A novel approach for the multiple-model multi-sensor Bernoulli filter (MM-MSBF) based on the theory of finite set statistics (FISST) is proposed for a single maneuvering target tracking in the presence of detection uncertainty and clutter. First, the FISST is used to derive the multi-sensor likelihood function of MSBF, and then combining the MSBF filter with the interacting multiple models (IMM) algorithm to track the maneuvering target. Moreover, the sequential Monte Carlo (SMC) method is used to implement the MM-MSBF algorithm. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.

Online resource management of a software system can take advantage of a performance model to predict the effect of proposed changes. However, the prediction accuracy may degrade if the performance model does not adapt to the changes in the system. This work considers the problem of using Kalman filters to track changes in both performance model parameters and system behavior. We propose a method based on the multiple-model Kalman filter. The method runs a set of Kalman filters, each of which models different system behavior, and adaptively fuses the output of those filters for overall estimates. We conducted case studies to demonstrate how to use the method to track changes in various system behaviors: performance modeling, process modeling, and measurement noise. The experiments show that the method can detect changes in system behavior promptly and significantly improve the tracking and prediction accuracy over the single-model Kalman filter. The influence of model design parameters and mode-model mismatch is evaluated. The results support the usefulness of the multiple-model Kalman filter for tracking performance model parameters in systems with time-varying behavior.

Packet loss and delay are the major network impairments for transporting real-time voice over IP networks. In the proposed system, multiple descriptions of the speech are used to take advantage of the packet path diversity. A new objective method is presented for predicting the perceived quality of multi-stream voice transmission. Also proposed is a multi-stream playout buffer algorithm, together with an adaptive parameter adjustment scheme, that maximizes the perceived speech quality via delay-loss trading. Experimental results showed that, compared to FEC-protected single-path transmission, the proposed multi-stream transmission scheme achieves significant reductions in delay and packet loss rates as well as improved speech quality.

This paper proposes a cross layer wireless VoIP service which integrates an Adaptive QoS Playout (AQP) algorithm, E-model, Stream Control Transmission Protocol (SCTP), IEEE 802.21 Media Independent Handover (MIH) middleware and two user motion detection services. The proposed AQP algorithm integrates the effect of playout control and lost packet retransmission based on the E-model. Besides, by using the partial reliable transmission service from SCTP and the handoff notification from MIH services in a cross layer manner, AQP can reduce the lateness loss rate and improve speech quality under high frame error rates. In the simulations, the performance of AQP is compared with a fixed playout algorithm and four adaptive playout strategies. The simulation results show that the lateness loss rate of AQP is 2% lower than that of existing playout algorithms and the R-factor is 16% higher than the compared algorithms when a network has 50 ms wired propagation delay and 2.5% frame error rate.

In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.

VoIP is one of the key technologies for recent telecommunication services. In addition to the migration from the conventional PSTN to IP networks, mobile networks will follow the PSTN in moving to an IP-based infrastructure. Due to limited radio resources, the speech bitrate in mobile networks must be more strongly compressed than is true in PSTN. This will lead to a heterogeneous network environment, in which different speech codecs are employed in fixed and mobile networks. Therefore, from the viewpoint of designing and managing the QoE (Quality of Experience) of end-to-end telephony services, establishing a method to evaluate the quality of VoIP in such a heterogeneous network environment is very important. The quality of speech communication services should be discussed in subjective terms. Subjective quality assessment is time-consuming and expensive, however, so objective quality assessment which estimates subjective quality without carrying out subjective quality experiments is desirable. To establish an objective method to evaluate the end-to-end quality of speech in a heterogeneous network environment, this paper proposes a method for estimating the end-to-end listening quality based on the quality in each individual segment. This method is very important because conventional technologies such as the E-model, which was standardized as ITU-T Recommendation G.107, cannot accurately estimate overall quality based on segmental qualities. The experimentals show that the proposed method offers better performance in terms of quality estimation than the conventional method.

VoIP, one of emerging technologies, offers high quality of real time voice services over IP-based broadband networks; however, the quality of voice would easily be degraded by IP network impairments such as delay, jitter and packet loss, hereon initiate the presence of new technologies to help solve out the problems. Among those, playout buffer at the receiving end can compensate for the jitter effects by its function of tradeoff between delay and loss. Adaptive smoothing algorithms are capable of the dynamical adjustment of smoothing size by introducing a variable delay based on the use of the network parameters so as to avoid the quality decay problem. This paper introduces an efficient and feasible perceived quality method for buffer optimization to achieve the best voice quality. This work formulates an online loss model which incorporates buffer sizes and applies the ITU-T E-model approach to optimize the delay-loss problem. Distinct from other optimal smoothers, the proposed optimal smoother can be applied for most codecs and carries the lowest complexity. Since the adaptive smoothing scheme introduces variable playback delays, the buffer re-synchronization between the capture and the playback becomes essential. This work also presents a buffer re-synchronization algorithm based on silence skipping to prevent unacceptable increase in the buffer preloading delay and even buffer overflow. Simulation experiments validate that the proposed adaptive smoother achieves significant improvement in the voice quality.

A triple density Error Diffusion for medical monochrome LCDs is proposed to improve their gray-scale precisions. In addition, a measurement technique of image qualities based on E-MSE (Eye model-based Mean Square Error) is proposed. Several conventional techniques for medical LCDs, such as Sub-Pixel Modulation and Error Diffusion, are evaluated based on E-MSE and the validity of the proposed technique is ensured objectively.

With the increasing demand for IP telephony services using Voice over IP (VoIP) technology, techniques for monitoring speech quality in actual networks are required to manage the quality of VoIP services constantly. Since the speech quality of VoIP is affected by IP network performance factors, non-intrusive methods of monitoring the quality of service (QoS) by passively measuring network performance are being watched with keen interest. VQmon technology is one of the non-intrusive quality monitoring methods. Although the monitoring functions of the VQmon for post-arrived packet behavior events at VoIP-gateways are effective, the estimating algorithm does not take differences in the implementations of VoIP-gateway products into account. We therefore propose a non-intrusive method of monitoring QoS that works in conjunction with ITU-T Recommendation P.862 "PESQ" that takes the characteristics of VoIP-gateway products into consideration. We compared the performance of non-intrusive quality monitoring technology such as VQmon and the proposed method in terms of estimating the accuracy of speech quality and mouth-to-ear delay. The experimental results revealed that the proposed method outperforms the conventional one, achieving sufficient accuracy for quality monitoring of VoIP services.