Wen-Rong Wu

Image contrast enhancement based on a histogram transformation of local standard deviation

The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CGs) to adjust the high-frequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (ILSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, we present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunts image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using our formulation, it can be shown that conventional ACEs use linear functions to compute the new CGs. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. Finally, simulations using some X-ray images are provided to demonstrate the effectiveness of our new algorithm.

Linear MMSE Transceiver Design in Amplify-and-Forward MIMO Relay Systems

We consider the precoding problem in an amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay system in which multiple antennas are equipped at the source, the relay, and the destination. Most existing methods for this problem only consider the design of the relay precoder, and some even ignore the direct link. In this paper, we consider a joint source/relay precoder design problem, taking both the direct and the relay links into account. Using a minimum-mean-square-error (MMSE) criterion, we first formulate the problem as a constrained optimization problem. However, it is found that the mean square error (MSE) is a highly nonlinear function of the precoders, and a direct optimization is difficult to conduct. We then design the precoders to diagonalize the MSE matrix in the cost function. To do that, we pose certain structural constraints on the precoders and derive an MSE upper bound. It turns out that minimization with respect to this bound becomes simple and straightforward. Using the standard Lagrange technique, we can finally obtain the solution with an iterative water-filling method. Simulation results show that the proposed method, with an additional precoder, outperforms the existing methods, in terms of either the MSE or the bit error rate (BER).

Adaptive AR modeling in white Gaussian noise

Autoregressive (AR) modeling is widely used in signal processing. The coefficients of an AR model can be easily obtained with a least mean square (LMS) prediction error filter. However, it is known that this filter gives a biased solution when the input signal is corrupted by white Gaussian noise. Treichler (1979) suggested the /spl gamma/-LMS algorithm to remedy this problem and proved that the mean weight vector can converge to the Wiener solution. In this paper, we develop a new algorithm that extends works of Vijayan et al. (1990), for adaptive AR modeling in the presence of white Gaussian noise. By theoretical analysis, we show that the performance of the new algorithm is superior to the /spl gamma/-LMS filter. Simulations are also provided to support our theoretical results.

New nonlinear algorithms for estimating and suppressing narrowband interference in DS spread spectrum systems

It has been shown that the narrowband (NB) interference suppression capability of a direct-sequence (DS) spread spectrum system can be enhanced considerably by processing the received signal via a prediction error filter. The conventional approach to this problem makes use of a linear filter. However, the binary DS signal, that acts as noise in the prediction process, is highly non-Gaussian. Thus, linear filtering is not optimal. Vijayan and Poor (1990) first proposed using a nonlinear approximate conditional mean (ACM) filter of the Masreliez (1975) type and obtained significant results. This paper proposes a number of new nonlinear algorithms. Our work consists of three parts. (1) We develop a decision-directed Kalman (DDK) filter, that has the same performance as the ACM filter but a simpler structure. (2) Using the nonlinear function in the ACM and the DDK filters, we develop other nonlinear least mean square (LMS) filters with improved performance. (3) We further use the nonlinear functions to develop nonlinear recursive least squares (RLS) filters that can be used independently as predictors or as interference identifiers so that the ACM or the DDK filter can be applied. Simulations show that our nonlinear algorithms outperform conventional ones.

Joint source/relay precoder design in nonregenerative cooperative systems using an MMSE criterion

This paper considers transmitter precoding in an amplify-and-forward cooperative system where multiple antennas are equipped at the source, the relay, and the destination. Existing methods for the problem only consider the design of the relay precoder. To further improve the performance, we include the source precoder into the design. Using a minimum-meansquare- error (MMSE) criterion, we propose a joint source/relay precoder design method, taking both the direct and relay links into account. It is shown that the MMSE is a highly nonlinear function of the precoding matrices, and a direct minimization is not feasible. To facilitate analysis, we propose to design the precoders toward first diagonalizing the MSE matrix of the relay link. This imposes certain structural constraints on both precoders that allow us to derive an analytically tractable MSE upper bound. By conducting minimization with respect to this bound, the solution can be obtained by an iterative water-filling technique.

Subband Kalman filtering for speech enhancement

Kalman filtering is an effective speech-enhancement technique, in which speech signals are usually modeled as autoregressive (AR) processes and represented in the state-space domain. Since AR coefficients identification and Kalman filtering require extensive computations, real-time implementation of this approach is difficult. This paper proposes a simple and practical scheme that overcomes these obstacles. Speech signals are first decomposed into subbands. Subband speech signals are then modeled as low-order AR processes, such that low-order Kalman filters can be applied. Enhanced fullband speech signals are finally obtained by combining the enhanced subband speech signals. To identify AR coefficients, prediction-error filters adapted by the LMS algorithm are applied. Due to noisy inputs, the LMS algorithm converges to biased solutions. The performance of the Kalman filter with biased parameters is analyzed. It is shown that accurate estimates of AR coefficients are not required when the driving-noise variance is properly estimated. New methods for making such estimates are proposed. Thus, we can tolerate biased AR coefficients and take advantage of the LMS algorithms simple structure. Simulation results show that speech enhancement in the subband domain not only greatly reduces the computational complexity, but also achieves better performance compared to that in the fullband domain.

Maximum Likelihood Timing and Carrier Frequency Offset Estimation for OFDM Systems With Periodic Preambles

Symbol timing offset (STO) and carrier frequency offset (CFO) estimation are two main synchronization operations in packet-based orthogonal frequency division multiplexing (OFDM) systems. To facilitate these operations, a periodic preamble is often placed at the beginning of a packet. CFO estimation has been extensively studied for the case of two-period preambles. In some applications, however, a preamble with more than two periods is available. A typical example is the IEEE802.11a/g wireless local area network system, which features a ten-period preamble. Recently, researchers have proposed a maximum likelihood (ML) CFO estimation method for such systems. This approach first estimates the received preamble using a least squares method and then maximizes the corresponding likelihood function. In addition to the standard calculations, this method requires an extra procedure to solve the roots of a polynomial function, which is disadvantageous for real-world implementations. In this paper, we propose a new ML method to solve the likelihood function directly and thereby perform CFO estimation. Our method can obtain a closed-form ML solution, without the need for the root-finding step. We further extend the proposed method to address the STO estimation problem as well as derive a lower bound on the estimation performance. Our simulations show that while the performance of the proposed method is either equal to or better than the existing method, the computational complexity is lower.

A robust adaptive generalized sidelobe canceller with decision feedback

Conventional generalized sidelobe canceller (GSC) is sensitive to a mismatch between the estimated and actual direction of arrival (DOA) of the desired signal. Such a mismatch induces signal cancellation in the GSC, and it severely degrades the beamforming performance. In this paper, we propose a new decision feedback (DF) technique to increase the robustness against the DOA mismatch. Our new scheme introduces a blind equalizer and a feedback filter in the GSC structure. We first derive Wiener solutions for the DF-GSC with perfectly matched and mismatched DOA and show that the problem of signal cancellation can be avoided. Then, we consider the adaptive GSC implementation in which the least-mean-square (LMS) algorithm is used for weight adaptation. In addition to the improved robustness, the proposed scheme also remedies the slow convergence problem inherent in the conventional adaptive GSC structure. The convergence behavior of the LMS-based DF-GSC is fully analyzed and the analytic signal-to-interference-plus-noise ratio (SINR) is also derived. Finally, simulation results demonstrate that while the proposed structure can considerably enhance the overall performance, it has greatly improved robustness as compared to other existing robust adaptive beamformers.

Low-Complexity ICI Mitigation Methods for High-Mobility SISO/MIMO-OFDM Systems

In orthogonal frequency-division multiplexing (OFDM) systems, it is generally assumed that the channel response is static in an OFDM symbol period. However, the assumption does not hold in high-mobility environments. As a result, intercarrier interference (ICI) is induced, and system performance is degraded. A simple remedy for this problem is the application of the zero-forcing (ZF) equalizer. Unfortunately, the direct ZF method requires the inversion of an N times N ICI matrix, where N is the number of subcarriers. When N is large, the computational complexity can become prohibitively high. In this paper, we first propose a low-complexity ZF method to solve the problem in single-input-single-output (SISO) OFDM systems. The main idea is to explore the special structure inherent in the ICI matrix and apply Newtons iteration for matrix inversion. With our formulation, fast Fourier transforms (FFTs) can be used in the iterative process, reducing the complexity from O (N3) to O (N log2 N). Another feature of the proposed algorithm is that it can converge very fast, typically in one or two iterations. We also analyze the convergence behavior of the proposed method and derive the theoretical output signal-to-interference-plus-noise ratio (SINR). For a multiple-input-multiple-output (MIMO) OFDM system, the complexity of the ZF method becomes more intractable. We then extend the method proposed for SISO-OFDM systems to MIMO-OFDM systems. It can be shown that the computational complexity can be reduced even more significantly. Simulations show that the proposed methods perform almost as well as the direct ZF method, while the required computational complexity is reduced dramatically.

A nonlinear IMM algorithm for maneuvering target tracking

In target tracking, the measurement noise is usually assumed to be Gaussian. However, the Gaussian modeling of the noise may not be true. Noise can be non-Gaussian. The non-Gaussian noise arising in a radar system is known as glint noise. The distribution of glint noise is long tailed and will seriously affect the tracking performance. We develop a new algorithm that can effectively track a maneuvering target in the glint environment The algorithm incorporates the nonlinear Masreliez filter into the interactive multiple model (IMM) method. Simulations demonstrate the superiority of the new algorithm. >