Signal Processing Commons^™

Temperature Determination And Scene Change Artifact Mitigation When Using Fourier-Transform Spectroscopy On Targets With Time-Varying Temperature, Kode A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz

Faculty Publications

Fourier-transform spectroscopy is a widely used technique for determining the spectral and thermal properties of a target. However, target temperature variations during measurement can compromise the spectral accuracy. Temperature fluctuations induce oscillations superimposed on the target spectrum. These oscillations, referred to as scene-change artifacts, degrade the spectral accuracy. The literature is divided, with theoretical predictions suggesting negligible artifacts and growing experimental evidence reporting significant artifacts. This paper presents a theory and experimental validation of scene-change artifacts originating from target temperature variations. Traditionally, the interferogram offset is assumed to be constant, an invalid assumption for a changing scene. The error is …

Comparison Of Anomaly Detection Methods On Event-Based Vision Sensor Data In A High Noise Environment, Will Johnston, Anthony L. Franz, Shannon R. Young, Rachel Oliver, Zachary Theis, Brian Mcreynolds, Michael L. Dexter

Faculty Publications

Event-based vision sensors (EVSs) provide unique frequency analysis opportunities due to their event data output and high temporal resolution. Anomaly detection methods used in hyperspectral analysis can be used on the event frequency spectra to detect targets. However, the introduction of a strong, flickering interfering source can reduce the EVS sensitivity and obscure targets of interest. Previous work presented a method showing that targets could still be detected through an overwhelming source using frequency analysis, background suppression, and statistical filtering. This paper extends that research and compares the ability of five different eigenanalysis anomaly detection methods (principal component background suppression …

Preliminary Sonification Of Enso Using Traditional Javanese Gamelan Scales, Sandy Hs Herho, Rusmawan Suwarman, Nurjanna J. Trilaksono, Iwan P. Anwar, Faiz R. Fajary

Northeast Journal of Complex Systems (NEJCS)

Sonification—the mapping of data to non-speech audio—offers an underexplored channel for representing complex dynamical systems. We treat El Niño-Southern Oscillation (ENSO), a canonical example of low-dimensional climate chaos, as a test case for culturally-situated sonification evaluated through complex systems diagnostics. Using parameter-mapping sonification of the Niño 3.4 sea surface temperature anomaly index (1870–2024), we encode ENSO variability into two traditional Javanese gamelan pentatonic systems (pelog and slendro) across four composition strategies, then analyze the resulting audio as trajectories in a two- dimensional acoustic phase space. Recurrence-based diagnostics, convex hull ge- ometry, and coupling analysis reveal that the sonification …

Cooperative Unmanned Aerial System (Uas) Geolocation Of Emitters, Christopher Peters

Electrical Engineering Theses and Dissertations

A collection of unmanned aerial systems (UAS) can be networked as a cooperative wireless sensor array to geolocate an unknown-location RF emitter using time-based measurements. In operation, however, environmental multipath and hardware errors in sensor positioning and timing can degrade emitter localization accuracy and limit the practicality of single-snapshot solutions. This dissertation evaluates time-of-arrival and time-difference-of-arrival (TOA/TDOA) geolocation for cooperative UAS arrays under realistic error sources and develops geometry-control strategies that actively reduce localization uncertainty through iterative UAS repositioning.

This work studies the Location on a Conic Axis (LOCA) method for emitter localization. Using Monte Carlo simulations with hardware error …

Ellie - Exteroceptive Light Locomotion In Eukaryote-Fungi, Brandon Etwarroo

Doctoral Dissertations and Master's Theses

Over the past decade, fungal research and its technological applications have expanded across multiple disciplines, including the emerging field of biohybrid systems. This thesis develops and evaluates a wireless, untethered mobile robot controlled by the action potential-like activity generated by Pleurotus ostreatus sporocarps under red, green, and blue optical stimulation. Light is applied to the sporocarps, the resulting electrical responses are recorded, and these signals are transmitted wirelessly to actuate the mobile robot. Both the action potential-like activity patterns and the robot’s movement trajectories were analyzed. The results demonstrate that wireless robotic control mediated by fungal electrophysiology is feasible. Overall, …

A Software-Defined Radio Testbed For Mimo-Ofdm Communications, Chaz G. Maschman

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

In this thesis, we implement a testbed for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems via GNU Radio. Specifically, we implement a configurable framework for the construction of MIMO-OFDM software-defined radio (SDR) systems as a GNU Radio module. The GNU Radio MIMO-OFDM module consists of multiple algorithmic blocks necessary for implementation of a MIMO-OFDM system. This includes a library for the generation of orthogonal or pseudo-random pilot sequences, amendments to the Schmidl-Cox protocol for MIMO synchronization, and the creation of click-and-drag GNU Radio blocks implementing the conversion of arbitrary data sent via external programs to MIMO-OFDM frames, the initial …

Using Ai To Predict Energy Expenditure In Lower Limb Prosthesis Users, Nelly Diaz, Siem Hadish

Posters - 2026

• Computer vision has evolved from simple image classification and object detection to analyzing human motion and biomechanics (1). • CNN’s are usually focused on image classification, but, in this case, we are not asking the model if a person is walking. • Many real-world problems require regression: Predicting a continuous number like energy expenditure of walking is a complex task. • It is essential for Prosthetists to understand energy expenditure of their prosthetic patients (2). • An amputee may use 20-30% more energy to walk. • In this project, we developed an AI model to analyze human motion and …

A Pretraining-Based Framework For On-Device Training Of Imu-Based Locomotion Mode Detection For Wearable Active Exoskeletons, Muhammad Tahir Khan

LSU Master's Theses

Active exoskeletons are being developed to support human movement in physically demanding industries such as construction. For these systems to work effectively, they must be able to correctly identify the user’s current activity. This process is known as locomotion mode detection and plays an important role in selecting the appropriate control parameters for exoskeletons. Many existing approaches use inertial measurement units (IMUs) to recognize these activities and have shown strong performance. However, most of these methods depend on large amounts of labeled data collected under specific conditions. As a result, they often do not perform well when applied to new …

Characterizing Atmospheric Turbulence With The Lunar Step Response Method, Patrick D. Carattini, Caleb J. Stilp, Katelyn M. Atkinson, Stephen C. Cain

Faculty Publications

Most methods that astronomers use to characterize the strength of atmospheric turbulence in and around their observatories use differential image motion monitors observing a star to provide the necessary data for the measurement. With the Moon becoming a greater national priority, the need to characterize atmospheric paths between observatories on Earth and the Moon is potentially going to grow in the future. To this end, the differential image motion monitor is not an ideal instrument for characterizing turbulence along paths between observatories and the Moon as the bright Moon makes it difficult to detect and locate stars in its vicinity. …

The Main Errors Of The Ultrasonic Sensor In Measuring Water Flow In Open Channels, Anvar Urolovich Djalilov

Chemical Technology, Control and Management

This article analyzes the use of ultrasonic sensors in measuring water flow and the main errors that may occur in this process. In the conducted scientific research, a time-pulse ultrasonic sensor was tested. The absolute, relative and repeatability errors of the sensor during water flow measurement were studied. The absolute error represents the largest difference between the value recorded by the sensor and the real value, affecting the overall accuracy of the measurement system. This error can vary depending on environmental factors, the design and operating principles of the sensor. During the experiment, the performance of this sensor was …

Flexible Fault-Tolerant Multi-Die Fpga-Based Architectures For Varying Space Environments, Yosof Ali Seif El Din Ali Maklad

Theses and Dissertations

It is well-known fact that spacecraft’s electronic components operate in an extreme harsh and varying space environments, beside changing orbit or passing through Van Allan Belts during orbital course results of radiation levels change. This thesis focuses on SRAM-based FPGA systems on-board of such spacecrafts, that are commonly utilized in space applications’ critical applications due to their capabilities and flexibility to reconfigure, since these systems are vulnerable to frequent negative impacts of ionizing radiation, thus inducing soft and hard errors leading to disastrous failures that could jeopardize the entire spacecraft. The soft errors’ effects are frequent yet can be mitigated, …

Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts, Kody A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz

Faculty Publications

Fourier-transform spectrometers are widely used for spectral measurements. Changes in the field of view during measurement introduce oscillations into the measured spectra known as scene-change artifacts. Field-of-view changes also introduce uncertainty about which target the measured spectrum represents. Though scene-change artifacts are often present in dynamic data, their significance is disputed in the current literature. This work presents a theoretical framework and experimental validation for scene-change artifacts. Field-of-view changes introduce variable interferogram offsets, which standard processing techniques assume are constant. The error between the interferogram offset and its estimate is Fourier-transformed, yielding scene-change artifacts, often confused with noise, in the …

Universal Sound Separation: Distance-Aware Mixture Simulation, Co-Occurrence Conditioning, And Chain-Of-Inference, Wonjun Park

Computer Science and Engineering Theses - Archive

Universal Sound Separation (USS) -- the task of disentangling arbitrary sound sources from a single-channel acoustic mixture -- remains an open challenge due to the ill-posed nature of the problem and the distributional gap between synthetic training data and real-world recordings. This thesis addresses three distinct bottlenecks in the USS pipeline: training data realism, inference strategy, and conditioning richness. We first present two knowledge-guided approaches to sound source separation. The first is a distance-aware mixing strategy that leverages Large Language Models (LLMs) to assign plausible loudness relationships between audio sources during training data synthesis. By querying an LLM about the …

Code-Net++: An Attention-Guided Deep Learning Framework With Grad-Cam-Based Explainability For Covid-19 Detection Using Chest X-Ray Images, Fareesa Amina, Dr Krishnanaik Vankdoth

Mansoura Engineering Journal

Chest radiograph imaging has emerged as a practical and scalable diagnostic modality for respiratory diseases, including COVID-19. However, accurate discrimination of COVID-19 manifestations from other pulmonary abnormalities remains challenging because of low contrast, imaging noise, and overlapping radiographic patterns. This work presents CODE-NET++, an enhanced attention-guided deep learning framework with Grad-CAM-based explainability for reliable COVID-19 detection using chest X-ray images. The proposed framework integrates adaptive trilateral filtering for image enhancement, Reverse Edge Attention Network (RE-Net) for lesion-aware segmentation, and an Enhanced LinkNet architecture with dilated convolutions for multiscale feature extraction and classification. Grad-CAM-based explainable artificial intelligence visualization is incorporated to …

Guitar Amplifier Directivity, Rachel C. Edelman, Brian E. Anderson, Samuel D. Bellows, Timothy W. Leishman

Directivity

No abstract provided.

Deep Learning For Wireless Communications, Swarada Ajit Kulkarni

Electrical Engineering Dissertations

The rapid evolution of wireless communication imposes stringent requirements for ultra-reliable, low-latency transmission in dynamic, interference-prone environments. Traditional model-driven signal processing struggles to adapt to nonlinear hardware effects, time-varying channels, and complex interference patterns. Deep learning (DL) offers a transformative, data-driven alternative, enabling end-to-end optimization and robust adaptation under uncertain propagation conditions.

This dissertation investigates deep learning architectures for intelligent and resilient wireless communication through three complementary contributions. The first introduces a Vision Transformer (ViT)-based modulation classification framework that leverages self-attention to capture local and global dependencies in spectrogram representations of Quadrature Amplitude Modulation (QAM) signals. The ViT achieves superior …

Delay-Doppler Integrated Sensing And Communications (Dd-Isac) With Predictive Beamforming, Mohammad Abdul Mobin, Yanjun Pan, Jingxian Wu

Electrical Engineering and Computer Science Faculty Publications and Presentations

A new delay-Doppler (DD) integrated sensing and communications (ISAC) framework is proposed for unmanned aerial vehicle (UAV) systems. In the DD-ISAC framework, both sensing and communications are performed by using the orthogonal delay Doppler division multiplexing (ODDM) waveforms, which unify sensing and communication designs through the unique ODDM waveform properties, such as local DD-domain bi-orthogonality and dual-resolution. Specifically, the dual-resolution property enables the generation of a range-Doppler map for accurate and low complexity sensing, and the bi-orthogonality minimizes interference for both sensing and communications. The ODDM waveforms are used in combination with the phase comparison monopulse technique and a scaled …

A Digital Calibration Source For 21 Cm Cosmology Telescopes, Kalyani Balkrishna Bhopi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Precise calibration of radio telescope beams and gains is a central requirement for 21 cm intensity mapping experiments, which aim to measure large scale cosmological structure through the redshifted emission line of neutral hydrogen. Bright astrophysical foregrounds dominate the sky at these frequencies, and separating them from the cosmological signal demands precise control over instrumental systematics, particularly the telescope beam and its frequency-dependent response. Existing aerial calibration sources are incoherent broadband emitters, detectable only as total power. They provide no direct phase information and suffer from poor sensitivity in low signal-to-noise regimes.

We present the Precision Emitter for 21cm Array …

Robotizing Complex Welding Processes Through Imitation Learning And Generative Models From Human Demonstration, Yue Cao

Theses and Dissertations--Electrical and Computer Engineering

Arc welding processes demand real-time adaptive control that current robotic systems cannot achieve autonomously. This dissertation develops a systematic framework to robotize complex welding by learning from human demonstration, integrating generative modeling, physics-informed reconstruction, and model-based imitation learning. First, human--robot collaboration systems are established for both Gas Tungsten Arc Welding (GTAW) and Double-Electrode Gas Metal Arc Welding, combining robotic teleoperation with virtual reality interfaces to capture high-quality operator demonstrations. Second, a physics-informed neural network framework reconstructs complete molten pool flow fields from high-speed imaging, enriching process understanding beyond direct sensor observation. Third, generative models, including a hybrid latent variational autoencoder …

Hyperglycemia Detection From Sigle - Lead Ecg Using A Hybrid Cnn & Transformer Model, Adam Ayomikun Ogunjembola

Graduate Theses, Dissertations, and Problem Reports (ETD)

Abstract
Hyperglycemia Detection from Single-Lead ECG using a Hybrid CNN & Transformer Model
Adam Ogunjembola

Diabetes Mellitus is known as high blood glucose. This high blood glucose level happens when the body has a problem with producing or using insulin. Insulin is a very important hormone that the pancreas makes to control how much glucose gets into the bloodstream and cells. Diabetes Mellitus has an effect on the body if it is not treated, such as damaging the blood vessels and nerves which can lead to stroke, kidney failure, heart attack and permanent loss of vision. Since people with diabetes …