A multiple channel active noise control (ANC) system with several secondary sources, error sensors, and reference sensors has been used for complicated noise fields. Centralized multiple channel ANC systems have been proposed, however implementation of such systems becomes difficult according to increase of control points. Distributed multiple channel ANC systems which have more than a controller are considered. This paper proposes a new implementation of distributed multiple channel ANC systems based on simultaneous equations methods. In the proposed algorithm, communications between controllers are permitted to distribute the computational burden and to improve the performance of noise reduction. This algorithm shows good performances for noise cancellation and tracking of changes in the error paths.

In this paper, we present a method to analyze the steady-state performance of a complex coefficient adaptive IIR notch filter which is useful for the rejection of multiple narrow-band interferences from broad-band signals in quadrature phase shift keying (QPSK) spread-spectrum communication systems. The adaptive notch filter based on the simplified gradient algorithm is considered. Analytical expressions have been developed for the conditional mean and variance of notch filter output. The signal-to-noise ratio improvement factor is also obtained from which the validity of the use of the notch filter can be concluded. Finally, the results of computer simulations are shown which confirm the theoretical predictions.

In this paper, we propose a new error feedback (EF) structure for 2-D separable-denominator digital filters described by a rational transfer function. In implementing two-dimensional separable-denominator digital filters, the minimum delay elements structures are common. In the proposed structure, the filter feedback-loop corresponding to denominator polynomial is placed at a different location compared to the commonly used structures. The proposed structure can minimize the roundoff noise more than the previous structure though the number of multipliers is less than that of previous one. Finally, we present a numerical example by designing the EF on the proposed structure and demonstrate the effectiveness of the proposed method.

Based on the Fornasini-Marchesini second local state-space (LSS) model, the coefficient sensitivities of two-dimensional (2-D) digital filters are analyzed in conjunction with frequency weighting functions. The overall sensitivity called the frequency-weighting sensitivity is then evaluated using the 2-D generalized Gramians that are newly introduced for the Fornasini-Marchesini second LSS model. Next, the 2-D filter structures that minimize the frequency-weighting sensitivity are synthesized for two cases of no constraint and scaling constraints on the state variables. Finally, an example is given to illustrate the utility of the proposed technique.

A simultaneous equations method is one of active noise control algorithms without estimating an error path. This algorithm requires identification of a transfer function from a reference microphone to an error microphone containing the effect of a noise control filter. It is achieved by system identification of an auxiliary filter. However, the introduction of the auxiliary filter requires more number of samples to obtain the noise control filter and brings a requirement of some undesirable assumption in the multiple channel case. In this paper, a new simultaneous equations method without the identification of the auxiliary filter is proposed. By storing a small number of input signals and error signals, we avoid this identification. Therefore, we can reduce the number of samples to obtain the noise control filters and can avoid the undesirable assumption. From simulation examples, it is verified that the merits of the ordinary method is also retained in the proposed method.

For multiple-channel active noise control (ANC) systems, distributed systems consisting of more than one controller are useful. In this paper, we propose a performance improvement algorithm for the distributed multiple-channel ANC system based on the simultaneous equations method. In the proposed algorithm, no estimation of error paths is required. This algorithm can provide good performance in canceling primary noises with auto-/cross-correlations and achieve stable noise reduction under a change of the error paths.

The minimization problem of an L2-sensitivity measure subject to L2-norm dynamic-range scaling constraints is formulated for a class of two-dimensional (2-D) state-space digital filters. First, the problem is converted into an unconstrained optimization problem by using linear-algebraic techniques. Next, the unconstrained optimization problem is solved by applying an efficient quasi-Newton algorithm with closed-form formula for gradient evaluation. The coordinate transformation matrix obtained is then used to synthesize the optimal 2-D state-space filter structure that minimizes the L2-sensitivity measure subject to L2-norm dynamic-range scaling constraints. Finally, a numerical example is presented to illustrate the utility of the proposed technique.

In Orthogonal Frequency Division Multiplexing (OFDM), the composite time signal exhibits a high peak-to-average power ratio (PAPR). Due to non-linearities of the transmit power amplifiers, this high PAPR generates in-band distortion, out of band noise (OBN) or spectral spreading, which degrades the bit-error rate (BER) performance. In this paper, we propose a simple way to combat this problem without sacrificing channel estimation and frequency-offset tracking accuracy, by designing a sub-optimal configuration of the pilot tones. The effectiveness of the newly designed pilot tones in reducing PAPR is validated by Monte-Carlo simulations. The corresponding improvement in BER is also verified by simulations under IEEE 802.11a standard settings, by using the channel with perfect CSI and the designed pilot-aided estimated channel for coherent detection.

To take intercarrier interference (ICI) attributed to time variations of the channel into consideration, the time- and frequency-selective (doubly-selective) channel is parameterized by a finite parameter model. By capitalizing on the finite parameter model to approximate the doubly-selective channel, a Kalman filter is developed for channel estimation. The ICI suppressing, reduced-complexity Viterbi-type Maximum Likelihood (RML) equalizer is incorporated into the Kalman filter for recursive channel tracking and equalization to improve the system performance. An enhancement in the channel tracking ability is validated by theoretical analysis, and a significant improvement in BER performance using the channel estimates obtained by the recursive channel estimation method is verified by Monte-Carlo simulations.

Block-state realization of state-space digital filters offers reduced implementation complexity relative to canonical state-space filters while filter's internal structure remains accessible. In this paper, we present a quantitative analysis on l2 coefficient sensitivity of block-state digital filters. Based on this, we develop two techniques for minimizing average l2-sensitivity subject to l2-scaling constraints. One of the techniques is based on a Lagrange function and some matrix-theoretic techniques. The other solution method converts the problem at hand into an unconstrained optimization problem which is solved by using an efficient quasi-Newton algorithm where the key gradient evaluation is done in closed-form formulas for fast and accurate execution of quasi-Newton iterations. A case study is presented to demonstrate the validity and effectiveness of the proposed techniques.

Based on the Fornasini-Marchesini second model, an efficient algorithm is developed to derive the characteristic polynomial and the inverse of the system matrix from the state-space parameters. As a result, the external description of the Fornasini-Marchesini second model is clarified. A technique for designing 2-D recursive digital filters in the frequency domain is then presented by using the Fornasini-Marchesini second model. The resulting filter approximates both magnitude and group delay specifications and its stability is always guaranteed. Finally, three design examples are given to illustrate the utility of the proposed technique.

Adaptive optimization of the notch bandwidth of a lattice-based adaptive infinite impulse response (IIR) notch filter is presented in this paper. The filter is used to improve the performance of a direct sequence spread spectrum (DSSS) binary phase shift keying (BPSK) communication system by suppressing a narrow-band interference at the receiver. A least mean square (LMS) algorithm used to adapt the notch bandwidth coefficient to its optimum value which corresponds to the maximum signal to noise ratio (SNR) improvement factor is derived. Bit error rate (BER) improvement gained by the DSSS communication system using the filter with the optimized notch bandwidth is also shown. Computer simulation results are compared with those obtained analytically to demonstrate the validity of theoretical predictions for various received signal parameters.

This paper presents a new implementation of fuzzy filters for edge-preserving smoothing of an image corrupted by impulsive and white Gaussian noise. This filter structure is expressed as an adaptive weighted mean filter that uses fuzzy control. The parameters of this filter can be adjusted by learning. Finally, simulation results demonstrate the effectiveness of the proposed technique.

The sub-recursive least squares (sub-RLS) algorithm estimates the coefficients of adaptive filter under the least squares (LS) criterion, however, does not require the calculation of inverse matrix. The sub-RLS algorithm, based on the different principle from the RLS algorithm, still provides a convergence property similar to that of the RLS algorithm. This paper first rewrites the convergence condition of the sub-RLS algorithm, and then proves that the convergence property of the sub-RLS algorithm successively approximates that of the RLS algorithm on the convergence condition.

Since most process systems have nonlinearities, it is necessary to consider the design of schemes to deal with such systems. In this paper, a new design scheme of PID controllers is proposed. This scheme is designed based on the internal model control (IMC) which is a kind of the model driven controllers. The internal model consists of the design-oriented model and the full model. The full model is designed by using the neural network. The primary PID control system is firstly constructed for the augmented system which is composed of the controlled object and the internal model, and this control system is designed by the pole-assignment method. Furthermore, the secondary PID controller is designed in order to remove the steady state error. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on a simulation example.

Based on the concept of polynomial operators, this paper explores generalized direct-form II structure and its state-space realization for two-dimensional separable-denominator digital filters of order (m, n) where a structure with 3(m+n)+mn+1 fixed parameters plus m+n free parameters is introduced and analyzed. An l2-scaling method utilizing different coupling coefficients at different branch nodes to avoid overflow is presented. Expressions of evaluating the roundoff noise for the filter structure as well as its state-space realization are derived and investigated. The availability of the m+n free parameters is shown to be beneficial as the roundoff noise measures can be minimized with respect to these free parameters by means of an exhaustive search over a set with finite number of candidate elements. The important role these parameters can play in the endeavors of roundoff noise reduction is demonstrated by numerical experiments.