Sensor node localisation using a stereo camera rig

In this paper, we use stereo vision processing techniques to detect and localise sensors used for monitoring simulated environmental events within an experimental sensor network testbed. Our sensor nodes communicate to the camera through patterns emitted by light emitting diodes (LEDs). Ultimately, we envisage the use of very low-cost, low-power, compact microcontroller-based sensing nodes that employ LED communication rather than power hungry RF to transmit data that is gathered via existing CCTV infrastructure. To facilitate our research, we have constructed a controlled environment where nodes and cameras can be deployed and potentially hazardous chemical or physical plumes can be introduced to simulate environmental pollution events in a controlled manner. In this paper we show how 3D spatial localisation of sensors becomes a straightforward task when a stereo camera rig is used rather than a more usual 2D CCTV camera

Observations of nitryl chloride and modeling its source and effect on ozone in the planetary boundary layer of southern China

Nitryl chloride (ClNO2) plays potentially important roles in atmospheric chemistry, but its abundance and effect are not fully understood due to the small number of ambient observations of ClNO2 to date. In late autumn 2013, ClNO2 was measured with a chemical ionization mass spectrometer (CIMS) at a mountain top (957 m above sea level) in Hong Kong. During 12 nights with continuous CIMS data, elevated mixing ratios of ClNO2 (>400 parts per trillion by volume) or its precursor N2O5 (>1000 pptv) were observed on six nights, with the highest ever reported ClNO2 (4.7 ppbv, 1 min average) and N2O5 (7.7 ppbv, 1 min average) in one case. Backward particle dispersion calculations driven by winds simulated with a mesoscale meteorological model show that the ClNO2/N2O5-laden air at the high-elevation site was due to transport of urban/industrial pollution north of the site. The highest ClNO2/N2O5 case was observed in a later period of the night and was characterized with extensively processed air and with the presence of nonoceanic chloride. A chemical box model with detailed chlorine chemistry was used to assess the possible impact of the ClNO2 in the well-processed regional plume on next day ozone, as the air mass continued to downwind locations. The results show that the ClNO2 could enhance ozone by 5-16% at the ozone peak or 11-41% daytime ozone production in the following day. This study highlights varying importance of the ClNO2 chemistry in polluted environments and the need to consider this process in photochemical models for prediction of ground-level ozone and haze. Key Points First observation of ClNO2 in the planetary boundary layer of China Combined high-resolution meteorological and measurement-constrained chemical models in data analysis ClNO2 enhances daytime ozone peak by 5-16% in well-processed PRD air.Department of Civil and Environmental Engineerin

Bio-Inspired, Odor-Based Navigation

The ability of moths to locate a member of the opposite sex, by tracking a wind-borne plume of odor molecules, is an amazing reality. Numerous scenarios exist where having this capability embedded into ground-based or aerial vehicles would be invaluable. The main crux of this thesis investigation is the development of a navigation algorithm which gives a UAV the ability to track a chemical plume to its source. Inspiration from the male moth\u27s, in particular Manduca sexta, ability to successfully track a female\u27s pheromone plume was used in the design of both 2-D and 3-D navigation algorithms. The algorithms were developed to guide autonomous vehicles to the source of a chemical plume. The algorithms were implemented using a variety of fuzzy controllers and ad hoc engineering approaches. The fuzzy controller was developed to estimate the location of a vehicle relative to the plume: coming into the plume, in the plume, exiting the plume, or out of the plume. The 2-D algorithm had a 60% to 90% success rate in reaching the source while certain versions of 3-D algorithm had success rates from 50% to 100%

Robotic Olfactory-Based Navigation with Mobile Robots

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. It has been viewed as challenging due to the turbulent nature of airflows and the resulting odor plume characteristics. The key to correctly finding an odor source is designing an effective olfactory-based navigation algorithm, which guides the robot to detect emitted odor plumes as cues in finding the source. This dissertation proposes three kinds of olfactory-based navigation methods to improve search efficiency while maintaining a low computational cost, incorporating different machine learning and artificial intelligence methods. A. Adaptive Bio-inspired Navigation via Fuzzy Inference Systems. In nature, animals use olfaction to perform many life-essential activities, such as homing, foraging, mate-seeking, and evading predators. Inspired by the mate-seeking behaviors of male moths, this method presents a behavior-based navigation algorithm for using on a mobile robot to locate an odor source. Unlike traditional bio-inspired methods, which use fixed parameters to formulate robot search trajectories, a fuzzy inference system is designed to perceive the environment and adjust trajectory parameters based on the current search situation. The robot can automatically adapt the scale of search trajectories to fit environmental changes and balance the exploration and exploitation of the search. B. Olfactory-based Navigation via Model-based Reinforcement Learning Methods. This method analogizes the odor source localization as a reinforcement learning problem. During the odor plume tracing process, the belief state in a partially observable Markov decision process model is adapted to generate a source probability map that estimates possible odor source locations. A hidden Markov model is employed to produce a plume distribution map that premises plume propagation areas. Both source and plume estimates are fed to the robot. A decision-making model based on a fuzzy inference system is designed to dynamically fuse information from two maps and balance the exploitation and exploration of the search. After assigning the fused information to reward functions, a value iteration-based path planning algorithm solves the optimal action policy. C. Robotic Odor Source Localization via Deep Learning-based Methods. This method investigates the viability of implementing deep learning algorithms to solve the odor source localization problem. The primary objective is to obtain a deep learning model that guides a mobile robot to find an odor source without explicating search strategies. To achieve this goal, two kinds of deep learning models, including adaptive neuro-fuzzy inference system (ANFIS) and deep neural networks (DNNs), are employed to generate the olfactory-based navigation strategies. Multiple training data sets are acquired by applying two traditional methods in both simulation and on-vehicle tests to train deep learning models. After the supervised training, the deep learning models are verified with unseen search situations in simulation and real-world environments. All proposed algorithms are implemented in simulation and on-vehicle tests to verify their effectiveness. Compared to traditional methods, experiment results show that the proposed algorithms outperform them in terms of the success rate and average search time. Finally, the future research directions are presented at the end of the dissertation

Sensory landscape impacts on odor-mediated predator-prey interactions at multiple spatial scales in salt marsh communities

This collection of research examines how changes in the sensory landscape, mediated by both odor and hydrodynamic properties, impact odor-mediated predator-prey interactions in salt marsh communities. I approached this research using an interdisciplinary framework that combined field and laboratory experimentation to address issues of scale and make connections between predator behavior and patterns of predation in the field. I explored a variety of interactions mediated by changes in the sensory landscape including; indirect effects of biotic structure on associated prey, predator responses to patches of prey with differing density and distribution, and dynamic interactions between predators and prey distributions. I found that biotic structure (oyster reefs [Crassostrea virginica]) has negative indirect effects on associated hard clam prey (Mercenaria mercenaria) through the addition of oyster reef odor cues that attract predators (blue crabs [Callinectes sapidus] and knobbed whelks [Busycon carica])and increase foraging success near the structural matrix. Variation in the structure of patch-scale prey odor plumes created by multiple prey results in predator-specific patterns of predation as a function of patch density and distribution which are mediated by differences in predator sensory ability. There is a potential negative feedback loop between blue crab predators and hard clam prey distributions; clam patches assume random within-patch distributions after exposure to blue crab predators, making the detection of patches by future blue crab predators more difficult. Sensory landscapes are also mediated by water flow, which transports prey odor plumes downstream to predators. Characterization of water flow in small-scale estuary systems indicates that values of turbulent flow parameters are highly context specific and depend on both tidal type (spring, neap, normal) and site. Wind and tidal range seem to be good predictors for wave components and turbulent components of fluctuating flow parameters, respectively, although the strength of their predictive ability is dependent on time scale. Modifications of the sensory landscape through changes in structurally-induced turbulence, mixing of individual plumes from multiple prey, and bulk velocity and turbulence characteristics need to be considered when formulating predictions as to the impact of predators on naturally occurring prey populations in the field.Ph.D.Committee Chair: Marc Weissburg; Committee Member: Donald Webster; Committee Member: Julia Kubanek; Committee Member: Lin Jiang; Committee Member: Mark Ha

Distributed Mobile Sensor Networks for Hazardous Applications

1Research Department for Underwater Acoustics and Marine Geophysics, Bundeswehr Technical Centre for Ships and Naval Weapons, Naval Technology and Research (WTD 71), Klausdorfer Weg 2, 24148 Kiel, Germany 2Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, 4555 Overlook Avenue S.W., Washington, DC 20375, USA 3Acoustic Research Laboratory, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 119077 4 Systems Technology Department, NATO Undersea Research Centre (NURC), Viale S. Bartolomeo 400, 19126 La Spezia, Ital

Radiative absorption enhancements by black carbon controlled by particle-to-particle heterogeneity in composition.

Black carbon (BC) absorbs solar radiation, leading to a strong but uncertain warming effect on climate. A key challenge in modeling and quantifying BC's radiative effect on climate is predicting enhancements in light absorption that result from internal mixing between BC and other aerosol components. Modeling and laboratory studies show that BC, when mixed with other aerosol components, absorbs more strongly than pure, uncoated BC; however, some ambient observations suggest more variable and weaker absorption enhancement. We show that the lower-than-expected enhancements in ambient measurements result from a combination of two factors. First, the often used spherical, concentric core-shell approximation generally overestimates the absorption by BC. Second, and more importantly, inadequate consideration of heterogeneity in particle-to-particle composition engenders substantial overestimation in absorption by the total particle population, with greater heterogeneity associated with larger model-measurement differences. We show that accounting for these two effects-variability in per-particle composition and deviations from the core-shell approximation-reconciles absorption enhancement predictions with laboratory and field observations and resolves the apparent discrepancy. Furthermore, our consistent model framework provides a path forward for improving predictions of BC's radiative effect on climate