Frequency Domain Filtering Research Articles

Hybrid-Domain Parallel Decision Feedback Equalization for Single-Carrier Block Transmission

To combat intersymbol interference (ISI) in single-carrier block transmission systems, the hybrid decision feedback equalizer (HDFE) is a low-complexity, symbol-by-symbol detector that combines a frequency-domain feedforward filter and a time-domain feedback filter. To mitigate the error propagation effect of HDFE while preserving the implementation advantages of frequency-domain equalization, we propose the parallel HDFE (P-HDFE) algorithm that operates, based on the reliability of the decision variable, as an ordinary HDFE or a sequence detector that consists of multiple HDFEs running in parallel. Our study shows that, at moderate to high signal-to-noise ratios (SNRs), P-HDFE outperforms HDFE with little increase in complexity. We analyze the symbol error rate of P-HDFE at high SNRs for the case of quaternary phase-shift keying and static ISI channels, accounting for error propagation and residual ISI. Simulations demonstrate the accuracy of the analysis.

A Sparse Modulation Signal Bispectrum Analysis Method for Rolling Element Bearing Diagnosis

Modulation signal bispectrum (MSB) analysis is an effective method to obtain the fault frequency for rolling bearing, but harmonics make fault frequency dense and even frequency aliasing. Carrier frequency of bearing is generally determined by its structure and inherent characteristics and changes with the increase of the damage degree, so it is hard to be accurately found. To solve these problems, this paper proposes a sparse modulation signal bispectrum analysis method. Firstly the vibration signal is demodulated by MSB analysis and its bispectrum is obtained. After the frequency domain filtering, the carrier frequency is computed based on the characteristics of energy concentration at the carrier frequency on MSB. By shift-frequency MSB (SF-MSB), the carrier frequency is moved to the coordinate origin, the entire MSB is shifted for the same distance, and SF-MSB is obtained. At last, the bispectrum is shifted to the frequency zero point and diagonal slices are performed to obtain a sparse representation of MSB. Experimental results show that sparse MSB (S-MSB) method can not only eliminate the interference of harmonic frequency, but also make the extracted characteristic frequency of fault more obvious.

Bandwidth-on-Demand Motion Control

In this brief, we introduce a “bandwidth-on-demand” variable-gain control (VGC) strategy that allows for a varying bandwidth of the feedback controller. The proposed VGC can achieve improved performance given time-varying, reference-dependent performance requirements compared with linear time-invariant (LTI) control suffering from design tradeoffs between low-frequency tracking performance and sensitivity to higher-frequency disturbances. The VGC consists of frequency-domain loop-shaped linear filters and a variable-gain element, which depends on reference information. We present easy-to-use controller design guidelines and data-based frequency-domain conditions to verify stability and convergence of the closed-loop system. Moreover, the ability of the “bandwidth-on-demand” controller to outperform LTI controllers is emphasized through experiments on an industrial nanopositioning motion stage.

A Load Identification Algorithm of Frequency Domain Filtering Under Current Underdetermined Separation

Non-intrusive load identification is important to load monitoring and smart power utilization. In order to effectively identify load status and obtain detailed power data, this paper explained a load identification algorithm based on load decomposition and frequency domain filtering. It realized load decomposition through solving an underdetermined problem of a single current signal under non-intrusive collection mode and then further achieved load identification. The underdetermined problem was optimized into a 1-D underdetermined problem. The solving model was constructed as separating the collecting current into two channels. According to the sparseness of the current frequency domain signal, the optimal solution was obtained by a two-step iterative shrinkage threshold algorithm. Therefore, each operation load can be decomposed independently. The frequency-domain filter group was conducted to filter the decomposition current, and load identification was realized based on the decision of the frequency component after filtering. The algorithm simplified the identification complexity, with a fast convergence rate and high identification accuracy. The performance was validated by the actually measured data. The load decomposition and identification was effective, which could accurately determine the load status, and obtain the current waveform of the independent load.

Learning spatially correlation filters based on convolutional features via PSO algorithm and two combined color spaces for visual tracking

Last years, we have seen an emergence of wide methods in visual object tracking topic as convolutional neural network combined with correlation filter such as hierarchical features (HCF) (Ma et al. 20). However, upon the fact that some features may cause the tracking failures, the existing methods are still suffering of handling complex object appearance changes such as fast motion, significant deformation and occlusions. Further, they learn the correlation filter in frequency domain using Fourier transform, which cause unwanted boundary effects, which severely degrade the quality of the tracking model. Moreover, these methods are incapable of dealing with the illumination variation because they rely only on RGB base for color sequences. In this paper, we propose a novel method, which addresses the pre-cited problems. As first contribution, we learn adaptively three correlation filters in the spatial domain, with hierarchical convolutional features extracted from specific layers. Indeed, we apply the Particle Swarm Optimization algorithm to solve the update model equation of the correlation filters. Second, we propose that the switching between RGB and HSV color bases, give a soft manner to handle the illumination variation. For this aim, an HSV-energy condition is presented to choose the appropriate color base resorting to the energy of the second HSV component. Extensive experiments on a common benchmark dataset, justify that the proposed method outperforms the state-of-art methods.

On the Shift Operator, Graph Frequency, and Optimal Filtering in Graph Signal Processing

Defining a sound shift operator for graph signals, similar to the shift operator in classical signal processing, is a crucial problem in graph signal processing (GSP), since almost all operations, such as filtering, transformation, prediction, are directly related to the graph shift operator. We define a set of energy-preserving shift operators that satisfy many properties similar to their counterparts in classical signal processing, but are different from the shift operators defined in the literature, such as the graph adjacency matrix and Laplacian matrix based shift operators, which modify the energy of a graph signal. We decouple the graph structure represented by eigengraphs and the eigenvalues of the adjacency matrix or the Laplacian matrix. We show that the adjacency matrix of a graph is indeed a linear shift invariant (LSI) graph filter with respect to the defined shift operator. We further define autocorrelation and cross-correlation functions of signals on the graph, enabling us to obtain the solution to the optimal filtering on graphs, i.e., the corresponding Wiener filtering on graphs and the efficient spectra analysis and frequency domain filtering in parallel with those in classical signal processing. This new shift operator based GSP framework enables the signal analysis along a correlation structure defined by a graph shift manifold as opposed to classical signal processing operating on the assumption of the correlation structure with a linear time shift manifold. Several illustrative simulations are presented to validate the performance of the designed optimal LSI filters.

Frequency-Domain Adaptive Kalman Filter With Fast Recovery of Abrupt Echo-Path Changes

The frequency-domain Kalman filter (FDKF) was successfully applied in echo-cancelation systems due to its fast initial convergence and robustness to double-talk. However, the reconvergence ability of the FDKF was not comprehensively resolved and analyzed in the literature. This letter presents the steady-state solutions of the predicted system distance and corresponding step size of the FDKF, which is found to be related to the signal-to-noise ratio and the transition parameter. On this basis, it is pointed out that a frequently observed slow reconvergence of the FDKF, when the sudden echo-path change occurs, can be mainly attributed to the over-estimated noise power spectral density. Finally, we propose a shadow filter approach to resolve the algorithm's lack of reconvergence. Computer simulations of acoustic echo cancelation confirm the improved performance of the proposed method.

An Improved Filtering Method for Quantum Color Image in Frequency Domain

In this paper we investigate the use of quantum Fourier transform (QFT) in the field of image processing. We consider QFT-based color image filtering operations and their applications in image smoothing, sharpening, and selective filtering using quantum frequency domain filters. The underlying principle used for constructing the proposed quantum filters is to use the principle of the quantum Oracle to implement the filter function. Compared with the existing methods, our method is not only suitable for color images, but also can flexibly design the notch filters. We provide the quantum circuit that implements the filtering task and present the results of several simulation experiments on color images. The major advantages of the quantum frequency filtering lies in the exploitation of the efficient implementation of the quantum Fourier transform.

Compositional Signatures in Acoustic Backscatter Over Vegetated and Unvegetated Mixed Sand‐Gravel Riverbeds

Multibeam acoustic backscatter has considerable utility for remote characterization of spatially heterogeneous bed sediment composition over vegetated and unvegetated riverbeds of mixed sand and gravel. However, the use of high‐frequency, decimeter‐resolution acoustic backscatter for sediment classification in shallow water is hampered by significant topographic contamination of the signal. In mixed sand‐gravel riverbeds, changes in the abiotic composition of sediment (such as homogeneous sand to homogeneous gravel) tend to occur over larger spatial scales than is characteristic of small‐scale bedform topography (ripples, dunes, and bars) or biota (such as vascular plants and periphyton). A two‐stage method is proposed to filter out the morphological contributions to acoustic backscatter. First, the residual supragrain‐scale topographic effects in acoustic backscatter with small instantaneous insonified areas, caused by ambiguity in the local (beam‐to‐beam) bed‐sonar geometry, are removed. Then, coherent scales between high‐resolution topography and backscatter are identified using cospectra, which are used to design a frequency domain filter that decomposes backscatter into the (unwanted) high‐pass component associated with bedform topography (ripples, dunes, and sand waves) and vegetation, and the (desired) low‐frequency component associated with the composition of sediment patches superimposed on the topography. This process strengthens relationships between backscatter and sediment composition. A probabilistic framework is presented for classifying vegetated and unvegetated substrates based on acoustic backscatter at decimeter resolution. This capability is demonstrated using data collected from diverse settings within a 386 km reach of a canyon river whose bed varies among sand, gravel, cobbles, boulders, and submerged vegetation.

Adaptive Feedback Cancellation Using a Partitioned-Block Frequency-Domain Kalman Filter Approach With PEM-Based Signal Prewhitening

Adaptive filtering based feedback cancellation is a widespread approach to acoustic feedback control. However, traditional adaptive filtering algorithms have to be modified in order to work satisfactorily in a closed-loop scenario. In particular, the undesired signal correlation between the loudspeaker signal and the source signal in a closed-loop scenario is one of the major problems to address when using adaptive filters for feedback cancellation. Slow convergence speed and limited tracking capabilities are other important limitations to be considered. Additionally, computationally expensive algorithms as well as long delays should be avoided, for instance, in hearing aid applications, because of power constraints, important to extend battery life, and real-time implementations requirements, respectively. We present an algorithm combining good decorrelation properties, by means of the prediction-error method based signal prewhitening, fast convergence, good tracking behavior, and low computational complexity by means of the frequency-domain Kalman filter, and low delay by means of a partitioned-block implementation.

Volcanic rocks distribution and basement structure in western–central Junggar Basin revealed by gravitational and magnetic data

The Junggar Basin is a large superimposed basin with multistage evolution. The Carboniferous volcanic rock in the middle‐lower part of the basin has been an important target of oil and gas exploration. Therefore, it is crucial to establish a set of efficient methods and techniques to find out the distribution of the Carboniferous volcanic rock and the tectonic factors related to the reservoir forming of the volcanic rock. Our research reveals that the density and magnetism of the Carboniferous volcanic rock are obviously higher than those of the Mesozoic and Cenozoic sedimentary rocks. With a series of frequency domain filters and boundary enhancement techniques, we determine the residual gravitational and magnetic anomalies caused by the Carboniferous volcanic rock. Combined with borehole and seismic data from the studied areas, the horizontal and vertical distributions of the Carboniferous volcanic rock are defined, and the lithologies of different types of volcanic rocks are predicted. Furthermore, the gravitational and magnetic anomalies are used to estimate the basement faults and topography. The regional deep faults and their secondary faults of the basement are outlined, and the model of basement relief is constructed. Finally, the effects of the fracture structure and the basement topography in the process of volcanic activity and hydrocarbon accumulation are fully discussed. These results provide fundamental information for optimal selection of the favorable area of volcanic reservoir.

Overlapping Signal Separation Method Using Superresolution Technique Based on Experimental Echo Shape

Overlapping signals separation is a difficult problem, where time windowing is unable to separate signals overlapping in time and frequency domain filtering is unable to separate signals with overlapping spectra. In this work, a simulation under MATLAB is implemented to illustrate the concept of overlapping signals. We propose an approach for resolving overlapping signals based on Fourier transform and inverse Fourier transform. The proposed approach is tested under MATLAB, and the simulation results validate the effectiveness and the accuracy of the proposed approach. The approach is developed using Gerchberg superresolution technique to cope with signals with low signal-to-noise ratio. For practical work, an echo shape determination is required to apply the proposed technique. The experimental results show accurate localization of multiple targets.

Statistical Convergence Analysis for Optimal Control of DFT-Domain Adaptive Echo Canceler

The frequency-domain adaptive filter (FDAF) is widely used in echo cancellation systems due to its low complexity and fast convergence rate. However, the FDAF algorithm with a fixed step size exhibits a tradeoff among the convergence rate, steady-state misalignment, tracking ability, and robustness to near-end speech interferences. Several variable step-size FDAF algorithms were presented to address this problem. However, the state-of-the-art variable step-size FDAF algorithms did not handle this problem comprehensively. This paper presents a new robust variable step-size control approach to the FDAF algorithm. Based on a statistical analysis of the FDAF algorithm, an optimal step size for each frequency bin is derived by minimizing the mean-square deviation (MSD) between the true weight vector and estimated weight vector at each frame. Calculation of the step size requires the system distance and the observation noise power spectral density (PSD). The system distance is estimated using the deterministic recursive equations of MSD and the noise PSD is computed using the magnitude squared coherence function between the far-end signal and error signal. Moreover, a close link between the proposed FDAF and the frequency-domain Kalman filter is revealed. Specifically, the work presented here can be understood as a means to adaptively monitor and control the underlying acoustic state space of the Kalman filter, including means for fast readaptation of the adaptive filter after abrupt echo path changes. Simulation results demonstrate that the proposed algorithm can achieve fast convergence and low steady-state misalignment. Furthermore, the algorithm is robust to the double-talk interferences, but it does not require an explicit double-talk detector.

A Strategy of Quantum Image Filtering in Frequency Domain

In the field of quantum image processing, the method for quantum image representation has been a hot issue. However the computational complexity of the whole preparation for the mature model (Flexible Representation of Quantum Images) is too high. In this paper we present a fast image storage method based on the transformation about two quantum image representation models. Then a filtering algorithm is proposed via using quantum Fourier transform. With low time complexity, the good performance of this filtering operation can be presented in the simulation experiments.

Analysis of Fingerprint Image Enhancement using Gabor Filtering with Different Orientation Field Values

<p>Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, , and radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features. </p>

Accurate mask-based spatially regularized correlation filter for visual tracking

Recently, discriminative correlation filter (DCF)-based trackers have achieved extremely successful results in many competitions and benchmarks. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier. However, this assumption will produce unwanted boundary effects, which severely degrade the tracking performance. Correlation filters with limited boundaries and spatially regularized DCFs were proposed to reduce boundary effects. However, their methods used the fixed mask or predesigned weights function, respectively, which was unsuitable for large appearance variation. We propose an accurate mask-based spatially regularized correlation filter for visual tracking. Our augmented objective can reduce the boundary effect even in large appearance variation. In our algorithm, the masking matrix is converted into the regularized function that acts on the correlation filter in frequency domain, which makes the algorithm fast convergence. Our online tracking algorithm performs favorably against state-of-the-art trackers on OTB-2015 Benchmark in terms of efficiency, accuracy, and robustness.

Accurate and Autonomous Star Acquisition Method for Star Sensor under Complex Conditions

Star sensor is a preferred attitude measurement device for its extremely high accuracy. Star acquisition is the essential and critical procedure, which is aiming at acquiring accurate star areas. However, degenerated acquisition results under complex conditions become one of the major restrictions for modern star sensor. In this paper, an accurate and autonomous star acquisition method is proposed. Mathematical morphology and variable thresholding are combined for accurate star extraction; motion PSF is estimated in frequency domain and nonlinear filter is adopted for star restoration. Accurate star acquisition can be achieved based on only one star image. Simulations and laboratory experiments are conducted for verification. Several existing methods are also reproduced for comparison. Acquisition results demonstrate that the proposed method is effective and an excellent performance can be achieved autonomously under complex conditions, along with more detected stars and improved acquisition accuracy.

Using composite sinusoidal patterns in structured-illumination reflectance imaging (SIRI) for enhanced detection of apple bruise

Structured-illumination reflectance imaging (SIRI) provides a new means for enhanced detection of defects in horticultural products. Implementing the technique relies on retrieving amplitude images by illuminating the object with sinusoidal patterns of single spatial frequencies, which, however, are limited in interrogating the tissues at different depths. This study presented a first exploration of using composite sinusoidal patterns that integrated two and three spatial frequencies of interest, in SIRI for enhanced detection of defects in food (e.g., bruises in apples). Three methods based on Fourier transform were proposed to retrieve amplitude images at different frequencies by using either phase shifting with or without spiral phase transform (SPT) or frequency-domain filtering. The phase-shifting method involves solving a linear system that is composed of multiple phase-shifted pattern images in the Fourier space, and SPT that acts as a two-dimensional quadrature transform operator is used to reduce the images needed for amplitude retrieval; while the filtering-based method directly extracts different frequency components from only one pattern image that are then subjected to SPT processing. The three methods were tested for dual-frequency and triple-frequency patterns through numerical simulations and experiments on the detection of fresh bruises in apples by SIRI. The phase-shifting methods showed good performance in terms of small demodulation errors and strong image contrast for bruise detection; the filtering-based method, although viable in numerical simulation, needed improvement due to the worst practical performance. In addition, more frequency components introduced in the pattern would deteriorate the performance of these methods, and grid composite patterns were superior over the fringe ones due to reduced interactions between different frequency components.

Adaptive threshold based frequency domain filter for periodic noise reduction

Restoration of images corrupted with periodic noise is a fundamental task in digital image processing since periodic noise affects all imaging processes. The paper presents an adaptive threshold based frequency domain filter for denoising periodic noise from corrupted digital images. The algorithm applies forward origin shifting and Fourier transform to convert spatial domain image to frequency domain image. The proposed algorithm adaptively determines the threshold function for identifying the noisy peak areas of frequency domain image. The identified noisy frequencies corresponding to noisy peak areas of frequency domain image are diffused by the minimum filter for the effective restoration of frequency domain images. The restored frequency domain image is applied with inverses of Fourier transform and shifting operations to reconstruct the final restored image in spatial domain. Experimental analysis on different images with varying noisy conditions shows that the proposed algorithm is capable of producing better restored images than other algorithms used in the comparative study.

Fourier transform‐based windowed adaptive switching minimum filter for reducing periodic noise from digital images

This paper presents a windowed adaptive switching minimum filter in frequency domain to restore images corrupted by periodic noise. Periodic noise frequencies that spread throughout the spatial domain image concentrate in frequency domain image as star-shaped peak regions. The proposed algorithm incorporates distinct stages of noisy frequency detection and correction. The noisy frequency detection stage has peak detection and noise map generation sub-stages to effectively identify noisy peak areas into a binary flag image from the directional image of the origin shifted Fourier transformed corrupted image. The proposed noise correction scheme restores the detected noisy areas of the corrupted frequency domain image with the minimum of nearest possible uncorrupted frequencies. Finally, inverse shifting and inverse Fourier transform operations generates the restored image. Experimental results in terms of subjective and objective metrics demarcate that the proposed periodic noise reduction filter is more effective in restoring images corrupted with periodic noise than other filters used in the comparative study.