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Automatic monitoring tools can be useful for assessing the health and welfare status of animals. Specifically, a computer-vision-based tracking tool could be helpful to remotely and automatically monitoring the behaviour of individual animals. However, animals housed in partly covered pens present a particular challenge for animal tracking due to the possibility for animals to disappear from and reappear in the field of view (FOV). The aim of this study was to develop a tracking method for weaner pigs housed in partly covered pens, with the particular aim to re-identify individuals when they reappear in the FOV. In this study a one-shot tracker in which the detection and re-identification (re-ID) branches were jointly trained was adopted for tracking pigs. Three associations based on re-ID features and intersection over union (IoU) were used for matching the correct ID, especially re-identifying individuals reappearing in the FOV. Two sets of short videos were selected to test the model, with a first set of two short videos (mean ± SD: 1m50s ± 20) and a second set of three short videos (mean ± SD: 10m08s ± 3m52s). The model reached the performance of 91.41% and 88.33% in MOTA and IDF1 on the first set of videos, and 81.17% in mean tracking percentage per individual on the second set. The test on one long video (from the same pen, length: 85 m) achieved a tracking percentage of 16.78% per individual. The suggested method improved automatic individual behaviour analysis in complex environments where animals can leave the FOV. • A long-term tracking algorithm was developed for group-housed pigs. • Re-ID features and IoU were used to associate and identify pigs entering the field of view. • An average tracking percentage of 16.78% was obtained on a 85-min-long video. [ABSTRACT FROM AUTHOR]

Highlights • We model pipe wall viscoelasticity during transient waves in a viscoelastic pipe. It is shown that, in the frequency domain, the viscoelastic effect is equivalent to changing the constant wave speed to be frequency-dependent. • Based on this viscoelastic model, matched-field processing (MF) method is applied for pipeline leakage detection. It is a 1D search of leak location along the pipe, independent of leak size. Therefore, it is fast and has a low optimization/computation complexity. • Experimental data at University of Perugia and Hong Kong University of Science and Technology (HKUST) are studied to evaluate the proposed MFP method. It is shown that MFP outperforms three representative methods in the literature (wavelet analysis, frequency response peak pattern, and inverse transient analysis) in the sense of lower localization error. Moreover, MFP leak localization is accurate even for a small leak (the flow ratio of leak and main pipe is approximately 10 • A profound discussion is given to explain the properties of MFP in the aspects of (i) physical complexity modeling; (ii) sufficient use of information from measurements; (iii) multi-sensor information fusion; and (iv) computation complexity. These discussions also guide the choice of data set, experimental setup design, and inverse method for leakage localization. Abstract This paper applies the matched-field processing (MFP) method to leakage localization in a viscoelastic pipe. The viscoelasticity of pipe wall is included in the governing equations of transient wave via the generalized Kelvin-Voigt model and its effect is finally translated into a frequency-dependent wave speed. Then, a leak is localized by MFP via a 1D search of leak location along the pipe, independent of the leak size. Transient experiments with viscoelastic pipe in the Water Engineering Laboratory at University of Perugia and in the Water Resources Research Laboratory at Hong Kong University of Science and Technology are studied. Experimental results demonstrate that the inclusion of pipe wall viscoelasticity and using more frequencies (instead of using only resonant frequencies) improve significantly the leak localization accuracy. It is shown that the MFP leak localization is accurate even for a small leak (the flow ratio of leak and main pipe is approximately 10%) in a noisy environment: among 50 transient experiments, the maximum error of MFP leak localization is only 1.14 m and in the other 49 experiments the error is always lower than 1 m. [ABSTRACT FROM AUTHOR]

The Hamlet test is an innovative device providing a complex environment for testing topographic memory in mice. Animals were trained in groups for weeks in a small village with a central agora, streets expanding from it towards five functionalized houses, where they can drink, eat, hide, run, interact with a stranger mouse. Memory was tested by depriving mice from water or food and analyzing their ability to locate the Drink/Eat house. Exploration and memory were analyzed in different strains, gender, and after different training periods and delays. After 2 weeks training, differences in exploration patterns were observed between strains, but not gender. Neuroanatomical structures activated by training, identified using FosB/ΔFosB immunolabelling, showed an involvement of the hippocampus-subiculum-parahippocampal gyrus axis and dopaminergic structures. Training increased hippocampal neurogenesis (cell proliferation and neuronal maturation) and modified the amnesic efficacy of muscarinic or nicotinic cholinergic antagonists. Moreover, topographical disorientation in Alzheimer's disease was addressed using intracerebroventricular injection of amyloid β 25–35 peptide in trained mice. When retested after 7 days, Aβ 25–35 -treated mice showed memory impairment. The Hamlet test specifically allows analysis of topographical memory in mice, based on complex environment. It offers an innovative tool for various ethological or pharmacological research needs. For instance, it allowed to examine topographical disorientation, a warning sign in Alzheimer's disease. [ABSTRACT FROM AUTHOR]

The phase-based motion estimation method is promising because the phase is sensitive to the motion information embedded in the pixel values. Unfortunately, complex environments such as extra textures and illumination changes exist in practice, whereas their effects have not been studied. This study theoretically discovers that the large spatial size and DC component of the filter adversely affect the performance of the motion estimation method under complex environments. The traditional method adopting the Gabor filter with a large bandwidth may suppress extra textures while cause a significant drift in the estimated motion due to the negative frequency and DC components. To tackle this, a log-Gabor-based new motion estimation method is proposed, in which the log-Gabor filter intrinsically without the DC and negative frequency components can extract the local phase and filter out the illumination change. A localization index based on the amplitude map is designed to determine the optimal parameters of the log-Gabor filter and maximize its spatial localization capability. Numerical and experimental examples demonstrate that the proposed method outperforms the traditional Gabor filter in motion estimation under the complex environment. [ABSTRACT FROM AUTHOR]