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The potential of Markov chain and cellular automata model with help of agents that play a vital role in a cities urbanisation through fuzziness in the data and hierarchal weights (for principal agents) have been used to understand and predict the urban growth for the Pune city, India. The model utilizes temporal land use changes with probable growth agents such as roads drainage networks, railway...
A conventional grey Markov model will produce large errors when it's used for the prediction of a sequence with large volatility. These errors can lead to prediction failure. So it is necessary to improve the model. In order to reduce the volatility, the logarithm of the original sequence is firstly calculated, and then the minimum error is used to replace the relative error of the grey model. After...
Previous analysis of content fingerprints has mainly focused on the case of independent and identically distributed fingerprints. Practical fingerprints, however, exhibit correlations between components computed from successive frames. In this paper, a Markov chain based model is used to capture the temporal correlations, and the suitability of this model is evaluated through experiments on a video...
An integrated sensor framework is described and simulated which demonstrates an intelligent or cognitive RF sensor detection method that detects unique stochastic behaviors of incoming event data. For signal surveillance / RF spectrum management sensors, detecting interesting human behavior and rapidly adapting the sensor to optimally process these behaviors is the challenge. The detection method...
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