Adaptive Filters

This paper presents a method for estimating the optimum memory size for identification of an unknown second-order Volterra kernel. As these structures may imply considerable computational demands, it is highly desirable to design adaptive realizations with a minimum number of coefficients. Therefore, we propose a combination scheme comprising two Volterra filters with time-variant sizes of the actually used quadratic kernels. By following some simple rules, the number of diagonals in the quadratic kernels is increased or decreased in order to find the optimum memory configuration in parallel to the coefficient adaptation. Thus, the arbitrary choice of the nonlinear system size is overcome by a dynamically growing/shrinking system. Experimental results for various signals and nonlinear scenarios demonstrate the effectiveness of the proposed method.

Adaptive filtering schemes are subject to different tradeoffs regarding their steady-state misadjustment, speed of convergence and tracking performance. To alleviate these compromises, a new approach has recently been proposed, in which two filters with complementary capabilities adaptively mix their outputs to get an overall filter of improved performance. Following this approach, in this paper we propose a new normalized rule for adapting the mixing parameter that controls the combination. The new update rule preserves the good features of the standard scheme and is more robust to changes in the filtering scenario, for instance when the signal to noise ratio (SNR) is time varying. The benefits of the normalized scheme are illustrated analytically and with a number of experiments in both stationary and tracking situations.

We present a novel simple blind adaptive compensation method for in-phase/quadrature (IQ) imbalance in m-ary quadrature amplitude modulation (mQAM) coherent optical fiber communication systems. IQ-imbalance compensation is integrated into butterfly-structured finite impulse response (FIR) filters, resulting in a significant computational effort reduction in comparison to conventional methods. A reduction in hardware complexity by a factor of about 3 is achieved by the proposed joint method. The proposed structure is experimentally validated with a 40Gbit/s 16-QAM signal. A 7-dB power penalty reduction is experimentally achieved at a bit error rate (BER) of 10 -3 in the presence of a 10° phase imbalance, confirming the effectiveness of the proposed algorithm. The equalization capability remains even in the presence of group velocity dispersion along the link, which is numerically confirmed with optical fiber transmission up to 1200 km and 20 phase imbalance.

In this work, we propose a design method for oversampled FIR DFT lter banks which have the paraunitary property, where the number of channel M is the multiple of decimation ratio D and the lter length is the multiple of M. Our proposed method is based on Householder factorization, which can keep the perfect reconstruction condition and the paraunitary property of lter banks in optimization process. In addition, we examine the linear phase property for oversampled DFT lter banks, and the design method of oversampled linear phase DFT lter banks. In order to show the eectiveness of our method, we give some design examples.

This paper addresses the issue of removal of broadband noise from audio recordings which are degraded by aging or limitations of the recording-reproduction mechanism. To achieve the noise reduction, the Kalman filter is applied to the digitized audio signals. Kalman filter is an optimum filter for minimizing the error variance between a measured signal and its estimation. This is a time domain method and is free from musical noise phenomena inherent in the popular spectral subtraction method which is a spectral domain method. Application of the Kalman filter requires the correct estimation of the measurement noise variance (background noise variance in audio) .Normally noise variance is estimated from silent regions of the signal. In audio applications this would be the first one or two seconds of the recordings. But this method may not be practical in all cases and especially if the signal SNR is very low. Hence in this work, the method of quantile based noise variance estimation is employed for determining the background noise variance. Performances of the filter with the two methods (silent region noise estimation and quantile based noise estimation) for broadband noise reduction in degraded audio signals are contrasted.

This paper introduces a new approach based on blind source separation (BSS) to mit igate intentional interference in BFSK d igital co mmun ication systems using frequency hopping spread spectrum technique. The use of BSS is possible thanks to adopting an adequate selection block to distinguish between the useful signal and other undesirable signals, hence, circu mvent the problem of amb iguity of permutation. An analytical calculation of the probability of error to predict the performance is done. The simulat ion results showed the effectiveness of this approach, whatever the level of the JSR and without using the fast frequency hopping alternative or error-correcting codes.

Handover is one of the key operations in the mobility management of long-term evolution (LTE)-based systems. Hard handover decided by handover margin and time to trigger (TTT) has been adopted in third Generation Partnership Project (3GPP) LTE with the purpose of reducing the complexity of network architecture. Various handover algorithms, however, have been proposed for 3GPP LTE to maximize the system goodput and minimize packet delay. In this paper, a new handover approach enhancing the existing handover schemes is proposed. It is mainly based on the two notions of handover management: lazy handover for avoiding ping-pong effect and early handover for handling real-time services. Lazy handover is supported by disallowing handover before the TTT window expires, while early handover is supported even before the window expires if the rate change in signal power is very large. The performance of the proposed scheme is evaluated and compared with two well-known handover algorithms base...

Electrocardiogram (ECG) signals are fundamental in the diagnosis and monitoring of cardiac conditions, but they are often contaminated by various types of noise, such as power line interference, baseline wander, and muscle artifacts. Traditional noise filtering techniques, including the Least Mean Squares (LMS) algorithm, have been widely used to remove steady-state noise. However, LMS filters alone are less effective when dealing with nonstationary noise. This paper proposes a novel Adaptive Hybrid LMS-Kalman Filter (AHLKF) algorithm that combines the strengths of the LMS and Kalman filtering approaches for enhanced real-time ECG signal processing. The AHLKF dynamically adjusts filter parameters to handle both stationary and non-stationary noise with improved performance while preserving salient ECG features and keeping computational complexity low for realtime wearable applications. Simulation results on MIT-BIH records demonstrate improved SNR and lower MSE relative to standalone LMS and Kalman filters.

Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancellation [1], array beamforming [2], channel equalization [3], and to more recent sensor network applications in surveillance, target localization and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], . Ranging from the simple Least-Mean-Squares (LMS) to sophisticated state-space algorithms, significant research has been carried out over the last 50 years to develop effective adaptive algorithms, in an attempt to improve their general properties in terms of convergence, tracking ability, steady-state misadjustment, robustness, or computational cost . Many design procedures and theoretical models have been developed, and many novel adaptive structures are continually proposed with the objective of improving filter behavior with respect to well-known performance tradeoffs (such as convergence rate versus steady-state performance), or incorporating available a priori knowledge into the learning mechanisms of the filters (e.g., to enforce sparsity).

Traditional acoustic echo cancelers use a linear model to represent the echo path. Nevertheless, many consumer devices include loudspeakers and audio power amplifiers that may generate significant nonlinear distortions, creating the need for acoustic echo cancelers to produce a nonlinear filter response. To address this issue, we propose a nonlinear acoustic echo cancellation algorithm based on the framework of kernel methods. We model the echo path as a Hammerstein system, and we propose a resource-efficient strategy to identify the nonlinear and linear parts. While the basic algorithm is presented as an iterative batch method, we show that a simple extension allows it to be used in online scenarios as well. Results for both types of scenarios show that the algorithm produces good results on a system with a clipping nonlinearity and a realistic room impulse response.

It is a well-known result of estimation theory that biased estimators can outperform unbiased ones in terms of expected quadratic error. In steady-state, many adaptive filtering algorithms offer an unbiased estimation of both the reference signal and the unknown true parameter vector. In this correspondence, we propose a simple yet effective scheme for adaptively biasing the weights of adaptive filters using an output multiplicative factor. We give theoretical results that show that the proposed configuration is able to provide a convenient bias vs variance tradeoff, leading to reductions in the filter mean-square error, especially in situations with a low signal-to-noise ratio (SNR). After reinterpreting the biased estimator as the combination of the original filter and a filter with constant output equal to 0, we propose practical schemes to adaptively adjust the multiplicative factor. Experiments are carried out for the normalized leastmean-squares (NLMS) adaptive filter, improving its mean-square performance in stationary situations and during the convergence phase.

Monochromatic documents claim for much less computer bandwidth for network transmission and storage space than their color or even grayscale equivalent. The binarization of historical documents is far more complex than recent ones as paper aging, color, texture, translucidity, stains, back-to-front interference, kind and color of ink used in handwriting, printing process, digitalization process, etc. are some of the factors that affect binarization. This article presents a new binarization algorithm for historical documents. The new global filter proposed is performed in four steps: filtering the image using a bilateral filter, splitting image into the RGB components, decision-making for each RGB channel based on an adaptive binarization method inspired by Otsu’s method with a choice of the threshold level, and classification of the binarized images to decide which of the RGB components best preserved the document information in the foreground. The quantitative and qualitative asses...

In this paper we compare the performances of different variants of split-spectrum algorithms and propose some new extensions based on the use of variable bandwidth filters equally spaced in frequency and energy gain equalized. Signal-to-Noise Ratio Gain and Flaw-to-Clutter Ratio Gain factors were selected as the figures of merit to make the comparisons among the different methods. We considered simulated ultrasonic signals using both stationary and non-stationary models for the grain noise, and real scans obtained in laboratory from low dispersive (aluminum) and high dispersive (cement) materials. Frequency Multiplication (FM) recombination method is revealed as the best option when combined with the new extensions.

Adaptive least mean square (LMS) predictors with independently low-order cascaded structures, such as the cascaded forward LMS (CFLMS) and cascaded forward-backward LMS (CFBLMS), have proven effective in combating the misadjustment and eigenvalue spread effects of linear predictors. Further developing this cascade structure, we study the fixed-point implementation of CFBLMS with applications to speech signals. Moreover, two groups of predictors with a total of six cases are compared. Group 1 employs the transversal structure for LMS, CFLMS, and CFBLMS algorithms. Group 2 employs the lattice structure for LMS, CFLMS, and CFBLMS algorithms. Experimental results show that, in group 1, the performance degradation of CFBLMS and CFLMS predictors becomes significant when the number of bits is reduced to 8, while that of the LMS predictor becomes significant when the number of bits is reduced to 9. On the other hand, in group 2, the performance degradation of CFBLMS and CFLMS predictors becomes significant when the number of bits is reduced to 5, while that of the LMS predictor becomes significant when the number of bits is reduced to 6. In both groups, the performances of CFBLMS and CFLMS are significantly superior to that of LMS, and CFBLMS is superior to CFLMS, in terms of the rate of convergence, misadjustment, and mean-square error (MSE).

Methods for removal of ground bounce in general and for subsurface utility mapping in particular are considered in this paper. The term ground bounce is often used for the effect due to the radar wave being reflected at both the air-ground interface and at the non-ideal antenna. Both mean, median, moving median and Principal Component Analysis techniques have been evaluated on GPR data sets collected with an elevated bow-tie array antenna. Moving median filter ground bounce removal and an online and realtime PCA based method have been implemented and tested as well. The Principal Component Analysis technique has been shown to have superior performance.

Novel and simple concepts for adaptive blockequalizers to be used in the downlink of the Universal Mobile Telecommunication System (UMTS) terrestrial radio access (UTRA) with frequency-division duplex (FDD) are presented. The adaptation of the block-equalizers using modified leastsquares (LS) or minimum mean-square error (MMSE) schemes is based on the continuously transmitted, code-multiplexed common pilot channel (CPICH). In contrast to classical adaptive filter approaches, the number of coefficients to be adapted corresponds to the number of propagation channel paths and is thus independent of the chosen block size. In time-varying multipath propagation scenarios, a remarkable performance gain in terms of uncoded bit-error rate is obtained with respect to blockequalizers constructed using conventionally estimated channel impulse responses.

This document provides a summary of the theory of discrete-time signals and dynamic systems. Whenever a computer is used in measurement, signal processing or control applications, the data (as seen from the computer) and systems involved are naturally discrete-time because a computer executes program code at discrete points of time. Theory of discrete-time dynamic signals and systems is useful in design and analysis of control systems, signal filters, and state estimators, and model estimation from time-series of process data (system identification). It is assumed that you have basic knowledge about systems theory of continuous-time systems -specifically differential equations, transfer functions, block diagrams, and frequency response.

Several studies assert that the random access procedure of the Long Term Evolution (LTE) cellular standard may not be effective whenever a massive number of simultaneous connection attempts are performed by terminals, as may happen in a typical Internet of Things or Smart City scenario. Nevertheless, simulation studies in real deployment scenarios are missing because many system-level simulators do not implement the LTE random access procedure in detail. In this paper, we propose a patch for the LTE module of ns-3, one of the most prominent open-source network simulators, to improve the accuracy of the routine that simulates the LTE Random Access Channel (RACH). The patched version of the random access procedure is compared with the default one and the issues arising from massive simultaneous access from mobile terminals in LTE are assessed via a simulation campaign. 2 AWGN (0 Hz) -14.2 dB -14.2 dB -16.4 dB -16.5 d ETU70 (270 Hz) -8 dB -7.8 dB -10.0 dB -10.1 d 4 AWGN (0 Hz) -16.9 dB -16.7 dB -19.0 dB -18.8 d ETU70 (270 Hz) -12.1 dB -11.7 dB -14.1 dB -13.9 d

We propose adopting a volumetric appreciation of light-modeling optical signals as finite-thickness wave-bodies with a boundary layer (Lu)-to design carrier chips that encode and route information in three spatial degrees (radius (r), envelope thickness (D), and phase). This approach leverages wavepacket physics and volumetric scattering models to improve multiplexing, resilience to particulate media, and new logic primitives based on controlled volumetric interference.