From Theory to Practice in Programming for Student-Athletes

This paper contends that program development for student-athletes must begin with a theoretical base if the program is to meet the psychological needs of the student-athletes and earn the respect of administrators and academicians. Theory combined with a program implementation model facilitates both the creation and evaluation of a program. A blueprint for one program developed via this method is provided. The article reviews some historical background and how athletic counselors should be more than eligibility counselors. It discusses the foundations of Purdue's program at its infancy

Multi-Modal Biometrics: Applications, Strategies and Operations

The need for adequate attention to security of lives and properties cannot be over-emphasised. Existing approaches to security management by various agencies and sectors have focused on the use of possession (card, token) and knowledge (password, username)-based strategies which are susceptible to forgetfulness, damage, loss, theft, forgery and other activities of fraudsters. The surest and most appropriate strategy for handling these challenges is the use of naturally endowed biometrics, which are the human physiological and behavioural characteristics. This paper presents an overview of the use of biometrics for human verification and identification. The applications, methodologies, operations, integration, fusion and strategies for multi-modal biometric systems that give more secured and reliable human identity management is also presented

Audio-Visual Speech Recognition using Red Exclusion an Neural Networks

PO BOX Q534,QVB POST OFFICE, SYDNEY, AUSTRALIA, 123

Automatic identification of epileptic and background EEG signals using frequency domain parameters

The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity. We compared different model based power spectral density estimation methods and different classification methods. Specifically, we used the autoregressive moving average as well as from Yule-Walker and Burg's methods, to extract the power density spectrum from representative signal samples. Local maxima and minima were detected from these spectra. In this paper, the locations of these extrema are used as input to different classifiers. The three classifiers we used were: Gaussian mixture model, artificial neural network, and support vector machine. The classification results are documented with confusion matrices and compared with receiver operating characteristic curves. We found that Burg's method for spectrum estimation together with a support vector machine classifier yields the best classification results. This combination reaches a classification rate of 93.33%, the sensitivity is 98.33% and the specificy is 96.67%

Performance Exploration of Multiple Classifiers with Grid Search Hyperparameter Tuning for Detecting Epileptic Seizures from EEG Signals

This study evaluates the performance of two-level classifications using dimensionality reduction methods to determine the risk level of epilepsy from EEG dataset. To diminish the complexity of EEG data, dimensionality reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA), and Principal Component Analysis (PCA) are utilized. The risk level of epilepsy classification from EEG dataset would then be carried out using three classifiers: Hidden Markov Model (HMM), Naïve Bayesian Classifier (NBC) and Gaussian Mixture Model (GMM). The Grid Search (GS) process is employed to tune the hyperparameters of GMM and NBC classifiers. This study analyzed twenty patients’ datasets. Performance evaluation of classifiers with and without GS hyperparameter tuning is examined, including performance index, sensitivity, specificity, and accuracy. The GMM classifier with the GS hyper-tuning approach for SVD dimensionality reduction technique achieved a higher accuracy of 98.18% than its counterpart classifiers

Designing Aedes mosquito traps: the evolution of the male Aedes sound trap by iterative evaluation insects

Effective surveillance of Aedes aegypti (Linnaeus, Diptera: Culicidae) is critical to monitoring the impact of vector control measures when mitigating disease transmission by this species. There are benefits to deploying male-specific traps, particularly when a high level of catch-specificity is desired. Here, the rationale behind the developmental process of an entirely new trap which uses a sound lure to capture male Ae. aegypti, the male Aedes sound trap (MAST), is presented as a target product profile with findings from developmental trials of key trap components and performance. Trial results suggest that the presence of a black base associated with the trap influenced male catches as did variations in size of this base, to a degree. Trap entrance shape didn’t influence catch rates, but entrance size did. No significant differences in catch rates were found when sound lures were set to intermittent or continuous playbacks, at volumes between 63–74 dB or frequencies of 450 Hz compared to 500 Hz. Additionally, adult males aged 3 days post-eclosion, were less responsive to sound lures set to 500 Hz than those 4 or 6 days old. Lastly, almost no males were caught when the MAST directly faced continual winds of 1.5 ms−1, but males were captured at low rates during intermittent winds, or if the trap faced away from the wind. The developmental process to optimising this trap is applicable to the development of alternate mosquito traps beyond Aedes sound traps and provides useful information towards the improved surveillance of these disease vectors