74 resultados para WAVELET TRANSFORM
Resumo:
How to learn an over complete dictionary for sparse representations of image is an important topic in machine learning, sparse coding, blind source separation, etc. The so-called K-singular value decomposition (K-SVD) method [3] is powerful for this purpose, however, it is too time-consuming to apply. Recently, an adaptive orthogonal sparsifying transform (AOST) method has been developed to learn the dictionary that is faster. However, the corresponding coefficient matrix may not be as sparse as that of K-SVD. For solving this problem, in this paper, a non-orthogonal iterative match method is proposed to learn the dictionary. By using the approach of sequentially extracting columns of the stacked image blocks, the non-orthogonal atoms of the dictionary are learned adaptively, and the resultant coefficient matrix is sparser. Experiment results show that the proposed method can yield effective dictionaries and the resulting image representation is sparser than AOST.
Resumo:
Background
Methods
Transform-Us! is a clustered randomised school-based intervention with four groups: sedentary behaviour intervention (SB-I), PA intervention (PA-I), combined PA+SB-I and control group. All children in grade 3 from 20 participating primary schools in Melbourne, Australia were eligible to complete annual evaluation assessments. The outcomes were the proportion of time spent in moderate-to-vigorous PA (MVPA) and light PA (LPA) during recess and lunchtime assessed by accelerometers. Potential mediators included: perceived social support from teachers; perceived availability of line markings; perceived accessibility of sports equipment; and perceived school play environment. Generalised linear models were used and mediation effects were estimated by product-of-coefficients (a·b) approach.
Results
268 children (8.2 years, 57% girls at baseline) provided complete data at both time points. A significant intervention effect on MVPA during recess in the SB-I and PA-I groups compared with the control group (proportional difference in MVPA time; 38% (95% CI 21% to 57%) and 40% (95% CI 20% to 62%), respectively) was found. The perceived school play environment was significantly positively associated with MVPA at recess among girls. An increase in perceived social support from teachers suppressed the PA+SB-I effect on light PA during recess (a·b= −0.03, 95% CI −0.06 to −0.00). No significant mediating effects on PA during recess and lunchtime were observed.
Conclusions
A positive perception of the school play environment was associated with higher MVPA during recess among girls. Future studies should conduct mediation analyses to explore underlying mechanisms of PA interventions.
Resumo:
The noninvasive brain imaging modalities have provided us an extraordinary means for monitoring the working brain. Among these modalities, Electroencephalography (EEG) is the most widely used technique for measuring the brain signals under different tasks, due to its mobility, low cost, and high temporal resolution. In this paper we investigate the use of EEG signals in brain-computer interface (BCI) systems.
Resumo:
After studying several reduction algorithms that can be found in the literature, we notice that there is not an axiomatic definition of this concept. In this work we propose the definition of weak reduction operators and we propose the properties of the original image that reduced images must keep. From this definition, we study whether two methods of image reduction, undersampling and fuzzy transform, satisfy the conditions of weak reduction operators.
Resumo:
The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.
Resumo:
This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.
