Stationary and mobile ground-based cameras can be used to scale ecological observations, relating pixel information in images to in situ measurements. Currently there are four CENS projects that involve using cameras for scaling ecological observations: 1. Scaling from one individual to the landscape. Pan-Tilt-Zoom cameras can be zoomed in on a tight focus on individual plants and parts of individuals and then zoomed out to get a landscape view, composed of the same and similar species. 2. Estimating photosynthesis over large areas with HDR. High Dynamic Range imaging is a technique to capture an absolute amount of reflected light in an image. For a meadow composed of similarly reflecting species, we can estimate light received by leaves and thus photosynthesis over a wide area. 3. Scaling soil surface temperature measurements. Soil surface temperatures and soil energy balance are related to solar radiation and air temperature. Sunflecks captured with a camera taking panoramic mosaics of images can be used to estimate the radiation load for large areas of unobstructed understory. 4. Expanding plant phenological observations with a nation-wide network of webcams. Twice-daily images from over 1000 internet-connected and freely available cameras have been collected since February 2008. The advance of Spring can be tracked as a "green-up" and related to satellite remote sensing signals.

The explanation of linguistic variation and change is one of the central questions in the language sciences. Functional explanations focus on how the needs and abilities of language users shape the distribution of linguistic structures that typically conventionalize -- e.g. structures that are harder to perceive or learn accurately are less likely to conventionalize accurately. Perceptibility effects are common sound patterns that seem closely related to the relative confusability of different speech sound sequences. One class of explanations -- purely phonological accounts --have assumed speakers (implicitly) know how confusability varies as a function of immediately adjacent sounds, and that this is a rich enough description of confusability to explain perceptibility effects. Chapter 2 shows that the perceptibility of tokens of any given sound in American English systematically varies based on a listener's incrementally-adjusted expectations about what the speaker intends to say, and shows that this variation is significantly greater than variation due to immediately adjacent sounds. To derive this result, I present a computational psycholinguistic model of word recognition and apply it to experimental confusability data and a transcribed lexicon of 10^4 words. I conclude that purely phonological accounts of perceptibility effects need to be more complicated and less modular than currently appreciated. Chapter 3 applies the same word recognition model and novel information-theoretic measures of confusability to two conversational corpora and shows that words that are more contextually confusable are lengthened in contexts where they are more confusable, and shortened where they are less so. This is a crucial step towards a linking hypothesis between the realtime perceptibility of different speech sound sequences and conventionalized perceptibility effects. Chapter 4 considers morphology. Prior research has observed an inverse relation between morphological complexity and demographic variables like speech community size and proportion of adult learners. Recent work has hypothesized that higher complexity may be helpful to child learners, and that populations with differing demographics constitute environments with different 'selection pressures' for language variants to 'evolve' in. I argue that mathematical formulations of Darwinian evolution suggest a more likely explanation: 'neutral' change caused by random fluctuations in variant frequency ('drift') is much more powerful in small populations and can easily overwhelm selection relative to large populations.

Introduction: Long QT syndrome (LQTS) is an uncommon disorder that can lead to potentially life-threatening dysrhythmias. LQTS can be genetic, acquired, or both.

Case Report: A 44-year-old female with well-controlled hypertension and asthma presented with chest tightness. An initial electrocardiogram yielded a normal corrected QT interval of 423 milliseconds (ms) (normal <480 ms in females). Albuterol was administered and induced agitation, tremulousness, and tachycardia. Follow-up electrocardiograms demonstrated extreme prolongation of the corrected QT interval to 633 ms and morphology change of the T wave. Lab values were later notable for hypokalemia and hypomagnesemia, attributable to a recently started thiazide diuretic. The patient was ultimately diagnosed with congenital LQTS after initial unmasking by albuterol in the emergency department.

Conclusion: LQTS can be unmasked or exacerbated by electrolyte abnormalities and QT prolonging medications.

The characterization of the solar radiation environment under a forest canopy is important for both understanding temperature-dependent biological processes and validating energy balance models. A modified sinusoidal model of soil heat conductivity was used to estimate subsurface temperature and heat flux from the uneven but periodic solar heating of the soil surface due to sun flecks from a forest canopy. Using a mobile sensor platform with an infrared thermometer along an 11 m transect, a sunfleck model of soil surface temperature was tested using soil surface temperature maxima, air temperatures, and photodiodes placed on the soil surface to measure sunflecks. A pan-tilt-zoom digital camera on a 10 m tower above the site was then used to capture a time series of panoramic images of sunflecks reflected from the soil surface and to scale the sunfleck temperature model to a wide area. Finally, this image-based model of surface temperatures was combined with the modified sinusoidal model for heat conduction to estimate soil subsurface temperatures and heat flux over a wide area due to sunflecks from a forest canopy.

Research results that advance the capabilities of autonomous underwater vehicles (AUVs) to conduct seabed surveys are described. These include the creation of a software framework to enable research and development in sensing and adaptive autonomy, a novel synthetic baseline navigation technique, and a magnetic sensing system that incorporates sense and react behaviors. Field experiments were conducted globally in a wide range of littoral environments to test hypotheses associated with the emerging field of autonomy as applied to underwater systems.

To facilitate sensor integration and provide a testbed for autonomous sense and react research, an onboard sensor processing and autonomy system was developed for the REMUS AUV using the Robot Operating System (ROS) that provides high-level control of the vehicle. Multiple vehicles outfitted with this system were used for seabed surveys, sensor evaluation, and engineering tests. This framework enabled the development of novel techniques for undersea navigation and magnetic sensing.

A synthetic baseline navigation technique that self-localizes an AUV using intermittent acoustic communications signals received by a single transducer is presented. The methodology is found to offer advantages over traditional acoustic-based navigation, in that it can operate with or without synchronized clocks, does not require acoustic transmissions dedicated to navigation, and can provide faster navigation solution convergence. The method uses the phase measurement at the output of a second-order phase-locked loop (PLL) to create fine-scale pseudo-range estimates in addition to, or in the absence of, a one-way travel time (OWTT) measurement based on the arrival time of the acoustic data packet. These range measurements are incorporated by an adaptive particle filter. This technique allows the vehicle navigation system to take advantage of multiple phase-derived range measurements made over the duration of a communication packet.

To enable geophysical and archaeological survey capabilities, a scalar magnetometer system has been developed and integrated into an AUV. Real-time signal processing mitigates platform effects of the vehicle. Development of autonomy for on-board processing and target detection, coupled with reacquisition behaviors, is found to increase the effective survey coverage rate by nearly 300% when searching for magnetic dipole targets.

The compact system collects data from a Micro-Fabricated Atomic Magnetometer (MFAM, Geometrics Corporation, San Jose, CA, USA), a total-field atomic magnetometer, and data from the sensor is both streamed to storage and made available to an onboard autonomy engine for real-time sense and react behaviors. Following characterization both in controlled laboratory conditions and at sea to determine its performance limits, methodologies for processing the magnetometer data to correct for interference and error introduced by the AUV platform were developed to improve sensing performance. When conducting seabed surveys, the developed autonomy is found to reliably detect and characterize targets of interest using physics-based algorithms designed to operate in real-time within the computational constraints of the AUV. Over the course of this research, the system was advanced to drive both single- and multiple-vehicle autonomous target reacquisition behaviors. Detailed results from surveys searching for submerged World-War II aircraft wrecks at locations worldwide are presented.