This paper reports on a method of cadence analysis for the discrimination between human and quadruped using a cheap seismic sensor. Previous works in the domain of seismic detection of human vs. quadruped have relied on the fundamental gait frequency. Slow movement of quadrupeds can generate the same fundamental gait frequency as human footsteps therefore causing the recognizer to be confused when quadruped are ambling around the sensor. Here we propose utilizing the cadence analysis of temporal gait pattern which provides information on temporal distribution of the gait beats. We also propose a robust method of extracting temporal gait patterns. Features extracted from gait patterns are modeled with optimum number of Gaussian Mixture Models (GMMs). The performance of the system during the test for discriminating between horse, dog, multiple people walk, and single human walk/run was over 95%.

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This paper presents a simplified nonlinear model for Dynamic Synapse Neural Network (DSNN) which is based on nonlinear dynamics of neurons in the hippocampus, using a recurrent neural network. The proposed model will be utilized in place of DSNN for various applications which require simpler implementation and faster training, maintaining the same performance as a nonlinear system model, classifier, or pattern recognizer. This model was tested in two different structure and training methods, by learning the input-output relationship of a few DSNNs with sets of experimentally-determined coefficients. The results showed that this model can capture DSNN's complicated nonlinear dynamics in a temporal domain with less computational cost and faster training.

This paper presents a nonlinear dynamic model for the purpose of modeling vowels uttered by patients who have problem in the control of voice box muscles. The proposed model will be utilized in the detection of speech pathologies and also automatic speech recognition systems to enhance patients' communication capabilities. The model of this study utilizes feedback, and also a sigmoid nonlinear function which is not included in the linear speech production models. The nonlinear function allows for the higher order dynamics of the signal to be captured and feedback increases dynamicity of the model. The model of the current research was applied to discriminate between few voice pathologies and normal cases. The statistical analysis of the parameters of the trained model showed that these parameters can provide independent and distinct features with which pathological classes can be discriminated.