Jacobian Adaptation (JA) has been successfully used in Automatic Speech Recognition (ASR) systems to adapt the acoustic models from the training to the testing noise conditions. In this work we present an improvement of JA for speaker verification, where a specific training noise reference is estimated for each speaker model. The new proposal, which will be referred to as Model-dependent Noise Reference Jacobian Adaptation (MNRJA), has consistently outperformed JA in our speaker verification experiments.

In this paper, we propose a technique for detection and real-time tracking of moving targets. This uses a color segmentation algorithm robust to irregular illumination variation and a line-based tracker. The former is based on statistical representation of a color. And, we can obtain a real-time property for detection and tracking of moving targets from the latter.

This study proposes a novel adaptive fuzzy control methodology to remove disadvantages of traditional fuzzy approximation based control. Meanwhile, the highly uncertain robot manipulator is taken as an application with either guaranteed robust tracking performances or asymptotic stability in a global sense. First, the design concept, namely, feedforward fuzzy approximation based control, is introduced for a simple uncertain system. Here the desired commands are utilized as the inputs of the Takagi-Sugeno (T-S) fuzzy system to closely compensate the unknown feedforward term required during steady state. Different to traditional works, the assumption on bounded fuzzy approximation error is not needed, while this scheme allows easier implementation architecture. Next, the concept is extended to controlling manipulators and achieves global robust tracking performances. Note that a linear matrix inequality (LMI) technique is applied and provides an easier gain design. Finally, numerical simulations are carried out on a two-link robot to illustrate the expected performances.

In this paper, we propose a new feature extraction method that combines both HMT-based denoising and weighted filter bank analysis for robust speech recognition. The proposed method is made up of two stages in cascade. The first stage is denoising process based on the wavelet domain Hidden Markov Tree model, and the second one is the filter bank analysis with weighting coefficients obtained from the residual noise in the first stage. To evaluate performance of the proposed method, recognition experiments were carried out for additive white Gaussian and pink noise with signal-to-noise ratio from 25 dB to 0 dB. Experiment results demonstrate the superiority of the proposed method to the conventional ones.

This paper addresses a robust supervisory control problem for uncertain timed discrete event systems (DESs) modeled as a set of some possible timed models. To avoid the state space explosion problem caused by tick transitions in timed models, the notion of eligible time bounds is presented. Based on the notion and activity (logical) models, this paper shows how the controllability condition of a given language specification is presented as a necessary and sufficient condition for the existence of a robust supervisor to achieve the specification for any timed model in the set.

Optimum wideband beam pattern synthesis methods are usually sensitive to antenna elements gain, phase and position errors. In this letter, these errors are taken into account in a constraint optimization process, and a generalized diagonal loading algorithm is obtained. Computer simulations indicate the robustness of this new method.

This paper proposes a new robust adaptive processing algorithm that is based on the extended least squares (ELS) method with running spectrum filtering (RSF). By utilizing the different characteristics of running spectra between speech signals and noise signals, RSF can retain speech characteristics while noise is effectively reduced. Then, by using ELS, autoregressive moving average (ARMA) parameters can be estimated accurately. In experiments on real speech contaminated by white Gaussian noise and factory noise, we found that the method we propose offered spectrum estimates that were robust against additive noise.

This paper is concerned with improving noise-robustness of a multi-SNR multi-band speaker identification system by introducing automatic adjustment of subband likelihood recombination weights. The adjustment is performed on the basis of subband power calculated from the noise observed just before the speech starts in the input signal. To evaluate the noise-robustness of this system, text-independent speaker identification experiments were conducted on speech data corrupted with noises recorded in five environments: "bus," "car," "office," "lobby," and "restaurant". It was found that the present method reduces the identification error by 15.9% compared with the multi-SNR multi-band method with equal recombination weights at 0 dB SNR. The performance of the present method was compared with a clean fullband method in which a speaker model training is performed on clean speech data, and spectral subtraction is applied to the input signal in the speaker identification stage. When the clean fullband method without spectral subtraction is taken as a baseline, the multi-SNR multi-band method with automatic adjustment of recombination weights attained 56.8% error reduction on average, while the average error reduction rate of the clean fullband method with spectral subtraction was 11.4% at 0 dB SNR.

Some conventional beamformers require the direction of the desired signal. The performance of such beamformers can substantially be degraded even in the presence of small error on the directional information. In this letter, we propose a prefilter-type beamforming scheme robust to directional error by employing a simple compensator. The performance of the proposed scheme is verified by computer simulation.

In this paper, we propose a robust adaptive algorithm for impulsive noise suppression. The perturbation of the input signal as well as the perturbation of the estimation error are restricted by M-estimation. The threshold used in M-estimation is obtained from the proposed adaptive variance estimation. Simulations show that the proposed algorithm is less vulnerable to the impulsive noise than the conventional algorithm.

This paper describes a noise robustness technique that normalizes the cepstral amplitude range in order to remove the influence of additive noise. Additive noise causes speech feature mismatches between testing and training environments and it degrades recognition accuracy in noisy environments. We presume an approximate model that expresses the influence by changing the amplitude range and the DC component in the log-spectra. According to this model, we propose a cepstral amplitude range normalization (CARN) that normalizes the cepstral distance between maximum and minimum values. It can estimate noise robust features without prior knowledge or adaptation. We evaluated its performance in an isolated word recognition task by using the Noisex92 database. Compared with the combinations of conventional methods, the CARN could improve recognition accuracy under various SNR conditions.

A novel public watermarking algorithm based on chaotic properties is proposed. Thanks to good randomness and easy reproducibility of chaos, firstly the watermark is permuted by chaotic sequences, then a small number of reference points are selected randomly in the middle frequency bands of DCT domain, and the variable number disorder watermark bits are embedded into the neighborhood of each reference point according to chaotic sequences. The experimental results show the invisibility and robustness.

We had previously proposed a fuzzy logic-based buffer management scheme (BMS) called fuzzy early detection (FED), which was designed to improve transmission control protocol (TCP) performance over the unspecified bit rate (UBR) service of asynchronous transfer mode (ATM) networks. Since a weakness in FED was discovered later, we present a refined version of it named FED+ here. Maintaining the design architecture and the algorithm of FED, FED+ further adopts a specific per virtual connection accounting algorithm to achieve its design goals. The effects of TCP implementation, TCP maximum segment size, switch buffer size and network propagation delay on FED+ performance are studied through simulation. Its performance is then compared with those of pure early packet discard (EPD), P-random early detection (P-RED) and FED. Our evaluations show that FED+ is superior to the others if the issues of efficiency, fairness, robustness, buffer requirement and the ease of tuning control parameters of a BMS are considered collectively.

In mobile multimedia environment, it is very important to minimize handoff latency due to mobility. In terms of reducing handoff latency, Hierarchical Mobile IPv6 (HMIPv6) can be an efficient approach, which uses a mobility agent called Mobility Anchor Point (MAP) in order to localize registration process. However, MAP can be a single point of failure or performance bottleneck. In order to provide mobile users with satisfactory quality of service and fault-tolerant service, it is required to cope with the failure of mobility agents. In, we proposed Robust Hierarchical Mobile IPv6 (RH-MIPv6), which is an enhanced HMIPv6 for fault-tolerant mobile services. In RH-MIPv6, an MN configures two regional CoA and registers them to two MAPs during binding update procedures. When a MAP fails, MNs serviced by the faulty MAP (i.e., primary MAP) can be served by a failure-free MAP (i.e., secondary MAP) by failure detection/recovery schemes in the case of the RH-MIPv6. In this paper, we investigate the comparative study of RH-MIPv6 and HMIPv6 under several performance factors such as MAP unavailability, MAP reliability, packet loss rate, and MAP blocking probability. To do this, we utilize a semi-Markov chain and a M/G/C/C queuing model. Numerical results indicate that RH-MIPv6 outperforms HMIPv6 for all performance factors, especially when failure rate is high.

This paper proposes a noise robust speech recognition method using prosodic information. In Japanese, the fundamental frequency (F0) contour represents phrase intonation and word accent information. Consequently, it conveys information about prosodic phrases and word boundaries. This paper first describes a noise robust F0 extraction method using the Hough transform, which achieves high extraction rates under various noise environments. Then it proposes a robust speech recognition method using multi-stream HMMs which model both segmental spectral and F0 contour information. Speaker-independent experiments are conducted using connected digits uttered by 11 male speakers in various kinds of noise and SNR conditions. The recognition error rate is reduced in all noise conditions, and the best absolute improvement of digit accuracy is about 4.5%. This improvement is achieved by robust digit boundary detection using the prosodic information.

Watermarking schemes have been extensively discussed and developed recently. People are usually facing the dilemma of two factors, robustness and transparency. To achieve these requirements, embedding the watermark message in the transform domain or the spatial domain is usually considered. In this paper, we will propose a blind image watermarking scheme based on vector quantization. By exploiting a modified binary tree splitting method, a stable codebook could be generated so that the watermark message could be novelly embedded and survive the JPEG compression and the Gaussian noise addition. The embedded message could be extracted without referring the host image. It makes the scheme more practical.

In this paper we describe a new framework of feature compensation for robust speech recognition, which is suitable especially for small devices. We introduce Delta-cepstrum Normalization (DCN) that normalizes not only cepstral coefficients, but also their time-derivatives. Cepstral Mean Normalization (CMN) and Mean and Variance Normalization (MVN) are fast and efficient algorithms of environmental adaptation, and have been used widely. In those algorithms, normalization was applied to cepstral coefficients to reduce the irrelevant information from them, but such a normalization was not applied to time-derivative parameters because the reduction of the irrelevant information was not enough. However, Histogram Equalization (HEQ) provides better compensation and can be applied even to the delta and delta-delta cepstra. We investigate various implementation of DCN, and show that we can achieve the best performance when the normalization of the cepstra and the delta cepstra can be mutually interdependent. We evaluate the performance of DCN using speech data recorded by a PDA. DCN provides significant improvements compared to HEQ. It is shown that DCN gives 15% relative word error rate reduction from HEQ. We also examine the possibility of combining Vector Taylor Series (VTS) and DCN. Even though some combinations do not improve the performance of VTS, it is shown that the best combination gives the better performance than VTS alone. Finally, the advantage of DCN in terms of the computation speed is also discussed.

FEC (Forward Error Correction) can repair the damage to communication quality due to packet loss. The growing requirement of FEC for high-quality video transmission is inevitable on broadband networks. We have designed and implemented FEC, and integrated it to our developed video transmission system named "mpeg2ts." Our goal is to make it possible to deploy this system on the broadband Internet. However, the problem with constant redundancy of FEC is that weakness to fluctuation of network condition. To resolve this problem, in this paper, we propose and evaluate an efficient FEC method for high-quality video transmission. The proposed mechanisms can provide robustness as well as saving of processing load and optimization of bandwidth consumption. Moreover, we integrate it into a system to deploy it on the real broadband Internet. Transmission experiment demonstrates availability of developed system deployed on the network.

There exists a class of nonlinear systems which fail to have a well-defined relative degree but have a robust relative degree. We have removed the full relative degree assumption which the previous results required, and have provided a local state observer for nonlinear systems that have robust relative degree γ n and have detectability property in some sense. The proposed observer utilizes the coordinate change which transforms the system into an approximate normal form. Using the proposed method, we constructed an observer for the ball and beam system on a vibrating frame. Simulation results reveal that substantial improvement in the performance is achieved compared with other local observers.

A novel geometrically invariant watermarking scheme based on gravity center is presented which treating the geometrically invariant gravity centers of host image and its supplement image as reference points. Thus watermark synchronization is obtained. Simulation results show the effectiveness of our scheme to the geometrical distortion including rotation and/or scaling.