1,433 research outputs found

    Knowledge-based vision for space station object motion detection, recognition, and tracking

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    Computer vision, especially color image analysis and understanding, has much to offer in the area of the automation of Space Station tasks such as construction, satellite servicing, rendezvous and proximity operations, inspection, experiment monitoring, data management and training. Knowledge-based techniques improve the performance of vision algorithms for unstructured environments because of their ability to deal with imprecise a priori information or inaccurately estimated feature data and still produce useful results. Conventional techniques using statistical and purely model-based approaches lack flexibility in dealing with the variabilities anticipated in the unstructured viewing environment of space. Algorithms developed under NASA sponsorship for Space Station applications to demonstrate the value of a hypothesized architecture for a Video Image Processor (VIP) are presented. Approaches to the enhancement of the performance of these algorithms with knowledge-based techniques and the potential for deployment of highly-parallel multi-processor systems for these algorithms are discussed

    Urease Activity in a Kentucky Bluegrass Turf

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    The components of a turfgrass ecosystem, including plants, an intervening layer of thatch and the underlying soil, influence the fate of topically applied urea fertilizer. The loss of urea N by ammonia volatilization may be governed by the rate of urea hydrolysis. The main objective of this study was to determine the extent of urease activity associated with turfgrass plant tissue, thatch, and the underlying soil. This information may help elucidate the mechanism of ammonia loss following urea application. Because a turfgrass stand frequently possesses an extensive thatch layer that may serve as the primary plant growth medium, additional objectives included: i) determining the effects of air drying and seasonal variation on the activity of urease in thatch; ii) determining the variability in thatch urease activity by analyzing multiple field samples; and iii) determining the variation of urease activity within a thatch profile. Turfgrass clippings, thatch, and underlying Flanagan silt loam soil (Aquic Argiudoll) samples were taken from a field-grown Kentucky bluegrass (Poa pratensis L.) turf in either September 1980 or March 1981. On a dry weight basis, urease activity was 18 to 30 times higher from turfgrass clippings and thatch than from soil. Air drying thatch increased urease activity by 20 % over moist samples while air drying soil samples had no apparent effect. Greenhouse incubation of winter-dormant thatch samples increased urease activity 40 %, presumably in response to the duration of increased temperature. Thatch urease activity varied between sampling sites but still remained extremely high compared to soil activity. Within each thatch sample (1 X 1 X 2 cm), urease activity was highest in the upper 1.0 cm of the profile. It was concluded that thatch urease activity was variable in nature depending upon seasonal conditions which contrasts sharply with extremely stable soil urease activities. These findings suggest that, because of the high level of urease in thatch, ammonia volatilization will occur from most urea-treated turfgrass stands, regardless of the type of underlying soil unless the urea is thoroughly washed into the soil

    A model of ant route navigation driven by scene familiarity

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    In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints

    Foliar Application of N and Fe to Kentucky Bluegrass

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    The goal of the professional lawn care industry is to provide the homeowner with a dark green weed-free lawn. Members of this industry are interested in techniques to enhance the color of a turfgrass stand in lieu of excessive N fertilization. The purpose of this research was to evaluate the use of foliar applications of Fe alone or in combination with N on the color response of Kentucky bluegrass (Poa pratensis L.). Iron sulfate or an iron chelate was applied at the rate of 1.1, 2.2, or 4.5 kg Fe ha–1 in combination with either 0, 25, or 49 kg N ha–1 to a mixed ‘Columbia’/‘Touchdown’ Kentucky bluegrass turf growing on a Catlin silt loam (fine-silty, mixed, mesic Typic Argiudoll). Color ratings and clipping weights were determined on a weekly basis until treatment effects were no longer significant. In a separate experiment, both sources of Fe were applied at rates of 1.1 to 72.4 kg Fe ha–1 to Kentucky bluegrass to evaluate phytotoxicity. The color enhancement due to Fe applications without N lasted from several weeks to several months depending on the weather following application. Use of Fe during cool wet periods enhanced turf color for only 2 to 3 weeks and therefore, was considered of limited value. Iron applications during cool dry periods enhanced turf color for several months. The treatment of 2.2 kg ha–1 of Fe from iron chelate was judged to be the most effective Fe treatment because the color enhancement was usually equal to that provided by a 4.5 kg rate of either source but it did not result in any discoloration as was found with the 4.5 kg rate. Combining Fe with the 25 kg ha–1 rate of N resulted in color enhancement equal to that caused by applying 49 kg ha–1 of N alone. The results of the study indicate that combining Fe with N can result in acceptable turfgrass color with lower rates of N. No permanent damage was caused to turfs receiving Fe at rates up to 72.2 kg ha–1 although foliar phytotoxicity was observed

    Desert Ants Learn Vibration and Magnetic Landmarks

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    The desert ants Cataglyphis navigate not only by path integration but also by using visual and olfactory landmarks to pinpoint the nest entrance. Here we show that Cataglyphis noda can additionally use magnetic and vibrational landmarks as nest-defining cues. The magnetic field may typically provide directional rather than positional information, and vibrational signals so far have been shown to be involved in social behavior. Thus it remains questionable if magnetic and vibration landmarks are usually provided by the ants' habitat as nest-defining cues. However, our results point to the flexibility of the ants' navigational system, which even makes use of cues that are probably most often sensed in a different context

    Coherent Control for a Two-level System Coupled to Phonons

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    The interband polarizations induced by two phase-locked pulses in a semiconductor show strong interference effects depending on the time tau_1 separating the pulses. The four-wave mixing signal diffracted from a third pulse delayed by tau is coherently controlled by tuning tau_1. The four-wave mixing response is evaluated exactly for a two-level system coupled to a single LO phonon. In the weak coupling regime it shows oscillations with the phonon frequency which turn into sharp peaks at multiples of the phonon period for a larger coupling strength. Destructive interferences between the two phase-locked pulses produce a splitting of the phonon peaks into a doublet. For fixed tau but varying tau_1 the signal shows rapid oscillations at the interband-transition frequency, whose amplitude exhibits bursts at multiples of the phonon period.Comment: 4 pages, 4 figures, RevTex, content change

    Ultrafast Spin Dynamics in Nickel

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    The spin dynamics in Ni is studied by an exact diagonalization method on the ultrafast time scale. It is shown that the femtosecond relaxation of the magneto-optical response results from exchange interaction and spin-orbit coupling. Each of the two mechanisms affects the relaxation process differently. We find that the intrinsic spin dynamics occurs during about 10 fs while extrinsic effects such as laser-pulse duration and spectral width can slow down the observed dynamics considerably. Thus, our theory indicates that there is still room to accelerate the spin dynamics in experiments.Comment: 4 pages, Latex, 4 postscript figure

    Using deep autoencoders to investigate image matching in visual navigation

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    This paper discusses the use of deep autoencoder networks to find a compressed representation of an image, which can be used for visual naviga-tion. Images reconstructed from the compressed representation are tested to see if they retain enough information to be used as a visual compass (in which an image is matched with another to recall a bearing/movement direction) as this ability is at the heart of a visual route navigation algorithm. We show that both reconstructed images and compressed representations from different layers of the autoencoder can be used in this way, suggesting that a compact image code is sufficient for visual navigation and that deep networks hold promise for find-ing optimal visual encodings for this task
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