This paper reviews several multi-source localization methods which estimate ITDs based on zero crossings (ZCs). Employing signal-to-noise ratio (SNR) estimation from ITD variances, these ZC-based source localization methods are more robust to diffuse noise than the cross-correlation (CC)-based one with less computational complexity. In order to take reverberant environments into account, two approaches detect intervals which dominantly contain direct-path components from sources to sensors because they may effectively provide reliable ITDDs corresponding to source directions. One accomplishes the detection by comparing the original and cepstral-prefiltering-processed envelopes, and the other searches sudden increase of acoustic energy by considering typical characteristics of acoustic reverberation. Experiments for comparison of these methods demonstrate that the approach with energy-based detection efficiently achieves multi-source localization in reverberant environments.

The target strength (TS) is calculated from the measured signal using the definition of the peak TS (PTS) or the integrated TS (ITS). These two types of TS sometimes give different results depending on what the pulse duration is. In this paper, we model the scattered time signal by the numerical code based on the physical diffraction theory and examine the effect of the pulse duration on the value of PTS or ITS. The transformed TS (TTS) for the frequency domain is used as a reference solution.

In this paper, we propose a new VSS-NLMS algorithm through a simple modification of the conventional NLMS algorithm, which leads to a low complexity algorithm with enhanced performance. The step size of the proposed algorithm becomes smaller as the error signal is getting orthogonal to the input vector. We also show that the proposed algorithm is an approximated normalized version of the KZ-algorithm and requires less computation than the KZ-algorithm. We carried out a performance comparison of the proposed algorithm with the conventional NLMS and other VSS algorithms using an adaptive channel equalization model. It is shown that the proposed algorithm presents good convergence characteristics under both stationary and non-stationary environments despites its low complexity.

In this paper, we suggest criteria for objective measurement of speech quality in mobile VoIP (Voice over Internet Protocol) services over wireless mobile internet such as mobile WiMAX networks. This is the case that voice communication service is available under other networks. When mobile VoIP service users in the mobile internet network based on packet call up PSTN and mobile network users, but there have not been relevant quality indexes and quality standards for evaluating speech quality of mobile VoIP. In addition, there are many factors influencing on the speech quality in packet network. Especially, if the degraded speech with packet loss transfers to the other network users through the handover, voice communication quality is significantly deteriorated by the transformation of speech codecs. In this paper, we eventually adopt the Gilbert-Elliot channel model to characterize packet network and assess the voice quality through the objective speech quality method of ITU-T P. 862. 1 MOS-LQO for the various call scenario from mobile VoIP service user to PSTN and mobile network users under various packet loss rates in the transmission channel environments. Our simulation results show that transformation of speech codecs results in the degraded speech quality for different transmission channel environments when mobile VoIP service users call up PSTN and mobile network users.

Identity recognition in real environment with a reliable mode is a key issue in human computer interaction (HCI). In this paper, we present a robust person identification system considering score-based optimal reliability measure of audio-visual modalities. We propose an extension of the modified reliability function by introducing optimizing parameters for both of audio and visual modalities. For degradation of visual signals, we have applied JPEG compression to test images. In addition, for creating mismatch in between enrollment and test session, acoustic Babble noises and artificial illumination have been added to test audio and visual signals, respectively. Local PCA has been used on both modalities to reduce the dimension of feature vector. We have applied a swarm intelligence algorithm, i.e., particle swarm optimization for optimizing the modified convection function's optimizing parameters. The overall person identification experiments are performed using VidTimit DB. Experimental results show that our proposed optimal reliability measures have effectively enhanced the identification accuracy of 7.73% and 8.18% at different illumination direction to visual signal and consequent Babble noises to audio signal, respectively, in comparison with the best classifier system in the fusion system and maintained the modality reliability statistics in terms of its performance; it thus verified the consistency of the proposed extension.

The level set based approach is one of active methods for contour extraction in image segmentation. Since Osher and Sethian introduced the level set framework in 1988, the method has made the great impact on image segmentation. However, there are some problems to be solved; such as multi-objects segmentation, noise filtering and much calculation amount. In this paper we address the drawbacks of the previous level set methods and propose an extension of the traditional fast level set to cope with the limitations. We introduce a relationship matrix, a new split-and-merge criterion, a modified Chan-Vese criterion and a novel filtering criterion into the traditional fast level set approach. With the segmentation experiments we evaluate the proposed method and show the promising results of the proposed method.