Engineering Commons^™

2-Channel Eeg Neurofeedback System, Tim Erwin, Donna Nikjou, Sebastian Turkewitz

Electrical Engineering

This work describes the design of an EEG-based neurofeedback system which provides users with real-time feedback on their level of focus or relaxation. By analyzing the spectral content of the brain activity measured via scalp electrodes, focus and relaxation levels can be quantified. Based on these measurements, live feedback in the form of moving bar graphs is provided to users, allowing them to gain awareness of their mental-state and more efficiently learn how to consciously relax or focus. This project covers the design of the system, including amplification and filtering stages, digitization, and signal processing. The system interfaces with a …

Performance Analysis Of Underground-To-Aboveground Communication In Agricultural Iot Networks, Irfana Ilyas Manzil

Theses

This thesis investigates the potential of LoRa, a low-power, wide-area networking technology, for establishing reliable communication between underground sensors and aboveground infrastructure. We comprehensively analyze LoRa's performance in both single-hop and multi-hop configurations, considering the impact of diverse environmental factors such as soil composition, moisture content, underground transmission distance, and path loss on signal propagation. We delve into the crucial role of the spreading factor (SF) within the LoRa communication system, analyzing its influence on network performance. Furthermore, we develop a comprehensive mathematical model for bit error rate (BER) under various channel conditions, including additive white Gaussian noise (AWGN) and …

Generative Data Augmentation: Using Dcgan To Expand Training Datasets For Chest X-Ray Pneumonia Detection, Ryan D. Maier

Master's Theses

Recent advancements in computer vision have demonstrated remarkable success in image classification tasks, particularly when provided with an ample supply of accurately labeled images for training. These techniques have also exhibited significant potential in revolutionizing computer-aided medical diagnosis by enabling the segmentation and classification of medical images, leveraging Convolutional Neural Networks (CNNs) and similar models. However, the integration of such technologies into clinical practice faces notable challenges. Chief among these is the obstacle of acquiring high-quality medical imaging data for training purposes. Patient privacy concerns often hinder researchers from accessing large datasets, while less common medical conditions pose additional hurdles …

Impact Force Helmet Device For Concussion Monitoring, Patrice Mulligan, Karina Mealey, Zack Borthwick, Benjamin Fick

General Engineering

To track head trauma in football, our device is placed inside a football helmet to measure both linear and gyroscopic (rotational) accelerations. These measurements are taken using an ADXL375 linear accelerometer and an LSM6DSOX accelerometer and gyroscope. The data from these sensors is processed and transmitted wirelessly by a SparkFun Thing Plus microcontroller to another SparkFun Thing Plus microcontroller connected to a laptop on the sidelines. Our program on the laptop stores the acceleration impact data into a .csv file for each player. If a likely concussive impact is detected, the program displays a popup warning. Testing results were promising …

Design And Characterization Of A Broadband Rf Switch Utilizing Surface Mount Devices, Daniel Bracamontes

Master's Theses

High frequency solid-state switches are critical elements in communication systems, radio frequency (RF) systems and instrumentation. Key parameters to an RF switch include insertion loss while on and off-state isolation. Power handling and linearity are important to consider for a cost-effective construction. This becomes a design challenge into K-band frequencies as components required need to be small, dielectric losses and transmission lines need to be physically matched for proper isolative and through states. This thesis presents a novel single pole eight throw (SP8T) hybrid design composed of commercially available surface mount technology solutions to achieve high isolation and low insertion …

Underwater Acoustic Telemetry For Marine Permaculture, Jonathan Lin, Harrison Shaw, Ben Shiverdaker, Mark Ellersick

Electrical and Computer Engineering Senior Theses

As a result of global warming and rising ocean temperatures and sea levels, seaweed forests are degrading. Seaweed biomass loss presents significant risks to coastal communities that rely on seaweed forests for food, livestock feed, fertilizer, and more. Marine Permaculture is the Climate Foundation’s regenerative farming initiative to grow seaweed outside of its intended habitat. The “Deep Cycling” technique involves positioning a submersible bed near the water surface during the day and lowering it at night, allowing for seaweed to receive sunlight and cold water. Prior work on this project included establishing a temperature control loop, though did not include …

Storage Solutions Within Memory Capacitor Technologies, Sam Anderson

Electrical and Computer Engineering Senior Theses

The need for efficient memory solutions is ever-increasing in the contemporary computing technology landscape, particularly in neuromorphic computing. This thesis introduces a novel approach to memory storage using memcapacitors, which store information via capacitance rather than voltage, offering a promising alternative to traditional memory systems. Through the development and simulation of memcapacitor technologies, this work explores their potential to enhance machine learning capabilities and reduce power consumption. Our research demonstrates that memcapacitors can be effectively implemented at the nano-scale, albeit with substantial manufacturing investment. We employed various optimization strategies, including circuit adjustments and behavioral modeling simulations, to maximize the efficiency …

Providing Cadence Feedback In Real-Time To Guide Cardiovascular Workouts, Levi O. Rash

Master's Theses

Cardiovascular workouts offer numerous health benefits, yet beginners often find it challenging to initiate them. Existing wearable technologies, although providing valuable feedback such as heart rate zones, often disrupt the workout flow and distract users due to the need for interaction with the wearable display. In response, we propose an alternative feedback mechanism: cadence, measured in steps per minute. This feedback mechanism uses multiplicative control to produce the correct cadence for the user’s target heart rate (HR). To model the HR and cadence relationship, a first-order system was used. The prototype implementation of this system was completed in Arduino, using …

Design And Analysis Of A New Buck-Boost Converter With A Low-Side Switch, Abdullah Awidah

Master's Theses

This work explores the design, simulation, construction, and analysis of a novel Non-isolated DC-DC Buck-Boost converter which has the advantage of incorporating a low-side switch compared to the traditional buck-boost which requires a high-side switch. This allows the use of a low-side driver which further simplifies the design and operation of the converter. The proposed Buck-Boost converter was constructed to provide -24 V output from an input range of 12V-18V with 15V nominal input at 10W maximum output power utilizing 500kHz switching frequency. Findings from simulations and hardware tests verify that the converter effectively provides the desired -24 V output …

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 …

Shuffled Faster Than Nyquist Signaling For Spectrally Efficient And Secure Wireless Communication, John Gharib

Master's Theses

This thesis investigates the implementation and performance of Shuffled Faster than Nyquist (SFTN) signaling, a communication method that enhances spectral efficiency and provides physical layer security (PLS) in wireless communications. In Faster than Nyquist signaling, the Nyquist inter-symbol interference (ISI) criterion is exceeded, thereby increasing spectral efficiency. By varying the transmission rate of symbols above the Nyquist rate, SFTN signaling is able to obfuscate the timing of transmitted symbols with ISI. The work in this thesis evaluates the performance of SFTN in Additive White Gaussian Noise (AWGN) channels and the MATLAB 802.11ax fading channels. Results show that while SFTN signaling …

An Analysis Of Indoor Air Quality At Cal Poly For Sensor Design, Isabella M. Santi

Master's Theses

Prior research has shown that indoor air quality (IAQ) impacts cognitive performance. At Cal Poly, many older buildings are unable to maintain appropriate IAQ because of their outdated ventilation systems and the increasing number of students in the rooms. This work analyzes the IAQ of different buildings at Cal Poly, with a focus on Building 20. Carbon dioxide, temperature, and relative humidity inside classrooms are collected using an integrated circuit sensor and a microcontroller. A total of 38 hours of data was collected, with 22 of those hours in Building 20 specifically. We find that unlike temperature and relative humidity, …

Power Flow Modeling And Analysis Of A Green Seaport Power System, Alejandra Zapata

Master's Theses

Indonesia has committed to achieving Net-Zero emissions by 2060, signaling a significant shift towards sustainability, which includes impactful initiatives such as the implementation of Green Seaports. This thesis focuses on designing and developing a model for a proposed Green Seaport power system and the subsequent performance of power flow analysis under various operating conditions. The model, constructed using MATLAB Simulink, underwent extensive testing and analysis, with a specific emphasis on the Battery Energy Storage System (BESS) operating modes, including individual charging, discharging, and simultaneous charging and discharging. This comprehensive investigation and analysis, involving 62 separate simulations, evaluated the impact of …

A Hybrid Computer To Solve The Selective Harmonic Minimization Problem In Single-Phase Inverters, Dillon Nguyen

Master's Theses

Power electronics possess forms of non-ideality due to the nonlinearity present in physical switches. These switches are driven with pulse width modulation (PWM), which properly down-converts or up-converts based on converter topology. When these switches turn on or off, they introduce the main proponent of efficiency loss: harmonics. This becomes a nonlinear optimization problem with many converters. The solution proposed in this thesis is an analog/digital hybrid computer that utilizes the speed of analog computation and the accuracy of digital computing together. A Plexim RT Box provides the programmability needed for algorithms, while the analog circuit provides low-power, high-speed computation …

Optimal False Data Injection (Fdi) In Simulated Cooperative Adaptive Cruise Control (Cacc) Systems, Lovro Dukic

Master's Theses

In the rapidly advancing field of autonomous vehicles, ensuring the security and reliability of self-driving systems is crucial. Autonomous vehicle systems, such as cooperative adaptive cruise control (CACC), must undergo significant research and testing before their integration into commercial intelligent transportation systems. CACC considers multiple vehicles in close proximity as a single entity, or platoon, with each vehicle equipped with a controller that uses sensor-based measurements and vehicle-to-vehicle (V2V) communication to control inter-vehicle spacing. While this system offers numerous potential benefits for traffic safety and efficiency, it is also susceptible to False Data Injection (FDI) attacks, which can cause the …

Design Of A Dynamic, Reconfigurable, Self-Balancing Battery Pack, Alexander Hallett Neiman

Master's Theses

This thesis details the design and testing of a reconfigurable battery array. Reconfigurable battery arrays, specifically for mobile applications, have the potential to reduce or eliminate use of auxiliary charging, balancing, and inverter systems. Design work included a bidirectional solid state switch and a cell-to-cell bidirectional current-limiting power converter. While the overall efficiency, size, and cost of the battery pack was not competitive with existing options, it demonstrated cell-to-cell balancing and native four-level square wave AC inverter output with an array of only two cells. This demonstrates viability of the concept as well as reveals important requirements and safety features …

Single-Polarization And Single-Mode Hybrid Hollow-Core Anti-Resonant Fiber Design At 2 Μm, Herschel Herring, Mohammad Al Mahfuz, Md Selim Habib

Electrical Engineering and Computer Science Faculty Publications

In this paper, to the best of our knowledge, a new
type of hollow-core anti-resonant fiber (HC-ARF) design using
hybrid silica/high-index material (HIM) cladding is presented
for single-polarization, high-birefringence, and endlessly single-
mode operation at 2 μm wavelength. We show that the inclusion
of a HIM layer in the cladding allows strong suppression of
𝑥−polarization, while maintaining low propagation loss and
single-mode propagation for 𝑦−polarization. The optimized HC-
ARF design includes a combination of low propagation loss,
high-birefringence, and polarization-extinction ratio (PER) or
loss ratio of 0.02 dB/m, 1.2×10−4, and >550 respectively, while the
loss of the 𝑥−polarization is >20 …

3d Automated Surgeon’S Hand Motion Assessment Using A Cascade Fuzzy Supervisor During Intelligent Box-Trainer System Skills Training In A Multi-Thread Video Processing, Fatemeh Rashidi Fathabadi

Dissertations

For certain surgical procedures, Minimally Invasive Surgery (MIS) has become more advantageous than traditional open surgery. Therefore, mastery of laparoscopic skills is an essential component of surgical training and requires considerable time and effort. The Fundamentals of Laparoscopic Surgery (FLS) program has been developed as a tool to improve and assess fundamental surgical skills. In fact, using a low-cost Box-Trainer or a simulator, laparoscopic surgeons are required to train using a set of structured tasks that can be objectively used to assess their laparoscopic skills, which must be mastered before carrying out real-life laparoscopic procedures. These tasks include peg transfer, …

Impact Of Operational Ladar Occlusions On Point Cloud Instance Segmentation, Andrew D. Gibson

Theses and Dissertations

Data exploitation techniques are the enabler for technological advancements in military ISR applications of ladar ISR. By identifying instances of military objects in observed scenes, point cloud deep learning models can unlock new standards of real-time information delivery to warfighters. Although current deep learning training datasets do not include real-world collection occlusions consistent with military applications, this research characterizes SPT model performance by adding occlusions to the DALESObjects dataset via artificial flyby simulations.We find that a baseline model trained on unoccluded data suffers performance degradation on both semantic and instance segmentation tasks when evaluated on occluded data, but that the …

Network Slicing And Noma Enabled Mobile Edge Computing For Next-Generation Networks, Mohammad Arif Hossain

Dissertations

The advent of next-generation wireless networks ushers in a new era of potential, harnessing cutting-edge technologies like mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing as pivotal drivers of transformation. Within this landscape, an innovative approach is proposed by introducing a NOMA-enabled network slicing technique within MEC networks. This approach aims to achieve multiple objectives: meeting stringent quality of service requirements, minimizing service latency, and enhancing spectral efficiency. By seamlessly integrating NOMA with network slicing in edge computing environments, significant reductions in overall latency are achieved, alongside ensuring optimal resource allocation for NOMA users. To address these …

Integrating Laser Charging And Drones For Secure Edge Computing, Weiqi Liu

Dissertations

Drone-mounted base stations (DBSs) have emerged as a promising solution to enhance the flexibility and coverage of wireless networks, potentially revolutionizing communication systems. This dissertation explores the integration of DBSs into 5G and beyond networks, focusing on methodologies to optimize their deployment and performance. A laser charging-enabled DBS framework is proposed to extend flight time and enhance network coverage. By leveraging laser charging technology, the DBS can receive continuous energy transmission from a ground-based charging station while providing communication services to users. The framework is formulated as an optimization problem to jointly maximize flight time and communication data rate, while …

Computational Microscopy For Biomedical Imaging With Deep Learning Assisted Image Analysis, Yuwei Liu

Dissertations

Microscopy plays a crucial role across various scientific fields by enabling structural and functional imaging with microscopic resolution. In biomedicine, microscopy contributes to basic research and clinical diagnosis. Conventionally, optical microscopy derives its contrast from the amplitude of the optical wave and provides visualization of the physical structure of the sample qualitatively. To understand the function at the cellular or tissue level, there is a need to characterize the sample quantitatively and explore contrast mechanisms other than light intensity. Image enhancement or reconstruction from microscopic imaging systems is known as computational microscopy, and it involves the application of computational techniques …

Interaction Of Particles With Plasma And Shock Produced By Pulsed Spark Discharge, Shomik Mukhopadhyay

Dissertations

Interactions of powders with high-temperature plasma and shockwaves occur in diverse scenarios, such as nuclear blasts, accidental industrial dust explosions, solid propellant combustion in explosive charges and when removing contaminants from surfaces. Electrostatic Discharge (ESD), known for generating shock and plasma, is a promising lab-scale technique for simulating these interactions. Studies with ESD involved placing powders near a spark-producing gap between electrodes and observing mechanical and chemical processes like particle motion and ignition. A limited range of spark conditions and material properties have been tested, which facilitated the development and validation of preliminary computational models describing this system. Significant gaps …

Machine Learning-Based Design Of Doppler Tolerant Radar, Kyle Peter Wensell

Dissertations

In this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of …

Decentralized Control Of Renewable Generation Systems, Milad Shojaee

Dissertations

As the global community struggles with the escalating challenges of climate change and environmental degradation, the transition to renewable energy sources has emerged as a paramount solution. Harnessing energy from renewable sources such as solar, wind, and hydro power not only alleviates the adverse effects of conventional fossil fuel energy sources, but also establishes a foundation for a sustainable and resilient future. Microgrids have been utilized as a feasible platform to integrate renewable energy sources into the electrical power generation networks. They include one or more generation units connected to nearby users, and are able to operate in both grid-connected …

Information Theoretic Bounds For Capacity And Bayesian Risk, Ian Zieder

Dissertations

In this dissertation, the problem of finding lower error bounds on the minimum mean-squared error (MMSE) and the maximum capacity achieving distribution for a specific channel is addressed. Presented are two parts, a new lower bound on the MMSE and upper and lower bounds on the capacity achieving distribution for a Binomial noise channel. The new lower bound on the MMSE is achieved via use of the Poincare inequality. It is compared to the performance of the well known Ziv-Zakai error bound. The second part considers a binomial noise channel and is concerned with the properties of the capacity-achieving distribution. …

Graph-Based Modeling And Optimization Of Wpt Systems For Evs, Matthew J. Hansen, Greg Droge, Abhilash Kamineni

Electrical and Computer Engineering Student Research

A model of a system of wireless power transfer (WPT) pads is developed, where each WPT pad is modeled as a node and the coupling between pads is modeled as graph edges. This modeling approach is generalized to admit primary, secondary, and booster coils, where power can flow among the pads and a pad can fill multiple roles. An excitation in one pad induces voltage and current in all neighboring pads, causing each pad to act as both a booster coil and either a transmitter or a receiver. Power flow through the entire system can be modeled with the graph …

Integrating Edge-Intelligence In Auv For Real-Time Fish Hotspot Identification And Fish Species Classification, U. Sowmmiya, J. Preetha Roselyn, Prabha Sundaravadivel

Electrical Engineering Faculty Publications and Presentations

Enhancing the livelihood environment for fishermen's communities with the rapid technological growth is essential in the marine sector. Among the various issues in the fishing industry, fishing zone identification and fish catch detection play a significant role in the fishing community. In this work, the automated prediction of potential fishing zones and classification of fish species in an aquatic environment through machine learning algorithms is developed and implemented. A prototype of the boat structure is designed and developed with lightweight wooden material encompassing all necessary sensors and cameras. The functions of the unmanned boat (FishID-AUV) are based on the user's …

Marking Estimation In Petri Nets Using Dynamic Mode Decomposition, Aditya Kale

Theses

Petri Nets (PNs) are a well-established framework for modeling and analyzing complex systems with interacting, concurrent processes. This thesis extends traditional Petri Net methodologies by integrating Dynamic Mode Decomposition (DMD), a technique originally developed for fluid dynamics, to analyze Continuous Petri Nets (CPNs). By applying DMD to CPN marking evolution, this research constructs a reduced-order model that captures its dynamics through dynamic modes and eigenvalues, enabling prediction of future markings without detailed knowledge of transition firings or underlying deterministic models. The principal contribution of this research is extending the kit of tools available for analysis of CPN dynamics, providing insights …

The Next Strike: Pioneering Forward-Thinking Attack Techniques With Rowhammer In Dram Technologies, Nakul Kochar

Theses

In the realm of DRAM technologies this study investigates RowHammer vulnerabilities in DDR4 DRAM memory across various manufacturers, employing advanced multi-sided fault injection techniques to impose attack strategies directly on physical memory rows. Our novel approach, diverging from traditional victim-focused methods, involves strategically allocating virtual memory rows to their physical counterparts for more potent attacks. These attacks, exploiting the inherent weaknesses in DRAM design, are capable of inducing bit flips in a controlled manner to undermine system integrity. We employed a strategy that compromised system integrity through a nuanced approach of targeting rows situated at a distance of two rows …