High-frequency Noise Suppression Research Articles

Dual Closed-Loop Position Observer With Nonlinearity-Decoupled Model for Feature- Flux-Based Sensorless SRM Drives

The feature-flux-based sensolress drives is attractive for switched reluctance motor (SRM) because its simple implementation and high reliability. However, its low model-utilization rate inevitably leads to the discrete position estimation manner. As a result, high frequency noise may cause significant estimation errors. For this problem, a position-adaptive dual closed-loop observer is proposed based on the nonlinearity decoupled model. First, a nonlinearity-decoupled flux model is studied to remodel the continuous linearized position-relevant function. Then, the position-adaptation law is designed to force the remodeled function adapt to the actual position. With the real-time regulation in the dual closed-loop structure, the continuous position estimation can be realized with high-frequency noise suppression. Finally, the studied observer is experimentally verified on an 8kW SRM platform.

A Dynamic High-frequency Noise Suppression Method of Gyroscope for Inertial Stabilized Platforms Based on An Adaptive Compensatory Disturbance Observer

A Dynamic High-frequency Noise Suppression Method of Gyroscope for Inertial Stabilized Platforms Based on An Adaptive Compensatory Disturbance Observer

Model-Free Predictive Control of Power Converters with Multifrequency Extended State Observers

The performance of model-free predictive control (MFPC), which is based on the ultra-local model, is highly dependent on the effectiveness of the extended-state observer (ESO). The standard linear ESO has a high-gain which enhances disturbance rejection attributes. Nevertheless, this ESO has two main limitations: i) it amplifies high-frequency measurement noises, and ii) it deteriorates disturbance rejection performance when the noise immunity is improved. Therefore, this article introduces a new technique to improve both the disturbance rejection and noise suppression of ESO without increasing the bandwidth: the multi-frequency-based ESO (MF-ESO). Two novel ESO structures that operate on the MF-ESO principle are analyzed: parallel and cascade ESOs. Extensive frequency domain analyses unravel the comparative capabilities of the MF-ESO structures for improved disturbance rejection and/or high-frequency noise suppression. A novel adaptive gain is proposed to improve CESO's dynamic performance. The superior features of the proposed MF-ESO are experimentally validated by the model-free predictive control of a grid-connected power converter.

Robust Predictive Current Control of PMSG Wind Turbines with Sensor Noise Suppression

Model predictive control (MPC) is an efficient and multi-functional control scheme for synchronous permanent magnet generators (PMSGs). However, the effective management of traditional MPC depends on precise system models. Multiple uncertainties of permanent magnet flux, motor inductance, filter inductance and parameter measurement noise will limit MPC’s performance. The conventional linear extended state observer (ESO) can perform robust predictive control of the ultralocal model of the PMSG system to cope with parameter mismatches. However, the ESO is limited in balancing disturbance rejection with measurement noise attenuation. Since the amplification of high-frequency noise pollution can lead to both poor control performance and system instability, this challenge is of significant importance. To solve the problem, a new hybrid parallel cascaded ESO (PCESO) model-free predictive control framework is proposed using the three-level neutral-point-clamped (NPC) power electronic converter, on both the machine side and grid side. Analytical discussions of the time and frequency domain characteristics of the PCESO demonstrate its superior characteristics over the ESO. The proposed method can effectively balance parameter mismatch, disturbance rejection and high-frequency noise suppression. Finally, the effectiveness of the proposed method, under uncertainties of parameter mismatches, measurement noise and permanent magnet flux, is verified through real-time hardware-in-the-loop tests on a back-to-back grid-tied PMSG interfaced with an NPC power converter.

An adaptive multiscale fully convolutional network for bearing fault diagnosis under noisy environments

Intelligent algorithms based on convolutional neural network (CNN) has demonstrated remarkable potential in diagnosing bearing faults. However, Accurate and robust fault diagnosis using CNNs in noisy environments remains a challenge. In this paper, we propose an end-to-end adaptive multiscale fully convolutional network (AMFCN) for intelligent bearing fault diagnosis under various noise environments. Firstly, we enhance noise adaptability by retrofitting raw signals with random sampling. Then the convolution with huge kernel is innovatively applied for wide-area temporal feature extraction and high-frequency noise suppression. By stacked residual adaptive multiscale convolution (Res_AM) blocks, the AMFCN can adaptively adjust the feature weights from different convolution scales and selectively focus on important features. The AMFCN outstrips five advanced baseline models across two bearing fault datasets with various noise environments. The proposed AMFCN significantly enhances the feature extraction ability, noise immunity, and robustness, surpassing conventional CNNs and other advanced multi-scale CNNs.

Peak deconvolution with significant noise suppression and stability using a facile numerical approach in Fourier space

The deconvolution problem for resolution and feature enhancement of analytical signals is known to be a noise-enhancing procedure to the extent that some authors have called it “violently unstable.” The sharpened result is frequently buried in high-amplitude noise in direct deconvolution using the Fourier division method. With a Gaussian deconvolving function, one can get a “Not-a-Number (NaN) error” due to division by near zero (complex) numbers in Fourier space. Herein, a simple numerical method that stabilizes the calculation of direct Fourier deconvolution and ensures that the noise is controlled is proposed. In this calculation, a small positive constant or symmetric positive peak function is added to the denominator during division in Fourier space, resulting in stability and high-frequency noise suppression. The key advantages of this non-iterative method are speed, numerical stability during division, noise suppression, peak area and location invariance, and minimization of the edge effects after deconvolution. Also, ringing effects in the baseline and negative overshoots are significantly mitigated using the modified denominator method. The numerical approach can be applied to enhance the analytical signals consisting of peaks, e.g., in NMR, IR, Raman spectroscopy, or separation sciences. Both symmetric and one-sided (asymmetric) broadening functions can be used for deconvolution in this method. Applications are also demonstrated for various test functions (square pulse, Voigt, or highly overlapped Gaussian peaks) with clear advantages with respect to the standard Fourier division method. The new denominator addition method is compared with two constrained deconvolution procedures in matrix formalism and Fourier domain.

Dual-laser self-injection locking to an integrated microresonator.

Diode laser self-injection locking (SIL) to a whispering gallery mode of a high quality factor resonator is a widely used method for laser linewidth narrowing and high-frequency noise suppression. SIL has already been used for the demonstration of ultra-low-noise photonic microwave oscillators and soliton microcomb generation and has a wide range of possible applications. Up to date, SIL was demonstrated only with a single laser. However, multi-frequency and narrow-linewidth laser sources are in high demand for modern telecommunication systems, quantum technologies, and microwave photonics. Here we experimentally demonstrate the dual-laser SIL of two multifrequency laser diodes to different modes of an integrated Si3N4 microresonator. Simultaneous spectrum collapse of both lasers, as well as linewidth narrowing and high-frequency noise suppression , as well as strong nonlinear interaction of the two fields with each other, are observed. Locking both lasers to the same mode results in a simultaneous frequency and phase stabilization and coherent addition of their outputs. Additionally, we provide a comprehensive dual-SIL theory and investigate the influence of lasers on each other caused by nonlinear effects in the microresonator.

Reconfigurable Architectures with High-Frequency Noise Suppression for Wearable ECG Devices.

Recent advances in electronics and microelectronics have aided the development of low-cost devices that are widely used as well-being or preventive monitoring devices by many people. Remote health monitoring, which includes wearable sensors, actuators, and modern communication and information systems, offers effective programs that allow people to live peacefully in their own homes while also being protected in some way. High-frequency noise, power-line interface, and baseline drift are prevalent during the data-acquisition system of an ECG signal, and they can limit signal understanding. They (noises) must be isolated in order to provide an appropriate diagnostic of the patient. When removing high-frequency components (noise) from an ECG signal with an FIR filter, the critical path delay increases considerably as the filter's duration increases. To reduce high-frequency noise, simple moving average filters with pipelining and look-ahead transformation techniques are extensively used in this study. With the use of pipelining and look-ahead techniques, the only objective is to increase the clock speed of the designs. The moving average filters (conventional and proposed) were created on an Altera Cyclone IV FPGA EP4CE115F29C7 chip using the Quartus II software v13.1 tool. Finally, performance metrics such logic elements, clock speed, and power consumption were compared and studied thoroughly. The recursive pipelined 8-tap MA filter with look-ahead approach outperforms the other designs (685.48 MHz) in this investigation.

Measurement of the gas flow stagnation temperature by the method of two identical thermocouples in the short-duration aerodynamic facilities

Investigations of the temperature and heat flux distributions over the external surface of flying vehicles are important for the development of hypersonic planes. For obtaining correct results, experiments are performed in short-duration high-enthalpy wind tunnels. Determination of the stagnation temperature of the flow in such facilities is a challenging task due to short start-up times and high flow temperature. In this paper a new method is proposed for determining the stagnation temperature of a gas flow in the short-duration aerodynamic wind tunnels with stepwise changes in its parameters. The method is based on using two identical thermocouples with different initial temperatures. It is shown that in the case of jump-like changes in flow parameters the instrument function of the difference of the thermocouple readings coincides with the instrument function of each thermocouple, and the instrument function itself is actually the normalized temperature difference. The instrument function obtained in the study allows temperature retrieval by the method of deconvolution. This approach is highly sensitive to random noise of experimental data; therefore, regularization methods are applied for high-frequency noise suppression. A procedure for choosing the regularization parameter for temperature reconstruction is proposed. The research novelty of the article is that the proposed method allows exact reproduction of the stepwise function and instrument function without any additional assumptions, while the measurements and dynamic calibration are performed in one experiment. Applicability of the method for obtaining the stagnation temperature in the short-duration high-enthrall wind tunnel IT-302 M ITAM SB RUS is demonstrated.

Wearable Wireless Sensors Network for ECG Telemonitoring Using Neural Network for Features Extraction

The technological progress of wireless communication, embedded systems and health offers innovative alternatives to medical care, in particular, telemonitoring and telediagnosis. ECG signal monitoring is a vital indicator in the control of heart disease. Nevertheless, one of the main challenges of remote monitoring of heart rate is the requirement of control in accordance with the service provided by hospital equipment. In this article, an approach to ECG telemonitoring based on wireless sensor networks combined with the Internet of Things (IoT) is proposed. The ECG signal is measured using a wearable sensor node allowing high-frequency noise suppression. The collected data is transmitted to the Gateway node, which performs complex processing including baseline and linear variations suppression using polynomial interpolation, extraction of R peaks using the Multilayer Perceptron Neural Network. It can determine the variation in heart rate by the using the extracted R signal. Thanks to IoT technology, the Gateway node is able to aggregate data into an IoT platform through an IoT cloud for visual telemonitoring of heart rate in real-time. The experimental results show that the system is effective and reliable for the collection, transmission, and display of ECG data in real time for the purpose of telemonitoring of patients with heart disease.

Maximally Flat IIR Smoothers With Repeated Poles and a Prescribed Delay

The problem of optimal high-frequency noise suppression and low-frequency signal transmission in sampled sensor systems is considered in this paper. Low-delay digital filters with linear phase and unity magnitude at the dc limit (i.e., maximally flat) are of particular interest. Savitzky–Golay smoothers with an infinite impulse response (IIR) are introduced and their properties are explored. The filter coefficients are derived via regression analysis using orthogonal Laguerre polynomials in the time domain. A generalized form with a shape parameter included in the error-weighting function is proposed. This extension may be used to further reduce the white noise gain of the filter. A design process that determines the optimal position of the repeated real poles, either to minimize the white noise gain, or to maximize the high-frequency attenuation, for a specified low-frequency group delay, is discussed. An alternative process for the design of a generic class of maximally flat repeated-pole filters is also presented. This allows colored noise to be handled and/or additional frequency-domain constraints, such as Nyquist flatness, to be applied.

High noise suppression using magnetically isotropic (CoFe-AlN)/(AlN) multilayer films

Magnetically isotropic (CoFe-AlN)n/(AlN)n+1 multilayer films, in which the number of CoFe-AlN magnetic layers n ranged from 1 to 27, were prepared by radio frequency sputtering to achieve noise suppression at gigahertz frequencies. The soft CoFe-AlN magnetic layers consisted of nanometer-sized CoFe ferromagnetic grains embedded in an insulating AlN amorphous matrix, while the insulating AlN layers comprised AlN columnar crystals. All films showed a similar frequency dependence of permeability and ferromagnetic resonance of 1.7 GHz. Noise suppression was evaluated using a microstrip line as a noise source by determining the in-line conductive loss and the near-field intensity picked up by magnetic field detective probes. High noise suppression effects were observed in every direction in the film plane. Maximum noise suppression values amounted to 60% for the in-line conductive loss and −20 dB for the magnetic near-field intensity at around 1.7 GHz in the 27-layer film. These high-frequency noise suppression levels may be attributed to eddy current losses and ferromagnetic resonance.

Efficient wavelet-based ECG processing for single-lead FHR extraction

This paper describes a novel model for fetal heart rate (FHR) monitoring from single-lead motherʼs abdomen ECG (AECG) measurements. This novel method is divided in two stages: the first step consists on a one-step wavelet-based preprocessing for simultaneous baseline and high-frequency noise suppression, while the second stage efficiently detects fetal QRS complexes allowing FHR monitoring. The presented structure has been simplified as much as possible, in order to reduce computational cost and thus enable possible custom hardware implementations. Moreover, the proposed scheme and its fixed-point modeling have been tested using real abdominal ECG signals, which allow the validation of the presented approach and provide high accuracy.

An Ultra-Low Power ECG Acquisition and Monitoring ASIC System for WBAN Applications

This paper proposes a power and area efficient electrocardiogram (ECG) acquisition and signal processing application sensor node for wireless body area networks (WBAN). This sensor node can accurately record and detect the QRS peaks of ECG waveform with high-frequency noise suppression. The proposed system is implemented in 0.18-μm complementary metal-oxide-semiconductor technology with two chips: analog front end integrated circuit (IC) and digital application specific integrated circuit (ASIC), where the analog IC consumes only 79.6 μW with area of 4.25 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and digital ASIC consumes 9 μW at 32 kHz with 1.2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Therefore, this ECG sensor node is convenient for long-term monitoring of cardiovascular condition of patients, and is very suitable for on-body WBAN applications.

High-frequency noise suppression in downsized circuits using magnetic granular films

Noise suppression in downsized circuits utilizing magnetic granular films is demonstrated. Films of Fe-Al-O with thickness of 2 /spl mu/m which exhibit very high /spl mu/ dispersion in the quasimicrowave band were found to work as noise suppressors. The films were attached onto a micro-strip line and the transmission characteristics were compared. This work is the first to verify that films with only 2 /spl mu/m thickness can play a role in suppression of high frequency conduction noise in the quasimicrowave band. The results suggest a possibility of thin film noise suppressors which can be integrated into micron scale semiconductor circuits.

Short- and long-term frequency stabilization of a He-Ne laser using a Fabry-Perot cavity locked to the lamb dip

We have developed a frequency-stabilization system for the He-Ne laser using an external Fabry-Perot cavity which is locked to the Lamb dip of the laser. The cavity serves as a frequency discriminator for high-frequency noise suppression, while the length of the Fabry-Perot cavity is controlled, reffered to the Lamb dip, in order to eliminate low-frequency fluctuations. The frequency noise of a laser stabilized with the cavity suspended in a vacuum tank can be suppressed to about $$0.1Hz/\sqrt {Hz} $$ (1) at around 100 Hz by an FM-sideband locking method. The oscillation frequency is fixed to the Lamb dip at low frequency by controlling the cavity length thermally. The frequency fluctuation around the Lamb dip was estimated to be ∼ 2.3 MHz.