The multicast mobile ad-hoc network (multicast MANET) is proposed for a sender sending packets to several receivers through a multicast session. Since members of a multicast group changes frequently, the issue of supporting secure authentication and authorization in multicast MANET becomes more critical than the network provided a fixed central authentication (CA) server. This work thus proposes a two-step secure authentication approach for the multicast MANET. First, a Markov chain trust model is proposed to determine the trust value (TV) for each one-hop neighbors. A nodepsilas trust value is analyzed from its previous trust manner in this group. The proposed trust model is proven as an ergodic continuous-time Markov chain model. Second, a node with the highest TV of a group will be selected as the CA server. For increasing reliability, the node with the second highest TV will be selected as the backup CA server that will take over CA when CA fails. The procedures of the secure authentication for group management are detailed. The security analysis of each procedure is analyzed to guarantee that the proposed approach achieves a secure reliable authentication in multicast MANET. Numerical results indicate that the analytical trust value of each mobile node is very close to that of simulation under various situations. The speed of the convergence of the analytical trust value shows that the analytical results are independent of initial values and the trust classes.

This study presents a recurrent wavelet-based neuro-fuzzy network with dynamic symbiotic evolution (RWNFN-DSE) model which combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). The proposed RWNFN-DSE is used to dynamic system processing. A novel evolution learning called dynamic symbiotic evolution (DSE) is used to tune the parameter of the RWNFN-DSE model. Better chromosomes will be initially generated while the better mutation points will be determined for performing dynamic-mutation. Simulation results have shown that the proposed RWNFN-DSE model obtains better performance than other existing models

This study focuses on two issues: parametric modeling of the channel and index assignment of codevectors, to design a vector quantizer that achieves high robustness against channel errors. We first formulate the design of a robust zero-redundancy vector quantizer as a combinatorial optimization problem leading to a genetic search for a minimum-distortion index assignment. The performance is further enhanced by the use of the Fritchman (1967) channel model that more closely characterizes the statistical dependencies between error sequences. This study also presents an index assignment algorithm based on the Fritchman model with parameter values estimated using a real-coded genetic algorithm. Simulation results indicate that the global explorative properties of genetic algorithms make them very effective in estimating Fritchman model parameters, and use of this model can match index assignment to expected channel conditions.

Speech repairs occur often in spontaneous spoken dialogues. The ability to detect and correct those repairs is necessary for every spoken language system. We present a framework to detect and correct speech repairs where all relevant levels of information, i.e., acoustics, lexic, syntax and semantics could be integrated. The basic idea is to reduce the search space for repairs as soon as possible by cascading filters that involve more and more features. At first an acoustic module generates hypotheses about the existence of a repair. Second a stochastic model suggests a correction for every hypothesis. Well scored corrections are inserted as new paths in the word lattice. A lattice parser then makes the final decision about accepting the repair. 1. INTRODUCTION Human utterances often contain erroneous parts where speakers correct previous words in their speech. The erroneous portions are not part of the intended word sequence. A speech system must be able to deal with such phenomena ...

This paper presents quality enhancement of sinusoidal transform coders (STC) via the development of new parametric models. First explored are the benefits of the Bark spectrum for use in the design of perceptual coding of the sine-wave amplitudes. According to our results, the proposed approach provides a uniform perceptual fit across the spectrum. To enhance the accuracy of phase representation, noncausal all-pole modeling of the vocal system is also discussed. Experimental results indicate that the use of new parametric models allows the STC to improve the phase accuracy as well as the synthetic speech quality.

Most LPC-based audio coders employ simplistic noise-shaping operations to perform psychoacoustic control of quantization noise. In this paper, we report on new approaches to exploiting perceptual masking in the design of adaptive quantization of LPC excitation parameters. Due to its localized spectral sensitivity, sinusoidal excitation representation is preferred to spectrally flat signals for use in excitation modeling. Simulation results indicate that the proposed multisinusoid excited coder can deliver high quality audio reproduction at the rate of 72 kb/s.