Drawing, Landscape

(Georgia Institute of Technology, 1948) Smith, Joseph N. ; Georgia Institute of Technology. College of Architecture
Resort hotel and islands recreation facility for Lake Sidney Lanier

(Georgia Institute of Technology, 1967) Gruber, Bruce B. ; Georgia Institute of Technology. College of Architecture
A junior college campus plan

(Georgia Institute of Technology, 1962) Twitty, Paul M. ; Georgia Institute of Technology. College of Architecture
Interior - Guest & Entertainment House

(Georgia Institute of Technology, 1946-06-19) Connell, Arnall T. ; Georgia Institute of Technology. College of Architecture
A revival of Atlanta's urban fabric : design of an intown residential condominium community

(Georgia Institute of Technology, 1986-08) Taylor, Jeffrey Scott ; Hughes, Rufus R. ; Architecture
Overcoming barriers to greater private sector involvement in transportation

(Georgia Institute of Technology, 1986) Ross, Catherine L. ; Georgia Institute of Technology. Office of Sponsored Programs ; Georgia Institute of Technology. College of Architecture ; Georgia Institute of Technology. Office of Sponsored Programs
Capturing Atlanta’s Food Environment: A Community Level Assessment of Three Disparate Areas

(Georgia Institute of Technology, 2011-05-06) Douangchai, Vanhvilai L. ; Georgia Institute of Technology. School of City and Regional Planning

Food system planning is a fairly new phenomenon in planning and has gained widespread momentum in recent years. Some of the key reasons why food system planning did not capture the attention of planners in the past are the perceptions of the food system as a “flow of products” that are disconnected from the built environment; as something that does not demand attention because it is functioning properly; and as something that does not relate to public services. In fact, it was not until 2005 that the American Planning Association (APA) held its first sessions on topics relating to food systems planning at its annual conference in San Francisco. Two years later, the APA’s Legislative and Policy Committee, Chapter Delegate Assembly, and Board of Directors adopted the Policy Guide on Community and Regional Food Planning. The realization that the food system has a significant impact on energy consumption, the environment, land use, zoning, disadvantaged groups, and public services, such as water and transportation has brought food system issues to the forefront—even mainstream media has eaten up the notion. For instance, there are reports about food accessibility on National Public Radio, documentaries about food transportation and distribution on Georgia Public Broadcasting, and websites dedicated to urban agriculture and community supported agriculture. CNN has a blog called Eatocracy that focuses on all aspects of food from cultural differences in food consumption to food deserts, a term that is often used but has not been well defined. Some refer to food deserts as simply areas that are devoid of supermarkets, while others refer to it as areas that lack stores that offer healthful foods. The Center for Disease Control and Prevention defines food deserts as “areas that lack access to affordable fruits, vegetables, whole grains, low-fat milk, and other foods that make up the full range of a healthy diet.” The Atlanta Local Food Initiative views food deserts as “areas where there is little or no fresh food available in under-served neighborhoods.” Although the definition of food deserts is loosely interpreted, there are commonly accepted characteristics of food deserts. Communities that have a prevalence of fast food restaurants and limited or no supermarkets or grocery stores are generally viewed as food deserts. These communities are typically where disadvantaged individuals, such as the elderly, carless, and low income households live and where there is a greater prevalence of chronic diseases. Escalating incidents of obesity and diabetes in American adults, adolescents, and children have raised concerns regarding the association between the food environment and the eating habits of Americans. In 2009, only two states had an obesity rate of less than 20%. The majority of states had an obesity rate of 25% or more, including Georgia, which had an obesity rate of 27.2%.4 Between 2006-2008, Hispanics and blacks had a greater obesity rate than whites, 21% and 51%, respectively. Childhood obesity has had a significant increase as well. The prevalence of obesity among children ages 2-5 increased from 5% during the 1971-1974 period to 10.4% during the 2007-2008 period. Among children ages 6-11, the increase was from 4% to 19.6%, and among children ages 12-19, the increase was from 6.1% to 18.1% for the same periods. According to the 2011 National Diabetes Fact Sheet (as cited by the American Diabetes Association), over 8% of Americans of all ages have diabetes. Approximately 11% of adults aged 20 or older have diabetes and approximately 27% of adults aged 65 or older have diabetes. One out of 400 children and adolescents has type 1 diabetes. In 2007-2009, among different races and ethnicities aged 20 or older, the prevalence of diabetes was the greatest among blacks (12.6%), followed by Hispanics (11.8%), Asians (8.4%), and whites (7.1%).6 A joint study conducted by the California Center for Public Health Advocacy, PolicyLink, and the UCLA Center for Health Policy Research found that there is a correlation between health and the types of food venues in a community. Using a ratio of the number of fast food restaurants and convenience stores to the number of supermarkets, produce vendors, and farmers’ markets, the team calculated the Retail Food Environment Index (RFEI) of over 43,000 individuals who participated in the California Health Interview Survey.7 A RFEI measures the prevalence of healthy versus unhealthy food venues, where fast food restaurants and convenience stores represent places that sell mostly unhealthy food. While supermarkets, produce vendors, and farmers’ markets represent places that sell more healthy food. The team concluded that high RFEI values correlate to both high obesity and diabetes rates and that low income communities have a higher RFEI than higher income communities.
A Small Episcopal Church

(Georgia Institute of Technology, 1948-06-04) Driscoll, S. Porter ; Georgia Institute of Technology. College of Architecture
A home intervention augmented reality tool for occupational therapists

(Georgia Institute of Technology, 2020-04-28) Aoyama, Hiroo ; Aflatoony, Leila ; Sanford, Jon ; Purdy, Tim ; Industrial Design

The purpose of this project is to design a tablet-based AR application for use by OTs in home care. This application would allow OTs to support individuals with physical impairment and disability when making home modifications. Specifically, OTs would be able to search and show assistive technologies (ATs) for individuals to purchase and install to compensate for their reduced abilities and maintain independent living. The main purpose of this project includes enabling the OTs and their clients to envision the most appropriate scenarios when purchasing and utilizing ATs in the home. Several research methods have been employed to inform and evaluate the AR design as described in the followings: 1) literature review of related studies on assistive technology and augmented reality, 2)semi-structured interviews to understand current challenges in home modifications of people with disabilities; 3) participatory workshops to co-design an AR prototype with the OTs and their clients. 4) prototyping the AR tool following an iterative process, and 5) User study to evaluate the product satisfaction with OTs. Our user study revealed the potential of AR to include the home environment context when considering ATs and to improve the involvement from people with physical impairments or disabilities (PwIDs) to make the process more people-focused, both of which can result in an increase of buy-in from PwIDs and a decrease of AT abandonment.
Hubs and homogeneity: improving content-based music modeling

(Georgia Institute of Technology, 2008-04-01) Godfrey, Mark Thomas ; Chordia, Parag ; Freeman, Jason ; Weinberg, Gil ; Music Technology

With the volume of digital media available today, automatic music recommendation services have proven a useful tool for consumers, allowing them to better discover new and enjoyable music. Typically, this technology is based on collaborative filtering techniques, employing human-generated metadata to base recommendations. Recently, work in content-based recommendation systems have emerged in which the audio signal itself is analyzed for relevant musical information from which models are built that attempt to mimic human similarity judgments. The current state-of-the-art for content-based music recommendation uses a timbre model based on MFCCs calculated on short segments of tracks. These feature vectors are then modeled using GMMs (Gaussian mixture models). GMM modeling of frame-based MFCCs has been shown to perform fairly well on timbre similarity tasks. However, a common problem is that of hubs , in which a relative small number of songs falsely appear similar to many other songs, significantly decreasing the accuracy of similarity recommendations. In this thesis, we explore the origins of hubs in timbre-based modeling and propose several remedies. Specifically, we find that a process of model homogenization, in which certain components of a mixture model are systematically removed, improves performance as measured against several ground-truth similarity metrics. Extending the work of Aucouturier, we introduce several new methods of homogenization. On a subset of the uspop data set, model homogenization improves artist R-precision by a maximum of 3.5% and agreement to user collection co-occurrence data by 7.4%. We also find differences in the effectiveness of the various homogenization methods for hub reduction, with the proposed methods providing the best results. Further, we extend the modeling of frame-based MFCC features by using a kernel density estimation approach to non-parametric modeling. We find that such an approach significantly reduces the number of hubs (by 2.6% of the dataset) while improving agreement to ground-truth by 5% and slightly improving artist R-precision as compared with the standard parametric model. Finally, to test whether these principles hold for all musical data, we introduce an entirely new data set consisting of Indian classical music. We find that our results generalize here as well, suggesting that hubness is a general feature of timbre-based similarity music modeling and that the techniques presented to improve this modeling are effective for diverse types of music.