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This paper presents a discrimination method of Sleep Apnea Syndrome (SAS) and early detection of potential Sleep Apnea Syndrome (Pre-SAS) using sleep breath sound, by which each subject can take the required data for the diagnosis at home only with a voice recorder. Sleep breath sound of around two hours is analyzed statistically for detecting SAS patients. Long silence sections in the sleep breath sound and the biggest sound pressure typically occurred after them are compared for distinguishing Pre-SAS, SAS patients and non-patients. The k-means method is applied for classifying the above sound data. The proposed method drastically reduces the time and effort required for SAS diagnosis compared to the current medical approaches. Experimental results show the effectiveness of the proposed method, by which 100 % accuracy of discrimination is achieved.

In this study, we proposed a sound localization method by using the binaural model for a real environment, in which the reflected sound and background noise is present. In order to estimate of horizontal angle, the reduction method of the initial reflected sound that combining the modified Thomposon-τ method and the modified sub-band peak hold processing, was proposed and its effectiveness has been performed. In addition, to create the ESR database of vertical angle, the modified Thompson-τ method is used for elimination of the abnormal value. In the estimation of the vertical angle, a new method that is called wins calculation method is proposed and it is strong in abrupt noise, without using the traditional cross-correlation method. The experiments were carried by using /a/ sound in real environment with 45[dB] background noise and 300[ms] reverberation time. As the results obtained that the estimate correct rate of the horizontal angle is 94.6%, the correct rate of the vertical angle is next 98.6%, and the validity of the proposed method was confirmed.

In this paper, we propose the novel directional Wavelet Transform based on the Complex Discrete Wavelet Transform(CDWT). The CDWT is one of the Wavelet Transform and widely used as space-frequency analysis method for the signal and the image. The CDWT decomposes the image into directional components from the frequency analysis. And then, we can get the border, discontinuities and edges in the image. This property is called Directional Selection and this property is expected to give us shape information and geometric features. But, previous Directional Selection only offers the six directional components and features. Thus we firstly design the directional filter that can extract many directional feature rather than previous one. Secondly we propose the novel directional Wavelet Transform combining with previous CDWT and designed directional filters. Finally, we apply the proposed method into medical image processing.

In this study, in order to verify the effectiveness of the variable-band-filters discrete wavelet transform (VBF-DWT) and construction method of the variable band filter (VBF), a fetal ECG extraction has been carried out and the main results obtained are shown as follows. The approach to configuration variable band filter (VBF) by selecting the frequency band only fetal ECG component presented is effective to configure the optimal base sensible signal. The extraction of the fetal electrocardiogram is successful by applying soft-thresholding to VBF-DWT, which uses the constructed VBF. The information extropy is selected as an evaluation index, and two kinds of ECG signal are used to evaluate the relative performance of the wavelet packet transform (WPT) and the proposed VBF-DWT. The first kind of ECG signal is synthesized using a maternal ECG, fetal ECG, and white noise; the second kind is real measured mixed noisy maternal-fetal ECG obtained using ICA. The results show that the VBF-DWT basis (designed using VBF) outperforms the WPT basis that was elected by the basis algorithm (BBA).

Spectrometric oil analysis is of great importance for wear condition monitoring of gearbox. In this context, the contents of main elements compositions in the bench test of heavy vehicle gearbox are obtained by atomic emission spectrometric oil analysis first. Then correlation analysis of the test data and wearing mechanism analysis are carried out to get the metal element which could be used to describe the wearing and failure of the gearbox. The spectrometric data after filling/changing oil are corrected, and the laws of the contents of main elements compositions during tests are expressed as linear functions. After that, the reliability assessment is executed with considering the degradation law and discreteness of test data, in which the mean and standard deviation of normal distribution of spectrometric oil data at each time point are adopted. Finally, the influences of the threshold are discussed. It has been proved that the contents of metal element Cu, which is got by spectrometric oil analysis of different samples, could be used to assess the reliability of heavy vehicle gearbox. The reason is that the metal element Cu is closely related to the general wear state of gearbox, and is easy to be measured. When the threshold of Cu content is treated as a constant, bigger threshold means higher reliability at the same time, and the mean value of threshold has significant impact on the reliability assessment results as R > 0.9. When the threshold is treated as a random variable, bigger dispersion of threshold means smaller slope of reliability against time, and also means lower reliability of gearbox as R > 0.9 at the same time. In this study, the spectrometric oil analysis and probability statistics are used together for the reliability assessment of gear box, which extends the application range of spectrometric analysis.

It is well known that histamines play an important role as a neurotransmitter in the central nervous system and participate in several physiological functions, such as the regulation of body temperature, food intake, circadian rhythm and analgesia. Since the first report by de Almeida and Izquierdo that histamines facilitate memory performance in mice, it has been revealed that the histaminergic nervous system in the brain plays a crucial role in learning and memory functions. First, we demonstrate the effects of histamines and histaminergic drugs on learning and memory using active avoidance and 8-arm radial maze tests. Many reports suggest that acetylcholine and N-methyl-D-aspartate (NMDA) receptors play an important role in learning and memory. In this report, we also describe the interaction between histamine-induced memory facilitation and acetylcholine or NMDA receptors.