Application of ISO 25178 standard for multiscale 3D parametric assessment of surface topographies

The objective of the present work is to discuss the potential of areal surface texture parameters as introduced and discussed by ISO standards 25178, as a widely recognized reference framework of indices and procedures, which can help and accelerate understanding of functional information. Such indices have been developed specifically for the micro-scale, however they can be successfully implemented also in the case of larger scales. Parameters extraction takes place in three main steps, independently from the scale: calibration, filtering and parameter extraction. The possibility of using the same approach and the same roughness parameters at different scales helps very much not only the post processing of surfaces data sets but also their interpretation, putting the basis for multiscale models

Ammonia and Greenhouse Gas emissions from slatted dairy barn floors cleaned by Robotic Scrapers

The design of animal housing and manure management systems are key factors in livestock farming. Frequent removal methods, in fact, allow for the reduction of gasses produced from fermentations of the organic matter contained in manure, that affect animal welfare and farmer health and are emitted from animal housings into the atmosphere as a consequence of ventilation. The present study aims to evaluate the performance of a Robotic Scraper (RS) operating on the floors in a full-scale, operative free-stall dairy barn. The research is focused on the evaluation of gaseous emissions from the two types of floors (concrete and rubber mat coated), and with and without RS operation. The floors with rubber coating demonstrated higher emission rates of ammonia (NH3), carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) compared to the uncovered concrete floors, both before and after RS operations. The operation of RS, furthermore, determined significant reduction of greenhouse gasses (GHG) but did not have relevant effect in terms of NH3 emission, which reduced only of 1.4% from concrete floors, but increase of 12.7% from rubber coated floors

Scanning Probe Microscopy for polymer film characterization in food packaging

Scanning probe microscopy (SPM) is a branch of microscopy allowing characterization of surfaces at the micro-scale by means of a physical probe (with a size of a few microns) raster scanning the sample. SPMs monitor the interaction between such probe and the surface and, depending on the specific physical principles causing the interaction, they allow generation of a quantitative map of topographic properties: geometrical, optical, electrical, magnetic, etc. This is of the greatest interest, in particular whenever functional surfaces have to be characterized in a quantitative manner. The present paper discusses the different applications of Scanning Probe Microscopy techniques for a thorough characterization of polymer surfaces, of specific interest in particular for the case of food packaging applications

Automatic detection of cow/calf vocalizations in free-stall barn

Precision livestock farming dictates the use of advanced technologies to understand, analyze, assess and finally optimize a farm\u2019s production collectively as well as the contribution of each single animal. This work is part of a research project wishing to steer the dairy farms\u2019 producers to more ethical rearing systems. To study cow\u2019s welfare, we focus on reciprocal vocalizations including mother-offspring contact calls. We show the set-up of a suitable audio capturing system composed of automated recording units and propose an algorithm to automatically detect cow vocalizations in an indoor farm setting. More specifically, the algorithm has a two-level structure: a) first, the Hilbert follower is applied to segment the raw audio signals, and b) second the detected blocks of acoustic activity are refined via a classification scheme based on hidden Markov models. After thorough evaluation, we demonstrate excellent detection results in terms of false positives, false negatives and confusion matrix

A comparison of low-cost techniques for three-dimensional animal body measurement in livestock buildings

Data about health and development of animals are still now mostly collected through manual measurements or visual observations but these kinds of methods of collecting data are causes of several problems. Alternatively, optical sensing techniques can be implemented in order to overcome limitations arising from manual contact measurements. The present research discusses metrological analysis of Structure from motion (SfM) photogrammetry approach, low-cost LiDAR scanning and Microsoft Kinect v1 depth camera to three- dimensional animal body measurement, with specific reference to pigs. Analyses were carried out on fiberglass model to get rid of animal movements. Scans were captured based on a segmented approach, where different portion of the body have been imaged during different frames acquisition tasks. The obtained results demonstrate the high potential of 3D Kinect. LiDAR show a higher RMS value respect to Kinect and SfM most probably due to the collection approach based on single profiles rather than on surfaces. Anyway, the RMS of relative noise ranges between 0.7 and 4 mm, showing a high accuracy of reconstructions even for the others techniques

Intracranial hematopoiesis in a patient with aids-related central nervous system lymphoma and severe pancytopenia

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NON-RIGID MULTI-BODY TRACKING IN RGBD STREAMS

To efficiently collect training data for an off-the-shelf object detector, we consider the problem of segmenting and tracking non-rigid objects from RGBD sequences by introducing the spatio-temporal matrix with very few assumptions – no prior object model and no stationary sensor. Spatial temporal matrix is able to encode not only spatial associations between multiple objects, but also component-level spatio temporal associations that allow the correction of falsely segmented objects in the presence of various types of interaction among multiple objects. Extensive experiments over complex human/animal body motions with occlusions and body part motions demonstrate that our approach substantially improves tracking robustness and segmentation accuracy

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe