Abstract: A visit-pattern-aware mobile advertising system is provided for urban commercial complexes. In order to provide highly relevant advertisements to mobile users, next visit place is predicted through a probabilistic reasoning technique based on the collected visit place history, and an advertisement to be provided is selected based on the predicted next visit place. Since the advertisement is selectively provided according to the next visit place predicted based on a visit place history of an advertisement receiver, it is possible to increase an advertisement effect.

Abstract: Hidden Markov models (HMMs) are used in a system for recognizing on-line English characters. Input data from a tablet is represented as chain codes and HMMs are trained in character units for recognition. During HMM training, imaginary strokes are inserted into actual character strokes of input data and distances between adjacent points of the strokes are normalized. The input data is converted into chain codes, HMM-trained and then constructed into circular HMMs. Characters to be recognized are inserted with imaginary strokes, normalized, converted into chain codes, and then fed into the constructed circular HMMs, thereby enabling recognition.