Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR)

Experimental Tests for Heritable Morphological Color Plasticity in Non-Native Brown Trout (Salmo trutta) Populations

The success of invasive species is frequently attributed to phenotypic plasticity, which facilitates persistence in novel environments. Here we report on experimental tests to determine whether the intensity of cryptic coloration patterns in a global invader (brown trout, Salmo trutta) was primarily the result of plasticity or heritable variation. Juvenile F1 offspring were created through experimental crosses of wild-caught parents and reared for 30 days in the laboratory in a split-brood design on either light or dark-colored gravel substrate. Skin and fin coloration quantified with digital photography and image analysis indicated strong plastic effects in response to substrate color; individuals reared on dark substrate had both darker melanin-based skin color and carotenoid-based fin colors than other members of their population reared on light substrate. Slopes of skin and fin color reaction norms were parallel between environments, which is not consistent with heritable population-level plasticity to substrate color. Similarly, we observed weak differences in population-level color within an environment, again suggesting little genetic control on the intensity of skin and fin colors. Taken as whole, our results are consistent with the hypothesis that phenotypic plasticity may have facilitated the success of brown trout invasions and suggests that plasticity is the most likely explanation for the variation in color intensity observed among these populations in nature

Assessing the effectiveness of specially protected areas for conservation of Antarctica's botanical diversity

Vegetation is sparsely distributed over Antarctica's ice-free ground, and distinct plant communities are present in each of the continent's 15 recently identified Antarctic Conservation Biogeographic Regions (ACBRs). With rapidly increasing human activity in Antarctica, terrestrial plant communities are at risk of damage or destruction by trampling, overland transport and infrastructure construction, and the impacts of anthropogenically introduced species, as well as uncontrollable pressures such as fur seal activity and climate change. Under the Protocol on Environmental Protection to the Antarctic Treaty, the conservation of plant communities can be enacted and facilitated through the designation of Antarctic Specially Protected Areas (ASPAs). In this study we examined the distribution within the 15 ACBRs of the 33 ASPAs whose explicit purpose includes protecting macroscopic terrestrial flora. Large omissions in the protection of Antarctic botanical diversity were found, with no protection of plant communities in six ACBRs and, in a further six, less than 0.4% of the ACBR area was included within an ASPA protecting vegetation. We completed the first normalised difference vegetation index (NDVI) satellite remote sensing survey to provide baseline data on the extent of vegetation cover in all ASPAs designated for plant protection in Antarctica. Protected vegetation cover within the 33 ASPAs totalled 16.1 km2 for the entire Antarctic continent, with over half of this within a single protected area. Over 96% of the protected vegetation was contained within two ACBRs, which together contribute only 7.8% of the continent's ice-free ground. We conclude that Antarctic botanical diversity is clearly inadequately protected, and call for systematic designation of ASPAs protecting plant communities across by the Antarctic Treaty Consultative Parties, the members of the governing body of the continen

Farm technical manual 1991

The Farm Technical Manual is a versatile reference book which brings into one place all manner of essential technical information required by farmers and others involved in the farming industry. The Manual has been designed with the practitioner in mind, providing data gleaned from many sources, but presented in non-technical language wherever possible

Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica

The thermal infrared portion of the electromagnetic spectrum has considerable potential for mineral and lithological mapping of the most abundant rock-forming silicates that do not display diagnostic features at visible and shortwave infrared wavelengths. Lithological mapping using visible and shortwave infrared hyperspectral data is well developed and established processing chains are available, however there is a paucity of such methodologies for hyperspectral thermal infrared data. Here we present a new fully automated processing chain for deriving lithological maps from hyperspectral thermal infrared data and test its applicability using the first ever airborne hyperspectral thermal data collected in the Antarctic. A combined airborne hyperspectral survey, targeted geological field mapping campaign and detailed mineralogical and geochemical datasets are applied to small test site in West Antarctica where the geological relationships are representative of continental margin arcs. The challenging environmental conditions and cold temperatures in the Antarctic meant that the data have a significantly lower signal to noise ratio than is usually attained from airborne hyperspectral sensors. We applied preprocessing techniques to improve the signal to noise ratio and convert the radiance images to ground leaving emissivity. Following preprocessing we developed and applied a fully automated processing chain to the hyperspectral imagery, which consists of the following six steps: (1) superpixel segmentation, (2) determine the number of endmembers, (3) extract endmembers from superpixels, (4) apply fully constrained linear unmixing, (5) generate a predictive classification map, and (6) automatically label the predictive classes to generate a lithological map. The results show that the image processing chain was successful, despite the low signal to noise ratio of the imagery; reconstruction of the hyperspectral image from the endmembers and their fractional abundances yielded a root mean square error of 0.58%. The results are encouraging with the thermal imagery allowing clear distinction between granitoid types. However, the distinction of fine grained, intermediate composition dykes is not possible due to the close geochemical similarity with the country rock

Engineering a detect and destroy skin probiotic to combat methicillin-resistant Staphylococcus aureus.

The prevalence and virulence of pathogens such as methicillin-resistant Staphylococcus (S.) aureus (MRSA), which can cause recurrent skin infections, are of significant clinical concern. Prolonged antibiotic exposure to treat or decolonize S. aureus contributes to development of antibiotic resistance, as well as depletion of the microbiome, and its numerous beneficial functions. We hypothesized an engineered skin probiotic with the ability to selectively deliver antimicrobials only in the presence of the target organism could provide local bioremediation of pathogen colonization. We constructed a biosensing S. epidermidis capable of detecting the presence of S. aureus quorum sensing autoinducer peptide and producing lysostaphin in response. Here, we demonstrate in vitro activity of this biosensor and present and discuss challenges to deployment of this and other engineered topical skin probiotics

Momentum Distribution in the Decay B-->J/psi+X

We combine the NRQCD formalism for the inclusive color singlet and octet production of charmonium states with the parton and the ACCMM model, respectively, and calculate the momentum distribution in the decay B-->J/psi+X. Neglecting the kinematics of soft gluon radiation, we find that the motion of the b quark in the bound state can account, to a large extent, for the observed spectrum. The parton model gives a satisfactory presentation of the data, provided that the heavy quark momentum distribution is taken to be soft. To be explicit, we obtain epsilon_p=O(0.008-0.012) for the parameter of the Peterson et al. distribution function. The ACCMM model can account for the data more accurately. The preferred Fermi momentum p_F=O(0.57 GeV) is in good agreement with recent studies of the heavy quark's kinetic energy.Comment: revised version to be published in Phys. Rev. D; 27 pages, LaTeX, 7 eps figures, uses a4wide.sty, epsfig.sty and amssymb.st

Incremental bounded model checking for embedded software

Program analysis is on the brink of mainstream usage in embedded systems development. Formal verification of behavioural requirements, finding runtime errors and test case generation are some of the most common applications of automated verification tools based on bounded model checking (BMC). Existing industrial tools for embedded software use an off-the-shelf bounded model checker and apply it iteratively to verify the program with an increasing number of unwindings. This approach unnecessarily wastes time repeating work that has already been done and fails to exploit the power of incremental SAT solving. This article reports on the extension of the software model checker CBMC to support incremental BMC and its successful integration with the industrial embedded software verification tool BTC EMBEDDED TESTER. We present an extensive evaluation over large industrial embedded programs, mainly from the automotive industry. We show that incremental BMC cuts runtimes by one order of magnitude in comparison to the standard non-incremental approach, enabling the application of formal verification to large and complex embedded software. We furthermore report promising results on analysing programs with arbitrary loop structure using incremental BMC, demonstrating its applicability and potential to verify general software beyond the embedded domain