Filtering by
- All Subjects: Radar
- All Subjects: Electric Vehicles
- Creators: Electrical Engineering Program
- Creators: Chakraborty, Partha
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
The honors thesis presented in this document describes an extension to an electrical engineering capstone project whose scope is to develop the receiver electronics for an RF interrogator. The RF interrogator functions by detecting the change in resonant frequency of (i.e, frequency of maximum backscatter from) a target resulting from an environmental input. The general idea of this honors project was to design three frequency selective surfaces that would act as surrogate backscattering or reflecting targets that each contains a distinct frequency response. Using 3-D electromagnetic simulation software, three surrogate targets exhibiting bandpass frequency responses at distinct frequencies were designed and presented in this thesis.
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.
We present in this paper a method to compare scene classification accuracy of C-band Synthetic aperture radar (SAR) and optical images utilizing both classical and quantum computing algorithms. This REU study uses data from the Sentinel satellite. The dataset contains (i) synthetic aperture radar images collected from the Sentinel-1 satellite and (ii) optical images for the same area as the SAR images collected from the Sentinel-2 satellite. We utilize classical neural networks to classify four classes of images. We then use Quantum Convolutional Neural Networks and deep learning techniques to take advantage of machine learning to help the system train, learn, and identify at a higher classification accuracy. A hybrid Quantum-classical model that is trained on the Sentinel1-2 dataset is proposed, and the performance is then compared against the classical in terms of classification accuracy.
As more electric vehicles (EVs) are adopted, users need a solution to meet their expectations when it comes to Level 2 EV Charging (EVC). Currently, Adaptive Charging (AC) Techniques are used in multi-unit, public, settings. In the future, AC should be utilized to provide an optimized charging experience for the EV user in a single-unit residential application. In this experiment, an Electric Vehicle simulation tool was created using Python. A training dataset was generated from Alternative Fuels and Data Center (EVI-Pro) using charging data from Phoenix, Arizona. Similarly, the utility price plan chosen for this exercise was SRP Electric Vehicle Price plan. This will be the cost-basis for the thesis. There were four cases that were evaluated by the simulation tool. (1) Utility Guided Scheduling (2) Automatic Scheduling (3) Off-Site Enablement (4) Bidirectional enablement. These use-cases are some of the critical problems facing EV users when it comes to charging at home. Each of these scenarios and algorithms were proven to save the user money in their daily bill. Overall, the user will need some sort of weighted scenario that considers all four cases to provide the best solution to the user. All four scenarios support the use of Adaptive Charging techniques in residential level 2 electric vehicle chargers. By applying these techniques, the user can save up to 90% on their energy bill while offsetting the energy grid during peak hours. The adaptive charging techniques applied in this thesis are critical to the adoption of the next generation electric vehicles. Users need to be enabled to use the latest and greatest technology. In the future, individuals can use this report as a baseline to use an Artificial Intelligence model to make an educated case-by-case decision to deal with the variability of the data.
This thesis investigates how to design a radar using a field–programmable gate array board to generate the radar signal, and process the returned signal to determine the distance and concentration of objects (in this case, ash). The purpose of using such a board lies in its reconfigurability—a design can (relatively easily) be adjusted, recompiled, and reuploaded to the hardware with none of the cost or time overhead required of a standard weather radar.
The design operates on the principle of frequency–modulated continuous–waves, in which the output signal frequency changes as a function of time. The difference in transmit and echo frequencies determines the distance of an object, while the magnitude of a particular difference frequency corresponds to concentration. Thus, by viewing a spectrum of frequency differences, one is able to see both the concentration and distances of ash from the radar.
The transmit signal data was created in MATLAB®, while the radar was designed with MATLAB® Simulink® using hardware IP blocks and implemented on the ROACH2 signal processing hardware, which utilizes a Xilinx® Virtex®–6 chip. The output is read from a computer linked to the hardware through Ethernet, using a Python™ script. Testing revealed minor flaws due to the usage of lower–grade components in the prototype. However, the functionality of the proposed radar design was proven, making this approach to radar a promising path for modern vulcanology.
This thesis investigates how to design a radar using a field–programmable gate array board to generate the radar signal, and process the returned signal to determine the distance and concentration of objects (in this case, ash). The purpose of using such a board lies in its reconfigurability—a design can (relatively easily) be adjusted, recompiled, and reuploaded to the hardware with none of the cost or time overhead required of a standard weather radar.
The design operates on the principle of frequency–modulated continuous–waves, in which the output signal frequency changes as a function of time. The difference in transmit and echo frequencies determines the distance of an object, while the magnitude of a particular difference frequency corresponds to concentration. Thus, by viewing a spectrum of frequency differences, one is able to see both the concentration and distances of ash from the radar.
The transmit signal data was created in MATLAB®, while the radar was designed with MATLAB® Simulink® using hardware IP blocks and implemented on the ROACH2 signal processing hardware, which utilizes a Xilinx® Virtex®–6 chip. The output is read from a computer linked to the hardware through Ethernet, using a Python™ script. Testing revealed minor flaws due to the usage of lower–grade components in the prototype. However, the functionality of the proposed radar design was proven, making this approach to radar a promising path for modern vulcanology.