Dual Image Cryptosystem Using Henon Map and Discrete Fourier Transform

This paper introduces an efficient image cryptography system.The pro-posed image cryptography system is based on employing the two-dimensional(2D)chaotic henon map(CHM)in the Discrete Fourier Transform(DFT).The proposed DFT-based CHM image cryptography has two procedures which are the encryption and decryption procedures.In the proposed DFT-based CHM image cryptography,the confusion is employed using the CHM while the diffu-sion is realized using the DFT.So,the proposed DFT-based CHM image crypto-graphy achieves both confusion and diffusion characteristics.The encryption procedure starts by applying the DFT on the image then the DFT transformed image is scrambled using the CHM and the inverse DFT is applied to get the final-ly encrypted image.The decryption procedure follows the inverse procedure of encryption.The proposed DFT-based CHM image cryptography system is exam-ined using a set of security tests like statistical tests,entropy tests,differential tests,and sensitivity tests.The obtained results confirm and ensure the superiority of the proposed DFT-based CHM image cryptography system.These outcomes encourage the employment of the proposed DFT-based CHM image cryptography system in real-time image and video applications.

THE MULTIPLICATIVE COMPLEXITY AND ALGORITHM OF THE GENERALIZED DISCRETE FOURIER TRANSFORM(GFT)

In this paper, we have proved that the lower bound of the number of realmultiplications for computing a length 2t real GFT(a,b) (a = ±1/2, b = 0 or b =±1/2, a = 0) is 2t+1 - 2t - 2 and that for computing a length 2t real GFT(a,b)(a =±1/2, b = ±1/2) is 2t+1 - 2. Practical algorithms which meet the lower bounds ofmultiplications are given.

Digital watermarking for still image based on discrete fractional fourier transform

Presents a digital watermarking technique based on discrete fractional Fourier transform (DFRFT), discusses the transformation of the original image by DFRFT, and the modification of DFRFT coefficients of the original image by the information of watermark, and concludes from experimental results that the proposed technique is robust to lossy compression attack.

Peak-to-Average Power Ratio Performance Analysis for Orthogonal Chirp Division Multiplexing Multicarrier Systems Based on Discrete Fractional Cosine Transform

In doubly selective fading channels, the orthogonal frequency division multiplexing (OFDM) multicarrier system may fail. Chirp like basis (fractional Fourier transform-fractional cosine transform) may be used instead of complex exponential basis in this case to improve the system performance. However, in multicarrier transmission, the high peak to average power ratio (PAPR) of the transmitted signal is one of the difficult problems that face both the chirp and the exponential basis. In this paper, an evaluation for the PAPR performance of a multicarrier system based on the fractional cosine transform (FrCT) is introduced and then compared with DFrFT and FFT. Moreover, applying the SLAM technique over these systems is provided to understand the behaviour of these systems when applying SLAM. Simulations verify that this system obtains a better PAPR performance. Moreover, further PAPR reduction can be gained using the well-known PAPR reduction methods. Moreover, applying SLAM technique improves the performance of (dB) by 4 dB to 5 dB and all systems become as competitive to each other when SLAM is applied. Finally, BER performance comparison among OFDM, Discrete Cosine Transform MCM (DCT- MCM), Discrete Hartley Transform MCM (DHT-MCM), DFrFT-OCDM and DFrCT- OCDM MCM systems was done by means of simulation over 100,000 multicarrier blocks for each one and showed that our proposed scenario gave the best performance.

Design of a Multifrequency Signal Parameter Estimation Method for the Distribution Network Based on HIpST

The application of traditional synchronous measurement methods is limited by frequent fluctuations of electrical signals and complex frequency components in distribution networks.Therefore,it is critical to find solutions to the issues of multifrequency parameter estimation and synchronous measurement estimation accuracy in the complex environment of distribution networks.By utilizing the multifrequency sensing capabilities of discrete Fourier transform signals and Taylor series for dynamic signal processing,a multifrequency signal estimation approach based on HT-IpDFT-STWLS(HIpST)for distribution networks is provided.First,by introducing the Hilbert transform(HT),the influence of noise on the estimation algorithm is reduced.Second,signal frequency components are obtained on the basis of the calculated signal envelope spectrum,and the interpolated discrete Fourier transform(IpDFT)frequency coarse estimation results are used as the initial values of symmetric Taylor weighted least squares(STWLS)to achieve high-precision parameter estimation under the dynamic changes of the signal,and the method increases the number of discrete Fourier.Third,the accuracy of this proposed method is verified by simulation analysis.Data show that this proposed method can accurately achieve the parameter estimation of multifrequency signals in distribution networks.This approach provides a solution for the application of phasor measurement units in distribution networks.

Seismic dynamic stability of double-slider rock slopes containing tension cracks

Earthquakes have significant impact on rock slopes,thus studying the seismic stability of double-slider rock slopes containing tension cracks is crucial.We proposed an analysis method on the seismic dynamic slope stability.This method utilizes discrete Fourier transform to decompose real earthquake waves into a combination of harmonic waves.These waves are then used in conjunction with the pseudo-dynamic method and safety factor calculation formula to compute the safety factor.This approach accurately captures the influence of seismic time history characteristics on the dynamic stability of double-slider rock slopes containing tension cracks.The minimum safety factor in the obtained time history curves of the safety factor reflects the most unfavorable state of the slopes under seismic effects.Quantitative analysis is conducted using six sets of actual earthquake ground motion data obtained from the Pacific Earthquake Engineering Research Center’s NGAWest2 ground-shaking record database.The conclusions are as follows:(1)There is an inverse correlation between the average seismic acceleration amplitude and the minimum safety factor.Conversely,the seismic acceleration amplitude standard deviation shows a positive correlation with the minimum safety factor.The global sensitivity of geometric parameters in the slope model is higher than other influencing factors.(2)The proposed dynamic stability analysis method can capture the dynamic characteristics of earthquakes,emphasizing the minimum safety factor of the slope in the seismic time history as a stability indicator.In contrast,the pseudo-static method may yield unsafe results.(3)A safety factor expression considering hydrostatic pressure is proposed.A negative correlation was observed between the height of the water level line and the minimum safety factor.

New shape clustering method based on contour DFT descriptor and modified SOFM neural network

A contour shape descriptor based on discrete Fourier transform(DFT)and a K-means algorithm modified self-organizing feature map(SOFM)neural network are established for shape clustering.The given shape is first sampled uniformly in the polar coordinate.Then the discrete series is transformed to frequency domain and constructed to a shape characteristics vector.Firstly,sample set is roughly clustered using SOFM neural network to reduce the scale of samples.K-means algorithm is then applied to improve the performance of SOFM neural network and process the accurate clustering.K-means algorithm also increases the controllability of the clustering.The K-means algorithm modified SOFM neural network is used to cluster the shape characteristics vectors which is previously constructed.With leaf shapes as an example,the simulation results show that this method is effective to cluster the contour shapes.

Windowing Techniques, the Welch Method for Improvement of Power Spectrum Estimation

This paper revisits the characteristics of windowing techniques with various window functions involved,and successively investigates spectral leakage mitigation utilizing the Welch method.The discrete Fourier transform(DFT)is ubiquitous in digital signal processing(DSP)for the spectrum analysis and can be efciently realized by the fast Fourier transform(FFT).The sampling signal will result in distortion and thus may cause unpredictable spectral leakage in discrete spectrum when the DFT is employed.Windowing is implemented by multiplying the input signal with a window function and windowing amplitude modulates the input signal so that the spectral leakage is evened out.Therefore,windowing processing reduces the amplitude of the samples at the beginning and end of the window.In addition to selecting appropriate window functions,a pretreatment method,such as the Welch method,is effective to mitigate the spectral leakage.Due to the noise caused by imperfect,nite data,the noise reduction from Welch’s method is a desired treatment.The nonparametric Welch method is an improvement on the periodogram spectrum estimation method where the signal-to-noise ratio(SNR)is high and mitigates noise in the estimated power spectra in exchange for frequency resolution reduction.The periodogram technique based on Welch method is capable of providing good resolution if data length samples are appropriately selected.The design of nite impulse response(FIR)digital lter using the window technique is rstly addressed.The inuence of various window functions on the Fourier transform spectrum of the signals is discussed.Comparison on spectral resolution based on the traditional power spectrum estimation and various window-function-based Welch power spectrum estimations is presented.

Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique

In this paper,we propose a hybrid forecasting model(HFM)for the short-term electric load forecasting using artificial neural network(ANN),discrete Fourier transformation(DFT)and principal component analysis(PCA)techniques in order to attain higher prediction accuracy.Firstly,we estimate Fourier coefficients by the DFT for predicting the next-day load curve with an ANN and obtain approximate load curves by applying the inverse discrete Fourier transformation.Approximate curves,together with other input variables,are given to the ANN to predict the next-day hourly load curves.Furthermore,we predict PCA scores to obtain approximate load curves in the first step,which are then given to the ANN again in the second step.Both DFT and PCA models use input variables such as calendrical and meteorological data as well as past electric loads.Applying those models for forecasting hourly electric load in the metropolitan area of Japan for January and May in 2018,we train our models using historical data since January 2008.The forecast results show that the HFM consisting of“ANN with DFT”and“ANN with PCA”predicts next-day hourly loads more accurately than the conventional three-layered ANN approach.Their corresponding mean average absolute errors show 2.7%for ANN with DFT,2.6%for ANN with PCA and 3.0%for the conventional ANN approach.We also find that in May,when electric demand is smaller with smaller fluctuations,forecasting errors are much smaller than January for all the models.Thus,we can conclude that the HFM would contribute to attaining significantly higher forecasting accuracy.

Automatic seizure detection with different time delays using SDFT and time-domain feature extraction

Automatic seizure detection is important for fast detection of the seizure because the way that the expert denotes and searches for seizure in the long signal takes time. The most common way to detect seizures automatically is to use an electroencephalogram(EEG). Many studies have used feature extraction that needs time for calculation. In this study, sliding discrete Fourier transform(SDFT) was applied for conversion to a frequency domain without using a window, which was compared with using window for feature selection. SDFT was calculated for each time series sample directly without any delay by using a simple infinite impulse response(IIR)structure. The EEG database of Bonn University was used to test the proposed method, and two cases were defined to examine a two-classifier feedforward neural network and an adaptive network-based fuzzy inference system. Results revealed that the maximum accuracies were 93% without delay and 99.8% with a one-second delay. This delay accrued because the average was taken for the results with a one-second window.

OUTPUT MAXIMIZATION CONTROL FOR VSCF WIND ENERGY CONVERSION SYSTEM USING EXTREMUM CONTROL STRATEGY

The energy conversion optimization control strategy is presented for a family of horizontal-axis variablespeed fixed-pitch wind energy conversion systems,working in the partial load region.The system uses a variablespeed wind turbine(VSWT)driving a squirrel-cage induction generator(SCIG)connected to a grid.A new maximum power point tracking(MPPT)approach is proposed based on the extremum seeking control principles under the assumption that the wind turbine model and its parameters are poorly known.The aim is to drive the average position of the operation point close to optimality.Here the wind turbulence is used as search disturbance instead of inducing new sinusoidal search signals.The discrete Fourier transform(DFT)process of some available measures estimates the distance of operation point to optimality.The effectiveness of the proposed MPPT approach is validated under different operation conditions by numerical simulations in MATLAB/SIMULINK.The simulation results prove that the new approach can effectively suppress the vibration of system and enhance the dynamic performance of system.

Two-Dimensional Interpolation Criterion Using DFT Coefficients

In this paper,we address the frequency estimator for 2-dimensional(2-D)complex sinusoids in the presence of white Gaussian noise.With the use of the sinc function model of the discrete Fourier transform(DFT)coefficients on the input data,a fast and accurate frequency estimator is devised,where only the DFT coefficient with the highest magnitude and its four neighbors are required.Variance analysis is also included to investigate the accuracy of the proposed algorithm.Simulation results are conducted to demonstrate the superiority of the developed scheme,in terms of the estimation performance and computational complexity.

THE DISTRIBUTION FUNCTIONS FOR THE LINEAR COMPLEXITY OF PERIODIC SEQUENCES

Linear complexity is an important standard to scale the randomicity of stream ciphers. The distribution function of a sequence complexity measure gives the function expression for the number of sequences with a given complexity measure value. In this paper, we mainly determine the distribution function of sequences with period over using Discrete Fourier Transform (DFT), where and the characteristics of are odd primes, gcd and is a primitive root modulo The results presented can be used to study the randomness of periodic sequences and the analysis and design of stream cipher.

Quantitative Evaluation of Flicker ERG Waveforms in Low Vision Patients

Flicker electroretinograms(FERGs)of 30Hz were recorded for 46eyes(23 cases)of low vision,including 38 eyes with retinal diseases and 8eyes with optic neuropathy,and for 39 normal eyes(33 cases).The recordedwave forms were analysed by two methods:(1)to measure the peak to peakamplitude and pseudophase directly,and(2)to measure the amplitude andphase of fundamental response component(30Hz)by discrete Fourier trans-form(DFT).The abnormality ratios between these two methods were com-pared in low vision pati...

Estimation of Aggregate Losses of Secondary Cancer Using PH-OPPL and PH-TPPL Distributions

Kenyan insurance firms have introduced insurance policies of chronic illnesses like cancer; however, they have faced a huge challenge in the pricing of these policies as cancer can transit into different stages, which consequently leads to variation in the cost of treatment. This has made the estimation of aggregate losses of diseases which have multiple stages of transitions such as cancer, an area of interest of many insurance firms. Mixture phase type distributions can be used to solve this setback as they can in-cooperate the transition in the estimation of claim frequency while also in-cooperating the heterogeneity aspect of claim data. In this paper, we estimate the aggregate losses of secondary cancer cases in Kenya using mixture phase type Poisson Lindley distributions. Phase type (PH) distributions for one and two parameter Poisson Lindley are developed as well their compound distributions. The matrix parameters of the PH distributions are estimated using continuous Chapman Kolmogorov equations as the disease process of cancer is continuous while severity is modeled using Pareto, Generalized Pareto and Weibull distributions. This study shows that aggregate losses for Kenyan data are best estimated using PH-OPPL-Weibull model in the case of PH-OPPL distribution models and PH-TPPL-Generalized Pareto model in the case of PH-TPPL distribution models. Comparing the two best models, PH-OPPL-Weibull model provided the best fit for secondary cancer cases in Kenya. This model is also recommended for different diseases which are dynamic in nature like cancer.

A ROBUST COLOR EDGE DETECTION ALGORITHM BASED ON THE QUATERNION HARDY FILTER

This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selecting suitable parameters to handle different levels of noise.In particular,the quaternion analytic signal,which is an effective tool in color image processing,can also be produced by quaternion Hardy filtering with specific parameters.Based on the QHF and the improved Di Zenzo gradient operator,a novel color edge detection algorithm is proposed;importantly,it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique.From the experimental results,we conclude that the minimum PSNR improvement rate is 2.3%and the minimum SSIM improvement rate is 30.2%on the CSEE database.The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.

一种稳健的BIM数据盲水印算法

为了对BIM模型在遭受攻击后进行快速有效的版权保护,提出一种稳健的BIM数据盲水印算法。首先,以原数据中的多面网格要素为单位,提取多面网格对象的所有网格顶点构造复数序列,对复数序列做DFT变换得到幅度系数和相位系数;其次,利用QIM方法,将经过Logistic映射置乱得到的水印信息,嵌入到DFT变换域的幅度系数中;最后,运用离散傅里叶逆变换(inverse discrete fourier transform,IDFT)得到含水印信息的BIM数据。在水印提取中,通过投票原则提取水印,无需原始数据参与,是一种盲水印算法。在实际应用中,因BIM模型中顶点坐标的相同值较多,被用来嵌入水印的有效载体较少,为解决这一问题,在误差容许范围内对原始坐标数据加入了随机噪声,以嵌入不同的水印,间接地增加了水印嵌入容量。实验表明,该算法能够很好的控制水印嵌入引起的数据误差,嵌入水印对BIM数据的精度影响小,且对BIM数据常见的随机删除实体、旋转、平移攻击和特殊攻击具有较好的稳健性,可为BIM数据的所有权提供切实可行的依据。

Complex Frequency Features for TCD Signal Analysis

In this paper, we report on using pattern recognition techniques for embolic signal(ES) detection based on transcranial doppler ultrasound(TCD) audio data collected via machine EMS-9(from Shenzhen Delicate Electronics, Co. Ltd).Firstly, we adopted complex discrete fourier transform to get spectra of audio recordings; secondly, we used principal component analysis(PCA) for the visualization of selected signals, which makes it easy and intuitive to verify whether a signal contains an embolic component; finally we designed the classifier with support vector machines(SVM) for detection. With contrast to traditional methods of ES detection systems, the proposed approach considers two channel signals from the audio data collected by single transducer, and there is no predefined features for classification. The primary experimental results on real data are promising.

Improvement of signal processing in Coriolis mass flowmeters for gas-liquid two-phase flow

As an increasingly popular flow metering technology,Coriolis mass flowmeter exhibits high measurement accuracy under single-phase flow condition and is widely used in the industry.However,under complex flow conditions,such as two-phase flow,the measurement accuracy is greatly decreased due to various factors including improper signal processing methods.In this study,three digital signal processing methods—the quadrature demodulation(QD)method,Hilbert method,and sliding discrete time Fourier transform method—are analyzed for their applications in processing sensor signals and providing measurement results under gas-liquid two-phase flow condition.Based on the analysis,specific improvements are applied to each method to deal with the signals under two-phase flow condition.For simulation,sensor signals under single-and two-phase flow conditions are established using a random walk model.The phase difference tracking performances of these three methods are evaluated in the simulation.Based on the digital signal processor,a converter program is implemented on its evaluation board.The converter program is tested under single-and two-phase flow conditions.The improved signal processing methods are evaluated in terms of the measurement accuracy and complexity.The QD algorithm has the best performance under the single-phase flow condition.Under the two-phase flow condition,the QD algorithm performs a little better in terms of the indication error and repeatability than the improved Hilbert algorithm at 160,250,and 420 kg/h flow points,whereas the Hilbert algorithm outperforms the QD algorithm at the 600 kg/h flow point.

Efficient and Stable Exponential Runge-Kutta Methods for Parabolic Equations

In this paper we develop explicit fast exponential Runge-Kutta methods for the numerical solutions of a class of parabolic equations.By incorporating the linear splitting technique into the explicit exponential Runge-Kutta schemes,we are able to greatly improve the numerical stability.The proposed numerical methods could be fast implemented through use of decompositions of compact spatial difference operators on a regular mesh together with discrete fast Fourier transform techniques.The exponential Runge-Kutta schemes are easy to be adopted in adaptive temporal approximations with variable time step sizes,as well as applied to stiff nonlinearity and boundary conditions of different types.Linear stabilities of the proposed schemes and their comparison with other schemes are presented.We also numerically demonstrate accuracy,stability and robustness of the proposed method through some typical model problems.