University of Houston

About: University of Houston is a education organization based out in Houston, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 23074 authors who have published 53903 publications receiving 1641968 citations.

Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning

Abstract: Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. HSI data often lie on sparse, nonlinear manifolds whose geometric and topological structures can be exploited via manifold-learning techniques. In this article, we focused on demonstrating the opportunities provided by manifold learning for classification of remotely sensed data. However, limitations and opportunities remain both for research and applications. Although these methods have been demonstrated to mitigate the impact of physical effects that affect electromagnetic energy traversing the atmosphere and reflecting from a target, nonlinearities are not always exhibited in the data, particularly at lower spatial resolutions, so users should always evaluate the inherent nonlinearity in the data. Manifold learning is data driven, and as such, results are strongly dependent on the characteristics of the data, and one method will not consistently provide the best results. Nonlinear manifold-learning methods require parameter tuning, although experimental results are typically stable over a range of values, and have higher computational overhead than linear methods, which is particularly relevant for large-scale remote sensing data sets. Opportunities for advancing manifold learning also exist for analysis of hyperspectral and multisource remotely sensed data. Manifolds are assumed to be inherently smooth, an assumption that some data sets may violate, and data often contain classes whose spectra are distinctly different, resulting in multiple manifolds or submanifolds that cannot be readily integrated with a single manifold representation. Developing appropriate characterizations that exploit the unique characteristics of these submanifolds for a particular data set is an open research problem for which hierarchical manifold structures appear to have merit. To date, most work in manifold learning has focused on feature extraction from single images, assuming stationarity across the scene. Research is also needed in joint exploitation of global and local embedding methods in dynamic, multitemporal environments and integration with semisupervised and active learning.

Concentrating solar thermoelectric generators with a peak efficiency of 7.4

Abstract: Concentrating solar power normally employs mechanical heat engines and is thus only used in large-scale power plants; however, it is compatible with inexpensive thermal storage, enabling electricity dispatchability. Concentrating solar thermoelectric generators (STEGs) have the advantage of replacing the mechanical power block with a solid-state heat engine based on the Seebeck effect, simplifying the system. The highest reported efficiency of STEGs so far is 5.2%. Here, we report experimental measurements of STEGs with a peak efficiency of 9.6% at an optically concentrated normal solar irradiance of 211 kW m−2, and a system efficiency of 7.4% after considering optical concentration losses. The performance improvement is achieved by the use of segmented thermoelectric legs, a high-temperature spectrally selective solar absorber enabling stable vacuum operation with absorber temperatures up to 600 ∘C, and combining optical and thermal concentration. Our work suggests that concentrating STEGs have the potential to become a promising alternative solar energy technology. Solar thermoelectric generators are a promising technology for converting solar energy into electricity, however their efficiency has been limited to 5.2%. Kraemer et al. report a solar thermoelectric generator with an efficiency of 9.6%, resulting in 7.4% efficiency in a concentrating solar thermoelectric system.

Performance analysis of MPI collective operations

Abstract: Previous studies of application usage show that the performance of collective communications are critical for high-performance computing. Despite active research in the field, both general and feasible solution to the optimization of collective communication problem is still missing. In this paper, we analyze and attempt to improve intra-cluster collective communication in the context of the widely deployed MPI programming paradigm by extending accepted models of point-to-point communication, such as Hockney, LogP/LogGP, and PLogP, to collective operations. We compare the predictions from models against the experimentally gathered data and using these results, construct optimal decision function for broadcast collective. We quantitatively compare the quality of the model-based decision functions to the experimentally-optimal one. Additionally, in this work, we also introduce a new form of an optimized tree-based broadcast algorithm, splitted-binary. Our results show that all of the models can provide useful insights into various aspects of the different algorithms as well as their relative performance. Still, based on our findings, we believe that the complete reliance on models would not yield optimal results. In addition, our experimental results have identified the gap parameter as being the most critical for accurate modeling of both the classical point-to-point-based pipeline and our extensions to fan-out topologies.

Search for organic and volatile inorganic compounds in two surface samples from the chryse planitia region of Mars.

Abstract: Two surface samples collected from the Chryse Planitia region of Mars were heated to temperatures up to 500 degrees C, and the volatiles that they evolved were analyzed with a gas chromatograph-mass spectrometer. Only water and carbon dioxide were detected. This implies that organic compounds have not accumulated to the extent that individual components could be detected at levels of a few parts in 10(9) by weight in our samples. Proposed mechanisms for the accumulation and destruction of organic compounds are discussed in the light of this limit.

The Physical Activity Resource Assessment (PARA) instrument: Evaluating features, amenities and incivilities of physical activity resources in urban neighborhoods

Abstract: Neighborhood environment factors may influence physical activity (PA). The purpose of this study was to develop and test a brief instrument to systematically document and describe the type, features, amenities, quality and incivilities of a variety of PA resources. The one-page Physical Activity Resource Assessment (PARA) instrument was developed to assess all publicly available PA resources in thirteen urban lower income, high ethnic minority concentration neighborhoods that surrounded public housing developments (HDs) and four higher income, low ethnic minority concentration comparison neighborhoods. Neighborhoods had similar population density and connectivity. Trained field coders rated 97 PA resources (including parks, churches, schools, sports facilities, fitness centers, community centers, and trails) on location, type, cost, features, amenities, quality and incivilities. Assessments typically took about 10 minutes to complete. HD neighborhoods had a mean of 4.9 PA resources (n = 73) with considerable variability in the type of resources available for each neighborhood. Comparison neighborhoods had a mean of 6 resources (n = 24). Most resources were accessible at no cost (82%). Resources in both types of neighborhoods typically had about 2 to 3 PA features and amenities, and the quality was usually mediocre to good in both types of neighborhoods. Incivilities at PA resources in HD neighborhoods were significantly more common than in comparison neighborhoods. Although PA resources were similar in number, features and amenities, the overall appearance of the resources in HD neighborhoods was much worse as indicated by substantially worse incivilities ratings in HD neighborhoods. The more comprehensive assessment, including features, amenities and incivilities, provided by the PARA may be important to distinguish between PA resources in lower and higher deprivation areas.