Detection Of Sinusoids Research Articles

Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method

In this research computer vision techniques are applied to borehole resistivity imaging in order to establish an alternative procedure to the mean square dip (MSD) processing. The MSD is regularly applied to detect sinusoids and dips automatically in borehole imaging and dipmeter logs. The present proposal is based on Gabor’s filters, morphological transformations, Hough’s transform, and clustering techniques. The MSD method and the computer vision proposal were tested in 1012 m of images, showing 7.986% of false positives for the MSD processing and 0.879% for the computer vision approach. This methodology tries to emulate the geologists behavior when they make image interpretation; instead of making correlations between resistivity curves like the MSD does. There are no special computer requirements, and it can be applied directly in the field for quick well-site dip results. This procedure can be easily integrated into log units and most commercial borehole-imaging processing software. The processing workflow was developed in python using standard libraries.

Passive Tone Detection for Moving Targets Based on Long-Time Coherent Integration

The detection of sinusoidal signals embedded in noise is an important topic in passive sonar signal processing. The performance of existing tone detection techniques is limited by the Doppler frequency shift, which is caused by the relative motion between moving targets and the receiver. In this study, an algorithm based on long-time coherent integration is proposed to achieve robust tonal signal detection under the influence of a Doppler frequency shift. First, the received narrowband signals radiated by moving targets are split into multiple segments through a window-length constraint. The results obtained by applying a discrete Fourier transform (DFT) to the data segments are modeled as a polynomial phase signal in the frequency domain. Then, the polynomial Radon-polynomial Fourier transform (PRPFT) is applied to simultaneously compensate the time-variant frequency drift and phase difference. To avoid the gain loss due to the discretization of the phase difference compensation in PRPFT, a phase compensation factor searching algorithm is proposed. The simulation results show that the proposed algorithm can provide higher frequency resolution and higher coherent integration gain even in the case of a severe Doppler shift. Furthermore, the results of sea trials demonstrate that the proposed method can perfectly achieve coherent integration for signals with a duration of several hundred seconds under different experimental conditions.

Weak Sinusoidal Signal Detection with CSI Model in Chaotic Interference

In small target detection under strong sea clutter or impact signal detection under machinery fault diagnosis, a weak sinusoidal signal with random amplitude is often contaminated by heavier chaotic noise, and the target information is difficult to detect. Traditional solutions, such as neural networks or stochastic resonance, can not effectively extract heteroscedasticity of data, which leads to weak signals not being detected. To overcome these limitations and improve the detection efficiency, an empirical likelihood ratio statistical method for detecting weak sinusoidal signals with random amplitude under strong chaotic interference is proposed. First, based on the reconstruction in the phase space of the 1‐D observed time series signal with embedding dimension and time delay, the presented method obtains a multivariate special temporal series as an input. Subsequently, the chaotic single index (CSI) statistical model is established for single‐step prediction, and it can be estimated by the nonparametric locally linear algorithm for minimizing the mean squares error. Finally, the empirical likelihood ratio statistical method is applied to detect weak sinusoidal signals with random amplitude. Simulated data and real data experiment results show that the proposed CSI model can better capture the weak target signal and detect effectively weak target signal under the chaotic interference.

Low-Speed Rotating Restart and Speed Recording for Free-Running Sensorless IPMSM Based on Ultrahigh Frequency Sinusoidal Wave Injection

In the low-speed operating range of sensorless interior permanent magnet synchronous motor (IPMSM), this article proposed an ultrahigh frequency (UHF) sinusoidal voltage injection method supplied by the control power supply, and the corresponding signal injection and detection system topology are designed. The proposed method replaces the traditional high-frequency injection method to continuously detect the rotor position by injecting UHF sinusoidal detection voltage into the three-phase motor windings during the main power supply (MPS) down, thereby avoiding the loss of rotor polarity information. When the MPS is restored, there is no need to wait for the convergence process of the position observer and the rotor polarity determination process. The sensorless IPMSM can immediately resume normal operation at low speed. For applications with low load inertia, the proposed method improves the rapidness of rotating restart, realizes zero-wait operation after MPS is restored, and reduces the speed drop during the power down. For electric vehicle (EV) applications, the proposed method meets the requirements of EVs event data recorder to continuously record the motor speed when the MPS is <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> . The experimental results verify the correctness of the theoretical analysis and the feasibility of the proposed method.

SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network

The detection and estimation of sinusoids is a fundamental signal processing task for many applications related to sensing and communications. While algorithms have been proposed for this setting, quantization is a critical, but often ignored modeling effect. In wireless communications, estimation with low resolution data converters is relevant for reduced power consumption in wideband receivers. Similarly, low resolution sampling in imaging and spectrum sensing allows for efficient data collection. In this work, we propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We incorporate signal reconstruction internally as domain knowledge within the network to enhance learning and surpass traditional algorithms in mean squared error and Chamfer error. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. This threshold provides insight into why neural networks tend to outperform traditional methods and into the learned relationships between the input and output distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data. We use the learning threshold to explain, in the one-bit case, how our estimators learn to minimize the distributional loss, rather than learn features from the data.

Signal detection and inference based on the beta binomial autoregressive moving average model

This paper proposes the beta binomial autoregressive moving average model (BBARMA) for modeling quantized amplitude data and bounded count data. The BBARMA model estimates the conditional mean of a beta binomial distributed variable observed over the time by a dynamic structure including: (i) autoregressive and moving average terms; (ii) a set of regressors; and (iii) a link function. Besides introducing the new model, we develop parameter estimation, detection tools, an out-of-signal forecasting scheme, and diagnostic measures. In particular, we provide closed-form expressions for the conditional score vector and the conditional information matrix. The proposed model was submitted to extensive Monte Carlo simulations in order to evaluate the performance of the conditional maximum likelihood estimators and of the proposed detector. The derived detector outperforms the usual ARMA- and Gaussian-based detectors for sinusoidal signal detection. We also presented an experiment for modeling and forecasting the monthly number of rainy days in Recife, Brazil.

A low complexity model order and frequency estimation of multiple 2-D complex sinusoids

Model order and frequency estimation of multiple 2-D complex sinusoids in additive white Gaussian noise are addressed. Frequency estimation follows the coarse-fine search strategy. Coarse estimates, obtained by locating maxima of the 2-D discrete Fourier transform, are refined in a two-stage procedure. In both stages, frequency refinement is based on three-point periodogram maximization. In order to provide accurate model order estimation (MOE) for a wide signal-to-noise ratio (SNR) range, our approach combines two metrics for sinusoid detection in the 2-D frequency domain, one for low and the other for high SNR values. The proposed frequency estimation method attains the Cramér-Rao lower bound and it outperforms parametric methods in terms of the estimation accuracy and numerical efficiency. Compared with information criterion-based methods, the proposed MOE approach is numerically more efficient, it does not require estimation of noise variance and hence does not suffer from overestimation at high SNR.

Pilot protection of hybrid MMC DC grid based on active detection

Considering the advantages and limitations of traditional identification method, combined with the strategy of active detection, the principle of DC grid pilot protection based on active detection is proposed to improve the sensitivity and reliability of hybrid MMC DC grid protection, and to ensure the accurate identification of fault areas in DC grid. By using the DC fault ride-through control strategy of the hybrid sub-module MMC, the fault current at the converter station DC terminal is limited. Based on the high controllability of hybrid MMC, sinusoidal fault detection signals with the same frequency are injected into the line at each converter station. Based on model recognition, the capacitance model condition is satisfied by the detected signals at both ends during external faults whereas not satisfied during internal faults. The Spearman correlation coefficients is then introduced, and the correlation discriminant of capacitance model is constructed to realize fault area discrimination of DC grid. The simulation results show that the active detection protection scheme proposed in this paper can accurately identify the fault area of DC grid, and is not affected by fault impedance and has low sampling rate requirement.

Efficient and Accurate Detection and Frequency Estimation of Multiple Sinusoids

The method for detection of complex sinusoids in additive white Gaussian noise and estimation of their frequencies is proposed. It contains two stages: 1) sinusoid detection (model order estimation) and coarse frequency estimation, and 2) fine frequency estimation. The proposed method operates in the frequency domain, i.e., it uses the discrete Fourier transform (DFT) as the main tool. Sinusoid detection is performed so that a fixed probability of false alarm is provided (Neymann–Pearson criterion). For both coarse and fine frequency estimations, the three-point periodogram maximization approach is used. Simulations are carried out for variable signal-to-noise ratio, variable frequency displacement between the sinusoids and variable offset from the frequency grid. The proposed method meets the Cramer-Rao lower bound in frequency estimation and practically does not depend on the frequency displacement except for very small displacement values. In terms of model order estimation accuracy, it outperforms the state-of-the-art approaches. The most expensive operation in the method is the calculation of the DFT. Therefore, in terms of calculation complexity, the proposed method is on par with the most efficient algorithms for multiple frequency estimations.

Sinusoidal-signal detection by active, noisy oscillators on the brink of self-oscillation

Determining the conditions under which an active system best detects sinusoidal signals is important for numerous fields. It is known that a quiescent, deterministic system possessing a supercritical Hopf bifurcation is more sensitive to sinusoidal stimuli the closer it operates to the bifurcation. To understand signal detection in many natural settings, however, noise must be taken into account. Herein we describe the detection of sinusoidal signals by noisy supercritical and subcritical Hopf oscillators. To distinguish an oscillator’s motion owing to sinusoidal forcing from that provoked by noise, we employ the phase-locked amplitude and vector strength, which are zero in the absence of an external signal. The phase-locked amplitude and entrainment to frequency-detuned forcing – but not resonant forcing – peak as functions of the control parameter. These peaks occur near but not at the bifurcations. Moreover, an oscillator can detect stimuli over the broadest frequency range when it spontaneously oscillates near a Hopf bifurcation. Although noise exerts the greatest effect on the phase-locked amplitude when a Hopf oscillator is near a Hopf bifurcation, the oscillator nevertheless performs best as a sinusoidal-signal detector when it operates close to the bifurcation. The oscillator’s ability to differentiate detuned signals from noise is greatest with it autonomously oscillates near to but not at the bifurcation.

Research on Weak Signal Detection with Duffing Oscillator Based on Virtual Instrument Technology

In the process of oil field development, acoustic telemetry system is an important means to realize the wireless communication between underground and ground. However, due to the attenuation of acoustic waves in the transmission process, the signal becomes very weak when it reaches the ground, so, weak signal detection is very important for acoustic telemetry system. This paper proposed a weak signal detection with chaotic oscillator based on virtual instrument technology. Firstly, analyzed the principle of the weak sinusoidal signal detection with Duffing chaotic oscillator. Especially the influence of the signal initial phase was discussed; and then the multi-phase- capture method based on virtual instrument technology was proposed, it was achieved by programming with LabVIEW. The results indicate that the method not only could effectively detect the weak inputted signal with random phase, but also it features progressiveness and practicability used for downhole acoustic telemetry system.

Fast and objective detection and analysis of structures in downhole images

Downhole acoustic and optical televiewer images, and formation microimager (FMI) logs are important datasets for structural and geotechnical analyses for the mineral and petroleum industries. Within these data, dipping planar structures appear as sinusoids, often in incomplete form and in abundance. Their detection is a labour intensive and hence expensive task and as such is a significant bottleneck in data processing as companies may have hundreds of kilometres of logs to process each year. We present an image analysis system that harnesses the power of automated image analysis and provides an interactive user interface to support the analysis of televiewer images by users with different objectives. Our algorithm rapidly produces repeatable, objective results. We have embedded it in an interactive workflow to complement geologists' intuition and experience in interpreting data to improve efficiency and assist, rather than replace the geologist. The main contributions include a new image quality assessment technique for highlighting image areas most suited to automated structure detection and for detecting boundaries of geological zones, and a novel sinusoid detection algorithm for detecting and selecting sinusoids with given confidence levels. Further tools are provided to perform rapid analysis of and further detection of structures e.g. as limited to specific orientations.

A Study of Periodograms Standardized Using Training Datasets and Application to Exoplanet Detection

When the noise affecting time series is colored with unknown statistics, a difficulty for sinusoid detection is to control the true significance level of the test outcome. This paper investigates the possibility of using training data sets of the noise to improve this control. Specifically, we analyze the performances of various detectors {applied to} periodograms standardized using training data sets. Emphasis is put on sparse detection in the Fourier domain and on the limitation posed by the necessarily finite size of the training sets available in practice. We study the resulting false alarm and detection rates and show that standardization leads in some cases to powerful constant false alarm rate tests. The study is both analytical and numerical. Although analytical results are derived in an asymptotic regime, numerical results show that theory accurately describes the tests' behaviour for moderately large sample sizes. Throughout the paper, an application of the considered periodogram standardization is presented for exoplanet detection in radial velocity data.

Newtonized Orthogonal Matching Pursuit: Frequency Estimation Over the Continuum

We propose a fast sequential algorithm for the fundamental problem of estimating frequencies and amplitudes of a noisy mixture of sinusoids. The algorithm is a natural generalization of Orthogonal Matching Pursuit (OMP) to the continuum using Newton refinements, and hence is termed Newtonized OMP (NOMP). Each iteration consists of two phases: detection of a new sinusoid, and sequential Newton refinements of the parameters of already detected sinusoids. The refinements play a critical role in two ways: (1) sidestepping the potential basis mismatch from discretizing a continuous parameter space, (2) providing feedback for locally refining parameters estimated in previous iterations. We characterize convergence, and provide a Constant False Alarm Rate (CFAR) based termination criterion. By benchmarking against the Cramer Rao Bound, we show that NOMP achieves near-optimal performance under a variety of conditions. We compare the performance of NOMP with classical algorithms such as MUSIC and more recent Atomic norm Soft Thresholding (AST) and Lasso algorithms, both in terms of frequency estimation accuracy and run time.

A New Data Extrapolation Approach Based on Spectral Partitioning

In this letter, we propose a new linear predictive (LP) data extrapolation approach. It involves partitioning the spectrum into multiple spectral subbands and using a different autoregressive (AR) process to model each subband. The new extrapolation approach is then combined with the classical discrete Fourier transform (DFT) to produce a new hybrid LP-DFT spectral estimator to address the detection and estimation problem of multiple sinusoids in a discrete data sequence. Simulation results demonstrate the superiority of the proposed hybrid technique over an existing popular hybrid LP-DFT technique, where a single AR process is used to model the entire spectrum of the data sequence.

Discrimination of steady state and transient state of dither extremum seeking control via sinusoidal detection

A major class of extremum seeking control (ESC) is based on the use of periodic dither perturbation of plant input for extracting the gradient information. Presence of the dither input into the steady state operation is undesirable in practice due to the possible excessive wear of actuators. It is thus beneficial to stop the dithering action after the ESC reaches its steady state. In this paper, we propose a method for automatically discriminating between the steady state and the transient state modes of extremum seeking control process using the sinusoidal detection techniques. Some design guidelines are proposed for the parameter selection of the relevant sinusoidal detection scheme. The proposed scheme is validated with simulation study on dynamic virtual plant of two building HVAC systems.

Acoustic recognition of multiple bird species based on penalised maximum likelihood

Automatic system for recognition of multiple bird species in audio recordings is presented. Time-frequency segmentation of the acoustic scene is obtained by employing a sinusoidal detection algorithm, which does not require any estimate of noise and is able to handle multiple simultaneous bird vocalizations. Each segment is characterized as a sequence of frequencies over time, referred to as a frequency track. Each bird species is represented by a hidden Markov model that models the temporal evolution of frequency tracks. The decision on the number and identity of bird species in a given recording is obtained based on maximizing the overall likelihood of the set of detected segments, with a penalization applied for increasing the number of bird models used. Experimental evaluations are performed on audio field recordings containing 30 bird species. The presence of multiple bird species is simulated by joining the set of detected segments from several bird species. Results show that the proposed method can achieve recognition performance for multiple bird species not far from that obtained for single bird species, and considerably outperforms majority voting methods.

A NEURAL NET FOR 2D-SLOPE AND SINUSOIDAL SHAPE DETECTION

2D-slope and sinusoidal shape detection are application specific tasks which are widely discussed in the literature. A neural network is presented which is able to learn a set of different slopes or a set of sinusoids of different frequencies and to detect test patterns after the training stage. The neural net is composed of input neurons, delay neurons and output neurons. The delay neurons form a set of tapped delay lines. Each delay line adapts to its specific signal propagation velocity. The signal propagation velocity vector field of the delay lines is learned by collectively tuning the signal propagation velocities. The neural net is fed with a set of spatiotemporal training patterns, such as bars of different slopes or sinusoids of different frequencies. After training, the net is tested with a random set of 2D- patterns. Unsupervised learning with a Boltzmann temperature term is assumed.

Automated detection of planar geologic features in borehole images

The interpretation of borehole images begins with the detection and classification of features—a time-consuming manual process subject to variations between interpreters. In seeking to automate the detection part for the most frequently picked features (which in circumferential images from clastic rock environments are sinusoids corresponding to planar or subplanar bedding surfaces and fractures), it is not necessary to pick all instances, but it is necessary to pick sufficient representative instances to satisfy the interpretation objective, accounting for a broad range of apparent dips, and allowing for the likelihood of fractures crossing bedding surfaces. A key challenge in this context is the minimization of false picks, as manual corrections would potentially negate the principal benefit of automation. A fast nonsubjective method is described for the detection of prominent discontinuities and the calculation of associated dip angles. It combines a gradient based approach for edge detection with a phase congruency method for validation, followed by a robust sinusoid detection technique. It has been evaluated on microresistivity images from wireline and logging-while-drilling tools, these images having a wide range of features with varying degrees of geologic complexity; the proportion of false positives in the case of noisy data is less than 5%, improving to better than 2% in the case of good-quality data. In contrast to manual picking, the method is fast and gives reproducible results. With potentially thousands of sinusoids in a single image, the method dramatically improves efficiency.

Random Phase Weak Sine Signal Detection Based on Autocorrelation and Chaos Theory

The paper expounds the basic principle of the weak signal detection on chaotic system at first , according to the actual situation that the signal phase is unknown Received in the communication and radar systems, a new chaos detection model is presented based on traditional chaos system, combined with autocorrelation detection technology , completed the random phase weak sinusoidal signal detection. The simulation analysis indicates that the proposed method succeeds in detecting Random Phase weak sinusoidal signal, and furthermore the signal-to-noise threshold is lower than either autocorrelation or chaotic oscillator.