Output Signal-to-noise Ratio Research Articles

A New Second-Order Tristable Stochastic Resonance Method for Fault Diagnosis

Vibration signals are used to diagnosis faults of the rolling bearing which is symmetric structure. Stochastic resonance (SR) has been widely applied in weak signal feature extraction in recent years. It can utilize noise and enhance weak signals. However, the traditional SR method has poor performance, and it is difficult to determine parameters of SR. Therefore, a new second-order tristable SR method (STSR) based on a new potential combining the classical bistable potential with Woods-Saxon potential is proposed in this paper. Firstly, the envelope signal of rolling bearings is the input signal of STSR. Then, the output of signal-to-noise ratio (SNR) is used as the fitness function of the Seeker Optimization Algorithm (SOA) in order to optimize the parameters of SR. Finally, the optimal parameters are used to set the STSR system in order to enhance and extract weak signals of rolling bearings. Simulated and experimental signals are used to demonstrate the effectiveness of STSR. The diagnosis results show that the proposed STSR method can obtain higher output SNR and better filtering performance than the traditional SR methods. It provides a new idea for fault diagnosis of rotating machinery.

An Investigation of Audibility Effects on Cochlear Implant Speech Perception Prediction.

Output Signal to Noise Ratio (OSNR) is the Signal to Noise Ratio (SNR) at the output of a cochlear implant (CI) sound processor. Whereas other prediction metrics typically predict mean speech-in-noise test scores for a group of subjects, an OSNR-based model has been shown to accurately predict scores for individual CI recipients. The OSNR model was unable to predict scores for aggressive Ideal Binary Mask (IdBM) sound processing. This algorithm calculated Input Signal to Noise Ratio (ISNR), in each CI channel, and applied a gain function to suppress noise when a gain threshold was exceeded.The current study investigated the effect of IdBM processing on the separate speech and noise signals to determine whether audibility was affecting intelligibility. A novel metric, "OSNR and Power" (OSNRP), which combined the effect of the reduction in output speech power with OSNR, was proposed.It was found that the IdBM reduced the output speech level, likely causing audibility issues, at poor ISNRs. OSNRP accurately predicted individual speech-in-noise test scores for aggressive IdBM.The novel OSNRP metric has potential as a tool for calculating optimum configurations for sound processor parameter settings for individual CI recipients. We propose using a prescribed set of reference test conditions, the results of which can be utilized to predict outcomes when using alternative sound processing parameters and techniques, and to tailor them to the individual needs of individual CI recipients.

Parameter Estimation of Weak Signal Based on the Steady Attractor of Duffing Oscillator

Duffing oscillator is one of the classic nonlinear system that can generate chaotic motion. Given the sensitivity to regular signals but immunity to noise of its chaotic attractor, the Duffing oscillator can be used for weak signal detection. Our recent study on other attractors of Duffing oscillator showed that the state transition of its steady attractor not only has the two major advantages of the chaotic attractor, but also has a specific advantage, that is, it has no transitional zone. In the nearby area of the steady attractor, noise may even cause stochastic resonance, which significantly increases the output signaltonoise ratio. For the first time, we present a measure function for the state transition of the steady attractor of Duffing oscillator and then proposed a novel estimation method for weak sinusoidal signal buried in strong noise. Simulations were conducted to show the efficiency of the proposed method, and results indicate that the proposed method can achieve estimation of amplitude and frequency for sinusoidal signal. Moreover, the proposed method has a higher estimation accuracy and a stronger anti-noise performance than the classical spectrum and maximum likelihood estimation method.

Signal Detection Algorithm Design Based on Stochastic Resonance Technology Under Low Signal-to-Noise Ratio

In the current 4th generation (4G) communication network, the base station with the same frequency transmission makes a serious interference among adjacent cells, and information transmission is susceptible to interference such as channel multipath fading and occlusion effect. Detecting effectively spectrum signal under low signal-to-noise ratio (SNR), directly affects the whole performance of the wireless communication network system. This paper designs an energy signal detection algorithm based on stochastic resonance technology which transforms noise’s signal energy into useful signal energy, and improves output SNR. The energy signal detection algorithm realizes the function of providing effective detection of signal under low SNR, and promotes the performance of the whole communication system.

An Adaptive Periodical Stochastic Resonance Method Based on the Grey Wolf Optimizer Algorithm and Its Application in Rolling Bearing Fault Diagnosis

As a weak signal processing method that utilizes noise enhanced fault signals, stochastic resonance (SR) is widely used in mechanical fault diagnosis. However, the classic bistable SR has a problem with output saturation, which affects its ability to enhance fault characteristics. Moreover, it is difficult to implement SR when the fault frequency is not clear, which limits its application in engineering practice. To solve these problems, this paper proposed an adaptive periodical stochastic resonance (APSR) method based on the grey wolf optimizer (GWO) algorithm for rolling bearing fault diagnosis. The periodical stochastic resonance (PSR) model can independently adjust the system parameters and effectively avoid output saturation. The GWO algorithm is introduced to optimize the PSR model parameters to achieve adaptive detection of the input signal, and the output signal-to-noise ratio (SNR) is used as the objective function of the GWO algorithm. Simulated signals verify the validity of the proposed method. Furthermore, this method is applied to bearing fault diagnosis; experimental analysis demonstrates that the proposed method not only obtains a larger output SNR but also requires less time for the optimization process. The diagnosis results show that the proposed method can effectively enhance the weak fault signal and has strong practical values in engineering.

Performance analysis of a parameter‐tuned bistable parallel array system for binary pulse amplitude modulation signal processing

In order to improve the signal-to-noise ratio (SNR) gain and lower the bit error rate (BER) when a weak signal is buried in the noise, the parameter-tuned stochastic resonator (PSR) is studied in more detail. In this study, the authors explore the impact of the number of branches on performance. Hence, they build the parameter-tuned bistable parallel array system, which consists of a group of PSRs and propose a novel method to derive the probability density function (PDF) of the output signal, which combines the conventional PDF based on the approximation theory with Gaussian distribution. Based on this method, an analytical expression for BER of the system is derived. It is of interest to note that for an input binary pulse amplitude modulation signal buried in noise, the weak signal can be detected by using the parameter-tuned bistable parallel array system. It is found that via the array PSR effect, the system output SNR gain can be improved. Moreover, the BER can also be reduced with the increase of the array size as the array size increases.

Target characteristics and correlation detection probability calculation method in photoelectric detection screen testing system

AbstractThe changed environment factors will affect the capture rate of target and the detection performance in photoelectric detection screen testing system, to improve the detection performance of system, it is very necessary to establish the target optical characteristic model and correlation detection probability calculation model. Based on the detection principle of unit photoelectric detection screen and the spatial geometric relation of four photoelectric detection screens testing system, this article establishes a new target radiation characteristics calculation model by using target's radiation luminance and radiation energy, gives the output signal expression function, derived the detection probability function by signal to noise ratio (SNR) and false alarm probability. Through calculation analysis, the results show that the detection probability of four photoelectric detection screens testing system is related to the target optical characteristic, the output photoelectric current and SNR of photoelectric detection receiving sensor can describe target detection performance. According to the experiments testing, the proposed calculation model can effectively evaluate the performance of photoelectric detection screen testing system.

A Comparison of ECG Signal Pre-processing Using FrFT, FrWT and IPCA for Improved Analysis

ObjectiveElectrocardiogram (ECG) is a diagnostic tool for recording electrical activities of the human heart non-invasively. It is detected by electrodes placed on the surface of the skin in a conductive medium. In medical applications, ECG is used by cardiologists to observe heart anomalies (cardiovascular diseases) such as abnormal heart rhythms, heart attacks, effects of drug dosage on subject's heart and knowledge of previous heart attacks. Recorded ECG signal is generally corrupted by various types of noise/distortion such as cardiac (isoelectric interval, prolonged depolarization and atrial flutter) or extra cardiac (respiration, changes in electrode position, muscle contraction and power line noise). These factors hide the useful information and alter the signal characteristic due to low Signal-to-Noise Ratio (SNR). In such situations, any failure to judge the ECG signal correctly may result in a delay in the treatment and harm a subject (patient) health. Therefore, appropriate pre-processing technique is necessary to improve SNR to facilitate better treatment to the subject. Effects of different pre-processing techniques on ECG signal analysis (based on R-peaks detection) are compared using various Figures of Merit (FoM) such as sensitivity (Se), accuracy (Acc) and detection error rate (DER) along with SNR. MethodsIn this research article, a new fractional wavelet transform (FrWT) has been proposed as a pre-processing technique in order to overcome the disadvantages of other existing commonly used techniques viz. wavelet transform (WT) and the fractional Fourier transform (FrFT). The proposed FrWT technique possesses the properties of multiresolution analysis and represents signal in the fractional domain which consists of representation in terms of rotation of signals in the time–frequency plane. In the literature, ECG signal analysis has been improvised using statistical pre-processing techniques such as principal component analysis (PCA), and independent component analysis (ICA). However, both PCA and ICA are prone to suffer from slight alterations in either signal or noise, unless the basis functions are prepared with a worldwide set of ECG. Independent Principal Component Analysis (IPCA) has been used to overcome this shortcoming of PCA and ICA. Therefore, in this paper three techniques viz. FrFT, FrWT and IPCA are selected for comparison in pre-processing of ECG signals. ResultsThe selected methods have been evaluated on the basis of SNR, Se, Acc and DER of the detected ECG beats. FrWT yields the best results among all the methods considered in this paper; 34.37dB output SNR, 99.98% Se, 99.96% Acc, and 0.036% DER. These results indicate the quality of biology-related information retained from the pre-processed ECG signals for identifying different heart abnormalities. ConclusionCorrect analysis of the acquired ECG signal is the main challenge for cardiologist due to involvement of various types of noises (high and low frequency). Twenty two real time ECG records have been evaluated based on various FoM such as SNR, Se, Acc and DER for the proposed FrWT and existing FrFT and IPCA preprocessing techniques. Acquired real-time ECG database in normal and disease situations is used for the purpose. The values of FoMs indicate high SNR and better detection of R-peaks in a ECG signal which is important for the diagnosis of cardiovascular disease. The proposed FrWT outperforms all other techniques and holds both analytical attributes of the actual ECG signal and alterations in the amplitudes of various ECG waveforms adequately. It also provides signal portrayals in the time-fractional-frequency plane with low computational complexity enabling their use practically for versatile applications.

High-Precision Light Spot Position Detection in Low SNR Condition Based on Quadrant Detector

In free space optical communications, long-distance transmission leads to the attenuation of beacon light, where we adopt a quadrant detector (QD) to receive the weak signal. However, the background light interferes so strongly that the output signal-to-noise ratio (SNR) of QD is at a low level, which causes a decrease in accuracy of the direct detection method. This requires finding a new light spot detection method, so an improved detection method is proposed. Because the dark current noise and the background light noise are both white noise, we adopt a Kalman filter to estimate the real output of four electric signals of QD. Unfortunately, running these through an amplifier introduces some direct current (DC) offsets into the signals. In order to balance the effect of the DC offsets, we consider using the modulation method, where we employ a sine signal to modulate the intensity of the beacon light at the transmitting end, after which we can give an inverse gain to move the center of signals to near zero to eliminate the DC offsets when we calculate the data. In Kalman filtering, we use the peak values of the signals in every period after the analog to digital converter (ADC) as the elements of the measurement matrix. Experimental results show that even when QD output SNR is about −10 dB, the detection root-mean-square errors decrease by 51.5% using the improved detection method compared with the direct detection method. Moreover, Kalman filtering does not require a large amount of data, which means it works efficiently, can reduce the cost of hardware resources, and is available for the real-time calculation of spot position.

A piecewise nonlinear stochastic resonance method and its application to incipient fault diagnosis of machinery

Since noise is beneficial to improve weak fault characteristic extraction in nonlinear systems where a stochastic resonance occurs, a new piecewise nonlinear stochastic resonance (PNSR) method is proposed to enhance and extract the incipient fault signatures of machinery and ensure their reliable operation. In this new PNSR method, a piecewise nonlinear potential is employed to overcome the saturated shortcoming of classical bistable potentials, thereby improving and highlighting the performance of stochastic resonance in weak fault characteristic extraction. Moreover, the proposed PNSR method can realize the adjustment of the potential structure by a single parameter, it avoids the shortcoming that the imperfect matching classical bistable stochastic resonance (CBSR) parameters results in low output signal-to-noise ratio (SNR), and further identify fault characteristics are difficult. In addition, the output SNR of the PNSR method is deduced by using adiabatic approximate theory to investigate the effect of system parameter on the PNSR method, and even has higher output SNR compared with the CBSR method and the proposed PNSR method. Finally, the proposed PNSR method is validated by utilizing simulation and bearing experiments. All diagnostic results show that the proposed PNSR method can effectively extract weak fault characteristics, thereby achieving the incipient fault diagnosis of machinery. Compared with the CBSR method, the proposed method has higher spectrum peak values in fault characteristic frequencies. Therefore, it has better recognition degree in whole frequency spectrum.

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing.

Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p < 0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats.

Methods of Power Line Interference Elimination in EMG Signal

Electromyogram (EMG) recordings are often corrupted by the wide range of artifacts, which one of them is power line interference (PLI). The study focuses on some of the well-known signal processing approaches used to eliminate or attenuate PLI from EMG signal. The results are compared using signal-to-noise ratio (SNR), correlation coefficients and Bland-Altman analysis for each tested method: notch filter, adaptive noise canceller (ANC) and wavelet transform (WT). Thus, the power of the remaining noise and shape of the output signal are analysed. The results show that the ANC method gives the best output SNR and lowest shape distortion compared to the other methods.

Exploiting Shadowing Stationarity for Antenna Selection in V2V Communications

Antenna selection (AS) techniques are considered as ideal candidates for vehicle-to-vehicle communications, since they improve system's performance and simultaneously satisfy the hardware and signal processing constraints that exist in these systems. However, the achieved gain over single antenna links is affected by the fast varying wireless channel, since AS is frequently performed using outdated versions of the signal-to-noise ratio (SNR). In this paper, we propose an AS technique that exploits the stationarity of large-scale fading. In this context, by employing shadowing information as an AS criterion, the negative consequences of the outdated channel state information (CSI) can be alleviated, since large-scale fading varies more slowly than small-scale fading. The performance of the proposed technique is analyzed using the criteria of outage probability and average output SNR. It is shown that the proposed scheme outperforms the corresponding one that is based on outdated CSI, especially in scenarios with mild fading/shadowing channel conditions. Moreover, the influence of correlated shadowing on the system's performance has been also analytically investigated. The main results have been also verified by empirical data based on measurement campaigns in non-stationary communication conditions.

Adaptive extended binary phase-shift keying waveform design algorithm for extended target detection

We discuss the problem of extended binary phase-shift keying (EBPSK)–modulated radar waveform design in the presence of colored Gaussian disturbance subject to peak-to-average-power ratio and power constraints. The research work aims at improving the target detection and parameter estimation performance with respect to Doppler shifts. We propose an EBPSK coding design algorithm by optimizing the output signal-to-noise ratio (SNR) in correspondence of the unknown target Doppler frequency. Under the same constraints as in the previous problem, we present EBPSK coding cognitive waveform design algorithm by estimating the target scattering coefficients (TSC) based on maximum output SNR criterion. The simulation experimental results showed that extended target detection performances of optimized EBSPK signals are better than those of the unoptimized EBSPK signal. The designed EBPSK signal attains better TSC estimation performance with relatively lower complexity than the EBPSK signal with arbitrary parameters in the cognitive radar system.

An Improved Method for the Position Detection of a Quadrant Detector for Free Space Optical Communication

In free space optical communication, a beacon light loses too much energy after a long-distance transmission and faces strong interference from background light. The beacon light illuminated on a quadrant detector (QD) is so weak that the output signal-to-noise ratio (SNR) of a QD is very low, which leads to a significant decrease in the accuracy of the direct position detection method. To solve this problem, an improved light spot position detecting method is proposed. Since the background light and the dark current noise are white noise, we could consider concentrating the energy of QD output signal at a certain frequency point to enhance the output SNR. Therefore, a cosine signal is used to modulate the intensity of a beacon light at the transmitting end. Then the QD output photocurrents are also cosine signals with the same frequency as the modulating signal. Putting the photocurrent signals into a cross-correlation operation with a reference signal, which is the same as the modulating signal, can enhance the QD output SNR at a certain frequency point. Unfortunately, the result of the classical cross-correlation is attenuated with increasing delay. It is hard to detect the amplitude of the classical cross-correlation result. So, we used cyclic cross-correlation to obtain a stable correlation result to detect its amplitude accurately. The experiment results show that even when the QD output SNR is less than −17 dB, the detection root-mean-square error of the proposed method is 0.0092 mm, which is a quarter of the direct position detection method. Moreover, this method only needs a small amount of data to accomplish the calculation and is especially suitable for real-time spot position detection.

A Novel Type of Stochastic Resonance Potential Well Model and Its Application

For a stochastic resonance system, the characteristics of the nonlinear model have an important influence on the output. To further improve the enhanced detection of stochastic resonance, a novel potential well stochastic resonance model is constructed to simultaneously solve the problem of the output saturation and high barrier in the classical bistable model. Analytical expressions of the Kramers rate and output signalto-noise ratio are presented, and weak signal detection is theoretically analyzed. The performance of the system based on the novel potential well model is simulated and analyzed. Finally, the proposed model is used for the detection of multiple high-frequency weak signals in an α-stable noise environment and a practical bearing fault signal. The simulation and experimental results demonstrate that the output of the stochastic resonance system proposed in this paper exhibits a large output signal-to-noise ratio and a high spectral peak at the characteristic frequency.

Radar HRRP Target Recognition via Semi-Supervised Multi-Task Deep Network

Feature representation based on the high resolution range profile (HRRP) is the key technology in radar automatic target recognition(RATR). In this paper, we design a deep-u-blind denoising network(DUBDNet) to extract features with high-noise-stability. The fully convolutional DUBDNet is based on autoencoder and employs fusion layers to transfer input features to high dimensional space. Then radar HRRP shift-robust convolutional neural network(RSRNet) is proposed as the classifier. In the experiment, two radar sensors are used to measure HRRP signals of warplanes and civil airplanes. RSRNet performs high robustness to HRRP time-shift sensitivity via testing translation data. This is also a proof that convolutional neural network(CNN) is shift-robust on HRRP target recognition. Trained with noise-to-noise, DUBDNet can achieve blind-denoising in low signal-to-noise ratio(SNR) and significantly improve the correct recognition rate of targets. When the SNR of input HRRP signals is less than 5 dB, DUBDNet can increase the SNR by 10 dB. When the input SNR is -15 dB, output SNR can be increased by 15 dB and the correct recognition rate of targets can be increased by 15%.

Time-Frequency Domain Turbo Equalization for Single-Carrier Underwater Acoustic Communications

Single-carrier frequency-domain equalization (SC-FDE) is a promising technique for coherent high-rate underwater acoustic (UWA) communications. In this paper, a time-frequency domain turbo equalization (TFD-TE) scheme is proposed for single-carrier modulation. The TFD-TE scheme is to couple a multi-channel frequency domain equalizer with phase rotation compensation and a low-order single-channel time-domain turbo equalizer. To mitigate the error propagation and further enhance receiver performance, a bidirectional equalizer that combined the soft outputs of a conventional equalizer and a backward equalizer is introduced into the proposed TFD-TE scheme (Bi-TFD-TF). The sparse channel is estimated by the improved proportionate normalized least mean squares (IPNLMS) algorithm block by block. Both the simulated and experimental results show the proposed TFD-TE can effectively improve system performance with acceptable complexity. The proposed TFD-TE scheme is evaluated using communication data collected from the DJK15 lake experiment. For the $1\times 4$ single-input multiple-output (SIMO) communication system using quadrature phase shift keying (QPSK) and 8-phase shift keying (8PSK) modulations, the proposed scheme achieves error-free transmission after a few iterations. Our receiver achieves a maximum raw data rate of 12 kbit/s and a reliable net data rate of 4 kbit/s within 4-kHz bandwidth at a range of 10.8 km. Compared with conventional frequency domain turbo equalization (FD-TE), the proposed TFD-TE scheme can enhance 0.8–1.3 dB in terms of output signal-to-noise ratio (OSNR). The related bi-TFD-TE provides an additional 0.8–1.7-dB OSNR improvements.

BER analysis of EGC receiver over correlated Nakagami-m fading channels with phase error and asynchronous CCI

Performance analysis of an L-branch Equal Gain Combining (EGC) receiver with phase estimation error and asynchronous Co-Channel Interference (CCI) over correlated Nakagami-m fading channel is presented. Both the desired and interfering signals are assumed to be Nakagami-m distributed. The output Signal to Noise Ratio (SINR) of the EGC combiner is derived. Closed form expression for the n-th moment of the output SINR is derived. Using the n-th moment expression, Average Bit Error Probability (ABEP) for Binary Phase Shift Keying (BPSK) modulation is derived using Moment Generating Function (MGF) and Pade approximation method. Numerical and simulation results of ABEP for various desired and interfering signal parameters are obtained. ABEP results for various degrees of phase errors are also obtained. The results for special cases are verified with the available results.

Dimension-reduced generalized likelihood ratio detection based on sampling of normal modes in deep ocean

In this paper, two generalized likelihood ratio (GLR) detectors are presented for the case of multiple snapshots of test data to detect the presence of an underwater acoustic source in the deep ocean. The two GLR detectors are termed the eigenvalue detector (EVD) and the constant false alarm rate eigenvalue detector (CFAR EVD), respectively. Theoretical analysis and numerical results show that for a given input signal-to-noise ratio (SNR) of the array, the GLR detectors achieve higher output SNRs when the spatial dimension of test data decreases. To further enhance the detection performances of the GLR detectors, we propose a dimension-reduced (DR-GLR) method based on array sampling of modal information. This DR-GLR method combines the characteristics of sound propagation and array receiving. According to normal mode theory, acoustic signals emitted from the acoustic source lie in the modal space spanned by the sampled modal information of the array. Resulting from the restriction of the array size, it often occurs in deep ocean when the dimension of " effective modal subspace” is less than that of the test data which is equivalent to the number of hydrophones. Based on this phenomenon, we reconstruct the modal information by merely retaining the " effective modal subspace” to formulate the dimension reduction matrix. The DR-GLR test statistics is deduced by employing the dimension reduction matrix when using the vertical linear array (VLA) and the horizontal linear array (HLA), respectively. The DR-GLR detectors when using an HLA require more computational amount than when using a VLA. Simulation experiments are conducted to analyze the detection performances of the two GLR detectors, and verify the performance improvement effects of DR-GLR detectors. The numerical results show that the CFAR EVD presents good robustness to the uncertainty of the noise power and the DR-GLR detectors outperform the GLR detectors in detection performance. It also turns out the acoustic signals received by the HLA lie in a lower-dimensional " effective modal subspace” than by the VLA, and thus when using an HLA the DR-GLR detectors present higher detection probabilities than using a VLA. Moreover, the smaller the dimension of the " effective modal subspace”, the better the performance improvement of the DR-GLR detectors will be. The dimension of the " effective modal subspace” increases with hydrophone spacing and/or the source frequency increasing.