Compressed sensing is an emerging field, which proposes that a small collection of linear projections of a sparse signal contains enough information for perfect reconstruction of the signal. In this thesis, we study the general problem of modeling and reconstructing spatially or temporally correlated sparse signals in a distributed scenario. The correlation among signals provides an additional information, which could be captured by joint sparsity models. After modeling the correlation, we propose two different reconstruction algorithms that are able to successfully exploit this additional information. The first algorithm is a very fast greedy algorithm, which is suitable for large scale problems and can exploit spatial correlation. The second algorithm is based on a thresholding algorithm and can exploit both the temporal and spatial correlation. We also generalize the standard joint sparsity model and propose a new model for capturing the correlation in the sensor networks.

Nitric oxide (•NO) is a molecule with diverse biological effects involved in both signaling and defense mechanisms in mammalian systems. The production of •NO is catalyzed from L-Arginine by a family of enzymes known as nitric oxide synthases (NOS). All mammalian isoforms contain an active site oxygenase domain and an electron-donating reductase domain joined by a calmodulin binding region. Recently, prokaryotic homologues to the oxygenase domain of mammalian NOS enzymes have been identified. Although several bacterial NOS (bNOS) enzymes have been characterized, their function is still unknown. Possible roles for this enzyme could include: (i) intercellular signaling to coordinate cellular/infectious activity, (ii) regulation via •NO-mediated posttranslational modifications, or (iii) nitration of compounds in different biosynthetic pathways. Efforts, contained in this thesis, to determine a probable role for this enzyme are two-fold: (i) via a search for possible interacting protein partners, and (ii) via a proteomic analysis of the effects of a knockout of the bNOS gene. Bacterial-NOS knockouts in Bacillus subtilis and Bacillus cereus have been created. 2D-Differential in gel electrophoresis (DIGE) has been used to analyze the proteomic effects of the expression of this gene in B. subtilis. Thus far 16 proteins which exhibited significant changes in expression, with a p value ≤ 0.05 and fold change ≥│2│, have been isolated and identified by peptide mass fingerprinting (PMF) in order to shed light on the relevance of this bacterial NOS homologue. The proteins which have been identified thus far are involved in cellular metabolism, amino acid metabolism and nitrogen metabolism. The identification of further proteins is required for a broader view of the impact of the expression of this gene on the proteome.

Nitric oxide (•NO) is a molecule with diverse biological effects involved in both signaling and defense mechanisms in mammalian systems. The production of •NO is catalyzed from L-Arginine by a family of enzymes known as nitric oxide synthases (NOS). All mammalian isoforms contain an active site oxygenase domain and an electron-donating reductase domain joined by a calmodulin binding region. Recently, prokaryotic homologues to the oxygenase domain of mammalian NOS enzymes have been identified. Although several bacterial NOS (bNOS) enzymes have been characterized, their function is still unknown. Possible roles for this enzyme could include: (i) intercellular signaling to coordinate cellular/infectious activity, (ii) regulation via •NO-mediated posttranslational modifications, or (iii) nitration of compounds in different biosynthetic pathways. Efforts, contained in this thesis, to determine a probable role for this enzyme are two-fold: (i) via a search for possible interacting protein partners, and (ii) via a proteomic analysis of the effects of a knockout of the bNOS gene. Bacterial-NOS knockouts in Bacillus subtilis and Bacillus cereus have been created. 2D-Differential in gel electrophoresis (DIGE) has been used to analyze the proteomic effects of the expression of this gene in B. subtilis. Thus far 16 proteins which exhibited significant changes in expression, with a p value ≤ 0.05 and fold change ≥│2│, have been isolated and identified by peptide mass fingerprinting (PMF) in order to shed light on the relevance of this bacterial NOS homologue. The proteins which have been identified thus far are involved in cellular metabolism, amino acid metabolism and nitrogen metabolism. The identification of further proteins is required for a broader view of the impact of the expression of this gene on the proteome.

Bacteria produce and respond to external stimuli using molecules termed autoinducers. Poultry meat contains inhibitors which interfere with AI-2 signaling. The primary objective of this work was to understand the effects of AI-2 on the virulence and growth of Salmonella Typhimurium, and if the introduction of AI-2 inhibiting compounds would influence these effects. Using DNA microarray analysis, expression of 1136 virulence-related genes in a Salmonella Typhimurium wild type and a luxS mutant strain, PJ002 (unable to produce AI-2), was monitored after exposure to treatments containing in vitro synthesized AI-2 (AI-2) and poultry meat (PM) inhibitors. Responding gene expression was unique in the presence of AI-2, with 23 genes differentially expressed at least 1.5-fold (p < 0.05). The combined AI-2 + PM treatment resulted in 22 genes being differentially expressed. Identification of inhibitory compounds was attempted using GC analysis on a hexane solvent extract obtained from a PM wash. From this analysis, chemical standards of linoleic, oleic, palmitic, and stearic acid were tested for inhibition using V. harveyi BB170. Combined fatty acids (FA) demonstrated inhibition against AI-2 at 60 % while 10-fold and 100-fold concentrations had inhibition of 84 % and 70 %, respectively. Growth of PJoo2, was studied using M-9 minimal medium with FA of varying concentrations, supplemented with either AI-2, or 1X phosphate buffered saline (PBS). Comparative analysis was done calculating the growth constants based on OD 600 values for each treatment. No significant difference in the combined FA + AI-2 treatments was observed against the AI-2 treatment. A significant increase in the growth rate constants of the AI-2 treatments was observed, however, compared to the PBS control (P = 0.01). Bacterial invasiveness, using a murine macrophage cell line, RAW 264.7, was also studied. AI-2 decreased cell invasiveness (P = 0.02), while the addition of combined FA improved invasiveness to normal levels. The results of these studies indicate that AI-2 does have an effect on the growth and virulence of Salmonella, but this is not uniformly modulated by the introduction of fatty acids, that inhibit AI-2 activity, suggesting that inhibition may be based on species specific transport systems.

Ice clouds occur quite frequently, yet so much about these clouds is unknown. In recent years, numerous investigations and field campaigns have been focused on the study of ice clouds, all with the ultimate goal of gaining a better understanding of microphysical and optical properties, as well as determining the radiative impact. Perhaps one of the most recognized instruments used for such research is the Moderate-resolution Imaging Spectroradiometer (MODIS), carried aboard the NASA EOS satellites Terra and Aqua. The present research aims to support ongoing efforts in the field of ice cloud research by use of observations obtained from Terra and Aqua MODIS. First, a technique is developed to infer ice cloud optical depth from the MODIS cirrus reflectance parameter. This technique is based on a previous method developed by Meyer et al. (2004). The applicability of the algorithm is demonstrated with retrievals from level-2 and -3 MODIS data. The technique is also evaluated with the operational MODIS cloud retrieval product and a method based on airborne ice cloud observations. From this technique, an archive of daily optical depth retrievals is constructed. Using simple statistics, the global spatial and temporal distributions of ice clouds are determined. Research has found that Aqua MODIS observes more frequent ice clouds and larger optical depths and ice water paths than does Terra MODIS. Finally, an analysis of the time series of daily optical depth values revealed that ice clouds at high latitudes, which are most likely associated with synoptic scale weather sytems, persist long enough to move with the upper level winds. Tropical ice clouds, however, dissipate more rapidly, and are in all likelihood associated with deep convective cells.

The Gulf of California and its surrounding land areas provide a classic example of recently rifted continental lithosphere. The recent tectonic history of eastern Baja California has been dominated by oblique rifting that began at ~12 Ma. Thus, extensional tectonics, bedrock lithology, long-term climatic changes, and evolving surface processes have controlled the tectono-geomorphological evolution of the eastern part of the peninsula since that time. In this study, digital elevation data from the Shuttle Radar Topography Mission (SRTM) from Baja California were corrected and enhanced by replacing artifacts with real values that were derived using a series of geostatistical techniques. The next step was to generate accurate thematic geologic maps with high resolution (15-m) for the entire eastern coast of Baja California. The main approach that we used to clearly represent all the lithological units in the investigated area was objectoriented classification based on fuzzy logic theory. The area of study was divided into twenty-two blocks; each was classified independently on the basis of its own defined membership function. Overall accuracies were 89.6 %, indicating that this approach was highly recommended over the most conventional classification techniques. The third step of this study was to assess the factors that affected the geomorphologic development along the eastern side of Baja California, where thirty-four drainage basins were extracted from a 15-m-resolution absolute digital elevation model (DEM). Thirty morphometric parameters were extracted; these parameters were then reduced using principal component analysis (PCA). Cluster analysis classification defined four major groups of basins. We extracted stream length-gradient indices, which highlight the differential rock uplift that has occurred along fault escarpments bounding the basins. Also, steepness and concavity indices were extracted for bedrock channels within the thirty-four drainage basins. The results were highly correlated with stream length-gradient indices for each basin. Nine basins, exhibiting steepness index values greater than 0.07, indicated a strong tectonic signature and possible higher uplift rates in these basins. Further, our results indicated that drainage basins in the eastern rift province of Baja California could be classified according to the dominant geomorphologic controlling factors (i.e., faultcontrolled, lithology-controlled, or hybrid basins).

This research demonstrates two building blocks for CMOS integrated sensor IC for molecular or chemical sensing. One of them for molecular sensing is the capacitance sensing circuit to detect the change of the dielectric constant of novel nanowell devices. The size of nanowell (10nm-100nm) enables high fidelity detection and analysis through Broadband Dielectric Spectroscopy (BDS) of the parallel-plate capacitor formed by the nanowell and the targeted molecules. The signal tranduction is done by a novel, continuous-time detection circuit using a low-noise lock-in architecture which generates the current output containing the information about the admittance of the sensor as a function of the frequency for BDS. This current signal is processed in the current domain by a low power current-mode A/D converter. The current signal transducer has a quasilinear capacitance resolution of 164pA/aF (at 1Ghz) and power consumption of only 30uW in 0.18um TSMC CMOS technology. Another building block is a low noise front end for feature extraction for gas and nanoparticle detection using Van der Waals sensors. The output of such a sensor consists of particle specific information in the low frequency range from 0 to 100 KHz in the form of stochastic fluctuations. Such detection schemes are termed as fluctuation enhanced sensing, which exploit the statistics of the noise in the low frequency spectrum. The front end consists of a low pass filter bank to process the amplified signal from a low-noise transimpedance amplifier. It handles the noise-like information signal from the sensor with filters having increasing cut-off frequencies. It is designed to operate at temperature as high as 200C with low leakage currents to maximize the stochastic fluctuation noise generation. The front-end system was fabricated with TSMC 0.18um technology and tested. The gain of the front-end circuit is at least 87dB and its power consumption with one transimpedance amplifier and 10 filters is just 1.1mW. Moreover, the worst-case maximum input current signal is 0.2uApp while satisfying 5% THD and the equivalent input current noise level is under 7nA. The front-end circuit demonstrates the considerably high dynamic range with the low noise input range suitable for applications for sensing using fluctuation enhanced techniques.

Soil moisture in the shallow subsurface is a primary hydrologic state governing land-atmosphere interaction at various scales. The primary objectives of this study are to model soil moisture in the root zone in a distributed manner and determine scaling properties of surface soil moisture using passive microwave remote sensing. The study was divided into two parts. For the first study, a root zone soil moisture assessment tool (SMAT) was developed in the ArcGIS platform by fully integrating a one-dimensional vadose zone hydrology model (HYDRUS-ET) with an ensemble Kalman filter (EnKF) data assimilation capability. The tool was tested with dataset from the Southern Great Plain 1997 (SGP97) hydrology remote sensing experiment. Results demonstrated that SMAT displayed a reasonable capability to generate soil moisture distribution at the desired resolution at various depths of the root zone in Little Washita watershed during the SGP97 hydrology remote sensing experiment. To improve the model performance, several outstanding issues need to be addressed in the future by: including "effective" hydraulic parameters across spatial scales; implementing subsurface soil properties data bases using direct and indirect methods; incorporating appropriate hydrologic processes across spatial scales; accounting uncertainties in forcing data; and preserving interactions for spatially correlated pixels. The second study focused on spatial scaling properties of the Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface soil moisture fields in a region with high row crop agriculture. A wavelet based multi-resolution technique was used to decompose the soil moisture fields into larger-scale average soil moisture fields and fluctuations in horizontal, diagonal and vertical directions at various resolutions. The specific objective was to relate soil moisture variability at the scale of the PSR footprint (800 m X 800 m) to larger scale average soil moisture field variability. We also investigated the scaling characteristics of fluctuation fields among various resolutions. The spatial structure of soil moisture exhibited linearity in the log-log dependency of the variance versus scale-factor, up to a scale factor of -2.6 (6100 m X 6100 m) irrespective of wet and dry conditions, whereas dry fields reflect nonlinear (multi-scaling) behavior at larger scale-factors.

This dissertation consists of three studies dealing with particulate organic carbon (POC). The first study describes the temporal and spatial variability of particulate matter (PM) and POC, and physical processes that affect the distribution of PM and POC with synchronous remote sensing data. The purpose of the second study is to develop POC algorithms in the Gulf of Mexico based on satellite data using numerical methods and to compare POC estimates with spectral radiance. The purpose of the third study is to investigate climatological variations from the temporal and spatial POC estimates based on SeaWiFS spectral radiance and physical processes, and to determine the physical mechanisms that affect the distribution of POC in the Gulf of Mexico. For the first and second studies, hydrographic data from the Northeastern Gulf of Mexico (NEGOM) study were collected on each of 9 cruises from November 1997 to August 2000 across 11 lines. Remotely sensed data sets were obtained from NASA and NOAA using algorithms that have been developed for interpretation of ocean color data from various satellite sensors. For the third study, we use the time-series of POC estimates, sea surface temperature (SST), sea surface height anomaly (SSHA), sea surface wind (SSW), and precipitation rate (PR) that might cause climatological variability and physical processes. The distribution of surface PM and POC concentrations were affected by one or more factors such as river discharge, wind stress, stratification, and the Loop Current/Eddies. To estimate POC concentration, empirical and model-based approaches were used using regression and principal component analysis (PCA) methods. We tested simulated data for reasonable and suitable algorithms in Case 1 and Case 2 waters. Monthly mean values of POC concentrations calculated with PCA algorithms. The spatial and temporal variations of POC and physical forcing data were analyzed with the empirical orthogonal function (EOF) method. The results showed variations in the Gulf of Mexico on both annual and inter-annual time scales.

Thesis (M.S.)--Kent State University, 2008. / Title from PDF t.p. (viewed Sept. 28, 2009). Advisor: Donna Witter. Keywords: chlorophyll; Lake Erie; remote sensing; algorithm; atmospheric correction. Includes bibliographical references (p. 58-66).