Active isolated stretching : an investigation of the mechanical mechanisms

The Active Isolated Stretching (AIS) technique proposes that by contracting a muscle (agonist) the opposite muscle (antagonist) will relax through reciprocal inhibition and lengthen without increasing muscle tension (Mattes, 2000). The clinical effectiveness of AIS has been reported but its mechanism of action has not been investigated at the tissue level. Proposed mechanisms for increased range of motion (ROM) include mechanical or neural changes, or an increased stretch tolerance. The purpose of the study was to investigate changes in mechanical properties, i.e. stiffness, of skeletal muscle in response to acute and long-term AIS stretching for the hamstring muscle group. Recreationally active university-aged students (female n=8, male n=2) classified as having tight hamstrings, by a knee extension test, volunteered for the study. All stretch procedures were performed on the right leg, with the left leg serving as a control. Each subject was assessed twice: at an initial session and after completing a 6-week AIS hamstring stretch training program. For both test sessions active knee extension (ROM) to a position of "light irritation", passive resisted torque and stiffness were determined before and after completion of the AIS technique (2x10 reps). Data were collected using a Biodex System 3 Pro (Biodex Medical Systems, NY, USA) isokinetic dynamometer. Surface electromyography (EMG) was used to monitor vastus lateralis (VL) and hamstring muscle activity during the stretching movements. Between test sessions, 2x10 reps of the AIS bent knee hamstring stretch were performed daily for 6-weeks.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

The Influence of Sex on the Relationship Between Arterial Mechanical Properties and Cardiovagal Baroreflex Sensitivity

Cardiovagal baroreflex sensitivity (cvBRS) demonstrates a strong relationship with arterial mechanical properties. Both cvBRS and arterial mechanics differ by sex such that males demonstrate greater cvBRS, yet lower large artery elasticity than females. Whether the relationship between cvBRS and arterial mechanics is similar in males and females remains unexamined. As a result, it is unclear whether arterial mechanics contribute to sex differences in cvBRS. This study investigated the cross-sectional relationship between cvBRS and arterial mechanical properties of the common carotid, carotid sinus and aortic arch (AA) in 36 (18 females) young, healthy normotensives. The cvBRS-arterial mechanics relationship did not reach statistical significance and did not differ by sex. Both cvBRS and AA distensibility were greater in females than males. Sex differences in cvBRS were eliminated after controlling for AA distensibility. These findings suggest that in this sample, AA elasticity may contribute to the greater cvBRS in females than males.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.