Estimating Stimulated Raman Scattering Noise In Cwdm O-Band Channels Induced By Two Classical Dwdm Sources In A Quantum Network Fiber Segment,
2024
Air Force Institute of Technology
Estimating Stimulated Raman Scattering Noise In Cwdm O-Band Channels Induced By Two Classical Dwdm Sources In A Quantum Network Fiber Segment, Kurt T. Spranger Ii
Theses and Dissertations
The purpose of this research is to estimate the stimulated Raman scattering noise induced in CWDM O-band channels by two DWDM classical sources in a terrestrial quantum optical network containing classical and quantum optical signal coexistence in the same fiber segment. A use case is defined and analyzed which extracts a single fiber segment from a notional Bell state measurement found in a notional terrestrial quantum network. A stimulated Raman scattering noise model is employed in a Python simulation to estimate and rank-order the five O-band channels with the least amount of relative induced stimulated Raman scattering noise when given …
A Sindy Hardware Accelerator For Efficient System Identification On Edge Devices,
2024
California Polytechnic State University, San Luis Obispo
A Sindy Hardware Accelerator For Efficient System Identification On Edge Devices, Michael Sean Gallagher
Master's Theses
The SINDy (Sparse Identification of Non-linear Dynamics) algorithm is a method of turning a set of data representing non-linear dynamics into a much smaller set of equations comprised of non-linear functions summed together. This provides a human readable system model the represents the dynamic system analyzed. The SINDy algorithm is important for a variety of applications, including high precision industrial and robotic applications. A Hardware Accelerator was designed to decrease the time spent doing calculations. This thesis proposes an efficient hardware accelerator approach for a broad range of applications that use SINDy and similar system identification algorithms. The accelerator is …
Random Forests For Detecting Weak Signals And Extracting Physical Information: A Case Study Of Magnetic Navigation,
2024
Autonomy and Navigation Technology Center, Air Force Institute of Technology
Random Forests For Detecting Weak Signals And Extracting Physical Information: A Case Study Of Magnetic Navigation, Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai, Aaron P. Nielsen
Faculty Publications
It has been recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth’s anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment. The accuracy of the detected anomaly field corresponds to a positioning accuracy in the range of 10–40 m. To increase the accuracy and reduce the uncertainty of weak signal detection as well as to directly obtain the position information, we exploit the machine-learning model of random forests that combines the output of multiple decision trees to give optimal values of the physical …
Small Unmanned Aircraft System Detection And Tracking With Audio, Computer Vision, And Deep Learning Techniques,
2024
Air Force Institute of Technology
Small Unmanned Aircraft System Detection And Tracking With Audio, Computer Vision, And Deep Learning Techniques, Anthony C. Brunson
Theses and Dissertations
sUAS present significant risks to local and federal agencies when under the control of negligent, reckless, or criminal operators. In the face of an escalating presence of sUAS in shared airspace with traditional aircraft, and their deployment in protected airspace as potential weapons, safeguarding personnel, facilities, and assets becomes paramount. This research seeks to address this emerging threat by investigating the efficacy of integrating low-cost distributed sensors and Machine learning (ML) models to enhance battlespace awareness and complement existing sensing platforms for real-time sUAS detection, classification, and localization. The thesis introduces the conceptualization and development of a Drone Detection Command …
Improving Rogue Radio Emitter Detection Using Siamese Networks,
2024
Air Force Institute of Technology
Improving Rogue Radio Emitter Detection Using Siamese Networks, Mason Wright
Theses and Dissertations
Radio Frequency Fingerprinting (RFF) is the process of creating discerning signatures of emitted radio signals, most often with the goal of identifying specific devices again in the future. The security benefits of this task are intended to build upon current software-based authentication by making use of multi-factor authentication (MFA), but the related task of being able to reject unwanted emitters is limited. This paper presents a Siamese network trained on two different extracted fingerprints of raw Wi-Fi signals, along with a verifier to perform classification and rogue device detection. It was found that fingerprints using the Distortion Reconstruction (DR) technique …
Tree Localization In A Plantation Using Ultra Wideband Signals,
2024
Purdue University
Tree Localization In A Plantation Using Ultra Wideband Signals, Akshat Verma
The Journal of Purdue Undergraduate Research
No abstract provided.
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
