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In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Boosting has been a very successful technique for solving a two-class classification problem. This algorithm only requires that the performance of each weak classifier is better than the random guessing (rather than 1/2) by combining weak classifiers. But the process of selecting weak classifier was too much time-consuming. All the features are needed to traverse for the threshold that achieves minimal error rate. To reduce the computation complexity, a novel method is proposed. Face recognition experiments using local binary pattern (LBP) features are conducted. The results show that the same classifier and error rate are achieved using the proposed training algorithm, and 30% of training time or even more is reduced. [ABSTRACT FROM AUTHOR]

The conventional direction of arrival (DOA) estimation algorithm with static sources assumption usually estimates the source angles of two adjacent moments independently and the correlation of the moments is not considered. In this article, we focus on the DOA estimation of moving sources and a modified particle filtering (MPF) algorithm is proposed with state space model of single acoustic vector sensor. Although the particle filtering (PF) algorithm has been introduced for acoustic vector sensor applications, it is not suitable for the case that one dimension angle of source is estimated with large deviation, the two dimension angles (pitch angle and azimuth angle) cannot be simultaneously employed to update the state through resampling processing of PF algorithm. To solve the problems mentioned above, the MPF algorithm is proposed in which the state estimation of previous moment is introduced to the particle sampling of present moment to improve the importance function. Moreover, the independent relationship of pitch angle and azimuth angle is considered and the two dimension angles are sampled and evaluated, respectively. Then, the MUSIC spectrum function is used as the "likehood" function of the MPF algorithm, and the modified PF-MUSIC (MPF-MUSIC) algorithm is proposed to improve the root mean square error (RMSE) and the probability of convergence. The theoretical analysis and the simulation results validate the effectiveness and feasibility of the two proposed algorithms. [ABSTRACT FROM AUTHOR]

Background: China bears the biggest stroke burden in the world. However, little is known about the current prevalence, incidence, and mortality of stroke at the national level, and the trend in the past 30 years.Methods: In 2013, a nationally representative door-to-door survey was conducted in 155 urban and rural centers in 31 provinces in China, totaling 480 687 adults aged ≥20 years. All stroke survivors were considered as prevalent stroke cases at the prevalent time (August 31, 2013). First-ever strokes that occurred during 1 year preceding the survey point-prevalent time were considered as incident cases. According to computed tomography/MRI/autopsy findings, strokes were categorized into ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage, and stroke of undetermined type.Results: Of 480 687 participants, 7672 were diagnosed with a prevalent stroke (1596.0/100 000 people) and 1643 with incident strokes (345.1/100 000 person-years). The age-standardized prevalence, incidence, and mortality rates were 1114.8/100 000 people, 246.8 and 114.8/100 000 person-years, respectively. Pathological type of stroke was documented by computed tomography/MRI brain scanning in 90% of prevalent and 83% of incident stroke cases. Among incident and prevalent strokes, ischemic stroke constituted 69.6% and 77.8%, intracerebral hemorrhage 23.8% and 15.8%, subarachnoid hemorrhage 4.4% and 4.4%, and undetermined type 2.1% and 2.0%, respectively. Age-specific stroke prevalence in men aged ≥40 years was significantly greater than the prevalence in women (P<0.001). The most prevalent risk factors among stroke survivors were hypertension (88%), smoking (48%), and alcohol use (44%). Stroke prevalence estimates in 2013 were statistically greater than those reported in China 3 decades ago, especially among rural residents (P=0.017). The highest annual incidence and mortality of stroke was in Northeast (365 and 159/100 000 person-years), then Central areas (326 and 154/100 000 person-years), and the lowest incidence was in Southwest China (154/100 000 person-years), and the lowest mortality was in South China (65/100 000 person-years) (P<0.002).Conclusions: Stroke burden in China has increased over the past 30 years, and remains particularly high in rural areas. There is a north-to-south gradient in stroke in China, with the greatest stroke burden observed in the northern and central regions. [ABSTRACT FROM AUTHOR]

There are plenty of weak audio analog signals in ocean, whose signal characteristic is usually an important basis for the target detection in ocean, therefore it is necessary to seek for approaches to detect the weak audio signal. Stochastic resonance, which is regarded as a novel method for detecting the weak signal, has been applied in many areas. However, the most researches concentrate on the single-frequency weak signal, and less research on the multi-frequency weak signal. Starting from the basic bistable stochastic resonance system, theoretical analysis on the multi-frequency analog signal with strong noise is carried out, and a method for detecting and recovering the multi-frequency analog signal is proposed, which is based on the average inversion and interpolation fitting. The analysis and simulation results present that, the multi-frequency analog signal is successfully detected and recovered in the condition that the input signal-to-noise ratio (SNR) is -22dB.Compared with the traditional filter method, the proposed method can effectively reduce the waveform distortion and phase delay of the multi-frequency signal, and it is especially suitable for the detection of multi-frequency weak signals whose frequencies are very close, which makes it possess good theoretical guidance and practical application value. [ABSTRACT FROM AUTHOR]