Recently, a cluster map based blind RBF equalizer (CM-BRE) has been proposed. By utilizing the underlying structure characteristics of RBF equalizer, the CM-BRE can be implemented by the combination of neural-gas algorithm (NGA) with several sorting operations. Although the CM-BRE is able to achieve almost identical performance with the optimal RBF equalizer, the high computational load mainly caused by NGA limits it's application. In this paper, we propose a downsizing method that employs the inter-relation among RBF centers and significantly reduces the NGA's computational load. Furthermore, a method to determine the feedback vector is derived, then CM-BRE is extended to a cluster map based blind RBF decision feedback equalizer (CM-BRDFE). The proposed CM-BRDFE also shows the close performance with the optimal RBF decision feedback equalizer in simulations.

The present paper investigates a two-dimensional (2-D) adaptive lattice filter used for modeling 2-D AR fields. The 2-D least mean square (LMS) lattice algorithm is used to update the filter coefficients. The proposed adaptive lattice filter can represent a wider class of 2-D AR fields than previous ones. Furthremore, its structure is also shown to possess orthogonality in the backward prediction error fields. These result in superior convergence and tracking properties to the adaptive transversal filter and other adaptive 2-D lattice models. Then, the convergence property of the proposed adaptive LMS lattice algorithm is discussed. The effectiveness of the proposed model is evaluated for parameter identification through computer simulation.

The purpose of this paper is to propose a novel cluster map based blind RBF equalizer for received signal constellation (RSC) independent channel, which belongs to RSC based blind equalization approach. Without channel estimator, firstly, the desired numbers of unlabeled RBF centers are obtained by an unsupervised clustering algorithm. Then a cluster map generated from the known RBF equalizer structure is used to partition the unlabeled centers into appropriate subsets merely by several simple sorting operations, which corresponds to the weight initialization. Finally, the weight is adjusted iteratively by an unsupervised least mean square (LMS) algorithm. Since the process of the weight initialization using the underlying structure of RBF equalizer is very effective, the proposed blind RBF equalizer can achieve almost identical performance with the optimal RBF equalizer. The validity of the proposed equalizer is also demonstrated by computer simulations.

In OFDM systems, the estimation/correction of carrier frequency offset (CFO) is crucial to maintain orthogonality among subcarriers. However, the CFO estimation suffers from DC offset (DCO) generated in low-cost direct-conversion receivers (DCRs). More seriously, in practice, DCO is time-varying due to the automatic gain control. In this paper, a novel CFO estimator in the presence of time-varying DCO is proposed. It is shown the residual DCO after high-pass filtering varies in a linear fashion. Based on this observation and the periodicity of the training sequence, we derive a CFO estimator independent of DCO. Also, the residual DCO can be estimated, using the obtained CFO. The validity of the proposed estimation method is demonstrated by simulations.

Gas or water leaks in pipes that are buried under ground or that are situated in the walls of buildings may occur due to aging or unpredictable accidents, such as earthquakes. Therefore, the detection of leaks in pipes is an important task and has been investigated extensively. In the present paper, we propose a novel leak detection method by means of acoustic wave. We inject an acoustic chirp signal into a target pipeline and then estimate the leak location from the delay time of the compressed pulse by passing the reflected signal through a correlator. In order to distinguish a leak reflection in a complicated pipeline arrangement, the reflection characteristics of leaks are carefully discussed by numerical simulations and experiments. There is a remarkable difference in the reflection characteristics between the leak and other types of discontinuity, and the property can be utilized to distinguish the leak reflection. The experimental results show that, even in a complicated pipe arrangement including bends and branches, the proposed approach can successfully implement the leak detection. Furthermore, the proposed approach has low cost and is easy to implement because only a personal computer and some commonly equipment are required.

In an orthogonal frequency division multiplexing (OFDM) receiver with direct-conversion architecture, carrier frequency offset (CFO) and direct-current offset (DCO), which cause severe performance degradation, need to be estimated and compensated. Recently, by investigating the subspace of OFDM signal after coarse DCO cancellation using time-domain average, we have proposed a nullspace-based estimator (NSE), for blind CFO and DCO estimation. In this paper, based on an analysis of the cost function of the NSE, we propose a common nullspace based estimator (CNSE). It is shown that by matching the frequency occupation of the received OFDM signal with CFO and DCO, the CNSE can achieve the full performance potential of the NSE. Also, the performance analysis reveals that the CNSE can asymptotically approach the Cramer-Rao bound (CRB) of OFDM CFO estimation in the presence of DCO. Finally the analysis results are confirmed by simulations.

In OFDM based mobile communication systems, channel variation during one symbol period introduces intercarrier interference (ICI). Conventional pilot-aided equalization mitigates the ICI at the price of band inefficiency. On the other hand, the blind or semi-blind equalization method, which utilizes the known statistic properties of the transmitted data, will raise system complexity. In this letter, without bandwidth-consuming pilots, a novel channel estimation and tracking method based on an iterative equalization technique (IET) is proposed. The proposed approach successfully achieves a good compromise between bandwidth efficiency and system complexity, and its validity is demonstrated by numerical simulations, especially for fast fading channel.

In this letter, a neural network (NN) for peak power reduction of an orthogonal frequency-division multiplexing (OFDM) signal is improved in order to suppress its computational complexity. Numerical experiments show that the amount of IFFTs in the proposed NN can be reduced to half, and its computational time can be reduced by 21.5% compared with a conventional NN. In comparison with the SLM, the proposed NN is effective to achieve high PAPR reduction and it has an advantage over the SLM under the equal computational condition.

We present an extended quarter-plane lattice model for generating two-dimensional (2-D) autoregressive fields. This work is a generalization of the extended lattice filter of diagonal form (ELDF) developed by Ertuzun et al. The proposed model represents a wider class of 2-D AR fields than conventional lattice models. Several examples are presented to demonstrate the applicability of the proposed model. Furthermore, the proposed structure is compared with other conventional lattice filters based on the computation of their entropy values.

In mobile OFDM systems, sub-carriers orthogonality will be broken due to Doppler shift, and this results in inter-carrier interference (ICI). Many methods have been proposed to compensate for this, however, these methods won't be suitable for fast fading caused by high mobile speed. In this letter, we propose a novel sampling theorem based pilot symbol-aided technique which can not only estimate the channel fading envelope (CFE) accurately under high relative Doppler frequency (RDF) but also achieve lower BER than conventional methods. The validity of the proposed method is demonstrated by computer simulations.

The purpose of this paper is to derive a novel fractionally spaced Bayesian decision feedback equalizer (FS-BDFE). The oversampling technique changes single input single output (SISO) linear channel to single input multiple output (SIMO) linear channel. The Bayesian decision variable in the FS-BDFE is defined as the product of Bayesian decision variables in the Bayesian decision feedback equalizers (BDFE) corresponding to each channels of the SIMO. It can be shown that the FS-BDFE has less decision error probability than the conventional BDFE. The effectiveness of the proposed equalizer is also demonstrated by the computer simulation.

The present paper examines a two-dimensional (2-D) joint-process lattice estimator and its implementation for image restoration. The gradient adaptive lattice (GAL) algorithm is used to update the filter coefficients. The proposed adaptive lattice estimator can represent a wider class of 2-D FIR systems than the conventional 2-D lattice models. Furthermore, its structure possesses orthogonality between the backward prediction errors. These results in superior convergence and tracking properties versus the transversal and other 2-D adaptive lattice estimators. The validity of the proposed model for image restoration is evaluated through computer simulations. In the examples, the implementation of the proposed lattice estimator as 2-D adaptive noise cancellator (ANC) and 2-D adaptive line enhancer (ALE) is considered.

In this paper, we propose a two-dimensional (2-D) least-squares lattice (LSL) algorithm for the general case of the autoregressive (AR) model with an asymmetric half-plane (AHP) coefficient support. The resulting LSL algorithm gives both order and space recursions for the 2-D deterministic normal equations. The size and shape of the coefficient support region of the proposed lattice filter can be chosen arbitrarily. Furthermore, the ordering of the support signal can be assigned arbitrarily. Finally, computer simulation for modeling a texture image is demonstrated to confirm the proposed model gives rapid convergence.

Penumbral imaging is a technique which uses the facts that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. The technique is based on a linear deconvolution. In this paper, a two-step method is proposed for decoding penumbral images. First a local-statistic filter based on adaptive windowing is applied to smooth the noise; then, followed by the conventional linear deconvolution. The simulation results show that the reconstructed image is dramatically improved in comparison to that without the noise-smoothing filtering, and the proposed method is also applied to real experimental X-ray imaging.

This paper proposes a novel Orthogonal frequency-division multiplexing (OFDM) system based on polynomial cancellation coded OFDM (PCC-OFDM). This proposed system can reduce peak-to-average power ratio (PAPR) by our neural phase rotator and it does not need any side information to transmit phase rotation factors. Moreover, this system can compensate the common phase error (CPE) by a proposed technique which allows estimating frequency offset at receiver. From numerical experiments, it is shown that our system can reduce PAPR and ICI at the same time and improve BER performance effectively.

The spectrum sculpting precoder (SSP) is a precoding scheme for sidelobe suppression of frequency division multiplexing (OFDM) signals. It can form deep spectral notches at chosen frequencies and is suitable for cognitive radio systems. However, the SSP degrades the error rate as the number of notched frequencies increases. Orthogonal precoding that improves the SSP can achieve both spectrum notching and the ideal error rate, but its computational complexity is very high since the precoder matrix is large in size. This paper proposes an effective and equivalent decomposition of the precoder matrix by QR-decomposition in order to reduce the computational complexity of orthogonal precoding. Numerical experiments show that the proposed method can drastically reduce the computational complexity with no performance degradation.

The conventional algorithms in the echo canceling system have drawback when they are faced with double-talk condition in noisy environment. Since the double-talk and noise signal are exist, then the error signal is contaminated to estimate the gradient correctly. In this paper, we define a new class of adaptive algorithm for tap adaptations, based on the correlation function processing. The computer simulation results show that the Correlation LMS (CLMS) and the Extended CLMS (ECLMS) algorithms have better performance than conventional LMS algorithm. In order to implement the ECLMS algorithm, the Frequency domain Extended CLMS (FECLMS) algorithm is proposed to reduce the computational complexity. However the convergence speed is not sufficient. In order to improve the convergence speed, the Wavelet domain Extended CLMS (WECLMS) algorithm is proposed. The computer simulation results support the theoretical findings and verify the robustness of the proposed WECLMS algorithm in the double-talk situation.

The purpose of this letter is to modify the correlation least mean squares algorithm using a sum of the lagged squared errors as the cost function and extend the modified CLMS algorithm to two-dimensional domain. The effectiveness of the proposed algorithm is shown by the computer simulation.

An adaptive joint-process IIR filter with generalized lattice structure is constructed. This filter can borrow both FIR and IIR features and simultaneously holds the well-known merits of lattice structure.

An adaptive multichannel IIR lattice predictor for k-step ahead prediction is constructed and the effectiveness of the proposed predictor is evaluated using digital simulations.