Path Planning for Two-Link Navigation in an Unknown Environment using webcam

In this article, we used image processing by a webcam connected on top of the arm robot. The robot navigation is in an unknown environment. Then start point and target point were determined for the robot, so the robot needs to have a program for path planning using Voronoi diagrams to find the path. After the possible path for moving the robot was found, the route information obtained was sent to the arm robot. The arm robot moves in the workspace and any time new information was processed via the webcam. The program was written using MATLAB software which at controls the robot’s movement the unknown environment

Umělé oplodnění a problematika "kdo je matkou" z pohledu coučasného ší'itského fiqhu

Studie se zabývá důležitou otázkou v rámci islámské bioetiky: kdo je matka, když je v procesu umělého oplodnění zahrnuto více žen? Ší'itští islámští právníci rozvinuly čtyři teorie o tom "kdo je matka". Pro plné pochopení jejich právních postojů a řešení je nutné sledovat celý právní proces stojící za finálním rozhodnutím. Cílem studie je vzhled do procesu idžtihádu, doplněného všemi argumenty, následnou kritikou a proti-argumenty. Určení původu (nasab) není pouze teoretický právní problém, má důležité právní konsekvence v oblasti dědictvní (irth), péče o dítě (Hadanah) a výživného (nafaqa).This study deals with an important question within the field of Islamic bioethics: who is the mother if more than one woman is involved in the process of artificial fertilization? The Shi‘i fuqahā ͗ have developed four theories about “being a mother”. In order to gain a genuine understanding of their legal solution, it is essential to perceive the whole legal process behind it. The objective behind this study is to present a unique insight into the whole ijtihād process, accompanied by supporting argumentation, subsequent criticism and counter-argumentation. Lineage determination (nasab) not only presents a theoretical legal problem; it is also important due to its legal consequences in regard to inheritance (irth), custody (ḥaḍānah), and maintenance (nafaqah)

Biometric Identification of Cattle via Muzzle Print Patterns and Deep Learning in a Few-Shot Learning Context

The dataset consists of 300 images of cattle faces. Each folder in the dataset represents individual cattle and the folder name correlates with the Cattle ID document for identification purposes

Biometric Identification of Cattle via Muzzle Print Patterns and Deep Learning in a Few-Shot Learning Context

Over the past decade, increasing global demand for livestock products has encouraged farmers to raise more stock. Tracking and monitoring the welfare of large numbers of livestock is, however, not an easy task. Poor animal welfare impacts greatly on consumer preference and negatively impacts productivity. Accurate recording of herd production depends on the identification of the animals. Individual animal identification allows producers to record and manage important animal information. A successful identification system should be able to identify an animal accurately and quickly. A wide range of traditional approaches including ear tagging, ear tattooing, hot ironing, freeze branding, ID collars, microchipping and visual markers such as paint have been used to track and identify individual animals within herds, but most of these approaches are invasive with the potential to cause pain and morbidity to stock. The current cattle identification approaches have significant limitations, however, recent innovations in human biometrics research such as face recognition systems offer promising alternative options. Biometric identification offers less invasive monitoring with corresponding benefits to livestock welfare. In this thesis, a general framework is proposed that is focused on using cattle muzzle print patterns as a biometric. Muzzle print patterns were selected based on prior research indicating that they are a highly distinctive biometric which is similar to a fingerprint. A full biometric identification system is developed spanning the automated detection and extraction of the muzzle region through to convolutional neural network (CNN) based classification of individual identity. Application specific challenges are also addressed, notably: (i) obtaining the large and representative cattle biometric identification dataset, (ii) developing and evaluating few-shot learning and metric matching approaches to minimise the number of training images required per individual and (iii) exploring a model updating approach to ensure that the biometric recognition model is robust as herd composition changes due to addition and removal of stock. Three different CNN network architectures (ResNet-50, VGG16 and MobileNetv2) are assessed along with two model training strategies (transfer learning and fine-tuning) and three metric matching assessments (Euclidean, Cosine, BrayCurtis). Model performance was evaluated with a particular emphasis on both performance (accuracy) and suitability for operational applications (smartphone or smart camera based biometric identification). This thesis focuses on three key studies and the collection of a novel dataset to support cattle biometric research. Dataset: There is a lack of publicly available datasets involving cattle muzzle biometrics. Furthermore, those studies conducted in the past were of limited scope and not reflective of many livestock commercial production settings. Specifically, both the diversity of breeds and the numbers of individual animals studied was limited. This thesis collects and publicly releases a large cattle muzzle biometrics dataset obtained under a controlled protocol from a commercial scale facility. The size of the dataset permits both investigations into biometric recognition algorithms and ensures the evaluations are of a sufficiently sized herd (300 individuals) to be relevant in livestock production environments. Study 1: Automated Cattle Biometric Identification System This study is composed of two parts: (i) muzzle detection and extraction and (ii) CNNbased individual cattle identification using muzzle patterns. Both parts comprise an integrated workflow within an automated cattle biometric identification system. The primary purpose of the muzzle detector is to detect the muzzle region-of-interest from frontal face images of cattle. Once the muzzle region is detected its corresponding bounding box coordinates are used to extract the sub-region of the image corresponding to the cattle’s muzzle. A muzzle detector was developed and applied using a YOLOv3 model with three different network resolutions. Results indicated that the higher resolution (1024x1024) network provides better precision detection and achieved 99.13% accuracy on a test dataset. Post muzzle detection a CNN classifier is applied to the muzzle extracts to classify the individual identity of an animal. A ResNet-50 network architecture utilising a transfer learning with fine-tuning strategy was employed to achieve a high classification accuracy (99.1%) on a test data set. Achieving such high muzzle detection and classification accuracies on a large and diverse test set of animals signifies that an automated cattle biometric identification system is feasible and fast becoming a reality. Study 2: Deep Feature Extraction, Metric Distances and Few-shot Learning. Study 1 indicated that a highly accurate biometric identification algorithm was feasible for small-medium scale cattle herd sizes. A major barrier, however, to the implementation of biometric identification systems in livestock production settings is the intensive data requirements for accurate model development. Few-shot learning (≤ 5 images per individual animal) was investigated as an option to reduce the intensive data requirements. Three CNN network architectures were examined (ResNet-50, VGG16, MobileNetv2) Another major challenge when implementing CNN models is that model memory requirements and computational resources required for model training grow greatly as the number of classes increase. This issue poses a problem for the future practical implementation of livestock biometrics on herds of hundreds or potentially thousands of animals. Solutions to these barriers are explored using deep feature learning (via fine-tuned CNN models to extract image features and removal of the CNN classification layer) and distance metric matching (Euclidean, Cosine, and BrayCurtis distances) comparing the similarities between query image CNN features and database image CNN features. The results obtained demonstrate that this procedure can successfully train a CNN muzzle biometric recognition model with high accuracy for both single (ResNet50 1-shot Cosine distance 95.73% Accuracy) and few training images per individual (ResNet50 5-shot Bray-Curtis distance 98.57% Accuracy). The ResNet50 architecture was found to provide the highest overall accuracy (98.57%) but required considerable computational resources and is best suited for cloud server applications but the MobileNetv2 architecture was also found to produce highly competitive and accurate results (97.86%), which is most suitable for smartphone or smart camera (single-board computer options). Study 3: Model Updating and avoiding Catastrophic Forgetting. Another challenge to the implementation of a livestock biometric identification system is the fact that herd structure is dynamic; individual animals enter and leave the herd on a routine basis. The set of individuals used to develop the training model is unlikely to correspond to the herd composition at future dates, so the biometric model has a temporal constraint before it is outdated and unable to identify all of the individual animals in the herd. Excellent results were obtained (ResNet50 3-shot Cosine 95.98% accuracy) demonstrating that the deep feature learning and metric matching strategy is sufficiently robust to the addition of new individuals to the herd. Importantly, the proposed approach can maintain model efficiency and reduce training cost and dependency on previous data whilst also learning new classes. Overall, this study reveals that computer vision can prove highly useful in terms of non-invasive animal identification, with excellent potential to improve livestock welfare. The results are anticipated to contribute to building a practical automatic biometric-based cattle identification system. The deep feature extraction coupled with metric matching strategy was demonstrated to produce excellent results in a manner suitable for this livestock biometric identification application. Finally, this study provides recommendations for future studies, concerning other data types, regions of interest, and classification strategies

Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle

Livestock welfare and management could be greatly enhanced by the replacement of branding or ear tagging with less invasive visual biometric identification methods. Biometric identification of cattle from muzzle patterns has previously indicated promising results. Significant barriers exist in the translation of these initial findings into a practical precision livestock monitoring system, which can be deployed at scale for large herds. The objective of this study was to investigate and address key limitations to the autonomous biometric identification of cattle. The contributions of this work are fourfold: (1) provision of a large publicly-available dataset of cattle face images (300 individual cattle) to facilitate further research in this field, (2) development of a two-stage YOLOv3-ResNet50 algorithm that first detects and extracts the cattle muzzle region in images and then applies deep transfer learning for biometric identification, (3) evaluation of model performance across a range of cattle breeds, and (4) utilizing few-shot learning (five images per individual) to greatly reduce both the data collection requirements and duration of model training. Results indicated excellent model performance. Muzzle detection accuracy was 99.13% (1024 × 1024 image resolution) and biometric identification achieved 99.11% testing accuracy. Overall, the two-stage YOLOv3-ResNet50 algorithm proposed has substantial potential to form the foundation of a highly accurate automated cattle biometric identification system, which is applicable in livestock farming systems. The obtained results indicate that utilizing livestock biometric monitoring in an advanced manner for resource management at multiple scales of production is possible for future agriculture decision support systems, including providing useful information to forecast acceptable stocking rates of pastures

Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle

Livestock welfare and management could be greatly enhanced by the replacement of branding or ear tagging with less invasive visual biometric identification methods. Biometric identification of cattle from muzzle patterns has previously indicated promising results. Significant barriers exist in the translation of these initial findings into a practical precision livestock monitoring system, which can be deployed at scale for large herds. The objective of this study was to investigate and address key limitations to the autonomous biometric identification of cattle. The contributions of this work are fourfold: (1) provision of a large publicly-available dataset of cattle face images (300 individual cattle) to facilitate further research in this field, (2) development of a two-stage YOLOv3-ResNet50 algorithm that first detects and extracts the cattle muzzle region in images and then applies deep transfer learning for biometric identification, (3) evaluation of model performance across a range of cattle breeds, and (4) utilizing few-shot learning (five images per individual) to greatly reduce both the data collection requirements and duration of model training. Results indicated excellent model performance. Muzzle detection accuracy was 99.13% (1024 × 1024 image resolution) and biometric identification achieved 99.11% testing accuracy. Overall, the two-stage YOLOv3-ResNet50 algorithm proposed has substantial potential to form the foundation of a highly accurate automated cattle biometric identification system, which is applicable in livestock farming systems. The obtained results indicate that utilizing livestock biometric monitoring in an advanced manner for resource management at multiple scales of production is possible for future agriculture decision support systems, including providing useful information to forecast acceptable stocking rates of pastures