Extension of a Classic Theory of the Low Frequency Dielectric Dispersion of Colloidal Suspensions to the High Frequency Domain

2010 ◽  
Vol 114 (39) ◽  
pp. 12520-12527 ◽  
Author(s):  
Constantino Grosse
Keyword(s):  
Frequency Domain ◽  
High Frequency ◽  
Low Frequency ◽  
Colloidal Suspensions ◽  
Dielectric Dispersion ◽  
Classic Theory

scholarly journals An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Dongju Chen ◽  
Shuai Zhou ◽  
Lihua Dong ◽  
Jinwei Fan
Keyword(s):  
Machine Tool ◽  
Frequency Domain ◽  
Surface Topography ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Time Frequency ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method

This paper presents a new identification method to identify the main errors of the machine tool in time-frequency domain. The low- and high-frequency signals of the workpiece surface are decomposed based on the Daubechies wavelet transform. With power spectral density analysis, the main features of the high-frequency signal corresponding to the imbalance of the spindle system are extracted from the surface topography of the workpiece in the frequency domain. With the cross-correlation analysis method, the relationship between the guideway error of the machine tool and the low-frequency signal of the surface topography is calculated in the time domain.

Evaluation of Surface Vibrations in the Low and High Frequency Domain

1995 ◽  
Author(s):  
Peter Fischer ◽  
Helmut J. Pradlwarter ◽  
Gerhart I. Schuëller
Keyword(s):  
Frequency Domain ◽  
High Frequency ◽  
Flow Characteristics ◽  
Low Frequency ◽  
Structural Response ◽  
Short Wave ◽  
Fe Model ◽  
Special Cases ◽  
Wide Range ◽  
Surface Vibrations

Abstract The frequency domain of many problems in structural dynamics encompasses a wide range, covering nearly static behavior up to vibration flow characteristics similar to heat transfer. This work presents an uniform approach for low and high frequency vibration analysis, which is based on Finite Element modeling of the structure. Vibrations in the low frequency range are determined by an efficient superposition technique of complex modes, which accounts accurately for any linear damping effect. The modal method is extended to the high frequency domain by applying different levels of averaging to the response and eigenfrequencies and by the introduction of random properties of modeshapes. The high frequency domain is defined by the size of the Finite Elements, i.e. short wave lengths of high frequency modeshapes cannot be represented by the FE-model. The response computation of isolated structures is extended to substructures of complex systems by prescribing stochastic multi-support base excitation at the substructure boundaries. It may be noted, that the presented approach of stochastic high frequency dynamics contains, as special cases, the expressions of the structural response of Statistical Energy Analysis, Bolotin’s integral method and the results of Asymptotic Modal Analysis.

The Pattern of Sympathovagal Balance Explored in the Frequency Domain

Physiology ◽  
1999 ◽  
Vol 14 (3) ◽  
pp. 111-117 ◽  
Author(s):  
Alberto Malliani
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
High Frequency ◽  
Low Frequency ◽  
Sympathovagal Balance ◽  
Heart Period ◽  
Reciprocal Regulation ◽  
Physiological Conditions

In most physiological conditions, sympathetic and vagal activities modulating heart period undergo a reciprocal regulation, leading to the concept of sympathovagal balance. This pattern can be indirectly quantified by computing the spectral powers of the oscillatory components corresponding to respiratory acts (high frequency) and to vasomotor waves (low frequency) present in heart rate variability.

A Comparison of Methods for the Coupled Analysis of Floating Structures

2007 ◽  
Author(s):  
Ying Min Low ◽  
Robin S. Langley
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
High Frequency ◽  
Geometric Nonlinearity ◽  
Low Frequency ◽  
Alternative Methods ◽  
Coupled Analysis ◽  
Intermediate Water ◽  
Low Frequency Motion ◽  
Frequency Domain Approach

The dynamic analysis of a deepwater floating platform and the associated mooring/riser system should ideally be fully coupled to ensure a reliable response prediction. It is generally held that a time domain analysis is the only means of capturing the various coupling and nonlinear effects accurately. However, in recent work it has been found that for an ultra-deepwater floating system (2000m water depth), the highly efficient frequency domain approach can provide highly accurate response predictions. One reason for this is the accuracy of the drag linearization procedure over both first and second order motions, another reason is the minimal geometric nonlinearity displayed by the mooring lines in deepwater. In this paper, the aim is to develop an efficient analysis method for intermediate water depths, where both mooring/vessel coupling and geometric nonlinearity are of importance. It is found that the standard frequency domain approach is not so accurate for this case and two alternative methods are investigated. In the first, an enhanced frequency domain approach is adopted, in which line nonlinearities are linearized in a systematic way. In the second, a hybrid approach is adopted in which the low frequency motion is solved in the time domain while the high frequency motion is solved in the frequency domain; the two analyses are coupled by the fact that (i) the low frequency motion affects the mooring line geometry for the high frequency motion, and (ii) the high frequency motion affects the drag forces which damp the low frequency motion. The accuracy and efficiency of each of the methods are systematically compared.

scholarly journals Acute Effect of Kapalbhati Yoga on Cardiac Autonomic Control Using Heart Rate Variability Analysis in Healthy Male Individuals

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Rajeev Gupta
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
High Frequency ◽  
Low Frequency ◽  
Acute Effect ◽  
Sympathetic Activation ◽  
Frequency Range ◽  
Healthy Male ◽  
Band Power

Kapalbhati is well known for improving cardiovascular health. But there are some reports of heart attacks while practising kapalbhati. We hypothesize that ill-effect of kapalbhati could be because of autonomic dysfunction to heart. In the present study, we aim to understand the acute effect of kapalbhati yoga on heart rate dynamics using heart rate variability (HRV) analysis. Resting heart rate (HR) varies widely in different individuals and during various physiological stresses, particularly, exercise it can go up to three-fold. These changes in heart rate are known as heart rate variability (HRV). Variability in heart rate reflects the control of autonomic system on the heart and which can be determined during brief periods of electrocardiographic (ECG) monitoring. HRV measures the effect of any physical exercise on the heart rate using time- and frequency-domain methods. Frequency-domain method involves power spectral analyses of the beat-to-beat intervals (R-R intervals) variability data. When total power vs. frequency, fast fourier transform analysis of R-R intervals data is done, it shows three well-defined peaks/rhythms in every individual, which contain different physiological information. Thus, the total spectral power of R-R intervals data can be divided into three components or bands viz., the very low frequency (VLF) band, the low-frequency (LF) band and the high frequency (HF) band. VLF represent very long time-period physiological phenomenon like thermoregulation, circadian rhythms etc. and thus are not seen in short-term recordings like in this work. LF band power represents long period physiological rhythms in the frequency range of 0.05- 0.15 Hz and LF band power increases as a consequence of sympathetic activation. HF band represent physiological rhythms in the frequency range of 0.15-0.5 Hz and they are synchronous with the respiration rate, and arise due to the intrathoracic pressure changes and mechanical vibrations caused by the breathing activity. In this work, twenty healthy male volunteers were trained in kapalbhati yoga and their ECG waveforms (2 min.) were obtained while doing kapalbhati (breathing at 1 Hz frequency for 2 min.) and were compared with the baseline (just 2 min. before the start) and post-kapalbhati (immediately 2 min. after completing the practice) HRV data. Our results showed a significant decrease in the time-domain measures i.e., NN50, pNN50 and the mean heart rate interval during-kapalbhati when compared statistically to the respective before practice or “pre”-kapalbhati (p < 0.05, student’s paired t-test) values. Frequency-domain indices showed that during-kapalbhati there is a significant increase (~48%) in the LF band power which suggests sympathetic activation and a significant increase (~88%) in the low frequency to the high frequency power ratio (LF/HF ratio) which indicates sympathetic system predominance. A significant decrease (~63%) in the HF component was also noted during-kapalbhati as compared to the “pre-kapalbhati” values which shows decrease in parasympathetic tone. Thus, these results suggest that during-kapalbhati there is drastic increase of sympathetic tone whereas parasympathetic activity is reduced. We propose these changes in autonomic system control on heart are responsible for the myocardial ischemic attacks induced during kapalbhati in some individuals.

scholarly journals Reconstruction of MR Images using Sparse Signal Sequences in Frequency Domain

2020 ◽  
Vol 9 (3) ◽  
pp. 895-902
Keyword(s):  
Compressed Sensing ◽  
Frequency Domain ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Component ◽  
High Frequency Component ◽  
Sparse Signal ◽  
Reference Image ◽  
Signal Acquisition ◽  
Coarse Scale

A new strategy for signal acquisition has emerged called Compressed Sensing (CS). The compressed sensing has gained attention in the filed of computer science, electrical engineering and mathematics. The Compressed Sensing is a mathematical approach of reconstructing a signal that is acquired from the dimensionally reduced data coefficients/less number of samples i.e. less than the Niquist rate. The data coefficients are high frequency component and low frequency component. The high frequency components are due to the rapid changes in the images (edges) and low frequency correspond provide the coarse scale approximation of the image, i.e. fine continuos surface. The idea is to retain only coarse scale approximation of the image i.e. the significant components that constitute the compressed signal. This compressed signal is the sparse signal which is so helpful during medical scenarios. During the Medical Resonance Imaging (MRI) scans, the patient undergoes many kinds difficulties like uncomfortness, patients are afraid of the scanning devices, h/she cannot be stable or changing his body positions slightly. Due to all these reasons, there can be a chance of acquiring only the less number of samples during the process of MRI scan. Even though the numbers of samples are less than the Nyquist rate, the reconstruction is possible by using the compressed sensing technique. The work has been carried out in the frequency domain to achieve the sparsity. The comparative study is done on percentage of different levels of sparsity of the signal. This can be verified by using Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Structural similarity (SSIM) methods which are calculated between the reference image and the reconstructed image. The finite dimensional signal has a sparsity and compressible representation. This sparsified data can be recovered from small set of linear, non-adaptive measurements. The implementation is done by using MATLAB.

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