Animal behavior is indicative of health status and changes in behavior can indicate health issues (i.e., illness, stress, or injury). Currently, human observation (HO) is the only method for detecting behavior changes that may indicate problems in group-housed pigs. While HO is effective, limitations exist. Limitations include HO being time consuming, HO obfuscates natural behaviors, and it is not possible to maintain continuous HO. To address these limitations, a computer vision platform (NUtrack) was developed to identify (ID) and continuously monitor specific behaviors of group-housed pigs on an individual basis. The objectives of this study were to evaluate the capabilities of the NUtrack system and evaluate changes in behavior patterns over time of group-housed nursery pigs. The NUtrack system was installed above four nursery pens to monitor the behavior of 28 newly weaned pigs during a 42-d nursery period. Pigs were stratified by sex, litter, and randomly assigned to one of two pens (14 pigs/pen) for the first 22 d. On day 23, pigs were split into four pens (7 pigs/pen). To evaluate the NUtrack system's capabilities, 800 video frames containing 11,200 individual observations were randomly selected across the nursery period. Each frame was visually evaluated to verify the NUtrack system's accuracy for ID and classification of behavior. The NUtrack system achieved an overall accuracy for ID of 95.6%. This accuracy for ID was 93.5% during the first 22 d and increased (P < 0.001) to 98.2% for the final 20 d. Of the ID errors, 72.2% were due to mislabeled ID and 27.8% were due to loss of ID. The NUtrack system classified lying, standing, walking, at the feeder (ATF), and at the waterer (ATW) behaviors accurately at a rate of 98.7%, 89.7%, 88.5%, 95.6%, and 79.9%, respectively. Behavior data indicated that the time budget for lying, standing, and walking in nursery pigs was 77.7% ± 1.6%, 8.5% ± 1.1%, and 2.9% ± 0.4%, respectively. In addition, behavior data indicated that nursery pigs spent 9.9% ± 1.7% and 1.0% ± 0.3% time ATF and ATW, respectively. Results suggest that the NUtrack system can detect, identify, maintain ID, and classify specific behavior of group-housed nursery pigs for the duration of the 42-d nursery period. Overall, results suggest that, with continued research, the NUtrack system may provide a viable real-time precision livestock tool with the ability to assist producers in monitoring behaviors and potential changes in the behavior of group-housed pigs.

Existing fiducial markers solutions are designed for efficient detection and decoding, however, their ability to stand out in natural environments is difficult to infer from relatively limited analysis. Furthermore, worsening performance in challenging image capture scenarios - such as poor exposure, motion blur, and off-axis viewing - sheds light on their limitations. E2ETag introduces an end-to-end trainable method for designing fiducial markers and a complimentary detector. By introducing back-propagatable marker augmentation and superimposition into training, the method learns to generate markers that can be detected and classified in challenging real-world environments using a fully convolutional detector network. Results demonstrate that E2ETag outperforms existing methods in ideal conditions and performs much better in the presence of motion blur, contrast fluctuations, noise, and off-axis viewing angles. Source code and trained models are available at https://github.com/jbpeace/E2ETag.

Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification network to assign unique identification to instances. The fixed cardinality of the targets is leveraged to create a continuous set of tracks and the forward-backward algorithm is used to assign ear-tag identification probabilities to each detected instance. Tracking achieves real-time performance on consumer-grade hardware, in part because it does not rely on complex, costly, graph-based optimizations. A publicly available, human-annotated dataset is introduced to evaluate tracking performance. This dataset contains 15 half-hour long videos of pigs with various ages/sizes, facility environments, and activity levels. Results demonstrate that the proposed method achieves an average precision and recall greater than 95% across the entire dataset. Analysis of the error events reveals environmental conditions and social interactions that are most likely to cause errors in real-world deployments.

Spatial skills have a known beneficiary role in STEM students’ academic success. This paper explores data relating to the role of spatial skills in electrical engineering problem solving which is a relatively under researched area. Data indicate a significant association between electrical engineering problem solving and spatial skills and a discussion around their potential causal role concludes the paper.

Abstract Rapid identification of morbid/injured pigs is essential for swine producers to ensure the health and well-being of each individual pig and ensure production efficiency. As such, there is a need to develop advanced technology as a means to further ensure the health and well-being of pigs. The objective of this study was to evaluate a novel computer vision, Deep-Frame – Detection and Tracking Platform (DF–DTP), for the ability to automatically identify/maintain identity and continuously track the activities of group-housed pigs. Utilizing a depth-enabled camera and multi-ellipsoid expectation maximization technology, 28 nursery pigs were continuously evaluated during the first 42 d of the nursery phase. Over the 42-d nursery period, the DF–DTP was capable of achieving a 93.7% accuracy for identifying and maintaining the identity of individual pigs. Through visual validation (10,544 observations), 642 identification errors were detected. Of the identification errors, 82% occurred when pigs were lying, 7.8% standing, 2.1% walking, 7.2% at the feeder (at the feeder), and 1.0% at the waterer. The DF–DTP was capable of a 96.2% accuracy rate for classification of an individual pig’s activity. Accuracy for classification of activities was 96.3% for walking, 96.3 for standing, 99.1 for lying, 86.4% for at the feeder, and 73.6% for at the waterer. Across the 42-d trial, the average time pigs spent 77.8% (+ 0.02) lying, 8.6% (+ 0.32) standing, 2.9% (+ 0.09) walking and traveled 943.1 meters/d (+ 195.9). Over time (d 1 – d 42), the time pigs spent lying, standing, walking, and meters/d decreased (P 0.001). As d within the nursery phase increased, time at the feeder increased (P < 0.001), there was no change (P = 0.11) for time at the waterer. Results of the study indicate that the DF–DTP is capable of accurately identifying, maintaining the identity of and continuously tracking the activity of group-housed nursery pigs.

Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new dataset containing 2000 annotated images with 24,842 individually annotated pigs from 17 different locations. The proposed method achieves over 99% precision and over 96% recall when detecting pigs in environments previously seen by the network during training. To evaluate the robustness of the trained network, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 91% precision and 67% recall. The dataset is publicly available for download.

The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches. Source code available at https://github.com/yanfengliu/layered_embeddings .

Maintaining the health and well‐being of animals is critical to the efficiency and profitability of livestock operations. However, it can be difficult to monitor the health of animals in large group‐housed settings without the assistance of technology. This study presents a system that uses depth images to continuously track individual pigs in a group‐housed environment. It is an alternative to traditional manual observation used by both researchers and producers for the analysis of animal activities and behaviours. The tracking method used by the system exploits the consistent shape and fixed number of the targets in the environment by applying expectation maximisation as a policy for fitting an ellipsoid to each target. Results demonstrate that the system can maintain the correct positions and orientations of 15 group‐housed pigs for an average of 19.7 min between failure events.

This paper describes a study to assess the demand of problems posed in undergraduate electrical engineering courses. This is part of a larger study to examine the gap between the demand of problems and the types of reasoning students use when attempting to solve them. The analysis uses a two-dimensional taxonomy table consisting of rows and columns that define cognitive processes and categories of knowledge, respectively. The cognitive process dimension (i. e., the rows of the table) contains six categories: remember, understand, apply, analyze, evaluate, and create. The knowledge dimension (i. e., the columns of the table) contains four categories: factual, conceptual, procedural, and metacognitive. Preliminary results suggest that the proposed two-dimension analysis can be successfully used to characterize the demand of electrical engineering problems.

Many applications require both the location and identity of objects in images and video. Most existing solutions, like QR codes, AprilTags, and ARTags use complex machine-readable fiducial markers with heuristically derived methods for detection and classification. However, in applications where humans are integral to the system and need to be capable of locating objects in the environment, fiducial markers must be human readable. An obvious and convenient choice for human readable fiducial markers are alphanumeric characters (Arabic numbers and English letters). Here, a method for classifying characters using a convolutional neural network (CNN) is presented. The network is trained with a large set of computer generated images of characters where each is subjected to a carefully designed set of augmentations designed to simulate the conditions inherent in video capture. These augmentations include rotation, scaling, shearing, and blur. Results demonstrate that training on large numbers of synthetic images produces a system that works on real images captured by a video camera. The result also reveal that certain characters are generally more reliable and easier to recognize than others, thus the results can be used to intelligently design a human-readable fiducial markers system that avoids confusing characters.

We offered professional development to in-service K-12 teachers. Teachers learned programming, and how to teach programming. During the subsequent academic year, they taught programming in their schools. We interviewed the teachers to better understand their experiences. This poster describes case studies of K-12 teachers as they teach programming for the first time. As this study is qualitative, it does not attempt to measure findings. Rather, in exploring individual teachers' experiences, we hope to benefit both future teachers who will need to teach computing as well as those who will be helping those teachers.

A practical, low-cost system is presented for continuous tracking of animals using depth-enabled multi-object tracking. The system is capable of producing detailed, longterm, continuous 3D movement data that can be used to detect eating/drinking, aggression, and a multitude of other social interactions. Results also demonstrate that physical parameters like weight can be reliably estimated by the system. The combination of movement data and physical parameters make it possible for the system to monitor growth rate, classify aggression, and detect early signs of compromised health allowing for individualized care and management in large group settings.

The ability to solve problems is a critical skill in all undergraduate engineering curricula. Students' capacity for problem solving is complicated by the fact that higher level reasoning skills, including the capacity for abstraction, are not innate in a person until the mid-twenties or later. The results of an exploratory study that looked at students' episodes of reasoning when solving problems in a sophomore level electrical circuits course are presented. Students' problem solving attempts are analyzed using the representation mapping framework developed by Hahn and Chater that is based on store representations of knowledge and how they are applied. This framework distinguishes between similarity and rules-based cognitive processes, and accounts for memory-bank, rules-based, similarity based and prototype types of reasoning. Students were asked to think aloud when solving specific problems selected by the course instructor. The interviews were recorded, transcribed, and analyzed in detail to identify the types of reasoning and the degree of abstraction in the students' problem solving attempts. This study demonstrated that representation mapping is useful framework for studying students' problem solving skills in electrical engineering.

Radio Frequency Identification (RFID) is an information exchange technology based on radio wave communication. It is also a possible solution to indoor localisation. Owing to multipath propagation and anisotropic interference in the indoor environment, theoretical propagation models are generally not sufficient for RFID-based localisation. In fact, the received radio frequency signal distribution may not even be monotonic and this makes range-based localisation algorithms less accurate. On the other hand, range-free localisation algorithms, such as k Nearest-Neighbour (kNN), require reference tags to be spread throughout the whole three-dimensional (3D) space which is frequently not practical. In this work, a hybrid real-time localisation algorithm that combines reference tags with Received Signal Strength Indicator (RSSI) ranging is introduced to improve RFID-based 3D localisation in indoor environments. The localisation algorithm is implemented in MATLAB and is synchronised with radio signal data in real-time. Results show that the proposed hybrid algorithm achieves an average 3D localisation error of 1.08 m which represents a significant improvement over algorithms that use only kNN or RSSI.

A new method is introduced for stereo matching that operates on minimum spanning trees (MSTs) generated from the images. Disparity maps are represented as a collection of hidden states on MSTs, and each MST is modeled as a hidden Markov tree. An efficient recursive message-passing scheme designed to operate on hidden Markov trees, known as the upward-downward algorithm, is used to compute the maximum a posteriori (MAP) disparity estimate at each pixel. The messages processed by the upward-downward algorithm involve two types of probabilities: the probability of a pixel having a particular disparity given a set of per-pixel matching costs, and the probability of a disparity transition between a pair of connected pixels given their similarity. The distributions of these probabilities are modeled from a collection of images with ground truth disparities. Performance evaluation using the Middlebury stereo benchmark version 3 demonstrates that the proposed method ranks second and third in terms of overall accuracy when evaluated on the training and test image sets, respectively.

Free Access Ieee Press Series on Digital and Mobile Communication Book Editor(s):Christian B. Schlegel, Christian B. SchlegelSearch for more papers by this authorLance C. Pérez, Lance C. PérezSearch for more papers by this author First published: 29 August 2015 https://doi.org/10.1002/9781119106319.oth1 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Trellis and Turbo Coding: Iterative and Graph-Based Error Control Coding, Second Edition RelatedInformation