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In this paper, a decision tree algorithm based on time-domain digital technique is developed for the identification and classification of diverse radar intra-pulse modulated signals for the electronic intelligence system in real-time. This includes linear frequency modulation, non-linear frequency modulation, stepped frequency modulation and bi-phase modulation. The received signal is digitised and the instantaneous phase and high accuracy instantaneous frequency are estimated. The instantaneous amplitude is also estimated to get the start and stop of the pulse. Instantaneous parameters are estimated using a moving autocorrelation technique. The proposed algorithm is employed on the instantaneous frequency and the modulation is identified. The modulation type and modulation parameter are important for unique radar identification when similar radars are operating in a dense environment. Simulations are carried out at various SNR conditions and results are presented. The model for algorithm is developed using a system generator and implemented in FPGA. These results are compared when the proposed algorithm is used with the existing digital in-phase and quadrature-phase (DIQ) technique of instantaneous frequency and amplitude estimation.

Ultrafast pulsed lasers can be used to measure the phenomena occurring at femtosecond (fs) (10−15 s) time scale which include photo synthesis, protein collapsing and sub-atomic vibrations. In normal course, the temporal profile of the nanosecond (ns) pulsed lasers can be estimated by using the standard detectors because the recombination time of the electron-hole pair is of the order of ns. Nevertheless, the electronic equipment and the standard detectors are insufficient to measure the temporal profile of the ultrafast pulsed lasers. In the present investigation, we compare various techniques to measure the temporal profile of the ultrafast lasers which is followed by the details of the experimental configuration of an autocorrelation technique. The temporal profile of the ultrafast pulsed lasers has shown various pulse width values in the experimental set-up when recorded at different configurations.

The shape of two objects hidden behind a thin scattering medium is retrieved by the presented method. One of the two objects keeps stationary, while the other one is supposed to be gradually moving, and the Euclidean distance between them is always beyond the range of the 3D optical memory effect. We capture two speckle patterns to image the two isolated objects by using a developed speckle-differential-based strategy and the traditional speckle autocorrelation technique. The feasibility of our method is demonstrated by theoretical analysis and a set of experiments.

In this work we theoretically confirm that the constant term of the measured signal of the Fringe-Resolved Autocorrelation technique using a two-photon absorption photodiode as sensor provides the same information as the intensity autocorrelation amplitude obtained from this technique. We achieve this result by computing the temporal intensity pulse distributions of focused femtosecond pulses around the focal region of an aberrated system and calculating both the intensity autocorrelation and the quadratic interferometric autocorrelation equation. Pulses with initial Full-Width Half-Maximum pulse widths of 50.0 and 20.0 [fs] were focused with a BK7 double convex lens.

During navigation, information at multiple scales needs to be integrated. Single-unit recordings in rodents suggest that gradients of temporal dynamics in the hippocampus and entorhinal cortex support this integration. In humans, gradients of representation are observed, such that granularity of information represented increases along the long axis of the hippocampus. The neural underpinnings of this gradient in humans, however, are still unknown. Current research is limited by coarse fMRI analysis techniques that obscure the activity of individual voxels, preventing investigation of how moment-to-moment changes in brain signal are organized and how they are related to behavior. Here, we measured the signal stability of single voxels over time to uncover previously unappreciated gradients of temporal dynamics in the hippocampus and entorhinal cortex. Using our novel, single voxel autocorrelation technique, we show a medial-lateral hippocampal gradient, as well as a continuous autocorrelation gradient along the anterolateral-posteromedial entorhinal extent. Importantly, we show that autocorrelation in the anterior-medial hippocampus was modulated by navigational difficulty, providing the first evidence that changes in signal stability in single voxels are relevant for behavior. This work opens the door for future research on how temporal gradients within these structures support the integration of information for goal-directed behavior.

Characterization of ultrashort vacuum and deep ultraviolet pulses is important in view of applications of those pulses for spectroscopic and dynamical imaging of atoms, molecules, and materials. We present an extension of the autocorrelation technique, applied for measurement of the pulse duration via a single Gaussian function. Analytic solutions for two-photon ionization of atoms by Gaussian pulses are used along with an expansion of the pulse to be characterized using multiple Gaussians at multi-color central frequencies. This approach allows one to use two-photon autocorrelation signals to characterize isolated ultrashort pulses and pulse trains, i.e., the time-dependent amplitude and phase variation of the electric field. The potential of the method is demonstrated using vacuum and deep ultraviolet pulses and pulse trains obtained from numerical simulations of macroscopic high harmonic spectra.

Trade winds localized within the Western Equatorial Pacific express lagged and statistically significant correlations to sunspot numbers as well as to streamflow in rivers of the Southern Rocky Mountains. Both correlation sets were integrated in a linear regression analysis to produce relatively accurate sub-decadal streamflow forecasts for an annual and a 5-year average. In comparison to the autocorrelation technique, the prototyped method yielded the highest correlations, the highest goodness-of-fit scores, and the lowest root mean squared errors, for both the 5-year average and the annual average assignments. Of all of the cases examined, the highest Kolmogorov-Smirnov test scores between observation and prediction were found for the single solar-based forecast 5 years in advance for the 60-month average streamflow of the Animas River in New Mexico.

We report on the formation of near-transform-limited sub-picosecond pulses from a mid-infrared quantum cascade laser with several Watts of peak power. Starting from a frequency-modulated phase-locked state, which most efficiently exploits the gain from the active region, ultrashort high peak power pulses are generated via external pulse compression. The temporal nature of these pulses is assessed by means of coherent beatnote interferometry, optical sampling and an interferometric autocorrelation technique.

International audience; Postfilamentation channel resulting from filamentation of freely propagating 744-nm, 5-mJ, 110-fs pulse in the corridor air is examined experimentally and in simulations. The longitudinal extension of postfilament was determined to be 55–95 m from the compressor output. Using single-shot angle-wavelength spectra measurements, we observed a series of red-shifted maxima in the spectrum, localized on the beam axis with the divergence below 0.5 mrad. In the range 55–70 m, the number of maxima and their red-shift increase with the distance reaching 1 μm, while the pulse duration measured by the autocorrelation technique is approximately constant. Further on, for distances larger than 70 m and up to 95 m, the propagation is characterized by the suppressed beam divergence and unchanged pulse spectrum. The pulse duration increases due to the normal air dispersion.

Seismic sequences are a powerful tool to locally infer geometrical and mechanical properties of faults and fault systems. In this study, we provided detailed location and characterization of events of the 3&ndash, 7 July 2020 Irpinia sequence (southern Italy) that occurred at the northern tip of the main segment that ruptured during the 1980 Irpinia earthquake. Using an autocorrelation technique, we detected more than 340 events within the sequence, with local magnitude ranging between &minus, 0.5 and 3.0. We thus provided double difference locations, source parameter estimation, and focal mechanisms determination for the largest quality events. We found that the sequence ruptured an asperity with a size of about 800 m, along a fault structure having a strike compatible with the one of the main segments of the 1980 Irpinia earthquake, and a dip of 50&ndash, 55&deg, at depth of 10.5&ndash, 12 km and 60&ndash, 65&deg, at shallower depths (7.5&ndash, 9 km). Low stress drop release (average of 0.64 MPa) indicates a fluid-driven initiation mechanism of the sequence. We also evaluated the performance of the earthquake early warning systems running in real-time during the sequence, retrieving a minimum size for the blind zone in the area of about 15 km.

Interpretation of averaged evoked potentials is difficult when the time relationship between stimulus and response is not constant. Later components are more prone to latency jitter, making them insufficiently reliable for routine clinical use even though they could contribute to greater understanding of the functioning of polysynaptic components of the afferent nervous system. This study is aimed at providing a simple but effective method of identifying and quantifying latency jitter in averaged evoked potentials. Autocorrelation techniques were applied within defined time windows on simulated jittered signals embedded within the noise component of recorded evoked potentials and on real examples of somatosensory evoked potentials. We demonstrated that the technique accurately identifies the distribution and maximum levels of jitter of the simulated components and clearly identifies the jitter properties of real evoked potential recording components. This method is designed to complement the conventional analytical methods used in neurophysiological practice to provide valuable additional information about the distribution of latency jitter within an averaged evoked potential. It will be useful for the assessment of the reliability of averaged components and will aid the interpretation of longer-latency, polysynaptic components such as those found in nociceptive evoked potentials.

In this work, the DC two-photon absorption signal offset ( D C T P A ) and the amplitude of the autocorrelation ( A A C ) are measured simultaneously around the focal point of an apochromatic microscope objective using the z-scan autocorrelation technique. The A A C is obtained from the nonlinear sensor response given by the two-photon-absorption, generated in a GaAsP photodiode, for femtosecond laser pulses. We verify that the change in the D C T P A signal along z is coincident with the amplitude of the intensity autocorrelation, and that the highest amplitude of the AC is reached at the same position as the highest amplitude of the D C T P A signal. The D C T P A signal is typically used as a reference for the alignment in a collinear intensity autocorrelator, and we show that it can also be used as a practical procedure to estimate the depth of focus. The D C T P A signal measurement allows us to locate the optimum spatial-temporal coupling given by the highest amplitude of the intensity autocorrelation. Additionally, we find a variation in the pulse duration within the same region due to the radially varying group delay dispersion.

This article studies a procedure that facilitates short-time, deterministic predictions of the wave-induced motion of a marine vessel, where it is understood that the future motion of the vessel is calculated ahead of time. Such predictions are valuable to assist in the execution of many marine operations (crane lifts, helicopter landings, etc.), as a specific prediction can be used to inform whether it is safe, or not, to carry out the particular operation within the nearest time horizon. The examined prediction procedure relies on observations of the correlation structure of the wave-induced response in study. Thus, predicted (future) values ahead of time for a given time history recording are computed through a mathematical combination of the sample autocorrelation function and previous measurements recorded just prior to the moment of action. Importantly, the procedure does not need input about the exciting wave system, and neither does it rely on off-line training. In the article, the prediction procedure is applied to experimental data obtained through model-scale tests, and the procedure's predictive performance is investigated for various irregular wave scenarios. The presented results show that predictions can be successfully made in a time horizon corresponding to about 8–9 wave periods ahead of current time (the moment of action).

Complex sequences with constant magnitude in the time domain and good aperiodic autocorrelation properties are of fundamental interest due to its applications. Their design typically involves the minimization of a nonlinear function that strives to make equal to zero the correlation coefficients in a region of interest. In this letter, the design of unimodular sequences whose aperiodic autocorrelation and aperiodic complementary autocorrelation vanish for a given set of lags is proposed. The sequences are obtained by minimizing a generalized weighted integrated sidelobe level that depends on their complete second-order characterization. Numerical results show that the proposed sequences are ideally suited to identify strictly linear as well as widely linear systems.

This paper studies the effect of autocorrelation on the smoothness of the trend of a univariate time series estimated by means of penalized least squares. An index of smoothness is deduced for the case of a time series represented by a signal-plus-noise model, where the noise follows an autoregressive process of order one. This index is useful for measuring the distortion of the amount of smoothness by incorporating the effect of autocorrelation. Different autocorrelation values are used to appreciate the numerical effect on smoothness for estimated trends of time series with different sample sizes. For comparative purposes, several graphs of two simulated time series are presented, where the estimated trend is compared with and without autocorrelation in the noise. Some findings are as follows, on the one hand, when the autocorrelation is negative (no matter how large) or positive but small, the estimated trend gets very close to the true trend. Even in this case, the estimation is improved by fixing the index of smoothness according to the sample size. On the other hand, when the autocorrelation is positive and large the simulated and estimated trends lie far away from the true trend. This situation is mitigated by fixing an appropriate index of smoothness for the estimated trend in accordance to the sample size at hand. Finally, an empirical example serves to illustrate the use of the smoothness index when estimating the trend of Mexico’s quarterly GDP.

Pseudo-random sequences with good statistical properties, such as low autocorrelation, high linear complexity and large 2-adic complexity, have been used in designing reliable stream ciphers. In this paper, we obtain the exact autocorrelation distribution of a class of binary sequences with three-level autocorrelation and analyze the 2-adic complexity of this class of sequences. Our results show that the 2-adic complexity of such a binary sequence with period N is at least (N + 1) − log2 (N + 1). We further show that it is maximal for infinitely many cases. This indicates that the 2-adic complexity of this class of sequences is large enough to resist the attack of the rational approximation algorithm (RAA) for feedback with carry shift registers (FCSRs).

In this paper, a noise robust formant frequency estimation scheme is developed based on a spectral model matching algorithm. Considering the vocal tract as an autoregressive system, a spectral model of repeated autocorrelation function RACF of band-limited speech signal is proposed. It is shown that because of the repeated autocorrelation operation on band-limited signal, the proposed model can exhibit prominent formant characteristics. First from given noisy speech observations, an adaptive band selection criterion is developed. Next, on each resulting band-limited noisy speech signal, a repeated autocorrelation operation is carried out, which not only reduces the effect of noise but also strengthens the dominant poles corresponding to the formant frequencies. Finally, spectrum of the RACF is computed and instead of direct spectral peak picking, a model fitting scheme is introduced to find out model parameters which lead to formant estimation. The proposed algorithm has been tested on natural vowels as well as some naturally spoken sentences in the presence of different environmental noises. It is found that the proposed scheme provides better formant estimation accuracy in comparison to some of the existing methods at low levels of signal-to-noise ratio.

Autocorrelation domain is a proper domain for clean speech signal and noise separation. In this paper, a method is proposed to decrease effects of noise on the clean speech signal, autocorrelation-based noise subtraction (ANS). Then to deal with the error introduced by assumption that noise and clean speech signal are uncorrelated, two methods are proposed. Also to improve recognition rate of speech recognition system, overestimation parameter is used. Finally, with the addition of energy and cepstral mean and variance normalization to features of speech, recognition rate has improved considerably in comparison to standard features and other correlation-based methods. The proposed methods are tested on the Aurora 2 database. Between different proposed methods, autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization (ANSSOEMV) method has a best recognition rate improvement in average than MFCC features which is 64.91% on the Aurora 2 database.

M-estimation (robust estimation) for the parameters in nonlinear mixed effects models using Fisher scoring method is investigated in the article, which shares some of the features of the existing maximum likelihood estimation: consistency and asymptotic normality. Score tests for autocorrelation and random effects based on M-estimation, together with their asymptotic distribution are also studied. The performance of the test statistics are evaluated via simulations and a real data analysis of plasma concentrations data.

Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time-delayed arrival from the event, we propose an autocorrelation-based stacking method that designs a denoising filter from all the traces, as well as a multi-channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace’s autocorrelation is centred at zero in the lag domain. The effect of white noise is concentrated near zero lag; thus, the filter design requires a predictable adjustment of the zero-lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with coloured noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces.

Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation requires the whole network to be observed. This is practically infeasible if network size is huge (e.g., Facebook, Twitter, Weibo, WeChat, etc). In that case, one has to rely on sampled network data to infer about spatial autocorrelation. By doing so, network relationships (i.e., edges) involving unsampled nodes are overlooked. This leads to distorted network structure and underestimated spatial autocorrelation. To solve the problem, we propose here a novel solution. By temporarily assuming that the spatial autocorrelation is small, we are able to approximate the likelihood function by its first order Taylor's expansion. This leads to the method of approximate maximum likelihood estimator (AMLE), which further inspires the development of paired maximum likelihood estimator (PMLE). Compared with AMLE, PMLE is computationally superior and thus is particularly useful for large scale network data analysis. Under appropriate regularity conditions (without assuming a small spatial autocorrelation), we show theoretically that PMLE is consistent and asymptotically normal. Numerical studies based on both simulated and real datasets are presented for illustration purpose.

The Legendre sequence possesses several desirable features of pseudorandomness in view of different applications such as a high linear complexity (profile) for cryptography and a small (aperiodic) autocorrelation for radar, gps, or sonar. Here we prove the first nontrivial bound on its arithmetic autocorrelation, another figure of merit introduced by Mandelbaum for errorcorrecting codes.

To improve the precision of frequency estimation, a phase correction autocorrelation-based frequency estimation method for sinusoidal signal is proposed. Firstly, a phase correction autocorrelation is developed to reduce the effect of non-half period sampling signal on autocorrelation. Secondly, reference signal is generated according to phase correction autocorrelation signal. Finally, an error function between phase correction autocorrelation signal and reference signal is constructed and frequency estimation is obtained by calculating the minimum of error function. To demonstrate the superiority of the proposed method, computational complexity is analyzed, simulations and experiments are performed. Theoretical analysis and simulations demonstrate that the proposed method reduces the influence of non- half period sampling signal and has better frequency estimation performance than the interpolated DFT method, the modified covariance method for correlation, the two-stage autocorrelation method and the expanded autocorrelation method. The measurement experiments of LFMCW radars validate the effectiveness and superiority of the proposed method in practice. Phase correction autocorrelation is devised to correct the phase and reduce bias.Proposed method reduces the effect of sinusoidal signal's non-half period sampling.Proposed method is computationally efficient.

Waveform separation based on matched filtering leads to autocorrelation sidelobe and cross correlation, which deteriorate the performance of multiple-input multiple-output synthetic aperture radar (MIMO-SAR). This letter investigates the performance of a waveform-separation approach employing an extended space-time coding (STC) scheme for MIMO-SAR. Using the autocorrelation property of multiphase complementary codes, we propose a novel STC scheme to effectively cancel out both the autocorrelation sidelobe and cross correlation. Using theoretical analysis also confirmed by simulations, we show that the proposed scheme decreases the sidelobe ratio while increasing the signal-to-noise ratio, leading to high-quality.

The stationary Markov process is considered and its circular autocorrelation function is investigated. More specifically, the transition density of the stationary Markov circular process is defined by two circular distributions, and we elucidate the structure of the circular autocorrelation when one of these distributions is uniform and the other is arbitrary. The asymptotic properties of the natural estimator of the circular autocorrelation function are derived. Furthermore, we consider the bivariate process of trigonometric functions and provide the explicit form of its spectral density matrix. The validity of the model was assessed by applying it to a series of wind direction data.

The autocorrelation method, proposed at first by [E. K. K. H. Salje, M. I. A. Carpenter, T. H. Malcherek, T. Boffa Ballaran, Eur. J. Min. 2000, 12, 503.] for the evaluation of FTIR data, is an efficient method the evaluation of data depending on external parameters like temperature, pressure, chemical composition etc. In this study a new single parameter value λ′(ω′) is proposed which is computational more efficient with a higher reproducibility than the Δcorr parameter proposed earlier. The method is additionally transferred from Fourier transform infrared (FTIR) spectroscopy to X-ray diffraction data. It is shown that the autocorrelation method has some beneficial properties compared to the often used Pearson’s r-value.

A novel digital signal processing-based method for phase noise measurement on sinusoidal signals is proposed. The method obtains the power spectral density of the phase noise by Fourier transforming its autocorrelation function. The autocorrelation function of the phase noise is in turn derived from the cyclic complementary autocorrelation function of the analytical version of the sinusoidal signal under test. The method proves that, taking advantage of the cyclostationarity properties of the signal under test, it is possible to improve the sensitivity in phase noise measurements by diminishing the effects of the instrumentation noise, which mainly limits the performance of the majority of the digital processing-based approaches.

This paper presents a Rough Set Theory (RST) based approach for predicting the surface contamination level of porcelain type insulators at different environmental condition. The sample insulators have been contaminated by Solid layer Method (SLM) according to IEC60507. Leakage current of the insulators are recorded at different contamination level and at different humidity conditions. In the proposed method, autocorrelation technique is used for feature selection from leakage current because it is well suited for non-stationary leakage current signals and cancels out the effect of noise present in the signal. Rough Set Theory is used for classification of extracted features as it suits well with the correlation technique. Result shows that the proposed method is able to detect the contamination level with acceptable range of accuracy.

We present relative astrometric measurements of visual binaries, made in 2015-2016 with the speckle camera pupil interferometry speckle camera and coronagraph (PISCO) at the 1-m Epsilon telescope of the C2PU facility (Observatoire de la Côte d'Azur, Calern site). Our observing list contains orbital couples as well as binaries whose motion is still uncertain. From our 1,233 observations of 1,173 multiple stars, we obtained 1,170 new measurements with angular separations in the range 0″.1 — 8″.2, and an average accuracy of 0″.0078. The mean error on the position angles is 0°.54. Most of the position angles were determined without the usual 180° ambiguity with the application of the direct vector autocorrelation technique and/or by inspection of the long integration files.

We consider the autocorrelation function technique for obtaining excitation spectra for indistinguishable particles. The interacting particles are described by coherent superpositions of configurations built from time-dependent spin-orbitals. The fermionic or bosonic character of the particles is taken into account by considering Slater determinants or permanents, respectively. The approach involves the calculation of overlaps between nonorthonormal Slater determinants for fermions and permanents for bosons. Efficient methods already exist for fermions. In the case of bosons, the evaluation of permanents generally scales exponentially with system size. We present an efficient approach for bosons for calculating the excitation spectrum, which circumvents this scaling. The approach is illustrated and validated by comparison with an analytical model for interacting bosons, for a system with a number of bosons so large that the autocorrelation technique could not be applied without the present development.We consider the autocorrelation function technique for obtaining excitation spectra for indistinguishable particles. The interacting particles are described by coherent superpositions of configurations built from time-dependent spin-orbitals. The fermionic or bosonic character of the particles is taken into account by considering Slater determinants or permanents, respectively. The approach involves the calculation of overlaps between nonorthonormal Slater determinants for fermions and permanents for bosons. Efficient methods already exist for fermions. In the case of bosons, the evaluation of permanents generally scales exponentially with system size. We present an efficient approach for bosons for calculating the excitation spectrum, which circumvents this scaling. The approach is illustrated and validated by comparison with an analytical model for interacting bosons, for a system with a number of bosons so large that the autocorrelation technique could not be applied without the present development.

We pursue locking of optical frequency comb for absolute frequency measurement. For the purpose, the characteristics of frequency comb are measured. At first, the characteristics in the fs pulse laser are measured by an autocorrelation technique. Second, the frequency stability of the reputation rate of an optical frequency comb based on a mode-locked fiber laser is measured. The repetition frequency is locked to a frequency generated from a synthesizer, which is referred to a Rubidium frequency standard. The stability was 3 Hz at averaging time of 1 s under locking. The best stability was better than 0.3 mHz at averaging time of 100 s. Finally, the wavelength spectrum of the pulse laser is broadened using self-phase modulation in highly nonlinear fibers. The spectrum bandwidth was broadened to 824 nm.

A signal processing technique utilizing autocorrelation of backscattered signals was designed and implemented in a 1.5 &micro, m all-fiber wind sensing Coherent Doppler Lidar (CDL) system to preprocess atmospheric signals. The signal processing algorithm&rsquo, s design and implementation are presented. The system employs a 20 kHz pulse repetition frequency (PRF) transmitter and samples the return signals at 400 MHz. The logic design of the autocorrelation algorithm was developed and programmed into a field programmable gate array (FPGA) located on a data acquisition board. The design generates and accumulates real time correlograms representing average autocorrelations of the Doppler shifted echo from a series of adjustable range gates. Accumulated correlograms are streamed to a host computer for subsequent processing to yield a line of sight wind velocity. Wind velocity estimates can be obtained under nominal aerosol loading and nominal atmospheric turbulence conditions for ranges up to 3 km.

An algorithm, based on frequency domain accumulation for the cyclic autocorrelation of MC-CDMA signals, is proposed to estimate the signal parameters: useful data duration, symbol duration, and chip duration in multipath fading channel. Firstly the cycle autocorrelation of received MC-CDMA signals is computed, then frequency domain accumulation is used to reduce the influence of noise. At last, through interval measurement between the peak pulses in different slices, parameters mentioned above can be estimated. Meanwhile, for the estimation of symbol duration, a novel accumulative average method is developed: by averaging amplitudes of the spectral lines in each column parallel to the time-delay axis in the three-dimensional diagram, the symbol duration can be obtained. The cyclic autocorrelation expression of MC-CDMA signal has been derived, and simulation results show that the proposed algorithm is effective and works well under the condition of low signal-to-noise ratio (SNR).

In the article, the application of the electrical spike, instead of the tone-burst, to the ultrasonic pulsed-Doppler velocity profiling the first time is presented. To generate ultrasound, the ultrasonic sensor is excited by the spike. For the Doppler method, the spike excitation was not used previously. It has been used with wideband signal processing methods. In our study, first, the fluid-velocity profile measurement using spike excitation has been carried out for single-phase pipe flow. The received data have been validated by the logarithmic law of the wall, for turbulent flow regime. The flow rate calculated by using the mean velocity profile well coincides with the flow-meter data. Furthermore, the spike signal and the Doppler method are used in the multiwave ultrasonic velocity profile (UVP) method, for the bubbly flow measurement. The experiments show that, if the spike signal is slowly damped, the generated ultrasonic wave is similar to the tone-burst that is required for the Doppler method, but in this case, usually the pulse length is smaller and depends on the damping and sensor characteristics. The autocorrelation technique is used for echo signal processing. The derived UVP systems inherit the advantages from both the spike excitation and Doppler method.

Display Omitted Gaussian noise samples are delta correlated; ACF(t)=ź(t).We can do spectrum sensing with the Euclidean distance between ACF(t) and a reference line.The Euclidean distance method performs better than the ED and ACF(1) methods. Spectrum sensing is an important aspect of cognitive radios. This paper describes a method for spectrum sensing based on the autocorrelation of the received samples. The proposed method was evaluated by means of experiments wherein the probabilities of detection and false alarm at different signal-to-noise ratios (SNRs) were observed. The platform used for the experiments was a set of Universal Software Radio Peripheralź (USRPź) devices acting as radio frequency front ends in combination with GNU Radio software. Since the signal processing was performed in the software domain, Gaussian noise of different levels was emulated by changing the standard deviation of a Python random number generator. In addition, the output power of a signal generator was varied to obtain different levels of SNR. A metric called the Euclidean distance was derived to analyze the autocorrelation of the samples received by the USRPź device in order to decide between two possible situations: only noise present or signal plus noise present. The proposed method was compared with two methods: one based on the value of the autocorrelation at the first lag and another one based on the power of the signal, known as energy detection spectrum sensing technique.

The traditional and most used measure for serial dependence in a time series is the autocorrelation function. This measure gives a complete characterization of dependence for a Gaussian time series, but it often fails for nonlinear time series models as, for instance, the generalized autoregressive conditional heteroskedasticity model (GARCH), where it is zero for all lags. The autocorrelation function is an example of a global measure of dependence. The purpose of this article is to apply to time series a well-defined local measure of serial dependence called the local Gaussian autocorrelation. It generally works well also for nonlinear models, and it can distinguish between positive and negative dependence. We use this measure to construct a test of independence based on the bootstrap technique. This procedure requires the choice of a bandwidth parameter that is calculated using a cross validation algorithm. To ensure the validity of the test, asymptotic properties are derived for the test functional and for the bootstrap procedure, together with a study of its power for different models. We compare the proposed test with one based on the ordinary autocorrelation and with one based on the Brownian distance correlation. The new test performs well. Finally, there are also two empirical examples.

Homogeneity of between-individual variance and autocorrelation coefficients is one of assumptions in the study of longitudinal data. However, the assumption could be challenging due to the complexity of the dataset. In the paper we propose and analyze nonlinear mixed models with AR(1) errors for longitudinal data, intend to introduce Huber's function in the log-likelihood function and get robust estimation, which may help to reduce the influence of outliers, by Fisher scoring method. Testing of homogeneity of variance among individuals and autocorrelation coefficients on the basis of Huber's M-estimation is studied later in the paper. Simulation studies are carried to assess performance of score test we proposed. Results obtained from plasma concentrations data are reported as an illustrative example.

Sequences with low aperiodic autocorrelation sidelobes are well known to have extensive applications in active sensing and communication systems. In this paper, we consider the problem of minimizing the weighted integrated sidelobe level (WISL), which can be used to design sequences with impulse-like autocorrelation and zero (or low) correlation zone. Two algorithms based on the general majorization-minimization method are developed to tackle the WISL minimization problem and the convergence to a stationary point is guaranteed. In addition, the proposed algorithms can be implemented via fast Fourier transform (FFT) operations and thus are computationally efficient, and an acceleration scheme has been considered to further accelerate the algorithms. Moreover, the proposed methods are extended to optimize the $\ell_{p}$-norm of the autocorrelation sidelobes, which lead to a way to minimize the peak sidelobe level (PSL) criterion. Numerical experiments show that the proposed algorithms can efficiently generate sequences with virtually zero autocorrelation sidelobes in a specified lag interval and can also produce very long sequences with much smaller PSL compared with some well known analytical sequences., arXiv admin note: text overlap with arXiv:1501.02252

In neutron coincidence counting using the shift register autocorrelation technique, a predelay is inserted before the opening of the ( R + A )-gate. Operationally the purpose of the predelay is to ensure that the ( R + A )- and A -gates have matched effectiveness, otherwise a bias will result when the difference between the gates is used to calculate the accidentals corrected net reals coincidence rate. The necessity for the predelay was established experimentally in the early practical development and deployment of the coincidence counting method. The choice of predelay for a given detection system is usually made experimentally, but even today long standing traditional values (e.g., 4.5 µs) are often used. This, at least in part, reflects the fact that a deep understanding of why a finite predelay setting is needed and how to control the underlying influences has not been fully worked out. In this paper we attempt to gain some insight into the problem. One aspect we consider is the slowing down, thermalization, and diffusion of neutrons in the detector moderator. The other is the influence of deadtime and electronic transients. These may be classified as non-ideal detector behaviors because they are not included in the conventional model used to interpret measurement data. From improved understanding of the effect of deadtime and electronic transients on the predelay bias in neutron coincidence counting, the performance of both future and current coincidence counters may be improved.

A new technique to quantify signal-to-noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson-Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first-order linear interpolation and nearest neighbourhood combined with first-order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation.