54 research outputs found

    Bayesian Classification of Flight Calls with a Novel Dynamic Time Warping Kernel

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    Abstract—In this paper we propose a probabilistic classifi-cation algorithm with a novel Dynamic Time Warping (DTW) kernel to automatically recognize flight calls of different species of birds. The performance of the method on a real world dataset of warbler (Parulidae) flight calls is competitive to human expert recognition levels and outperforms other classifiers trained on a variety of feature extraction approaches. In addition we offer a novel and intuitive DTW kernel formulation which is positive semi-definite in contrast with previous work. Finally we obtain promising results with a larger dataset of multiple species that we can handle efficiently due to the explicit multiclass probit likelihood of the proposed approach1

    Final report for LDRD Project 93633 : new hash function for data protection.

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    The security of the widely-used cryptographic hash function SHA1 has been impugned. We have developed two replacement hash functions. The first, SHA1X, is a drop-in replacement for SHA1. The second, SANDstorm, has been submitted as a candidate to the NIST-sponsored SHA3 Hash Function competition

    Testing an Emerging Paradigm in Migration Ecology Shows Surprising Differences in Efficiency between Flight Modes

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    To maximize fitness, flying animals should maximize flight speed while minimizing energetic expenditure. Soaring speeds of large-bodied birds are determined by flight routes and tradeoffs between minimizing time and energetic costs. Large raptors migrating in eastern North America predominantly glide between thermals that provide lift or soar along slopes or ridgelines using orographic lift (slope soaring). It is usually assumed that slope soaring is faster than thermal gliding because forward progress is constant compared to interrupted progress when birds pause to regain altitude in thermals. We tested this slope-soaring hypothesis using high-frequency GPS-GSM telemetry devices to track golden eagles during northbound migration. In contrast to expectations, flight speed was slower when slope soaring and eagles also were diverted from their migratory path, incurring possible energetic costs and reducing speed of progress towards a migratory endpoint. When gliding between thermals, eagles stayed on track and fast gliding speeds compensated for lack of progress during thermal soaring. When thermals were not available, eagles minimized migration time, not energy, by choosing energetically expensive slope soaring instead of waiting for thermals to develop. Sites suited to slope soaring include ridges preferred for wind-energy generation, thus avian risk of collision with wind turbines is associated with evolutionary trade-offs required to maximize fitness of time-minimizing migratory raptors

    Follicular fluid content and oocyte quality: from single biochemical markers to metabolomics

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    The assessment of oocyte quality in human in vitro fertilization (IVF) is getting increasing attention from embryologists. Oocyte selection and the identification of the best oocytes, in fact, would help to limit embryo overproduction and to improve the results of oocyte cryostorage programs. Follicular fluid (FF) is easily available during oocyte pick-up and theorically represents an optimal source on non-invasive biochemical predictors of oocyte quality. Unfortunately, however, the studies aiming to find a good molecular predictor of oocyte quality in FF were not able to identify substances that could be used as reliable markers of oocyte competence to fertilization, embryo development and pregnancy. In the last years, a well definite trend toward passing from the research of single molecular markers to more complex techniques that study all metabolites of FF has been observed. The metabolomic approach is a powerful tool to study biochemical predictors of oocyte quality in FF, but its application in this area is still at the beginning. This review provides an overview of the current knowledge about the biochemical predictors of oocyte quality in FF, describing both the results coming from studies on single biochemical markers and those deriving from the most recent studies of metabolomic

    Energy-Neutral Data Collection Rate Control for IoT Animal Behavior Monitors

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    Energy-neutral operation (ENO) is a major concern for Internet of things (IoT) sensor systems. Animals can be tagged with IoT sensors to monitor their movement and behavior. These sensors wirelessly upload collected data and can receive parameters to change their operation. Typically, the behavior monitors are powered by a battery where the system relies upon harvesting solar radiation for sustainable operation. Solar panels typically are used as the harvesting mechanism and can have a level of uncertainty regarding consistent energy delivery due to factors such as adverse weather, foliage, time of day, and individual animal behavior. The variability of available energy inevitably creates a trade-off in the rate at which data can be collected with respect to incoming and stored energy. The objective of this research was to investigate and simulate methods and parameters that can control the data collection rate of an IoT behavior monitor to achieve sustained operation with unknown and random energy harvesting. Analysis and development of a control system were performed by creating a software model of energy consumption and then simulating using different initial conditions and random energy harvesting rates for evaluation. The contribution of this effort was the exploration into the usage of a discrete-time gain scheduled Proportional–Integral–Derivative (PID) that was tuned to a specific device configuration, using battery state of charge as an input, and found to maintain a battery level set-point, reject small solar harvesting energy disturbances, and maintain a consistent data collection rate throughout the day

    An Instrumented Golden Eagle’s (Aquila chrysaetos) Long-Distance Flight Behavior

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    One-second-processed three-dimensional position observations transmitted from an instrumented golden eagle were used to determine the detailed long-range flight behavior of the bird. Once elevated from the surface, the eagle systematically used atmospheric gravity waves, first to gain altitude, and then, in multiple sequential glides, to cover over 100 km with a minimum expenditure of its metabolic energy
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