recontextualization of textiles in such exploratory quest into the human senses has proven to be critical. Computational science, algorithmic logic and digital devices have always been rooted and closely interwoven with textile crafts and practices. Recent technological advancements have further combined technology and textile, generating interactive textile surfaces, constructing endless possibilities for multisensorial experiences. In this presentation we will examine how we can weave a sensitive …
Unsupervised Machine Learning In Wireless Sensor Measurements And Hyperspectral Imaging,
2024
University of Texas at Arlington
Unsupervised Machine Learning In Wireless Sensor Measurements And Hyperspectral Imaging, Abrar U. Alam
Electrical Engineering Dissertations - Archive
The increasing demand for real-time analysis of sensor data in dynamic environments necessitates innovative approaches to data clustering. This work introduces a novel Online Kernel Clustering (OKC) framework that efficiently determines time-varying clustering configurations without requiring training data. The proposed method employs sparse kernel factorization, guided by a time-dependent metric to quantify the closeness of kernel similarity matrices to a block diagonal structure. By processing data sequentially, the OKC framework is tailored for non-stationary settings. The optimization process integrates block coordinate descent, difference-of-convex functions minimization, and projected sub-gradient descent to iteratively update kernel covariance matrices and cluster memberships online. Numerical …
All-Optical Signal Processing With Fiber-Based Parametric Wavelength Converters,
2024
University of Texas at Arlington
All-Optical Signal Processing With Fiber-Based Parametric Wavelength Converters, Cheng Guo
Electrical Engineering Dissertations - Archive
The optical signal degradation by optical amplifier noise set the fundamental limit of link reach in the fiber-optics networks. The industrial solution is to use the optical-electrical-optical (OEO) regenerator to clean up the noise at the expense of high-speed electronics and extra cost of laser and photodetectors. All-optical signal processing, enabled by nonlinear optics and optical fiber, intrinsically provides 2-order of magnitude higher processing bandwidth and seamless interface to fiber communication channels. However, there is no robust phase-preserving regenerator that has been experimentally demonstrated without sophisticated polarization tuning and without instable interferometric structure. In this project, we explore the applications …
Neural Net Estimation Of Discriminants Posterior Probability Vector,
2024
University of Texas at Arlington
Neural Net Estimation Of Discriminants Posterior Probability Vector, Harshvardhan Harshvardhan
Electrical Engineering Theses - Archive
Interpreting multi-layer perceptron (MLP) classifier outputs as posterior probabilities is a well-established practice in machine learning and is supported in the literature. However, several authors point out that MLP outputs are very poor estimates of the posterior probabilities. This is demonstrated for classifiers with and without nonlinear output activation. Achieving this reliability depends on key factors such as model complexity, sufficient training data availability, and optimization techniques' effectiveness. In practice, these requirements are not met, resulting in suboptimal probability estimates. Our approach introduces an innovative method based on the softmax output. The method aim to refine MLP discriminants into more …
Artificial Intelligence Enabled Machinery Fault Detection And Diagnosis Using Vibro-Acoustic Signals,
2024
University of Kentucky
Artificial Intelligence Enabled Machinery Fault Detection And Diagnosis Using Vibro-Acoustic Signals, Srinivasa Rao Ippili
Theses and Dissertations--Mechanical and Aerospace Engineering
In various industries, the early detection of faults in rotating machinery is crucial to prevent system failures and ensure customer satisfaction. Typically, vibration measurement and diagnosis are employed for fault detection, but this process faces challenges in automation due to the complexity of installing and maintaining accelerometers, particularly in end-of-line quality control or pre-installed machinery health assessments. Acoustic signals, as a form of mechanical wave, offer an alternative for monitoring machinery while in operation. Unlike accelerometers, acoustic transducers are non-contact and easy to set up, enabling real-time data collection without interrupting equipment operation. However, utilizing acoustic signals in manufacturing poses …
Non-Invasive Monitoring Device For Early Detection Of Breast Cancer Related Lymphedema,
2024
University of New Hampshire
Non-Invasive Monitoring Device For Early Detection Of Breast Cancer Related Lymphedema, Amy Prendergast
Honors Theses and Capstones
Breast Cancer Related Lymphedema (BCRL) is a common co-morbidity in cancer survivors following neoadjuvant therapies such as chemotherapy, radiation, and/or surgery. It is brought about by the disruption in the lymphatic system (think lymph node biopsy) that leads to a buildup of lymphatic fluid in the arm. Current diagnostic strategies for this condition are merely retroactive, and fairly limited in the parameters that are examined to ensure patient well-being long term. We hypothesize that with an approach that mimics bioimpedance spectroscopy analysis, we will be able to provide a clinical support tool that would better determine early stages of lymphedema …
Investigation Of Delta-Focused Ictal Electrical Source Imaging In Refractory Focal Epilepsy,
2024
University of Kentucky
Investigation Of Delta-Focused Ictal Electrical Source Imaging In Refractory Focal Epilepsy, Jared A. Rybarczyk
Theses and Dissertations--Electrical and Computer Engineering
Refractory focal epilepsy is characterized by the presence of seizures that cannot be controlled via anti-seizure medications. For patients suffering from this form of epilepsy, accurate identification of the seizure onset zone is a crucial step for many modalities of treatment. Electrical source imaging (ESI) allows for estimation of the seizure onset zone from electroencephalography. EEG feature extraction is an important step that can impact the final accuracy of source estimates. This work provides a review of 23 ictal ESI studies and proposes a delta-focused ictal ESI methodology. Our proposed delta-focused ictal ESI is implemented across 33 refractory focal epilepsy …
A Novel Processor Architecture Implementing The Stacked Error Diffusion Algorithm And Its Zynq-Based Realization,
2024
University of Kentucky
A Novel Processor Architecture Implementing The Stacked Error Diffusion Algorithm And Its Zynq-Based Realization, Qishi Hu
Theses and Dissertations--Electrical and Computer Engineering
Digital halftoning reproduces continuous-tone images using patterns of black and white dots, while multitoning extends this concept by incorporating inks with intermediate intensities. These techniques are extensively utilized in the printing industry to accommodate the limited range of inks available in printers. Stacked error diffusion is a high-quality multitoning algorithm that adheres to the blue-noise dithering standard. This thesis research studies the potential parallelism inherent in the algorithm and introduces the design of a novel processor architecture optimized for efficient execution. The architecture is realized on an FPGA development board featuring a Zynq SoC. Additionally, the hardware prototype can also …
Information-Theoretic Learning Framework Based On Covariance Operators On Reproducing Kernel Hilbert Spaces,
2024
University of Kentucky
Information-Theoretic Learning Framework Based On Covariance Operators On Reproducing Kernel Hilbert Spaces, Jhoan Keider Hoyos Osorio
Theses and Dissertations--Electrical and Computer Engineering
Information theory provides tools to quantify uncertainty, dependence, and similarity between probability distributions, which are crucial for addressing various machine-learning problems. However, estimating these quantities is challenging because data distributions are usually unknown, and only observations are available for analysis. In this dissertation, we advance the field of information-theoretic learning by developing a comprehensive framework using kernel methods for analyzing probability distributions using reproducing kernel Hilbert spaces (RKHS). By leveraging covariance operators in this representation space, we propose approaches to estimate a set of fundamental information-theoretic quantities, that, because of their resemblance with conventional quantities in information theory, we call …
Decompositions Of Nonlinear Input-Output Systems To Zero The Output,
2024
Old Dominion University
Decompositions Of Nonlinear Input-Output Systems To Zero The Output, W. Steven Gray, Kurusch Ebrahimi-Fard, Alexander Schmeding
Electrical & Computer Engineering Faculty Publications
Consider an input–output system where the output is the tracking error given some desired reference signal. It is natural to consider under what conditions the problem has an exact solution, that is, the tracking error is exactly the zero function. If the system has a well defined relative degree and the zero function is in the range of the input–output map, then it is well known that the system is locally left invertible, and thus, the problem has a unique exact solution. A system will fail to have relative degree when more than one exact solution exists. The general goal …
Sparse Representation Learning For Temporal Networks,
2024
University at Albany, State University of New York
Sparse Representation Learning For Temporal Networks, Maxwell Mcneil
Electronic Theses & Dissertations (2024 - present)
Temporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data …
An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation,
2024
Air Force Institute of Technology
An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation, Jeffrey Choate, Derek Worth, Scott Nykl, Clark N. Taylor, Brett J. Borghetti, Christine M. Schubert Kabban
Faculty Publications
Achieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective …
Passive Wireless Corrosion And Temperature Detection In High-Temperature Environments,
2024
West Virginia University
Passive Wireless Corrosion And Temperature Detection In High-Temperature Environments, Noah Lane Strader
Graduate Theses, Dissertations, and Problem Reports (ETD)
This work focuses on the theory and development of LC sensors for high temperature and corrosion measurement for stainless steel and copper surfaces with power industry and general corrosion detection applications. The LC resonators were fabricated via screen printing an Ag inductor on an alumina substrate. The LC design was modeled using the ANSYS HFSS modeling package. The LC passive wireless sensors operate with resonant frequencies centered at 85-110 MHz. The wireless response of the LC sensor was interrogated and received by a radio frequency signal generator and spectrum analyzer at temperatures from 50-800 °C for copper ground planes and …
Implementing Associative Learning Using Neuromorphic Robot,
2024
Michigan Technological University
Implementing Associative Learning Using Neuromorphic Robot, Vinay Kumar Pillalamarri
Dissertations, Master's Theses and Master's Reports
Associative learning, a key cognitive process seen across the animal kingdom, enables organisms to form connections between stimuli and adapt their behaviors based on past experiences. A particularly powerful example is fear conditioning, where animals learn to associate a neutral stimulus with an aversive one, allowing them to predict and avoid potential threats. Inspired by this mechanism, this project implements associative learning on an unmanned ground vehicle (UGV) to develop adaptive behavior through neuromorphic principles. Utilizing Nengo for neural modeling, the UGV learns to associate visual (red color) and tactile (vibration) stimuli through Hebbian learning, a biologically inspired synaptic adaptation …
Gnss Software Defined Radio: History, Current Developments, And Standardization Efforts,
2024
Universitat der Bundeswehr Munchen
Gnss Software Defined Radio: History, Current Developments, And Standardization Efforts, Thomas Pany, Dennis Akos, Javier Arribas, M. Zahidul H. Bhuiyan, Pau Closas, Fabio Dovis, Ignacio Fernandez-Hernandez, Carles Fernandez-Prades, Sanjeev Gunawardena, Todd Humphreys, Zaher M. Kassas, Jose A. Lopez Salcedo, Mario Nicola, Mario L. Psiaki, Alexander Rugamer, Yong-Jin Song, Jong-Hoon Won
Faculty Publications
Taking the work conducted by the global navigation satellite system (GNSS) software-defined radio (SDR) working group during the last decade as a seed, this contribution summarizes, for the first time, the history of GNSS SDR development. This report highlights selected SDR implementations and achievements that are available to the public or that influenced the general development of SDR. Aspects related to the standardization process of intermediate-frequency sample data and metadata are discussed, and an update of the Institute of Navigation SDR Standard is proposed. This work focuses on GNSS SDR implementations in general-purpose processors and leaves aside developments conducted on …
Information Access For Infrastructurally-Challenged Environments And Beyond Through Mutually Aware Spectrum Sharing Technologies,
2024
University at Albany, State University of New York
Information Access For Infrastructurally-Challenged Environments And Beyond Through Mutually Aware Spectrum Sharing Technologies, Karyn Doke
Electronic Theses & Dissertations (2024 - present)
The Radio Frequency (RF) spectrum is scarce and to make it available for new mobile wireless services, regulators are forced to re-allocate spectrum from existing services or develop mechanisms to share spectrum with new entries. Television White Space (TVWS) and Citizen Broadband Radio Service (CBRS) are two examples of recently commercialized spectrum sharing technologies. TVWS enables sharing among fixed wireless broadband technologies (secondary users) and terrestrial TV broadcast services (primary users). CBRS enables spectrum sharing among 5G/LTE (secondary users) and naval radar (primary users). With both technologies, a central database determines when it is safe for secondary users to operate …
Detecting Bearing Race Defects With Inductive Magnetic Reluctance Sensors And Artificial Neural Networks,
2024
Georgia Southern University
Detecting Bearing Race Defects With Inductive Magnetic Reluctance Sensors And Artificial Neural Networks, Collin Daly
College of Graduate Studies: Theses & Dissertations
This work proposes a method of detecting physical damage to bearing races in a rotational assembly by means of magnetic reluctance sensors generating a signal from a rotating gear-tooth wheel. A nominally sinusoidal signal is generated based on the rotation of a gearwheel with regularly spaced voids and lands. Detection is based on the time variance of the signal periodically in relation to the gearwheel and the bearing damage. The purpose of this work is to propose a process to detect and classify bearing race defects using existing sensors and neural networks for hazardous area equipment applications.
Estimating And Detecting Slow-Wave Events In Eeg Signals,
2023
Washington University in St. Louis
Estimating And Detecting Slow-Wave Events In Eeg Signals, Zhenghao Xiong
McKelvey School of Engineering Graduate Student Theses & Dissertations
Slow wave activity (SWA) is an electroencephalogram (EEG) pattern commonly occurring during anesthesia and deep sleep, and is hence a candidate biomarker to quantify such states and understand their connection to various phenotypes. SWA consists of individual slow waves (ISW), high-amplitude deflections lasting for approximately 0.5 to 1 second, and occurring quasi-periodically. This latter fact poses a challenge for conventional power spectral density EEG analysis methods that perform best when there is persistency of oscillatory activity. In this work, we pursue a time-domain detection framework for identifying and quantifying ISWs as a metric for SWA. Our method works, in essence, …
Energy Efficiency And Fault Tolerance In Open Ran And Future Internet,
2023
Technological University Dublin
Energy Efficiency And Fault Tolerance In Open Ran And Future Internet, Saish Urumkar, Byrav Ramamurthy, Sachin Sharma
Conference papers
Open Radio Access Networks (Open RAN) repre- sent a promising technological advancement within the realm of the future internet. Research efforts are currently directed towards enhancing energy efficiency and fault tolerance, which are critical aspects for both Open RAN and the future internet landscape. In the context of energy saving in Open RAN, there exists a spectrum of methods for achieving energy efficiency. These methods include the toggling of on/off states for different hardware resources such as base station units, distributed units, and radio units. Conversely, for enhancing fault tolerance in Open RAN, Software-Defined Networking (SDN) and OpenFlow based techniques …
