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Bird classification using audio data can be beneficial in assisting ornithologists, bird watchers and environmentalists. However, due to the complex environment in the jungles, it is difficult to identify birds by visual inspection. Hence, identification via acoustical means may be a better option in such an environment. This study aims to classify endemic Bornean birds using their sounds. Thirty-five (35) acoustic features have been extracted from the pre-recorded soundtracks of birds. In this paper, a novel approach for selecting an optimum number of features using Linear Discriminant Analysis (LDA) has been proposed to give better classification accuracy. It is found that using a Nearest Centroid (NC) technique with LDA produces the optimum classification results of bird sounds at 96.7% accuracy with reduced computational power. The low computational complexity is an added advantage for handheld portable devices with minimal computing power, which can be used in birdwatching expeditions. Comparison results have been provided with and without LDA using NC and Artificial Neural Network (ANN) classifiers. It has been demonstrated that both classifiers with LDA outperform those without LDA. Maximum accuracies for both NC and ANN with LDA, with NC and the ANN classifiers requiring 7 and 10 LDAs to achieve the optimum accuracy, respectively, are 96.7%. However, ANN classifier with LDA is more computationally complex. Hence, this is significant as the simpler NC classifier with LDA, which does not require expensive processing power, may be used on the portable and affordable device for bird classification purposes. [ABSTRACT FROM AUTHOR]

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients. Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rectification. Then, feature extraction was conducted through a multi-level decomposition via a wavelet transform. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection. The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection. [ABSTRACT FROM AUTHOR]

Electric load identification and classification for smart grid environment can improve the power service for both consumers and producers. The main concept of electric load identification and classification is to disaggregate various loads and categorize them. In this paper, a new practical method for electric load identification and classification is presented. The method is based on using a power monitor to analyze a real measured current waveform of a grid-connected device. A set number of features is extracted using the currents' physical components-based power theory decomposition. Using currents' physical components ensures a constant number of features, which maintains the signal's characteristics regardless of the harmonic content. These features are used to train a supervised classifier based on two techniques: artificial neural network and nearest neighbor search. The theory is outlined, and experimental results are shown. This paper demonstrates high accuracy performance in identifying an electric load from a designated database. Furthermore, the results show a definite classification of an untrained operation state of a device to the closest trained operation state, for example, the excitation angle of a dimmer. In a comparative study, the method is shown to outperform other state-of-the-art techniques, which are based on harmonic components. [ABSTRACT FROM AUTHOR]