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Instant intercommunion techniques such as Instant Messaging (IM) are widely popularized. Aiming at such kind of large scale mass communication media, clustering on its text content is a practical method to analyze the characteristic of text content in instant messages, and find or track the social hot topics. However, key words in one instant message usually are few, even latent; moreover, single message can not describe the conversational context. This is very different from general document and makes common clustering algorithms unsuitable. A novel method called WR − KMeans is proposed, which synthesizes related instant messages as a conversation and enriches conversation's vector by words which are not included in this conversation but are closely related with existing words in this conversation. WR − KMeans performs clustering like k-means on this extended vector space of conversations. Experiments on the public datasets show that WR − KMeans outperforms the traditional k-means and bisecting k-means algorithms. [ABSTRACT FROM AUTHOR]

A novel de-noising method, called matching pursuit method, for improving the signal-to-noise ratio (SNR) of Doppler ultrasound blood flow signals is proposed. Using this method, the Doppler ultrasound signal is first decomposed into a linear expansion of waveforms, called time-frequency atoms, which are selected from a redundant dictionary named Gabor functions. Then a decay parameter-based algorithm is employed to determine the decomposition times. Finally, the de-noised Doppler signal is reconstructed using the selected components. The SNR improvements and the maximum frequency estimation precision with simulated Doppler blood flow signals have been used to evaluate a performance comparison based on the wavelet, the wavelet packets and the matching pursuit de-noising algorithms. From the simulation and clinical experiment results, it is concluded that the performance of the matching pursuit approach is the best for the Doppler ultrasound signal de-noising. [ABSTRACT FROM AUTHOR]

This paper presents some preliminary efforts towards a new methodology which harmonically blends deterministic and stochastic frameworks in information processing, including identification, signal processing, communications, system design, etc. We begin with a discussion on distinctive features of the two frameworks and explanation of compelling reasons and motivating issues for introducing such a combined framework. Using persistent identification as an example, we demonstrate the application and utility of the methodology. Lower and upper bounds on identification errors are obtained for systems subject to both deterministic unmodelled dynamics and random external disturbances. [ABSTRACT FROM AUTHOR]