Description:
Objective: To describe the physiological responses to tournament tennis in relation to prevailing environmental conditions, match notation, and skills that underpin performance. Design: 14 male professional tennis players (mean (SD) age, 21.4 (2.6) years; height, 183.0 (6.9) cm; body mass, 79.2 (6.4) kg) were studied while contesting international tennis tournaments. Environmental conditions, match notation, physiological (core temperature, hydration status, heart rate, blood variables), and performance indices (serve kinematics, serve velocity, error rates) were recorded. Results: Hard and clay court tournaments elicited similar peak core temperature (38.9 (0.3) v 38.5 (0.6)°C) and average heart rate (152 (15) v 146 (19) beats/min) but different body mass deficit (1.05 (0.49) v 0.32 (0.56)%, p<0.05). Average pre-match urine specific gravity was 1.022 (0.004). Time between points was longer during hard court matches (25.1 (4.3) v 17.2 (3.3) s, p<0.05). Qualitative analysis of first and second serves revealed inverse relations between the position of the tossing arm at ball release and the position of the ball toss and progressive match time (respectively, r= -0.74 and r= -0.73, p<0.05) and incurred body mass deficit (r=0.73 and r=0.73, p<0.05). Conclusions: Participants began matches in a poor state of hydration, and experienced moderate thermoregulatory strain and dehydration during competition. These adverse physiological conditions may compromise performance and influence notational analyses.
Description:
In the last few years, there have been many works in the area of hybrid neural learning algorithms combining a global and local based method for training artificial neural networks. In this paper, we discuss various connection strategies that can be applied to a special kind of a hybrid neural learning algorithm group, one that combines a genetic algorithm-based method with various least square-based methods like QR factorization. The relative advantages and disadvantages of the different connection types are studied to find a suitable connection topology for combining the two different learning methods. The methodology also finds the optimum number of hidden neurons using a hierarchical combination methodology structure for weights and architecture. We have tested our proposed approach on XOR, 10 bit odd parity, and some other real-world benchmark data sets, such as the hand-writing character dataset from CEDAR, Breast cancer, and Heart Disease from the UCI machine learning repository.
Description:
In a transform domain distributed video coding scheme, the correlation between the current encoding unit, e.g. block and slice, and the corresponding side-information is modeled using a virtual channel. This correlation model is then used for rate allocation, quantization, and Wyner-Ziv coding. Since the encoder can only have an estimate of the correlation instead of the exact knowledge of the side-information, the decoder will fail to recover the quantized transformed coeffi- cients with a nonzero probability. In this paper, we propose to integrate a scheme at the decoder to recover the undecoded coefficients using the spatial smoothness property of individual video frames. Simulation results demonstrated that, at different decoding failure probabilities, a transformed coeffi- cient recovery scheme can significantly improve the quality of videos in terms of both PSNR and SSIM.
Description:
In a transform domain distributed video coding scheme, the correlation between the current encoding unit, e.g. block and slice, and the corresponding side-information is modeled using a virtual channel. This correlation model is then used for rate allocation, quantization, and Wyner-Ziv coding. Since the encoder can only have an estimate of the correlation instead of the exact knowledge of the side-information, the decoder will fail to recover the quantized transformed coeffi- cients with a nonzero probability. In this paper, we propose to integrate a scheme at the decoder to recover the undecoded coefficients using the spatial smoothness property of individual video frames. Simulation results demonstrated that, at different decoding failure probabilities, a transformed coeffi- cient recovery scheme can significantly improve the quality of videos in terms of both PSNR and SSIM
Description:
The task of classifying the genre of polyphonic music signals is traditionally done using only low level features of the signal. In this paper high level features have been applied to improve the task of music genre classification. The use of statistical chord features and chord progression information in conjunction with low level features are proposed in this paper. The chord progression information is manifested in genre probability descriptors calculated using a pattern matching algorithm. Our proposed method provides an improvement of 12.4% in the classification results over a commonly compared technique.
Description:
Most existing data models and transmission policies for sensor network assume uniform periodic data generation and unconstrained transmission range for sensor nodes, both assumptions being too restrictive to capture and analyze real- world operation for practical deployment. In this paper, we consider these two practical aspects and present a new transmission policy formulated after (i) stochastic data model where a set of events occur with certain probabilities and rate of data generation by a sensor varies based on sensed event and (ii) limited transmission range of sensors. Assuming co-centric rings around the base station, located at a generic location (internal or external to the network area), ring thickness and hope sizes over lifetime is determined by formatting an optimization problem where nodes in each ring may transmit data at different hop sizes at a given instant and also vary hop sizes over lifetime. Performance analysis shows significant improvement in network lifetime and better uniformity in energy usage distribution in the proposed policy irrespective of network size and maximum allowable transmission range of nodes.
Description:
Existing contention based nonpersistent medium access control protocols in Wireless Sensor Network (WSN) do not perform well in high contention. Their performances are affected by occurrence of collision due to uniform probability distribution in choosing Time Slot (TS) during backoff period. To address this issue nonuniform probability distribution was proposed. However success rate still drops for higher number of contenders. In this paper we propose CSMA/s (Collision Sense Multiple Access /per Slot based), a new approach in nonuniform contender-aware probability distribution for choosing TS in the backoff period. Rather than taking a premeditated fixed value for contender population size, our proposed scheme embeds neighborhood population size into its bedrock to automatically converge to the actual number of contenders which enables the contender to adaptively choose TS in the backoff period for reducing collision. This method produces better success rate and lower latency for even very high number of contenders.
Description:
Higher order thinking skills have been identified as desirable although elusive outcomes of many educational curricula. Through a qualitative case study, the alignment between the three levels of the curriculum: intended, implemented, and attained, was examined to determine the tensions and possibilities in the development of mathematical and thinking skills in senior secondary students in Gippsland, a large regional area of Victoria, Australia. Probability was the mathematical content area of focus. Data from document analysis of the intended curriculum, textbooks as the implemented curriculum, and assessments as the attained curriculum, was combined with qualitative data from semi-structured interviews with twenty students and fourteen senior secondary mathematics teachers. These diverse data sources scaffolded each other to identify tensions and possibilities influencing development of student thinking in senior secondary mathematics. This research demonstrated that the flow of content via the intended-implemented-attained curriculum was not adequate to describe all the influences on student learning. The lens of Activity Theory (Engeström, 2001) came closer to capturing the related complexities whereby the textbooks, calculators, bound reference books and assessments, combined with the balance of agency demonstrated by the teachers and students, were found to both support and cause tensions within the activity system. Probability was found to be a valuable topic to study in relation to the development of thinking skills due to its relevance in decision making, how it linked many areas of mathematics and the uniqueness of the classic, experimental, and subjective views of probability. This study is significant in the contribution it makes to understanding the tensions and possibilities associated with the development of mathematical thinking relating to probability through the lens of Activity Theory. While the intended curriculum encouraged a range of thinking skills, this intended curriculum could be implemented in a way that promotes memorisation rather than the intended higher order thinking. This study concludes with recommendations for the curriculum designers, textbook publishers, teachers, and students which may support the development of mathematical and thinking skills.
Description:
Higher order thinking skills have been identified as desirable although elusive outcomes of many educational curricula. Through a qualitative case study, the alignment between the three levels of the curriculum: intended, implemented, and attained, was examined to determine the tensions and possibilities in the development of mathematical and thinking skills in senior secondary students in Gippsland, a large regional area of Victoria, Australia. Probability was the mathematical content area of focus. Data from document analysis of the intended curriculum, textbooks as the implemented curriculum, and assessments as the attained curriculum, was combined with qualitative data from semi-structured interviews with twenty students and fourteen senior secondary mathematics teachers. These diverse data sources scaffolded each other to identify tensions and possibilities influencing development of student thinking in senior secondary mathematics. This research demonstrated that the flow of content via the intended-implemented-attained curriculum was not adequate to describe all the influences on student learning. The lens of Activity Theory (Engeström, 2001) came closer to capturing the related complexities whereby the textbooks, calculators, bound reference books and assessments, combined with the balance of agency demonstrated by the teachers and students, were found to both support and cause tensions within the activity system. Probability was found to be a valuable topic to study in relation to the development of thinking skills due to its relevance in decision making, how it linked many areas of mathematics and the uniqueness of the classic, experimental, and subjective views of probability. This study is significant in the contribution it makes to understanding the tensions and possibilities associated with the development of mathematical thinking relating to probability through the lens of Activity Theory. While the intended curriculum encouraged a range of thinking skills, this intended curriculum could be implemented in a way that promotes memorisation rather than the intended higher order thinking. This study concludes with recommendations for the curriculum designers, textbook publishers, teachers, and students which may support the development of mathematical and thinking skills.
Description:
This study presents a reliability-constrained adaptive-robust multi-resolution model for generation maintenance scheduling (GMS) problem considering the uncertainty sources of electricity demand, wind power generation, and equipment unavailabilities. In the proposed tri-level adaptive-robust model, a polyhedral uncertainty set is used to model the electricity demand and wind power generation fluctuations. In addition, equipment unavailabilities as discrete uncertainty sources are modelled in the reliability sub-problem where the expected energy not supplied is determined as a reliability criterion. Accordingly, the proposed model obtains a robust maintenance schedule for generating units immunised against the worst realisation of electricity demand and wind power generation while satisfying the reliability constraint considering equipment unavailabilities. Moreover, maintenance and operation periods are specifically modelled using different resolutions in the proposed multi-resolution GMS approach. To solve the proposed reliability-constrained adaptive-robust multi-resolution model, a new solution approach including Benders cut, reliability cut, and block coordinate descent method is presented. Numerical results on two test systems show the effectiveness of both the proposed GMS model and the proposed solution approach.