This study measure the effect of temperature on a neural network's ability to detect and classify solar panel faults. It's well known that temperature negatively affects the power output of solar panels. This has consequences on their output data and our ability to distinguish between conditions via machine learning.
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One answer to the lack of general knowledge for alternative energy and integration topics is seen in the workforce development content Laboratory of Energy and Power Solutions has generated for the past 6 years. LEAPS is a world-changing organization that…
One answer to the lack of general knowledge for alternative energy and integration topics is seen in the workforce development content Laboratory of Energy and Power Solutions has generated for the past 6 years. LEAPS is a world-changing organization that provides both technical and business solutions in areas of grid modernization, workforce development, and global energy access that facilitates the global transition to a resilient, low-carbon economy. This paper will aim to explain the contributions of David Hobgood, an Arizona State University senior, to LEAPS workforce development content through the course of the Spring 2022 semester. This paper goes into detail on the process of completing this educational content, amplifies key aspect, and presents the results of a two week pilot that presented the generated content.
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The idea for this thesis emerged from my senior design capstone project, A Wearable Threat Awareness System. A TFmini-S LiDAR sensor is used as one component of this system; the functionality of and signal processing behind this type of sensor…
The idea for this thesis emerged from my senior design capstone project, A Wearable Threat Awareness System. A TFmini-S LiDAR sensor is used as one component of this system; the functionality of and signal processing behind this type of sensor are elucidated in this document. Conceptual implementations of the optical and digital stages of the signal processing is described in some detail. Following an introduction in which some general background knowledge about LiDAR is set forth, the body of the thesis is organized into two main sections. The first section focuses on optical processing to demodulate the received signal backscattered from the target object. This section describes the key steps in demodulation and illustrates them with computer simulation. A series of graphs capture the mathematical form of the signal as it progresses through the optical processing stages, ultimately yielding the baseband envelope which is converted to digital form for estimation of the leading edge of the pulse waveform using a digital algorithm. The next section is on range estimation. It describes the digital algorithm designed to estimate the arrival time of the leading edge of the optical pulse signal. This enables the pulse’s time of flight to be estimated, thus determining the distance between the LiDAR and the target. Performance of this algorithm is assessed with four different levels of noise. A calculation of the error in the leading-edge detection in terms of distance is also included to provide more insight into the algorithm’s accuracy.
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This thesis is done as an extension of the development of an electrical engineering capstone project. The goal of the capstone is to create a system that can receive a 2.4 GHz Wi-Fi signal out to a range of 300…
This thesis is done as an extension of the development of an electrical engineering capstone project. The goal of the capstone is to create a system that can receive a 2.4 GHz Wi-Fi signal out to a range of 300 meters and then use it to point in the direction of a given Wi-Fi source. The design process of the capstone system is described in depth and the results of the proposed design are presented. The thesis work explores how this system can achieve a dual band capability at both 2.4 GHz and 5 GHz Wi-Fi bands. So, a slotted patch antenna system with a slotted ground plane was designed and tested and proved to deliver the ideal characteristics for accurate signal tracking.
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Most machine learning algorithms, and specifically neural networks, utilize vector-matrix multiplication (VMM) to process information, but these calculations are CPU intensive and can have long run-times. This issue is fundamentally outlined by the von Neumann bottleneck. Because of this undesirable…
Most machine learning algorithms, and specifically neural networks, utilize vector-matrix multiplication (VMM) to process information, but these calculations are CPU intensive and can have long run-times. This issue is fundamentally outlined by the von Neumann bottleneck. Because of this undesirable expense associated with performing VMM via software, the exploration of new ways to perform the same calculations via hardware have grown more popular. When performed with hardware that is specialized to perform these calculations, VMM becomes far more power-efficient and less time consuming. This project expands upon those principles and seeks to validate the use of RRAM in this hardware.
The flexibility of the conductance of RRAM makes these devices a strong contender for hardware-driven VMM calculation for neural network computing. The conductance of these devices is affected by the pulse width of a voltage signal sent across the devices at each node. This pulse is produced on-chip and can be modified by user inputs. The design of this pulse- producing circuit, as well as the simulated and physical functionality of the design, is discussed in this Honors Thesis.
Simulation and physical testing of the pulse-producing design on the ASIC have verified correct operation of the design. This operation is imperative to the future ability of the ASIC to perform accurate VMM.
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This thesis project explores the TID susceptibility of 12nm FinFETs. Along with the basic effects, the mechanisms and patterns of these effects are analyzed and reported.
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The stability of cheerleading stunts is crucial to athlete safety and team success. Consistency in stunt technique contributes to success in stunting skills, giving a team the tools to win competitions. Increased stunt technique reduces the chances of falls and…
The stability of cheerleading stunts is crucial to athlete safety and team success. Consistency in stunt technique contributes to success in stunting skills, giving a team the tools to win competitions. Increased stunt technique reduces the chances of falls and the severity of those falls. Proper technique also prevents injuries caused by improper positions that place pressure on the lower back and shoulders. Bases must maintain strong technique with proper lines of support in order to maximize stunt stability. Through exploration of the EmbeddedML system, involving a neural network implemented using a SensorTile, cheerleading motions can be successfully classified. Using this system, it is possible to identify motions that result in both weak and injurious positions almost instantly. By alerting athletes to these incorrect motions, improper stunt technique can be corrected quickly and without the involvement of a coach. This automated technique correction would be incredibly beneficial to the sport of competitive cheerleading
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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…
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.
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In wireless communication systems, the process of data transmission includes the estimation of channels. Implementing machine learning in this process can reduce the amount of time it takes to estimate channels, thus, resulting in an increase of the system’s transmission…
In wireless communication systems, the process of data transmission includes the estimation of channels. Implementing machine learning in this process can reduce the amount of time it takes to estimate channels, thus, resulting in an increase of the system’s transmission throughput. This maximizes the performance of applications relating to device-to-device communications and 5G systems. However, applying machine learning algorithms to multi-base-station systems is not well understood in literature, which is the focus of this thesis.
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This paper discusses the process of creating and testing the haptic feedback wearable that utilizes a sweeping Light Detection and Ranging sensor. This design comes as an extension to the capstone project for electrical engineers. The design works by attaching…
This paper discusses the process of creating and testing the haptic feedback wearable that utilizes a sweeping Light Detection and Ranging sensor. This design comes as an extension to the capstone project for electrical engineers. The design works by attaching a LiDAR sensor to a sweeping servo motor, and whenever an object is detected by the sensor, a motor will vibrate to notify the user that an object is nearby. The design incorporates four motors so that the user will have a sense of where an obstacle is coming from and be able to navigate around that obstacle. The design was tested for its accuracy in distance and angle measurement, its efficiency when it came to processing the data, and the uncertainty of the sensor due to beam spreading. Plotting the results for the distance and angle accuracy showed that the design is capable of accurate measurements. The implementation of the code was also very efficient and had no issues with latency when processing the data from the sensor. There was also uncertainty at the larger ranges for the sensor.
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