This paper presents a robust reduced order observer for a class of Lipschitz nonlinear systems with external disturbance. Sufficient conditions on the existence of the proposed observer are characterized by linear matrix inequalities. It is also shown that the proposed observer design can reduce the effect on the estimation error of external disturbance up to the prescribed level. Finally, a numerical example is provided to verify the proposed design method.

In prior work, contact welding phenomena were observed in automotive relays during break of motor inrush current. The switching performance of the type of relay investigated could be correlated with the parameters: over-travel, coil suppression, and the break current. In the present work the author further explores the impact of both the contact material (silver tin oxide versus fine grain silver) and the contact surface topography (brand new and pre-aged contacts). He further assesses the robustness of the system "relay" with those parameters using the Taguchi methods for robust design. Furthermore, the robustness of two alternative automotive relay types will be discussed.

To solve the problem of distributed multisensor fusion, the optimal linear methods can be used in Gaussian noise models. In practice, channel noise distributions are usually non-Gaussian, possibly heavy-tailed, making linear methods fail. By combining a classical tool of optimal linear fusion and a robust statistical method, the two-stage MAD robust fusion (MADRF) algorithm is proposed. It effectively performs both in symmetrically and asymmetrically contaminated Gaussian channel noise with contamination parameters varying over a wide range.

In this paper we describe a new framework of feature combination in the cepstral domain for multi-input robust speech recognition. The general framework of working in the cepstral domain has various advantages over working in the time or hypothesis domain. It is stable, easy to maintain, and less expensive because it does not require precise calibration. It is also easy to configure in a complex speech recognition system. However, it is not straightforward to improve the recognition performance by increasing the number of inputs, and we introduce the concept of variance re-scaling to compensate the negative effect of averaging several input features. Finally, we propose to take another advantage of working in the cepstral domain. The speech can be modeled using hidden Markov models, and the model can be used as prior knowledge. This approach is formulated as a new algorithm, referred to as Hypothesis-Based Feature Combination. The effectiveness of various algorithms are evaluated using two sets of speech databases. We also refer to automatic optimization of some parameters in the proposed algorithms.

This paper addresses the problem of joint transceiver design for Tomlinson-Harashima Precoding (THP) in the multiuser multiple-input-multiple-output (MIMO) downlink under both perfect and imperfect channel state information at the transmitter (CSIT). For the case of perfect CSIT, we differ from the previous work by performing stream-wise (both inter-user and intra-user) interference pre-cancelation at the transmitter. A minimum total mean square error (MT-MSE) criterion is used to formulate our optimization problem. By some convex analysis of the problem, we obtain the necessary conditions for the optimal solution. An iterative algorithm is proposed to handle this problem and its convergence is proved. Then we extend our designed algorithm to the robust version by minimizing the conditional expectation of the T-MSE under imperfect CSIT. Simulation results are given to verify the efficacy of our proposed schemes and to show their superiorities over existing MMSE-based THP schemes.

Software defined radio (SDR) technology has been widely applied for its powerful universality and flexibility in the past decade. To address the issue of bandpass sampling of multiband signals, a novel and efficient method of finding the minimum valid sampling frequency is proposed. Since there are frequency deviations due to the channel effect and hardware instability in actual systems, we also consider the guard-bands between downconverted signal spectra in determining the minimum sampling frequency. In addition, the case that the spectra within the sampled bandwidth are located in inverse placement can be avoided by our proposed method, which will reduce the complexity of the succeeding digital signal process significantly. Simulation results illustrate that the proper minimum sampling frequency can be determined rapidly and accurately.

We present likelihood-ratio test (LRT) for detecting a signal in the presence of a known colored clutter, a white noise and a strong jammer with unknown nonstationary power. We have suggested the test allowing to remove completely all components of the jammer. It has been obtained the asymptotic inverse covariance matrix of the clutter with the jammer when the jammer power tends to infinite. Using this formula we developed the asymptotic LRT detection test. The performance of the new test statistic is analyzed and compared with well known eigencanceler-based detector. The effect of the jammer removing on the performance is evaluated for an example scenario.

In this letter, a two channel frequency domain speech enhancement algorithm is proposed. The algorithm is designed to achieve better overall performance with relatively small array size. An improved version of adaptive null-forming is used, in which noise cancelation is implemented in auditory subbands. And an OM-LSA based postfiltering stage further purifies the output. The algorithm also features interaction between the array processing and the postfilter to make the filter adaptation more robust. This approach achieves considerable improvement on signal-to-noise ratio (SNR) and subjective quality of the desired speech. Experiments confirm the effectiveness of the proposed system.

The Letter deals with constant false alarm rate (CFAR) detection of random Gaussian target signals embedded in Gaussian clutter with unknown covariance. The proposed detector is analyzed on the assumption that clutter covariance is not known and a random target signal has low-rank property. The low-dimensional subspace-based approach leads to a robust false alarm rate (RFAR) detector. The detection performance loss and the false alarm stability loss to unknown clutter covariance have been evaluated for example scenario.

In this paper, we analyze the robustness for low-density parity-check (LDPC) codes over the Gilbert-Elliott (GE) channel. For this purpose we propose a density evolution method for the case where LDPC decoder uses the mismatched parameters for the GE channel. Using this method, we derive the region of tuples of true parameters and mismatched decoding parameters for the GE channel, where the decoding error probability approaches asymptotically to zero.

Crosstalk delay within an on-chip bus can induce severe transmission performance penalties. The Bus-grouping Asynchronous Transmission (BAT) scheme is proposed to mitigate the performance degradation. Furthermore, considering the distinct spatial locality of transition distribution on some types of buses, we use the locality to optimize the BAT. In terms of the implementation, we propose the Differential Counter Cluster (DCC) synchronous mechanism to synchronize the data transmission, and the Delay Active Shielding (DAS) to protect some critical signals from crosstalk and optimize the routing area overhead. The BAT is scalable with the variation of bus width with little extra implementation complexity. The effectiveness of the BAT is evaluated by focusing on the on-chip buses of a superscalar microprocessor simulator using the SPEC CPU2000 benchmarks. When applied to a 64-bit on-chip instruction bus, the BAT scheme, compared with the conservative approach, Codec and Variable Cycle Transmission (DYN) approaches, improves performance by 55+%, 10+%, 30+%, respectively, at the expense of 13% routing area overhead.

This letter addresses the problem of robust transceiver design for the multiuser multiple-input-multiple-output (MIMO) downlink where the channel state information at the base station (BS) is imperfect. A stochastic approach which minimizes the expectation of the total mean square error (MSE) of the downlink conditioned on the channel estimates under a total transmit power constraint is adopted. The iterative algorithm reported in [2] is improved to handle the proposed robust optimization problem. Simulation results show that our proposed robust scheme effectively reduces the performance loss due to channel uncertainties and outperforms existing methods, especially when the channel errors of the users are different.

The problem of designing a robust adaptive control for nonlinear systems with uncertain time-varying parameters is addressed. The upper bound of uncertain parameters, considered even in control coefficients, are not required to be known. An adaptive tracking controller is presented and, using the Lyapunov theory, the closed-loop stability and tracking error convergence is shown. In order to improve the performance of the method, a robust mechanism is incorporated into the adaptive controller yielding a robust adaptive algorithm. The proposed controller guarantees the boundedness of all closed-loop signals and robust convergence of tracking error in spite of time-varying parameter uncertainties with unknown bounds. The parametric uncertain systems under consideration describes a wide class of nonlinear circuits and systems. As an application, a novel parametric model is derived for nonlinear Chua's circuit and then, the proposed method is used for its control. The effectiveness of the method is demonstrated by some simulation results.

In this paper, a robust sound source localization approach is proposed. The approach retains good performance even when model errors exist. Compared with previous work in this field, the contributions of this paper are as follows. First, an improved broad-band and near-field array model is proposed. It takes array gain, phase perturbations into account and is based on the actual positions of the elements. It can be used in arbitrary planar geometry arrays. Second, a subspace model errors estimation algorithm and a Weighted 2-Dimension Multiple Signal Classification (W2D-MUSIC) algorithm are proposed. The subspace model errors estimation algorithm estimates unknown parameters of the array model, i.e., gain, phase perturbations, and positions of the elements, with high accuracy. The performance of this algorithm is improved with the increasing of SNR or number of snapshots. The W2D-MUSIC algorithm based on the improved array model is implemented to locate sound sources. These two algorithms compose the robust sound source approach. The more accurate steering vectors can be provided for further processing such as adaptive beamforming algorithm. Numerical examples confirm effectiveness of this proposed approach.

This work addresses the issue on the robustness performance in M-ary quantization watermarking. If the encoded messages are arranged in the order of Gray Code such that adjacent messages differ at only one bit, this work demonstrates the robustness will be substantially improved in low DNR scenarios. Furthermore, the two-bit quantization watermarking can outperform the LUT approach which also provides the robustness improvement in the high-noisy environments.

In this letter, we propose a new histogram equalization method to compensate for acoustic mismatches mainly caused by corruption of additive noise and channel distortion in speech recognition. The proposed method employs an improved test cumulative distribution function (CDF) by more accurately smoothing the conventional order statistics-based test CDF with the use of window functions for robust feature compensation. Experiments on the AURORA 2 framework confirmed that the proposed method is effective in compensating speech recognition features by reducing the averaged relative error by 13.12% over the order statistics-based conventional histogram equalization method and by 58.02% over the mel-cepstral-based features for the three test sets.

Robustness and invisibility are two contrary constraints for robust invisible watermarking. Instead of the conventional strategy with human visual system (HVS) model, this paper presents a content-adaptive approach to further optimize the constraint between them. To reach this target, the entropy-based and integrated HVS (IHVS) based measures are constructed so as to adaptively choose the suitable components for watermark insertion and detection. Such a kind of scheme potentially gives rise to synchronization problem between the encoder and decoder under the framework of blind watermarking, which is then solved by incorporating the repeat-accumulate (RA) code with erasure and error correction. Moreover, a new hidden Markov model (HMM) based detector in wavelet domain is introduced to reduce the computation complexity and is further developed into a posterior one to avoid the transmission of HMM parameters with only a little sacrifice of detection performance. Experimental results show that the proposed algorithm can obtain considerable improvement in robustness performance with the same distortion as the traditional one.

This paper provides insights on the status of broadband optical access market and technologies in Europe and on the expected trends for the next generation optical access networks. The final target for most operators, cities or any other player is of course FTTH (Fibre To The Home) deployment although we can expect intermediate steps with copper or wireless technologies. Among the two candidate architectures for FTTH, PON (Passive Optical Network) is by far the most attractive and cost effective solution. We also demonstrate that Ethernet based optical access network is very adequate to all-IP networks without any incidence on the level of quality of service. Finally, we provide feedback from a FTTH pilot network in Colmar (France) based on Gigabit Ethernet PON technology. The interest of this pilot lies on the level of functionality required for broadband optical access networks but also on the development of new home network configurations.

This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In this approach, the adaptive fuzzy-neural network (FNN) in AFNO is adopted on line to model the nonlinear term in the transmitter. Additionally, the master's unknown states can be reconstructed from one transmitted state using observer design in the slave end. Synchronization is achieved when all states are observed. The utilized scheme can adaptively estimate the transmitter states on line, even if the transmitter is changed into another chaos system. On the other hand, the robustness of AFNO can be guaranteed with respect to the modeling error, and external bounded disturbance. Simulation results confirm that the AFNO design is valid for the application of chaos synchronization.

A robust routing algorithm was developed based on reinforcement learning that uses (1) reward-weighted principal component analysis, which compresses the state space of a network with a large number of nodes and eliminates the adverse effects of various types of attacks or disturbance noises, (2) activity-oriented index allocation, which adaptively constructs a basis that is used for approximating routing probabilities, and (3) newly developed space compression based on a potential model that reduces the space for routing probabilities. This algorithm takes all the network states into account and reduces the adverse effects of disturbance noises. The algorithm thus works well, and the frequencies of causing routing loops and falling to a local optimum are reduced even if the routing information is disturbed.