Wavelet based spectral correlation method for DPSK chip rate estimation

The spectral correlation function (SCF) is an efficient tool for analyzing the BPSK signal. In a low signal-to-noise (SNR) condition, however, spectral lines associated with the BPSK signal chip rate in SCF may be corrupted by the quadratic characteristics of the SCF. By use of the quadratic property of SCFs and narrow-band characteristics of the BPSK signal, this paper proposes a wavelet transform (WT) based SCF algorithm to detect and estimate the BPSK chip rate in a low SNR environment. Examples are illustrated to validate the proposed method.

The spectral correlation function (SCF) is an efficient tool for analyzing cyclostationary signals. In a low signal-to-noise (SNR) conditions, however, the spectral lines associated with the SCF signal chip rate may be corrupted. The most general measurements of SCF are based on averaging methods to diminish the effect of noise. Here, the noise contribution is discussed and a new method for the estimation of parameters of modulated signals is brought forward.

In many engineering fields, distance to targets is very important. General distance measurement method uses a time delay between transmitted and reflected waves, but it is difficult to estimate the short distance. On the other hand, the method using phase interference to measure the short distance has been known in the field of microwave radar. Therefore, we have proposed the distance estimation method based on interference between transmitted and reflected audible sound, which can measure the distance between microphone and target with one microphone and one loudspeaker. However, the distance measurement performance of the proposed method was degraded by additional noise in noisy environment. In this paper, the acoustic distance measurement method based on the interference between the transmitted wave and the reflected waves is expanded to a noise-robust method in a noisy environment. In order to measure distance in a low-signal-to-noise ratio (low-SNR) environment, we introduce a concept from synchronous addition and spectral subtraction to the proposed method. Evaluation experiments in a real environment reveal that the proposed method was valid and effective because the proposed method could measure distance in a low-SNR environment.

By generating ultrasonic waves at two different frequencies onto a cracked structure, modulations due to crack-induced nonlinearity can be observed in the corresponding ultrasonic response. This nonlinear wave modulation phenomenon has been widely studied and proven capable of detecting a fatigue crack at a very early stage. However, under field conditions, other exogenous vibrations exist and the modulation components can be buried under ambient noises, making it difficult to extract the modulation components simply by using a spectral density function. In this study, the nonlinear modulation components in the ultrasonic response were extracted using a spectral correlation function (the double Fourier transform) with respect to time and time lag of a signal’s autocorrelation. Using spectral correlation, noise or interference, which is spectrally overlapped with the nonlinear modulation components in the ultrasonic response, can be effectively removed or reduced. Only the nonlinear modulation components are accentuated at specific coordinates of the spectral correlation plot. A damage feature is defined by comparing the spectral correlation value between nonlinear modulation components with other spectral correlation values among randomly selected frequencies. Then, by analyzing the statistical characteristics of the multiple damage feature values obtained from different input frequency combinations, fatigue cracks can be detected without relying on baseline data obtained from the pristine condition of the target structure. In the end, an experimental test was conducted on aluminum plates with a real fatigue crack and the test signals were contaminated by simulated noises with varying signal-to-noise ratios. The results validated the proposed technique.

Fast Averaged Cyclic Periodogram method to compute spectral correlation and coherence

Low earth orbit (LEO) satellite communication systems suffer a large and fast-varying Doppler shift which has to be estimated and compensated before any demodulation processing. In this paper, a novel Doppler shift estimation and compensation method is proposed. A cascading algorithm consisting of a time-vary Burg (TV-Burg) spectral analyzer and an alpha-beta filter is designed to provide a Doppler estimate at each sampling point recursively. Doppler compensation is then applied to each data sample using the corresponding estimate. The proposed method has the following two main characteristics. Firstly, neither satellite ephemerides nor training sequences is required for estimating the Doppler frequency. That is, it is a blind estimation method. Secondly, all processing units of this method are time-recursive, and they can update the outputs whenever a new data sample is received. As a result, the proposed method is able to track the fast time-varying Doppler shift continuously, thus reducing the processing budget at each sampling point and making it hardware-efficient. Simulation results demonstrate the effectiveness of the proposed method in a low SNR environment. Furthermore, the proposed method has been implemented on the Xilinx ZYNC-7100 FPGA and verified on a general-purpose

Let X = (Xt)t≥0 be a stochastic process issued from $x \in \mathbb{R}$ that admits a marginal stationary measure v, i.e. vPtf = vf for all t ≥ 0, where $\textbf{P}_t\,f(x)= \mathbb{E}_x[f(\textbf{X}_t)]$ . In this paper, we introduce the (resp. biorthogonal) spectral projections correlation functions which are expressed in terms of projections.” Also, update first published online date, if available. into the eigenspaces of Pt (resp. and of its adjoint in the weighted Hilbert space L2 (v)). We obtain closed-form expressions involving eigenvalues, the condition number and/or the angle between the projections in the following different situations: when X = X with X = (Xt)t ≥ 0 being a Markov process, X is the subordination of X in the sense of Bochner, and X is a non-Markovian process which is obtained by time-changing X with an inverse of a subordinator. It turns out that these spectral projections correlation functions have different expressions with respect to these classes of processes which enables to identify substantial and deep properties about their dynamics. This interesting fact can be used to design original statistical tests to make inferences, for example, about the path properties of the process (presence of jumps), distance from symmetry (self-adjoint or non-self-adjoint) and short-to-long-range dependence. To reveal the usefulness of our results, we apply them to a class of non-self-adjoint Markov semigroups studied in Patie and Savov (to appear, Mem. Amer. Math. Soc., 179p), and then time-change by subordinators and their inverses.

Cyclostationarity is an important characteristic of signals. The orthogonal frequency-division multiplexing (OFDM) signal is cyclostationary. Explicit formulas for the spectral correlation density (SCD) for OFDM signal are derived. To illustrate the spectral correlation characteristics of OFDM signal, the magnitudes of the spectral correlation functions are graphed in graphical terms as the heights of surfaces above a bi-frequency plane. The accurate analytic expression of the SCD function is very helpful for blind signal processing.

The reductase unit of p-hydroxyphenylacetate hydroxylase contains flavin mononucleotide (FMN) as a cofactor. Fluorescence decay curves measured by fluorescence up-conversion method were remarkably dependent on monitored emission wavelength. The fluorescence lifetime was shorter at the shorter emission wavelengths and longer at the longer wavelengths. Spectral shift correlation function of p-coumaric acid in water and FMN in C1 protein in buffer solution were expressed by two-exponential functions. Correlation times, phi1 and phi2, of p-coumaric acid were 0.053 and 0.650 ps, respectively, which was similar to previous works. phi1 and phi2 of C1 were 0.455 and 250 ps, respectively. The Stokes shift from t=0 to t=infinity was 2200 cm(-1), while it is 500 cm(-1) in the static Stokes shift obtained by the solvent effect of the fluorescence spectrum under static excitation. This suggests that the isoalloxazine ring of FMN in C1 is exposed in hydrophilic environment. Such large Stokes shift was unusual among flavoproteins. The biphasic decay of the spectral correlation function in C1 was discussed and compared to the biphasic decay of tryptophan in proteins.

In this paper, we attempt to find a unified framework in which the statistics of wave propagation in random media can be understood as strengths of scattering and absorption increase. First, we discuss the weak scattering, diffusive limit without absorption. In this limit, the suppression of transmission by weak localization, the distribution of total transmission and the intensity correlation functions with displacement and polarization rotation are all described in terms of the dimensionless conductance so that these effects are explicitly linked. When absorption is introduced, the dimensionless conductance can no longer serve as a fundamental scaling parameter, but the variance of the total transmission is still able to chart the changing statistical character of propagation and localization with sample size. By examining transport at a fixed time following pulsed excitation, the affect of absorption can be removed while the growing impact of localization can be clearly discerned. The functional form of probability distributions of intensity and total transmission and of the spatial and polarization intensity correlation functions in the time domain are the same as in the frequency domain. The connection of mesoscopic fluctuations to localization can be seen in the spectral correlation function of the field, which is the Fourier transform of average pulsed transmission. The spectral field correlation function can be expressed as a product of the correlation function of the field normalized to the average amplitude in a given configuration and of the square root of the total transmission.

To explore the application of Gaofen-5’s thermal infrared (TIR) data, algorithm development and validations needs to be conducted before its launch; and image simulation is necessary to provide datasets for such research. In this paper, using ASTER imagery as the data source, a simulation method is proposed for Gaofen-5’s four TIR bands’ emissivity images. For the first 3 bands, emissivity images are generated through translation models built on spectral correlation between emissivity of Gaofen-5 bands and those of ASTER bands. For the 4th TIR band of Gaofen-5, we propose a spectral matching and spectral mixing method for emissivity image generation. To test the feasibility of our simulation method, ASTER thermal infrared emissivities are simulated and then compared with their actual values. Average relative errors of these simulated emissivities are 4.04%, 2.98%, 1.74%, −4.02% and −2.79% in 5 ASTER TIR bands, indicating that our proposed method can be used for emissivity image generation.

The microscopic spectral correlations of the Dirac operator in Yang-Mills theories coupled to fermions in $2+1$ dimensions can be related to three universality classes of random matrix theory. In the microscopic limit the orthogonal ensemble corresponds to a theory with two colors and fermions in the fundamental representation and the symplectic ensemble corresponds to an arbitrary number of colors and fermions in the adjoint representation. Using the new method of Widom, we derive an expression for the two scalar kernels which through quaternion determinants gives all spectral correlation functions in the Gaussian orthogonal ensemble (GOE) and in the Gaussian symplectic ensemble (GSE) with all fermion masses equal to zero. The result for the GOE is valid for an arbitrary number of fermions while for the GSE we have results for an even number of fermions.

This presentation briefly reviews the modeling of finite energy signals using a linear time-varying system excited by white noise. General properties of this model are consistent with intuition regarding transients produced by physical systems. The implications of these assumed model properties for observable features to obtain a detector based on the spectral correlation function are presented. The results of testing the proposed detector vis-a-vis a conventional power spectral detector are given.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this work, an attempt has been made to analyze the cyclostationarity of surface electromyography (sEMG) signals recorded during dynamic contraction of biceps brachii muscle. Twenty five healthy adult volunteers have participated in this study. The recorded signals are preprocessed and segmented into three zones, namely, non-fatigue zone, first muscle discomfort zone and fatigue zone. These signals are subjected to strip spectral correlation algorithm to estimate the spectral correlation density (SCD). Cyclic domain profile (CDP) is extracted from the normalized SCD magnitude. The area of CDP is calculated for all the three zones. The results show that strip spectral correlation algorithm based SCD estimation is able to demonstrate the cyclostationary property of sEMG signal in all three zones. It is also observed that the normalized SCD magnitude spectrum is distinct for all cases. The CDP area shows the significant variation in fatigue zone. Further it appears that cyclostationarity based on SCD can be used to differentiate different neuromuscular and pathological conditions.

Spectral un mixing has been an important technique for hyperspectral imagery processing. In spectral unmixing methods that are based on the original non-negative matrix factorization (NMF) model, unmixing accuracy is limited by the lack of appropriate constraints that derived from the inherent properties of hyperspectral images. It was shown that the correlation analysis can be extended for NMF-based spectral unmixing model, based on the advantage that the spatial and spectral correlation features can provide necessary constraints for NMF to overcome related problems and obtain better performance. However, many correlation constrained NMF models prefer adopting only spatial correlation, the neglect of spectral correlation may limit the accuracy and application scope. This letter presents a novel method of imposing spatial and spectral correlation constraints simultaneously on the NMF-based unmixing model by adopting Markov Random Field (MRF) and complexity pursuit respectively. The related issues, including reducing the deviation of endmembers' spatial energy distribution and substituting the predictability of signal for piecewise smoothness of spectra, were studied together. Experiments showed that the new proposed NMF model was superior to some existing NMF models with only spatial correlation constraint in terms of estimation accuracy of endmember spectra and fractional abundances.