This paper proposes a two dimensional quaternion valued singular spectrum analysis based method for enhancing the hyperspectral image. Here, the enhancement is for performing the object recognition, but neither for improving the visual quality nor suppressing the artifacts. In particular, the two dimensional quaternion valued singular spectrum analysis components are selected in such a way that the ratio of the interclass separation to the intraclass separation of the pixel vectors is maximized. Next, the support vector machine is employed for performing the object recognition. Compared to the conventional two dimensional real valued singular spectrum analysis based method where only the pixels in a color plane is exploited, the two dimensional quaternion valued singular spectrum analysis based method fuses four color planes together for performing the enhancement. Hence, both the spatial information among the pixels in the same color plane and the spectral information among various color planes are exploited. The computer numerical simulation results show that the overall classification accuracy based on our proposed method is higher than the two dimensional real valued singular spectrum analysis based method, the three dimensional singular spectrum analysis based method, the multivariate two dimensional singular spectrum analysis based method, the median filtering based method, the principal component analysis based method, the Tucker decomposition based method and the hybrid spectral convolutional neural network (hybrid SN) based method.
This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the electroencephalograms into various brain wave components. However, nonlinear phase distortions will be introduced by these filters. To address this issue, this paper applies empirical mode decomposition to decompose the electroencephalograms into various intrinsic mode functions and categorize them into four groups. In addition, common features used to analyze electroencephalograms are energy and entropy. However, because there are only two features, the available information is limited. To address this issue, this paper extracts 11 different physical quantities from each group of intrinsic mode functions, and these are employed as the features. Finally, this paper uses the random forest to perform activity recognition. It is worth noting that the conventional approach for performing activity recognition is based on a single type of signal, which limits the recognition performance. In this paper, a multi-modal system based on electroencephalograms, image sequences, and motion signals is used for activity recognition. The numerical simulation results show that the percentage accuracies based on three types of signal are higher than those based on two types of signal or the individual signals. This demonstrates the advantages of using the multi-modal approach for activity recognition. In addition, our proposed empirical mode decomposition-based method outperforms the conventional filtering-based method. This demonstrates the advantages of using the nonlinear and adaptive time frequency approach for activity recognition.
Prehypertension is a new risky disease defined in the seventh report issued by the Joint National Commission. Hence, detecting prehypertension in time plays a very important role in protecting human lives. This study proposes a method for categorising blood pressure values into two classes, namely the class of healthy blood pressure values and the class of prehypertension blood pressure values, as well as estimating the blood pressure values continuously only by employing photoplethysmograms. First, the denoising of photoplethysmograms is performed via a discrete cosine transform approach. Then, the features of the photoplethysmograms in both the time domain and the frequency domain are extracted. Next, the feature vectors are categorised into the two classes of blood pressure values by a multimodel fusion of the classifiers. Here, the support vector machine, the random forest and the K-nearest neighbour classifier are employed for performing the fusion. There are two types of blood pressure values. They are the systolic blood pressure values and the diastolic blood pressure values. For each class and each type of blood pressure values, support vector regression is used to estimate the blood pressure values. Since different classes and different types of blood pressure values are considered separately, the proposed method achieves an accurate estimation. The computed numerical simulation results show that the proposed method based on the multi-model fusion of the classifiers achieves both higher classification accuracy and higher regression accuracy than the individual classification methods.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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