Signal Processing Commons^™

Constructing Orthonormal Bases With The Residuals Of Successive Approximations, An Introduction To Multiresolution Analysis, Elijah J. Guptill

Master's Theses

Wavelets and wavelet analysis are used in the study of signal processing, quantum field theory, functional analysis, multifractal analysis, and various other areas of mathematics. Multiresolution analysis provides a framework for building a wavelet basis of $\mathcal{L}^{2}(\mathbb{R})$ from a scaling function $\phi$, whose dyadic dilations and translations, $\{2^{j /2}\phi(2^{j}x-k):j,k\in \mathbb{Z}\}$, approximate $\mathcal{L}^{2}(\mathbb{R})$. One of the key properties of  $\phi$ is that it must satisfy $\phi(x)=\sum_{k\in \mathbb{Z}}{p_{k}2^{j /2}\phi(2^{j}x-k)}$ with respect to the norm on $\mathcal{L}^{2}(\mathbb{R})$. This equation is called a two-scale difference equation. Such equations enforce a regularity on the ordinary generating function $2^{-1 /2}\sum_{k\in \mathbb{Z}}{p_{k}z^{k}}$, known as the quadrature condition. …

Efficient Mathematical Modeling And Synthesis Of Realistic Musical Instrument Sounds, Andrew Chookaszian

Master's Theses

This thesis develops and evaluates a compact parametric additive synthesis model for isolated musical instrument tones. The method analyzes a single-note recording, estimates its fundamental frequency, extracts harmonic amplitude and frequency behavior, and stores the sound as a reduced set of interpretable parameters. These parameters include the note duration, pitch, per-harmonic amplitude envelopes, phase information, and amplitude- and frequency-modulation vibrato parameters. The stored model is then used to resynthesize the tone without directly using the original audio waveform.

The model was evaluated using synthetic signals, real instrument samples, objective error metrics, storage comparisons, pitch and duration modification tests, and listening …

A Study On Quantization And Hardware Cost Tradeoffs In A 5g Nr Ldpc Decoder On Fpga, Giancarlo Acosta

Master's Theses

The fixed-point implementation of a 5G New Radio LDPC decoder forces a tradeoff between precision and hardware cost, governed by the variable node word length W_L, the check node message width W_R, and the normalized min-sum correction factor α. This thesis characterizes how these three parameters affect both error correction performance and FPGA resource utilization for an LDPC decoder on an established LDPC decoder architecture. A bit-accurate MATLAB core model records bit error rate (BER) and frame error rate (FER) while a verified HDL Coder model generates synthesizable VHDL for Vivado synthesis, and both are swept …

Deep Learning For Affect Recognition: A Comparative Study Of Physiological Sensor-Based And Facial Image-Based Approaches, Ravi Panchal, Ravi Panchal

Master's Theses

This thesis investigates deep learning approaches for affect recognition using wearable physiological signals and facial image data. The sensor-based component evaluates stress and affect recognition on the WESAD dataset using wrist-based physiological windows and examines multiple temporal modeling strategies, including convolutional, recurrent, hybrid CNN-LSTM, attention-based, ensemble, and time-frequency approaches.

The image-based component evaluates hard-label facial expression recognition on AffectNet+ using pre-trained ResNet-50, EfficientNet-B3, and ConvNeXt-Tiny architectures across Easy, Challenging, and Difficult subsets representing different levels of expression ambiguity.

Experimental results show that the proposed Multi-Branch Attention CNN-BiLSTM (MBA-CNN-BiLSTM) model achieves the strongest wearable stress- and affect-recognition performance among the evaluated …

Practical Multimodal Wearable Sensing For Functional Upper Extremity Primitive Classification With Application To Stroke Rehabilitation, Nicholas Weiss

Master's Theses

Stroke often causes long-term weakness and impaired motor control in the upper extremity (UE), making everyday tasks such as reaching, grasping, and moving objects more difficult. Restoring functional arm use is therefore a central goal of post-stroke rehabilitation. Measuring affected arm use continuously and objectively is important because isolated clinical assessments may not fully capture how the affected arm is used during therapy or daily life. Wearable sensors offer a promising approach for monitoring, but raw sensor signals are difficult to interpret directly. Functional movement primitives address this issue by describing UE behavior as smaller, task-agnostic movement units.

This thesis …

Deterministic Methods To Improve The Field-Of-View For Direction Finding Using Sparse Digital Arrays, Nolan J. Egging

Master's Theses

Direction finding algorithms are used with digital phased arrays to determine the incoming angle of arrival (AoA) of an incident signal. These algorithms, and direction finding as a whole, have a wide range of civilian and military applications from radar, electronic reconnaissance, mobile communication, et cetera. However, for situations where the spacing between antenna elements needs to be large, gating lobes appear in the radiation pattern of analog arrays. This work demonstrates that for digital beamforming algorithms, the field of view (FoV) of a uniform linear digital array matches the grating lobe free range of a similarly spaced analog array. …

Leveraging Machine-Learning Algorithms In Two Car Crash Detection Systems On A Custom Dataset, Addison Jacob Sandvik

Master's Theses

Traffic accidents pose a significant threat to public safety, causing millions of deaths and injuries worldwide each year. While efforts to reduce accidents have seen limited progress in recent years, improving emergency response times through automated detection systems is a promising avenue for saving lives. This thesis describes the development of machine learning-based traffic accident detection systems, exploring both video classification and image detection models. The models are trained on a new dataset deemed the Cal Poly Traffic Accident Dataset, an extension of the existing Car Accident Detection and Prediction (CADP) dataset with a precise collision annotations. Two systems were …

Coherent Synchronization For Distributed Digital Phased Arrays, Zachary C. Numa

Master's Theses

Distributed digital phased arrays are rising technologies that help enable applications such as search and rescue operations, wireless communication, radar navigation, and military operations, among many others. Due to their improved angular resolution, digital phased arrays offer superior direction-finding capabilities compared to traditional analog phased array systems. However, this improvement comes at the cost of increased complexity—specifically, the need for precise synchronization of phase, time, and frequency across physically separated nodes. Without synchronization, the distributed phased array's gain and direction-of-arrival (DoA) estimations deteriorate significantly.

There are multiple aspects to implementing and synchronizing a non-stationary distributed digital phased array. This research …

Single-Sideband Pulse Width Modulation For Parametric Acoustic Arrays, Douglas Liu

Master's Theses

Parametric acoustic arrays are directional loudspeakers that operate using ultrasonic carriers to project sound within a narrow beam. Input audio is first modulated onto an ultrasonic carrier and transmitted through air, where it self-demodulates into audible frequencies in the far field.

This thesis introduces a method for preprocessing audio into scaled quadrature signals using a passive analog polyphase filter. These signals are modulated using microcontroller-generated waveforms to create quadrature ultrasonic pulse-width modulation (PWM) signals. The modulated outputs are combined through a wired-OR summer, producing single-sideband ultrasonic content at the desired frequency of 40 kHz. This signal is amplified through high-efficiency …

A Heuristic Approach To Portrait Segmentation And Its Application To Synthetic Bokeh Generation, Charles J. Snead

Master's Theses

Segmentation of portrait images is an important technique used to separate the foreground and background of an image. This separation of layers is useful for selectively applying post-processing techniques to enhance the quality of the image, such as blurring the background. Automatic portrait segmentation is a complex process that can be completed with a high degree of accuracy using deep learning with neural networks, but training and inference are often very computationally expensive. This thesis aims to take a heuristic approach to portrait segmentation by combining classical image processing and computer vision techniques into a solution that can be run …

Optimizing And Training An Svm-Based Breast Cancer Tumor Classifier, Kevin Lopatka

Master's Theses

With advancements in technology, turning to machine learning has become a popular choice for aiding clinicians in the diagnoses of breast cancer malignancies. While the neural networking approach has been vetted thoroughly, this work aims to take advantage of traditional machine learning techniques; mainly support vector machine learning and the optimizing of feature extraction. The discrete-wavelet transform is used in the feature extraction stage of machine learning. Previous works that use this feature extraction technique are analyzed and expanded upon by utilizing a variety of different wavelets as well as other color-spaces with the goal of achieving higher result metrics …

Enhanced Post-Capture Automatic White Balance For Srgb Images, Eric Huang

Master's Theses

Automatic white balancing (AWB) aims to correct color casts caused by varying illumination conditions, typically assuming access to RAW sensor data. However, many real-world applications involve only sRGB images that have already been processed by in-camera pipelines. In these cases, traditional AWB algorithms often underperform due to the nonlinear transformations done by these pipelines.

This thesis builds upon a data-driven color correction framework introduced by Afifi et al. that relies on RGB-UV histograms and learned color transforms. A revised automatic white balancing (AWB) framework that improves both color accuracy and runtime efficiency is proposed. A fallback routine is implemented to …

Design And Implementation Of An Inverted Short Baseline Acoustic Positioning System, Jakob Frabosilio

Master's Theses

This document details the design, implementation, testing, and analysis of an inverted short baseline acoustic positioning system. The system presented here is an above-water, air-based prototype for an underwater acoustic positioning system; it is designed to determine the position of remotely-operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs) in the global frame using a method that does not drift over time.

A ground-truth positioning system is constructed using a stacked hexapod platform actuator, which mimics the motion of an AUV and provides the true position of an ultrasonic microphone array. An ultrasonic transmitter sends a pulse of sound towards …

Enhancing Direction Finding Accuracy In Perturbed Digital Arrays Via Rf Ranging-Based Self Calibration, Ariel Freiman

Master's Theses

Direction finding with radio-frequency (RF) waves have numerous applications in radio navigation, wireless localization, emergency aid, and air traffic control, among others. Direction-finding using digital arrays outperforms traditional analog techniques but requires precise knowledge of the location of the array elements to obtain accurate results. Array perturbations can lead to algorithm failures and false detection, compromising direction-finding capabilities.

This research proposes implementing a Matched Filter - Least Square (MF-LS) algorithm for Two-Way Ranging (TWR) to enhance direction-finding accuracy in arrays with perturbed element locations. The MF-LS algorithm leverages the properties of matched filters to accurately determine element positions by measuring …

Harmonic Flow Modeling And Analysis Of A Green Hybrid Ac/Dc Seaport Power System, Krishan Kaushal Ram, Taufik, Helen Yu, Siddharth Vyas

Master's Theses

This thesis aims to design and develop a model for a proposed Green Seaport power system and perform harmonic analysis. The model was developed using MATLAB Simulink and tests were performed by dividing the system into several Battery Energy Storage System (BESS) operating modes such as simultaneous charging and discharging, simultaneous charging, and simultaneous discharging. For each mode, BESS state of charge and other factors such as solar irradiance for the PV system, load levels and power factor were varied to observe the impact on system’s voltage Total Harmonic Distortion (THD) level and current Total Demand Distortion (TDD). 62 separate …

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 …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Range-Doppler Map Processing Chain For Marine Radar On Fpga Of An Rfsoc, Nickolas W. Ogilvie

Master's Theses

To improve the accuracy and resolution of the measurements, radar systems employ increasingly complex, resource-intensive signal processing, larger bandwidths, and higher carrier frequencies. However, implementing these improvements requires more expensive, complex electronics that are larger and use more power. Using RFSoC (Radio Frequency System on Chips) in radars can address these challenges. By combining processors, an FPGA (Field Programmable Gate Array), and RF data converters into a single integrated circuit, RFSoCs allow for radar electronics that are physically smaller, use less power, and are simpler to design. As using RFSoCs to perform RF data conversion for radars has been already …

Music Visualization Using Source Separated Stereophonic Music, Hannah Eileen Chookaszian

Master's Theses

This thesis introduces a music visualization system for stereophonic source separated music. Music visualization systems are a popular way to represent information from audio signals through computer graphics. Visualization can help people better understand music and its complex and interacting elements. This music visualization system extracts pitch, panning, and loudness features from source separated audio files to create the visual. Most state-of-the art visualization systems develop their visual representation of the music from either the fully mixed final song recording, where all of the instruments and vocals are combined into one file, or from the digital audio workstation (DAW) data …

Neural Network Based Diagnosis Of Breast Cancer Using The Breakhis Dataset, Ross E. Dalke

Master's Theses

Breast cancer is the most common type of cancer in the world, and it is the second deadliest cancer for females. In the fight against breast cancer, early detection plays a large role in saving people’s lives. In this work, an image classifier is designed to diagnose breast tumors as benign or malignant. The classifier is designed with a neural network and trained on the BreakHis dataset. After creating the initial design, a variety of methods are used to try to improve the performance of the classifier. These methods include preprocessing, increasing the number of training epochs, changing network architecture, …

Indoor Positioning Using Synchronized Ultrasonic Ofdma Signals, Julian Bartolone

Master's Theses

This paper proposes a method of short-range indoor localization using differential phase measurements of synchronized two-tone ultrasonic signals in an Orthogonal Frequency Multiple Access (OFDMA) scheme. This indoor positioning system (IPS) operates at an ultrasonic frequency of approximately 40kHz and synchronizes using an infrared signal. The OFDMA scheme allows for a receiver to process the signals from multiple transmitters continuously without the signals interfering with each other. The phases of the signals are measured using Goertzel Filters, allowing for low-complexity frequency content analysis. A MATLAB simulation using the proposed localization method is performed using four transmitter nodes in the corners …

Comparison Of Hilbert Transform And Derivative Methods For Converting Ecg Data Into Cardioid Plots To Detect Heart Abnormalities, Robert George Goldie

Master's Theses

Electrocardiogram (ECG) time-domain signals contain important information about the heart. Several techniques have been proposed for creating a two-dimensional visualization of an ECG, called a Cardioid, that can be used to detect heart abnormalities with computer algorithms. The derivative method is the prevailing technique, which is popular for its low complexity, but it can introduce distortion into the Cardioid plot without additional signal processing. The Hilbert transform is an alternative method which has unity gain and phase shifts the ECG signal by 90 degrees to create the Cardioid plot. However, the Hilbert transform is seldom used and has historically been …

An Exploratory Study Of Pulse Width And Delta Sigma Modulators, Logan B. Penrod

Master's Theses

This paper explores the noise shaping and noise producing qualities of Delta-Sigma Modulators (DSM) and Pulse-Width Modulators (PWM). DSM has long been dominant in the Delta Sigma Analog-to-Digital Converter (DSADC) as a noise-shaped quantizer and time discretizer, while PWM, with a similar self oscillating structure, has seen use in Class D Power Amplifiers, performing a similar function. It has been shown that the PWM in Class D Amplifiers outperforms the DSM [1], but could this advantage be used in DSADC use-cases? LTSpice simulation and printed circuit board implementation and test are used to present data on four variations of these …

An Adaptive Approach To Gibbs’ Phenomenon, Jannatul Ferdous Chhoa

Master's Theses

Gibbs’ Phenomenon, an unusual behavior of functions with sharp jumps, is encountered while applying the Fourier Transform on them. The resulting reconstructions have high frequency oscillations near the jumps making the reconstructions far from being accurate. To get rid of the unwanted oscillations, we used the Lanczos sigma factor to adjust the Fourier series and we came across three cases. Out of the three, two of them failed to give us the right reconstructions because either it was removing the oscillations partially but not entirely or it was completely removing them but smoothing out the jumps a little too much. …

Indoor Positioning Using Acoustic Pseudo-Noise Based Time Difference Of Arrival, Nicholas J. Luong

Master's Theses

The Global Positioning System (GPS) provides good precision on a global scale, but is not suitable for indoor applications. Indoor positioning systems (IPS) aim to provide high precision position information in an indoor environment. IPS has huge market opportunity with a growing number of commercial and consumer applications especially as Internet of Things (IoT) develops. This paper studies an IPS approach using audible sound and pseudo-noise (PN) based time difference of arrival (TDoA). The system’s infrastructure consists of synchronized speakers. The object to be located, or receiver, extracts TDoA information and uses multilateration to calculate its position. The proposed IPS …

Distance Estimation Using Ofdm Signals For Ultrasonic Positioning, Kyman Huang

Master's Theses

This paper describes a method of estimating distance via Time-of-Flight (TOF) measurement using ultrasonic Orthogonal Frequency Division Multiplexing (OFDM) signals. Using OFDM signals allows the signals and their sub-carriers to remain orthogonal to each other while continuously transmitting. This estimation method is based on the change of phase of a traveling wave as it propagates through a medium (air for ultrasonic signals). By using signals containing multiple tones, the phase change between each frequency component is slightly different. This phase difference is dependent on the distance traveled and can thus be used to estimate distance. This paper studies the impact …

The Design, Testing, And Analysis Of A Constant Jammer For The Bluetooth Low Energy (Ble) Wireless Communication Protocol, Aiku Shintani

Master's Theses

The decreasing cost of web-enabled smart devices utilizing embedded processors, sensors, and wireless communication hardware have created an optimal ecosystem for the Internet of Things (IoT). IEEE802.15.4, IEEE802.11ah, WirelessHART, ZigBee Smart Energy, Bluetooth (BT), and Bluetooth Low Energy (BLE) are amongst the most commonly used wireless standards for IoT systems. Each of these standards has tradeoffs concerning power consumption, range of communication, network formation, security, reliability, and ease of implementation. The most widely used standards for IoT are Bluetooth, BLE, and Zigbee. This paper discusses the vulnerabilities in the implementation of the PHY and link layers of BLE. The link …

Visual Speech Recognition Using A 3d Convolutional Neural Network, Matthew Rochford

Master's Theses

Main stream automatic speech recognition (ASR) makes use of audio data to identify spoken words, however visual speech recognition (VSR) has recently been of increased interest to researchers. VSR is used when audio data is corrupted or missing entirely and also to further enhance the accuracy of audio-based ASR systems. In this research, we present both a framework for building 3D feature cubes of lip data from videos and a 3D convolutional neural network (CNN) architecture for performing classification on a dataset of 100 spoken words, recorded in an uncontrolled envi- ronment. Our 3D-CNN architecture achieves a testing accuracy of …

Development Of A Model And Imbalance Detection System For The Cal Poly Wind Turbine, Ryan Miki Takatsuka

Master's Theses

This thesis develops a model of the Cal Poly Wind Turbine that is used to determine if there is an imbalance in the turbine rotor. A theoretical model is derived to estimate the expected vibrations when there is an imbalance in the rotor. Vibration and acceleration data are collected from the turbine tower during operation to confirm the model is useful and accurate for determining imbalances in the turbine.

Digital signal processing techniques for analyzing the vibration data are explored and tested with simulation data. This includes frequency shifts, lock-in amplifiers, phase-locked loops, discrete Fourier transforms, and decimation filters. The …

Design Of A Low-Cost Data Acquisition System For Rotordynamic Data Collection, Gregory S. Pellegrino

Master's Theses

A data acquisition system (DAQ) was designed based on the use of a STM32 microcontroller. Its purpose is to provide a transparent and low-cost alternative to commercially available DAQs, providing educators a means to teach students about the process through which data are collected as well as the uses of collected data. The DAQ was designed to collect data from rotating machinery spinning at a speed up to 10,000 RPM and send this data to a computer through a USB 2.0 full-speed connection. Multitasking code was written for the DAQ to allow for data to be simultaneously collected and transferred …