scholarly journals On the Potential of Least Squares Response Method for the Calibration of Superconducting Gravimeters

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Mahmoud Abd El-Gelil ◽  
Spiros Pagiatakis ◽  
Ahmed El-Rabbany
Keyword(s):  
Least Squares ◽  
Time Domain ◽  
Scale Factor ◽  
Linear Regression Method ◽  
Superconducting Gravimeters ◽  
Frequency Domains ◽  
The Time Domain ◽  
Response Method ◽  
Filter Response ◽  
Time And Frequency Domains

One of the most important operating procedures after the installation of a superconducting gravimeter (SG) is its calibration. The calibration process can identify and evaluate possible time variability in the scale factor and in the hardware anti-aliasing filter response. The SG installed in Cantley, Canada is calibrated using two absolute gravimeters and the data are analysed in the time and frequency domains to estimate the SG scale factor. In the time domain, we use the weighted linear regression method whereas in the frequency domain we use the least squares response method. Rigorous statistical procedures are applied to define data disturbances, outliers, and realistic data noise levels. Using data from JILA-2 and FG5-236 separately, the scale factor is estimated in the time and frequency domains as−78.374±0.012 μGal/V and−78.403±0.075 μGal/V, respectively. The relative accuracy in the time domain is 0.015%. We cannot identify any significant periodicity in the scale factor. The hardware anti-aliasing filter response is tested by injecting known waves into the control electronics of the system. Results show that the anti-aliasing filter response is stable and conforms to the global geodynamics project standards.

Effect of Time and Frequency Manipulation on Syllable Perception in Developmental Dyslexics

1997 ◽  
Vol 40 (4) ◽  
pp. 912-924 ◽  
Author(s):  
Ken I. McAnally ◽  
Peter C. Hansen ◽  
Piers L. Cornelissen ◽  
John F. Stein
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Developmental Dyslexia ◽  
Stop Consonant ◽  
Stop Consonants ◽  
Systematic Improvement ◽  
Frequency Domains ◽  
The Time Domain ◽  
Time And Frequency Domains

Many people with developmental dyslexia have difficulty perceiving stop consonant contrasts as effectively as other people and it has been suggested that this may be due to perceptual limitations of a temporal nature. Accordingly, we predicted that perception of such stimuli by listeners with dyslexia might be improved by stretching them in time—equivalent to speaking slowly. Conversely, their perception of the same stimuli ought to be made even worse by compressing them in time—equivalent to speaking quickly. We tested 15 children with dyslexia on their ability to identify correctly consonant-vowel-consonant (CVC) stimuli that had been stretched or compressed in the time domain. We also tested their perception of the same CVC stimuli after the formant transitions had been stretched or compressed in the frequency domain. Contrary to our predictions, we failed to find any systematic improvement in their performance with either manipulation. We conclude that simple manipulations in the time and frequency domains are unlikely to benefit the ability of people with dyslexia to discriminate between CVCs containing stop consonants.

scholarly journals Asymptomatic Severe Vagal and Sympathetic Cardiac Denervation in Holmes-Adie’s Syndrome

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
B. Estañol ◽  
R. C. Callejas-Rojas ◽  
S. Cortés ◽  
R. Martínez-Memije ◽  
O. Infante-Vázquez ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Domain ◽  
Blood Pressure Variability ◽  
Standing Position ◽  
Cardiac Denervation ◽  
Frequency Domains ◽  
Systolic Blood Pressure Variability ◽  
Adie's Syndrome ◽  
The Time Domain ◽  
Time And Frequency Domains

A 40-year-old woman was found to have bilateral Adie’s pupils and generalized muscle stretch areflexia. She did not have orthostatic hypotension but, in an ECG strip in the office, she appeared to have an almost fixed heart rate. We thus studied the heart rate variability (HRV) and the systolic blood pressure variability (SBPV) in supine and standing position and also during rhythmic breathing. We found a decreased HRV in the time domain with very low standard deviation in supine and standing position and during rhythmic breathing. HRV in the frequency domain was low with a decrease in the absolute power of HF and LF and a decrease in the sympathovagal balance in supine and standing positions. SBPV in the time and frequency domains was found to be normal. This patient with Holmes-Adie syndrome had an asymptomatic severe loss of HRV and a preserved SBPV. The global decrease in the HRV in the time and frequency domains indicated that she had both vagal and sympathetic cardiac denervation, whereas the preserved SBPV suggested normal innervation of the blood vessels.

scholarly journals A New Method for Time Series Filtering near Endpoints

2008 ◽  
Vol 25 (4) ◽  
pp. 534-546 ◽  
Author(s):  
Anthony Arguez ◽  
Peng Yu ◽  
James J. O’Brien
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Time Domain ◽  
Southern Oscillation ◽  
Fundamental Property ◽  
Great Promise ◽  
Constraint Forces ◽  
Variance Method ◽  
Equal Variance ◽  
The Time Domain

Abstract Time series filtering (e.g., smoothing) can be done in the spectral domain without loss of endpoints. However, filtering is commonly performed in the time domain using convolutions, resulting in lost points near the series endpoints. Multiple incarnations of a least squares minimization approach are developed that retain the endpoint intervals that are normally discarded due to filtering with convolutions in the time domain. The techniques minimize the errors between the predetermined frequency response function (FRF)—a fundamental property of all filters—of interior points with FRFs that are to be determined for each position in the endpoint zone. The least squares techniques are differentiated by their constraints: 1) unconstrained, 2) equal-mean constraint, and 3) an equal-variance constraint. The equal-mean constraint forces the new weights to sum up to the same value as the predetermined weights. The equal-variance constraint forces the new weights to be such that, after convolved with the input values, the expected time series variance is preserved. The three least squares methods are each tested under three separate filtering scenarios [involving Arctic Oscillation (AO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO) time series] and compared to each other as well as to the spectral filtering method—the standard of comparison. The results indicate that all four methods (including the spectral method) possess skill at determining suitable endpoints estimates. However, both the unconstrained and equal-mean schemes exhibit bias toward zero near the terminal ends due to problems with appropriating variance. The equal-variance method does not show evidence of this attribute and was never the worst performer. The equal-variance method showed great promise in the ENSO project involving a 5-month running mean filter, and performed at least on par with the other realistic methods for almost all time series positions in all three filtering scenarios.

A Frequency-Domain INS/GPS Dynamic Response Method for Bridging GPS Outages

2010 ◽  
Vol 63 (4) ◽  
pp. 627-643 ◽  
Author(s):  
Mohammed El-Diasty ◽  
Spiros Pagiatakis
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Dynamic Response ◽  
Dynamic System ◽  
Least Squares ◽  
Frequency Domain ◽  
Impulse Response ◽  
Time Domain ◽  
Response Model ◽  
Response Method

We develop a new frequency-domain dynamic response method to model integrated Inertial Navigation System (INS) and Global Positioning System (GPS) architectures and provide an accurate impulse-response-based INS-only navigation solution when GPS signals are denied (GPS outages). The input to such a dynamic system is the INS-only solution and the output is the INS/GPS integration solution; both are used to derive the transfer function of the dynamic system using Least Squares Frequency Transform (LSFT). The discrete Inverse Least Squares Frequency Transform (ILSFT) of the transfer function is applied to estimate the impulse response of the INS/GPS system in the time domain. It is shown that the long-term motion dynamics of a DQI-100 IMU/Trimble BD950 integrated system are recovered by 72%, 42%, 75%, and 40% for north and east velocities, and north and east positions respectively, when compared with the INS-only solution (prediction mode of the INS/GPS filter). A comparison between our impulse response model and the current state-of-the-art time-domain feed-forward neural network shows that the proposed frequency-dependent INS/GPS response model is superior to the neural network model by about 26% for 2D velocities and positions during GPS outages.

A CFD APPROACH TO THE COMPUTATION OF THE ACOUSTIC RESPONSE OF EXHAUST MUFFLERS

2005 ◽  
Vol 13 (02) ◽  
pp. 301-316 ◽  
Author(s):  
A. BROATCH ◽  
X. MARGOT ◽  
A. GIL ◽  
F. D. DENIA
Keyword(s):  
Numerical Solution ◽  
Time Domain ◽  
Cfd Simulation ◽  
Transmission Loss ◽  
Three Dimensional ◽  
Alternative Procedure ◽  
Acoustic Response ◽  
Frequency Domains ◽  
The Time Domain ◽  
Numerical Approaches

The study of the three-dimensional acoustic field inside an exhaust muffler is usually performed through the numerical solution of the linearized equations. In this paper, an alternative procedure is proposed, in which the full equations are solved in the time domain. The procedure is based on the CFD simulation of an impulsive test, so that the transmission loss may be computed and compared with measurements and other numerical approaches. Also, the details of the flow inside the muffler may be studied, both in the time and the frequency domains. The results obtained compare favorably with a conventional FEM calculation, mostly in the ability of the procedure to account for dissipative processes inside the muffler.

scholarly journals Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

2021 ◽  
pp. 135481662110584
Author(s):  
Ying Wang ◽  
Hongwei Zhang ◽  
Wang Gao ◽  
Cai Yang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Spillover Effects ◽  
Short Term ◽  
Time Frequency ◽  
Frequency Domains ◽  
Media Hype ◽  
The Time Domain ◽  
The Impact

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

scholarly journals Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

2019 ◽  
Vol 9 (23) ◽  
pp. 4990 ◽  
Author(s):  
Jusas ◽  
Samuvel
Keyword(s):  
Feature Extraction ◽  
Time Domain ◽  
Extraction Process ◽  
Computer Interface ◽  
Eeg Signals ◽  
Frequency Bands ◽  
Fisher Ratio ◽  
Frequency Domains ◽  
Subject Specific ◽  
The Time Domain

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R2, Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher′s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher′s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods.

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