Signal Smoothing Research Articles

A New Approach to ARMAX Signals Smoothing: Application to Variable-Q ARMA Filter Design

In this paper, we propose a new approach to signal smoothing when the data are generated (or represented) by an autoregressive moving average with exogenous inputs (ARMAX) model. In this approach, the original ARMAX recurrence relation is directly employed and combined with a constrained least squares optimization framework to filter out both system and measurement noise components and estimate the desired signal in form of block-wise matrix formulation. The approach is also driven from a forward backward filtering, which is accomplished through linear time invariant system. While the impulse response of the proposed filter can be found using deconvolution operator, a closed-form expression is presented for its impulse response without resorting to any transform methods. Two examples of its applications for variable-Q filter design and spectral density estimation are given, which demonstrate the present approach is more effective and robust than the existing variable-Q filter designs in the literature and it can be used to improve the spectral density estimation.

New intelligent control strategy by robust neural network algorithm for real time detection of an optimized maximum power tracking control in photovoltaic systems

To increase the power output of a PV module or a field of PV modules, an electronic controller is incorporated between the PV generator and the load, whose role and main objective is the continuous monitoring of the maximum power point of the PV generator commonly known as MPPT (Maximum Power Point Tracking) and this in general per action on a DC-DC conversion device.The regulation and control techniques provide the impedance matching function, transferring to the load the maximum electrical power output from the PV generator in any the temperature and sunshine conditions.The development of a revolutionary method based on neural algorithms for the prediction of an instantaneous command is the main objective in our work.Indeed, the paper presents a new control strategy for the photovoltaic PV, it is a command based on Neuronal Network technique. It is the first time that this technique has been introduced, and proposed by the authors in synthesizing control laws for the converters of electronic power.The new technical algorithm based on Neural Networks, is designed to be more robust in performance with respect to tracking speed and precision.Moreover, this new successful technical research, provides a robust neural structure compared to the noisy empirical data used for the prediction of the command. Consequently a smooth control signal without oscillation, targeting exactly the expected optimal control with an independent control of the sampling frequency of the system.This study, which is followed by a simulation, has enabled us to consolidate the idea that the new Neural Network controller when compared to their classical counterparts, and obtains the best performances concerning the speed of tracking and precision.The robustness of the networks of neurons opposite the noise of measurements, like, the smoothness of the power signal of PV system generated during the application of the neuronal order, will qualify this command as a practical alternative to the disadvantages recorded on the levels of the classical methods.

Enhancing the Performance of One Dimensional Median Filter

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.

Contrast Enhancement via Dual Graph Total Variation-Based Image Decomposition

Images captured in low lighting environment suffer from both low luminance contrast and noise corruption. However, most existing contrast enhancement algorithms only consider contrast boosting, which tends to reveal or amplify noise that is originally not visible in the dark areas. In this paper, we propose a joint contrast enhancement and denoising algorithm, which is based on structure/texture layer decomposition via minimization of dual forms of graph total variation (GTV). Specifically, the structure layer is expected to be generally smoothing but with sharp edges at the foreground background boundaries, for which we propose a quadratic form of GTV (QGTV) as the prior that promotes signal smoothness along graph structure. For the texture layer, a re-weighted GTV (RGTV) is tailored to noise removal while preserving true image details. We provide theoretical analysis about the filtering behavior of these two priors. Furthermore, a boost factor is derived per patch via optimal contrast-tone mapping to improve the overall brightness level of the patch. Finally, an optimization objective function is formulated, which casts image decomposition, brightness boosting, and noise reduction into a unified optimization framework. We further propose a fast approach to efficiently solve the optimization and provide analysis about the convergency. The experimental results show that the proposed method outperforms the state-of-the-art works in subjective, objective, and statistical quality evaluation.

A Graph Signal Processing Approach to Study High Density EEG Signals in Patients with Disorders of Consciousness.

Graph signal processing (GSP) is a novel approach to analyse multi-dimensional neuroimaging data, constraining functional measures by structural characteristics in a single framework (i.e. graph signals). In this approach, functional time series are assigned to the vertices of the underlying weighted graph and GSP analysis is performed in each time point of the signal. Here we used GSP to study local brain connectivity changes in patients with disorders of consciousness based on resting state high density electroencephalography (hdEEG) recordings. Total variation of the graph signals is a measure of signal smoothness over the underlying graph. In this study, we constructed the underlying graph based on the geometrical distances between each electrode pairs in such a way that local smoothness of the signal can be studied. Total variation analysis in α-band showed that in the pathological states of altered consciousness, local short range communication of brain regions in this frequency band is stronger than in healthy states which shows that information is segregated in local regions in patients with disorders of consciousness.

Biological surface electromyographic switch and necklace-type button switch control as an augmentative and alternative communication input device: a feasibility study

Augmentative and alternative communication (AAC) is an approach used to supplement, improve, and support the communication of those with speech or language impairments. We developed an AAC device for diverse approaches, using an electromyographic (EMG) switch and a necklace-type button switch. The EMG switch comprised an EMG signal processor and a switch interface processor. EMG signals were processed using an electrode through the stages of signal acquisition, amplification, filtering, rectification, and smoothing. In the switch interface processor, the microprocessor determined the switch as ON or OFF in response to an input EMG signal and then converted the EMG signal into a keyboard signal, which was transmitted to a smart device via Bluetooth communication. A similar transmission process was used for the necklace-type button switch, and switch signals were input and processed with general-purpose input/output. The first and second feasibility tests for the EMG switch and button switch were conducted in a total of three test sessions. The result of the feasibility test indicated that the major inconvenience and desired improvement associated with the EMG switch were the intricacy of the AAC device settings. The major inconveniences and desired improvements for the necklace-type button switch involved device shifting, volume and weight, and inconvenience in fixing the switch in various directions. Thus, based on the first and second feasibility tests, we developed an additional device. Finally, the EMG switch and necklace-type button switch developed to remedy the inconveniencies had high feasibility.

Local Smoothness of Graph Signals

Analysis of vertex-varying spectral content of signals on graphs challenges the assumption of vertex invariance and requires the introduction of vertex-frequency representations as a new tool for graph signal analysis. Local smoothness, an important parameter of vertex-varying graph signals, is introduced and defined in this paper. Basic properties of this parameter are given. By using the local smoothness, an ideal vertex-frequency distribution is introduced. The local smoothness estimation is performed based on several forms of the vertex-frequency distributions, including the graph spectrogram, the graph Rihaczek distribution, and a vertex-frequency distribution with reduced interferences. The presented theory is illustrated through numerical examples.

Neural controller for the smoothness of continuous signals: an electrical grid example

In this paper, the use of artificial neural networks (ANNs) is proposed to manage the local demand of different electric grid elements to smooth their aggregated consumption. The ANNs are based on the load automation of the local electric behavior, following a local strategy but affecting to the global system. In an electrical grid, there is no possibility to share information between the users because anonymity must be warranted. Therefore, a solution to the problem is elaborated with the minimum information possible without the need for communication between the users. A grid environment and behavior of different users is simulated.

Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors.

Ambient backscatter communication enables passive sensors to convey sensing data on ambient RF signals in the air at ultralow power consumption. To extract data bits from such signals, threshold-based decoding has generally been considered, but suffers against Wi-Fi signals due to severe fluctuation of OFDM signals. In this paper, we propose a pattern-matching-based decoding algorithm for Wi-Fi backscatter communications. The key idea is the identification of unique patterns of signal samples that arise from the inevitable smoothing of Wi-Fi signals to filter out noisy fluctuation. We provide the mathematical basis of obtaining the pattern of smoothed signal samples as the slope of a line expressed in a closed-form equation. Then, the new decoding algorithm was designed to identify the pattern of received signal samples as a slope rather than classifying their amplitude levels. Thus, it is more robust against signal fluctuation and does not need tricky threshold configuration. Moreover, for even higher reliability, the pattern was identified for a pair of adjacent bits, and the algorithm decodes a bit pair at a time rather than a single bit. We demonstrate via testbed experiments that the proposed algorithm significantly outperforms conventional threshold-based decoding variants in terms of bit error rate for various distances and data rates.

Classification of Sand Grains by Terahertz Time-Domain Spectroscopy and Chemometrics

A novel method combining terahertz time-domain spectroscopy (THz-TDS) with chemometrics was proposed for classification of sand samples from different deserts and grain sizes. THz-TDS combined with chemometrics was firstly utilized for identifying the source of sand samples and analyzing the sizes of sand granule. Terahertz time-domain transmittance spectra were collected and then Savitzky–Golay smoothing and orthogonal signal correction (OSC) approaches were used for spectral pretreatment. Principal component analysis, partial least squares discriminant analysis, and support vector machine were used to establish classification models of the sand samples. The prediction correctness ranged between 94 and 97%. Scanning electron microscopy was used to investigate the morphology of the sand samples. The sand samples were analyzed by energy dispersive spectroscopy and the elemental content distributions were obtained. Fourier transform infrared absorption spectra of the sand samples were also collected. THz-TDS combined with multivariate modeling methods provided reliable and useful information to determine the provenance of the sands originated from different deserts in northern China. This work reveals that THz-TDS can be a useful tool for classification of sand from different deserts.

A Comparative Study of Enhanced Infrared Image Processing for Foreign Object Detection in Lightweight Composite Honeycomb Structures

The interest toward lightweight composites in the aeronautical industry grows year by year. The challenge is the identification and characterization of defects by using an integration of different techniques. The use of the infrared thermography (IRT) method for the inspection of lightweight composites is poorly documented in the open literature, due to the low heat diffusion through the honeycomb cores. In this study, IRT was used to retrieve the unknown positions of internal inserted objects in three different lightweight composite structures, by using three new thermal image processing integration methods. The corresponding post-processed results were compared against the traditional principal component thermography and pulsed phase thermography image processing techniques. The comparison showed that the integrated signal smoothing processing enhanced the image quality compared to the established techniques. Then, a thermal-physical analysis corresponding to the experimental results was conducted, in order to explain the experimental results. Finally, the advantages and disadvantages of the different presented methods when applied on the different lightweight composite structures were summarized.

Soft Variable Structure Control of Linear Systems via Desired Pole Paths

In this article, a novel method of soft variable structure control of linear systems, desired pole paths, is proposed. The proposed method is helpful to reach a fast response when a continuous control signal and satisfaction of some constraints are desired. The method selects a desired path for closed loop poles instead of the exact location of poles, then the pole placement in this path is determined by solving an optimization problem subject to a control signal constraint leading to a suboptimal control structure. The stability of the proposed method is provided based on the multivariable circle criterion and the Kalman-Yakubovich-Popov lemma. A design parameter is also introduced in this paper which can adjust a tradeoff between speed of response and smoothness of the control signal. Simulation of a satellite model shows an improvement in shortening the settling time and softening the control signal compared to published soft variable structure control schemes. DOI: http://dx.doi.org/10.5755/j01.itc.47.3.18805

Fault detection and diagnosis algorithms for HV-LED lamp driven by segmented linear solution using first-order difference calculation and peak-detection methods

Fault detection and diagnosis (FDD) algorithms based on first-order difference calculation and peak-detection methods for HV-LED lamp driven by segmented linear solution is proposed. The proposed FDD algorithms analyze the illuminance fluctuation characteristics of HV-LED lamp, and then identify whether the HV-LED lamp occurs fault or not and confirm the fault-type. The proposed FDD algorithms can be divided into five steps: illuminance signal smoothing, illuminance signal process using first-order difference calculation, difference-value sequence smoothing, peak detection of difference-value sequence, and fault-type recognition. Taking the HV-LED lamp driven by four-segment linear driver for example, the illuminance data sets of the fault-free and three different faults are investigated for algorithm verification experiments. The experimental results demonstrate the proposed FDD algorithms can effectively detect and diagnose the fault-type of HV-LED lamp driven by segmented linear driver, and locate the faulty HV-LED sub-strings.

Optimization of sampling rate and smoothing improves classification of high frequency power in electrocorticographic brain signals

Objective. High-frequency band (HFB) activity, measured using implanted sensors over the cortex, is increasingly considered as a feature for the study of brain function and the design of neural-implants, such as Brain-Computer Interfaces (BCIs). One common way of extracting these power signals is using a wavelet dictionary, which involves the selection of different temporal sampling and temporal smoothing parameters, such that the resulting HFB signal best represents the temporal features of the neuronal event of interest. Typically, the use of neuro-electrical signals for closed-loop BCI control requires a certain level of signal downsampling and smoothing in order to remove uncorrelated noise, optimize performance and provide fast feedback. However, a fixed setting of the sampling and smoothing parameters may lead to a suboptimal representation of the underlying neural responses and poor BCI control. This problem can be resolved with a systematic assessment of parameter settings. Approach. With classification of HFB power responses as performance measure, different combinations of temporal sampling and temporal smoothing values were applied to data from sensory and motor tasks recorded with high-density and standard clinical electrocorticography (ECoG) grids in 12 epilepsy patients. Main results. The results suggest that HFB ECoG responses are best performed with high sampling and subsequent smoothing. For the paradigms used in this study, optimal temporal sampling ranged from 29 Hz to 50 Hz. Regarding optimal smoothing, values were similar between tasks (0.1–0.9 s), except for executed complex hand gestures, for which two optimal possible smoothing windows were found (0.4–0.6 s and 0.9–2.7 s). Significance. The range of optimal values indicates that parameter optimization depends on the functional paradigm and may be subject-specific. Our results advocate a methodical assessment of parameter settings for optimal decodability of ECoG signals.

Signal Smoothing with PLS Regression.

Smoothing of instrumental signals is an important prerequisite in data processing. Various smoothing methods were suggested through the last decades each having their own benefits and drawbacks. Most of the filtering methods are based on averaging in a certain window (e.g., Savitzky-Golay) or on frequency-domain representation (e.g., Fourier filtering). The present study introduces novel approach to signal filtering based on signal variance through PLS (projections on latent structures) regression. The influence of filtering parameters on the smoothed spectrum is explained and real world examples are shown.

Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint

Non-intrusive load monitoring (NILM) is a cost-effective technique for extracting device-level energy consumption information by monitoring the aggregated signal at the entrance of the electric power. With the large-scale deployment of smart metering, NILM should ideally be designed to operate purely on the low-rate data from smart meters. In this paper, an approach based on Graph Shift Quadratic Form constrained Active Power Disaggregation (GSQF-APD) is proposed, which is built upon matrix factorization and introduces graph shift quadratic form constraint according to piecewise smoothness of the power signal. In addition, a two-step iterative optimization method is designed to solve this problem. The first step minimizes the regularization term to find the signal with minimum variation, and then the second step uses the simulated annealing (SA) algorithm to iteratively minimize the objective function and constraint based on the total graph variation minimizer. Using one open-access dataset, the strength of GSQF-APD is demonstrated through three sets of experiments. The numerical results show the superior performance of GSQF-APD, with Graph Laplacian Quadratic Form constrained Active Power Disaggregation (GLQF-APD) and the state-of-the-art NILM methods as benchmarks.

Static/Dynamic Filtering for Mesh Geometry.

The joint bilateral filter, which enables feature-preserving signal smoothing according to the structural information from a guidance, has been applied for various tasks in geometry processing. Existing methods either rely on a static guidance that may be inconsistent with the input and lead to unsatisfactory results, or a dynamic guidance that is automatically updated but sensitive to noises and outliers. Inspired by recent advances in image filtering, we propose a new geometry filtering technique called static/dynamic filter, which utilizes both static and dynamic guidances to achieve state-of-the-art results. The proposed filter is based on a nonlinear optimization that enforces smoothness of the signal while preserving variations that correspond to features of certain scales. We develop an efficient iterative solver for the problem, which unifies existing filters that are based on static or dynamic guidances. The filter can be applied to mesh face normals followed by vertex position update, to achieve scale-aware and feature-preserving filtering of mesh geometry. It also works well for other types of signals defined on mesh surfaces, such as texture colors. Extensive experimental results demonstrate the effectiveness of the proposed filter for various geometry processing applications such as mesh denoising, geometry feature enhancement, and texture color filtering.

Time-fractional diffusion equation for signal smoothing

The time-fractional diffusion equation is used for signal smoothing. Compared to the classical diffusion equation, the time-fractional diffusion equation has another adjustable time-fractional derivative order to control the diffusion process. Therefore, some simulated signals are used to compare the smoothing performance between the time-fractional diffusion equation and the classical diffusion equation as well as between classical smoothing methods (regularization method, Savitzky–Golay method and wavelet method). In the end, the time-fractional diffusion filtering is applied in an NMR spectrum smoothing. Results indicate that the time-fractional diffusion filtering is advantage over the classical diffusion filtering and their smoothing performance is better than that of classical smoothing methods.

Enhanced Infrared Image Processing for Impacted Carbon/Glass Fiber-Reinforced Composite Evaluation.

In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided.

On the use of signal smoothness estimates and signal similarity in bearing fault detection

The concepts of signal smoothness and signal similarity are very common in digital signal processing. In this paper, both concepts have been explored in the ball bearing failure diagnostics, where the bearing fault, bearing inner race and outer race faults were analysed. It was found out that the signal smoothness and the signal similarity estimates might reduce computational tolerance in the vibro-acoustic signal analysis. On this basis, numerous experiments have been carried out to demonstrate usefulness of the above-mentioned estimates (as useful parameters of signal features) in the signal quality analysis.