25,338 research outputs found

    Evaluierung von sechs Fotofallenmodellen hinsichtlich der Eignung für Fang-Wiederfang Methoden beim Eurasischen Luchs (Lynx lynx)

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    Digital outdoor cameras are increasingly used in wildlife research because they allow species inventories, population estimates, and behavior or activity observations. Which camera model is suitable and practical depends on environmental conditions, focus species and specific scientific questions posed. Here we focused on testing cameras appropriate for elusive species that can be identified visually owing to individual coat patterns. Specifically the camera should be adequate for calculating the minimum population of Eurasian Lynx (Lynx lynx) during a systematic monitoring with camera traps. Therefore we tested six digital camera models with regard to trigger speed and the image quality necessary for visual identification of pacing lynx on trails. The decision if a camera model is adequate for the scientific goal was regulated due to priority levels under laboratory conditions. Only one camera model proved to be suitable for camera-trap monitoring. Our practical camera test can be used to evaluate newer models of digital cameras as they become available. This application opens an avenue for a non-invasive population monitoring of rare and elusive species in a low mountain range area.Digitale Fotofallen werden weltweit in der Wildtierforschung eingesetzt. Die Einsatzgebiete sind vielfältig, sie reichen von Artenbestandsaufnahmen und Populationsschätzungen über die Verhaltensforschung bis hin zu Aktivitätsanalysen. Das jeweilig eingesetzte Kameramodell muss an die Aufnahmesituation und die Zielsetzung der Analyse angepasst sein. Das Ziel unseres Fotofallentests war es, ein Modell zu finden, welches für die visuelle Identifizierung von Fellmustern des Eurasischen Luchses geeignet ist. Die Fotofalle soll in einem systematischen Monitoring für die minimale Anzahl der im Gebiet vorkommenden Luchse und deren Populationsschätzung mit Fang-Wiederfang Methoden eingesetzt werden können. Bei dem Test von sechs Fotofallenmodellen, fiel das Hauptaugenmerk auf die Auslösegeschwindigkeit und die Bildqualität welche die nötigen Faktoren für die Sicherstellung der visuellen Identifikation von schreitenden Luchsen am Wildwechsel darstellen. Zur Entscheidungsfindung der Eignung eines Fotofallenmodells für die Fragestellung definierten wir Prioritätslevel unter Laborbedingungen. Es stellte sich heraus, dass nur ein Fotofallenmodell die Ansprüche erfüllte. Der praktische Fotofallentest kann für neuerscheinende Fotofallenmodelle adaptiert werden. Diese Anwendung eröffnet die Möglichkeit für ein nicht invasives Monitoring in Mittelgebirgslandschaften

    Allan Variance Analysis as Useful Tool to Determine Noise in Various Single-Molecule Setups

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    One limitation on the performance of optical traps is the noise inherently present in every setup. Therefore, it is the desire of most experimentalists to minimize and possibly eliminate noise from their optical trapping experiments. A step in this direction is to quantify the actual noise in the system and to evaluate how much each particular component contributes to the overall noise. For this purpose we present Allan variance analysis as a straightforward method. In particular, it allows for judging the impact of drift which gives rise to low-frequency noise, which is extremely difficult to pinpoint by other methods. We show how to determine the optimal sampling time for calibration, the optimal number of data points for a desired experiment, and we provide measurements of how much accuracy is gained by acquiring additional data points. Allan variances of both micrometer-sized spheres and asymmetric nanometer-sized rods are considered.Comment: 14 pages, 6 figures, presented at SPIE Optics+Photonics 2009 in San Diego, CA, US

    Method and apparatus for positioning a robotic end effector

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    A robotic end effector and operation protocol for a reliable grasp of a target object irrespective of the target's contours is disclosed. A robotic hand includes a plurality of jointed fingers, one of which, like a thumb, is in opposed relation to the other. Each finger is comprised of at least two jointed sections, and provided with reflective proximity sensors, one on the inner surface of each finger section. Each proximity sensor comprises a transmitter of a beam of radiant energy and means for receiving reflections of the transmitted energy when reflected by a target object and for generating electrical signals responsive thereto. On the fingers opposed to the thumb, the proximity sensors on the outermost finger sections are aligned in an outer sensor array and the sensors on the intermediate finger sections and sensors on the innermost finger sections are similarly arranged to form an intermediate sensor array and an inner sensor array, respectively. The invention includes a computer system with software and/or circuitry for a protocol comprising the steps in sequence of: (1) approach axis alignment to maximize the number of outer layer sensors which detect the target; (2) non-contact contour following the target by the robot fingers to minimize target escape potential; and (3) closing to rigidize the target including dynamically re-adjusting the end effector finger alignment to compensate for target motion. A signal conditioning circuit and gain adjustment means are included to maintain the dynamic range of low power reflection signals

    Multiple Instance Curriculum Learning for Weakly Supervised Object Detection

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    When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects. To address this challenge, we incorporate object segmentation into the detector training, which guides the model to correctly localize the full objects. We propose the multiple instance curriculum learning (MICL) method, which injects curriculum learning (CL) into the multiple instance learning (MIL) framework. The MICL method starts by automatically picking the easy training examples, where the extent of the segmentation masks agree with detection bounding boxes. The training set is gradually expanded to include harder examples to train strong detectors that handle complex images. The proposed MICL method with segmentation in the loop outperforms the state-of-the-art weakly supervised object detectors by a substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201

    A compact holographic optical tweezers instrument

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    Holographic optical tweezers have found many applications including the construction of complex micron-scale 3D structures and the control of tools and probes for position, force, and viscosity measurement. We have developed a compact, stable, holographic optical tweezers instrument which can be easily transported and is compatible with a wide range of microscopy techniques, making it a valuable tool for collaborative research. The instrument measures approximately 30×30×35 cm and is designed around a custom inverted microscope, incorporating a fibre laser operating at 1070 nm. We designed the control software to be easily accessible for the non-specialist, and have further improved its ease of use with a multi-touch iPad interface. A high-speed camera allows multiple trapped objects to be tracked simultaneously. We demonstrate that the compact instrument is stable to 0.5 nm for a 10 s measurement time by plotting the Allan variance of the measured position of a trapped 2 μm silica bead. We also present a range of objects that have been successfully manipulated

    Automatic Recognition of Mammal Genera on Camera-Trap Images using Multi-Layer Robust Principal Component Analysis and Mixture Neural Networks

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    The segmentation and classification of animals from camera-trap images is due to the conditions under which the images are taken, a difficult task. This work presents a method for classifying and segmenting mammal genera from camera-trap images. Our method uses Multi-Layer Robust Principal Component Analysis (RPCA) for segmenting, Convolutional Neural Networks (CNNs) for extracting features, Least Absolute Shrinkage and Selection Operator (LASSO) for selecting features, and Artificial Neural Networks (ANNs) or Support Vector Machines (SVM) for classifying mammal genera present in the Colombian forest. We evaluated our method with the camera-trap images from the Alexander von Humboldt Biological Resources Research Institute. We obtained an accuracy of 92.65% classifying 8 mammal genera and a False Positive (FP) class, using automatic-segmented images. On the other hand, we reached 90.32% of accuracy classifying 10 mammal genera, using ground-truth images only. Unlike almost all previous works, we confront the animal segmentation and genera classification in the camera-trap recognition. This method shows a new approach toward a fully-automatic detection of animals from camera-trap images

    Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package

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    This paper describes the R package crqa to perform cross-recurrence quantification analysis of two time series of either a categorical or continuous nature. Streams of behavioral information, from eye movements to linguistic elements, unfold over time. When two people interact, such as in conversation, they often adapt to each other, leading these behavioral levels to exhibit recurrent states. In dialogue, for example, interlocutors adapt to each other by exchanging interactive cues: smiles, nods, gestures, choice of words, and so on. In order for us to capture closely the goings-on of dynamic interaction, and uncover the extent of coupling between two individuals, we need to quantify how much recurrence is taking place at these levels. Methods available in crqa would allow researchers in cognitive science to pose such questions as how much are two people recurrent at some level of analysis, what is the characteristic lag time for one person to maximally match another, or whether one person is leading another. First, we set the theoretical ground to understand the difference between 'correlation' and 'co-visitation' when comparing two time series, using an aggregative or cross-recurrence approach. Then, we describe more formally the principles of cross-recurrence, and show with the current package how to carry out analyses applying them. We end the paper by comparing computational efficiency, and results' consistency, of crqa R package, with the benchmark MATLAB toolbox crptoolbox. We show perfect comparability between the two libraries on both levels

    Mathieu beams as versatile light moulds for 3D micro particle assemblies

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    We present tailoring of three dimensional light fields which act as light moulds for elaborate particle micro structures of variable shapes. Stereo microscopy is used for visualization of the 3D particle assemblies. The powerful method is demonstrated for the class of propagation invariant beams, where we introduce the use of Mathieu beams as light moulds with non-rotationally-symmetric structure. They offer multifarious field distributions and facilitate the creation of versatile particle structures. This general technique may find its application in micro fluidics, chemistry, biology, and medicine, to create highly efficient mixing tools, for hierarchical supramolecular organization or in 3D tissue engineering
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