High Frequency Noise Research Articles

A bandwidth enhanced multilayer electromagnetic bandgap structure to reduce the simultaneous switching noise

<abstract> <p>A bandwidth enhanced multilayer Electromagnetic Band Gap (EBG) structure to reduce the simultaneous switching noise (SSN) in high frequency operating circuits, which useful for the satellite communication application, is presented in this paper. A proposed stack structure is mathematically analyzed by the dispersion method and transmission matrix method. Simulation results show good mitigation of SSN in scattering parameters and signal integrity in terms of eye diagrams. We have also checked for power integrity analysis using self-impedance. The proposed structure gives a good SSN suppression at -30 dB from 817 MHz to 26.32 GHz, around 25.50 GHz bandwidth and also reduces the cavity mode resonance within the stopband range. The proposed multilayer structure is compared with planar EBG plane and reference board. It is also compared with published results.</p> </abstract>

Analysis of High-frequency Combustion Noise Generation Mechanism in Pre-chamber Jet Combustion Gasoline Engine

Analysis of High-frequency Combustion Noise Generation Mechanism in Pre-chamber Jet Combustion Gasoline Engine

Buck Converter Current Measurement Using Differential Amplifier

The accuracy of the measured current is a preeminent parameter for Current Control based Power Converter applications to ensure genuine operation of the designed converter. The current measurement accuracy can be affected by several parameters which includes the type of technology used, components used for the selected technology, aging, usage, operating and environmental conditions. The effect of gain resistors and their manufacturing tolerances on differential amplifier-based buck converter current measurement is investigated in this work. The analysis mainly focused on the output voltage variation and its accuracy with respect to the change in gain resistance tolerances. The gain resistors with 5%, 1%, 0.5% and 0.1% manufacturing tolerances taken for the worst-case analysis and the calculated performance results are compared and verified with the simulation results. The Operational amplifiers (Op-Amp) for high frequency power converter applications must operate in a high frequency noise environment and the intended current measuring system must manage common mode noise disturbances paired with the signal to be measured. Based on the Common Mode Rejection Ratio (CMRR) the common mode voltages and noise signals will effectively get filtered out. Lesser CMRR results in lower common mode signal rejection, resulting in poor precision and noise rejection. In differential amplifiers, the CMRR predominantly depends on gain resistors. So, the variations in Common Mode Rejection Ratio due to gain resistor tolerances also analyzed and compared with the output voltage variations. Besides the effects of resistor tolerances, this paper also examines the effect of Op-Amp offset voltage on output accuracy specifically for low magnitude input currents. The obtained results from this analysis clearly shows that the gain resistors with 0.1% tolerance gives maximum accuracy with improved CMRR and accuracy at low magnitude input currents will get well improved by using Op-Amps with Low Offset voltage specifications.

Active Disturbance Rejection Control of Quadrotor UAVs Based on Joint Observation and Feedforward Compensation

Under the influence of uncertain parameters and external disturbance, the accuracy is low and the response is slow of attitude and trajectory tracking in the flight control of the quadrotor unmanned air vehicles (UAV). In order to solve the problems, Because of the excellent adaptability and noise suppression ability of the extended Kalman filter for nonlinear system problems , it is used to suppress the high-frequency signal disturbance to reduce the estimation burden of the extended state observer, and jointly with the expanded state observer to estimate the total disturbance formed by the system uncertainty parameters and external disturbances to reduce system reliance on accurate models, and the differential values of perturbation estimated values is used by feedforward compensation to improve the tracking accuracy under abrupt disturbances and to overcome the phase lag caused by abrupt disturbances. Extended Kalman filtering, extended state observer with the feedforward compensation and PD controller with error compensation are comprehensively integrated into an improved active disturbance rejection controller which can suppress high frequency noise and abrupt disturbances to the better extent. Simulation and experiment results show that the joint observer can effectively reduce the observation error amplitude, correct the observation phase lag in advance, and obtain more accurate state information, and that the improved active disturbance rejection controller can better meet requirements of quadrotor UAV for fast response, efficient and stable control. Therefore, it can accurately and efficiently complete complex trajectory tracking operations.

A cascade dead-zone extended state observer for a class of systems with measurement noise

<abstract><p>For high frequency noise, a new $ 2n $-th order cascade extended state observer with dynamic dead-zone structure is proposed in this paper. Dead zone dynamic consists of two parts. One is to "trim" the effect of noise by cutting off the part that falls in the dead zone. The other part pushes the dead zone amplitude to converge to 0 as soon as possible to ensure the convergence of the estimation error. Moreover, in the cascade structure, the high-gain parameter grows only to a second power, thus avoiding excessive amplification of the measurement noise and solving numerical implementation problems. The design procedure ensures that the extended state observer is input-to-state stable. Numerical simulations show the improvement in terms of total disturbance estimation and noise attenuation. The frequency-domain analysis of the proposed ESO using the describing function method investigates the effect of the dead zone nonlinear parameter on the performance of a closed-loop system.</p></abstract>

Aerodynamic-induced Effects of Artificial Subglottic Stenosis on Vocal Fold Model Phonatory Response

ObjectiveSubglottic stenosis (SGS) is characterized by a narrowing of the trachea near the cricotracheal junction and impairs breathing. SGS may also adversely affect voice quality, but for reasons that are not fully understood. The purpose of this study is to provide experiment-based data concerning the effects on phonation of airway obstruction due to SGS. Study DesignBasic science MethodsA device simulating a SGS of adjustable severity ranging from 36% to 99.8% obstruction was created. Self-oscillating synthetic VF models were mounted downstream of the device and data were acquired to evaluate the effects of the obstruction on phonatory response. ResultsOnset pressures were relatively insensitive to obstructions of up to approximately 80% to 90% reductions in subglottic airway area and sharply increased thereafter. Flow rate (under conditions of constant pressure), flow resistance, and fundamental frequency all exhibited similar degrees of sensitivity to SGS obstruction as onset pressure. High-frequency noise became significant by 80% obstruction. Glottal area appeared to be less sensitive, not being affected until approximately 90% obstruction. ConclusionsConsistent with previous computational studies, this study found that aerodynamic, acoustic, and vibratory responses of self-oscillating VF models were largely unaffected by SGS until approximately 80% to 90% obstruction, and significantly affected at higher obstructions. This suggests that Grades I and II stenoses are unlikely to introduce subglottic airway aerodynamic disturbances that are sufficient in and of themselves to significantly alter phonatory output. The SGS model introduces a framework for future benchtop studies involving subglottic and supraglottic airway constrictions.

High-frequency transcranial random noise stimulation enhances unfamiliar face matching of high resolution and pixelated faces

Face identification is useful for social interactions and its impairment can lead to severe social and mental problems. This ability is also remarkably important in applied settings, including eyewitness identification and ID verification. Several studies have demonstrated the potential of Transcranial Random Noise Stimulation (tRNS) to enhance different cognitive skills. However, research has produced inconclusive results about the effectiveness of tRNS to improve face identification. The present study aims to further explore the effect of tRNS on face identification using an unfamiliar face matching task. Observers firstly received either high-frequency bilateral tRNS or sham stimulation for 20 min. The stimulation targeted occipitotemporal areas, which have been previously involved in face processing. In a subsequent stage, observers were asked to perform an unfamiliar face matching task consisting of unaltered and pixelated face pictures. Compared to the sham stimulation group, the high-frequency tRNS group showed better unfamiliar face matching performance with both unaltered and pixelated faces. Our results show that a single high-frequency tRNS session might suffice to improve face identification abilities. These results have important consequences for the treatment of face recognition disorders, and potential applications in those scenarios whereby the identification of faces is primordial.

OSCILADOR DE MICRO-ONDAS COMO FONTE DE SINAL

This work presents the development of a prototype of a microwave oscillator with a fixed frequency, around 3 GHz, with microstrip lines, using a high frequency and low noise bipolar transistor, through the scattering parameters S, with the aim of support the practical activities developed in the RF laboratory of the electrical engineering course at IFSUL. Computational tools are used to develop the equations for the conversion of the scattering parameters, for the impedance matching calculations, for the fabrication of the microstrip lines and for the transistor polarization. Finally, two prototypes are built and the results are presented. The prototypes worked properly, highlighting the one designed with the aid of a computational tool.

High frequency noise reduction in ECG signal by adaptive filter using FxLMS algorithm

High frequency noise reduction in ECG signal by adaptive filter using FxLMS algorithm

Speckle Noise-Free Interconnective Holographic Projection

Generally, speckle noise is regarded as unavoidable in holographic projection, and it results from unexpected high spatial frequency components of diffracted light at the sharp edge of pixel openings in a spatial light modulator. Speckle noise typically reduces image contrast and degrades the image quality of the holographic projection. In this study, we propose a novel holographic optical interconnection method free of speckle noise in holographic projection. This optical interconnection is achieved by using a holographic optical element (HOE). The HOE is designed to reconstruct Gaussian beams with low divergence. These Gaussian beams become points which form target images at desired depths. Since the Gaussian beam from the HOE does not share the same position with other Gaussian beams, there is no interference at the projection image. Therefore, the image is composed of the points from the Gaussian beams and there is no reason for unexpected high spatial frequency noise to appear on the image. In this paper, we fabricate the HOE, produced with our specially manufactured hologram printer, where the directions of two Gaussian beams with low divergence are controlled by goniometers. We experimentally demonstrated a speckle noise-free interconnective holographic projection. Two images are successfully formed at different depths by optically connecting two points in pairs.

Image quality and scan time optimisation for in situ phase contrast x-ray tomography of the intervertebral disc

In-line phase contrast synchrotron tomography combined with in situ mechanical loading enables the characterisation of soft tissue micromechanics via digital volume correlation (DVC) within whole organs. Optimising scan time is important for reducing radiation dose from multiple scans and to limit sample movement during acquisition. Also, although contrasted edges provided by in-line phase contrast tomography of soft tissues are useful for DVC, the effect of phase contrast imaging on its accuracy has yet to be investigated. Due to limited time at synchrotron facilities, scan parameters are often decided during imaging and their effect on DVC accuracy is not fully understood. Here, we used previously published data of intervertebral disc phase contrast tomography to evaluate the influence of i) fibrous image texture, ii) number of projections, iii) tomographic reconstruction method, and iv) phase contrast propagation distance on DVC results. A greater understanding of how image texture influences optimal DVC tracking was obtained by visualising objective function mapping, enabling tracking inaccuracies to be identified. When reducing the number of projections, DVC was minimally affected by image high frequency noise but with a compromise in accuracy. Iterative reconstruction methods improved image signal-to-noise and consequently significantly lowered DVC displacement uncertainty. Propagation distance was shown to affect DVC accuracy. Consistent DVC results were achieved within a propagation distance range which provided contrast to the smallest scale features, where; too short a distance provided insufficient features to track, whereas too long led to edge effect inconsistencies, particularly at greater deformations. Although limited to a single sample type and image setup, this study provides general guidelines for future investigations when optimising image quality and scan times for in situ phase contrast x-ray tomography of fibrous connective tissues.

The influence of rosin, blowing agent, and mould type on the sound insulation of paper-based foams

Wooden foams have been extensively researched in recent years because of their special structure and green features. At the same time, noise pollution is increasing, prompting sound insulation to become another research focus. In this study, a three-factor, five-level orthogonal experiment was designed, and 25 sets of specimens were produced for testing and analysis in 19 frequency bands. To measure the sound insulation, two criteria were used: the material sound loss rate (for determining mass) and the material sound loss rate (for determining volume sound). After measuring, these two values were compared. It was found that the material was highly isolated from high frequency noise. The amount of adhesive used and the choice of mould had a considerable effect on the sound insulation of the specimens as the foaming rate was varied. Good sound insulation can be achieved when the foaming rate is very low and when the foaming rate is very high.

Effect of the Indium Compositions in Tri-Gate InxGa1−xAs HEMTs for High-Frequency Low Noise Application

In this paper, we investigated the tri-gate InxGa1−xAs high electron mobility transistors (HEMTs) with different indium compositions for low noise applications. The tri-gate configuration in this work exhibits better device performances of the transconductance (gm), the current gain cutoff frequency (fT), the maximum oscillation frequency (fmax), and the minimum noise figure compared with the conventional planar structure due to the improved gate controllability. The indium contents of 20%, 40%, and 100% tri-gate devices were also fabricated for comparison. The resistances and leakage current were measured to investigate the effect between the indium compositions and the noise figure. Experimental results exhibited that the tri-gate In0.4Ga0.6As mHEMT with neither the highest transconductance nor the lowest gate leakage current exhibited the lowest minimum noise figure (NFmin) of 2 dB when operating at 50 GHz. The study reveals that device architecture and indium composition are important factors in the determination of noise performance.

Comprehensive study of health effects of plasma technology occupational environment: Exposure to high frequency and intensity noise and toxic gases

ObjectivesTo evaluate on animal models the health effects of the combined or separate exposure to main chemical and physical hazards of plasma-based material processing technology environment. Materials and methodsMale Wistar rats were exposed to actual levels of hazardous factors in plasma technology occupational environment: i.e., ozone and nitrogen oxides (O3 and NOx) in respective concentrations of 0.5 mg/m3 and 1.0 mg/m3 and high-frequency (1000–1600 Hz) of 112 dB intensity noise for 3 h/day, 5 days/week for 12 weeks, with a recovery period of 1 month. ResultsExposure to noise or its combination with chemical factors (ozone, nitrogen oxides) causes non-specific CNS changes testifying for significant excitation dominance, especially in the case of joint exposure. Histological examination of rats’ brain in experimental revealed a pronounced increase in blood filling of small vessels on the tenth day of the experiment, with subsequent intensification of vascular alterations and eventually to cerebral edema. The exposure to noise significantly reduced total thymus, bone marrow and spleen cell numbers and these was also more pronounced under the joint impact of noise and toxic gases. Thymus, but not bone marrow or spleen, mitotic activity was as well reduced under the same modes of exposure. Cytological investigation of film preparations of subcutaneous connective tissue revealed that joint exposure led to microcirculatory disorders, increased number of dark mast cells and reduced degranulation processes indicative of increased autoregulatory processes effective at microvasculature level. ConclusionsHigh-frequency and intensity noise is main stressor factor that has negative impact on CNS and immune system, morphology and functioning of hematopoietic organs (spleen, bone marrow, thymus) and connective tissue. Its negative impact is significantly potentiated by concurrent exposure to ozone and nitrogen oxide, while exposure only to these toxic gases has no significant effect on the above targets.

Alignment-invariant signal reality reconstruction in hyperspectral imaging using a deep convolutional neural network architecture

The energy resolution in hyperspectral imaging techniques has always been an important matter in data interpretation. In many cases, spectral information is distorted by elements such as instruments’ broad optical transfer function, and electronic high frequency noises. In the past decades, advances in artificial intelligence methods have provided robust tools to better study sophisticated system artifacts in spectral data and take steps towards removing these artifacts from the experimentally obtained data. This study evaluates the capability of a recently developed deep convolutional neural network script, EELSpecNet, in restoring the reality of a spectral data. The particular strength of the deep neural networks is to remove multiple instrumental artifacts such as random energy jitters of the source, signal convolution by the optical transfer function and high frequency noise at once using a single training data set. Here, EELSpecNet performance in reducing noise, and restoring the original reality of the spectra is evaluated for near zero-loss electron energy loss spectroscopy signals in Scanning Transmission Electron Microscopy. EELSpecNet demonstrates to be more efficient and more robust than the currently widely used Bayesian statistical method, even in harsh conditions (e.g. high signal broadening, intense high frequency noise).

Structured random receptive fields enable informative sensory encodings.

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

Dimensional X-Ray computed tomography: Apparent feature form error and its effect on measurement tasks

X-ray computed tomography (XCT) provides a unique set of dimensional measurement capabilities suitable for assessing complex geometry. However, due to the origins of XCT as a medical imaging method and qualitative non-destructive examination technique, only recently has XCT been rigorously investigated as a dimensional metrology tool. Although the data produced via XCT may be leveraged with conventional coordinate metrology techniques, the instrument physics and volumetric reconstruction methods which govern the accuracy and precision of the data are complex, workpiece-specific, and non-negligible. In particular, there is minimal prior work which has studied complex multi-step metrology tasks such as datum reference frame (DRF) construction and feature position measurement. In this work, a qualified artifact was used to evaluate the fitness of XCT for such a task. Multiple XCT systems were used, and multiple instrument magnification levels compared. The form error of planar and cylindrical features was evaluated. Further, the influence of individual feature reconstruction on the development of datums and upstream position measurements was studied. It was found that negative, i.e., bore-like, and positive, i.e., pin-like configurations of certain features were subject to distinct XCT artifact effects which caused upstream measurement errors when these features were used to construct a DRF. High magnification and X-ray scatter produced high spatial frequency noise in surface reconstruction while beam hardening and beam penetration distance variation produced gross apparent form error. The latter was associated with greater measurement errors in feature position measurement within an DRF.

Reduction of artifacts and noise in small electrogram datasets without manual annotation using transfer machine learning

Abstract Background/Introduction Mapping AF is challenging. Monophasic action potentials (MAPs) show that most of the recorded signals are not representing the atrial activity, and arise from far-field or other artifacts. Thus, reducing noise in electrophysiological signals is essential, yet it can be difficult for cross-talk from other chambers and pacing. Strategies to reduce noise include template matching, averaging, and smoothing, but all of them have major limitations. Furthermore, expert interpretation requires knowledge to discriminate signals from noise, but is subjective. Purpose We hypothesised a) that atrial and ventricular electrograms with varying artifact and noise can be denoised using autoencoder neural networks (NNs) without requiring manual annotation and in a reproducible manner, and b) we could train these NNs on a large available dataset ventricular signals, then apply transfer learning to the original smaller atrial dataset. We applied this approach to MAPs, which have interpretable shapes and would help identifying local from far-field signals helping in diagnosis, mapping and ablation. Methods We first trained with 5706 left and right ventricular MAPs from 42 patients with ischemic cardiomyopathy (age 65±13y; Fig. 1A), with 60% for training, 20% (validation) and 20% (testing). Transfer learning and parameter-tuning were then used to apply this NN to a smaller sample of atrial MAPs (N=641 from 21 patients, 67±5y, 13 women; Fig. 2B, D, F). The autoencoder was used to eliminate pacing artifacts in ventricular MAPs (Fig. 1B, C) and denoise atrial MAPs (Fig. 2C, E, G) by reconstructing key learned features. The accuracy of the reconstruction was evaluated using Pearson Correlation Coefficient (PCC) and a novel similarity coefficient (SC). No manual annotation was required to identify noisy signals. Results The trained NN encoder learned key features of ventricular MAPs and reconstructed these clean signals with a SC=0.91±0.16 and PCC=0.99±0.01 (Fig. 1A). With this training, the NN was able to denoise ventricular MAPs with pacing artifact (Fig. 1B, C). After fine-tuning, the NN learned key signal features (upstroke, triangular descent, terminus) and thus reduced diverse noise without specific training or manual annotation. Namely, it was able to reconstruct atrial MAPs eliminating ventricular noise, high frequency noise and truncated signals (Fig. 2). Conclusions Machine learned encoder-decoders are powerful tools that can learn essential features of atrial and ventricular signals and hence isolate noise. Transfer learning is effective when large datasets are unavailable for training. This approach can separate atrial beats from far-field ventricular beats and other sources of noise. The ability to eliminate a diverse range of noise improves this approach over existing techniques and may have far-reaching applications in electrophysiology, such as mapping and ablation. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH

Lombard effect and speech intelligibility by frequency content

The Lombard Effect is an increase in vocal effort in response to rising noise levels and disturbance in the communication environment. The objective of this study is to evaluate the Lombard Effect during four intensity levels from 45dB(A) to 75dB(A) of low-frequency, medium-frequency, and high-frequency energy noise to measure the effect of disturbance and vocal discomfort on the speaker’s intent to communicate, as well as the effect on speech intelligibility. Twelve conditions were randomly presented and recorded for each participant with the three types of noise and levels. At each condition, 20 participants were asked to read a passage. Immediately following each reading, participants were asked to rate the amount of noise disturbance and vocal comfort they had experienced. After, the speech intelligibility was evaluated by asking the participants to repeat the sentences of the QuickSin test emitted by a Head and Torso Simulator. The medium-frequency energy noise showed the highest Lombard Effect and the stronger decrease in intelligibility. In the conditions with noise with mid-frequency energetic content, the decrease in intelligibility was drastic with the increase in noise level. Low-frequency noises minimally impact speech intelligibility. High-frequency noises show little change in intelligibility with the increase in noise level.

A High-Performance Low Complex Design and Implementation of QRS Detector Using Modified MaMeMi Filter Optimized with Mayfly Optimization Algorithm

Electrocardiogram (ECG) is considered as the important diagnostic tests in medical field for detecting the cardiac anomalies. But, the ECG signals are polluted with numerous noise from power line intrusion, muscle noise, baseline wander, motion artifacts, low frequency noise signals, high frequency noise signals and T-wave, which automatically affects the QRS profile. The existing method provides the result in lesser accuracy with higher rate of error detection. To overcome these issues, QRS detector using modified maximum mean minimum (MoMaMeMi) filter optimized with mayfly optimization algorithm (QRS-MoMaMeMi-MOA) is proposed in this paper for less computational cost along with resource requirements. The proposed filter design consists of two phases for detecting QRS detector, such as filtering process associated to the enhancement and detection phase. Initially, the ECG data are taken from MIT/BIH arrhythmia dataset (MIT-AD). For eradicating the baseline wander in ECG data, MaMeMi filter is used. For expanding the performance of the modified MaMeMi filter, filter parameters, such as [Formula: see text] and [Formula: see text] are optimized by MOA to accomplish the best values and measure the performance of the whole QRS detector. For high frequency noise suppression in ECG data, the range function, noise subtractors, modified triangular detector are used. Then, heart beat detection can be done with the help of adaptive thresholding technique. The proposed filter design is carried out in MATLAB and implemented on field programmable gate arrays (FPGAs). The proposed QRS-MoMaMeMi-MOA filter design had 0.93%, 0.12% and 0.19% higher accuracy and 89.32%, 50% and 62% low detection error rate, compared to the existing filters, like Kalman filtering based adaptive threshold algorithm for QRS complex detection (QRS-KF-ATA), QRS detection of ECG signal utilizing hybrid derivative with MaMeMi filter by efficiently removing the baseline wander (QRS-HD-MaMeMi), and knowledge-based QRS detection operated by cascade of moving average filters (QRS-CAF). Then, the device utilization of the proposed FPGA implementation of the QRS-MoMaMeMi-MOA filter provides 95.556% and 71.428% lower power usage compared with the existing algorithms, like Kalman filtering based adaptive threshold algorithm for QRS complex detection in FPGA (FPGA-QRS-KF-ATA), and efficient architecture for QRS detection in FPGA utilizing integer Haar wavelet transform (FPGA-QRS-IHWT).