Adaptive Bandpass Filter Research Articles

Dynamic Harmonic Phasor Estimation by Adaptive Taylor-Based Bandpass Filter

The finite impulse response (FIR) harmonic estimators based on the Taylor signal model have recently received increased attention due to their capability to accurately estimate harmonic phasors under dynamic conditions of a power system. However, these filters may produce a large estimation error when the frequency deviation from the nominal value is high. Online calculation of the FIR filter bank coefficients may improve the estimation accuracy but at the cost of increased computational complexity. To address this issue, in this article, we propose a dynamic harmonic estimation algorithm that aims to enhance measurement accuracy at both nominal and off-nominal frequencies and has a reduced computational complexity. The proposed algorithm estimates the harmonic parameters by means of the Taylor-based bandpass filter while compensating for the effects of the neighboring spectral components. Numerical simulation results show that the proposed algorithm outperforms the available methods.

Extraction of Quasi-Monochromatic Gravity Waves from an Airglow Imager Network

An algorithm has been developed to isolate the gravity waves (GWs) of different scales from airglow images. Based on the discrete wavelet transform, the images are decomposed and then reconstructed in a series of mutually orthogonal spaces, each of which takes a Daubechies (db) wavelet of a certain scale as a basis vector. The GWs in the original airglow image are stripped to the peeled image reconstructed in each space, and the scale of wave patterns in a peeled image corresponds to the scale of the db wavelet as a basis vector. In each reconstructed image, the extracted GW is quasi-monochromatic. An adaptive band-pass filter is applied to enhance the GW structures. From an ensembled airglow image with a coverage of 2100 km × 1200 km using an all-sky airglow imager (ASAI) network, the quasi-monochromatic wave patterns are extracted using this algorithm. GWs range from ripples with short wavelength of 20 km to medium-scale GWs with a wavelength of 590 km. The images are denoised, and the propagating characteristics of GWs with different wavelengths are derived separately.

Memristor Based Planar Tunable RF Circuits

Memristors have been recently proposed as an alternative to incorporate switching along with traditional CMOS circuits. Adaptive impedance and frequency tuning are an essential and challenging aspect in communication system design. To enable both, a matching network based on switchable capacitors with fixed inductors is proposed in this paper where the switching is done by memristive switches. This paper analyzes the operation of memristors as a switch and a matching network based on memristors which adaptively tunes with impedance and frequency. With three capacitor banks of each 0.5 pF resolution and two fixed inductors, matching for antenna impedance ranging from 20 to 200[Formula: see text]Ohms and for frequencies ranging from 0.9 to 3.2[Formula: see text]GHz is reported. Thereafter, an adaptive planar band-pass filter is implemented on CMOS technology with two metal layers. This adaptive frequency tunable band-pass filter uses a [Formula: see text] network with resonator tanks in both arms that operates at 2.45 GHz. It is tunable from 2.8[Formula: see text]GHz to 7.625[Formula: see text]GHz range. This tunability is achieved using tunable spiral inductor based on memristive switches. The proposed filter layout is implemented and simulated in ANSYS Designer. The initialization and the programming circuitry to enable adaptive switching of the memristive devices has to be addressed. Since RF memristive devices are not commercially available, circuit level simulations are done as a proof of concept to validate the expected results.

Noise reduction for desert seismic data using spectral kurtosis adaptive bandpass filter

In view of the heterogeneity and week similarity of random noise in the desert seismic exploration, and lots of random noise focused on low frequency, the traditional bandpass filter and wavelet transform are used to separate the signal and noise. Although there are some denoising effects, the noise cannot be suppressed well, and effective signal is damaged to some extent. Because of the above shortcomings, we propose a bandpass filter denoising method based on spectral kurtosis in this paper. This method is based on the signal and the random noise’s energy distribution characteristics in the frequency domain. First, through short-time Fourier transform (STFT), the spectral kurtosis of noisy signals is obtained. Second, we design a new threshold by the obtained spectral kurtosis, the value of spectral kurtosis greater than the threshold is preserved, and the spectral kurtosis less than the threshold is set to 0. So, the method realises the adaptive choice of the filter passband, getting an adaptive bandpass filter. At the same time, the noise can be suppressed to a greater extent while the effective signal is retained very well. The noise removal results of synthetic data and actual data show that the proposed method has very good denoising performance and amplitude preserving capability.

Noncontact pulse wave detection by two-band infrared video-based measurement on face without visible lighting

In this paper, we propose a novel noncontact pulse wave monitoring method that is robust to fluctuations in illumination through use of two-band infrared video signals. Because the proposed method uses infrared light for illumination, the method can be used to detect a pulse wave on a human face without visible lighting. The corresponding two-band pixel values in the video signals can be separated into hemoglobin and shading components by application of a separation matrix in logarithmic space for the two pixel values. Because the shading component has been separated, the extracted hemoglobin component is then robust to fluctuations in the illumination. The pixel values in the region of interest were spatially averaged over all the pixels of each frame. These averaged values were then used to form the raw trace signal. Finally, the pulse wave and the corresponding pulse rate were obtained from the raw trace signal through several signal processing stages, including detrending, use of an adaptive bandpass filter, and peak detection. We evaluated the absolute error rate for the pulse rate between the estimated value and the ground truth obtained using an electrocardiogram. In the experiments, we found that the performance of the proposed method was greatly improved compared with that of conventional methods using single-band infrared video.

Fingerprint enhancement rooted in the spectra diffusion by the aid of the 2D adaptive Chebyshev band-pass filter with orientation-selective

2D adaptive Chebyshev band-pass filter (ACBF) with orientation-selective, a novel fingerprint enhancement filter, is designed in this paper. The fingerprint enhancement is deeply rooted in the spectra diffusion by performing the 2D ACBF with orientation-selective in the frequency domain. The process of the enhancement is to have two phases: fingerprint is first enhanced by using Gabor filter and histogram equalization (HE), and then the pre-enhanced fingerprint is enhanced based on spectra diffusion by using the 2D ACBF with orientation-selective in frequency domain. In the first stage, the fingerprint quality can be improved in some extent. In the second stage, first, the qualities of patches are evaluated by the coherence of point orientations. Second, the adaptive parameters of the 2D ACBF are estimated, further, the 2D ACBF with orientation-selective is designed. Finally, the fingerprint image is enhanced based on spectra diffusion by using the 2D ACBF with orientation-selective. Experimental results on the database of FVC2000 and FVC2004 show that the proposed method achieves better results in comparison with other methods, and can significantly improves the performance of automatic fingerprint identification system (AFIS).

Robust heart rate estimation using wrist-type photoplethysmographic signals during physical exercise: an approach based on adaptive filtering

Photoplethysmographic (PPG) signals are easily corrupted by motion artifacts when the subjects perform physical exercise. This paper introduces a two-step processing scheme to estimate heart rate (HR) from wrist-type PPG signals strongly corrupted by motion artifacts. Adaptive noise cancellation, using normalized least-mean-square algorithm, is first performed to attenuate motion artifacts and reconstruct multiple PPG waveforms from different combinations of corrupted PPG waveforms and accelerometer data. An adaptive band-pass filter is then used to track the common instantaneous frequency component (i.e. HR) of the reconstructed PPG waveforms. The proposed HR estimation scheme was evaluated on two datasets, composed of records from running subjects and subjects performing different kinds of arm/forearm movements and resulted in average absolute errors of 1.40 ± 0.60 and 4.28 ± 3.16 beats-per-minute for these two datasets, respectively. Importantly, the proposed method is fully automatic, induces an average estimation delay of 0.93 s, and is therefore suitable for real-time monitoring applications.

Automatic monitoring refractive index variations of transient solution during electrochemical reactions

Abstract We propose and experimentally demonstrate the automatic measurement of 2D refractive index (RI) changes in aqueous solution at electrode/electrolyte interface based on a Mach-Zehnder interferometer. During the electrochemical reactions, local variations of the electrolyte’s RI, which correlate with the concentration of dissolved species, change the phase of the object beam when the beam pass through the electrolyte. With the application of an adaptive band-pass filter, the RI changes can be obtained automatically from interferograms, which enable direct visualization of the concentration change of the soluble species. As a proof of principle demonstration, the system is employed to detect the soluble species during the potentiodynamic sweep of the copper electrode in 0.5 mol dm−3 NaBr solution at 10 mV s−1. The RI changes at the Cu/NaBr solution interface provide direct evidence of the reaction mechanisms of copper corrosion in NaBr solution.

협대역 초음파 신호를 이용한 시간 영역에서의 감쇠 지수 예측

The VSA(Video Signal Analysis) method is the time-domain approach for estimating ultrasonic attenuation which utilizes the envelop signals from backscattered rf signals. The echogenicity of backscattered ultrasonic signals, however, from deeper depths are distorted when the broadband transmit pulse is used and it degrades the estimation accuracy of attenuation coefficients. We propose the modified VSA method using adaptive bandpass filters according to the centroid shift of echo signals as a pulse propagates. The technique of dual-reference diffraction compensation is also proposed to minimize the estimation errors because the difference of attenuation properties between the reference and sample aggravates the estimation accuracy when the differences are accumulated in deeper depth. The proposed techniques minimize the distortion of relative echogenicity and maximize the signal-to-noise ratio at the given depth. Simulation results for numerical tissue-mimicking phantoms show that the Rectangular-shaped filter with the appropriate center frequency exhibits the best estimation performance and the technique of the dual-reference diffraction compensation dramatically improves accuracy for the region after the beam focus.

Phase retrieval of optical fringe pattern using wavelet ridge section and adaptive bandpass filter

To improve the accuracy of fringe projection profilometry for a single deformed pattern, an instantaneous phase retrieval method is proposed using a wavelet ridge section and an adaptive bandpass filter based on dyadic wavelets. First, we present a concept and assumption named wavelet ridge section to depict the instantaneous phases of the deformed fringe signals, which are also degraded by noise, and then introduce a formula to calculate the width of the wavelet ridge section. Furthermore, an adaptive bandpass filter is designed for extracting the wavelet subsignals corresponding to the wavelet ridge sections to reconstruct the analytical signal. Finally, the instantaneous phase of the distorted fringe pattern is effectively retrieved. All parameters of our method are designed to be adaptive for different fringe patterns. Our experiments indicate that the proposed method is effective for measurements and outperforms other existing mainstream wavelet transform profilometry techniques, not only in accuracy but also in noise suppression performance.

Single-Phase Grid Voltage Frequency Estimation Using Teager Energy Operator-Based Technique

This paper reports the performance of a technique for the estimation of single-phase grid voltage fundamental frequency under distorted grid conditions. The technique combines a Teager energy operator (TEO) with a frequency adaptive bandpass filter (BPF). The TEO is based on three consecutive samples and is used to estimate the fundamental frequency. The BPF relies on a recursive discrete Fourier transform (RDFT) and an inverse RDFT, and is used to extract the normalized amplitude of the grid voltage fundamental component. The technique is computationally efficient and can also reject the negative effects caused by dc offset and harmonics. It requires less computational effort, can provide faster estimation, and is also less affected by harmonics as compared with a technique relying on the RDFT-based decomposition of the single-phase system into orthogonal components. The performance of the technique is verified using both simulation and experimental results.

Motion-Resistant Remote Imaging Photoplethysmography Based on the Optical Properties of Skin

Remote imaging photoplethysmography (RIPPG) can achieve contactless monitoring of human vital signs. However, the robustness to a subject’s motion is a challenging problem for RIPPG, especially in facial video-based RIPPG. The RIPPG signal originates from the radiant intensity variation of human skin with pulses of blood and motions can modulate the radiant intensity of the skin. Based on the optical properties of human skin, we build an optical RIPPG signal model in which the origins of the RIPPG signal and motion artifacts can be clearly described. The region of interest (ROI) of the skin is regarded as a Lambertian radiator and the effect of ROI tracking is analyzed from the perspective of radiometry. By considering a digital color camera as a simple spectrometer, we propose an adaptive color difference operation between the green and red channels to reduce motion artifacts. Based on the spectral characteristics of photoplethysmography signals, we propose an adaptive bandpass filter to remove residual motion artifacts of RIPPG. We also combine ROI selection on the subject’s cheeks with speeded-up robust features points tracking to improve the RIPPG signal quality. Experimental results show that the proposed RIPPG can obtain greatly improved performance in accessing heart rates in moving subjects, compared with the state-of-the-art facial video-based RIPPG methods.

Conventional Synchronous Reference Frame Phase-Locked Loop is an Adaptive Complex Filter

Despite the wide acceptance and use of the conventional synchronous reference frame phase-locked loop (SRF-PLL), no transfer function describing its actual input-output relationship has been developed so far. Arguably, the absence of such transfer function has hampered the application of SRF-PLL as a filter or controller inside the closed-loop control systems. In this paper, the transfer function describing the actual input-output relationship of the conventional SRF-PLL is presented. Using this transfer function, it is shown that the conventional SRF-PLL is a first-order adaptive complex bandpass filter. The accuracy of this transfer function is confirmed through numerical results.

A Software-Defined Radio Based Adaptive HRPT/APT Signal Receiver

The paper discusses the design, implementation, and test of a software-defined radio (SDR) based receiver to acquire HRPT (high resolution picture transmission) and APT (automatic picture transmission) signals of NOAA weather satellites simultaneously, recognizes modulation types, and demodulates the signals. The captured HRPT RF signal is downconverted to the 137.5 MHz band in the RF (radio frequency) front-end and then combined with the APT RF signal. The combined signals are then downconverted and digitized at the programmable receiver platform. The digitized data are spectrally analyzed with short-time FFT (fast Fourier transform) to obtain an instantaneous spectrum for adjustment of the adaptive digital band-pass filter. The modulation recognition block identifies split-phase PSK (phase shift keying) for HRPT and FM (frequency modulation) for APT. The HRPT signal is then demodulated with a Costas loop because the two side bands have more power than the carrier. The APT signal is demodulated in an FM demodulator with Zoom-FFT and automatic gain control (AGC) to maintain the brightness of the whole weather map according to the brightness of the synchronization mark. The system functions are developed and realized in an SDR software platform. The resulting SDR-based receiver is then used to receive and process real signals.

Ionosphere phase decontamination method based on subspace in sky‐wave OTHR

In general, ionosphere phase contamination can expand the Bragg peak of sea clutter and the echo signal from a target in sky-wave over-the-horizon radar (OTHR). By exploiting the subspace algorithm, an effective approach of ionosphere phase decontamination is proposed to solve the above-mentioned problems. In this new scheme, the stronger one of two spreading Bragg peaks is extracted by an adaptive sliding-window bandpass filter, and the instantaneous phase of the spreading Bragg peak is obtained by the subspace algorithm. Both the theoretical analysis and the experimental results of the real OTHR data have confirmed the feasibility of this new method.

The use of an adaptive bandpass filter as an observer in the control loop of the international space station

For a parametrically uncertain model of the International Space Station (ISS), an algorithm using an adjustable model is proposed; on the basis of this algorithm, a self-tuning discrete bandpass filter is designed. The asymptotic stability of the proposed algorithm is ensured using the novel method for solving the matrix completion problem where the completed matrix is required to be stable. The use of the symbolic method for solving the matrix completion problem makes it possible to obtain a universal solution of the self-tuning bandpass filter design problem.

Four-band Digital Ground-based Scatterometer System

In the design of Digital Ground-based Radar Scatterometer, Digital Signal Processing(DSP) is used to do IF searching, IF estimation, adaptive band-pass filtering, detecting, etc and achieve high measuring speed and accuracy. Four-band scatterometer,which includes S-Band, C-Band, X-Band and Kuband is realized with this method. The system structure, algorithm and the experiment results are introduced in this paper.

Adaptive Phasor and Frequency-Tracking Schemes for Wide-Area Protection and Control

The steady-state performance of phasor measurement units (PMUs) is well standardized in the recently issued revised IEEE Std. C37.118. The Western Electricity Coordinating Council (WECC) has developed dynamic performance requirements for PMU filters as a means to guarantee better, uniform PMU response under dynamic conditions, such as power swings and changing harmonics. These have been endorsed by North American Synchrophasor Initiative (NASPI) for its wide-area monitoring infrastructure. The main purpose of this paper is to present a new framework for designing PMU filtering algorithms capable of meeting or exceeding the WECC/NASPI requirements, while achieving an optimum transient response time. To this end, an adaptive complex bandpass filter derived from the exponentially modulated filter bank theory has been devised. It is built from freely chosen low-pass filter prototypes that fulfill the WECC requirements. The static and dynamic performances of two specific schemes dedicated to control and monitoring with 4- and 7-cycle response-time, respectively, are ascertained under noisy waveforms and changing harmonics with system frequency varying from 40 to 80 Hz. The center-frequency adaptation approach is shown to be intrinsically superior to the frequency compensation scheme, especially under fast varying frequency and changing harmonics.

Local fringe density determination by adaptive filtering

We demonstrate a method to easily and quickly determine the local fringe density map of a fringe pattern. The method is based on an isotropic adaptive bandpass filter that is tuned at different frequencies. The modulation map after applying a specific bandpass frequencies filter presents a maximum response in the regions where the bandpass filter and fringe frequencies coincide. We show a set of simulations and experimental results that prove the effectiveness of the proposed method.

1/f Neural Noise Reduction and Spike Feature Extraction Using a Subset of Informative Samples

This article describes a study on neural noise and neural signal feature extraction, targeting real-time spike sorting with miniaturized microchip implementation. Neuronal signature, noise shaping, and adaptive bandpass filtering are reported as the techniques to enhance the signal-to-noise ratio (SNR). A subset of informative samples of the waveforms is extracted as features for classification. Quantitative and comparative experiments with both synthesized and animal data are included to evaluate different feature extraction approaches. In addition, a preliminary hardware implementation has been realized using an integrated circuit.