A multi-modal interface for road planning tasks using vision, haptics and sound

The planning of transportation infrastructure requires analyzing many different types of geo-spatial information in the form of maps. Displaying too many of these maps at the same time can lead to visual clutter or information overload, which results in sub-optimal effectiveness. Multimodal interfaces (MMIs) try to address this visual overload and improve the user\u27s interaction with large amounts of data by combining several sensory modalities. Previous research into MMIs seems to indicate that using multiple sensory modalities leads to more efficient human-computer interactions when used properly. The motivation from this previous work has lead to the creation of this thesis, which describes a novel GIS system for road planning using vision, haptics and sound. The implementation of this virtual environment is discussed, including some of the design decisions used when trying to ascertain how we map visual data to our other senses. A user study was performed to see how this type of system could be utilized, and the results of the study are presented

Diagnostic techniques in plasma research.

http://www.archive.org/details/diagnostictechni00newcU.S. Army (USA) autho

Identifying Target Populations for Screening or Not Screening Using Logic Regression

Colorectal cancer remains a significant public health concern despite the fact that effective screening procedures exist and that the disease is treatable when detected at early stages. Numerous risk factors for colon cancer have been identified, but none are very predictive alone. We sought to determine whether there are certain combinations of risk factors that distinguish well between cases and controls, and that could be used to identify subjects at particularly high or low risk of the disease to target screening. Using data from the Seattle site of the Colorectal Cancer Family Registry (C-CFR), we fit logic regression models to combine risk factor information. Logic regression is a methodology that identifies subsets of the population, described by Boolean combinations of binary coded risk factors. This method is well suited to situations in which interactions between many variables result in differences in disease risk. Neither the logic regression models nor stepwise logistic regression models fit for comparison resulted in criteria that could be used to direct subjects to screening. However, we believe that our novel statistical approach could be useful in settings where risk factors do discriminate between cases and controls, and illustrate this with a simulated dataset

A Neighborhood-Wide Association Study (NWAS): Example of prostate cancer aggressiveness

Purpose Cancer results from complex interactions of multiple variables at the biologic, individual, and social levels. Compared to other levels, social effects that occur geospatially in neighborhoods are not as well-studied, and empiric methods to assess these effects are limited. We propose a novel Neighborhood-Wide Association Study(NWAS), analogous to genome-wide association studies(GWAS), that utilizes high-dimensional computing approaches from biology to comprehensively and empirically identify neighborhood factors associated with disease. Methods Pennsylvania Cancer Registry data were linked to U.S. Census data. In a successively more stringent multiphase approach, we evaluated the association between neighborhood (n = 14,663 census variables) and prostate cancer aggressiveness(PCA) with n = 6,416 aggressive (Stage≥3/Gleason grade≥7 cases) vs. n = 70,670 non-aggressive (Stage<3/Gleason grade<7) cases in White men. Analyses accounted for age, year of diagnosis, spatial correlation, and multiple-testing. We used generalized estimating equations in Phase 1 and Bayesian mixed effects models in Phase 2 to calculate odds ratios(OR) and confidence/credible intervals(CI). In Phase 3, principal components analysis grouped correlated variables. Results We identified 17 new neighborhood variables associated with PCA. These variables represented income, housing, employment, immigration, access to care, and social support. The top hits or most significant variables related to transportation (OR = 1.05;CI = 1.001–1.09) and poverty (OR = 1.07;CI = 1.01–1.12). Conclusions This study introduces the application of high-dimensional, computational methods to large-scale, publically-available geospatial data. Although NWAS requires further testing, it is hypothesis-generating and addresses gaps in geospatial analysis related to empiric assessment. Further, NWAS could have broad implications for many diseases and future precision medicine studies focused on multilevel risk factors of disease

The Freshman, vol. 5, no. 12

The Freshman was a weekly, student newsletter issued on Mondays throughout the academic year. The newsletter included calendar notices, coverage of campus social events, lectures, and athletic teams. The intent of the publication was to create unity, a sense of community, and class spirit among first year students

The Freshman, vol. 5, no. 11

The Freshman was a weekly, student newsletter issued on Mondays throughout the academic year. The newsletter included calendar notices, coverage of campus social events, lectures, and athletic teams. The intent of the publication was to create unity, a sense of community, and class spirit among first year students

The Freshman, vol. 5, no. 18

The Freshman was a weekly, student newsletter issued on Mondays throughout the academic year. The newsletter included calendar notices, coverage of campus social events, lectures, and athletic teams. The intent of the publication was to create unity, a sense of community, and class spirit among first year students

The Freshman, vol. 5, no. 15

The Freshman was a weekly, student newsletter issued on Mondays throughout the academic year. The newsletter included calendar notices, coverage of campus social events, lectures, and athletic teams. The intent of the publication was to create unity, a sense of community, and class spirit among first year students

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform radiometric calibration. A kernel-driven semiempirical BRDF correction method based on the Ross Thick-Li Sparse (RTLS) model was used to normalize the data for both changes in solar elevation and sensor view angle differences attributed to pixel location within the field of view. Following rigorous radiometric and BRDF corrections, novel rule-based methods were developed to conduct automatic soil removal; and a newly proposed approach was used for image quality assessment; additionally, shadow masking and plot-level feature extraction were carried out. Our results show that the automated calibration, processing, storage, and analysis pipeline developed in this work can effectively handle massive amounts of hyperspectral data and address the urgent challenges related to the production of sustainable bioenergy and food crops, targeting methods to accelerate plant breeding for improving yield and biomass traits