Analysis and optimization of power spectrum on EBPSK modulation in throughput-efficient wireless system

In order to satisfy increasingly greater demand for the performance of communication systems, a throughput efficient wireless system based on the extended binary phase shift keying （EBPSK） modulation is presented. Simultaneously, corresponding analysis of power spectra is also given with a brief process. The optimal waveform is proposed without useful information loss, by removing linear spectra presenting periodic components. On this basis, the reasonable definition of bandwidth is discussed, which indicates that the EBPSK belongs to the category of the ultra narrow band （UNB） throughput-efficient communication. Meanwhile, the modulation parameters＇ effects on bandwidth, transmission rate and transmission performance are analyzed. Results illustrate the validity of theoretical analysis and spectrum optimization. Results also prove that this UNB system can obtain good bit error rate （BER） performance with high spectra efficiency.

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis

Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

A reweighted damped singular spectrum analysis method for robust seismic noise suppression

(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem.However,in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers.The presence of this kind of noise,called erratic noise,makes singular spectrum analysis(SSA)reconstruction unstable and has undesirable effects on the final results.We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA)method.It incorporates the damped SSA,an improved version of SSA,into a reweighted framework.The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise.The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations.The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance.The feasibility of RD-SSA is validated via both synthetic and field data examples.

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.

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.

POWER SPECTRUM ANALYSIS OF ABNORMAL ENG

Two hundred and eight copies ENG recorded during Barany’s test with 20℃water irrigation method were selected for power spectrum analysis. Their diagnosis were determined or confirmed by operation and pathological examination ahead. The results indicated that: in normal cases, the maximum amplitude frequency (Famax) should be over 1 Hz; the difference of Famax between two ears should be less than 1 Hz; the spectrum histogram should display as form "∧" ; the frequency components should be abundant and mostly distributed within 8 Hz as well. If the Famax was less than 0.5Hz, the difference of Famax was over 1 Hz, the histogram showed as "L" form, the frequency components decreased significantly or even disappeared, a function reduce or loss of inner ear or Ⅷnerve would be suggested.

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.

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

Impact Analysis of the 2011 Fukushima Nuclear Power Plant Accidents by Running Spectrum Analysis on Newspaper

Huge amount of digital data of the Great East Japan Earthquake is provided by the highly-developed digital data technology. But the method and technique for analysis of these huge digital data are not developed sufficiently. This paper proposes a running spectrum technique for text data and analyzing changes of disaster phase during the disaster management cycle. Impact analysis of the nuclear power plant accidents have been performed by using Fukushima Minpo newspaper for its verification. The result shows the dynamic characteristics of the nuclear power plant accidents. As the time interval B becomes longer, the analysis data is used from wide range period along with the smoothing effect. When observing different time intervals B, fewer keywords have been ranked in the longer time intervals of B. The proposed technique is a powerful tool to effective and efficient disaster response and management. analyze effectively the huge amount of digital data for the

FLUORESCENCE SPECTRUM ANALYSIS OF ETHER-WATER SOLUTION BASED ON GAUSSIAN DECOMPOSITION METHOD

The fluorescence spectrum of the ether-water solution excited by the ultraviolet light with the wavelength of 245 nm is experimentally detected. Based on the second derivative analysis, the fluorescence spectrum of the ether-water solution is used as Gaussian decomposition and seven Gaussian spectral lines are obtained. The center wavelength, the peak intensity and the half peak bandwidth of each Gaussian spectral line are measured, and the multi-peak fitting is made by using Gaussian primitive parameters. The highest and the lowest oscillation energy level differences in the ground state of each Gaussian spectrum are calculated. It is found that there are seven types of luminescent association molecules formed by ether and water molecules in different configurations existed in the solution. The location of each optimum absorption wavelength and the half peak bandwidth of the Gaussian spectral line is different. The energy level difference with the central wavelength of 304 nm attains the maximum value The result can contribute to the study of the molecular association in ether-water solution.

A Slice Analysis-Based Bayesian Inference Dynamic Power Model for CMOS Combinational Circuits

To improve the accuracy and speed in cycle-accurate power estimation, this paper uses multiple dimensional coefficients to build a Bayesian inference dynamic power model. By analyzing the power distribution and internal node state, we find the deficiency of only using port information. Then, we define the gate level number computing method and the concept of slice, and propose using slice analysis to distill switching density as coefficients in a special circuit stage and participate in Bayesian inference with port information. Experiments show that this method can reduce the power-per-cycle estimation error by 21.9% and the root mean square error by 25.0% compared with the original model, and maintain a 700 ＋ speedup compared with the existing gate-level power analysis technique.

Multi-Strategy Dynamic Spectrum Access in Cognitive Radio Networks: Modeling, Analysis and Optimization

Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA strategy, which might not be suited to the dynamic network environment. In this paper, we propose a multi-strategy DSA(MS-DSA) system, where the primary and the secondary system share spectrum resources with multiple DSA strategies simultaneously. To analyze the performance of the proposed MS-DSA system, we model it as a continuous-time Markov chain(CTMC) and derive the expressions to compute the corresponding performance metrics. Based on this, we define a utility function involving the concerns of effective throughput, interference quantity on primary users, and spectrum leasing cost. Two optimization schemes, named as spectrum allocation and false alarm probability selection, are proposed to maximize the utility function. Finally, numerical simulations are provided to validate our analysis and demonstrate that the performance can be significantly improved caused by virtues of the proposed MS-DSA system.

Power Allocation and Performance Analysis of the Collaborative NOMA Assisted Relaying Systems in 5G

Serving multiple cell-edge mobile terminals poses multifaceted challenges due to the increased transmission power and interferences, which could be overcome by relay communications. With the recent advancement of 5G technologies, non-orthogonal multiple access(NOMA) has been used at relay node to transmit multiple messages simultaneously to multiple cell-edge users. In this paper, a Collaborative NOMA Assisted Relaying(CNAR) system for 5G is proposed by enabling the collaboration of source-relay(S-R) and relay-destination(R-D) NOMA links. The relay node of the CNAR decodes the message for itself from S-R NOMA signal and transmits the remaining messages to the multiple cell-edge users in R-D link. A simplified-CNAR(S-CNAR) system is then developed to reduce the relay complexity. The outage probabilities for both systems are analyzed by considering outage behaviors in S-R and R-D links separately. To guarantee the data rate, the optimal power allocation among NOMA users is achieved by minimizing the outage probability. The ergodic sum capacity in high SNR regime is also approximated. Our mathematical analysis and simulation results show that CNAR system outperforms existing transmission strategies and S-CNAR reaches similar performance with much lower complexity.

Rural Power System Load Forecast Based on Principal Component Analysis

Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.

Short-range Climate Prediction Experiment of the Southern Oscillation Index Based on the Singular Spectrum Analysis

The Southern Oscillation Index (SOI) time series is analyzed by means of the singular spectrum analysis (SSA) method with 60-month window length. Two major oscillatory pairs are found in the series whose periods are quasi-four and quasi-two years respectively. The auto-regressive model, which is developed on the basis of the Maximum Entropy Spectrum Analysis, is fitted to each of the 9 leading components including the oscillatory pairs. The prediction of SOI with the 36-month lead is obtained from the reconstruction of these extrapolated series. Correlation coefficient between predicted series and 5 months running mean of observed series is up to 0.8. The model can successfully predict the peak and duration of the strong ENSO event from 1997 to 1998. It's also shown that the proper choice of reconstructed components is the key to improve the model prediction.

Anti-Noise Performance of the FT Continuous Zoom Analysis Method for Discrete Spectrum

As a discrete spectrum correction method, the Fourier transform （FT） continuous zoom analysis method is widely used in vibration signal analysis, but little effort had been made on this method＇s anti-noise performance. It is widely believed that the analysis accuracy of the method can be substantially improved by increasing the zoom multiple, however, with the zoom multiple increases, the frequency estimation accuracy may decline sometimes in practices. Aiming at the problems above, this paper analyzes the sources of frequency estimation error when a harmonic signal mixed with and without noise is processed using the FT continuous zoom analysis. According to the characteristics that the local maximum of the zoom spectrum may be wrongly selected when the signal is corrupted with noise, the number of wrongly selected spectrum lines is deduced under different signal-to-noise ratio and local zoom multiple, and then the maximum frequency estimation error is given accordingly. The validity of the presented analysis is confirmed by simulations results. The frequency estimation accuracy of this method will not improve any more under the influence of noise, and there is a best zoom multiple, when the zoom multiple is larger than the best zoom multiple; the maximum frequency estimation error will fluctuate back and forth. The best zoom multiple curves under different signal-to-noise ratios given provide a theoretical basis for the choice of the appropriate zoom multiples of the FT continuous zoom analysis method in engineering applications.

Frequency spectrum analysis on micro-seismic signal of rock bursts induced by dynamic disturbance

Blasting and breaking of hard roof are main inducing causes of rock bursts in coal mines with danger of rock burst,and it is important to find out the frequency spectrum distribution laws of these dynamic stress waves and rock burst waves for researching the mechanism of rock burst.In this paper,Fourier transform as a micro-seismic signal conversion method of amplitude-time character to amplitude-frequency character is used to analyze the frequency spectrum characters of micro-seismic signal of blasting,hard roof breaking and rock bursts induced by the dynamic disturbance in order to find out the difference and relativity of different signals.The results indicate that blasting and breaking of hard roof are high frequency signals,and the peak values of dominant frequency of the signals are single.However,the results indicate that the rock bursts induced by the dynamic disturbance are low frequency signals,and there are two obvious peak values in the amplitude-frequency curve witch shows that the signals of rock bursts are superposition of low frequency signals and high frequency signals.The research conclusions prove that dynamic disturbance is necessary condition for rock bursts,and the conclusions provide a new way to research the mechanism of rock bursts.

Independent component analysis approach for fault diagnosis of condenser system in thermal power plant

A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.

Hydrodynamic Analysis of a Semi-submersible Wind-Tidal Combined Power Generation Device

Energy shortages and environmental pollution are becoming increasingly severe globally. The exploitation and utilization of renewable energy have become an effective way to alleviate these problems. To improve power production capacity, power output quality, and cost effectiveness, comprehensive marine energy utilization has become an inevitable trend in marine energy development. Based on a semi-submersible wind-tidal combined power generation device,a three-dimensional frequency domain potential flow theory is used to study the hydrodynamic performance of such a device. For this study, the RAOs and hydrodynamic coefficients of the floating carrier platform to the regular wave were obtained. The influence of the tidal turbine on the platform in terms of frequency domain was considered as added mass and damping. The direct load of the tidal turbine was obtained by CFX.FORTRAN software was used for the second development of adaptive query workload aware software, which can include the external force. The motion response of the platform to the irregular wave and the tension of the mooring line were calculated under the limiting condition(one mooring line breakage). The results showed that the motion response of the carrier to the surge and sway direction is more intense, but the swing amplitude is within the acceptable range. Even in the worst case scenario, the balance position of the platform was still in the positioning range, which met the requirements of the working sea area. The safety factor of the mooring line tension also complied with the requirements of the design specification. Therefore, it was found that the hydrodynamic performance and motion responses of a semi-submersible wind-tidal combined power generation device can meet the power generation requirements under all design conditions, and the device presents a reliable power generation system.