Low Frequency Components Of Signal Research Articles

Annoyance caused by simultaneous exposure to noise and seat vibrations inside of a mini excavator: multimodal interaction and the effects of changes in low-frequency content of the sound signal

Drivers of commercial vehicles are exposed to noise and vibrations during their occupation. The signal characteristics of these exposures can lead to different degrees of annoyance perception, even below the legal daily exposure limits. In addition, simultaneous exposure to sound and vibration may lead to potential interaction effects between the two stimuli, influencing the total annoyance perception. Therefore, a perception experiment was designed to ask the participants about the perceived total annoyance caused by simultaneous noise and seat vibrations in commercial vehicles, presenting modified sound and seat vibrations, recorded inside of a mini-excavator. Multimodal interaction effects of noise and vibrations were investigated by changing the intensity of both stimuli. Furthermore, the influence of low-frequency signal components of the sound signal with and without simultaneous seat vibrations was investigated by specific changes in the sound spectrum.

A method for measuring capacitive current in distribution networks based on injection signal method

Abstract In view of the problems that exist in the current distribution network, such as the narrow range of measurement capacitance to the ground and the difficulty of selecting the appropriate signal frequency. Based on the phasor method, this paper proposes a method for measuring capacitance current in a distribution network based on injected signal and analyzes and calculates the error of the method. The measurement technique involves inputting two constant-amplitude signals—one high-frequency and one low-frequency—into a zero-sequence PT (Potential Transformer) configured with an open delta connection. Based on the equivalent circuit models for both high and low-frequency signals, the high-frequency signal’s increased frequency allows the magnitude to be disregarded, simplifying the calculations. Using a simplified equation for the low-frequency signal component, this method enables rapid calculation of the capacitor current in the distribution network. Based on MATLAB software, the simulation calculation model is established, and the simulation results show that the measurement method is simple to calculate and has high accuracy, which can be used to measure the large-capacity grounding capacitance current test, so as to improve the measurement accuracy of the capacitance current of medium and low voltage distribution network.

An Optimized Heading De-Noising Method of the Polarization Compass for Improving Orientation Accuracy

In recent years, the polarization compass (PC) has been extensively applied in the field of autonomous navigation due to its strong autonomy and anti-interference characteristics. Nevertheless, the existing research shows that the PC with high extinction ratio polarization sensor still offers poor orientation accuracy since there is a great quantity of noise in the heading data output from the compass. Here, we present a novel and promising scheme to denoise heading data to improve deficiency in heading data denoising of existing research. The purpose of this study aims to solve the problem that the orientation accuracy of PC caused by noise needs to be improved. Concretely, the major points of this research include an effective separation strategy of noise and useful signal based on the optimized variational mode decomposition (OVMD) and an effective heading data denoising method based on an adaptive singular spectrum analysis (AD-SSA). OVMD based on the center frequency is employed to decompose the original noisy heading data into a finite number of band-limited data intrinsic mode functions (DIMFs) so that the best number of decomposed layers for the noising data can be obtained. Subsequently, the above decomposed DIMFs are divided into low-frequency signal components, mixed components, and high-frequency noise components exploiting the maximum similar eigenvalue of the autocorrelation matrix corresponding to each DIMF as a classification method. An AD-SSA uses the correlation coefficient to further suppress the noise in classified DIMFs. These approaches are combined effectively to improve the orientation accuracy of the PC significantly and very contribute to the navigation application of unmanned mobile platforms such as unmanned aerial vehicles (UAVs). Compared to existing prior arts, the effectiveness of the proposed heading denoising scheme for the PC is demonstrated through the results obtained from the heading data sets on two representative sunny and dust days.

De-noising magnetotelluric data using variational mode decomposition combined with mathematical morphology filtering and wavelet thresholding

De-noising magnetotelluric (MT) data has become a key research topic closely related to the application of the MT method itself. Variational mode decomposition (VMD), a newly developed non-recursive signal decomposition method, has been applied to the denoising of MT data. However, some high-frequency noise may remain after VMD denoising. Moreover, the useful low-frequency signal and large-scale noise cannot always be easily separated, resulting in the loss of useful low-frequency signal components during signal reconstruction. To overcome this problem, a new time series editing strategy is proposed, which combines wavelet thresholding (WT) and mathematical morphology filtering (MMF) for further processing of the VMD output. WT is used to remove the remaining high-frequency noise from the retained signal after VMD, while MMF is utilized to extract the useful low-frequency signal components from the signal rejected by VMD. Finally, the WT and MMF outputs are merged to obtain the final denoising results. The proposed method was evaluated using a simulated dataset and two real datasets collected in the Hami Basin of East Tianshan, China. The simulated data tests showed that the proposed method has superior denoising ability compared to other time-domain methods, while the real data study showed that the quality of MT responses obtained from the proposed method is superior to that from conventional methods, especially for the low-frequency band.

Medical Image Segmentation Algorithm for Three-Dimensional Multimodal Using Deep Reinforcement Learning and Big Data Analytics.

To avoid the problems of relative overlap and low signal-to-noise ratio (SNR) of segmented three-dimensional (3D) multimodal medical images, which limit the effect of medical image diagnosis, a 3D multimodal medical image segmentation algorithm using reinforcement learning and big data analytics is proposed. Bayesian maximum a posteriori estimation method and improved wavelet threshold function are used to design wavelet shrinkage algorithm to remove high-frequency signal component noise in wavelet domain. The low-frequency signal component is processed by bilateral filtering and the inverse wavelet transform is used to denoise the 3D multimodal medical image. An end-to-end DRD U-Net model based on deep reinforcement learning is constructed. The feature extraction capacity of denoised image segmentation is increased by changing the convolution layer in the traditional reinforcement learning model to the residual module and introducing the multiscale context feature extraction module. The 3D multimodal medical image segmentation is done using the reward and punishment mechanism in the deep learning reinforcement algorithm. In order to verify the effectiveness of 3D multimodal medical image segmentation algorithm, the LIDC-IDRI data set, the SCR data set, and the DeepLesion data set are selected as the experimental data set of this article. The results demonstrate that the algorithm's segmentation effect is effective. When the number of iterations is increased to 250, the structural similarity reaches 98%, the SNR is always maintained between 55 and 60 dB, the training loss is modest, relative overlap and accuracy all exceed 95%, and the overall segmentation performance is superior. Readers will understand how deep reinforcement learning and big data analytics test the effectiveness of 3D multimodal medical image segmentation algorithm.

Investigation on flow pattern evolution and restraint of vibration energy to bubbles in vibrated dense medium fluidized bed

Vibrated dense medium fluidized bed (VDMFB) provides a feasible and low-cost method for fine coal separation. In this study, empirical mode decomposition (EMD) was used to study the evolution of the dominant flow pattern and the restraint effect of vibration energy on the bubble in VDMFB. The internal pressure fluctuation signal of VDMFB was analyzed by EMD, and the intrinsic mode functions (IMFs) of the signal were extracted, and the corresponding relationship between the IMFs and dominant fluidization behavior was verified. The intermediate-frequency signal components (IMF-3 ~ IMF-4) and low-frequency signal components (IMF-5 ~ IMF-8) corresponded to the particle oscillation of emulsion phase and bubble behavior in the particle bed, respectively. Additionally, the energy variation of IMFs in different frequency bands was closely related to the evolution of dominant fluidization behavior. Further, the intermediate-frequency energy decreased and the low-frequency energy increased gradually along the bed height; additionally, the intersection point Tr existed between the two curves, indicating that the dominant energy controlling the flow pattern of the bed changed from vibration to bubbles. The constraint range of vibration energy on bubble behavior was defined, and the quasi-dispersed micro-bubble fluidization environment suitable for fine coal separation was identified in VDMFB, in which the steady vibration energy effectively controlled the unsteady bubble.

A Novel Denoising Model of Underwater Drilling and Blasting Vibration Signal Based on CEEMDAN

In underwater drilling and blasting engineering, the blasting vibration signal is mixed with a mass of noises due to the complexity of monitoring environment, the error of monitoring sensors and the reflection of propagation medium. In order to accurately obtain the characteristics of vibration signal, a novel denoising model is established. The complete ensemble empirical mode decomposition with adaptive noise is used to decompose the original signal, and the objective function of the filtering algorithm is used to obtain the optimal denoising signal. The results indicate that the model can not only successfully remove the high-frequency noise but also has no effect on the low-frequency signal components, which verifies the reliability and validity of the denoising model.

Capacitively-Coupled CMOS VCSEL Driver Circuits

A capacitively-coupled laser diode driver (LDD) for common-cathode vertical-cavity surface-emitting lasers (VCSELs) designed in 65 nm CMOS intended for short-reach parallel optical interconnects is presented. The LDD consists of a capacitively-coupled voltage-mode high-frequency path and a direct-coupled path that provides low-frequency signal components. By increasing the low-frequency path bandwidth an order of magnitude compared to previous capacitively-coupled drivers, the on-chip coupling capacitor is reduced to 1.05 pF, occupying 3 times less area than prior art. The driver incorporates capacitively-coupled two-tap pre-emphasis to equalize the VCSEL’s optical response. The VCSEL was modulated with an optical modulation amplitude (OMA) of up to +5.1 dBm and an extinction ratio of 9 dB at 15 Gb/s, the highest extinction ratio achieved in high-speed anode-driving CMOS LDDs. The driver is programmable and is also demonstrated with +2.3 dBm OMA at a power consumption of only 30 mW, corresponding to an energy efficiency of 2 pJ/bit.

EEG analysis via multiscale Lempel-Ziv complexity for seizure detection.

Robust seizure detection and seizure prediction continues to be a challenge. Lempel-Ziv Complexity (LZC) is one of the features that has shown to be relevant in seizure detection. Recent work has shown that augmenting LZC can be beneficial to emphasize variations in amplitude or frequency when analyzing biomedical signals. In this paper, we present a first look into evaluating the feasibility of using a recently proposed feature stemmed from LZC, namely the Multiscale Lempel-Ziv Complexity (MLZC) for seizure detection. MLZC does not allow the high-frequency signal components to be overwhelmed by the low frequency signal components when calculating complexity values. We compare MLZC and LZC for identifying seizures for three cases and show MLZC can provide a clear separation between non-ictal and ictal periods for all three cases using a single threshold over 7 recordings and 7 seizures per patient, whereas LZC provided such a clear separation for only one of the patients.

Optical precursor with four-wave mixing and storage based on a cold-atom ensemble.

We observed optical precursors in four-wave mixing based on a cold-atom gas. Optical precursors appear at the edges of pulses of the generated optical field, and propagate through the atomic medium without absorption. Theoretical analysis suggests that these precursors correspond to high-frequency components of the signal pulse, which means the atoms cannot respond quickly to rapid changes in the electromagnetic field. In contrast, the low-frequency signal components are absorbed by the atoms during transmission. We also showed experimentally that the backward precursor can be stored using a Raman transition of the atomic ensemble and retrieved later.

Super-resolution spectral analysis and signal reconstruction of magnetic Barkhausen noise

An investigation was performed based on frequency domain analysis and reconstruction for the magnetic Barkhausen noise (MBN). The power spectrum density (PSD) of MBN was analyzed based on autoregressive (AR) model. The relations between PSD peaks and mechanical properties of high strength steel sheet were studied. The energy of low frequency signal components decreases as hardness increases, and increases as tensile stress increases. The energy of high frequency signal components increases as hardness increases, and decreases as tensile stress increases. After identification, the MBN signal was reconstructed by discrete Gabor expansion which remains desired signal components and removes undesired ones.

Classification of the waxy condition of durum wheat by near infrared reflectance spectroscopy using wavelets and a genetic algorithm

Near infrared (NIR) reflectance spectroscopy has been applied to the problem of differentiating four genotypes of durum wheat: ‘waxy’, Wx A1 null null, wx-B1 null and wild type. The test data consisted of 95 NIR reflectance spectra of wheat samples obtained from a USDA-ARS wheat breeding program. A two-step procedure for pattern recognition analysis of NIR spectral data was employed. First, the wavelet packet transform [14,15] was applied to the NIR reflectance data using wavelet filters at different scales to extract and separate low-frequency signal components from high frequency noise components. By applying these filters, each reflectance spectrum was decomposed into wavelet coefficients that represented the sample's constituent frequencies. Second, wavelet coefficients characteristic of the waxy condition of the wheat samples were identified using a genetic algorithm for pattern recognition. The pattern recognition GA employed both supervised and unsupervised learning to identify wavelet coefficients that optimized clustering of the spectra by genotype in a plot of the two largest principal components of the data. By sampling key feature subsets, scoring their PC plots, and tracking those genotypes and samples that were difficult to classify, the pattern recognition GA was able to identify a set of wavelet coefficients whose PC plot showed clustering of the wheat samples on the basis of their ‘waxy’ condition. Object validation was also performed to assess the predictive ability of the proposed NIR method to identify the ‘waxy’ condition of the wheat. An overall classification success rate of 78% was achieved for the spectral data.

Precise Driver-Side Equalization of Capacitive Coupling Connector

Capacitive coupling connector subjects to excessive inter-symbol interference with the increasing of signal transmission rate. To solve this problem, a driver-side equalization scheme is proposed, and the calculation method of precise equalization ratio for this scheme is also given. The equalization scheme is implemented through select appropriate equalization ratio to increase high frequency components of signal and reduce the low frequency components of signal at the driver-side at the drive-side. It can reduce the inter-symbol interference and improve pulse magnitude effectively. Simulation and calculation results show that the equalization scheme can increase the pulse amplitude in receiver-side, eliminate inter-symbol interference and improve signal transmission rate greatly.

Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI

Real-time fMRI allows analysis and visualization of the brain activity online, i.e. within one repetition time. It can be used in neurofeedback applications where subjects attempt to control an activation level in a specified region of interest (ROI) of their brain. The signal derived from the ROI is contaminated with noise and artifacts, namely with physiological noise from breathing and heart beat, scanner drift, motion-related artifacts and measurement noise. We developed a Bayesian approach to reduce noise and to remove artifacts in real-time using a modified Kalman filter. The system performs several signal processing operations: subtraction of constant and low-frequency signal components, spike removal and signal smoothing. Quantitative feedback signal quality analysis was used to estimate the quality of the neurofeedback time series and performance of the applied signal processing on different ROIs. The signal-to-noise ratio (SNR) across the entire time series and the group event-related SNR (eSNR) were significantly higher for the processed time series in comparison to the raw data. Applied signal processing improved the t-statistic increasing the significance of blood oxygen level-dependent (BOLD) signal changes. Accordingly, the contrast-to-noise ratio (CNR) of the feedback time series was improved as well. In addition, the data revealed increase of localized self-control across feedback sessions. The new signal processing approach provided reliable neurofeedback, performed precise artifacts removal, reduced noise, and required minimal manual adjustments of parameters. Advanced and fast online signal processing algorithms considerably increased the quality as well as the information content of the control signal which in turn resulted in higher contingency in the neurofeedback loop.

5–10 Gb/s 70 mW Burst Mode AC Coupled Receiver in 90-nm CMOS

A low power burst mode receiver architecture is presented which can be used for AC coupled links where low frequency signal components are attenuated by the channel. The nonlinear path comprises a hysteresis latch that recovers the missing low frequency content and a linear path that boosts the high frequency component by taking advantage of the high pass channel response. By optimally combining them, the front-end recovers NRZ signals up to 13 Gb/s burning only 26 mW in 90 nm CMOS. A low power- and area-efficient clock recovery scheme uses the linear path to injection lock an oscillator. A simple theory and simulation technique for ILO-based receivers is discussed. The clock recovery technique is verified with experimental results at 5-10 Gb/s in 90 nm CMOS consuming 70 mW and acquiring lock within 1.5 ns.

Endogenous circannual rhythm in luteinizing hormone secretion: insight from signal analysis coupled with mathematical modelling

In sheep, as in many vertebrates, the seasonal pattern of reproduction is timed by the annual photoperiodic cycle, characterized by seasonal changes in the day length. The photoperiodic information is translated into a circadian profile of melatonin secretion. After multiple neuronal relays (within the hypothalamus), melatonin affects gonadotrophin-releasing hormone (GnRH) secretion, which in turn controls ovarian cyclicity. The pattern of GnRH secretion is mirrored by that of luteinizing hormone (LH) secretion, whose plasmatic level can be easily measured. We addressed the question of whether there exists an endogenous circannual rhythm in a tropical sheep (Blackbelly) population that exhibits clear seasonal ovarian activity when ewes are subject to temperate latitudes. We based our analysis on LH time series collected in the course of 3 years from ewes subject to a constant photoperiodic regime. Owing to intra- and interanimal variability and unequal sampling times, the existence of an endogenous rhythm is not straightforward. We have used time-frequency signal processing methods, and especially the smooth pseudo-Wigner-Ville distribution, to extract possible hidden rhythms from the data. To further investigate the low-frequency (LF) and high-frequency (HF) components of the signals, we have designed a simple mathematical model of the LH plasmatic level accounting for the effect of experimental sampling times. The model enables us to (i) confirm the existence of an endogenous circannual rhythm as detected by the LF signal component, (ii) investigate the action mechanism of the photoperiod on the pulsatile pattern of LH secretion (control of the interpulse interval), and (iii) conclude that the HF component is mainly due to the experimental sampling protocol.

Performance Characterization of a Stellar Interferometer

This article discusses a frequency-domain system identification method developed to address specific challenges arising in the SIM application. The main challenges concern the need to incorporate frequency-weighted performance measures as well as the need to treat large systematic errors occurring naturally in the intended application. In general, systematic errors enter into frequency-domain analysis through various pathways, such as finite data lengths, the choice of window function (for example, Hanning or Hamming) used for spectral estimation, the presence of large low-frequency signal components, and artifacts of detrending filters.

Damage detection in an aircraft foam sandwich panel using nonlinear elastic wave spectroscopy

A novel damage detection technique, based on nonlinear elastic wave spectroscopy (NEWS) approach, is presented in this paper. This technique detects the presence of structural changes by monitoring the presence of harmonics and sidebands generated by the interaction between a low frequency and a high frequency harmonic excitation signal, due to the nonlinear material behaviour caused by the presence of damage. The proposed methodology was tested on a sandwich plate after being impacted by a foreign object under low velocity impact conditions. The high frequency signal was modulated in amplitude and the changes of the structural response, in terms of harmonic and sidebands amplitude, were recorded. The spectra and time frequency representation (TFR) were evaluated using wavelet transformations (WT). The experimental harmonic and sidebands amplitude resulted in close agreement with the theoretical behaviour of nonlinear behaviour of damaged materials. More specifically, the 3rd harmonics of the low frequency signal component showed a quadratic dependence with the low frequency response amplitude, as predicted by the theory for a hysteretic nonlinear material. Differently, the experimental second sidebands of the high frequency signal resulted bounded by curves representing the second sideband behaviour for pure hysteretic material (lower bound) and classical nonlinear material (upper bound). In particular, for small low frequency response amplitudes, the experimental second sidebands tended to be closer to the classical nonlinear material curve, while for the largest amplitudes investigated the response resembled a hysteretic material behaviour. The results showed that the proposed methodology was capable of detecting successfully the presence of impact damage and can be used as a first assessment of the presence of damage in aircraft structures where the presence of damage should be quickly estimated.

Baseline Wander Compensation for the Perpendicular Magnetic Recording Channel

Baseline wander compensation allows the effective use of low-frequency signal components in a perpendicular recording channel. In this paper, simple baseline wander models are presented, the associated compensation algorithms are developed, and their performances are formulated. The effect of loop delay on the compensation algorithms is also addressed. Simulation results quantify the performances of the proposed compensation algorithms.

Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy

Several current brain imaging techniques rest on the assumption of a tight coupling between neural activity and hemodynamic response. The nature of this neurovascular coupling, however, is not completely understood. There is some evidence for a decoupling of these processes at the onset of neural activity, which manifests itself as a momentary increase in the relative concentration of deoxyhemoglobin (HbR). The existence of this early component of the hemodynamic response function, however, is controversial, as it is inconsistently found. Near infrared spectroscopy (NIRS) allows quantification of levels of oxyhemoglobin (HbO 2) and HbR during task performance in humans. We acquired NIRS data during performance of simple motor and visual tasks, using rapid-presentation event-related paradigms. Our results demonstrate that rapid, event-related NIRS can provide robust estimates of the hemodynamic response without artifacts due to low-frequency signal components, unlike data from blocked designs. In both the motor and visual data the onset of the increase in HbO 2 occurs before HbR decreases, and there is a poststimulus undershoot. Our results also show that total blood volume (HbT) drops before HbO 2 and undershoots baseline, raising a new issue for neurovascular models. We did not find early deoxygenation in the motor data using physiologically plausible values for the differential pathlength factor, but did find one in the visual data. We suggest that this difference, which is consistent with functional magnetic resonance imaging (fMRI) data, may be attributable to different capillary transit times in these cortices.