Linear Adaptive Filter Research Articles

Robust Feature Matching for Remote Sensing Image Registration via Linear Adaptive Filtering

As a fundamental and critical task in feature-based remote sensing image registration, feature matching refers to establishing reliable point correspondences from two images of the same scene. In this article, we propose a simple yet efficient method termed linear adaptive filtering (LAF) for both rigid and nonrigid feature matching of remote sensing images and apply it to the image registration task. Our algorithm starts with establishing putative feature correspondences based on local descriptors and then focuses on removing outliers using geometrical consistency priori together with filtering and denoising theory. Specifically, we first grid the correspondence space into several nonoverlapping cells and calculate a typical motion vector for each one. Subsequently, we remove false matches by checking the consistency between each putative match and the typical motion vector in the corresponding cell, which is achieved by a Gaussian kernel convolution operation. By refining the typical motion vector in an iterative manner, we further introduce a progressive strategy based on the coarse-to-fine theory to promote the matching accuracy gradually. In addition, an adaptive parameter setting strategy and posterior probability estimation based on the expectation-maximization algorithm enhance the robustness of our method to different data. Most importantly, our method is quite efficient where the gridding strategy enables it to achieve linear time complexity. Consequently, some sparse point-based tasks may inspire from our method when they are achieved by deep learning techniques. Extensive feature matching and image registration experiments on several remote sensing data sets demonstrate the superiority of our approach over the state of the art.

A Comprehensive Review on Nature Inspired Neural Network based Adaptive Filter for Eliminating Noise in Medical Images.

Various kind of medical imaging modalities are available for providing noninvasive view and for analyzing any pathological symptoms of human beings. Different noise may appear in those modalities at the time of acquisition, transmission, scanning, or at the time of storing. The removal of noises from the digital medical images without losing any inherent features is always considered a challenging task because a successful diagnosis relies on them. Numerous techniques have been proposed to fulfill this objective, and each having their own benefits and limitations. In this comprehensive review article, more than 65 research articles are investigated to illustrate the applications of Artificial Neural Networks (ANN) in the field of biomedical image denoising. In particular, the zest of this article is to highlight the hybridized filtering model using nature-inspired algorithms and artificial neural networks for suppression of noise. Various other techniques, such as fixed filter, linear adaptive filters and gradient descent learning based neural network filter are also included. This article envisages how to train ANN using derivative free nature-inspired algorithms, and its performance in various medical images modalities and noise conditions.

A Commutation Error Compensation Strategy for High-Speed Brushless DC Drive Based on Adaline Filter

The commutation errors undermine the control performance of a brushless direct current (BLdc) drive especially at high-speed operation. In this article, a novel commutation error compensation strategy for BLdc drive that eliminates the phase deviation between the current and the back electromotive force is proposed. The d- axis current is introduced and used as the criterion to determine the commutation errors. Since the phase currents of BLdc drive contain large amounts of harmonics, which makes the d- axis current hard to detect, a least-mean-square-algorithm-based adaptive linear neuron filter is proposed. The optimal phase advance angle is adaptively generated by reducing the filtered d- axis current to zero. The proposed method does not need the motor parameter information or additional hardware, and can be implemented in general inverter controllers. The effectiveness of the proposed strategy is verified by both simulation and experimental results.

Generalized combined nonlinear adaptive filters: From the perspective of diffusion adaptation over networks

Combination of nonlinear adaptive filters (CNAF) is gaining popularity as an effective solution to enhance the filter performance by addressing compromises with respect to parameter and structure settings. However, most existing algorithms study the optimal choice of different filters while neglecting the internal structure optimization of filters. This limitation restricts the performance of CNAF. In this work, we propose a new scheme of the CNAF from the perspective of diffusion adaptive over network. Instead of combining the filters in a parallel manner, we consider to organize the candidate filters in a network manner. Specifically, a network with two subnetworks is constructed, and nodes in each subnetwork serve as linear adaptive filters and nonlinear adaptive filters respectively. A generalized CNAF (GCNAF) is obtained by linking the nodes of the network. The proposed GCNAF allows information exchange and sharing among nodes, so as to enhance the performance of the combined filters. As a result, search direction of each filter is also adjusted by combining those of other filters via the diffusion over networks. We show some representative, state-of-the-art combined adaptive filters are special cases of the proposed framework. Simulations with an acoustic problem demonstrate the effectiveness of the proposed GCNAF.

Multiple Frequency Tracking and Mitigation Based on RSPWVD and Adaptive Multiple Linear Kalman Notch Filter

In this paper, the multiple frequency tracking and mitigation method based on the reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) and an adaptive multiple linear Kalman notch filter (AMLKNF) is proposed to track and mitigate multiple global positioning system (GPS) interference signals. Compared to the conventional notch filters, the AMLKNF has a faster convergence rate and smaller signal loss. However, for using this method, we need to know the notch frequency which is the interference frequency to remove. Thus, we use the RSPWVD method, one of the time-frequency analysis methods. In addition, an adaptation logic is designed in the AMLKNF to adjust the notch depth according to the power of the jamming signal. Matlab-based simulations are performed for analyzing the performance of the proposed method. The simulation results show that the proposed algorithm can efficiently track and eliminate interference well compared to the conventional methods.

Nyström Kernel Algorithm Under Generalized Maximum Correntropy Criterion

The kernel adaptive filters (KAFs) based on the minimum mean square error (MMSE) criterion in reproducing kernel Hilbert space (RKHS) improve the performance of linear adaptive filters but result in instability issues and large burdens of computation and memory in impulsive noises. To this end, a novel Nystrom kernel recursive generalized maximum correntropy (NKRGMC) with probability density rank-based quantization (PRQ) sampling (NKRGMC-PRQ) algorithm is proposed to improve filtering performance, robustness, and computational efficiency of the traditional KAFs in this letter. In a fixed dimensional network structure, the proposed NKRGMC-PRQ algorithm can achieve a comparable performance to KAFs with low computational complexity. Monte Carlo simulations are conducted to validate the superiorities of NKRGMC-PRQ in terms of filtering accuracy, computational complexity, and robustness.

Complex-Valued Adaptive Filtering Based on the Random Fourier Features Method

The complex kernel adaptive filters (CKAFs) developed in complex reproducing kernel Hilbert space (RKHS) improve the performance of complex linear adaptive filters, but result in large burdens of computation and memory. To address these issues, a novel complex random Fourier features mapping (CRFFM) is proposed to approximate the kernel-induced mapping by applying the complexification of real RKHSs, and thus projects the complex-valued data from the original data space into the fixed-dimensional complex random Fourier features space (RFFS). To combat complex-valued non-Gaussian noises, a complex Cauchy loss function is presented, and the complex random Fourier features recursive complex Cauchy (CRFFRCC) algorithm is therefore proposed by the combination of CRFFM and the stochastic recursive method. The proposed CRFFRCC with a linear filter structure can reduce the computational and space complexities of CKAFs, significantly. Monte Carlo simulations conducted in the complex-valued nonlinear channel equalization validate the superiorities of CRFFRCC.

Power prediction for electric vehicles using online machine learning

Accurate range prediction of an electric vehicle is an important open problem, affecting among others the adoption and market penetration of electric vehicles. Current range prediction systems suffer several practical limitations. Most critically these systems employ models in which all vehicle-specific parameters are required to be known. In this paper, we propose a methodology for predicting power and mission energy of electric vehicles that does not require knowledge of vehicle-specific parameters nor a drive-train model. The proposed method uses a data-driven approach grounded entirely on available vehicle sensor data. In particular, the predictive model is obtained by applying machine learning techniques, and in order to adapt to changing conditions in real-time, the specific class of kernel adaptive filtering algorithms is employed. Kernel adaptive filtering extends the theory of linear filters with concepts from kernel methods in order to construct nonlinear adaptive filtering algorithms that exhibit properties such as universal approximation capabilities and convexity in training, requiring only modest computational complexity. After providing an overview of the most relevant properties of kernel adaptive filters, we evaluate the proposed prediction methodology on data obtained in nine vehicle trial runs, comparing the performance of one linear adaptive filter, one online trained neural network and two state-of-the-art kernel adaptive filters.

Adaptive prediction model for effective electrical machine maintenance

Purpose The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy. Design/methodology/approach The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA) time series prediction. ARMA prediction has been carried out using different combinations of AR and MA methods in order to make prediction with less Mean Squared Error (MSE). Findings The prediction error indicates the inadequacy of the model to estimate the data characteristics, which has been resolved at the subsequent stage by cascading an adaptive least mean square finite impulse response filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence. Research limitations/implications The componentized data prediction based on time series and cascade adaptive filter algorithm decomposes the non-stationary data characteristics for predictive maintenance. Evaluation of the model with different combination of time series algorithms and parameter settings of adaptive filter has been carried out to illustrate the performance of the prediction model. This prediction accuracy is compared with existing linear adaptive filter prediction using MSE as comparison index. The wide margin in the MSE values substantiates the prediction efficiency of the proposed model for machine data. Originality/value This model predicts the dynamic machine data with component decomposition at high accuracy, which enables to interpret the system response under dynamic conditions efficiently.

140/180/204-Gbaud OOK Transceiver for Inter- and Intra-Data Center Connectivity

We report on an on – off keying intensity-modulation and direct-detection C-band optical transceiver capable of addressing all datacenter interconnect environments at well beyond 100 Gbaud. For this, the transmitter makes the use of two key InP technologies: a 2:1 double heterojunction bipolar transistor selector multiplexer and a monolithically integrated distributed-feedback laser traveling-wave electro-absorption modulator, both exceeding 100-GHz of 3-dB analog bandwidth. A preamplified 110-GHz PIN photodiode prior to a 100-GHz analog-to-digital converter complete the ultrahigh bandwidth transceiver module; the device under study. In the experimental work, which discriminates between intra- and inter-data center scenarios (dispersion unmanaged 120, 560, and 960 m; and dispersion-managed 10 and 80 km of standard single-mode fiber), we evaluate the bit-error rate evolution against the received optical power at 140, 180, and 204 Gbaud on – off keying for different equalization configurations (adaptive linear filter with and without the help of short-memory sequence estimation) and forward error correction schemes (hard-decision codes with 7% and 20% overhead); drawing conclusions from the observed system-level limitations of the respective environments at this ultrahigh baudrate, as well as from the operation margins and sensitivity metrics. From the demonstration, we highlight three results: successful operation with >6-dB sensitivity margin below the 7% error-correction at 140 Gbaud over the entire 100 m–80 km range with only linear feed-forward equalization. Then, the transmission of a 180-Gbaud on – off keying carrier over 80 km considering 20% error-correction overhead. Finally, a 10-km communication at 204 Gbaud on – off keying with up to 6 dB sensitivity margin, and regular 7% overhead error-correction.

MIMO beamforming system for speech enhancement in realistic environment with multiple noise sources

Multiple noise sources in a realistic environment severely degrade the quality and intelligibility of the desired speech signal, thus posing a severe problem for many speech applications. Several noise reduction algorithms have been proposed with a main goal to solve this problem. However, the good performances of such algorithms are severely impaired in realistic environment under multi-noise sources condition. In this paper, the author treats the noise cancellation system as a multiple-input multiple-output (MIMO) beamformer system. The proposed approach consists of two steps. First, the noise signals are generated by applying the white noise sources to a MIMO AR system. Then, the noisy microphone signals are sequentially processed by employing multi-channel linear prediction error filters (MCLPEFs) and multi-channel adaptive noise estimation filters (MCANEFs) in the lower path of the proposed beamformer. The MCLPEFs are used to whiten the input signals, while the MCANEFs are used as a MIMO system identification to perform the modeling process of the noise signals. Finally, the noise signals in the upper path are subtracted from the estimated noises in the lower path to recover an enhanced speech signal. Moreover, the performance of the proposed MIMO approach was validated under a realistic environment with real noise sources.

A BDS Interference Suppression Technique Based on Linear Phase Adaptive IIR Notch Filters.

The continuous wave interferences (CWIs) and the narrow-band interferences (NBIs) have significantly impacted the acquisition, tracking and positioning accuracy of Beidou Navigation Satellite System (BDS) receivers. As an interference suppression technology with a simple structure and low hardware cost, the adaptive infinite-duration impulse response (IIR) notch filter has been widely employed in the receivers to mitigate the CWIs and the NBIs. However, the nonlinear phase characteristics introduced by the IIR notch filters into the navigation receivers, may cause the distortion of navigation signals. It also leads to amplitude and phase distortion of the correlation peak in acquisition and loop tracking, which consequently brings on positioning errors in the measurement domain. This problem, however, has been ignored by many previous papers. Meanwhile, some other researchers came up with the idea of equalizers and all-pass networks compensating the distortion, which is also highly infeasible. Therefore, we propose a new method of an adaptive linear phase IIR notch filter with low hardware cost which is composed of three parts—the complex IIR notch filters, the IIR linear phase structure, and the adaptive and variable step-size algorithms. Applying this method, interference suppression and the correlation peak distortion compensation can be achieved with a modest increase hardware cost. This paper compares the performance of the new method with other IIR filters in both CWI and NBI scene and presents the effects of its parameters on the anti-jamming performance. Simulation results show that the proposed module has better anti-jamming performance in NBI scene and can compensate for the correlation peak distortion in the meantime.

Potential improvements of lung and prostate MLC tracking investigated by treatment simulations.

Intrafraction tumor motion during external radiotherapy is a challenge for the treatment accuracy. A novel technique to mitigate the impact of tumor motion is real-time adaptation of the multileaf collimator (MLC) aperture to the motion, also known as MLC tracking. Although MLC tracking improves the dosimetric accuracy, there are still residual errors. Here, we investigate and rank the performance of five prediction algorithms and seven improvements of an MLC tracking system by extensive tracking treatment simulations. An in-house-developed MLC tracking simulator that has been experimentally validated against an electromagnetic-guided MLC tracking system was used to test the prediction algorithms and tracking system improvements. The simulator requires a Dicom treatment plan and a motion trajectory as input and outputs all motion of the accelerator during MLC tracking treatment delivery. For lung tumors, MLC tracking treatments were simulated with a low and a high modulation VMAT plan using 99 patient-measured lung tumor trajectories. For prostate, tracking was also simulated with a low and a high modulation VMAT plan, but with 695 prostate trajectories. For each simulated treatment, the tracking error was quantified as the mean MLC exposure error, which is the sum of the overexposed area (irradiated area that should have been shielded according to the treatment plan) and the underexposed area (shielded area that should have been irradiated). First, MLC tracking was simulated with the current MLC tracking system without prediction, with perfect prediction (Perfect), and with the following five prediction algorithms: linear Kalman filter (Kalman), kernel density estimation (KDE), linear adaptive filtering (LAF), wavelet-based multiscale autoregression (wLMS), and time variant seasonal autoregression (TVSAR). Next, MLC tracking was simulated using the best prediction algorithm and seven different tracking system improvements: no localization signal latency (a), doubled maximum MLC leaf speed (b), halved MLC leaf width (c), use of Y backup jaws to track motion perpendicular to the MLC leaves (d), dynamic collimator rotation for alignment of the MLC leaves with the dominant target motion direction (e), improvements 4 and 5 combined (f), and all improvements combined (g). All results are presented as the mean residual MLC exposure error compared to no tracking. In the prediction study, the residual MLC exposure error was 47.0% (no prediction), 45.1% (Kalman), 43.8% (KDE), 43.7% (LAF), 42.1% (wLMS), 40.1% (TVSAR), and 36.5% (Perfect) for lung MLC tracking. For prostate MLC tracking, it was 66.0% (no prediction), 66.9% (Kalman), and 63.4% (Perfect). For lung with TVSAR prediction, the residual MLC exposure error for the seven tracking system improvements was 37.2%(1), 38.3%(2), 37.4%(3), 34.2%(4), 30.6%(5), 27.7%(6), and 20.7%(7). For prostate with no prediction, the residual MLC exposure error was 61.7%(1), 61.4%(2), 55.4%(3), 57.2%(4), 47.5%(5), 43.7%(6), and 38.7%(7). For prostate, MLC tracking was slightly better without prediction than with linear Kalman filter prediction. For lung, the TVSAR prediction algorithm performed best. Dynamic alignment of the collimator with the dominant motion axis was the most efficient MLC tracking improvement except for lung tracking with the low modulation VMAT plan, where jaw tracking was slightly better.

Comparison Study of Linear Adaptive Equalization Filters for Image Transmission in OFDM Systems

Comparison Study of Linear Adaptive Equalization Filters for Image Transmission in OFDM Systems

51.5 Tb/s Capacity over 17,107 km in C+L Bandwidth Using Single-Mode Fibers and Nonlinearity Compensation

We transmit 51.5 Tb/s capacity over 17,107 km with C+L band erbium doped fiber amplifiers (EDFAs) and demonstrate a record single mode fiber capacity-distance product of 881 Pb/s × km. This is achieved by using a performance optimized multidimensional coded modulation format with hybrid probabilistic and geometric constellation shaping. This 4D-PS-7/12-40APSK modulation format is designed to approach the Shannon limit and to maximize the performance of nonlinearity compensation in comparison to conventional and probabilistically shaped two-dimensional formats. The receiver digital signal processing uses multistage nonlinearity compensation that includes fast least-mean-square equalizer and generalized filter in addition to digital back propagation. Adaptive linear filters are aided by coded modulation decisions. We experimentally study the contribution of each algorithm versus transmission distance and show a steady increase in the total nonlinearity compensation benefit up to ∼10,000 km and saturation afterwards. An average total nonlinearity compensation benefit of 1.75 dBQ is achieved after 17,107 km over a 80 nm optical bandwidth at the designed amplifier power. In addition, we show that nonlinearity compensation techniques used in our experiments are not affected by potential correlation among neighboring channels.

Field programmable gate array implementation of fuzzy variable step size adaptive linear element for adaptive frequency estimation

Accurate estimation of power signal frequency is an important requirement for many application areas that include system protection, energy quality monitoring and instrumentation. Though significant efforts have been made since long to develop potent algorithms for accurate estimation of power signal frequency, still their accuracy and convergence speed are a challenge under sudden frequency drift and variations. Therefore, this study focuses on a low complexity adaptive linear element filter using quadratic signal model, whose parameters are adjusted using a fast variable step size fuzzy logic-based learning algorithm to provide better convergence and noise rejection properties for the estimation of frequency from noisy and distorted signals. In addition, the new filter has also been implemented on a field programmable gate array hardware and Xilinx 14.2 with Sysgen software for the tracking of dynamic signal parameters in single and three phase power networks. Various numerical and experimental results are addressed for estimation of frequency of time varying sinusoids.

Adaptive learning based data-driven models for predicting hourly building energy use

Accurately predicting energy usage in buildings is of great importance in various efforts on improving building energy efficiencies such as fault detection and diagnostics, building-grid interactions, and building commissioning. Data-driven approach and first-principle approach are two commonly used methods in developing models for predicting building energy use. In this paper, several data-driven methods including multiple linear regression, adaptive linear filter algorithms (least mean square (LMS), normalized least mean square (nLMS), and recursive least square (RLS)), and Gaussian mixture model regression (GMMR) are employed to predict hourly energy usages in two buildings. One building is a synthetic large-size office building from DOE reference building models. The hourly building energy consumption was predicted using the energy simulation model for one year under Chicago climate. The other building is an existing office building located in Des Moines, Iowa. The actual hourly building energy consumption of the existing building was obtained through building submeters. The accuracies of these data-driven models for predicting energy usages of the two buildings are compared. The GMMR models outperform the adaptive filter methods in this study. Both the GMMR and adaptive filter methods meet the model calibration criteria defined by the ASHRAE Guideline 14.

70.46 Tb/s Over 7,600 km and 71.65 Tb/s Over 6,970 km Transmission in C+L Band Using Coded Modulation With Hybrid Constellation Shaping and Nonlinearity Compensation

We demonstrate 70.46 Tb/s transmission over 7,600 km and 71.65 Tb/s transmission over 6,970 km with C+L band EDFAs using a multidimensional coded modulation format with hybrid probabilistic and geometric constellation shaping. The proposed format (4D-PS-9/12-56APSK) is designed to approach Shannon limit and to improve the performance of nonlinearity compensation over conventional and probabilistically shaped 2-D formats. The receiver DSP uses multistage nonlinearity compensation that includes fast least-mean-square equalizer and generalized filter in addition to digital back propagation. The adaptive linear filters are aided by coded modulation decisions. We study the contribution of each algorithm and show an average total nonlinearity compensation benefit of 1.4 dBQ at the designed amplifier power.

Improving battery voltage prediction in an electric bicycle using altitude measurements and kernel adaptive filters

The time-varying nature of consumption patterns is critical in the development of reliable electric vehicles and real-time schemes for assessing energy autonomy. Most of these schemes use battery voltage observations as a primary source of information and neglect variables external to the vehicle that affect its autonomy and help to characterise the behaviour of the battery as main energy storage device. Using an electric bicycle as case study, we show that the incorporation of external variables (e.g., altitude measurements) improves predictions associated with evolution of the battery voltage in time. We achieve this by proposing a novel kernel adaptive filter for multiple inputs and with a data-dependent dictionary construction. This allows us to model the dependency between battery voltage and altitude variations in a sequential manner. The proposed methodology combines automatic discovery of the relationship between voltage and altitude from data, and a kernel-based voltage predictor to address an important issue in reliability of electric vehicles. The proposed method is validated against a standard kernel adaptive filter, fixed linear filters and adaptive linear filters as baselines on the short- and long-term prediction of real-world battery voltage data.

Single‐phase solar grid‐interfaced system with active filtering using adaptive linear combiner filter‐based control scheme

This study deals with a control scheme for single-stage solar photovoltaic (SPV) grid-interfaced system. The voltage-source inverter (VSI) is a power electronic interface between SPV array and the grid. The VSI provides power quality features, i.e. harmonics mitigation, power factor correction and perturb and observe maximum power point tracking for single-stage SPV grid-interfaced system. The SPV array supplies active power to the non-linear load and grid through VSI, during daytime only or when SPV generation is more than the load power. To increase the utilisation of VSI, it is used as an active power filter at night, to mitigate harmonics and to provide active power to the load from the utility grid. The proposed adaptive linear combiner filter has the capability to extract harmonics and enhances the power quality in SPV grid-interfaced system. It has also satisfied an IEEE-519 standard of harmonics by improving the quality of power of SPV grid-integrated system. Simulation results of the SPV system are presented, which are simulated using MATLAB and response of system under non-linear load and varying environmental condition are also studied experimentally on a developed prototype in the laboratory.