Adaptive Filtering Technique Research Articles

Implementation of Adaptive Noise Canceller System for Audio-Related Applications

This paper describes the performance of Adaptive Noise Cancellation system. Basic concept of adaptive noise canceller is to process signals from two input sources and to reduce the level of undesired noise with adaptive filtering techniques. Adaptive filtering techniques play vital role in wide range of applications. An implementation of adaptive noise cancellation system is used to remove undesired noise from a received signal for various audio related applications that has been developed and implemented by MATLAB. The dual channel adaptive noise cancellation system uses an adaptive filter with least mean square algorithm to cancel noise component from primary signal picked up by primary sensor. Various parameters such as convergence behavior, tracking ability of the algorithm, signal to noise ratio, mean square error etc. of ANC system are studied, analyzed for various applications of adaptive noise cancellation and the same are discussed in this paper.

P and M Class Phasor Measurement Unit Algorithms Using Adaptive Cascaded Filters

The new standard C37.118.1 lays down strict performance limits for phasor measurement units (PMUs) under steady-state and dynamic conditions. Reference algorithms are also presented for the performance (P) and measurement (M) class PMUs. In this paper, the performance of these algorithms is analyzed during some key signal scenarios, particularly those of offnominal frequency, frequency ramps, and harmonic contamination. While it is found that total vector error (TVE) accuracy is relatively easy to achieve, the reference algorithm is not able to achieve a useful rate of change of frequency (ROCOF) accuracy. Instead, this paper presents alternative algorithms for P and M class PMUs, which use adaptive filtering techniques in real time up to 10-kHz sample rates, allowing consistent accuracy to be maintained across a ±33% frequency range. ROCOF errors can be reduced by factors of > 40 for P class and > 100 for M class devices.

The Research of Signal Processing in Ultrasonic Nondestructive Testing and Evaluation

The development of signal processing technology not only improves the reliability of qualitative and quantitative ultrasound detection, but also promotes the sensitivity and precision. This paper introduces the new progress of signal processing technology in application of Ultrasonic Nondestructive Testing, including the basic principle, characteristic and localization of Wavelet Transform, Adaptive Filter Technique, Artificial Neural Network and Support Vector Machine application in Ultrasonic Testing, and the trend of development in the future.

Intercepted signals for ionospheric science

AbstractThe ISIS array (Intercepted Signals for Ionospheric Science) is a distributed, coherent software radio array designed for the study of geospace phenomena by observing the scatter of ambient radio frequency (RF) signals. ISIS data acquisition and analysis is performed using the MIDAS‐M platform (Millstone Data Acquisition System ‐ Mobile). Observations of RF signals can be performed between HF and L‐band using the Array nodes and appropriate antennas. The deployment of the Array focuses on observations of the plasmasphere boundary layer. We discuss the concept of the coherent software radio array, describe the ISIS hardware, and give examples of data from the system for selected applications. In particular, we include the first observations of E region irregularities using the Array. We also present single‐site passive radar observations of both meteor trails and E region irregularities using adaptive filtering techniques.

FUZZY CLUSTERING OF IMAGE TRADEMARK DATABASE AND PREPROCESSING USING ADAPTIVE FILTER AND KARHUNEN–LOÈVE TRANSFORM

In this paper, an efficient preprocessing module has been described which focuses on building a trademark database that can be used for developing a trademark retrieval system. The preprocessing module focuses on noise removal from the trademark images using an adaptive filtering technique using Wiener filters, followed by Karhunen–Loève transform that makes the trademark search process rotation invariant by rotating the object along positive y direction. Since the registered trademarks are huge in number and will increase invariantly in the future it will be strenuous for the search system to search for similarity in such huge database. Intention was to reduce the search space hence fuzzy clustering has been applied. All these preprocessing steps make a retrieval system more efficient and reduce computation cost.

Traditional Color Image Enhancement Based on Adaptive Filter

Color image enhancement is a very important pre-processing stage in face detection and face recognition applications especially when the environment is very dark. In this paper, a color color image enhancement with the adaptive filter and discrete wavelet transform (DWT) is proposed. Discrete wavelet transform is used to improve image enhancement and stationary wavelet transform is used to reduce the halo distortion in gray scale images only and wavelet cannot work in color images. In adaptive filter technique is to reduce halo distortion in color and gray images. The adaptive filter algorithm finds the importance of color information in color image enhancement and utilizes color space conversion to obtain a much better visibility. Experimental results show that adaptive filter to reducing halo, color distortion and produce better visibility compare to DWT. Keywords-Adaptive filter, color video enhancement, HVS, color space conversion and image fusion

Area Optimized Adaptive Noise Cancellation System Using FPGA for Ultrasonic NDE Applications

This paper describes a method for noise removal from ultrasonic signals used in NDE (Non Destructive Evaluation) applications. The method used is FPGA based adaptive noise cancellation. Indeed adaptive filtering is one of the core technologies in digital signal processing. We can find numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including system identification, adaptive equalization, adaptive noise cancellation and echo cancellation. Characteristics of signals that are generated by systems of the above application are not known a priori. Under this condition, a significant improvement in performance can be achieved by using adaptive rather than fixed filters. An adaptive filter is a self-designing filter that uses a recursive algorithm (known as adaptation algorithm or adaptive filtering algorithm) to design itself. Here the algorithm used is normalized least mean square algorithm, which has good convergence and stability. Keywords; Adaptive noise cancellation, NLMS algorithm, NDE applications, ultrasonic grain noise.

Research on the Improvement of Inverter Current’s Real Measuring Quality

In order to improve the inverter current quality，a kind of space vector control method is presented，which has no deadtime and has small torque ripple．The research on improving inverter current quality has been done according to improving electric current harmonic and adopting adaptive filtering technique on the electronic current，and got a better effect on the electronic current real measuring quality

RANDOM NOISE CANCELLATION IN BIOMEDICAL SIGNALS USING VARIABLE STEP SIZE GRIFFITH LMS ADAPTIVE LINE ENHANCER

This paper presents a new method of random noise cancellation for removing artefacts from biomedical signals using an adaptive line enhancer (ALE). The ALE is implemented using proposed time domain variable step size Griffith least mean square (VSGLMS) algorithm. The technique is based on the adaptation of the gradient of the error surface. The method makes both the step size and the gradient free from observation noise and reduces the gradient mis-adjustment error. Here, both the gradient and the scale factor for the step size are free from the input noise effects, which makes the algorithm robust to both stationary and non-stationary observation noise. Further the additional computational load involved for this is marginal. The VSGLMS adaptive filter technique for ALE is tested on noise cancellation of two types of bio-medical signals — separation of electro cardiogram (ECG) signal from a background of electro myogram (EMG) and heart sound signal (HSS) from lung sound signal (LSS). Application of VSGLAM–ALE for the separation of HSS from LSS and ECG from EMG has been demonstrated using synthetic White Gaussian noise (WGN). It is found that VSGLMS–ALE can separate the desired signals like ECG or HSS at an input SNR of -5 dB to 27 dB. The performance of VSGLMS is compared with state-of-the-art least mean square LMS–ALE and normalised LMS–ALE. The results of PSDs, time domain waveforms, and mean square error (MSE) have proven that VSGLMS performs better than advanced techniques.

Gearbox Fault Features Extraction Using Vibration Measurements and Novel Adaptive Filtering Scheme

Vibration signals measured from a gearbox are complex multicomponent signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures, and a substantial amount of noise. This paper presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS) algorithm is examined and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio), which makes meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a healthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25% tooth removal) and fault 2 (50% tooth removal) under loads (0%, and 80% of the total load). The experimental results show remarkable improvements and enhance gear condition features. This paper illustrates that the new approach offers a more effective way to detect early faults.

Efficient Computational Construction of Weighted Protein-Protein Interaction Networks Using Adaptive Filtering Techniques Combined with Natural Selection-Based Heuristic Algorithms

The analysis of protein-protein interactions (PPIs) is crucial to the understanding of cellular processes. In recent years, a variety of computational methods have been developed to supplement the interactions that have been detected experimentally. The article’s main objective is to present a novel classification framework for predicting PPIs combining the advantages of two algorithmic methods’ categories. State-of-the-art adaptive filtering techniques were combined with the most contemporary heuristic methods which are based in the natural selection process. The authors’ goal is to find a simple mathematical equation that governs the best classifier enabling the extraction of biological knowledge. The proposed methodology assigns a confidence score to each protein pair and as a result a weighted PPI network is constructed. All possible combinations of the selected adaptive filtering and heuristic techniques were used and comparisons were made to explore the classifiers with the highest performance and interpretability.

Estimation of brainstem auditory evoked potentials using a nonlinear adaptive filtering algorithm

In this study, we introduce a novel nonlinear system not only for tracking of both the latency and amplitude variations in brainstem auditory evoked potential (BAEP), but also for reduction of single-trial numbers in BAEP pattern extraction process. Trial-to-trial variations in auditory evoked potential (AEP) are very important in quantifying dynamical properties of the nervous system and in specifying the group-specific effects in clinical applications. Due to the nonlinear dynamics of the AEP, a nonlinear adaptive filtering technique is considered as a powerful tool for tracking such variations. Therefore, we have designed a wavelet network-based nonlinear adaptive filter (WaNe-NAF) satisfying asymptotic stability in the sense of Lyapunov. The simulation results are verified that the proposed WaNe-NAF can effectively track the trial-to-trial variations. We have also compared the WaNe-NAF with the most widely used ensemble averaging technique using real measured human BAEP data. The WaNe-NAF shows promise for requiring less number of ensembles than conventional ensemble averaging method to attain adequate signal quality. As a result, the proposed filtering system is suggested as a powerful tool in AEP acquisition and processing systems.

Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network

In a distributed parameter estimation problem, during each sampling instant, a typical sensor node communicates its estimate either by the diffusion algorithm or by the incremental algorithm. Both these conventional distributed algorithms involve significant communication overheads and, consequently, defeat the basic purpose of wireless sensor networks. In the present paper, we therefore propose two new distributed algorithms, namely, block diffusion least mean square (BDLMS) and block incremental least mean square (BILMS) by extending the concept of block adaptive filtering techniques to the distributed adaptation scenario. The performance analysis of the proposed BDLMS and BILMS algorithms has been carried out and found to have similar performances to those offered by conventional diffusion LMS and incremental LMS algorithms, respectively. The convergence analyses of the proposed algorithms obtained from the simulation study are also found to be in agreement with the theoretical analysis. The remarkable and interesting aspect of the proposed block-based algorithms is that their communication overheads per node and latencies are less than those of the conventional algorithms by a factor as high as the block size used in the algorithms.

Adaptive Noise Cancellation for speech Employing Fuzzy and Neural Network

Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications such as noise cancellation. Noise cancellation is a common occurrence in today telecommunication systems. The LMS algorithm which is one of the most efficient criteria for determining the values of the adaptive noise cancellation coefficients are very important in communication systems, but the LMS adaptive noise cancellation suffers response degrades and slow convergence rate under low Signal-to-Noise ratio (SNR) condition. This paper presents an adaptive noise canceller algorithm based fuzzy and neural network. The major advantage of the proposed system is its ease of implementation and fast convergence. The proposed algorithm is applied to noise canceling problem of long distance communication channel. The simulation results showed that the proposed model is effectiveness.

Adaptive Noise Cancellation for speech Employing Fuzzy and Neural Network

Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications such as noise cancellation. Noise cancellation is a common occurrence in today telecommunication systems. The LMS algorithm which is one of the most efficient criteria for determining the values of the adaptive noise cancellation coefficients are very important in communication systems, but the LMS adaptive noise cancellation suffers response degrades and slow convergence rate under low Signal-to-Noise ratio (SNR) condition. This paper presents an adaptive noise canceller algorithm based fuzzy and neural network. The major advantage of the proposed system is its ease of implementation and fast convergence. The proposed algorithm is applied to noise canceling problem of long distance communication channel. The simulation results showed that the proposed model is effectiveness.

선형적 위상배열 코일구조의 시뮬레이션을 통한 민감도지도의 공간 해상도 및 필터링 변화에 따른 MR-SENSE 영상재구성 평가

Parallel imaging technique can provide several advantages for a multitude of MRI applications. Especially, in SENSE technique, sensitivity maps were always required in order to determine the reconstruction matrix, therefore, a number of difference approaches using sensitivity information from coils have been demonstrated to improve of image quality. Moreover, many filtering methods were proposed such as adaptive matched filter and nonlinear diffusion technique to optimize the suppression of background noise and to improve of image quality. In this study, we performed SENSE reconstruction using computer simulations to confirm the most suitable method for the feasibility of filtering effect and according to changing order of polynomial fit that were applied on variation of spatial resolution of sensitivity map. The image was obtained at 0.32T(Magfinder II, Genpia, Korea) MRI system using spin-echo pulse sequence(TR/TE = 500/20 ms, FOV = 300 mm, matrix = <TEX>$128{\times}128$</TEX>, thickness = 8 mm). For the simulation, obtained image was multiplied with four linear-array coil sensitivities which were formed of 2D-gaussian distribution and the image was complex white gaussian noise was added. Image processing was separated to apply two methods which were polynomial fitting and filtering according to spatial resolution of sensitivity map and each coil image was subsampled corresponding to reduction factor(r-factor) of 2 and 4. The results were compared to mean value of geomety factor(g-factor) and artifact power(AP) according to r-factor 2 and 4. Our results were represented while changing of spatial resolution of sensitivity map and r-factor, polynomial fit methods were represented the better results compared with general filtering methods. Although our result had limitation of computer simulation study instead of applying to experiment and coil geometric array such as linear, our method may be useful for determination of optimal sensitivity map in a linear coil array.

A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation

State-of-charge (SOC) and capacity estimation plays an essential role in many battery-powered applications, such as electric vehicle (EV) and hybrid electric vehicle (HEV). However, commonly used joint/dual extended Kalman filter (EKF) suffers from the lack of accuracy in the capacity estimation since (i) the cell voltage is the only measurable data for the SOC and capacity estimation and updates and (ii) the capacity is very weakly linked to the cell voltage. The lack of accuracy in the capacity estimation may further reduce the accuracy in the SOC estimation due to the strong dependency of the SOC on the capacity. Furthermore, although the capacity is a slowly time-varying quantity that indicates cell state-of-health (SOH), the capacity estimation is generally performed on the same time-scale as the quickly time-varying SOC, resulting in high computational complexity. To resolve these difficulties, this paper proposes a multiscale framework with EKF for SOC and capacity estimation. The proposed framework comprises two ideas: (i) a multiscale framework to estimate SOC and capacity that exhibit time-scale separation and (ii) a state projection scheme for accurate and stable capacity estimation. Simulation results with synthetic data based on a valid cell dynamic model suggest that the proposed framework, as a hybrid of coulomb counting and adaptive filtering techniques, achieves higher accuracy and efficiency than joint/dual EKF. Results of the cycle test on Lithium-ion prismatic cells further verify the effectiveness of our framework.

Autonomous estimation of Allan variance coefficients of onboard fiber optic gyro

Satellite motion included in gyro output disturbs the estimation of Allan variance coefficients of fiber optic gyro on board. Moreover, as a standard method for noise analysis of fiber optic gyro, Allan variance has too large offline computational effort and data storages to be applied to online estimation. In addition, with the development of deep space exploration, it is urged that satellite requires more autonomy including autonomous fault diagnosis and reconfiguration. To overcome the barriers and meet satellite autonomy, we present a new autonomous method for estimation of Allan variance coefficients including rate ramp, rate random walk, bias instability, angular random walk and quantization noise coefficients. In the method, we calculate differences between angle increments of star sensor and gyro to remove satellite motion from gyro output, and propose a state-space model using nonlinear adaptive filter technique for quantities previously measured from offline data techniques such as the Allan variance method. Simulations show the method correctly estimates Allan variance coefficients, R = 2.7965exp−4 °/h2, K = 1.1714exp−3 °/h1.5, B = 1.3185exp−3 °/h, N = 5.982exp−4 °/h0.5 and Q = 5.197exp−7 ° in real time, and tracks degradation of gyro performance from initail values, R = 0.651 °/h2, K = 0.801 °/h1.5, B = 0.385 °/h, N = 0.0874 °/h0.5 and Q = 8.085exp−5 °, to final estimations, R = 9.548 °/h2, K = 9.524 °/h1.5, B = 2.234 °/h, N = 0.5594 °/h0.5 and Q = 5.113exp−4 °, due to gamma radiation in space. The technique proposed here effectively isolates satellite motion, and requires no data storage and any supports from the ground.

Mitigation of 50-60 Hz power line interference in geophysical data

[1] The analysis of ELF/VLF radio data has broad applications for ionospheric and magnetospheric phenomena, lightning activity, long-range communications, and geophysical prospecting. However, recordings of ELF/VLF data on the ground are adversely affected by the presence of electromagnetic fields from 50–60 Hz power lines, whose harmonics can extend to many kilohertz and interfere with the detection of natural and man-made signals. Removal of this interference is complicated by the time-varying fundamental frequency of power lines and strongly varying characteristics across different power grids. We discuss two methods for isolation and then subtraction of this interference, by an adaptive filtering technique and with least squares matrix analysis. Methods for estimating the time-varying frequency are also discussed. A few variants of these techniques are applied both to simulated data and then to real data. It is found that least squares isolation gives superior results, although the adaptive filter is potentially more effective for poorly behaved power line interference with rapidly changing fundamental frequencies as well as being computationally more efficient.

Classification-Based Adaptive Filtering for Multiframe Blind Image Restoration

In this paper, the blind restoration of a scene is investigated, when multiple degraded (blurred and noisy) acquisitions are available. An adaptive filtering technique is proposed, where the distorted images are filtered, classified and then fused based upon the classification decisions. Finite normal-density mixture (FNM) models are used to model the filtered outputs at each iteration. For simplicity, fixed number of Gaussian components (classes) is, initially, considered for each degraded frame and the selection of the optimal number of classes is performed according to the global relative entropy criterion. However, there exist cases where dynamically varying FNM models should be considered, where the optimal number of classes is selected according to the Akaike information criterion. The iterative application of classification and fusion, followed by optimal adaptive filtering, converges to a global enhanced representation of the original scene in only a few iterations. The proposed restoration method does not require knowledge of the point-spread-function support size or exact alignment of the acquired frames. Simulation results on synthetic and real data, using both fixed and dynamically varying FNM models, demonstrate its efficiency under both noisy and noise-free conditions.