Autocorrelation Of Signal Research Articles

Central venous pressure estimation from ultrasound assessment of the jugular venous pulse.

ObjectivesAcquiring central venous pressure (CVP), an important clinical parameter, requires an invasive procedure, which poses risk to patients. The aim of the study was to develop a non-invasive methodology for determining mean-CVP from ultrasound assessment of the jugular venous pulse.MethodsIn thirty-four adult patients (age = 60 ± 12 years; 10 males), CVP was measured using a central venous catheter, with internal jugular vein (IJV) cross-sectional area (CSA) variation along the cardiac beat acquired using ultrasound. The resultant CVP and IJV-CSA signals were synchronized with electrocardiogram (ECG) signals acquired from the patients. Autocorrelation signals were derived from the IJV-CSA signals using algorithms in R (open-source statistical software). The correlation r-values for successive lag intervals were extracted and used to build a linear regression model in which mean-CVP was the response variable and the lagging autocorrelation r-values and mean IJV-CSA, were the predictor variables. The optimum model was identified using the minimum AIC value and validated using 10-fold cross-validation.ResultsWhile the CVP and IJV-CSA signals were poorly correlated (mean r = -0.018, SD = 0.357) due to the IJV-CSA signal lagging behind the CVP signal, their autocorrelation counterparts were highly positively correlated (mean r = 0.725, SD = 0.215). Using the lagging autocorrelation r-values as predictors, mean-CVP was predicted with reasonable accuracy (r2 = 0.612), with a mean-absolute-error of 1.455 cmH2O, which rose to 2.436 cmH2O when cross-validation was performed.ConclusionsMean-CVP can be estimated non-invasively by using the lagged autocorrelation r-values of the IJV-CSA signal. This new methodology may have considerable potential as a clinical monitoring and diagnostic tool.

Discrete Lissajous and Recton Functions: A New Method for Frequency Response Measurements

An innovative and completely new technique so called discrete Lissajous functions and recton functions for signal analysis and measurement is explained. The contribution of the study is that it is a new digital method of obtaining information on frequency, change of frequency, phase and phase shift as well as auto-correlation, cross-correlation and energy functions of signals in digital form on-line that may be difficult to be provided in analog form and available for measurement and control applications at each sampling instance directly. The discrete Lissajous figures and recton functions can also be sketched in digital image form for further analysis of signals and systems. They are based on an algorithm utilizing discrete convolutions of discrete time signals. They can be depicted in 3D form. They give more information than classical analog Lissajous figures obtained on an oscilloscope screen. They can be used for several applications from chaotic systems to biomedical applications requiring to finding correlations, energies as well as frequency and phase information of the signals and controlling such systems. In this study, the application of them to systems steady-state, mainly frequency response analysis is explained after giving basic definitions.

Characterization of Localized Atmospheric Turbulence Layer Using Laser Light Backscattered off Moving Target

A concept of atmospheric turbulence characterization using laser light backscattered off a moving unresolved target or a moving target with a glint is considered and analyzed through wave-optics numerical simulations. The technique is based on analysis of the autocorrelation function and variance of the power signal measured by the target-in-the-loop atmospheric sensing (TILAS) system composed of a single-mode-fiber-based optical transceiver and the moving target. It is shown that the TILAS received power signal autocorrelation function strongly depends on the turbulence distribution and is weakly sensitive to the turbulence strength, while the signal variance equally depends on these parameters. Assuming the atmospheric turbulence model can be represented by a single spatially localized turbulence layer and the target position and speed are known independently, consecutive analysis of the autocorrelation function and variance of the TILAS signal allows evaluation of both the turbulence layer strength and position along the optical propagation path. It is also demonstrated that the autocorrelation function can potentially be used for the atmospheric turbulence outer scale estimation.

Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework

Rolling bearing defects in induction motors are usually diagnosed using vibration signal analysis. For accurate detection of rolling bearing defects, appropriate feature extraction from vibration signals is necessary, failure of which may lead to incorrect interpretation. Considering the above fact, this article presents an autocorrelation aided feature extraction method for diagnosis of rolling bearing defects. To this end, the vibration signals of healthy as well as different faulty bearings were recorded using accelerometers and autocorrelation of the respective vibration signals were done to examine their self-similarity in time scale. Following this, several statistical, hjorth as well as non-linear features were extracted from the respective vibration correlograms and were subjected to feature reduction using recursive feature elimination technique. The dimensionally reduced top ranked feature vectors were subsequently fed to a random forest classifier for classification of vibration signals. A large number of experiments were carried out for (i) three different fault diameters at (ii) four different shaft speeds and also at (iii) two different sampling frequencies. Besides, for each condition, six binary class and one multiclass classification problem is also addressed in this paper, resulting in a total 112 different classification tasks. It was observed that the proposed method has yielded very high accuracy in identifying different bearing defects which can be practically implemented for automated bearing fault detection of induction motors.

Iterative learning approach to active noise control of highly autocorrelated signals with applications to machinery noise

This study describes an iterative learning approach to the active control of machinery noise with high autocorrelation properties. In contrast to typical active noise control solutions, which work by adapting the transfer function of the controller, in the iterative learning control one adapts the control signal itself. Special care was taken to develop a generic solution that can handle different sorts of secondary path models including very long and non-minimum phase finite impulse response filters. To achieve that, the authors used spectral factorisation and exploit the fact that, for non-minimum phase systems, a stable inverse can be constructed if the causality constraint is relaxed and later restored by taking advantage of the periodicity of the attenuated signal. The resulting controller can be efficiently implemented on a sample-to-sample calculation basis. The behaviour and the performance of the proposed scheme are studied using computer simulations and real-world experiments on noises from an electric transformer and functional magnetic resonance imaging device. The proposed solution was also compared to normalised feedforward filtered-X least mean squares algorithm and performed much better in terms of attenuation, convergence, and robustness.

Forecast Optimization via Parameter Tuning: Performance Gain and Overfit

Most predictors used in systematic trading are designed as functions of available data streams. The functional form of these signals involves tuning a number of parameters such as the trading frequency, sample sizes, entry and exit thresholds, stop losses, and so on to improve the performance of the predictor. It is well known that such tuning leads to overfitting, meaning that the constructed signal fits historical returns well but may poorly predict their future behavior. One of the central questions that has not been previously addressed is how much each fitting parameter contributes to the overall performance improvement. In this article, this question is addressed both quantitatively and qualitatively. The author concentrates on a simple but very common in practice fitting technique consisting of smoothing the signal using the weighted moving average. It is shown that the improvement of the Sharpe ratio mainly depends on the signal autocorrelation and its alpha decay parameter. The findings are confirmed by numerical simulations. This result sheds more light on the overfitting phenomenon in the context of investment strategy construction and reveals the sources of performance improvement in forecast design. TOPICS:Big data/machine learning, portfolio construction, simulations, statistical methods Key Findings • Fitting the parameters of return predictors on historical data unavoidably leads to statistical overfitting, which may result in partial or total failure of the signal in the out-of-sample period. • This article introduces a novel framework capable of precisely estimating the level of overfitting or performance gain arising from optimization over every parameter. • An illustration of the power of the new methodology is provided using the ubiquitous example of exponential moving average smoothing of the forecast.

A Theoretical Study on a Novel Cartesian Product Operator Formalism Applicable to Strongly-Coupled Two-Spin 1/2 Systems in Solution-State and Solid-State MAS NMR Spectroscopy

Abstract In solution-state and solid-state MAS (Magic Angle Spinning) NMR (Nuclear Magnetic Resonance) spectroscopy, a comprehensive product operator formalism applicable to general two-spin 1/2 systems, including strongly-coupled systems, is proposed. The formalism employs standard product operators, based on the orthogonal coordinate system, easily evaluating the effects of RF (Radio Frequency) pulse sequences. Solid-state anisotropic interactions modulated by MAS are treated in the perturbation theory, yielding striking solid-state effects, whereas the absence of those interactions leads to solution-state cases with strong couplings. Solid-state MAS INADEQUATE spectra are found to give rise to auto-correlation signals even for the spin pairs possessing an identical isotropic chemical shift.

DETECTING SYSTEMATIC AND RANDOM COMPONENT OFSURFACE ROUGHNESS SIGNAL

The solution of the problem of separating the initial one-dimensional signal into two components –systematic and random –has an extremely wide practical application not only in the theory of information and communication (and related disci-plines), but also in mechanical engineering disciplines. For example, mechanical engineering technology being a science discipline includes the teaching about the surface quality of the machined parts and researching the surface roughness after machining these parts by cutting and grinding. The paper shows that the theoretical and actual values of roughness parameters differ significantly (up to 20 times) due to the influence of a random component that is present in the roughness signal together with a systematic com-ponent. It is necessary to identify the share of each of these components in the specified surface quality parameters in accordance with the method proposed in the paper. This method allows detecting the systematic and random components of the signal and isbased on the analysis of the signal autocorrelation function. Practical examples of this analysis are considered in detail formilled surface profilogram obtained experimentally. Both milling, which creates irregularities on the machined surface, and measurement of these irregularities are performed on modern CNC equipment: machining center 500V/5 and computer measuring station T8000, respectively. The developed and shown by examples signal separation technique is also applicable in other fields of science, technol-ogy and manufacturing. For example, when determining the signal to noise ratio in the theory of information and communication, in the field of telecommunications and telemetry, radio engineering, etc.

Autocorrelated Envelopes for early fault detection of rolling bearings

The rolling element bearings are extensively applied in rotating machines, and they are the most susceptible components in rotating machines. Early fault detection of bearings is to prevent machines from such typical failures and subsequent consequences. In this paper a detector based on Ensemble Average of Autocorrelated Envelopes (EAAE) is proposed to identify the early occurrence faults in rolling element bearings, of which the fault induced vibration signals are inevitably contaminated or masked by both additive background noise and random phase noise (or slippage between bearing components). To enhance the cyclostationary characteristics for fault detection, it utilizes the phase synchronization property of autocorrelation signals for aligning the cyclostationary signals in the lag domain to achieve an effective ensemble average which allows both types of random influences to be suppressed significantly. As a result, this detector shows very high performance of robustness in extracting the local fault signatures, which is verified by simulation signals and experimental investigations and benchmarked by the recent milestone method of Spectral Correlation (SC).

OCT Image Restoration Using Non-Local Deep Image Prior

In recent years, convolutional neural networks (CNN) have been widely used in image denoising for their high performance. One difficulty in applying the CNN to medical image denoising such as speckle reduction in the optical coherence tomography (OCT) image is that a large amount of high-quality data is required for training, which is an inherent limitation for OCT despeckling. Recently, deep image prior (DIP) networks have been proposed for image restoration without pre-training since the CNN structures have the intrinsic ability to capture the low-level statistics of a single image. However, the DIP has difficulty finding a good balance between maintaining details and suppressing speckle noise. Inspired by DIP, in this paper, a sorted non-local statics which measures the signal autocorrelation in the differences between the constructed image and the input image is proposed for OCT image restoration. By adding the sorted non-local statics as a regularization loss in the DIP learning, more low-level image statistics are captured by CNN networks in the process of OCT image restoration. The experimental results demonstrate the superior performance of the proposed method over other state-of-the-art despeckling methods, in terms of objective metrics and visual quality.

DETECTING SYSTEMATIC AND RANDOM COMPONENT OF SURFACE ROUGHNESS SIGNAL

The solution of the problem of separating the initial one-dimensional signal into two components – systematic and random – has an extremely wide practical application not only in the theory of information and communication (and related disciplines), but also in mechanical engineering disciplines. For example, mechanical engineering technology being a science discipline includes the teaching about the surface quality of the machined parts and researching the surface roughness after machining these parts by cutting and grinding. The paper shows that the theoretical and actual values of roughness parameters differ significantly (up to 20 times) due to the influence of a random component that is present in the roughness signal together with a systematic component. It is necessary to identify the share of each of these components in the specified surface quality parameters in accordance with the method proposed in the paper. This method allows detecting the systematic and random components of the signal and is based on the analysis of the signal autocorrelation function. Practical examples of this analysis are considered in detail for milled surface profilogram obtained experimentally. Both milling, which creates irregularities on the machined surface, and measurement of these irregularities are performed on modern CNC equipment: machining center 500V/5 and computer measuring station T8000, respectively. The developed and shown by examples signal separation technique is also applicable in other fields of science, technology and manufacturing. For example, when determining the signal to noise ratio in the theory of information and communication, in the field of telecommunications and telemetry, radio engineering, etc.

Adaptive Detection and Localization Exploiting the IEEE 802.11ad Standard

In this work, we exploit the sector level sweep of the IEEE 802.11ad communication standard to implement an opportunistic radar at mmWaves and derive an adaptive procedure for detecting multiple targets (echoes) and estimating their parameters. The proposed detector/estimator extracts the prospective echoes one-by-one from the received signal, after removing the interference caused by the previously detected (stronger) targets. Examples are provided to assess the system performance, also in comparison with the canonical matched-filter peak-detector and the Cram\'er-Rao bounds. Results indicate that the proposed method is robust against the signal spillover and the near-far problem caused by the imperfect auto-correlation of the probing signal and, for the same probability of false alarm, grants detection and localization performances close to those previously obtained in a simplified single-target scenario.

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for improving the utilization of spectrum resources. In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. Firstly, we analyze the cyclic autocorrelation of an OFDM signal and the cyclic spectrum obtained by the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM), and the cyclic spectrum is normalized to gray scale processing to form a cyclic autocorrelation gray scale image. Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. Simulation experiments show that this method can complete the spectrum sensing task by taking advantage of the cycle spectrum, which has better spectrum sensing performance for OFDM signals under a low signal-noise ratio (SNR) than traditional methods.

A 5 GS/s 29 mW Interleaved SAR ADC With 48.5 dB SNDR Using Digital-Mixing Background Timing-Skew Calibration for Direct Sampling Applications

This article presents a 16-channel 5 GS/s time-interleaved (TI) SAR ADC for a direct-sampling receiver that employs a digital-mixing background timing mismatch calibration to compensate for timing-skew errors. It uses a first-order approximation to obtain the derivative of the autocorrelation of the input signal, subsequently used to evaluate the explicit amount of the timing-skew. Therefore, this allows a digital background calibration of the timing-skew, avoiding extra analog circuits. The proposed 16-channel TI ADC uses a splitting-combined monotonic DAC switching method for the individual SAR channel to achieve a trade-off of simple switching and small common-mode voltage variation of the comparator. The prototype, implemented in 28 nm CMOS, reaches a 48.5/47.8 dB SNDR with an input signal of 2.38/4.0 GHz after the proposed background timing mismatch calibration, respectively. Furthermore, the ADC core’s power consumption is 29 mW sampling at 5 GS/s, with a Walden FoM of 26.7 fJ/conv.-step and a Schreier FoM of 157.9 dB.

Autocorrelation Ensemble Average of Larger Amplitude Impact Transients for the Fault Diagnosis of Rolling Element Bearings

Rolling element bearings are one of the critical elements in rotating machinery of energy engineering systems. A defective roller of bearing moves in and out of the load zone during each revolution of the cage. Larger amplitude impact transients (LAITs) are produced when the defective roller passes the load zone centre and the defective area strikes the inner or outer races. A series of LAIT segments with higher signal to noise ratio are separated from a continuous vibration signal according to the bearing geometry and kinematics. In order to eliminate the phase errors between different LAIT segments that can arise from rotational speed fluctuations and roller slippages, unbiased autocorrelation is introduced to align the phases of LAIT segments. The unbiased autocorrelation signals make the ensemble averaging more accurate, and hence, archive enhanced diagnostic signatures, which are denoted as LAIT-AEAs for brevity. The diagnostic method based on LAIT separation and autocorrelation ensemble average (AEA) is evaluated with the datasets captured from real bearings of two different experiment benches. The validation results of the LAIT-AEAs are compared with the squared envelope spectrums (SESs) yielded based on two state-of-the-art techniques of Fast Kurtogram and Autogram.

Emitter Signal Waveform Classification Based on Autocorrelation and Time-Frequency Analysis

Emitter signal waveform recognition and classification are necessary survival techniques in electronic warfare systems. The emitters use various techniques for power management and complex intra-pulse modulations, which can create what looks like a noisy signal to an intercept receiver, so emitter signal waveform recognition at a low signal-to-noise ratio (SNR) has gained increased attention. In this study, we propose an autocorrelation feature image construction technique (ACFICT) combined with a convolutional neural network (CNN) to maintain the unique feature of each signal, and a structure optimization for CNN input layer called hybrid model is designed to achieve image enhancement of the signal autocorrelation, which is different from using a single image combined with CNN to complete classification. We demonstrate the performance of ACFICT by comparing feature images generated by different signal pre-processing algorithms, and the evaluation indicators are signal recognition rate, image stability degree, and image restoration degree. This paper simulates six types of the signals by combining ACFICT with three types of hybrid model, the simulation results compared with the literature show that the proposed methods not only has a high universality, but also better adapts to waveform recognition at low SNR environment. When the SNR is –6 dB, the overall recognition rate of the method reaches 88%.

Combining autocorrelation signals with delay multiply and sum beamforming algorithm for ultrasound imaging.

Beamformer is one of the most important components in ultrasound imaging system. The delay and sum (DAS) beamforming algorithm has been widely used in recent decades due to its simplicity and robustness. However, it has poor impact on resolution and contrast. A new beamformer named filtered delay multiply and sum (F-DMAS) which was an alternative of delay multiply and sum (DMAS) was proposed to overcome these shortcomings of DAS. Although F-DMAS partially enhances the image quality, its performance still has room for improvement. Therefore, a novel beamformer named lag-based delay multiply and sum (L-DMAS) which combines autocorrelation signals with DMAS algorithm is proposed by us to improve its efficiency. Field II was employed to synthesize a point target phantom and a cyst phantom to compare the performance between DAS, F-DMAS, double stage delay multiply and sum (DS-DMAS), and L-DMAS. We also estimate the performance of four algorithms on experimental data and in vivo data. These results show that both DS-DMAS and L-DMAS are better than DAS and F-DMAS in each case. In some cases, DS-DMAS and L-DMAS have little difference in performance, but in other cases, L-DMAS outperforms DS-DMAS. Graphical Abstract.

Shear wave structure of a transect of the Los Angeles basin from multimode surface waves and H/V spectral ratio analysis

SUMMARYWe use broad-band stations of the ‘Los Angeles Syncline Seismic Interferometry Experiment’ (LASSIE) to perform a joint inversion of the Horizontal to Vertical spectral ratios (H/V) and multimode dispersion curves (phase and group velocity) for both Rayleigh and Love waves at each station of a dense line of sensors. The H/V of the autocorrelated signal at a seismic station is proportional to the ratio of the imaginary parts of the Green’s function. The presence of low-frequency peaks (∼0.2 Hz) in H/V allows us to constrain the structure of the basin with high confidence to a depth of 6 km. The velocity models we obtain are broadly consistent with the SCEC CVM-H community model and agree well with known geological features. Because our approach differs substantially from previous modelling of crustal velocities in southern California, this research validates both the utility of the diffuse field H/V measurements for deep structural characterization and the predictive value of the CVM-H community velocity model in the Los Angeles region. We also analyse a lower frequency peak (∼0.03 Hz) in H/V and suggest it could be the signature of the Moho. Finally, we show that the independent comparison of the H and V components with their corresponding theoretical counterparts gives information about the degree of diffusivity of the ambient seismic field.

Detection of Nonadjacent Rotor Faults in Induction Motors via Spectral Subtraction and Autocorrelation of Stray Flux Signals

In this paper, statistical signal processing techniques are applied to electromotive force signals captured in external coil sensors for adjacent and nonadjacent broken bars detection in induction motors. An algorithm based on spectral subtraction analysis is applied for broken bar identification, independent of the relative position of the bar breakages. Moreover, power spectrum analyses enable the discrimination between healthy and faulty conditions. The results obtained with experimental data prove that the proposed approach provides good results for fault detectability. Moreover, the identification of the faults, and the signal correlation indicator to prove the results are also presented for different positions of the flux sensor.

MOCZ for Blind Short-Packet Communication: Practical Aspects

We investigate practical aspects of a recently introduced blind (noncoherent) communication scheme, called modulation on conjugate-reciprocal zeros (MOCZ). MOCZ is suitable for a reliable transmission of sporadic and short-packets at ultra-low latency and high spectral efficiency via unknown multipath channels, which are assumed to be static over the receive duration of one packet. The information is modulated on the zeros of the transmitted discrete-time baseband signal's z- transform. Because of ubiquitous impairments between the transmitter and receiver clocks, a carrier frequency offset occurs after down-conversion to the baseband. This results in a common rotation of the zeros. To identify fractional rotations of the base angle in the zero-pattern, we propose an oversampled direct zero-testing decoder to identify the most likely one. Integer rotations correspond to cyclic shifts of the binary message, which we determine by cyclically permutable codes (CPC). Additionally, the embedding of CPCs into cyclic codes, enables additive error-correction which reduces the bit-error-rate tremendously. Furthermore, we exploit the trident structure in the signal's autocorrelation for an energy based detector to estimate timing offsets and the effective channel delay spread. We finally demonstrate how this joint data and channel estimation can be largely improved by receive antenna diversity at low SNR.