Adaptive Bandpass Filter Research Articles

Selective noise control by adaptive bandpass filter and secondary aural cartilage source

When a transducer is put on an aural cartilage, the transmitted vibration generates sound in the external auditory canal (i.e., cartilage conduction). Different from conventional earphones, the cartilage conduction technology enables the use of earphones without occluding the canal. However, the opening ears allow both desired sounds (e.g., speech) and unwanted noises to reach the eardrums. To minimize only the noise selectively, our study introduced filtered-x least means square algorithm combined with adaptive bandpass filter (ABF-FxLMS). The ABF-FxLMS stored the narrowband noise separating from a speech and transferred it to the FxLMX as a secondary sound source produced via the cartilage conduction. The adaptive bandpass filter actively adjusted to changes in the center frequency of noise, and the ABF-FxLMS ensured effective noise reduction permitting the passing of speech. Our simulations demonstrated achieving approximately 19 dB reduction in the noise while keeping the speech quality. A coming cartilage conduction device will enable conversations in noisy environments.

Low-complexity adaptive multipath interference and channel noise mitigation scheme based on optimized detection for bandwidth-limited IM/DD systems.

In this Letter, we propose a low-complexity adaptive multipath interference (MPI) and channel noise mitigation (AMCM) scheme in the receiver digital signal processing (DSP) for bandwidth-limited intensity modulation and direct detection (IM/DD) transmission systems. Following channel equalization, MPI and channel noise are distributed in the low- and high-frequency parts, respectively, and exhibit the characteristics of bandstop filtering. The proposed AMCM is designed based on optimized detection, which incorporates an adaptive bandpass filter (BPF) and a log-maximum a posteriori estimation with a lookup table-based fixed number of surviving states (LUT-based FS-MAP) decoder. The adaptive BPF is capable of mitigating the MPI and channel noise based on spectral distribution. Moreover, the LUT-based FS-MAP decoder can eliminate intersymbol interference (ISI) introduced by the BPF. The proposed AMCM is implemented in an O-band 56-Gbaud IM/DD optical 4-level pulse amplitude modulation (PAM-4) system with a 10.7-GHz bandwidth over a 10-km standard single-mode fiber with different linewidths. The results demonstrate that the proposed AMCM scheme can enhance signal-to-interference ratio (SIR) tolerance to 11 dB with only three real-valued multiplications per symbol, achieving a 7% hard-decision forward error correction threshold. To the best of our knowledge, for the first time, this represents the inaugural instance of optimized detection being employed for MPI mitigation.

Robust Heart Rate Variability Measurement from Facial Videos.

Remote Photoplethysmography (rPPG) is a contactless method that enables the detection of various physiological signals from facial videos. rPPG utilizes a digital camera to detect subtle changes in skin color to measure vital signs such as heart rate variability (HRV), an important biomarker related to the autonomous nervous system. This paper presents a novel contactless HRV extraction algorithm, WaveHRV, based on the Wavelet Scattering Transform technique, followed by adaptive bandpass filtering and inter-beat-interval (IBI) analysis. Furthermore, a novel method is introduced to preprocess noisy contact-based PPG signals. WaveHRV is bench-marked against existing algorithms and public datasets. Our results show that WaveHRV is promising and achieves the lowest mean absolute error (MAE) of 10.5 ms and 6.15 ms for RMSSD and SDNN on the UBFCrPPG dataset.

Регульований смуговий фільтр на основі кільцевих резонаторів для мікрохвильових застосунків

The paper presents the principle of a microwave adaptive band-pass filter based on the cascade of ring resonators. The filter is performed as a planar microstrip technology with thickness of around 1 mm to ensure compactness of the device. The ring resonators, being a part of metamaterials, can be considered as an equivalence of an oscillator and is conventionally used in the microwaves. While a quite number of the filters with predefined parameters are well-known, the adaptive filters the throughput and output characteristics of which can be adjusted depending on the input ones is the topical problematic nowadays and, at least, for the closest future. We have investigated in the paper the possibilities of adjustment the considered filter’s transmission characteristics through the ring design, distance between them, and discussed other features, which have impact. For example, it allows expanding of the filter bandwidth from 250 to 60 MHz for simultaneous change of a distance between the adjacent rings. We have suggested the approach how to do the filter time-dependent. A varactor diode inserted into the gap of the middle ring is controlled with an independent external source and can adjust the filter bandwidth from 80 up to 140 MHz for the varactor capacitance variation from 16 to 6 pF (bias voltage variation from 1 to 7 V) that covers the existing communication networks, such as mobile generations, Bluetooth, Wi-Fi, etc. and can be applied for modern smart technologies of the Internet-of-Things for a remote control. It becomes possible because different sensing elements, such as photodiodes, Hall effect sensors, photoresistors, etc., can be exploited as the aforementioned external source.

Impedance-Edited Multiple Low-Frequency Harmonic Current Adaptive Suppression

In the two-stage inverter, a large amount of low-frequency harmonic current will emerge in the front-end dc-dc converter and input dc voltage source. The low-frequency harmonic current will reduce the service life of the dc source and decrease the efficiency of the system. Additionally, the main components of low-frequency harmonic current fluctuate with uncertainty. To address those issues, an adaptive current suppression method based on impedance editing of the source-stage dc-dc converter is proposed in this paper. Compared with the traditional active power filter (APF), the proposed adaptive low-frequency harmonic current suppression method does not require additional circuits, which will never decrease the system efficiency but reduce the cost. More importantly, when the amplitude of the main components of harmonic current fluctuates, adaptive multiple bandpass filters are proposed to adaptively suppress the main harmonic current, which never influences the dynamic performance. In addition, the virtual impedance is connected in parallel with the bus capacitor to reduce the bus-voltage overshoot. The key parameters design of the proposed control strategy is given and the dynamic response of the proposed control strategy is also discussed. Finally, a prototype of 900W was fabricated and tested.

Adaptive Band-Pass Filter and VMD-Esprit Based Multi-Mode Monitoring Method for Broadband Electromagnetic Oscillation in “Double High” Power Systems

With the development of new power systems with high proportions of renewable energy and high proportions of power electronics equipment, the influence of broadband electromagnetic oscillations in power systems is becoming more and more significant. In order to better grasp the dynamic characteristics of broadband oscillations, a new adaptive band-pass filter and VMD-Esprit based multi-modal monitoring method is proposed for broadband electromagnetic oscillation in “double high” power systems. First, based on the mode frequency and amplitude information provided by FFT mode detection, the proposed adaptive band-pass filter adaptively sets the center frequency, bandwidth, and other parameters and extracts or separates the voltage/current signal in each frequency band. Second, the filtered signals are corrected and compensated, and then the VMD modal decomposition of each frequency band signal is combined with Esprit for parameter identification so as to obtain the waveform and parameter information of each mode. Finally, the separation, correction, and parameter identification of multi-mode broadband oscillation waveforms are carried out. The experimental results show that frequency division processing can reduce the computation and improve real-time performance. In the processing of signals in the frequency band, the center frequency, bandwidth, and other parameters can be adjusted adaptively under different conditions of single-mode composition or multi-mode composition, which improves the accuracy of VMD decomposition and increases the flexibility of signal processing. Meanwhile, it overcomes the defects such as the inaccuracy of traditional mode recognition, which provides a new idea for broadband electromagnetic oscillation analysis.

A Novel Acceleration-Based Approach for Monitoring the Long-Term Displacement of Bridge Cables

The cables of the long-span bridge are usually featured as ultra-low frequency, hence making the acceleration unable to accurately capture the information, e.g. damping ratios, for assessing the cable state assessment and mitigating the excessive structural vibration. The displacement was approved to be more sensitive to the low-frequency vibration than the acceleration. However, there is still a lack of effective method to accurately monitor the long-term displacements of bridge cables using reference-free methods. To address this issue, this paper develops a novel acceleration-based approach for monitoring the long-term displacements of the cables of long-span bridges. In the monitoring scheme, recursive least squares method is utilized to conduct baseline correction in the time domain integration of acceleration. An adaptive band-pass filtering method considering cable vibration characteristics is used to eliminate noise, thus avoiding the difficulty of selecting the cut-off frequency by experience in traditional methods. A numerical test of an analytical cable model and a field experiment of the hanger of a full-scale suspension bridge are applied to the applicability and robustness of the developed method. Result shows that adaptive band-pass filter considering the vibration characteristics is suitable for estimating the displacements of the cables. The estimated displacements using the developed method agree well with the background truth in both time and frequency domains.

Difference mode decomposition for adaptive signal decomposition

Adaptive extraction of concerned components (CC) from mixed frequency components remains to be a challenging topic in various research domains. Most existing adaptive mode decomposition algorithms for extracting CC, such as wavelet transform, wavelet packet transform, singular value decomposition, empirical mode decomposition, empirical wavelet transform, and variational mode decomposition, are intrinsically adaptive band-pass filter banks and they face the following two tough problems. The first problem is that they cannot separate CC from interferential components in same frequency bands. The second problem is that it is not fully effective in automatically selecting CC distributed in different frequency bands by using some designed criteria. In this paper, a new decomposition approach called difference mode decomposition (DMD) is proposed to adaptively decompose a mixed signal into CC, reference components, and noise, and enrich the domain of adaptive mode decomposition. The proposed DMD relies on convex optimization and Fourier transform, and its decomposition is mathematically justified and composes physical interpretations. Analyses of simulated and real-world bearing and gear vibration signals are used to verify the effectiveness and superiority of the proposed DMD over existing adaptive mode decomposition algorithms. It is demonstrated that the proposed DMD can effectively extract CC such as repetitive transients caused by bearing and gear faults. Moreover, since the proposed DMD is based on Fourier transform expanded by trigonometric basis functions, the proposed DMD can be easily extended to other domains by expanding basis functions besides the frequency domain.

Mixed reality-based brain computer interface system using an adaptive bandpass filter: Application to remote control of mobile manipulator

Brain-computer interface (BCI) systems based on mixed reality (MR) have promising applications in assisting people with disabilities to control manipulators. Using MR glasses instead of a computer screen to display visual stimulator can effectively avoid frequent switching of attention between the visual stimulator and the manipulator. When the manipulator moves out of the sight of the subject, the subject may not be able to control it accurately. Our system uses Microsoft Hololens2 as the display device to synchronize the command matrix with a live view of the mobile manipulator's position, thus tracking the position in real-time. Another problem in previous studies is that they have good accuracy in trained subjects, however, the accuracy drops dramatically when faced with untrained subjects, suggesting poor generalization capabilities. In our study, an adaptive filtering method combined with convolutional neural networks (CNN) is proposed, which has few learning parameters and fast convergence, and can improve the generalization ability of the system in the face of untrained subjects. When faced with untrained subjects, the average accuracy of our method was 93.04%, and the average ITR was 20.96 bits/min. All subjects can successfully complete the grasping task without colliding with obstacles. The results show that the BCI system developed in this study has strong practicability and high research significance.

Ionosphere Delay Extraction in GNSS using DSP Algorithm

With multiple GNSS availability and wider applications, many positioning modes like SPS, DGNSS, RTK and PPP have been developed to cover these flexible employments. Currently, some intricate influences such as ionosphere delays and environmental interference have caused obvious impacts to accurate positioning, and they are therefore formed highlighted research points for worldwide researchers and scholars. To deal with the ionosphere delay, this submission has proposed an intrinsic approach to identify and extract the ionospheric disturbance from GNSS observations in which Digital Signal Process (DSP) is employed to form an adaptive band pass filter for combined observations in the form of either code minus phase (CMP) or double differential (DD) carrier phase. Related mechanism for identification and extraction of the ionosphere delay using DSP are introduced and analyzed. We have also discussed the separation of ionosphere delay with multipath caused by signal reflections from nearby surroundings when deformation monitoring is conducted on some restricted environments. Effects achieved by DSP have been verified combining with typical SPS and carrier based relative positioning conducted in Guangzhou metro construction sites. Results exhibit that the suggested standalone approach to extract ionosphere delays is an innovative technical frame to achieve accurate positioning regardless of assistant messages from base stations or satellite navigation broadcast.

Fourier-Based Adaptive Signal Decomposition Method Applied to Fault Detection in Induction Motors

Time-frequency analysis is commonly used for fault detection in induction motors. A variety of signal decomposition techniques have been proposed in the literature, such as Wavelet transform, Empirical Mode Decomposition (EMD), Multiple Signal Classification (MUSIC), among others. They have been successfully used in many works related with the topic. Nevertheless, the studied signals present amplitude changes and chirp-type frequency components that are difficult to track and isolate with the aforementioned techniques. The contribution of this work is the introduction of a novel technique for time-frequency signal decomposition that is based on an adaptive band-pass filter and the Short Time Fourier Transform (STFT), namely Fourier-Based Adaptive Signal Decomposition (FBASD) method. This method is capable of tracking and extracting nonstationary time-frequency components within a user-selected frequency band. With these components, a methodology for detecting and classifying broken rotor bars in induction motors using the startup transient current is also proposed.

Real-time heave motion measurement by adaptive band-pass filter based on strapdown INS

Heave measurement methods are beneficial to the advancement of ocean engineering and modern navigation technology. Unfortunately, these methods have several drawbacks in practice. Firstly, the high-pass filter used in these methods introduces positive phase and then decreases the real-time performance of the heave measurement. Secondly, the peak-to-peak error existing in traditional methods is typical 2%. In order to address the above issues, a novel heave measurement method was proposed in this study. It employs a band-pass filter instead of the high-pass filter existing in traditional methods. The band-pass filter possesses a zero-phase and zero-magnitude-attenuation at center frequency. Weighted-frequency Fourier linear combiner (WFLC) method, as an adaptive frequency estimator, is designed to ensure algorithm accuracy under unknown sea conditions. The proposed method eliminates the peak-to-peak error existing in traditional methods, and it avoids phase correction by adding an addition filter. The simulation and trial results demonstrated efficiency of the proposed heave measurement method and illustrated that it has a higher accuracy than conventional methods. This method plays an important role in ocean engineering.

Vibration Compensation Control of BPMSM With Dead-Time Effect Based on Adaptive Neural Network Band-Pass Filter

In order to solve the problem of rotor vibration for the bearingless permanent magnet synchronous motor, a vibration compensation control method based on adaptive neural network band-pass filter (ANNBPF) is proposed. First, the mechanism of rotor vibration is obtained by theoretical analysis. Second, the ANNBPF is used to extract the components related to the frequency of the unbalanced vibration and the dead-time (DT) vibration to realize the vibration compensation control. Additionally, the DT effect is compensated by detecting the current polarity to improve the compensation effect. Finally, a vibration compensation control system based on ANNBPF and current polarity detection is constructed.

Online Deflection Compensation of a Flexible Hydraulic Loader Crane Using Neural Networks and Pressure Feedback

The deflection compensation of a hydraulically actuated loader crane is presented. Measurement data from the laboratory are used to design a neural network deflection estimator. Kinematic expressions are derived and used with the deflection estimator in a feedforward topology to compensate for the static deflection. A dynamic deflection compensator is implemented, using pressure feedback and an adaptive bandpass filter. Simulations are conducted to verify the performance of the control system. Experimental results showcase the effectiveness of both the static and dynamic deflection compensator while running closed-loop motion control, with a 90% decrease in static deflection.

A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO$_2$ Monitoring Using Smartphone Cameras

Blood oxygen saturation (SpO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula>) is an important indicator forpulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula>. Most of these works are contact-based, requiring users to cover a phone&#x2019;s camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26&#x0025; when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25&#x0025;.

A Novel Adaptive Bandpass Filter Based PLL for Grid Synchronization Under Distorted Grid Conditions

In the current scenario, the integration of a renewable energy sources (RESs) with variable power production into power grids requires a power converter with robust control techniques. In order to formulate the control strategy meticulously, a fast and accurate detection of grid phase angle is necessary. Hence, frequency and phase angle of the grid voltages are vital components to guarantee the synchronization for grid-connected converter. Additionally, a phase locked loop (PLL)-based control algorithm is needed to generate the accurate reference current for the grid-connected converter. The conventional adaptive low-pass filter (ALPF)-based PLL is used as a synchronization tool but requires more settling time during interharmonics and dc offset grid conditions in addition to higher THD of the grid currents/voltages. In this article, a novel adaptive bandpass filter (ABPF) is proposed and can be added as a prefilter of PLL structures without modifying the base design. To confirm the adaptive nature of the ABPF the gradient algorithm is used to maximize the normalized mean square output and self-adjust the center frequency of the filter during distorted grid conditions. The 3-D surface plot can be used to provide the system stability and dynamic response of the filter. For effectiveness of the proposed study, ABPF is added as prefilter to dq-frame cascaded delayed signal cancellation (dqCDSC) PLL and MAF PLL and detailed comparative study is made in this article. In addition to that, the proposed ABPF-based dqCDSC PLL and ABPF-based MAF PLL methods are analyzed using MATLAB/Simulink environment. Finally, performance is tested with real-time via dSPACE-1104 hardware-in-loop (HIL) simulation.

Recursive DFT-Based Method for Fast and Accurate Estimation of Three-Phase Grid Frequency

This letter proposes a method for fast and accurate estimation of three-phase grid frequency under unbalanced and distorted conditions. It includes a recursive discrete Fourier transform-based frequency adaptive bandpass filter for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</i> – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β</i> signals generated by the Clarke transform and a two-point-based algorithm for frequency estimation. The proposed method is relatively simple to implement and can eliminate adverse influences created by the imbalances, dc offset and both odd and even harmonics. It can also provide fast estimation with a response time of almost one fundamental cycle under grid disturbances. Moreover, it can present improved performance when compared to several methods reported in the technical literature. The advantages of the proposed method are confirmed by both simulated and real-time experimental results.

Multiband Wavelet Age Modeling for a ∼293 m (∼600 kyr) Sediment Core From Chew Bahir Basin, Southern Ethiopian Rift

The use of cyclostratigraphy to reconstruct the timing of deposition of lacustrine deposits requires sophisticated tuning techniques that can accommodate continuous long-term changes in sedimentation rates. However, most tuning methods use stationary filters that are unable to take into account such long-term variations in accumulation rates. To overcome this problem we present herein a new multiband wavelet age modeling (MUBAWA) technique that is particularly suitable for such situations and demonstrate its use on a 293 m composite core from the Chew Bahir basin, southern Ethiopian rift. In contrast to traditional tuning methods, which use a single, defined bandpass filter, the new method uses an adaptive bandpass filter that adapts to changes in continuous spatial frequency evolution paths in a wavelet power spectrum, within which the wavelength varies considerably along the length of the core due to continuous changes in long-term sedimentation rates. We first applied the MUBAWA technique to a synthetic data set before then using it to establish an age model for the approximately 293 m long composite core from the Chew Bahir basin. For this we used the 2nd principal component of color reflectance values from the sediment, which showed distinct cycles with wavelengths of 10–15 and of ∼40 m that were probably a result of the influence of orbital cycles. We used six independent 40Ar/39Ar ages from volcanic ash layers within the core to determine an approximate spatial frequency range for the orbital signal. Our results demonstrate that the new wavelet-based age modeling technique can significantly increase the accuracy of tuned age models.

An adaptive multi band-pass filter algorithm and its application in fault diagnosis of rolling bearing

Construction of an optimal band-pass filter for effective envelope demodulation spectral (EDS) analysis of rolling element bearing has been studied widely and amount of methods have been arising. However, most of these methods only get the envelope demodulation analysis result of a specific frequency band. In fact, multiple resonance bands might be caused when rolling bearing fails, especially when compound fault arises, and some key components buried in the original signal are often neglected by the above methods such as fast Kurtogram and Autogram algorithms. Therefore, it is particularly necessary to establish a multi-band pass filter algorithm for EDS. In the paper, an adaptive multi-band pass filter method based on signal energy is proposed, and then the square wave envelope analysis method is used for multi-band pass filtered signal to extract the fault characteristic frequency of rolling bearing. In addition, since the phase of the signal retains a lot of useful information of the original signal, the phase information of the multi-band filtered signal is retained and used for signal reconstruction. Not only the early weak fault feature could be extracted, but also the compound fault feature of rolling bearing could also be extracted by the proposed method, which is verified thorough simulation and experiments.

Method of automatic search for odor-induced patterns in bioelectric activity of a rat olfactory bulb

Introduction: The olfactory system in chronic animal experiments is studied according to the records of bioelectric activity recorded with microelectrode arrays located on the dorsal surface of the olfactory bulb. It is believed that the response of the olfactory bulb bioelectric activity to the odorant presentation is associated with fluctuations synchronized by respiration and belonging to the range of so-called gamma rhythms. Purpose: To develop a method of automatic search for time boundaries of odor-induced gamma rhythm patterns in the bioelectric activity of an olfactory bulb. Results: The complexity of automatic search for odor-induced patterns are due to masking them by spontaneous oscillations of gamma rhythms unrelated to the olfactory system activity. Besides, the modal frequency of the patterns is a priori unknown and changes dynamically during an experiment on an anesthetized animal. The Hausdorff distance is used as a cost function of the search. In order to increase its sensitivity, an adaptive band-pass filter was synthesized based on a multichannel singular spectrum analysis. Its passband in the analyzed time sample corresponds best to the spectrum of the required patterns. The results of experiments on rats are presented. The use of band-pass filters based on the Fourier transform with parameters unchanged during the experiment due to the non-stationary nature of the frequency characteristics of the desired patterns does not ensure their effective search. In particular, when using the Butterworth band-pass filter in the search algorithm, significant errors were observed in determining the time boundaries of the epochs of patterns, and omissions of patterns. But the use of a synthesized adaptive band-pass filter provided reliable automatic search for patterns and determination of their time boundaries with a high accuracy. Algorithm failures in this case were observed only in rat motion artifacts. Practical relevance: The developed method extends the existing tools used to study the olfactory system of a living organism.