BackgroundAlignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.ResultsA new alignment-free sequence similarity analysis method, called SSAW is proposed. SSAW stands for Sequence Similarity Analysis using the Stationary Discrete Wavelet Transform (SDWT). It extracts k-mers from a sequence, then maps each k-mer to a complex number field. Then, the series of complex numbers formed are transformed into feature vectors using the stationary discrete wavelet transform. After these steps, the original sequence is turned into a feature vector with numeric values, which can then be used for clustering and/or classification.ConclusionsUsing two different types of applications, namely, clustering and classification, we compared SSAW against the the-state-of-the-art alignment free sequence analysis methods. SSAW demonstrates competitive or superior performance in terms of standard indicators, such as accuracy, F-score, precision, and recall. The running time was significantly better in most cases. These make SSAW a suitable method for sequence analysis, especially, given the rapidly increasing volumes of sequence data required by most modern applications.

In order to reduce the hazards of biochemical terrorist attacks to the countries and regions, Bayesian network model was used to identify poison according to the observed preliminary symptoms of the poisoning people. As the collected dataset had the characteristics of high-dimensional and small sample, we proposed a Bayesian network structure learning algorithm based on dataset extension, correction and feature selection, which could learn an effective Bayesian network structure from small sample of high-dimensional datasets. And it was verified to be correct and valid on the UCI datasets and biochemical poison dataset.

Power spectrum analysis can make EEG which the amplitude changes with time transformation for spectrum chart which the EEG power changes with time. From the spectrum chart the distribution of the α-wave, the θ-wave, the δ-wave and the β-wave, and the change of rhythm can be observed directly. On this study, the 1/f wave had been applied on the treatment of patients with mental disease , the analysis and the research of EEG before and after the 1/f wave electrical stimulation. The results show that, the 1/f wave electric stimulation has a significant effect for mental disease patients which were caused by structural damage.

In the acquisition process of ECG, noise, which mainly consists of power line interference baseline drift and the EMG interference, often exists due to the instrument, the human body and other aspects. This noise mixed with the ECG, will causes ECG distortion, which makes the whole ECG waveform blurred, and impacts the subsequent signal processing and analysis. In this paper, Coif4 wavelet is used to make the ECG decomposed by 8 scale; at the same time, the wavelet decomposition and reconstruction method is used to remove baseline drift, and then the improved wavelet threshold method is used to remove power line interference and the EMG interference waveform to obtain accurate geocentric, providing a more accurate basis for the medical diagnosis.