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Description
In this research, I surveyed existing methods of characterizing Epilepsy from Electroencephalogram (EEG) data, including the Random Forest algorithm, which was claimed by many researchers to be the most effective at detecting epileptic seizures [7]. I observed that although many papers claimed a detection of >99% using Random Forest, it was not specified “when” the detection was declared within the 23.6 second interval of the seizure event. In this research, I created a time-series procedure to detect the seizure as early as possible within the 23.6 second epileptic seizure window and found that the detection is effective (> 92%) as early as the first few seconds of the epileptic episode. I intend to use this research as a stepping stone towards my upcoming Masters thesis research where I plan to expand the time-series detection mechanism to the pre-ictal stage, which will require a different dataset.
ContributorsBou-Ghazale, Carine (Author) / Lai, Ying-Cheng (Thesis director) / Berisha, Visar (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2022-05
Description
Machine learning has been increasingly integrated into several new areas, namely those related to vision processing and language learning models. These implementations of these processes in new products have demanded increasingly more expensive memory usage and computational requirements. Microcontrollers can lower this increasing cost. However, implementation of such a system on a microcontroller is difficult and has to be culled appropriately in order to find the right balance between optimization of the system and allocation of resources present in the system. A proof of concept that these algorithms can be implemented on such as system will be attempted in order to find points of contention of the construction of such a system on such limited hardware, as well as the steps taken to enable the usage of machine learning onto a limited system such as the general purpose MSP430 from Texas Instruments.
ContributorsMalcolm, Ian (Author) / Allee, David (Thesis director) / Spanias, Andreas (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05
Description
The ability of magnetic resonance imaging (MRI) to image any part of the human body without the effects of harmful radiation such as in CAT and PET scans established MRI as a clinical mainstay for a variety of different ailments and maladies. Short wavelengths accompany the high frequencies present in high-field MRI, and are on the same scale as the human body at a static magnetic field strength of 3 T (128 MHz). As a result of these shorter wavelengths, standing wave effects are produced in the MR bore where the patient is located. These standing waves generate bright and dark spots in the resulting MR image, which correspond to irregular regions of high and low clarity. Coil loading is also an inevitable byproduct of subject positioning inside the bore, which decreases the signal that the region of interest (ROI) receives for the same input power. Several remedies have been proposed in the literature to remedy the standing wave effect, including the placement of high permittivity dielectric pads (HPDPs) near the ROI. Despite the success of HPDPs at smoothing out image brightness, these pads are traditionally bulky and take up a large spatial volume inside the already small MR bore. In recent years, artificial periodic structures known as metamaterials have been designed to exhibit specific electromagnetic effects when placed inside the bore. Although typically thinner than HPDPs, many metamaterials in the literature are rigid and cannot conform to the shape of the patient, and some are still too bulky for practical use in clinical settings. The well-known antenna engineering concept of fractalization, or the introduction of self-similar patterns, may be introduced to the metamaterial to display a specific resonance curve as well as increase the metamaterial’s intrinsic capacitance. Proposed in this paper is a flexible fractal-inspired metamaterial for application in 3 T MR head imaging. To demonstrate the advantages of this flexibility, two different metamaterial configurations are compared to determine which produces a higher localized signal-to-noise ratio (SNR) and average signal measured in the image: in the first configuration, the metamaterial is kept rigid underneath a human head phantom to represent metamaterials in the literature (single-sided placement); and in the second, the metamaterial is wrapped around the phantom to utilize its flexibility (double-sided placement). The double-sided metamaterial setup was found to produce an increase in normalized SNR of over 5% increase in five of six chosen ROIs when compared to no metamaterial use and showed a 10.14% increase in the total average signal compared to the single-sided configuration.
ContributorsSokol, Samantha (Author) / Sohn, Sung-Min (Thesis director) / Allee, David (Committee member) / Jones, Anne (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2022-05
Description
In this work, a highly sensitive strain sensing technique is developed to realize in-plane strain mapping for microelectronic packages or emerging flexible or foldable devices, where mechanical or thermal strain is a major concern that could affect the performance of the working devices or even lead to the failure of the devices. Therefore strain sensing techniques to create a contour of the strain distribution is desired.
The developed highly sensitive micro-strain sensing technique differs from the existing strain mapping techniques, such as digital image correlation (DIC)/micro-Moiré techniques, in terms of working mechanism, by filling a technology gap that requires high spatial resolution while simultaneously maintaining a large field-of-view. The strain sensing mechanism relies on the scanning of a tightly focused laser beam onto the grating that is on the sample surface to detect the change in the diffracted beam angle as a result of the strain. Gratings are fabricated on the target substrates to serve as strain sensors, which carries the strain information in the form of variations in the grating period. The geometric structure of the optical system inherently ensures the high sensitivity for the strain sensing, where the nanoscale change of the grating period is amplified by almost six orders into a diffraction peak shift on the order of several hundred micrometers. It significantly amplifies the small signal measurements so that the desired sensitivity and accuracy can be achieved.
The important features, such as strain sensitivity and spatial resolution, for the strain sensing technique are investigated to evaluate the technique. The strain sensitivity has been validated by measurements on homogenous materials with well known reference values of CTE (coefficient of thermal expansion). 10 micro-strain has been successfully resolved from the silicon CTE extraction measurements. Furthermore, the spatial resolution has been studied on predefined grating patterns, which are assembled to mimic the uneven strain distribution across the sample surface. A resolvable feature size of 10 µm has been achieved with an incident laser spot size of 50 µm in diameter.
In addition, the strain sensing technique has been applied to a composite sample made of SU8 and silicon, as well as the microelectronic packages for thermal strain mappings.
The developed highly sensitive micro-strain sensing technique differs from the existing strain mapping techniques, such as digital image correlation (DIC)/micro-Moiré techniques, in terms of working mechanism, by filling a technology gap that requires high spatial resolution while simultaneously maintaining a large field-of-view. The strain sensing mechanism relies on the scanning of a tightly focused laser beam onto the grating that is on the sample surface to detect the change in the diffracted beam angle as a result of the strain. Gratings are fabricated on the target substrates to serve as strain sensors, which carries the strain information in the form of variations in the grating period. The geometric structure of the optical system inherently ensures the high sensitivity for the strain sensing, where the nanoscale change of the grating period is amplified by almost six orders into a diffraction peak shift on the order of several hundred micrometers. It significantly amplifies the small signal measurements so that the desired sensitivity and accuracy can be achieved.
The important features, such as strain sensitivity and spatial resolution, for the strain sensing technique are investigated to evaluate the technique. The strain sensitivity has been validated by measurements on homogenous materials with well known reference values of CTE (coefficient of thermal expansion). 10 micro-strain has been successfully resolved from the silicon CTE extraction measurements. Furthermore, the spatial resolution has been studied on predefined grating patterns, which are assembled to mimic the uneven strain distribution across the sample surface. A resolvable feature size of 10 µm has been achieved with an incident laser spot size of 50 µm in diameter.
In addition, the strain sensing technique has been applied to a composite sample made of SU8 and silicon, as well as the microelectronic packages for thermal strain mappings.
ContributorsLiang, Hanshuang (Author) / Yu, Hongbin (Thesis advisor) / Poon, Poh Chieh Benny (Committee member) / Jiang, Hanqing (Committee member) / Zhang, Yong-Hang (Committee member) / Arizona State University (Publisher)
Created2014
Description
This creative project is a part of the work being done as a Senior Design Project in which an autonomous solar charge controller is being developed. The goal of this project is to design and build a prototype of an autonomous solar charge controller that can work independently of the power grid. This solar charge controller is being built for a community in Monument Valley, Arizona who live off grid. The controller is designed to step down power supplied by an array of solar panels to charge a 48V battery and supply power to an inverter. The charge controller can implement MPPT (Maximum Power Point Tracking) to charge the battery and power the inverter, it also is capable of disconnecting from the battery when the battery is fully charged and reconnecting when it detects that the battery has discharged. The charge controller can also switch from supplying power to the inverter from the panel to supplying power from the battery at low sun or night. These capabilities are not found in solar charge controllers that are on the market. This project aims to achieve all these capabilities and provide a solution for the problems being faced by the current solar charge controller
ContributorsSingh, Khushi (Author) / Goryll, Michael (Thesis director) / Kitchen, Jennifer (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2021-12
Description
This thesis details the design process of a variable gain amplifier (VGA) based circuit which maintains a consistent output power over a wide range of input power signals. This effect is achieved by using power detection circuitry to adjust the gain of the VGA based on the current input power so that it is amplifier to a set power level. The paper details the theory behind this solutions as well as the design process which includes both simulations and physical testing of the actual circuit. It also analyses results of these tests and gives suggestions as to what could be done to further improve the design. The VGA based constant output power solution was designed as a section of a larger circuit which was developed as part of a senior capstone project, which is also briefly described in the paper.
ContributorsMeyer, Sheldon (Author) / Aberle, James (Thesis director) / Chakraborty, Partha (Committee member) / Electrical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
While SPICE circuit simulation software gives researchers and industry accurate information regarding the behavior and characteristics of circuits, the auditory effect of SPICE circuit simulation on audio circuits is not well documented. This project takes a thoroughly analyzed and popular audio effect circuit called the Ibanez Tubescreamer and simulates its distortion effect on a .wav file in order to hear the effect of SPICE simulation. Specifically, the TS-808 schematic is drawn in the SPICE program LTSPICE and simulated using generated sinusoids and recorded .wav files. Specific components are imported using .MODEL and .SUBCKT to accurately represent the diodes, bipolar transistors, op amps, and other components in order to hear how each component affects the response. Various transient responses are extracted as .wav files and assembled as figures in order to characterize the result of the circuit on the input. Once the actual circuit is built and debugged, all of the same transient analysis is applied and then compared to the SPICE simulation figures gathered in the digital simulation. These results are then compared along with a subjective hearing test of the digital simulation and analog circuit in order to test the validity of the SPICE simulations. The digital simulations reveal that the distortion follows the signature characteristics of Ibanez Tubescreamer which shows that SPICE simulation will give insight into the real effects of audio circuits modeled in SPICE programs. Diodes--such as Silicon, Germanium, Zener, Red LEDs and Blue LEDs--can dramatically change the waveforms and sound of the inputs within the circuit where as the Op-amps--such as the JRC4558, TL072, and NE5532--have little to no effect on the waveforms and subjective effects on the output .wav files. After building the circuit and hearing the difference between the analog circuit and digital simulation, the differences between the two are apparent but very similar in nature--proving that the SPICE simulation can give meaningful insight into the sound of the actual analog circuit. Some of the differences can be explained by the variance of equipment and environment used in recording and playback. Since this project did not use high fidelity audio recording equipment and consistency in the equipment used for playback, it is uncertain if the simulation and actual circuit could be classified as completely accurate. Any further work on the project would be recording and playing back in a constant environment and looking into a wider range of specific components instead of looking into one permutation.
ContributorsMacias, Cole Thomas (Author) / Goryll, Michael (Thesis director) / Yu, Shimeng (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
The thesis explores the avenues of machine learning principles in object detection using TensorFlow 2 Object Detection API Libraries for implementation. Integrating object detection capabilities into ESP-32 cameras can enhance functionality in the capstone dragster application and potential applications, such as autonomous robots. The research implements the TensorFlow 2 Object Detection API, a widely used framework for training and deploying object detection models. By leveraging the pre-trained models available in the API, the system can detect a wide range of objects with high accuracy and speed. Fine-tuning these models using a custom dataset allows us to enhance their performance in detecting specific objects of interest. Experiments to identify strengths and weaknesses of each model's implementation before and after training using similar images were evaluated The thesis also explores the potential limitations and challenges of deploying object detection on real-time ESP-32 cameras, such as limited computational resources, costs, and power constraints. The results obtained from the experiments demonstrate the feasibility and effectiveness of implementing object detection on ESP-32 cameras using the TensorFlow2 Object Detection API. The system achieves satisfactory accuracy and real-time processing capabilities, making it suitable for various practical applications. Overall, this thesis provides a foundation for further advancements and optimizations in the integration of object detection capabilities into small, low-power devices such as ESP-32 cameras and a crossroad to explore its applicability for other image-capturing and processing devices in industrial, automotive, and defense sectors of industry.
ContributorsMani, Vinesh (Author) / Tsakalis, Konstantinos (Thesis director) / Jayasuriya, Suren (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05