Texture Feature Extraction Method for Ground Nephogram Based on Contourlet and the Power Spectrum Analysis Algorithm

It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection.In this paper,a new method is presented to capture the contour edge,texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm.More abundant texture information is extracted.Cloud images can be obtained a multiscale and multidirection decomposition.The coefficient matrix from Contourlet transform of ground nephogram is calculated.The energy,mean and variance characteristics calculated from coefficient matrix are composed of the feature information.The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis.Then Support Vector Machines(SVM)classifier is used to classify according to the frequency information of the trend graph of data series.It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds.

A Combined Numerical-Experimental Study on the Noise Power Spectrum Produced by an Electromagnetic Sensor for Slurry Flow

The signals generated by electromagnetic flow sensors used for slurry fluids are often affected by noise interference produced by interaction with the slurry itself.In this study,the power spectrum characteristics of the signal are studied,and an attempt is made to determine the relationship between the characteristics of the related noise and the velocity and concentration of the slurry fluid.Dedicated experiments are conducted and the related power spectrum curve is obtained processing the signal measured by the sensor with Matlab.Numerical simulations are also carried out in the frame of an Eulerian approach in order get additional insights into the considered problem through comparison with the experimental results.The following conclusions are drawn:(1)The intensity of noise is directly proportional to the number of solid particles colliding with the electrode of the electromagnetic flow sensor per unit time,and to the square of the average velocity of the flow layer near the pipe wall.(2)With an increase in the slurry noise intensity,the power spectrum curve shifts upward in the logarithmic coordinate system(and vice versa).

AUTOREGRESSIVE MODEL AND POWER SPECTRUM CHARATERISTICS OF CURRENT SIGNAL IN HIGH FREQUENCY GROUP PULSE MICRO-ELECTROCHEMICAL MACHINING

在高频率组脉搏的极间差距尺寸的鉴定(HGPECM ) 微电气化学的用机器制造主要被讨论。汽车回归(AR ) 越过阴极和阳极流动的组脉搏电流的模型与不同处理在不同状况下面被创造参数和极间差距尺寸。基于的在电流表明的 AR 模型显示 AR 模型的顺序关于不同处理条件和极间差距尺寸显然是不同的；而且，它关于动态系统的稳定性是不同的，即格林动态系统的函数的白噪音反应是多样的。另外，力量光谱方法与不同极间差距尺寸关于当前的信号在动态时间系列的分析被使用，结果证明那在那里存在一座最强壮的力量光谱山峰，典型力量光谱(每秒字符数)，到水流，与不同极间差距有关的信号在 0 应用软件公牛的范围缩放 5 kHz.Therefore ，当前的信号的每秒字符数能实现极间差距的鉴定。

Earthquake accelerogram simulation with statistical law of evolutionary power spectrum

By using the technique for evolutionary power spectrum proposed by Nakayama and with reference to the Kameda formula, an evolutionary spectrum prediction model for given earthquake magnitude and distance is established based on the 80 near-source acceleration records at rock surface with large magnitude from the ground motion database of western U.S.. Then a new iteration method is developed for generation of random accelerograms non-stationary both in amplitude and frequency which are compatible with target evolutionary spectrum. The phase spectra of those simulated accelerograms are also non-stationary in time and frequency domains since the interaction between amplitude and phase angle has been considered during the generation. Furthermore, the sign of the phase spectrum increment is identified to accelerate the iteration. With the proposed statistical model for predicting evolutionary power spectra and the new method for generating compatible time history, the artificial random earthquake accelerograms non-stationary both in amplitude and frequency for certain magnitude and distance can be provided.

Universal form of the power spectrum of the aero-optical aberration caused by the supersonic turbulent boundary layer

Based on the measurement of one-dimensional(1D) optical path difference(OPD) of the supersonic turbulent boundary layer, an analytical form for the power spectrum of the two-dimensional(2D) OPD is obtained with its structure function and under the locally homogeneous isotropic assumption. The universality of this spectrum is argued, and its validity is checked by the comparison with experimental result. The potential applications of this model in theoretical and numerical studies are emphasized. Another contribution of this work is around the application of correlation function to analyzing the statistics of OPD. Based on our results and other results published elsewhere, we show that the OPD is often not stationary,and one should be cautious about using this tool.

Research and Implementation of Text Similarity System Based on Power Spectrum Analysis

The paper proposed the research and implement of text similarity system based on power spectrum analysis. It is not difficult to imagine that the signals of brain are closely linked with writing process. So we build text modeling and set pulse signal function to get the power spectrum of the text. The specific detail is getting power spectrum from economic field to build spectral library, and then using the method of power spectrum matching algorithm to judge whether the test text belonged to the economic field. The method made text similarity system finish the function of text intelligent classification efficiently and accurately.

The power spectrum and correlation of flow noise for an axisymmetric body in water

Understanding the physical features of the flow noise for an axisymmetric body is important for improving the performance of a sonar mounted on an underwater platform. Analytical calculation and numerical analysis of the physical features of the flow noise for an axisymmetric body are presented and a simulation scheme for the noise correlation on the hydrophones is given. It is shown that the numerical values of the flow noise coincide well with the analytical values. The main physical features of flow noise are obtained. The flow noises of two different models are compared and a model with a rather optimal fore-body shape is given. The flow noise in horizontal symmetry profile of the axisymmetric body is non-uniform, but it is omni-directional and has little difference in the cross section of the body. The loss of noise diffraction has a great effect on the flow noise from boundary layer transition. Meanwhile, based on the simulation, the noise power level increases with velocity to approximately the fifth power at high frequencies, which is consistent with the experiment data reported in the literature. Furthermore, the flow noise received by the acoustic array has lower correlation at a designed central frequency, which is important for sonar system design.

Effect of focused ultrasound stimulation at different ultrasonic power levels on the local field potential power spectrum

Local field potential(LFP) signals of the rat hippocampus were recorded under noninvasive focused ultrasound stimulation(FUS) with different ultrasonic powers. The LFP mean absolute power was calculated with the Welch algorithm at the delta, theta, alpha, beta, and gamma frequency bands. The experimental results demonstrate that the LFP mean absolute power at different frequency bands increases as the ultrasound power increases.

BP-Neural-Network-Based Tool Wear Monitoring by Using Wav elet Decomposition of the Power Spectrum

In a drilling process, the power spectr um of the drilling force is related to the tool wear and is widely applied in the monitoring of tool wear. But the feature extraction and identification of the po wer spectrum have always been an unresolved difficult problem. This paper solves it through decomposition of the power spectrum in multilayers using wavelet tra nsform and extraction of the low frequency decomposition coefficient as the enve lope information of the power spectrum. Intelligent identification of the tool w ear status is achieved in the drilling process through fusing the wavelet decomp osition coefficient of the power spectrum by using a BP(Back Propagation) neural network. The experimental results show that the features of the power spectrum can be extracted efficiently through this method, and the trained neural network s show high identification precision and the ability of extension.

Fourier Power Spectrum Analysis of Exons for the Period-3 Behavior

The period-3 behaviors of 105 exons from 20 genes in human were studied by Fourier power spectrum. The results indicated that not all exons show the period-3 behavior. The exons were adjusted in order to make them accord with the order of the protein translated, and we found that the period-3 character is relation to the length of exons and the bases distribution in the three codon position. Furthermore, as long as the exons with period-3 behavior accord with the order of protein translated, they would exhibit the synonymous codons usage preference, and the codons with g/c at the third position are used in higher frequency. The results are significant to the gene prediction and the research on the introns.

Improved significance testing of wavelet power spectrum near data boundaries as applied to polar research

When one applies the wavelet transform to analyze finite-length time series, discontinuities at the data boundaries will distort its wavelet power spectrum in some regions which are defined as a wavelength-dependent cone of influence (COI). In the COI, significance tests are unreliable. At the same time, as many time series are short and noisy, the COI is a serious limitation in wavelet analysis of time series. In this paper, we will give a method to reduce boundary effects and discover significant frequencies in the COI. After that, we will apply our method to analyze Greenland winter temperature and Baltic sea ice. The new method makes use of line removal and odd extension of the time series. This causes the derivative of the series to be continuous (unlike the case for other padding methods). This will give the most reasonable padding methodology if the time series being analyzed has red noise characteristics.

Blind Image Quality Assessment Based on Wavelet Power Spectrum in Perceptual Domain

Blind image quality assessment(BIQA) can assess the perceptual quality of a distorted image without a prior knowledge of its reference image or distortion type. In this paper, a novel BIQA model is developed in wavelet domain. Considering the multi-resolution and band-passing characteristics of discrete wavelet transform(DWT), an improvement over the power spectrum is put forward, i.e., dubbed wavelet power spectrum(WPS)estimation. Then, the concept of directional WPS is applied to simplify the calculation. Moreover, a rotationally symmetric modulation transfer function(MTF) of human visual system(HVS) is integrated as a filter, which makes the metric to be consistent with the human vision perception and more discriminative. Experiments are conducted on the LIVE databases and three other databases, and the results show that the proposed metric is highly correlated with subjective evaluations and it competes well with other state-of-the-art metrics in terms of effectiveness and robustness.

Measurement Quantization Describes History of Universe—Quantum Inflation, Transition to Expansion, CMB Power Spectrum

Developing a comprehensive model of the early universe that describes events and conditions prior to recombination has proved difficult. Using a new approach, we express Heisenberg’s uncertainty principle in terms of measures and counts of those measures to resolve an expression consisting entirely of counts. The description allows us to resolve explicit values for discrete measures. With these values, we present new expressions describing the earliest epoch and the transition event that initiates expansion. We determine the quantity, age, density, and temperature of the cosmic microwave background (CMB). Moreover, we approach the CMB power spectrum anew, describing each mass/energy distribution, its physical significance, its peak temperature, and the effects of relativity. We do not engage in fitting or modification of the existing laws of physics. The approach is classical and correlates both quantum and cosmological phenomena with descriptive expressions that are measurable, verifiable, and falsifiable.

A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

Spectrum sensing is an essential component to realize the cognitive radio, and the requirement for real-time spectrum sensing in the case of lacking prior information, fading channel, and noise uncertainty, indeed poses a major challenge to the classical spectrum sensing algorithms. Based on the stochastic properties of scalar transformation of power spectral density(PSD), a novel spectrum sensing algorithm, referred to as the power spectral density split cancellation method(PSC), is proposed in this paper. The PSC makes use of a scalar value as a test statistic, which is the ratio of each subband power to the full band power. Besides, by exploiting the asymptotic normality and independence of Fourier transform,the distribution of the ratio and the mathematical expressions for the probabilities of false alarm and detection in different channel models are derived. Further, the exact closed-form expression of decision threshold is calculated in accordance with Neyman–Pearson criterion. Analytical and simulation results show that the PSC is invulnerable to noise uncertainty,and can achive excellent detection performance without prior knowledge in additive white Gaussian noise and flat slow fading channels. In addition, the PSC benefits from a low computational cost, which can be completed in microseconds.

MODAL PARAMETERS EXTRACTION WITH CROSS CORRELATION FUNCTION AND CROSS POWER SPECTRUM UNDER UNKNOWN EXCITATION

In most of real operational conditions only response data are measurable while the actual excitations are unknown, so modal parameter must be extracted only from responses. This paper gives a theoretical formulation for the cross correlation functions and cross power spectra between the outputs under the assumption of white noise excitation. It widens the field of modal analysis under ambient excitation because many classical methods by impulse response functions or frequency response functions can be used easily for modal analysis under unknown excitation. The Polyreference Complex Exponential method and Eigensystem Realization Algorithm using cross correlation functions in time domain and Orthogonal Polynomial method using cross power spectra in frequency domain are applied to a steel frame to extract modal parameters under operational conditions. The modal properties of the steel frame from these three methods are compared with those from frequency response functions analysis. The results show that the modal analysis method using cross correlation functions or cross power spectra presented in this paper can extract modal parameters efficiently under unknown excitation.

Effects of Different Extinction for Morphine-CPP on Hippocampal EEG Power Spectrum in Mice

The relationship between hippocampal electroencephalogram(EEG)power spectrum and the extinction of addiction memory was investigated.Forty KM mice(Kunming mice)that had successfully established morphine(MOR)-conditioned place preference(CPP)were divided into four groups:saline-training extinction(SAL-TE),SAL-natural extinction(SAL-NE),MORTE,MOR-NE,for extinction treatment and EEG recording in the dorsal and ventral hippocampus(DH/VH).Results show that the CPP score of MOR-TE significantly decreased and the total,βandγbands power spectrum of MOR-TE was suppressed.Notably,the total power from the VH was more correlated with the extinction method,and the TE total power was always lower than NE in SAL or MOR group.Naloxone enhanced the EEG total power and all bands(DH)orβandγbands(VH)power in the SAL group.However,in the MOR-TE group,onlyβandγbands power in VH increased(P<0.05).The results suggest that TE can promote the extinction of opiate addictive memory more effectively than NE,and the extinction of MOR-CPP may have a stronger correlation with changes of VH-EEG power spectrum.

Passive sonar identification (Ⅲ): Feature extraction and pattern plates of double-frequency spectrum as well as average power spectrum

This series of papers deal with vessel recognition. The project is conducted by using fuzzy neural networks and basing on the spectra of vessel radiated-noise. Based on the studies of a large amount of ship radiated-noise data, which has been collected from actual ships on the sea, effectively recognizable features are extracted. Such features include line-spectrum features, stationary and nonstationary spectrum features as well as rhythm features. Finally the categorization are tested by unknown samples on the sea, including 33 surface vessels, 8 underwater vessels in 30 operating conditions. Methods for memorization and classilication are also explored in the project. Paper (Ⅲ) is the thirird in the series. It deals with the extraction method of modulation information in double-frequency power spectrum and the establishment of pattern plate of double-frequency spectrum as well as average power spectrum. To extract features from double-frequency spectrum, the tendency of wave is subtracted from the wave of each channel and the modulation of high frequency is compensated. The modulation degree of lines is shown by relative Value and converted to fuzzy value by fuzzy function. The pattern-plate of double-frequency spectrum memorises stable line and its respective modulation strength. The pattern-plate of average power spectrum memorizes the spectra mean of typical samples and the standard

Changes in the power spectrum of electrical signals in maize leaf induced by osmotic stress

To quantify the characteristics of the power spectrum of plant electrical signals, we defined the following concepts:spectral edge frequency (SEF), spectral center frequency (SCF), power index (PI) and power spectral entropy (PSE). These parameters were used to examine and quantify changes in the power spectrum of electrical signals in maize leaves under osmotic stress. In the absence of osmotic stress, the SEF of the electrical signal in maize leaves was approx. 0.2 Hz and the SCF was approx. 0.1 Hz. The electrical signal in maize leaves was mainly a slow wave signal with a frequency of 0-0.1 Hz. After 2 h osmotic stress, the SEF and SCF of the electrical signal increased to higher frequencies. The proportion of the fast wave frequency also increased to 0.1-0.2 Hz, resulting in a dramatic increase in PSE. We also found that the changes in PSE and SCF were significantly correlated during osmotic stress. We propose that the changes in the PSE and SCF in maize leaves can be used as a sensitive signal indicating water deficit in leaf cells under osmotic stress. Thus, measurement of SCF or PSE of electrical signals in maize leaves could be used to develop early warning and rapid diagnosis techniques for the water demands of plants.

Classical and modern power spectrum estimation for tune measurement in CSNS RCS

Precise measurement of betatron tune is required for good operating condition of CSNS RCS.The fractional part of betatron tune is important and it can be measured by analyzing the signals of beam position from the appointed BPM.Usually these signals are contaminated during the acquisition process,therefore several power spectrum methods are used to improve the frequency resolution.In this article classical and modern power spectrum methods are used.In order to compare their performance,the results of simulation data and IQT data from J-PARC RCS are discussed.It is shown that modern power spectrum estimation has better performance than the classical ones,though the calculation is more complex.

Matched Filter Based Spectrum Sensing When Primary User Has Multiple Power Levels

In this paper,we investigate the matched filter based spectrum sensing in a more reasonable cognitive radio(CR) scenario when the primary user(PU) has more than one transmit power levels,as regulated in most standards,i.e.,IEEE 802.11 Series,GSM,LTE,LTE-A,etc.This new multiple primary transmit power(MPTP) scenario is specialized by two different targets:detecting the presence of PU and identifying the power level.Compared to the traditional binary sensing where only the presence of PU is checked,SU may attain more information about the primary network(making CR more "intelligent") and design the subsequent optimization strategy.The key technology is the multiple hypothesis testing as opposed to the traditional binary hypothesis testing.We discuss two situations under whether the channel phase is known or not,and we derive the closed form solutions for decision regions and several performance metrics,from which some interesting phenomenons are observed and the related discussions are presented.Numerical examples are provided to corroborate the proposed studies.