Interferometric hydrophone reference leg low frequency compensation

Wavelet transform can decompose the input signal into high and low frequency signal. The low frequency signal includes main performances of the signal. However, the high frequency signal contains many noises. Wavelet de-noising can be realized by reconstructing the rest signal after the high frequency signal is flatted. According to the effect of noise on hydrological time series, this paper studies hydrological time series including measured noise with wavelet de-noising method. Firstly, wavelet de-noising is done with MATLAB, and then trend component, periodic component and random component are extracted from the hydrological time series respectively. At last, the time series model based on wavelet de-noising is established by superposition to simulate and forecast the groundwater dynamic variation of Chahayang irrigation district. The forecasting precision of this model is good. So it is reasonable to research the groundwater dynamic variation of Chahayang irrigation district through the model. At the same time, this paper can provide scientific basis for sustainable utilization of groundwater resources, making reasonable irrigation system and the development of paddy field in Chahayang irrigation district.

Design, development, fabrication and evaluation of the dynamics of a graphene based underwater acoustic vector sensor: A simulation and experimental study

In the attack of the encryption algorithm ARCFOUR(RC4), new method of machine learning based on Wavelet Transform(WT) and Principal Component Analysis(PCA)is proposed. WT is used to reconstruct the new signals to extract information from the original signals effectively. Components in low and high frequency and reconstructed signals are made by WT. In order to analyze the impact of the signals on the success rate of prediction, four kinds of signals are adapted respectively for dimensionality reduction, which contains initial signals, reconstructed signals, and the signals in low and high frequency. By the classification of Support Vector Machine (SVM), the results show that the effect of the reconstructed signals is the best one. The reconstructed signals reduce the noise influence. In the range of 500 dimensions, the classification effect of the reconstructed signals is obviously better than others. As the dimension increases, the effect becomes small. The effect of signals in low frequency is more effective than that of the original in most of dimensions. The classification success rate is still high with fewer dimensions. In the four kinds of signals, the effect of the signals in high frequency is the worst. The results show that WT combined with PCA is a good method to handle with classification.

A new wavelet domain algorithm for speech enhancement is presented. The main idea of the proposed algorithm can be described as follows: noisy speech is first split into low frequency band signal and high frequency band signal via one scale of DWT. Then, three scales of DWT and three scales of WPD are applied to the low frequency signal and the high frequency signal respectively. The denoised speech is reconstructed finally. To prevent the degradation of unvoiced sounds during noise reduction, this algorithm divides noisy speech samples into unvoiced segments and voiced segments and then applies multi-threshold to them. Compared with DWT and WPD, the new algorithm can preserve more unvoiced sounds and provide better performance as shown by computer simulation results

To improve the accuracy of forecasting stock prices, a new stock index prediction approach is proposed, which based on wavelet analysis combines the autoregressive integrated moving average (ARIMA) and artificial neural network. The non-stationary share price index series are decomposed and reconstructed into one low frequency signal and several high frequency signals by wavelet; the approximate stationary low frequency signal is predicted using ARIMA forecasting model, and the high frequency signals are forecasted using Elman neural network models; the prediction result of each layer are mixed by the radial basis function(RBF)neural network and the result is the final prediction. Examples show that the prediction of the combined forecasting model is precise.

A neuron carries out its functions in networks receiving contacts from roughly 104 presynaptic neurons. Such a dense connectivity profile for a single neuron may give rise to enormous complex neuronal topologies, which might be very difficult to understand the underlying mechanisms of neurological functions and diseases. Extensive experimental data from neuroanatomical studies have uncovered that neural networks include some recurring topologies of microcircuits, known as network motifs which serve as characteristic building blocks of complex networks [1,2]. Therefore, it is widely assumed that a clear explanation on dynamical and functional features of these network motifs can be considered as the first step to understand large networks. Following this motivation, in this study, we investigate the Vibrational Resonance (VR) phenomenon in a triple-neuron feed-forward-loop (FFL) which is one of the most significant brain network motifs, shown in Figure ​Figure1.1. VR is a physical phenomenon found in nonlinear systems where a weak signal can be detected and processed by the system with the assistance of another high frequency signal. It is very similar to the well-known stochastic resonance phenomenon, where the role of noise is replaced in VR by a high frequency signal. In recent years, there exists a growing interest in applications of VR to neuroscience because two frequency signals are pervasive in neural systems, i.e. bursting neurons exhibit two widely different time scales, simultaneous arrival of vocal signals having distinct frequencies to auditory neurons. Due to the significance of its potential in neural signal processing, the VR phenomenon has already been studied with computational models of neurons and their networks [3,4]. However, a population of excitatory and inhibitory neurons has not been considered, yet. Here, we study the VR dynamics depending on whether the neurons in the FFL motifs are excitatory or inhibitory considering eight possible structural configurations of the considered microcircuit (Table ​(Table1).1). Such an approach provides us to test the functional influences of structural configuration on VR dynamics in neural populations. Figure 1 Schematic illustration of the considered feed-forward-loop motif. LF and HF refers to low and high frequency signals, respectively. Neuron 1 and 3 is considered as input and output neurons, respectively. Table 1 Eight possible FFL types. We found that the weak signal transmission in the network via the VR mechanism is possible depending on both the coupling strength between neurons and the network structure. For instance, when a weak coupling strength is present between connected neurons, weak signal injected to the input neuron can be transmitted to the output with only T8-FFL motif. In this case, signal transmission is impossible for other types of network motifs. For intermediate coupling strengths, signal transmission performance is high for all motifs and motif type does not change very much VR dynamics. Finally, for strong coupling strengths, the best VR performance is obtained with T1-FFL motif where all the neurons in the network are excitatory. We also clarify the mechanisms that underlies the differences in performance of network motifs.

Non-unit transient based boundary protection for UHV transmission lines

The optimum vibrational resonance in a time-delay bistable system driven by bihiarmonic signals is discussed in this paper. The theoretically expression for the response amplitude gain of low frequency signal in the time-delay bistable system is deduced, and the effects of time delay parameter on the optimum vibrational resonance peak and the required amplitude of high frequency signal are investigated. It is shown that the optimum vibrational resonance can be achieved by adjusting the high frequency signal amplitude and time delay parameter jointly. Meanwhile, the optimum vibrational resonance appeared periodically with time delay parameter and the period is equal to the period of low-frequency signal. The amplitude of high-frequency signal required for the optimum vibrational resonance can be fixed or varied with different time delay parameter depending on the ratio of the frequencies between biharmonic signals.

The prevalence of late potentials after myocardial infarction depends on the site of the infarction. This may be caused by the different activation onsets of the anterior and inferior myocardial segments. Therefore, in anterior infarcts the high frequency signals may be concealed within the QRS whereas in the inferior infarcts they last beyond the end of the QRS. We compared the timing and the spatial patterns of high frequency intra-QRS signals (IQSs) in the different infarction sites. We investigated 14 patients with anterior infarcts, 17 patients with inferior infarcts, and 10 healthy subjects. 31-lead magnetocardiograms were recorded in left precordial position and averaged. The QRS signals were smoothed with a Savitzky-Golay filter. The smoothed QRS signals were subtracted from the measured ones. The difference of the signals (frequency band of about 60-200 Hz) representing the high frequency components was quantified. The percentage of the high frequency signals was calculated for the entire QRS, for the first and for the second half, respectively. We found that in patients with anterior infarcts the high frequency components predominantly appeared in the first half of the QRS whereas in inferior infarcts these components predominantly appeared in the second half of the QRS. The different infarction sites were associated with different spatial patterns of the high frequency signals on the body surface. In healthy subjects there was not such a preferential association of time intervals and high frequency signals. Late potentials are the special case of high frequency signals appearing in the terminal QRS. It is the general property of the myocardium to generate high frequency signals associated with the depolarization of infarcted tissue. The timing of such signals and the spatial distribution patterns on the body surface may help to identify the location of the sources.

Background/Aims: Overactive bladder is a frequently encountered urological disease that has debilitating effects on quality of life. Some hypotheses have been brought forward in order to explain the main pathophysiology underlying this clinical phenomenon, however the exact mechanism remains unclear and the relevant reports are limited in number, confined to female patients and have conflicting results. Therefore, we aimed to examine one of these hypotheses -autonomic nervous system dysfunction- in male patients with overactive bladder. Methods: We enrolled 41 male patients with overactive bladder and 43 healthy males in this study. Their electrocardiographic signals are recorded in the prevoiding and postvoiding periods and then converted to heart rate variability parameters which may reveal autonomic nervous system dysfunction. Results: Standard deviation of normal-to-normal interval, square root of the mean squared differences of successive normal-to-normal intervals, low frequency signal and low frequency/high frequency ratio were found to be significantly lower while high frequency signal was significantly higher in the patient group compared to healthy controls (p<0.05). Conclusions: Heart rate variability was attenuated in male patients with overactive bladder and parasympathetic predominance was observed during both prevoiding and postvoiding periods, contrary to some of the previous reports revealing sympathetic predominance in female patients.

1. The upstroke of the action potential was used as a signal of variable frequency in order to measure the apparent membrane capacity of cardiac Purkyne fibres.2. The upstroke in Na-Tyrode can be regarded as a high frequency signal. Action potentials in Na-free, Sr-Tyrode are characterized by a very small rate of depolarization (5 V/sec) and conduction velocity (15 cm/sec) and were used as a low frequency signal.3. On changing from Na- to Sr-Tyrode the apparent membrane capacity increased by a factor of 3.77 and approached the value estimated by the square-current technique.4. Cable properties (space and time constants, internal core and membrane resistance) were found to be constant when changing from Na- to Sr-Tyrode.5. The significance of the frequency dependent character of the membrane capacity is discussed in relation to propagation and form of the action potential.

Interannual to multidecadal climate forcings on groundwater resources of the U.S. West Coast

We present results of a new imaging technique capable of detecting echoes from microbubbles and eliminating those emanating from non-oscillating structures (tissue). The method is based on mixing a low frequency signal and a high frequency signal. The LF signal modulates the microbubble size by making them vibrate between compression and rarefaction phases. During these two phases, an HF signal is transmitted to image the microbubbles, sensing the same bubble, but at two different stages, small and large. Simulations were carried out using the modified Herring equation using 1 cycle at 0.5 MHz and 7 cycles at 3.5 MHz. The results show that the bubbles respond differently compared to single frequency excitation. Optical observations of contrast microbubbles were carried out using a Brandaris high-speed camera. Two transducers were used to transmit 0.5 MHz and 3.5 MHz signals. The optical observations were carried out at a frame rate of 14 MHz and 128 successive frames were recorded. The oscillation of a 4 /spl mu/m diameter Sonovue microbubble was observed. The optical results show that the bubble response at 3.5 MHz depends on the phase of the LF signal. A larger response is obtained when the bubble is compressed compared to the response at the expansion phase. The decorrelation between these signals is significantly high enough to be used as a parameter to detect contrast microbubbles and discriminate them from tissues. These preliminary results demonstrate the feasibility of this linear approach in improving the contrast detection.

We have studied the stochastic behavior of Fitzhugh–Nagumo neuron-like model (FN) induced by subthreshold external stimuli. Our analysis based on three standard measures: the power spectrum, interspike interval distribution (ISI) and autocorrelation function shows that it is possible to define a characteristic time scale which can be identified in the response of the system for a wide range of frequencies. In contrast to previous studies we have focused our attention on high frequency signals which could be of interest for real systems such as nervous fibers in the auditory system. We report behaviors which resemble those of classical deterministic oscillators but never the stochastic resonance phenomenon typical of low frequency signals.

Analysis of geophone recordings from an OBS survey offshore Norway shows that excellent long-offset signals may be measured for frequencies below 2 Hz, and as low as 0.25 Hz. This observation is in contrast to the hydrophone recordings that do not show such excellent long-offset low frequency signals. We think that there are at least two factors that cause this: First, the fact that the receiver ghost spectrum is non-zero for the geophone component while the hydrophone ghost spectrum approaches zero for low frequencies, respectively. Second, the hydrophone component is more affected by noise created by (long wave length) ocean water waves compared to the vertical geophone component. Furthermore, a critical factor is the intrinsic low frequency response of the geophone and the hydrophone, which is a critical factor that controls what frequencies we may record. In our case, we find that the low frequency geophone response is sufficient to measure signals that are above the background noise level. We think that this opens a new possibility for low frequency seismic acquisition: Use seabed geophones that are specifically designed to measure low frequency (0.1-5 Hz) signals. If successful, this might open new possibilities for imaging below complex salt structures, below basalt and for full waveform inversion.