Impulse Response Low-pass Filter Research Articles

Online evaluation of resistance spot welding quality and defect classification

Resistance spot welding (RSW) is widely used in industrial product manufacturing, and weld quality is critical to ensure the safety and reliability of mechanical structures. With product quality improvement, the online monitoring of weld quality has become an urgent issue. Therefore, a method for online evaluation of RSW quality and automatic classification of welding defects based on mild steel is proposed. By acquiring the welding current and electrode voltage signals of the welding process in real time, a finite impulse response low-pass filter based on the Blackman window function is designed to perform zero-phase filtering of the dynamic resistance (DR). The influence of three welding parameters on the nugget diameter and shear strength of spot welding of DC01 mild steel was studied through the control variable method to determine the optimum welding parameters. Since the main defects of the RSW process are classified as expulsion defects and incomplete fusion defects, minor adjustments were made to the welding parameters to produce expulsion and incomplete fusion defects. Features in the time domain are extracted from the DR curves signals of normal, expulsion, and incomplete fusion welding spots, and the performance of the extracted features in the classifier is analyzed. Improvements are made to classify normal and expulsion spots with low accuracy, a new method is proposed that can accurately classify the quality of the welding spots. Through online evaluation tests of quality on 200 welding spots, the online evaluation test showed results that closely matched the validation test results. The results of the classification performance tests show that this study can be used for online evaluation of RSW quality and automatic classification of the welding spot defects of the DC01.

Implementation of Optimized Low Pass Filter for ECG filtering using Verilog

Electrocardiogram is a standard method used for the diagnosis of heart related disease. QRS complex plays an important role in Electrocardiogram signal processing since it is the prominent feature of Electrocardiogram signal. One of the important modules in the QRS detection algorithm is filtering. Electrocardiogram signal is processed to filter out unwanted signal through digital filtering. The main objective of this paper is to compare the resource utilization of hardware realization consumed between Direct Form I structure and Direct Form II structure. In this work, Infinite Impulse Response low pass filter to remove high frequency noise is designed with a passband frequency and stopband frequency of 5 and 25 Hz respectively. The designed filter is verified using Matlab Filter Design Analysis tool and realized in hardware using Verilog. Both the results show that the unwanted signals in the raw ECG signal are attenuated through the designed filter. The resource utilization result shows improvement with optimized Direct Form II implementation. The amount of look up tables, flip flop and digital signal processing used with Direct Form II structure shows a reduction to 0.26%, 0.12% and 2.50% respectively compared to 1.17%, 0.20%, 2.92% of utilization with Direct Form I structure.

Blind Sea Clutter Suppression for Spaceborne GNSS-R Target Detection

It has been demonstrated that delay-Doppler (DD) maps (DDMs) retrieved from spaceborne Global Navigation Satellite Systems Reflectometry missions contain sea clutter and also potential sea targets. Examples of sea targets include sea ice, oil rigs, and oil slicks. To distinguish sea targets from sea clutter, one can estimate the sea clutter and subtract it from the DDM, leaving behind only the sea target's signature on the DDM. This is computationally expensive and will not be robust when the sea clutter estimate is poor or if there are DD tracking errors. This article presents two blind methods for the suppression of sea clutter: first, an adaptive filter and second, an infinite impulse response low-pass filter. We use real DDMs collected by TechDemoSat-1 to show the autoregressive, hence temporally correlated nature of the DDM time series, and present evidence that two blind methods can suppress clutter at least as effectively as the existing method, albeit at a fraction of its computational cost.

Digital spotlighting filtering optimization for SAR imaging

In this paper, a synthetic aperture radar (SAR) image formation simulator is used to objectively evaluate the parameter selection within the digital spotlighting process. Specifically, recommendations for the filter type and filter order of the low-pass filters used in the range and azimuth decimation processes within the digital spotlighting algorithm are determined to maximize image quality and minimize computational cost. Results show that a finite impulse response low-pass filter with a Taylor (overline {n}=5) window applied provides the highest image quality over a wide range of filter orders and decimation factors. Additionally, a linear relationship between filter length and decimation factor is found.

Computing efficiency of the three-stage interpolated low pass filters

Undoubted advantages of finite impulse response filters are their unconditional stability, the absence of limit cycles and the possibility of implementing a filter that does not introduce phase distortion. The disadvantage of such filters is the large cost required to compute the response. This paper considers three-stage interpolated finite impulse response low-pass filters. The maximum values of the interpolation factors are determined. Dependences of the coefficient of computational efficiency and the coefficient of increase in the registers of the three-stage interpolated low-pass filter on the values of the interpolation factors, the widths of the passband and the transition band are obtained. Relations for determining the optimal values of interpolation factors corresponding to the maximal value of computational efficiency coefficient are obtained. In addition, the dependencies of the maximum coefficient of computational efficiency and the optimal coefficient of increase in the registers of the three-stage interpolated low-pass filter on the widths of the passband and the transition band at the optimum values of the interpolation factors are obtained. Considered three-stage interpolated low-pass filters should be used in the case when the required stopband is significantly less than the sampling rate. In this case, three- stage interpolated filters require less computational resources for calculating the response than the two-stage interpolated filters or filter implemented by the transversal structure.

Semi-real-time removal of baseline fluctuations in electrocardiogram (ECG) signals by an infinite impulse response low-pass filter (IIR-LPF)

The suppression of baseline disturbance in electrocardiogram (ECG) is necessary to avoid distorting diagnostic features in the data. In order to remove baseline drift, digital filter specifications had been designing along with detrending fluctuation algorithms. However, all these methods require a high amount of off-line computation. To address this, we propose a new method to remove baseline drift in ECG signals with semi-real-time computation while compensating for the phase distortion from the frequencies in the passband of the infinite impulse response low-pass filter and estimating its sample delay for the filtered signal. Regarding evaluation of the proposed filter, we can find the fact that the signal-to-noise ratio increases by approximately 3 dB after applying the filter to the MIT-BIH stress data consisted of arrhythmias corrupted with the intentional baseline wander and the disturbances due to muscle interactions as well as electrode–skin impedance mismatch during the electrocardiogram recordings.

An efficient local operator-based Q-compensated reverse time migration algorithm with multistage optimization

Most existing [Formula: see text]-compensated reverse time migration ([Formula: see text]-RTM) algorithms are based on pseudospectral methods. Because of the global nature of pseudospectral operators, these methods are not ideal for efficient parallelization, implying that they may suffer from high computational cost and inefficient memory usage for large-scale industrial problems. In this work, we reported a novel [Formula: see text]-RTM algorithm — the multistage optimized [Formula: see text]-RTM method. This [Formula: see text]-RTM algorithm uses a finite-difference method to compensate the amplitude and the phase simultaneously by uniquely combining two techniques: (1) a negative [Formula: see text] method for amplitude compensation and (2) a multistage dispersion optimization technique for phase correction. To prevent high-frequency noise from growing exponentially and ruining the imaging results, we apply a finite impulse response low-pass filter using the Kaiser window. The theoretical analyses and numerical experiments demonstrate that this [Formula: see text]-RTM algorithm precisely recovers the decayed amplitude and corrects the distorted phase caused by seismic attenuation effects, and hence produces higher resolution subsurface images with the correct structural depth information. This new method performs best in the frequency range of 10–70 Hz. Compared with pseudospectral [Formula: see text]-RTM methods, this [Formula: see text]-RTM approach offers nearly identical imaging quality. Based on local numerical differential operators, this [Formula: see text]-RTM method is very suitable for parallel computing and graphic processing unit implementation, an important feature for large 3D seismic surveys.

On the Selection of Optimum Savitzky-Golay Filters

Savitzky-Golay (S-G) filters are finite impulse response lowpass filters obtained while smoothing data using a local least-squares (LS) polynomial approximation. Savitzky and Golay proved in their hallmark paper that local LS fitting of polynomials and their evaluation at the mid-point of the approximation interval is equivalent to filtering with a fixed impulse response. The problem that we address here is, “how to choose a pointwise minimum mean squared error (MMSE) S-G filter length or order for smoothing, while preserving the temporal structure of a time-varying signal.” We solve the bias-variance tradeoff involved in the MMSE optimization using Stein's unbiased risk estimator (SURE). We observe that the 3-dB cutoff frequency of the SURE-optimal S-G filter is higher where the signal varies fast locally, and vice versa, essentially enabling us to suitably trade off the bias and variance, thereby resulting in near-MMSE performance. At low signal-to-noise ratios (SNRs), it is seen that the adaptive filter length algorithm performance improves by incorporating a regularization term in the SURE objective function. We consider the algorithm performance on real-world electrocardiogram (ECG) signals. The results exhibit considerable SNR improvement. Noise performance analysis shows that the proposed algorithms are comparable, and in some cases, better than some standard denoising techniques available in the literature.

Least squared error FIR filters with flat amplitude or group delay constraints

Finite impulse response lowpass filters with prescribed number of zeros at /spl omega/=/spl pi/ and either prescribed group delay flatness or prescribed amplitude flatness and linear phase are designed by minimizing a squared error. A simple orthogonal projection of least squared error filters (that are often available in closed form) onto a linear subspace determined via Thiran filters (for group delay flatness) and via Herrmann filters (for amplitude flatness) gives the desired filters.

Constant beamwidth IIR lowpass filter for data sampled at nonuniform rate

A simple modification to the algorithm for a first-order infinite impulse response lowpass filter to allow the use of nonuniformly sampled data is described. This real-time algorithm uses measurements of the intersample interval to modify the recursion weight so that the filter cutoff frequency is kept constant.