Gait, the walking pattern of individuals, is one of the most important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as the gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and view angle. To remedy this issue, we propose a novel AutoEncoder framework to explicitly disentangle pose and appearance features from RGB imagery and the LSTM-based integration of pose features over time produces the gait feature. In addition, we collect a Frontal-View Gait (FVG) dataset to focus on gait recognition from frontal-view walking, which is a challenging problem since it contains minimal gait cues compared to other views. FVG also includes other important variations, e.g., walking speed, carrying, and clothing. With extensive experiments on CASIA-B, USF and FVG datasets, our method demonstrates superior performance to the state of the arts quantitatively, the ability of feature disentanglement qualitatively, and promising computational efficiency.
These findings indicate that some auditory dysfunctions may be present in patients with mild and moderate OSAS, and the damages were aggravated with the severity of OSAS, which suggests that speech-ABR may be a potential biomarker in the diagnosis and evaluation at early stage of OSAS.
Combining the filtering performance of cascaded bistable stochastic resonance with measurement capability of fractal box dimension for non-linear characteristics of signals, a method of mechanical fault diagnosis based on cascaded bistable stochastic resonance and fractal box dimension was presented. The experiment results showed that this method removed high frequency noise efficiently and obtained precise fractal dimension. In order to implement mechanical fault diagnosis, the non-linear characteristics of mechanical vibration signals were measured by fractal dimension accurately.
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