Components In Frequency Domain Research Articles

Arid and semiarid rangeland responses to non-stationary temporal dynamics of environmental drivers

Temporal ecology is a growing research field aimed at understanding how temporal dynamics structure ecological systems in a changing environment. Ecosystems are exposed to accelerating environmental changes —mainly driven by climate change— that challenge their future survival. The main feature of this planetary process is that statistical properties such as mean, variance and autocorrelation are not constant over time. While this non-stationary climate change characteristic is well known, our premise is that far too much attention is given to ecosystems stationary responses, where annual cycles and seasonal changes of vegetation productivity are the main indicators for addressing environmental changes and planning rangeland management. Hence, shifts in other relevant intra-annual and inter-annual cycles are overlooked. We emphasize that a rigorous and systematic tool aimed at addressing these kinds of temporal changes in vegetation dynamics is lacking. We proposed an operative framework aimed at tackling the ability of rangelands to develop non-stationary responses in short periods, in the face of non-stationary changes in the dynamics of external factors driving its temporal functioning. In particular, we measured non-stationary dynamics of rangeland productivity by means of shifts in the hierarchy of frequency domain components over time of different rangelands’ photosynthetic activity from North-West Patagonia, Argentina, as measured by the Normalized Difference Vegetation Index (NDVI). We emphasize the importance of a relevant temporal dimension of ecosystems with new insights into both the understanding of environmental changes impact on terrestrial ecosystems and the implementation of rangelands adaptive management.

Data-Wise Spatial Regional Consistency Re-Enhancement for Hyperspectral Image Classification

Effectively using rich spatial and spectral information is the core issue of hyperspectral image (HSI) classification. The recently proposed Diverse Region-based Convolutional Neural Network (DRCNN) achieves good results by weighted averaging the features extracted from several predefined regions, thus exploring the use of spatial consistency to some extent. However, such feature-wise spatial regional consistency enhancement does not effectively address the issue of wrong classifications at the edge of regions, especially when the edge is winding and rough. To improve the feature-wise approach, Data-wise spAtial regioNal Consistency re-Enhancement (“DANCE”) is proposed. Firstly, the HSIs are decomposed once using the Spectral Graph Wavelet (SGW) to enhance the intra-class correlation. Then, the image components in different frequency domains obtained from the weight map are filtered using a Gaussian filter to “debur” the non-smooth region edge. Next, the reconstructed image is obtained based on all filtered frequency domain components using inverse SGW transform. Finally, a DRCNN is used for further feature extraction and classification. Experimental results show that the proposed method achieves the goal of pixel level re-enhancement with image spatial consistency, and can effectively improve not only the performance of the DRCNN, but also that of other feature-wise approaches.

A neuronal theta band signature of error monitoring during integration of facial expression cues.

Error monitoring is the metacognitive process by which we are able to detect and signal our errors once a response has been made. Monitoring when the outcome of our actions deviates from the intended goal is crucial for behavior, learning, and the development of higher-order social skills. Here, we explored the neuronal substrates of error monitoring during the integration of facial expression cues using electroencephalography (EEG). Our goal was to investigate the signatures of error monitoring before and after a response execution dependent on the integration of facial cues. We followed the hypothesis of midfrontal theta as a robust neuronal marker of error monitoring since it has been consistently described as a mechanism to signal the need for cognitive control. Also, we hypothesized that EEG frequency-domain components might bring advantage to study error monitoring in complex scenarios as it carries information from locked and non-phase-locked signals. A challenging go/no-go saccadic paradigm was applied to elicit errors: integration of facial emotional signals and gaze direction was required to solve it. EEG data were acquired from twenty healthy participants and analyzed at the level of theta band activity during response preparation and execution. Although theta modulation has been consistently demonstrated during error monitoring, it is still unclear how early it starts to occur. We found theta power differences at midfrontal channels between correct and error trials. Theta was higher immediately after erroneous responses. Moreover, before response initiation we observed the opposite: lower theta preceding errors. These results suggest theta band activity not only as an index of error monitoring, which is needed to enhance cognitive control, but also as a requisite for success. This study adds to previous evidence for the role of theta band in error monitoring processes by revealing error-related patterns even before response execution in complex tasks, and using a paradigm requiring the integration of facial expression cues.

Parsimonious truncated Newton method for time-domain full-waveform inversion based on the Fourier-domain full-scattered-field approximation

To exploit Hessian information in full-waveform inversion (FWI), the matrix-free truncated Newton method can be used. In such a method, Hessian-vector product computation is one of the major concerns due to the huge memory requirements and demanding computational cost. Using the adjoint-state method, the Hessian-vector product can be estimated by zero-lag crosscorrelation of the first-/second-order incident wavefields and the second-/first-order adjoint wavefields. Different from the implementation in frequency-domain FWI, Hessian-vector product construction in the time domain becomes much more challenging because it is not affordable to store all of the time-dependent wavefields. The widely used wavefield recomputation strategy leads to computationally intensive tasks. We have developed an efficient alternative approach to computing the Hessian-vector product for time-domain FWI. In our method, discrete Fourier transform is applied to extract frequency-domain components of involved wavefields, which are used to compute wavefield crosscorrelation in the frequency domain. This makes it possible to avoid reconstructing the first- and second-order incident wavefields. In addition, a full-scattered-field approximation is proposed to efficiently simplify the second-order incident and adjoint wavefield computation, which enables us to refrain from repeatedly solving the first-order incident and adjoint equations for the second-order incident and adjoint wavefields (re)computation. With our method, the computational time can be reduced by 70% and 80% in viscous media for Gauss-Newton and full-Newton Hessian-vector product construction, respectively. The effectiveness of our method is also verified in the frame of a 2D multiparameter inversion, in which our method almost reaches the same iterative convergence of the conventional time-domain implementation.

Reliable detection of pulse arrival time for partial discharge location in HV cable based on improved Allen-Bear algorithm

In multi-sensor data based partial discharge (PD) source location in high voltage (HV) cable, poor reliability of detection of pulse arrival time against noises, waveform distortion, amplitude attenuation is the major barriers to practical application. In this paper, an improved Allen-Bear PD pulse detection algorithm, which combines the advantage of the characteristic functions of the two time-window energy ratio based detection algorithms, is proposed for robust PD location. The most critical contribution of proposed method is the extension of frequency domain component compared to the characteristic functions of the original algorithm. Simulation and physical experiment results demonstrate that the proposed method not only ensures the detection accuracy of the PD pulse arrival time, but also improves the reliability under harsh practical scenarios. It performs significantly better than the existing detection algorithm in terms of anti-noise, anti-waveform distortion, time window length variation, etc. It has significant value for practical applications.

Association of Short-term Heart Rate Variability and Sudden Unexpected Death in Epilepsy.

We compared heart rate variability (HRV) in sudden unexpected death in epilepsy (SUDEP) cases and living epilepsy controls. This international, multicenter, retrospective, nested case-control study examined patients admitted for video-EEG monitoring (VEM) between January 1, 2003, and December 31, 2014, and subsequently died of SUDEP. Time domain and frequency domain components were extracted from 5-minute interictal ECG recordings during sleep and wakefulness from SUDEP cases and controls. We identified 31 SUDEP cases and 56 controls. Normalized low-frequency power (LFP) during wakefulness was lower in SUDEP cases (median 42.5, interquartile range [IQR] 32.6-52.6) than epilepsy controls (55.5, IQR 40.7-68.9; p = 0.015, critical value = 0.025). In the multivariable model, normalized LFP was lower in SUDEP cases compared to controls (contrast -11.01, 95% confidence interval [CI] -20.29 to 1.73; p = 0.020, critical value = 0.025). There was a negative correlation between LFP and the latency to SUDEP, where each 1% incremental reduction in normalized LFP conferred a 2.7% decrease in the latency to SUDEP (95% CI 0.95-0.995; p = 0.017, critical value = 0.025). Increased survival duration from VEM to SUDEP was associated with higher normalized high-frequency power (HFP; p = 0.002, critical value = 0.025). The survival model with normalized LFP was associated with SUDEP (c statistic 0.66, 95% CI 0.55-0.77), which nonsignificantly increased with the addition of normalized HFP (c statistic 0.70, 95% CI 0.59-0.81; p = 0.209). Reduced short-term LFP, which is a validated biomarker for sudden death, was associated with SUDEP. Increased HFP was associated with longer survival and may be cardioprotective in SUDEP. HRV quantification may help stratify individual SUDEP risk. This study provides Class III evidence that in patients with epilepsy, some measures of HRV are associated with SUDEP.

Attack Detection on Images Based on DCT-Based Features

As reproduction of images can be done with ease, copy detection has increasingly become important. In the duplication process, image modifications are likely to occur and some alterations are deliberate and can be viewed as attacks. A wide range of copy detection techniques has been proposed. In our study, content-based copy detection, which basically applies DCT-based features for images, namely, pixel values, edges, texture information and frequency-domain component distribution, is employed. Experiments are carried out to evaluate robustness and sensitivity of DCT-based features from attacks. As different types of DCT-based features hold different pieces of information, how features and attacks are related can be shown in their robustness and sensitivity. Rather than searching for proper features, use of robustness and sensitivity is proposed here to realize how the attacked features have changed when an image attack occurs. The experiments show that, out of ten attacks, the neural networks are able to detect seven attacks namely, Gaussian noise, S&P noise, Gamma correction (high), blurring, resizing (big), compression and rotation with mostly related to their sensitive features.

Sleep deprivation reduces vagal tone during an inspiratory endurance task in humans.

Sleep deprivation alters inspiratory endurance by reducing inspiratory motor output. Vagal tone is involved in exercise endurance. This study aimed to investigate the effect of sleep deprivation on vagal tone adaptation in healthy subjects performing an inspiratory effort. Vagal tone was assessed using Heart Rate Variability normalized units of frequency domain component HF (high frequency) before, at the start, and the end of an inspiratory loading trial performed until exhaustion by 16 volunteers after one night of sleep deprivation and one night of normal sleep, where sleep deprivation reduced the inspiratory endurance by half compared to the normal sleep condition (30 min vs 60 min). At rest, heart rate was similar in sleep deprivation and normal sleep conditions. In normal sleep condition, heart rate increased during inspiratory loading task; this increase was greater in sleep deprivation condition. In normal sleep condition, vagal tone increased at the beginning of the trial. This vagal tone increase was absent in sleep deprivation condition. Sleep deprivation abolished vagal tone response to inspiratory load, possibly contributing to a higher heart rate during the trial and to a reduced inspiratory endurance. NCT02725190.

Obesity, Nutrition and Heart Rate Variability.

Heart rate variability (HRV) represents the activity and balance of the autonomic nervous system and its capability to react to internal and external stimuli. As a measure of general body homeostasis, HRV is linked to lifestyle factors and it is associated with morbidity and mortality. It is easily accessible by heart rate monitoring and gains interest in the era of smart watches and self-monitoring. In this review, we summarize effects of weight loss, training, and nutrition on HRV with a special focus on obesity. Besides weight reduction, effects of physical activity and dietary intervention can be monitored by parameters of HRV, including its time and frequency domain components. In the future, monitoring of HRV should be included in any weight reduction program as it provides an additional tool to analyze the effect of body weight on general health and homeostasis. HRV parameters could, for example, be monitored easily by implementation of an electrocardiogram (ECG) every two to four weeks during weight reduction period. Indices presumibly showing beneficial changes could be a reduction in heart rate and the number of premature ventricular complexes as well as an increase in standard deviation of normal-to-normal beat intervals (SDNN), just to name some.

Research on the Influence of Time-Varying Excitation on Vibration Characteristics of the Spiral Bevel Geared Transmission System with Broken Teeth

Due to heavy and alternating loads of working conditions, spiral bevel gears are prone to broken tooth failures. To solve the problem of vibration characteristic of spiral bevel geared transmission with broken tooth failures that is unknown, this study, considering time-varying mesh stiffness and friction excitation, proposed a torsional vibration dynamic model of spiral bevel geared transmission, which has more simple transmission path and the smaller signal attenuation. First, the time-varying excitations of various broken tooth failure are calculated and introduced into the torsional vibration dynamic model. The vibration response of spiral bevel geared transmission with various broken tooth failures is analysed in the time-frequency domain. Then, the sensitivity of the time-domain statistical index and the frequency domain components to different broken tooth failures are studied. Finally, the correctness of the simulation is verified by experiment. The results show that the crest factor is sensitive to minor tooth failure (10–30%), while kurtosis is sensitive to severe failure (30–60%). With the increase of degrees of broken tooth failure, the energy of the low-frequency band increases obviously.

P‐8.4: Holographic Color Display by Controlling the Spectral Components in A Continuous White Light Source

Holographic color display is realized with white light as illumination by controlling the spectral components in frequency domain. Spectral components control method using the parameters of existing high resolution spatial light modulator (SLM) is demonstrated. An optical system including a white light source, an achromatic collimating lens, a high resolution spatial light modulator, a 4f optical filtering system and an achromatic lens as eyepiece is designed and developed for holographic color display. The effectiveness of the proposed technique is proved by experiments.

Flow Regimes Identification-based Multidomain Features for Gas–Liquid Two-Phase Flow in Horizontal Pipe

Accurate flow regimes identification is of great significance for the deep understanding of the flow structure and ensuring the safe and economic operation of actual production in the multiphase flow process. For the flow regimes identification of the gas-liquid two-phase flow in the horizontal pipe, a multidomain features processing scheme is proposed. The water holdup data are acquired by conductance rings sensor, and processed by the improved empirical wavelet transform (EWT) to obtain the Fourier spectrum in the frequency domain and decomposition components in the multiscale domain. The obtained multidomain feature information is quantified to get the high-dimensional feature vectors of different flow regimes. To make the features more representative and realize the visualization of the structural relationship between the flow regime samples, the isometric feature mapping (Isomap) method in manifold learning is used to synthesize the feature structure relationships and map them to low-dimensional space. The obtained low-dimensional feature vectors of training samples are input into the support vector machine (SVM) to acquire the flow regimes identification model. Compared with other flow regimes identification schemes that feature reduction uses different manifold learning methods, or feature extraction is not in multidomain, or data preprocessing uses traditional wavelet transform, the proposed scheme can better retain the global structural relationship of the flow regimes, has the characteristics of strong stability, and the identification rate of the gas-liquid two-phase flow regimes in the horizontal pipe is over 95%.

Towards the numerical ground-borne vibrations predictive models as a design tool for railway lines: A starting point THE

In recent decades, High-Speed Railway (HSR) lines have become one of the most extended and environmental-friendly ways to plan new mass transport networks. These systems are directly influenced by its operational speed generated dynamic effects and the areas where it runs through. This necessarily requires to predict ground-borne vibrations generated by trains passing-by populated areas and its influence zone.Trends in ground-borne measurements, prediction models, and isolation systems are usually performed for maximum operation speed. This method implies the maximum dynamic forces which are suitable for structural calculations (generally developed in time domain) but not necessary for vibration related issues (emission and/or transmission). Additionally, these studies are mainly focused on urban areas where maximum operational speed are frequently far from railways service’s top speeds.Related to frequency domain, it is known that upper frequencies are not the most disturbing ones. In fact, European structural standards usually cut frequencies off at 30 Hz, so much relevant information for vibrational prediction is ignored due to it does not influence structural issues.Moreover, current common predictive numerical models usually apply punctual loads (birth & death) that are disposed to run in certain speed conditions. This method, which is considered valid for time domain analysis, are identified to be incomplete for frequency domain components due to its discontinuous application of loads.The implementation of contact theories in the wheel-rail interface implies a continuous load application, refining the obtained results but increasing computational cost.In this study, different scenarios are compared varying inner and boundary conditions of a model, with the aim of validate results and optimize resources by obtaining a parametrical influence study that will show how different assumptions and cases could condition ground-borne vibrational studies results.

Analysis of Factors Affecting the Accuracy of Moving Force Identification

In this study, the influence of the static component in the total force and the effective frequency bandwidth on the accuracy of force identification has been investigated. The acceleration and bending moment responses at different locations of a simply supported beam under different moving forces are numerically measured. The fast Fourier transformation is also introduced to analyze the frequency-domain component of the dynamic responses of the beam. Simulation results show that the dynamic characteristics of the vehicle, such as the frequency of dynamic vehicle load, have significant effect on the proportion of static component in the total vehicle load; the higher the proportion of static component in the total force, the higher the identification accuracy. In addition, the wider the effective frequency bandwidth, the higher the identification accuracy. The numerical results also show that both the proportion of static component in the total force and the effective frequency bandwidth vary with the type and location of measurement. To more accurately identify the moving force, it is necessary to analyze first the static component and frequency characteristics of the measured responses and to select appropriate type and location of measurement.

Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic

Non-intrusive load monitoring is a vital part of an overall load management scheme. One major disadvantage of existing non-intrusive load monitoring methods is the difficulty to accurately identify loads with similar electrical characteristics. To overcome the various switching probability of loads with similar characteristics in a specific time period, a new non-intrusive load monitoring method is proposed in this paper which will modify monitoring results based on load switching probability distribution curve. Firstly, according to the addition theorem of load working currents, the complex current is decomposed into the independently working current of each load. Secondly, based on the load working current, the initial identification of load is achieved with current frequency domain components, and then the load switching times in each hour is counted due to the initial identified results. Thirdly, a back propagation (BP) neural network is trained by the counted results, the switching probability distribution curve of an identified load is fitted with the BP neural network. Finally, the load operation pattern is profiled according to the switching probability distribution curve, the load operation pattern is used to modify identification result. The effectiveness of the method is verified by the measured data. This approach combines the operation pattern of load to modify the identification results, which improves the ability to identify loads with similar electrical characteristics.

Holographic Color Display by Controlling the Spectral Components in A Continuous White Light Source

Holographic color display is realized with white light as illumination by controlling the spectral components in frequency domain. Spectral components control method using the parameters of existing high resolution spatial light modulator (SLM) is demonstrated. An optical system including a white light source, an achromatic collimating lens, a high resolution spatial light modulator, a 4f optical filtering system and an achromatic lens as eyepiece is designed and developed for holographic color display. The effectiveness of the proposed technique is proved by experiments.

Exploiting Level 1 and Level 3 features of fingerprints for liveness detection

Fingerprint-based biometric systems are designed to authenticate and provide authorized access to the users in various security applications. However, such systems can be jeopardized by different kinds of presentation and spoof attacks by using different spoof materials. In this paper, an improved feature descriptor named as Quantized Fundamental Fingerprint Features (Q-FFF) is proposed for live fingerprint detection. The proposed feature descriptor integrates Level 1 and Level 3 features of fingerprints. Level 3 features of fingerprints such as ridge contours are extracted by the weighted linear combination (WLC) of magnitude of perceived stimuli and local contrast information in spatial domain. The novel magnitude of perceived stimuli with modified Weber law is proposed to compute the perceived stimuli of initial pixel intensities. The weights are explicitly determined by sigmoid function to interpolate the sparse information. Level 1 features such as ridge orientation is computed by retaining only the significant frequency components in frequency domain. Both ridge contours and ridge orientation are explicitly quantized into predefined intervals. The quantized feature sets are then integrated to populate a 2-D histogram. Performance evaluation of Q-FFF is determined on the three standard datasets of LivDet competition i.e. LivDet 2011, 2013 and 2015. Results comparison showed a reduction of Average Error Rates (AER) to 5.28, 2.0 and 4.62 on LivDet 2011, LivDet 2013 and LivDet 2015, respectively.

Study of Parasympathetic Activity in Newly Diagnosed Patients of Moderate Depression Using High Frequency of HRV

Introduction: - Heart Rate Variability (HRV) is a noninvasive technique to measure parasympathetic activities of heart. In this study, we measured the Parasympathetic activity in newly diagnosed patients of moderate depression by High Frequency (HF) parameter of HRV.Depression is the leading cause of mental disability and by the year 2020, depression is projected to reach 2nd place of the ranking of disease disability-adjusted life year (DALY). Objective: Parasympathetic activity in newly diagnosed patients of moderate depression. Method:- this study was planned on 40 newly diagnosed male patients of moderate depression according to ICD-10 (the international classification of diseases-10) taken from department of Psychiatry PGIMS Rohtak and 40 healthy males subjects of same age group. High Frequency (HF) parameter of HRV was recorded by using RMS polyrite machine. Result: - The HF (ms 2 ) component in frequency domain of HRV in patients of moderate depression in group II (166.55±41.48) compared to normal subject in group I (179.48±41.68) which reduced but not significantly. The HF expressed in normalized unit (HFnu) and group II (35.30±12.58) were compared to Group I (38.74±1276) and (Table 5) which reduced but not significantly. And HF expressed in power percent (HFpp) and values of group II (18.83±5.04) were compared to the Group I (20.58±5.02), the reduction was not significant. Conclusion:-We found that Parasympathetic activity was not reduced significantly in patients of moderate depression. So the parasympathetic activity is not a problem to cause cardiac disease in patients of moderate depression.

Electrodiagnostic (EDX) Predictors of Sensory Neuropathy in Primary Hypothyroidism and its Association with Age and Body Mass Index (BMI): A Cross Sectional Study

Introduction: - Heart Rate Variability (HRV) is a noninvasive technique to measure parasympathetic activities of heart. In this study, we measured the Parasympathetic activity in newly diagnosed patients of moderate depression by High Frequency (HF) parameter of HRV. Depression is the leading cause of mental disability and by the year 2020, depression is projected to reach 2nd place of the ranking of disease disability-adjusted life year (DALY). Objective: Parasympathetic activity in newly diagnosed patients of moderate depression. Method:- this study was planned on 40 newly diagnosed male patients of moderate depression according to ICD-10 (the international classification of diseases-10) taken from department of Psychiatry PGIMS Rohtak and 40 healthy males subjects of same age group. High Frequency (HF) parameter of HRV was recorded by using RMS polyrite machine. Result: - The HF (ms2) component in frequency domain of HRV in patients of moderate depression in group II (166.55±41.48) compared to normal subject in group I (179.48±41.68) which reduced but not significantly. The HF expressed in normalized unit (HFnu) and group II (35.30±12.58) were compared to Group I (38.74±1276) and which reduced but not significantly. And HF expressed in power percent (HFpp) and values of group II (18.83±5.04) were compared to the Group I (20.58±5.02), the reduction was not significant. Conclusion:-We found that Parasympathetic activity was not reduced significantly in patients of moderate depression. So the parasympathetic activity is not a problem to cause cardiac disease in patients of moderate depression.

The Rational Spectral Method Combined with the Laplace Transform for Solving the Robin Time-Fractional Equation

In this paper, the rational spectral method combined with the Laplace transform is proposed for solving Robin time-fractional partial differential equations. First, a time-fractional partial differential equation is transformed into an ordinary differential equation with frequency domain components by the Laplace transform. Then, the spatial derivatives are discretized by the rational spectral method, the linear equation with the parameter s is solved, and the approximation Ux,s is obtained. The approximate solution at any given time, which is the numerical inverse Laplace transform, is obtained by the modified Talbot algorithm. Numerical experiments are carried out to demonstrate the high accuracy and efficiency of our method.