Simple Adaptive Filter Research Articles

SOGI-FLL Grid Frequency Monitoring with an Error-Based Algorithm for a Better Response in Face of Voltage Sag and Swell Faults

The SOGI-FLL (second-order generalized-integrator frequency-locked-loop) is a well-known and simple adaptive filter that allows estimation of the parameters of the grid voltage with a small computational burden. However, this structure has shown to be sensitive to the events of voltage sags and swell faults, especially to voltage sags that deeply distort the estimated frequency. In this paper an algorithm is proposed to face the fault that modifies the SOGI-FLLs gains in order to achieve a better transient response with a reduced perturbation in the estimated frequency. The algorithm uses the SOGI’s instantaneous and absolute error to detect the fault and change the SOGI-FLL gains during the fault. Moreover, the average of the absolute error is used for returning to normal operating conditions. The average value is obtained by means of a single low pass filter (LPF). The approach is easy to implement and represents a low computational burden for being implemented into a digital processor. The performance is evaluated by using simulations and real-time Typhoon Hardware in the Loop (HIL) results.

New SOGI-FLL Grid Frequency Monitoring with a Finite State Machine Approach for Better Response in the Face of Voltage Sag and Swell Faults

The SOGI-FLL (Second-Order Generalized Integrator–Frequency-Locked Loop) is a well-known and simple adaptive filter that allows for estimating the parameters of grid voltage with a small computational burden. However, the SOGI-FLL has been shown to be especially sensitive to voltage sags and voltage swells, which deeply distort the estimated parameters, especially the frequency. This problem can be alleviated by simply using a saturation block at the Frequency-Locked Loop (FLL) output to limit the impact of distortion on the estimated frequency. Improving upon this straightforward approach, in this paper we propose the use of a finite state machine (FSM) for the definition of the different states of the SOGI-FLL frequency response during a voltage sag or swell fault. The FSM approach allows for applying different gains during the fault, enhancing the SOGI-FLL transient response. The performance of the FSM-based SOGI-FLL is evaluated by using simulation results, which show a better and faster response to these kinds of faults.

Analog Least Mean Square Loop for Self-Interference Cancellation: A Practical Perspective.

Self-interference (SI) is the key issue that prevents in-band full-duplex (IBFD) communications from being practical. Analog multi-tap adaptive filter is an efficient structure to cancel SI since it can capture the nonlinear components and noise in the transmitted signal. Analog least mean square (ALMS) loop is a simple adaptive filter that can be implemented by purely analog means to sufficiently mitigate SI. Comprehensive analyses on the behaviors of the ALMS loop have been published in the literature. This paper proposes a practical structure and presents an implementation of the ALMS loop. By employing off-the-shelf components, a prototype of the ALMS loop including two taps is implemented for an IBFD system operating at the carrier frequency of 2.4 GHz. The prototype is firstly evaluated in a single carrier signaling IBFD system with 20 MHz and 50 MHz bandwidths, respectively. Measured results show that the ALMS loop can provide 39 dB and 33 dB of SI cancellation in the radio frequency domain for the two bandwidths, respectively. Furthermore, the impact of the roll-off factor of the pulse shaping filter on the SI cancellation level provided by the prototype is presented. Finally, the experiment with multicarrier signaling shows that the performance of the ALMS loop is the same as that in the single carrier system. These experimental results validate the theoretical analyses presented in our previous publications on the ALMS loop behaviors.

Quality Enhancement of Video Denoising Algorithm with Combined Effect of Different Views

An unwanted factor in digital images and videos called as Noise is responsible for hiding the details and destroying image information. Hence denoising has great importance in the detail restoration and to improve the quality measures. A simple adaptive median filter (SAMF) is one of the most promising algorithm capable of denoising the videos to better extent, but incapable to use all the neighboring pixel information while noise processing and hence quality of the denoised video can be further improved if all neighboring pixel information is considered. To make this possible the SAMF is combined with the multi-view denoising andthen a fusion strategy is used to generate enhanced denoised video sequence. The performance of the combined system is measured through the image quality matrix called as Pick Signal to Noise Ratio (PSNR) at different noise contamination level.

Effective impulse noise reduction method based on local correlation

This paper proposes a local correlation-based switching median filter for impulse noise reduction. The proposed algorithm consists of two main steps, detection and correction. In the first step, noise detection is performed using a scalable detection mask and morphological operation. In the second step, a corrupted pixel is corrected using the local correlation between the uncorrupted pixels in the mask. The experimental results showed that the proposed method can reduce impulse noise significantly and preserve more edge information than the existing state-of-art methods. In addition, the proposed method outperforms the most effective two schemes, which are simple adaptive median filter (SAM) and iterative adaptive switching median filter (IASMF) by an average of 1·68 and 0·49 dB, respectively. Therefore, we believe that the proposed method can be a useful tool for impulse noise-related products such as television, infrared-based devices and digital cameras.

Evaluation of a model for voice enhancement system of two channels

A dual channel system for speech enhancement is proposed and evaluated under babbling noise. A two stage dual channel structure of adaptive filters is used with different microphones, one directional for receiving the desired signal and the other omni directional for receiving the ambient noise which for this model is mainly babbling. Simple Root Mean Square (RMS), Itakura-Saito Distance (ISD) and subjective evaluation tests are made in order to ease comparisons. Using a simple adaptive filter, it is not enough to reduce background noise while maintaining the intelligibility of enhanced speech. Therefore, a noise emphasizing channel is used in order to achieve a better noise canceling. This work is done as precedent for the implementation of a system capable of diminish the noise from voices different than the target by using adaptive filter structures offering good results but small enough to be embedded in hearing aid devices.

An Edge-Based Adaptive Method for Removing High-Density Impulsive Noise from an Image While Preserving Edges

This paper presents an algorithm for removing high-density impulsive noise that generates some serious distortions in edge regions of an image. Although many works have been presented to reduce edge distortions, these existing methods cannot sufficiently restore distorted edges in images with large amounts of impulsive noise. To solve this problem, this paper proposes a method using connected lines extracted from a binarized image, which segments an image into uniform and edge regions. For uniform regions, the existing simple adaptive median filter is applied to remove impulsive noise, and, for edge regions, a prediction filter and a line-weighted median filter using the connected lines are proposed. Simulation results show that the proposed method provides much better performance in restoring distorted edges than existing methods provide. When noise content is more than 20 percent, existing algorithms result in severe edge distortions, while the proposed algorithm can reconstruct edge regions similar to those of the original image. Keywords: Impulsive noise, edge region, uniform region, binarized image, median filter.

Efficient and Simplified Adaptive Noise Cancelers for ECG Sensor Based Remote Health Monitoring

In this paper, several simple and efficient sign and error nonlinearity-based adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancellation of noise in electrocardiographic (ECG) signals. The proposed implementation is suitable for applications such as biotelemetry, where large signal to noise ratios with less computational complexity are required. These schemes mostly employ simple addition, shift operations and achieve considerable speed up over the other least mean square (LMS)-based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal-to-noise ratio and computational complexity.

Decentralized activation in sensor networks – global games and adaptive filtering games

This paper considers two methodologies for decentralized sensor activation in wireless sensor networks for energy-efficient monitoring. First, decentralized activation in wireless sensor networks is investigated using the theory of global games. Given a large number of sensors which can operate in either an energy-efficient ''low-resolution'' monitoring mode, or a more costly ''high-resolution'' mode, the problem of computing and executing a strategy for mode selection is formulated as a global game with diverse utilities and noise conditions. We formulate Bayes-Nash equilibrium conditions for which a simple threshold strategy is competitively optimal for each sensor, and propose a scheme for decentralized threshold computation. The second class of results we consider is in a non-Bayesian context where sensors deploy simple adaptive filtering algorithms and the global behavior converges to the set of correlated equilibria.

Modified-filtered-u LMS algorithm for active noise control and its application to a short acoustic duct

This paper presents a new adaptive algorithm for active noise control (ANC) that can be effectively applicable to a short acoustic duct, such as the intake system of an automobile engine, where the stability and fast convergence of the ANC system is particularly important. The new algorithm, called the modified-filtered-u LMS algorithm (MFU-LMS), is developed based on the recursive filtered-u LMS algorithm (FU-LMS) incorporating the simple hyper-stable adaptive recursive filter (SHARF) to ensure the control stability and the variable step size to enhance the convergence rate. The MFU-LMS algorithm is implemented by purely experimental ways, and is applied to active control of noise in a short acoustic duct, and is validated using two experimental cases of which the primary noise sources are a sinusoidal signal embedded in white noise and a chirp signal. The experimental results demonstrate that the proposed MFU-LMS algorithm gives a considerably better performance than other conventional algorithms, such as the filtered-x LMS (FX-LMS) and the FU-LMS algorithms.

Adaptive PD+I Control of a Switch-Mode DC–DC Power Converter Using a Recursive FIR Predictor

This paper presents an alternative technique for the adaptive control of power electronic converter circuits. Specific attention is given to the adaptive control of a dc-dc converter. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). The method is computationally efficient and based around a dichotomous coordinate descent (DCD) algorithm. The DCD-recursive least squares (RLS) algorithm has been employed as the adaptive PEF to reduce the computational complexity of existing RLS algorithms for efficient hardware implementation. Results show that the DCD-RLS is able to improve the dynamic performance and convergence rate of the adaptive gains (filter taps) within the controller. In turn, this yields a significant improvement in the overall dynamic performance of the closed-loop control system, particularly in the event of abrupt parameter changes. The proposed controller uses an adaptive proportional-derivative+integral (PD +I) structure which, alongside the DCD algorithm, offers an effective substitute to a conventional proportional-integral-derivative (PID) controller. The nonadaptive integral controller (+I), introduced in the feedback loop, increases the excitation of the filter tap weight and ensures good regulation. The approach results in a fast adaptive controller with self-loop compensation. This is required to minimize the prediction error signal and, in turn, minimize the voltage error signal in the loop by automatically calculating the optimal pole locations. The prediction error signal is further minimized through a second-stage FIR filter (adaptation gain stage). This ensures that the adaptive gains converge to their optimal value. This paper presents detailed simulation analysis and experimental validation on a prototype synchronous dc-dc buck converter. The experimental results clearly demonstrate the superior dynamic performance and voltage regulation compared to conventional PID and adaptive LMS control schemes, with only a modest increase in the computational burden to the microprocessor.

Simple adaptive filtering scheme to improve measurement accuracy of gyroscope on angular motion base

The objective of this work is to improve the measurement accuracy of a gyroscope on a angular motion base with a simple adaptive filter scheme. Two main topics are highlighted in this work. The first topic is to show building a dual-process model employed for the conventional Kalman filter. The second topic is to show developing a modified noise adaptive algorithm when measurement noise and process noise are unknown. The experimental results are presented to show that the simple adaptive filtering scheme outperforms the other conventional scheme in this paper in terms of noise reduction.

Simple adaptive median filter for the removal of impulse noise from highly corrupted images

This paper presents a simple, yet efficient way to remove impulse noise from digital images. This novel method comprises two stages. The first stage is to detect the impulse noise in the image. In this stage, based on only the intensity values, the pixels are roughly divided into two classes, which are "noise-free pixel" and "noise pixel". Then, the second stage is to eliminate the impulse noise from the image. In this stage, only the "noise-pixels" are processed. The "noise-free pixels " are copied directly to the output image. The method adaptively changes the size of the median filter based on the number of the "noise-free pixels " in the neighborhood. For the filtering, only "noise-free pixels " are considered for the finding of the median value. The results from 100 test images showed that this proposed method surpasses some of the state-ofart methods, and can remove the noise from highly corrupted images, up to noise percentage of 95%. Average processing time needed to completely process images of 1600times1200 pixels with 95% noise percentage is less than 2.7 seconds. Because of its simplicity, this proposed method is suitable to be implemented in consumer electronics products such as digital television, or digital camera.

A Self-Adjusting Simple Hyperstable Adaptive Recursive Algorithm

Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Hyperstability theory when simplified and adapted for digital signal processing offers a new class of IIR filters, simple hyperstable adaptive recursive filters (SHARFs), which is directly related to strictly positive real (SPR) transfer functions. One of the most important drawbacks of the SHARF algorithm is the presence of the unknown denominator in the transfer function that must be SPR in order to ensure convergence. In this paper, SHARF is investigated with SPR transfer functions designed without any priori knowledge of the filter parameters by the pole–zero placement on the unit circle method and made self-adjusting. To demonstrate self-adjustment of the algorithm, SHARF algorithm using constraint least-mean square (LMS) method is applied to a pure four-pole autoregressive process.

Acquisition performance for NDA symbol‐timing recovery with simple adaptive first‐order loops

AbstractPowerful feedforward estimators for symbol‐timing recovery are more or less mandatory for efficient acquisition in time‐division multiple access (TDMA) modems. However, in many cases, feedback solutions are much less complex from the computational point of view. The latter can be an attractive alternative for continuous transmission of data like DVB, where rapid acquisition is not so important as with TDMA. In order to describe analytically the acquisition performance of first‐order loops with Gardner detectors, it is shown that the related analog model can be successfully applied. In the sequel, this approach is used to demonstrate that the acquisition process may be accelerated significantly via simple adaptive loop filters. Copyright © 2006 AEIT

블록 경계 영역 특성을 이용한 블록 부호화 영상에서의 양자화 잡음 제거

본 논문에서는 블록 기반으로 부호화된 영상에 대하여 블록 경계 영역 특성에 따른 적응적 필터링을 이용한 양자화 잡음 제거 알고리즘을 제안하였다 제안한 방법에서는 블록 경계에 인접한 네 개 화소들의 통계적 특성을 이용하여 각 블록 경계 영역을 평탄 부영역(smooth sub-region)과 복잡 부영역(complex sub-region)으로 분류한 후, 인접 블록간 영역 특성을 이용하여 서로 다른 블록간 필터링을 수행한다. 먼저 인접 블록 모두 평탄 부영역인 경우에는 평탄 블록 경계 영역 중 블록화 현상이 발생하지 않은 영역도 존재하기 때문에 계산량을 줄이기 위하여 평탄 영역 중 블록화 현상이 발생한 영역만을 검출하여 필터링을 수행한다. 그리고, 두 부영역이 서로 다른 부영역인 경우에 대하여서는 기존의 방법들과는 달리 실제 에지 성분을 보존하면서 블록화 현상과 에지 주위에서 발생하는 링잉 현상을 동시에 제거하기 위하여 인접 블록의 영역 특성에 따라 적응적으로 일차원 필터링을 수행한다. 두 부영역이 모두 복잡 부영역일 경우에는 블록화 현상을 제거하면서 실제 에지를 보존하기 위하여 블록화 강도 및 양자화 파라미터에 따라 블록 경계 영역의 두 화소에 대하여 필터링을 수행한다. 모의실험 결과를 통하여 제안한 방법이 객관적 화질 및 주관적 화질 측면에서 기존의 방법보다 그 성능이 우수함을 확인하였다. In this paper, we propose a novel post-filtering algorithm with low computational complexity that improves the visual quality of decoded images using block boundary classification and simple adaptive filter (SAF). At first, each block boundary is classified into smooth or complex sub-region. And for smooth-smooth sub-regions, the existence of blocking artifacts is determined using blocky strength. And simple adaptive filtering is processed in each block boundary area. The proposed method processes adaptively, that is, a nonlinear 1-D 8-tap filter is applied to smooth-smooth sub-regions with blocking artifacts, and for smooth-complex or complex-smooth sub-regions, a nonlinear 1-D variant filter is applied to block boundary pixels so as to reduce the blocking and ringing artifacts. And for complex-complex sub-regions, a nonlinear 1-D 2-tap filter is only applied to adjust two block boundary pixels so as to preserve the image details. Experimental results show that the proposed algorithm produced better results than those of conventional algorithms both subjective and objective viewpoints.

SU-FF-J-25: Long Term Prediction of Respiratory Motion with Artificial Neural Network Based Adaptive Filtering Techniques

Purpose: Respiratory motion prediction is a key component affecting the accuracy of respiration gated and tumor tracking radiotherapy. The aim of this research was to investigate ways to improve long term prediction of respiratory motion with artificial neural network based adaptive filtering techniques. Method and Materials: An artificial neural network (ANN) based adaptive linear filter was used to predict respiratory motion for 30 motion traces (4 mins duration each) obtained from 7 patients both with (audio-visual biofeedback) and without breathing training. Sequential training of the network was implemented using the Widrow-Hoff training algorithm after proper determination of the maximum stable learning rate. A signal history of 5000 msec was used for training. The errors from prediction were compared to those obtained with a simplistic adaptive linear filter from earlier work. The effect of breathing training on the predictive ability of the ANN based was also determined. Results: Magnitude of geometric errors from prediction for the ANN based adaptive linear filter (∼ 2 mm − 1σ)were less than 50% of the magnitude of respiratory motion (5 mm − 1σ), especially for longer response times (> 600 msec). This represented a 40 % improvement in accuracy over a simple adaptive linear filter without any learning features. Breathing training with audio-visual biofeedback resulted in a slight reduction in errors from prediction. Conclusion: Artificial neural networks offer unique features that can help improve the adaptive capabilities of predictive filters. Further improvements in prediction of respiratory motion are possible with further developments using such a framework. Supported by: NCI RO1 93626

A New Adaptive Integration Methodology for Estimating Flux in Induction Machine Drives

This paper proposes a simple adaptive notch filter for the elimination of the dc component in the integration of signals used for the flux estimation in high performance ac drives. This integration method is composed of two identical adaptive noise cancellers using a linear neural network with just one bias weight. Its behavior has been investigated in simulation as applied to electrical drives and compared with other four traditional integration algorithms. A test bench has been then developed for its experimentation in a field oriented controlled induction machine. It has been verified that this integration algorithm outperforms the other algorithms in estimating the rotor flux even at low speeds.

Visual awareness and the cerebellum: possible role of decorrelation control

The two roles in awareness most often suggested for the cerebellum are (i) keeping the details of motor skills away from forebrain computation, and (ii) signaling to the forebrain when a sensory event is not predictable from prior motor commands. However, it is unclear how current models of the cerebellum could carry out these roles. Their architecture, based on the seminal ideas of Marr and Albus, appears to need 'motor error' to learn correct motor commands. However, since motor error is the difference between the actual motor command and what the command should have been, it is a signal unavailable to the organism in principle. We propose a possible solution to this problem, termed decorrelation control, in which the cerebellum learns to decorrelate the motor command sent to the muscles from the sensory consequences of motor error. This method was tested in a linear model of oculomotor plant compensation in the vestibulo-ocular reflex. A copy of the eye-movement command was sent as mossy-fiber input to the flocculus, represented as a simple adaptive filter version of the Marr-Albus architecture. The sensory consequences of motor error were retinal slip, delivered as climbing fiber input to the flocculus. A standard anti-Hebbian learning rule was used to decorrelate the two. Simulations of the linearized problem showed the method to be effective and robust for plant compensation. Decorrelation control is thus a candidate algorithm for the basic cerebellar microcircuit, indicating how it could achieve motor learning using only signals available to the system. Such learning might then enable the cerebellum to free up visual awareness, and also, by providing a sensory signal decorrelated from motor command, supply awareness with crucial information about the external world.

Subband neural networks prediction for on-line audio signal recovery

In this paper, a subbands multirate architecture is presented for audio signal recovery. Audio signal recovery is a common problem in digital music signal restoration field, because of corrupted samples that must be replaced. The subband approach allows for the reconstruction of a long audio data sequence from forward-backward predicted samples. In order to improve prediction performances, neural networks with spline flexible activation function are used as narrow subband nonlinear forward-backward predictors. Previous neural-networks approaches involved a long training process. Due to the small networks needed for each subband and to the spline adaptive activation functions that speed-up the convergence time and improve the generalization performances, the proposed signal recovery scheme works in online (or in continuous learning) mode as a simple nonlinear adaptive filter. Experimental results show the mean square reconstruction error and maximum error obtained with increasing gap length, from 200 to 5000 samples for different musical genres. A subjective performances analysis is also reported. The method gives good results for the reconstruction of over 100 ms of audio signal with low audible effects in overall quality and outperforms the previous approaches.