Multi-sensor classification of tennis strokes

In this work, we investigate tennis stroke recognition using a single inertial measuring unit attached to a player’s forearm during a competitive match. This paper evaluates the best approach for stroke detection using either accelerometers, gyroscopes or magnetometers, which are embedded into the inertial measuring unit. This work concludes what is the optimal training data set for stroke classification and proves that classifiers can perform well when tested on players who were not used to train the classifier. This work provides a significant step forward for our overall goal, which is to develop next generation sports coaching tools using both inertial and visual sensors in an instrumented indoor sporting environment

Energy Storage:Maximising Irelands Wind Energy Potential

Ireland plan\u27s to generate up to 40% of its electricity from wind generation by 2020. This thesis outlines the problems that may be faced by the electricity system and illustrates the benefits that large scale energy storage can bring to the electricity system when trying to integrate large amounts of wind energy. Energy storage is currently a topical subject in Ireland as wind penetration increases and problems such as curtailment loom. This thesis outlines the storage capacities required to sufficiently aid the integration of wind energy in Ireland and outlines the value that large scale energy storage can bring to the Irish electricity system. Models of the system load and wind generation profile are devised and wind penetration scenarios representing 13%, 20%, 40% and 60% wind penetration are developed. These wind penetration scenarios are analysed and the curtailment levels associated with them are calculated. Storage is then introduced to the system models and these are analysed. The improvements in system operation are outlined and the reduction in curtailment and required conventional generation are calculated. Popular generation adequacy assessment techniques are investigated and a generation adequacy assessment is carried out on the system models. Finally the value introduced to the system by adding the energy storage system is quantified by estimating the amount of conventional generation that has been offset by its introduction. The analysis shows that energy storage adds little or no value to the Irish electricity system when penetration levels of wind generation are under 20%. At penetration levels of 40% and 60%, energy storage significantly increases the amount of wind energy that is absorbed by the system and reduces the levels of curtailment and required conventional generation

TennisSense: a platform for extracting semantic information from multi-camera tennis data

In this paper, we introduce TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches. Our algorithms for extracting useful metadata from the overhead court camera are described and evaluated. We track the tennis ball using motion images for ball candidate detection and then link ball candidates into locally linear tracks. From these tracks we can infer when serves and rallies take place. Using background subtraction and hysteresis-type blob tracking, we track the tennis players positions. The performance of both modules is evaluated using ground-truthed data. The extracted metadata provides valuable information for indexing and efficient browsing of hours of multi-camera tennis footage and we briefly illustrative how this data is used by our tennis-coach playback interface

Combining inertial and visual sensing for human action recognition in tennis

In this paper, we present a framework for both the automatic extraction of the temporal location of tennis strokes within a match and the subsequent classification of these as being either a serve, forehand or backhand. We employ the use of low-cost visual sensing and low-cost inertial sensing to achieve these aims, whereby a single modality can be used or a fusion of both classification strategies can be adopted if both modalities are available within a given capture scenario. This flexibility allows the framework to be applicable to a variety of user scenarios and hardware infrastructures. Our proposed approach is quantitatively evaluated using data captured from elite tennis players. Results point to the extremely accurate performance of the proposed approach irrespective of input modality configuration

How to access resources and archives focused on Aboriginal Records, Family History and Storylines

This session will feature 3 x 20 min presentations about the key historical archives in the state of WA, followed by 30 mins for questions and discussion. All presenters will describe how the public, and researchers, can gain access to these materials

Establishing a Solution Strategy for Electrical Demand Forecasting in Ireland

Electrical demand is driven by economic and human activity, which has obvious daily, weekly and yearly cycles as well as a long-term trend and special periods such as bank holidays, Christmas etc., all of which are reflected in load data. These characteristics of electrical demand must inevitably be incorporated into any demand-forecasting model. However, with the exception of a few papers, the vast bulk of the literature on electrical demand forecasting is concerned with forecasting techniques. This paper proposes several methods with which to quantify the characteristics of Irish electrical load data prior to modelling

Data Fusion Model for Irish Electricity Load Forecasting

Weather inputs are an important factor in load forecasting. The arrival of a weather front or introduction of weather forecast errors lead to a change in the significance of weather as input. A data fusion of individual models is introduced, which uses a priori information regarding the significance of weather inputs in these situations, to produce a superior forecast to any individual model

Data Fusion Model for Irish Electricity Load Forecasting

Weather inputs are an important factor in load forecasting. The arrival of a weather front or introduction of weather forecast errors lead to a change in the significance of weather as input. A data fusion of individual models is introduced, which uses a priori information regarding the significance of weather inputs in these situations, to produce a superior forecast to any individual model

Describing Function Approximation For Biomedical Engineering Applications

This paper focuses on the determination of suitable approximations for sigmoid-type nonlinear characteristics, which are common to physiological systems, particularly cardiovascular regulatory systems. These sigmoid nonlinearities have been implicated in the development of limit cycle oscillations in blood pressure. Approximations of the sigmoid are required since the describing function is not calculable for the all representations of the sigmoid characteristic. In this paper, we present a new approximation, which gives a better overall approximation of the sigmoid and hence, can assist the use of describing functions in the diagnostic analysis of cardiovascular function

24-Hour Electrical Load Data - A Time Series or a Set of Independent Points?

The paper investigates whether a time series or a set of independent points is a more appropriate description of 24-hour Irish electrical load data. A set of independent points means that the load at each hour of the day is independent from the load at any other hour. The data is first split into 24 series, one for each hour of the day i.e. a 1am 2am 3am series etc. These are called parallel series. The linear cross-correlation's of the parallel series are used to indicate independence. While the loads at 9am and 6pm to 8pm appear independent the remaining loads are highly inter-correlated. This suggests that 24-hour electrical load data has a dual nature. Two techniques are used to test this hypothesis. The first technique models each parallel series using neural networks. This technique is found to be computationally expensive. The second technique uses a hybrid technique called the Multi Time Scale (MTS) technique. This models 24-hour electrical load data as a time series that can be adjusted by 5 parallel forecasts and a daily cumulative model. The results show that the MTS forecasts are superior to the parallel forecasts except for 9am and 6pm to 8pm. A composite model using neural networks for 9am and 6pm to 8pm and the MTS model elsewhere takes advantage of the dual nature of the data reducing error and computational expense