Analytic Model of Microcantilevers as Low Frequency Generator

A FPGA-based two-channel low frequency signal generator and analysis system has been designed to analyze the occurrence and analysis of low frequency signal. It uses FPGA for low frequency signal producing control chip module and analysis module. Control signal is sent to the FPGA by keys, FPGA generates frequency range step adjustable low frequency signal. Through the adder, fast Fourier transform is used to extract frequency domain signal by analysis module FPGA, the oscilloscope measures frequency amplitude and other information, to achieve low frequency signal frequency domain analysis capabilities. The system whose encapsulation is compact has friendly interface and it is very easy to operate and demonstrate.

Generation of low-frequency signals in the ionosphere has direct applications for over-the-horizon radar and related processes. The mechanism proposed in this study uses a ground-based modulated rf signal to resonantly excite plasma oscillations in the ionosphere's F layer, creating low-frequency signals that propagate back to the earth's surface. The authors find that a 94-GHz signal, modulated at 9 MHz and operated at 1 MW, can generate a 9-MHz signal on the ground, 500 km from the interaction regime in the ionosphere, at intensities sufficient for detection. This points to practical mobile radar units and atmospheric monitoring.

This paper presents a reconstructive oscillator based sinusoidal signal generator which can produce both high and low frequency signals by switching the oscillator into different mode. In addition, analog and digital signals can be produced concurrently in both modes. Also, signal amplitude and oscillation frequency can be precisely controlled compared with pure analog signal generator. Except for a 1-bit D/A converter and analog filter, the circuits are entirely constructed by digital circuits. One can trade the digital hardware complexity to moderate the difficulties in designing the analog reconstructive filter. The generated high and low frequency signals are suitable to serve as the test stimuli in built-in self test methodology for A/D converters to extract the transmission and static parameters, respectively

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.

The response of the global ionosphere to solar flares is an important topic in the field of space weather. The global ionospheric response to solar flares is comprehensively analyzed from the perspectives of total electron content (TEC) and very low frequency (VLF) signals by using solar flare data on the eruption days of X-class flares from 2006 to 2019, including flare level, flare duration, geographical location, and local time. In addition, the relationship between X-ray flux and VLF phase variation is studied through correlation analysis. The concepts of disturbance intensity (DI) and disturbance angle are defined, and a DI evaluation model is established to help detect the difference in sensitivity to solar flares between TEC and VLF signals. Results show the following. (1) The higher the flare level and the longer the duration, the greater the disturbance to the ionosphere. Simultaneously, a phenomenon exists in which the disturbance caused by low-level and long-lasting flares is greater than that caused by high-level flares. (2) VLF phase variation and flare level exhibit a good correlation, and they are also closely related to geographical location and local time. The disturbance degree of a station facing the sun is more evident than that of a station facing away from the sun. The TEC disturbance of stations in the morning (local time) is more obvious than that in the afternoon, and disturbance increases along the direction of Earth’s rotation. (3) When DI is at the same level, the lowest flare level that causes TEC response is higher than the lowest flare level that causes VLF signal response. The disturbance angle of TEC is unevenly distributed within the interval [0°, 90°], and that of VLF signals is more than 85°. The sensitivity of VLF signals to flare response is considerably higher than that of TEC, and the difference between the two even stride across DI level.

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

Very low frequency (VLF) (2 to 20 Hz) and low frequency (LF) (20 to 200 Hz) physical noise models and previous measurements are discussed and compared to low frequency, high resolution noise measurements. These data show long-term persistent directional characteristics associated with distant shipping lanes and density patterns. Short-term averages show a temporally dynamic field composed of resolved distant shipping and uncorrelated background noise. Calibrated low frequency transmission loss and signal coherence length estimates are presented for down-slope to deep ocean and bottom-limited sound channels. The transmission results compare favorably with previous measurements and computations performed with range-dependent water column and sediment characteristics. These results emphasize the importance of the coherent contribution from coastal shipping to the mid-ocean noise field. Vertical directionality measurements by Anderson et. al. (1972) show a broad angular distribution of noise intensity near the horizontal at low frequencies and a peaked distribution about the horizontal at high frequencies. This broad angular distribution near the horizontal is consistent with sound propagating down-slope via a bottom reflectivity which favors lower frequencies. The frequency variation near the horizontal was found smooth and indicates that in addition to surface ships, environmental noise influences the vertical directionality.

Cochlear implant (CI) recipients with residual hearing show improved performance with the addition of low-frequency acoustic stimulation (electro-acoustic stimulation, EAS). The present study sought to determine whether a synthesized first formant (F1) signal provided benefit to speech recognition in simulated EAS hearing and to compare such benefit with that from other low-frequency signals. A further aim was to determine if F1 amplitude or frequency was more important in determining benefit and if F1 benefit varied with formant bandwidth. In two experiments, sentence recordings from a male speaker were processed via a simulation of a partial insertion CI, and presented to normal hearing listeners in combination with various low-frequency signals, including a tone tracking fundamental frequency (F0), low-pass filtered speech, and signals based on F1 estimation. A simulated EAS benefit was found with F1 signals, and was similar to the benefit from F0 or low-pass filtered speech. The benefit did not differ significantly with the narrowing or widening of the F1 bandwidth. The benefit from low-frequency envelope signals was significantly less than the benefit from any low-frequency signal containing fine frequency information. Results indicate that F1 provides a benefit in simulated EAS hearing but low frequency envelope information is less important than low frequency fine structure in determining such benefit.

A low frequency living beings electrotherapy instrument is designed which is based on the transcutaneous electrical nerve stimulation. This system uses ATmega32 (MCU) to control its output and display. The hardware part includes the keyboard, the LCD, the alarm circuit and output circuit .The other part is software, which includes keyboard module, LCD module, pulse creating module and so on. The human-computer interaction can be realized well by the keyboard and LCD module. The pulse creating module controls the frequency, the duty-ratio and time of treatment. Introduction In the area of treatment, physical therapy is a common method of treatment. It is wide application because of the course of treatment without pain, little side effects and significant effect on certain diseases. Investigation shows that the fixed frequency signal is long applied to the stimulation of the human body, the signal will weaken the effect of the human body treatment . Therefore, it is significant for the stimulation of the body that the output signal frequency is independent, which can be well adapted to overcome the ability of human tissue, make the patient obtain the optimal therapeutic effect. The output signal of traditional physical therapy instrument has a certain regularity, which can make the body produce the adaptability and reduce the effect. The core of living beings electrotherapy instrument is the output signal waveform generation module, which relies on Atmega32 to achieve. The PWM module of Atmega32 can control the shape, frequency and amplitude of the output signal, and the output waveform can be generated randomly, applicable symptoms. The system can well solve the shortage of traditional electrotherapy instrument system, such as the treatment of adaptability of output signal, polarization voltage smaller, patient comfort and simple operation etc.. Therefore, it has a certain practical value to research and development of living beings electrotherapy. Electrotherapy principle The human body is a good conductor of electricity, has certain conductivity. During the treatment, the pulse current is applied to the deep body tissues and stimulate tissue to produce a variety of regular and irregular shrinkage . Such action by the organization can reduce the pain sensation, promote parts of the organization metabolism, but also has anti-inflammatory, removing blood stasis, adjusting the endocrine and metabolic disorders, improving local tissue blood circulation, eliminating excess fat, dredging the channel and other effects [3] . The pulse signal frequency is lower than 1000Hz is defined as the low frequency signal in medicine. The use of low frequency electrical stimulation of tissue to recuperate the body way is referred to as low frequency electrotherapy. Low frequency electrical signals applied to human tissue, stimulates the human tissue, which can accelerate the blood flow of partial tissue and alleviate the pain. Treatment principle of the system is based on transcutaneous electrical nerve stimulation therapy .The low frequency electrical signal is inputted to the human body by the skin and plays a role in healing and alleviate pain, which is known as TENS. TENS is widely used clinically, which can promote the local blood circulation, alleviate the pain and promote the development of bones. International Conference on Materials, Environmental and Biological Engineering (MEBE 2015) © 2015. The authors Published by Atlantis Press 448 People know about more the pain mechanism along with the progress of electronic and medical knowledge and the improving of people living conditions. Firstly the meridian theory thinks that the disease occurrence and development are related the acupoints of main and collateral channels. Later, the research indicates that the main and collateral channels and acupoints have the characteristic of potential high and low impedance and the impedance can change when the electrical signal effects them, which can make the human body appear acid, hemp, pain, swelling and other physiological phenomena. Therefore, the meridian and points which can reflect the function of muscle tissue, organ or physical condition should be chosen, the potential of the body can quickly restore balance, the meridian smooth, and the body is healthy by imposing its corresponding pulse signal. The overall design of the system The structure diagram of the system is shown in figure 1. It mainly consists of two parts: based on the ATmega32 control circuit and PWM interface circuit. The core of the control circuit is ATmega32, which has abundant resources and general equipment interface and includes key module, LCD module, PWM output module, sampling module, alarm module. The PWM Interface circuit includes filter circuit and power amplifier circuit. Atmage32 ADC LCD interface Keyboard interface PWM output display module Keyboard module alarm module signal sampling output circuit electrode

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.

An interferometer inhibits a received low frequency acoustic signal that iselow the pass band of interest from appearing in the output. The interferometer has a conventional optical hydrophone in the signal leg to sense both the high and low frequencies of an acoustic signal. The reference leg has means for accepting a low frequency acoustic signal to modulate the coherent light path length while inhibiting the desired high frequency signal. On recombining the signals from both the signal and reference legs the low frequency signal appearing in both legs is canceled and only the high frequency signal appearing in the signal leg reaches the output. In an alternate embodiment the reference leg mandrel is placed internal to the sensor mandrel and provides a low frequency compensation chamber.

Non-unit transient based boundary protection for UHV transmission lines

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.

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.

This paper presents an algorithm specifically developed for filtering low frequency signals. The application is related to weed detection into aerial images where crop lines are detected as repetitive structures. Theoretical bases of this work are presented first. Then, two methods are compared to select low frequency signals and their limitations are described. A decomposition based on wavelet packet is used to combine advantages of both methods. This algorithm allows a high selectivity of low frequency signals with an interesting computation time. At last, a complete algorithm for weed/crop classification is explained and a few results are shown.