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Autonomous Planetary Rover Team Final Project Report
Get PDFThis design team was tasked to develop autonomous movement, sensing, and navigation capabilities on a rover platform developed by a summer research team working under Dr. Kevin Nickels. The rover is comprised of four subsystems that strive to meet the requirements by establishing movement capabilities through power delivery and control, odometry feedback of the wheels, obstacle detection, and navigation. This report evaluated each design against the requirement they were intended to meet. The following report describes the final design of each of these subsystems, explains the testing performed on each subsystem, and evaluates the results of these tests against the design requirements.
The design constraints of rover size and budget are maintained by our final design by delivering a final design that fits through a standard CSI doorframe, and not exceeding the total budget of $2400. The final deliverable satisfies the requirements of battery specifications, incline traversal, display of map and estimated position, and obstacle detection. The final design failed to demonstrate an ability to traverse over an obstacle of 2 inches. The team was unable to demonstrate completion of the remaining requirements because of significant failures of the motors described later in the report.
In the process of delivering the project requirements, extensive modifications and redesigns to the provided platform were necessary. The frame received from the project sponsor was in a nonfunctional state. The team performed significant modifications to the rover frame to allow for proper movement of the rover. Also, the provided motor drivers failed in preliminary testing, requiring the team to experimentally evaluate the operational requirements of the motors and select and integrate new motor drivers into the final design.
Overall, the team delivered a functioning prototype that met many of the project requirements, and all the design constraints. The rover was able to move, detect obstacles, and plan navigation through an environment. Unfortunately, the motors suffered a thermally induced failure during testing, precluding the completion of the remaining tests
Remote Heart Diagnosis
Get PDFFor those in impoverished communities or remote regions, obtaining adequate healthcare can be a burden. Furthermore, limited access to specialists like cardiologists can make curable conditions a death sentence by leading them to be identified too late. An essential factor in the identification of heart conditions is the use of an electrocardiograph to measure the signal of the heart. In this project, the Remote Heart Diagnosis Team endeavored to design and build a prototype capable of remotely collecting and analyzing an electrocardiogram and displaying the results to a cardiologist in any location for review.
The prototype is composed of five main components. The first component is a printed circuit board designed to record the electrocardiogram. The second is a Raspberry Pi and touchscreen programmed to guide the user through the collection process, compile patient data and read the output of the circuit, run an artificial intelligence algorithm, and store all the information in the third component, a remotely deployed database, using a wireless connection. The fourth component is a website that accesses the database and allows doctors to view and interact with the device data. The final component is a three dimensional printed casing that houses the circuit, microcomputer, and touchscreen.
In early stages of testing, the team identified the need to transfer the circuit from a breadboard to a printed circuit board as the circuit often failed after being moved due to loosened wires. The team also discovered that the noise in the circuit was dependent on the wall outlet being used, leading to the addition of a filter in the circuit. As shown in the success of all but one final test, the prototype meets all expected qualifications, allowing for the changes in the potential diagnoses with the approval of the project sponsor. The only design requirement not achieved was in regards to the ability of the website to replicate a commercial electrocardiogram in form with 90% accuracy; however, as there were limitations with the commercial electrocardiogram used in terms of details in the data, measurement methods, and accuracy, the visuals were deemed reasonable due to their similarity to a traditional electrocardiogram.
Overall, the prototype is a fully functional proof of concept, as it is able to measure a clean electrocardiogram signal from a patient and collect their biographical data, analyze the electrocardiogram using a deployed artificial intelligence network, and store the results in a manner that can be remotely accessed on a website. This prototype is only a proof of concept, however, as, while the developed artificial intelligence network proved that the deployment and use of this type of network is possible, the network is unable to achieve functional accuracy due to a limited dataset. Before commercialization of the prototype, the neural network would need to be retrained using a large dataset of electrocardiograms collected using the device and labeled by a trained cardiologist. Furthermore, the neural network architecture and its ramifications in terms of classification should be reviewed with a professional cardiologist to ensure that image classification has the potential for functional accuracy. In addition, while outside the scope of this project, security code would need to be added to protect the website and transmissions before the prototype could be commercialized to comply with patient privacy laws and medical information regulations. Therefore, while the prototype was successful in achieving the desired functionality and in meeting the requirements, additional improvements will need to be made prior to the expansion of use
Age Prediction by DNA Methylation in Neural Networks
No full textAging is traditionally thought to be caused by complex and interacting factors such as DNA methylation. The traditional formula of DNA methylation aging is based on linear models and little work has explored the effectiveness of neural networks, which can learn non-linear relationships. DNA methylation data typically consists of hundreds of thousands of feature space and a much less number of biological samples. This leads to overfitting and a poor generalization of neural networks. We propose Correlation Pre-Filtered Neural Network (CPFNN) that uses Spearman Correlation to pre-filter the input features before feeding them into neural networks. We compare CPFNN with the statistical regressions (i.e. Horvaths and Hannums formulas), the neural networks with LASSO regularization and elastic net regularization, and the Dropout Neural Networks. CPFNN outperforms these models by at least 1 year in term of Mean Absolute Error (MAE), with a MAE of 2.7 years. We also test for association between the epigenetic age with Schizophrenia and Down Syndrome (p=0.024 and
Writing and Drawing: Knowledge of “Traditional Indigenous Midwives”
Get PDFThis paper aims to discuss the construction of the “traditional indigenous midwife” category in the context of public health policies on pregnancy, labor and childbirth care in Roraima, Brazil. Based on statements given by indigenous women and men in two sets of situations - the training courses offered by the Ministry of Health and in the Midwives, Praying men and Shamans Meetings held in Região das Serras, Raposa Serra do Sol Indigenous Land, Brazil - this work seeks to consider how the sensible knowing of these men and women who call themselves midwives is transformed into the category of “traditional indigenous knowledge”. In addition, I will examine the writing and drawing records produced by midwives, pointing to the ways in which traditional indigenous knowledge transforms itself and takes new shapes in relation to the conceptual logic of scientific knowledge embedded in public health policies
The Use of DISC Behavioral Profiling and Training: An Innovative Pedagogical Strategy to Enhance Learning and Future Career Opportunities in Sport Management and Sport Coaching Higher Education Classrooms
Get PDFImplementing effective training and education programs is of critical importance for sport management and sport coaching academic education programs. This exploratory research examined the implementation and effectiveness of DISC behavioral profiling in sport management and sport coaching classrooms at the university level. Over four academic years (eight semesters), pre- and post-tests were collected from multiple samples of sport management and sport coaching students (N = 216) at two universities in the United States. Students received a personalized DISC behavioral profile and educational activities were used to enhance the value of the behavioral profiling initiatives. Using pre- and post-activity surveys of the knowledge and skills gained during in-course activities, paired sample t-test showed positive and significant results for 11 of 16 measured areas. The findings suggest that behavioral profiling tools and activities within sport management and sport coaching curricula can enhance student’s self-awareness and help develop leadership skills which will prepare for future career opportunities. Limitations and opportunities for future research are also presented
Tap ‘Follow’ #FitFam: A Process of Social Media Microcelebrity
Get PDFThe practice of microcelebrity in social media has become part of the internet’s mainstream, and has led to the rise of influencers–trusted tastemakers in an industry niche–who are playing increasingly larger cultural and economic roles. Scholars have examined this topic since Senft introduced it in 2001, shedding light on strategies and practices of popular influencers, as well as the cultural milieu contributing to microcelebrity practices. Missing from the literature, however, is an explanation of how these popular microcelebrities reached their social media influencer status. Thus, through phenomenological interviews with 24 participants in multiple areas of the fitness sector, this study presents a general seven-step process by which these individuals became microcelebrities and leveraged their followings. Three findings are particularly noteworthy. First, a process detailing how influencers reached their status contributes to our theoretical understanding of microcelebrity by offering contextual factors and general steps experienced by influencers. Second, although microcelebrity practices are characterised by intentional self-commodification, most influencers in this study began their careers accidentally. Third, social media may be altering the traditional career paths of fitness professionals, especially as it relates to educations and credentials, which can be substituted with body capital. Future research may utili(z)e this process as a framework to investigate specific influencer strategies over time or at certain career stages, the meaning ascribed to influencers and microcelebrity practices, and influencer motivation related to individual context. Findings also encourage continued examination of social media’s effects on the fitness industry as a whole
Growth Factor Binding Peptides in Poly (Ethylene Glycol) Diacrylate (PEGDA)-Based Hydrogels for an Improved Healing Response of Human Dermal Fibroblasts
Get PDFGrowth factors (GF) are critical cytokines in wound healing. However, the direct delivery of these biochemical cues into a wound site significantly increases the cost of wound dressings and can lead to a strong immunological response due to the introduction of a foreign source of GFs. To overcome this challenge, we designed a poly(ethylene glycol) diacrylate (PEGDA) hydrogel with the potential capacity to sequester autologous GFs directly from the wound site. We demonstrated that synthetic peptide sequences covalently tethered to PEGDA hydrogels physically retained human transforming growth factor beta 1 (hTGFβ1) and human vascular endothelial growth factor (hVEGF) at 3.2 and 0.6 ng/mm2, respectively. In addition, we demonstrated that retained hTGFβ1 and hVEGF enhanced human dermal fibroblasts (HDFa) average cell surface area and proliferation, respectively, and that exposure to both GFs resulted in up to 1.9-fold higher fraction of area covered relative to the control. After five days in culture, relative to the control surface, non-covalently bound hTGFβ1 significantly increased the expression of collagen type I and hTGFβ1 and downregulated vimentin and matrix metalloproteinase 1 expression. Cumulatively, the response of HDFa to hTGFβ1 aligns well with the expected response of fibroblasts during the early stages of wound healing
COVID-19 Mortality Across ZIP codes in Four Major Texas Cities
No full textThe COVID-19 pandemic has continued to perpetuate adverse health outcomes across the U.S. Ongoing work has emphasized the role of the social determinants of health (SdoH) in producing unequal outcomes for cases and fatalities from the virus. In furthering their efforts, this study investigates contributions to differences in mortality rates across ZIP codes. A ZIP code level analysis provides a mechanism to explore contextual processes that operate within counties, especially as variation in large metropolitan areas are under-valued in county-level analyses of the pandemic. Fixed-effects Ordinary Least Squares (OLS) regressions are conducted to examine the relationship between socio-environmental factors and COVID-19 mortality across ZIP codes within the four largest counties in Texas: Bexar County, Dallas County, Harris County, and Tarrant County. Results indicate there is significant association between poverty rate and COVID-19 mortality rates across ZIP Codes. The full model maintains a predictive power of 70%, emphasizing the influence of SEF in perpetuating disparities. These findings underscore the importance of examining health inequalities at the local level
Persistent Mapping of Sensor Data for Medium-Term Autonomy
Get PDFFor vehicles to operate in unmapped areas with some degree of autonomy, it would be useful to aggregate and store processed sensor data so that it can be used later. In this paper, a tool that records and optimizes the placement of costmap data on a persistent map is presented. The optimization takes several factors into account, including local vehicle odometry, GPS signals when available, local map consistency, deformation of map regions, and proprioceptive GPS offset error. Results illustrating the creation of maps from previously unseen regions (a 100 m × 880 m test track and a 1.2 km dirt trail) are presented, with and without GPS signals available during the creation of the maps. Finally, two examples of the use of these maps are given. First, a path is planned along roads that have been seen exactly once during the mapping phase. Secondly, the map is used for vehicle localization in the absence of GPS signals