This paper presents a new feature extraction method for robust speech recognition based on the autocorrelation mel frequency cepstral coefficients (AMFCCs) and a variable window. While the AMFCC feature extraction method uses the fixed double-dynamic-range (DDR) Hamming window for higher-lag autocorrelation coefficients, which are least affected by noise, the proposed method applies a variable window, depending on the frame energy and periodicity. The performance of the proposed method is verified using an Aurora-2 task, and the results confirm a significantly improved performance under noisy conditions.

This paper presents a novel approach for robust impulsive synchronization of uncertain complex dynamical networks, each node of which possesses chaotic dynamics with different parameters perturbation and external disturbances as well as unknown but bounded network coupling effects. A new sufficient condition is proposed that guarantees the global robust synchronizing of such a network. Finally, the effectiveness of the proposed approach is evaluated by performing simulations on two illustrative examples.

This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur'e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

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.

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.

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.

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.

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.

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.

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.

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.

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.

We present a novel approach to developing Machine Learning (ML) based decoding models for extracting a watermark in the presence of attacks. Statistical characterization of the components of various frequency bands is exploited to allow blind extraction of the watermark. Experimental results show that the proposed ML based decoding scheme can adapt to suit the watermark application by learning the alterations in the feature space incurred by the attack employed.