Modeling a Sensor to Improve its Efficacy

Robots rely on sensors to provide them with information about their surroundings. However, high-quality sensors can be extremely expensive and cost-prohibitive. Thus many robotic systems must make due with lower-quality sensors. Here we demonstrate via a case study how modeling a sensor can improve its efficacy when employed within a Bayesian inferential framework. As a test bed we employ a robotic arm that is designed to autonomously take its own measurements using an inexpensive LEGO light sensor to estimate the position and radius of a white circle on a black field. The light sensor integrates the light arriving from a spatially distributed region within its field of view weighted by its Spatial Sensitivity Function (SSF). We demonstrate that by incorporating an accurate model of the light sensor SSF into the likelihood function of a Bayesian inference engine, an autonomous system can make improved inferences about its surroundings. The method presented here is data-based, fairly general, and made with plug-and play in mind so that it could be implemented in similar problems.Comment: 18 pages, 8 figures, submitted to the special issue of "Sensors for Robotics

Interactions between two gypsy moth (Lymantria dispar L.) pathogens, nuclear polyhedrosis virus and Entomophaga maimaiga (Entomophthorales: Zygomycetes).

The gypsy moth, Lymantria dispar L., is one of the most damaging pests of the deciduous forests in the United States. It was accidentally introduced from Europe in 1868 by an amateur naturalist in eastern Massachusetts. High density gypsy moth populations are regulated primarily by a nuclear polyhedrosis virus (LdNPV). LdNPV is transmitted by feeding the LdNPV contaminated foliage or the contaminated egg chorion on the way out from the egg by a larva. In 1989, an entomophthoralean fungus, Entomophaga maimaiga Humber, Shimazu et Soper was discovered in the northeastern United States, which caused massive epizootic in both low and high density gypsy moth populations. My study focused on the interactions between E. maimaiga and LdNPV. Laboratory bioassays in which I inoculated gypsy moth larvae with LdNPV and E. maimaiga at the same time indicated that the majority of dually inoculated larvae die from E. maimaiga because of the shorter incubation period of E. maimaiga (5-7 days) compared to LdNPV (14 days) at 20\sp\circC. When the larvae were inoculated with E. maimaiga, 10 days after LdNPV inoculation, there was an apparent synergistic effect of E. maimaiga with LdNPV. Dually inoculated larvae died producing LdNPV propagules, 1-2 days earlier than the larvae inoculated with LdNPV alone. Small-scale field experiments conducted in mesh-bags showed that artificial rainfall increases the E. maimaiga transmission. In a naturally occurring, moderate density gypsy moth population, I found that the LdNPV infection level was little affected by the presence of E. maimaiga. Host heterogeneity is suspected as one of the factors leading non-linear LdNPV transmissions. I showed that the host heterogeneity cannot explain the E. maimaiga epizootic observed in low density populations. I experimentally demonstrated this by comparing the E. maimaiga infection rates in feral (experienced the E. maimaiga/LdNPV epizootic in their parental generations) and laboratory reared (with no epizootic experience) larvae. This is probably due to the short period to which the North American gypsy moths have been exposed to E. maimaiga, so these gypsy moths have not had chance to develop resistance against E. maimaiga

Towards Identification of Relevant Variables in the observed Aerosol Optical Depth Bias between MODIS and AERONET observations

Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this paper, we present a general framework towards identifying relevant set of variables responsible for the observed bias. We present a general framework to identify the possible factors influencing the bias, which might be associated with the measurement conditions such as the solar and sensor zenith angles, the solar and sensor azimuth, scattering angles, and surface reflectivity at the various measured wavelengths, etc. Specifically, we performed analysis for remote sensing Aqua-Land data set, and used machine learning technique, neural network in this case, to perform multivariate regression between the ground-truth and the training data sets. Finally, we used mutual information between the observed and the predicted values as the measure of similarity to identify the most relevant set of variables. The search is brute force method as we have to consider all possible combinations. The computations involves a huge number crunching exercise, and we implemented it by writing a job-parallel program

Irrigation Water Quality—A Contemporary Perspective

In the race to enhance agricultural productivity, irrigation will become more dependent on poorly characterized and virtually unmonitored sources of water. Increased use of irrigation water has led to impaired water and soil quality in many areas. Historically, soil salinization and reduced crop productivity have been the primary focus of irrigation water quality. Recently, there is increasing evidence for the occurrence of geogenic contaminants in water. The appearance of trace elements and an increase in the use of wastewater has highlighted the vulnerability and complexities of the composition of irrigation water and its role in ensuring proper crop growth, and long-term food quality. Analytical capabilities of measuring vanishingly small concentrations of biologically-active organic contaminants, including steroid hormones, plasticizers, pharmaceuticals, and personal care products, in a variety of irrigation water sources provide the means to evaluate uptake and occurrence in crops but do not resolve questions related to food safety or human health effects. Natural and synthetic nanoparticles are now known to occur in many water sources, potentially altering plant growth and food standard. The rapidly changing quality of irrigation water urgently needs closer attention to understand and predict long-term effects on soils and food crops in an increasingly fresh-water stressed world

Irrigation Water Quality—A Contemporary Perspective

In the race to enhance agricultural productivity, irrigation will become more dependent on poorly characterized and virtually unmonitored sources of water. Increased use of irrigation water has led to impaired water and soil quality in many areas. Historically, soil salinization and reduced crop productivity have been the primary focus of irrigation water quality. Recently, there is increasing evidence for the occurrence of geogenic contaminants in water. The appearance of trace elements and an increase in the use of wastewater has highlighted the vulnerability and complexities of the composition of irrigation water and its role in ensuring proper crop growth, and long-term food quality. Analytical capabilities of measuring vanishingly small concentrations of biologically-active organic contaminants, including steroid hormones, plasticizers, pharmaceuticals, and personal care products, in a variety of irrigation water sources provide the means to evaluate uptake and occurrence in crops but do not resolve questions related to food safety or human health effects. Natural and synthetic nanoparticles are now known to occur in many water sources, potentially altering plant growth and food standard. The rapidly changing quality of irrigation water urgently needs closer attention to understand and predict long-term effects on soils and food crops in an increasingly fresh-water stressed world

Maximum Joint Entropy and Information-Based Collaboration of Automated Learning Machines

We are working to develop automated intelligent agents, which can act and react as learning machines with minimal human intervention. To accomplish this, an intelligent agent is viewed as a question-asking machine, which is designed by coupling the processes of inference and inquiry to form a model-based learning unit. In order to select maximally-informative queries, the intelligent agent needs to be able to compute the relevance of a question. This is accomplished by employing the inquiry calculus, which is dual to the probability calculus, and extends information theory by explicitly requiring context. Here, we consider the interaction between two question-asking intelligent agents, and note that there is a potential information redundancy with respect to the two questions that the agents may choose to pose. We show that the information redundancy is minimized by maximizing the joint entropy of the questions, which simultaneously maximizes the relevance of each question while minimizing the mutual information between them. Maximum joint entropy is therefore an important principle of information-based collaboration, which enables intelligent agents to efficiently learn together.Comment: 8 pages, 1 figure, to appear in the proceedings of MaxEnt 2011 held in Waterloo, Canad

Labile carbon and soil texture control nitrogen transformation in deep vadose zone

Understanding transient nitrogen (N) storage and transformation in the deep vadose zone is critical for controlling groundwater contamination by nitrate. The occurrence of organic and inorganic forms of carbon (C) and nitrogen and their importance in the deep vadose zone is not well characterized due to difficulty in sampling and the limited number of studies. We sampled and characterized these pools beneath 27 croplands with different vadose zone thicknesses (6–45 m).We measured nitrate and ammonium in different depths for the 27 sites to evaluate inorganic N storage. We measured total Kjeldahl nitrogen (TKN), hot-water extractable organic carbon (EOC), soil organic carbon (SOC), and δ13C for two sites to understand the potential role of organic N and C pools in N transformations. Inorganic N stocks in the vadose zone were 21.7–1043.6 gm−2 across 27 sites; the thicker vadose zone significantly stored more inorganic N (p \u3c 0.05). We observed significant reservoirs of TKN and SOC at depths, likely representing paleosols that may provide organic C and N to subsurface microbes. The occurrence of deep C and N needs to be addressed in future research on terrestrial C and N storage potential. The increase of ammonium and EOC and δ13C value in the proximity of these horizons is consistent with N mineralization. An increase of nitrate, concurrent with the sandy soil texture and the water-filled pore space (WFPS) of 78%, suggests that deep vadose zone nitrification may be supported in vadose zones with organic-rich layers such as paleosol. A profile showing the decrease of nitrate concentrations, concurrent with the clay soil texture and the WFPS of 91%, also suggests denitrification may be an important process. Our study shows that microbial N transformation may be possible even in deep vadose zone with co-occurrence of C and N sources and controlled by labile C availability and soil texture. Inclydes supplementary materials

Ferrihydrite Reduction Increases Arsenic and Uranium Bioavailability in Unsaturated Soil

Redox driven mobilization and plant uptake of contaminants under transiently saturated soil conditions need to be clarified to ensure food and water quality across different irrigation systems. We postulate that solid-phase iron reduction in anoxic microsites present in the rhizosphere of unsaturated soil is a key driver for mobilization and bioavailability of contaminants under nonflooded irrigation. To clarify this, two major crops, corn and soybean differing in iron uptake strategies, were grown in irrigated synthetic soil under semiarid conditions with gravimetric moisture content ∼12.5 ± 2.4%. 2-line ferrihydrite, which was coprecipitated with uranium and arsenic, served as the only iron source in soil. Irrespective of crop type, reduced iron was detected in pore water and postexperiment rhizosphere soil confirming ferrihydrite reduction. These results support the presence of localized anoxic microsites in the otherwise aerobic porous bulk soil causing reduction of ferrihydrite and concomitant increase in plant uptake of comobilized contaminants. Our findings indicate that reactive iron minerals undergo reductive dissolution inside anoxic microsites of primarily unsaturated soil, which may have implications on the mobility of trace element contaminants such as arsenic and uranium in irrigated unsaturated soils, accounting for 55% of the irrigated area in the US. Includes supplemental materials

Source apportionment and health risk assessment of nitrate in foothill aquifers of Western Ghats, South India

The present research reports the level of nitrate (NO3-), associated health risks and possible sources of contamination in groundwater from south India. Many samples (32%) are above or approaching the recommended level of NO3- for safe drinking water. The correlation analysis indicates different sources of NO3- contamination in different regions rather than a common origin. The isotopic measurements provide information about potential nitrogen sources contributing NO3- to the groundwater. Based on isotope analysis, the sources of NO3- in the groundwater of this region are likely to be from (a) septic sewage (b) organic nitrogen (animal and livestock excreta) (c) sewage (domestic & chemical fertilizers). Among the sample analyzed sewage, manure and septic sewage contribute 46%, 23% and 31% NO3- to groundwater. The HQ \u3e 1 indicates non-carcinogenic health risk due to consumption of high NO3- in drinking water. Among the studied age groups, infants are exposed to higher risk than children and adults. Results indicate that groundwater of this region is polluted with NO3- due to anthropogenic activities. Continuous consumption of such water may pose serious health risk to the residents