The field of radio broadcast requires the cohesion of several different skill sets in order to be a success. KHEA Radio has used a traditional form of teaching, which means taking a one-on-one approach. Taking this approach has worked for years in the past and has been the only option for teaching. The down side to this method of teaching is that it requires one seasoned employee to stop their work and take the time to train a new employee. Because of the significant void in the area of instructional content for radio sound engineering, my co-worker and I had to troubleshoot this console and basically teach ourselves its functions. I saw the need for better instructional content on the Internet and in print based on my own experiences. The skills used to create the following instructional content were gained from course work at Arizona State University. The graduate department of Technical Communication makes every effort to equip students with varied skills that can be applied to different fields within the overall scheme of technical communication. This guide serves as a tool for radio broadcast novices to learn the basics of sound board operation.

YourBrandPartner.com exists to provide content to those seeking specific advice and information on purchasing custom promotional items. For this investigation, I conducted a usability test with a select user group to identify user experience issues. The primary goal of this research was to conduct general usability testing through large group survey and a small in-person usability testing group. I designed surveys and tests to investigate if users experienced difficulties in finding the information they were looking for on the website. Based on the results of this study, I recommend reviewing the visual design of the website, increasing site speed, creating a better experience between the blog and e- commerce interactions, and creating an environment that is more accommodating of where the user is in the buying process. This full report includes expanded participant feedback, methodology behind the study, and full recommendations for improvement.

The purpose of this applied project was to research and recommend to Phoenix Children’s Hospital (PCH) improvements to their website in order to provide parents whose child has been newly diagnosed with cancer the most clear and appropriate health information. I conducted a study in order to analyze and evaluate the health information content currently provided to parents at PCH. This was done by through qualitative coding methods on both printed documents provided by The Emily Center Library, as well as interviews conducted with three Hematology/Oncology nurses at PCH. Additionally, I researched the current literature surrounding this topic in order to provide a background of information. Based on the results, I recommended that PCH offer parents a comprehensive cancer database in which all provided information would be searchable via their website. This database would also allow them to expand on their two topic focuses: home care and emotional support. Additionally, I recommended that parents are provided information on how to identify credible and non- credible sources on the Internet so that they can find information that is truly medically valuable when searching for information on their own. Lastly, I offered future recommendations that will require continued research so that PCH’s provided health information can continue to grow and improve.

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

Self-efficacy in engineering, engineering identity, and coping in engineering have been shown in previous studies to be highly important in the advancement of one’s development in the field of engineering. Through the creation and deployment of a 17 question survey, undergraduate and first year masters students were asked to provide information on their engagement at their university, their demographic information, and to rank their level of agreement with 22 statements relating to the aforementioned ideas. Using the results from the collected data, exploratory factor analysis was completed to identify the factors that existed and any correlations. No statistically significant correlations between the identified three factors and demographic or engagement information were found. There needs to be a significant increase in the data sample size for statistically significant results to be found. Additionally, there is future work needed in the creation of an engagement measure that successfully reflects the level and impact of participation in engineering activities beyond traditional coursework.

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

In collaboration with Moog Broad Reach and Arizona State University, a<br/>team of five undergraduate students designed a hardware design solution for<br/>protecting flash memory data in a spaced-based radioactive environment. Team<br/>Aegis have been working on the research, design, and implementation of a<br/>Verilog- and Python-based error correction code using a Reed-Solomon method<br/>to identify bit changes of error code. For an additional senior design project, a<br/>Python code was implemented that runs statistical analysis to identify whether<br/>the error correction code is more effective than a triple-redundancy check as well<br/>as determining if the presence of errors can be modeled by a regression model.