The interpretation of tactile stimuli empowers humans to identify substances, distinguish materials, and engage in tactile communication. For stimulus design in human-computer interaction, objective similarity measures improve efficiency and save costs. Inspired by the fact that biological systems are robust in recognizing multimedia stimuli, we propose a neuromorphic method for similarity measurement. The method is divided into two steps. First, tactile information is translated into biological representations by mimicking a low-threshold mechanoreceptor through a physiological neuronal model. Then, three measures are nominated to assess the similarity of neural spike trains from the following perspectives: interval spike counting, temporal matching, and vector space embedding. Regression analysis showed that the linearity of these measures was significant, indicating that the filtering ability of the physiological neuron model is robust. One of the measures is selected for comparison with the signal-to-noise ratio, structural similarity, and hybrid metric. The results suggest that the correlation between the predictions of our method and the subjective evaluation is stable, above 0.9 for each experimental stimulus. We achieve a mutual interpretation between quantitative measures of vibrotactile similarity and subjective cognitive outcomes. Furthermore, the feasibility of this method in material classification has been substantiated through an exploratory experiment.

MPEG-4 encoded video is expected to account for a large portion of the traffic in future wireline and wireless networks, but there are only a few studies that evaluate networking protocols and resource management schemes with such new object-based encoded video. We explore the viability of multifractal analysis in modeling MPEG-4 video traffic. First, we prove that MPEG-4 video traffic exhibits multifractal characteristics through multifractal formalism and spectral analysis. Then we apply a multifractal wavelet model to the analysis and synthesis of MPEG-4 video traffic. This model uses the Haar wavelet transform and puts a constraint on the wavelet coefficients to guarantee positivity, which results in a fast O(N) algorithm to synthesize N-point data sets. The statistical numeric results and fit procedure show this model's flexibility and accuracy in MPEG-4 video modelling.