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Abstract: The steady flow of new research results and developments in the field of adaptive equalization that was witnessed for at least the last four decades is clearly evidenced by the many footprints of success it left behind and shows no sign of ending. The thrust of research and implementation in this field is mainly powered by the use of the well-known mean-square cost function upon which relies the ubiquitous least-mean square (LMS) algorithm. However, such an algorithm is well-known to lead to sub-optimal solutions in the real world that is largely dominated by non-Gaussian interference signals. The use of a non-mean-square cost function would successfully tackle these types of interference signals but would invariably involve a higher computational cost. To address these important practical issues, this paper proposes a new adaptive equalization technique that combines both the least-mean-fourth (LMF) algorithm, which is governed by a non-mean-square cost function, with a power-of-two quantizer (PTQ) in the coefficient update process, which greatly reduces the computational cost involved and which therefore makes the proposed technique applicable to time-varying environments. This paper not only elaborates on the basic idea behind the proposed technique but also defines the necessary assumptions and provides a thorough statistical performance analysis (including a study of the convergence behavior) of the combined algorithm LMF-PTQ that is at the core of the proposed technique. An extensive simulation work was carried out and showed that the theoretical predictions are very well substantiated. [Copyright &y& Elsevier]

Multimodulus algorithms (MMA) based adaptive blind equalizers mitigate inter-symbol interference and recover carrier-phase in communication systems by minimizing dispersion in the in-phase and quadrature components of the received signal using the respective components of the equalized sequence in a decoupled manner. These equalizers are mostly incorporated in bandwidth-efficient digital receivers which rely on quadrature amplitude modulation (QAM) signaling. The nonlinearities in the update equations of these equalizers tend to lead to difficulties in the study of their steady-state performance. This paper presents originally the steady-state excess mean-square-error (EMSE) analysis of different members of multimodulus equalizers MMA p – q in a non-stationary environment using energy conservation arguments, and thus bypassing the need for working directly with the weight error covariance matrix. In doing so, the exact and approximate expressions for the steady-state mean-square-error of several MMA based blind equalization algorithms are derived, including MMA2–2, MMA2–1, MMA1–2, and MMA1–1. The accuracy of the derived analytical results is validated using Monte–Carlo experiments and found to be in close agreement. [ABSTRACT FROM AUTHOR]

Abstract: It is recently shown that algorithms derived from random matrix theory (RMT) can provide superior performance for spectrum sensing, which corresponds to the task of detecting the presence of primary users in cognitive radio. The essence of the RMT-based methods is to utilize the distribution of extremal eigenvalues of the received signal sample covariance matrix (SCM), namely, the Tracy–Widom (TW) distribution. Although the TW distribution is quite useful in spectrum sensing, computationally demanding numerical evaluation is required because it does not have an explicit closed-form expression. In this paper, we devise two novel volume-based detectors by exploiting the determinant of the SCM or volume to distinguish between the signal-presence and signal-absence cases. With the use of RMT, we accurately produce the theoretical decision threshold for one of the detectors under the Gaussian noise assumption. Simulation results are included to illustrate the effectiveness of the volume-based detectors. [Copyright &y& Elsevier]

Abstract: This paper shows a new algorithm to improve the performance of IQ demodulators and frequency converters exhibiting gain and phase imbalances between their branches. This algorithm does not require any input calibration signal and is independent of the input signal level. It exploits the spectral coherence (SC) concept using a monobit kernel to achieve optimization targets with minimum time to convergence, low computational load, and a wide range of input levels. Its effectiveness is shown through a low-IF receiver that improves its image rejection ratio (IRR) from 30 dB to 60 dB. [Copyright &y& Elsevier]

Abstract: This paper presents new algorithms that exploit uncorrelation between adjacent data samples produced by IFFT in ADSL systems. These algorithms update time-domain equalizer (TEQ) coefficients by adjacent lag autocorrelation minimization (ALAM) to shorten the channel. ALAM is an addition to the family of several existing autocorrelation-based algorithms that can achieve similar or better performance at lower complexity compared to other algorithms. The complexity reduction is achieved by designing a cost function without the sum-square, and further utilizing symmetrical TEQ property to reduce the complexity of the adaptation of TEQ to half of other existing algorithms. Furthermore, to avoid the limitations of lower bitrate after reaching the maximum bitrate and high complexity, an adaptive TEQ using equal-taps constraint (ETC) is introduced to maximize the bitrate along with achieving the lowest complexity. In addition, a simplified approach is used to calculate the expectation to reduce the computational complexity for ALAM and the other existing autocorrelation-based algorithms. Simulation results demonstrate that the proposed algorithms and modified existing algorithms achieve similar or better performance figures with reduced complexities. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a reversible data hiding algorithm for grayscale images. Specifically, our algorithm is based on the histogram modification technique. The premise of this algorithm is that a histogram is constructed from the differences between each pixel and its neighbors. In the data embedding process, a modified histogram shifting scheme is used to embed a secret message into the pixels whose pixel difference is located at the peak value within the histogram. Experimental results show that our algorithm can achieve higher embedding capacity and imperceptible distortion. Performance comparisons with other existing algorithms are also provided to demonstrate the feasibility of our proposed algorithm in reversible data hiding. [Copyright &y& Elsevier]

Abstract: Selective partial update of the adaptive filter coefficients has been a popular method for reducing the computational complexity of least mean-square (LMS)-type adaptive algorithms. These algorithms use a fixed step-size that forces a performance compromise between fast convergence speed and small steady state misadjustment. This paper proposes a variable step-size (VSS) selective partial update LMS algorithm, where the VSS is an approximation of an optimal derived one. The VSS equations are controlled by only one parameter, and do not require any a priori information about the statistics of the system environment. Mean-square performance analysis will be provided for independent and identically distributed (i.i.d.) input signals, and an expression for the algorithm steady state excess mean-square error (MSE) will be presented. Simulation experiments are conducted to compare the proposed algorithm with existing full-update VSS LMS algorithms, which indicate that the proposed algorithm performs as well as these algorithms while requiring less computational complexity. [Copyright &y& Elsevier]

Abstract: The environment of the communication system will be more complicated in the future. Therefore, the adaptive array alone cannot cancel all the interferences in future communication system. Many space–temporal joint equalizers (STE) have been proposed to further enhance the capability of canceling the co-channel interferences (CCI) and the multi-path inter-symbol interferences (ISI). The recursive least square (RLS) algorithm of those joint methods will suffer the problems of system structure is complex, the convergent rate of performance is slow or number of calculations is large. In this paper, we propose a simple RLS algorithm (SRLS) of the STE to reduce the computation load, simplify the system structure and improve the system performance. The SRLS algorithm uses the output signal of STE to adjust the weights of adaptive array and equalizer simultaneously without using a transversal filter. Since there is no transversal filter required in the system, the system is simpler in comparison to the conventional STE. Besides, the RLS algorithm of conventional STE adjusts the weights of equalizer after the weights of adaptive array. The convergent rate of weights is slowly in those STE systems. The SRLS algorithm adjusts the weights of adaptive array and equalizer simultaneously. Therefore, the convergent rate of system will improve by the SRLS. Furthermore, the SRLS algorithm is found with input vectors of adaptive array and equalizer separately. Another STE method, which combines the input vectors of adaptive array and equalizer into a large dimension input vector, needs more number of calculations than the SRLS method. Thus, the SRLS of STE can simplify the complexity of system structure, reduce the computation load and improve the convergent performance of system effectively. [Copyright &y& Elsevier]

Abstract: This paper aims at analyzing the performance of various fast methods for the computation of pseudo Zernike moments (PZMs) with respect to their time complexity and numerical stability. Based on these different types of existing methods, four new methods are also proposed. A computational framework is provided which integrates the fast calculation of pseudo Zernike polynomials (PZPs), the angular functions and symmetry/anti-symmetry property of complex kernel functions. It is shown that the recursive methods that compute PZMs of all orders for an image of resolution pixels reduce the time complexity from to . Experimental analysis for numerical stability of various methods is provided which could be useful for the evaluation of these methods. It is observed that recursive methods are numerically stable for high orders of moments and their reconstruction error declines with increase in image size and moment order. [Copyright &y& Elsevier]

Abstract: In this paper, we have proposed two extensions to pixon-based image modeling. The first one is using bicubic interpolation instead of bilinear interpolation and the second one is using fuzzy filtering method, aiming to improve the quality of the pixonal image. Finally, partial differential equations (PDEs) are applied on the pixonal image for noise removing. The proposed algorithm has been examined on variety of standard images and their performance compared with the existing algorithms. Experimental results show that in comparison with the other existing methods, the proposed algorithm has a better performance in denoising and preserving image edges. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of joint block diagonalization (JBD) of a set of given matrices. As is known that the nonunitary JBD algorithm has some advantages over the existing orthogonal one for convolutive blind source separation (CBSS). However, the nonunitary JBD algorithm is prone to converge to some unexpected degenerate solutions (singular or ill-conditioned solutions). Especially for the matrices of large dimension or the case that the number of the diagonal blocks is relatively large, the performances of the nonunitary JBD algorithm degrade more severely. To eliminate the degenerate solutions, we optimize a penalty term based weighted least-squares criterion and thus develop a fast efficient algorithm. The performance of the proposed algorithm is evaluated by computer simulations and compared with the existing state-of-the-art nonunitary JBD algorithm. The simulation results demonstrate the robustness and performance improvement of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, a low complexity data demodulation algorithm is proposed that requires time of arrival information of the received signal exclusively. As an application example, the algorithm is applied to an ultra-wideband impulse radio communication system with pulse position modulation. The algorithm is insensitive to a common time delay for all pulses, that means, it does not require an accurate synchronization between the transmitter and the receiver. For the performance estimation, only a symbol synchronization is assumed, i.e., that there is a priori knowledge which pulse marks the beginning of a received data symbol. The performance of the proposed algorithm is evaluated for straightforward time of arrivals estimators, such as a maximum detector or a threshold detector. It is shown that the proposed algorithm outperforms a least squares algorithm in all considered scenarios. In particular, an increased robustness against additive white Gaussian noise, impulse like noise, and multiuser interference is demonstrated as well as an improved performance for multipath propagation channels. [Copyright &y& Elsevier]

Abstract: In this paper, a new and simple palmprint recognition solution based on sparse representation is suggested. It is shown that when the aim is to recover a palmprint from a limited number of observations as a linear combination of measurements of the same palmprint class, the ensuing representation in intrinsically very sparse. It can be efficiently computed by solving an norm convex minimisation problem. When combined with well known subspace feature selection techniques such as PCA and LDA as well as with downsampled images, our tests, which have been carried out on 250 classes of the widely used PolyU database, have yielded an EER as low as 0.11% depending on the palmprints selected during the enrolment phase. Coupled with an execution time as short as 8.4 ms, the obtained results outperform similar work in the literature including EigenPalms, FisherPalms and Gabor based palmprint matching algorithms, which shows the effectiveness of the new solution. [Copyright &y& Elsevier]

Abstract: In this paper, we verify how far electric disturbance signals can be compressed without compromising the analysis of encoded fault records. A recently proposed compression algorithm, referred to as Damped Sinusoidal Matching Pursuit (DSMP) has the remarkable feature of obtaining both compact and physically interpretable representations. However, for fault analysis applications, one is primarily interested in how accurate can be the analysis performed on compressed signals, instead of evaluating mean-squared error figures. Unlike previous works in digital fault records compression, the performance of the DSMP compression method is evaluated using a protocol based on fault analysis procedures commonly performed by expert engineers. This protocol is applied for comparing the results obtained in the analysis of both uncompressed records and their compressed versions at different compression ratios. The results show that the DSMP is a reliable compression system since it achieves high compression ratios (6.4:1) without causing fault analysis misinterpretation. [Copyright &y& Elsevier]

Abstract: The dithered signed-error constant modulus algorithm (DSE-CMA) is an approach less computationally than CMA but exhibits ill-convergence. On the contrary, the concurrent CMA with soft decision-directed scheme () improves the convergence rate of CMA by adding SDD but brings lots of computational complexity. As a tradeoff, in this paper we combine the virtues of DSE-CMA and SDD, then propose a new algorithm, which compensates the phase shift and provides convergence faster than DSE-CMA with lower complexity than . More importantly, an approximation of its steady-state mean square error performance is derived. Simulation results show the superiority of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, a new FIR adaptive filtering algorithm is proposed. The approach uses a variable step-size and the instantaneous value of the autocorrelation matrix in the coefficient update equation that leads to an improved performance. Convergence analysis of the algorithm has been presented. Simulation results show that the algorithm performs better than the Transform Domain LMS with Variable Step-Size (TDVSS) in stationary Additive White Gaussian Noise (AWGN) and Additive Correlated Gaussian Noise (ACGN) environments in a system identification setting. It is shown that the algorithm has a performance better than RLS and very similar to RRLS algorithm with a considerable reduction in computational complexity. Additionally, the performance of the proposed algorithm is shown to be superior to that of the Stabilized Fast Transversal Recursive Least Squares (SFTRLS) algorithm under the same conditions. [Copyright &y& Elsevier]

Abstract: Square Contour Algorithm (SCA) is a relatively new blind equalization algorithm which reduces the dispersion between the output of the equalizer and a square contour. In this paper we develop Frequency Domain Square Contour Algorithm (FD-SCA) which is based on frequency domain implementation of block based SCA. Simulation results reveal that FD-SCA exhibits the same performance as that of existing time domain SCA with significantly low computational complexity. The performance of the FD-SCA in terms of convergence rate is further enhanced using normalization in frequency bins. The complete hardware architecture for the proposed FD-SCA based on ASM design flow along with implementation results is also presented. [Copyright &y& Elsevier]

Abstract: This paper presents a secure, robust, and blind adaptive audio watermarking algorithm based on singular value decomposition (SVD) in the discrete wavelet transform domain using synchronization code. In our algorithm, a watermark is embedded by applying a quantization-index-modulation process on the singular values in the SVD of the wavelet domain blocks. The watermarked signal is perceptually similar to the original audio signal and gives high quality output. Experimental results show that the hidden watermark data is robust to additive noise, resampling, low-pass filtering, requantization, MP3 compression, cropping, echo addition, and denoising. Performance analysis of the proposed scheme shows low error probability rates. The data embedding rate of the proposed scheme is 45.9 bps. The proposed scheme has high payload and superior performance against MP3 compression compared to the earlier audio watermarking schemes. [Copyright &y& Elsevier]

Abstract: It has been found that envelopes established by extrema in the empirical mode decomposition cannot always depict the local characteristics of a signal very well. This is due in part to the slight oscillations characterized as hidden scales which are almost left untreated during the sifting process. When involving hidden scales, the intrinsic mode function usually contains at a given instance multiple oscillation modes. In view of this, based on inflection points this paper presents a new decomposition algorithm called ‘oblique-extrema empirical mode decomposition’ to settle these problems. With this algorithm, any signal can be decomposed into a finite number of ‘oblique-extrema intrinsic mode functions’ which may possess better-behaved Hilbert transforms and produce more accurate instantaneous frequencies. It can suppress the effect of hidden scales and gets one step further in extracting finer scales. Experimental results demonstrate good performances of this new method. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new time-varying step-size for the affine projection (AP) algorithm based on the minimization of the mean-square error (MSE) at each time instant. The step-size is dependent on accessible quantities and, therefore, does not need approximation. In addition, we show how the new step-size control equations can be incorporated into the fast AP (FAP) algorithm leading to a reduced complexity implementation of the proposed algorithm. The algorithm improved performance characteristics are verified by simulation experiments. [Copyright &y& Elsevier]

Abstract: In this paper, a particle swarm optimization (PSO) is used to design the quadrature mirror filter (QMF) banks with linear phase in frequency domain. A unique PSO is developed to optimize filter bank coefficients to match the ideal system response. The three main attributes used in assessing the performance of filter are reconstruction error, mean square error in pass band and mean square error in stop band. The proposed PSO based method produces almost the same result as that of the existing methods. As compared to the existing methods this method is very simple to implement for the QMF bank optimization. To implement the proposed PSO algorithm, a Matlab program is developed and three examples have been presented to illustrate the performance of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper presents three multiuser detectors structures: the conventional detector, the MMSE and the adaptive detector. The error probability performances are evaluated and compared one another, as functions of signal to noise ratio, for a system with four users, and different intercorrelation coefficients and users amplitudes. Based on those simulations, a number of conclusions are drawn with respect to the effects of the intercorrelation between users or unequal amplitudes on the detectors performances. [Copyright &y& Elsevier]