A Novel Multi-View Table Tennis Umpiring Framework

This research investigates the development of a low-cost multi-view umpiring framework, as an alternative to the current expensive systems that are almost exclusively restricted to elite professional sports. Table tennis has been selected as the testbed because, while automating the process is challenging, it has many different complex match elements including the service, return and rallies, which are governed by a strict set of regulations. The focus is mainly on the rally element rather than the whole match. Ball detection and tracking in video frames are undertaken to determine reliably the ball position relative to key reference objects like the table surface and net, and the ball’s flight path is used to determine the rally’s status. While a low-cost option has benefits, it is technically challenging due to the limited number of cameras and generally low video resolution used. This thesis presents a portable multi-view umpiring framework that identifies each state change in a rally. It makes three significant contributions to knowledge: i) a reliable ball detection strategy that accurately detects the location of the ball in low-resolution sequences; ii) a novel framework for ball tracking using a multi-view system, and iii) a new state-machine based evaluation system for analysing table tennis rallies. In a series of ten different test scenarios, the system achieved an average of 94% system detection rate and 100% accurate decisions. A test sequence of duration 1 s can be processed in 8 s, leading to a delay of only 7 s, which is considered acceptable for practical purposes. This solution has the potential to reform the way matches are umpired, providing objectivity in resolving disputed decisions. It affords an economic technology for amateur players, while the multi-view facility is extendible to other relevant ball-based sports. Finally, the ball flight path analysis mechanism can be a valuable training tool for skills development

Tracking a table tennis ball for umpiring purposes using a multi-agent system

Tracking a table tennis ball for umpiring purposes is a challenging task as, in real-match scenarios, the ball travels fast and can become occluded or merged with other background objects. This paper presents the design of a multi-view based tracking system that can overcome the challenges of tracking a ball in real match sequences. The system has been tested on a complete table tennis rally and the results are very promising. The system is able to continuously track the ball with only marginal variations in detection. Furthermore, the initialization of the multi-camera system means it is both a portable and cost-effective solution for umpiring purposes

Tracking a table tennis ball for umpiring purposes

This study investigates tracking a table-tennis ball rapidly from video captured using low-cost equipment for umpiring purposes. A number of highly efficient algorithms have been developed for this purpose. The proposed system was tested using sequences capture from real match scenes. The preliminary results of experiments show that accurate and rapid tracking can be achieved even under challenging conditions, including occlusion and colour merging. This work can contribute to the development of an automatic umpiring system and also has the potential to provide amateur users open access to a detection tool for fast-moving, small, round objects

Incremental learning algorithm based on support vector machine with Mahalanobis distance (ISVMM) for intrusion prevention

In this paper we propose a new classifier called an incremental learning algorithm based on support vector machine with Mahalanobis distance (ISVMM). Prediction of the incoming data type by supervised learning of support vector machine (SVM), reducing the step of calculation and complexity of the algorithm by finding a support set, error set and remaining set, providing of hard and soft decisions, saving the time for repeatedly training the datasets by applying the incremental learning, a new approach for building an ellipsoidal kernel for multidimensional data instead of a sphere kernel by using Mahalanobis distance, and the concept of handling the covariance matrix from dividing by zero are various features of this new algorithm. To evaluate the classification performance of the algorithm, it was applied on intrusion prevention by employing the data from the third international knowledge discovery and data mining tools competition (KDDcup'99). According to the experimental results, ISVMM can predict well on all of the 41 features of incoming datasets without even reducing the enlarged dimensions and it can compete with the similar algorithm which uses a Euclidean measurement at the kernel distance

Highly Cytotoxic Xanthones from Cratoxylum cochinchinense Collected in Myanmar

Eight xanthones and one anthraquinone, together with four common triterpenoids, have been isolated from the barks of Cratoxylum cochinchinense, collected in Myanmar. The structures of the metabolites were elucidated by spectroscopic data analysis and their antiproliferative activities were measured against six human tumor cell lines, by using the MTT assay. Pruniflorone N (1) showed a significant cytotoxicity against all cancer cells with IC50 values in the range 3-9 μM, on average higher than the anticancer drug cisplatin. Instead, compounds 2 and 3 exhibited high antiproliferative activity against some specific cell lines

Swarm Intelligence Based Feature Selection for High Dimensional Classification: A Literature Survey

Feature selection is an important and challenging task in machine learning and data mining techniques to avoid the curse of dimensionality and maximize the classification accuracy. Moreover, feature selection helps to reduce computational complexity of learning algorithm, improve prediction performance, better data understanding and reduce data storage space. Swarm intelligence based feature selection approach enables to find an optimal feature subset from an extremely large dimensionality of features for building the most accurate classifier model. There is still a type of researches that is not done yet in data mining. In this paper, the utilization of swarm intelligence algorithms for feature selection process in high dimensional data focusing on medical data classification is form the subject matter. The results shows that swarm intelligence algorithms reviewed based on state-of-the-art literature have a promising capability that can be applied in feature selections techniques. The significance of this work is to present the comparison and various alternatives of swarm algorithms to be applied in feature selections for high dimensional classification

A Review of Common Medicinal Plants in Chin State, Myanmar:

Promising sources of novel bioactive compounds include plants growing in several third-world countries where the local flora is still largely uninvestigated. A paradigmatic example is represented by medicinal plants growing in Myanmar, especially in Chin State, in northwestern Myanmar. This is one of the least developed areas of the country where the people still use natural remedies derived from a rich biodiversity. This review mainly covers the investigations done on phytochemical constituents and biological activities of 20 medicinal plants, namely Alangium chinense, Anemone obtusiloba, Anneslea fragrans, Antidesma bunius, Croton oblongifolius, Embelia tsjeriam-cottam, Ficus heterophylla, Gaultheria fragrantissima, Hydnocarpus kurzii, Leea macrophylla, Leucas cephalotes, Millingtonia hortensis, Myrica nagi, Olax scandens, Pimpinella heyneana, Pterospermum semisagittatum, Ruellia tuberosa, Smilax zeylanica, Stemona burkillii, and Tadehagi triquetrum, that have long been used in the Chin State for curing various diseases. These plants have been selected on the basis of their medicinal uses not only in Myanmar but also in the related Ayurvedic healing system. Moreover, besides their medicinal importance, most of them grow in the Chin State more abundantly than in other regions of Myanmar. Although the efficacy of some of these plants have been verified scientifically, the chemical constituents and biological activities of most of them still need to be investigated to confirm the claimed therapeutic effects

Comparative Study for Text Document Classification Using Different Machine Learning Algorithms

Classification is a supervised learning method: the goal is finding the labels of the unknown object. In the real world, the tedious amounts of manual works are required to label the unknown documents. The system is initially trained by labeled documents by using one of the supervise machine learning algorithm and then applied trained model to predict the label of the unknown documents.  The framework of text document classification consists of: input text document, pre-processing, feature extraction and classification. The analysis four common classification methods are performed: Naïve Bayes, Decision Tree, Support Vector Machine and K-nearest neighbors for text document classification. The main focus of this paper is to present comparative study of different exiting classification methods for text document classification. The experiment performed different classification methods on the Enron Email Dataset and measure classification accuracy, true positive, true negative, false positive and false negative to compare the performance of different classification methods

A multi-view automatic table tennis umpiring framework

This paper presents a low-cost, portable, multi-view table tennis umpiring framework, as a viable alternative to the current expensive systems which are almost exclusively restricted to elite professional sports. Table tennis has been selected as the sport to evaluate this framework primarily because it comprises many different complex match elements, including the service, return and rally elements, which are governed by a strict set of regulations which need to be umpired. The aim is to develop novel methods to analyse and judge the legality of such key match facets, with ball detection and tracking in video frames being integral to reliably and accurately determining the ball’s position and flight path during rallies. While a low-cost option is attractive and offers several benefits, it is a technically challenging problem due to the small number and generally low-resolution cameras that are used. A novel multi-view camera setup and multi-agent system (MAS) framework is presented, which comprises computationally lightweight agents which detect and track the table tennis ball, create a 3D representation of the flight path of the ball, predict the ball’s trajectory, and identify and analyse key facets in a table tennis rally. The MAS correctly detects all state transitions in seven test table tennis sequences with minimal latency and while the processing rate of a standard computer may be unable to analyse long rallies in real time, the potential of running the MAS on a parallel architecture is a propitious alternative. The MAS is also scalable, enabling additional camera pairs to be deployed to achieve enhanced accuracy and coverage. The framework affords the potential to reform the way matches are umpired, especially for amateur players, providing an economic and objective manner of dispute resolution, while the multi-view facility is extendible to other relevant ball-based sports. The ball flight path analysis mechanism can be exploited as a valuable training tool for skill development

Health systems resilience in fragile and shock-prone settings through the prism of gender equity and justice: implications for research, policy and practice

Fragile and shock-prone settings (FASP) present a critical development challenge, eroding efforts to build healthy, sustainable and equitable societies. Power relations and inequities experienced by people because of social markers, e.g., gender, age, education, ethnicity, and race, intersect leading to poverty and associated health challenges. Concurrent to the growing body of literature exploring the impact of these intersecting axes of inequity in FASP settings, there is a need to identify actions promoting gender, equity, and justice (GEJ). Gender norms that emphasise toxic masculinity, patriarchy, societal control over women and lack of justice are unfortunately common throughout the world and are exacerbated in FASP settings. It is critical that health policies in FASP settings consider GEJ and include strategies that promote progressive changes in power relationships. ReBUILD for Resilience (ReBUILD) focuses on health systems resilience in FASP settings and is underpinned by a conceptual framework that is grounded in a broader view of health systems as complex adaptive systems. The framework identifies links between different capacities and enables identification of feedback loops which can drive or inhibit the emergence and implementation of resilient approaches. We applied the framework to four different country case studies (Lebanon, Myanmar, Nepal and Sierra Leone) to illustrate how it can be inclusive of GEJ concerns, to inform future research and support context responsive recommendations to build equitable and inclusive health systems in FASP settings