Weak Pulse Signal Research Articles

Double-Layer Robust Broad Logistic Regression for Detecting Weak Pulse Signal in Chaotic Interference

Double-Layer Robust Broad Logistic Regression for Detecting Weak Pulse Signal in Chaotic Interference

Design of Pulse Signal Acquisition System of Adaptive Wide Dynamic Range Based on ADS125H02

In order to solve the problem of wide input range of photocurrent, aiming at the shortage of existing pulse signal acquisition system, an adaptive pulse signal acquisition system based on ADS125H02 is designed, which has high collection accuracy, low noise, anti-interference, wide input range, high integration, etc. The measurement range of the system can reach ±10 V, and the accuracy is 0.009 μV. Experimental tests show that the design of the system can extract weak pulse signals, and has good practicality.

Generalized Resonance Sensor Based on Fiber Bragg Grating

In response to the difficulty of weak detection of early bearing damage, resonance demodulation technology and the principle of fiber Bragg grating sensing strain were combined to design a fiber Bragg grating generalized resonance sensor, which can extract the weak pulse signal of weak detection of early bearing’s early damage from rolling bearing. First, a principle of resonance dynamics of second-order mechanical systems based on fiber Bragg grating and generalized resonance principles is proposed. Second, the basic structure of the sensor is designed. Then, ANSYS finite element simulation is used to analyze the natural frequency of the sensor. Finally, the natural frequency value of the sensor was obtained through experiments. The experimental results of proof-of-principle show that the experimental results are consistent with the theoretical predictions. The theoretical model is accurate, which verifies the feasibility of the sensor.

Distributed Sensor Local Linear Fusion Detection of Weak Pulse Signal in Chaotic Background

This paper combines the distributed sensor fusion system with the signal detection under chaotic noise to realize the distributed sensor fusion detection from chaotic background. First, based on the short‐term predictability of the chaotic signal and its sensitivity to small interference, the phase space reconstruction of the observation signal of each sensor is carried out. Second, the distributed sensor local linear autoregressive (DS‐LLAR) model is constructed to obtain the one‐step prediction error of each sensor. Then, we construct a Bayesian risk model and also obtain the corresponding conditional probability density function under each sensor’s hypothesis test which firstly needs to fit the distribution of prediction errors according to the parameter estimation. Finally, the fusion optimization algorithm is designed based on the Bayesian fusion criterion, and the optimal decision rule of each sensor and the optimal fusion rule of the fusion center are jointly solved to effectively detect the weak pulse signal in the observation signal. Simulation experiments show that the proposed method which used a distributed sensor combined with a local linear model can effectively detect weak pulse signals from chaotic background.

Detection of pulse signal in chaotic noise background using extreme learning machine

It is still a challenge to detect the useful signals under chaotic noise background with effective methods. Difficults such as suppression of useful signals, large computation and low sensitivity commonly exist the new method based on the nonlinear characteristics of signals, such as neural network method, and traditional methods. On the contrast, The extreme learning machine has advantages of strong nonlinear approximation, simple structure, high precision, fast learning and training speed, etc. On this basis, this paper proposes a method of utilizing extreme learning machine based on least square to get the output weight, in order to train the model to detect the weak pulse signal.

Phase-noise estimation using Bayesian inference for discretely modulated measurement-device-independent continuous-variable quantum key distribution

Measurement-device-independent continuous-variable quantum key distribution (MDI-CVQKD) protocol calibrates the phase reference frames by transmitting local oscillator pulses between two legitimate parties. The above implementation leaves massive loopholes; in particular, an eavesdropper can attack the system by manipulating the power of the local oscillator pulses. In this paper, a Bayesian phase-noise estimation model is proposed to estimate the phase drift and its uncertainty in the MDI-CVQKD protocol. The model employs the Bayesian estimation of the very weak quantum signal pulses and eliminates the necessity of transmitting local oscillator pulses between two legitimate parties. Moreover, the Bayesian inference algorithm has the characteristic of high robustness to noise and can achieve a well-motivated confidence interval of the estimated eigenphase. Comparing with its Gaussian counterpart, we adopt the discrete modulation in the MDI-CVQKD protocol, which allows a much better reconciliation efficiency at low signal-to-noise ratio. With simpler implementation, the proposed Bayesian phase-noise estimation model precisely avoids the security bottleneck due to the transmitted local oscillator pulses.

Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often submerged in strong chaotic noise, and the targeted signal data are difficult to recover. Traditional schemes, such as Elman neural network (ENN), backpropagation neural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to extract the weak signal embedded in a chaotic background. To improve the estimating accuracy, a novel estimating method for aiming at extracting problem of weak pulse signal buried in a strong chaotic background is presented. Firstly, the proposed method obtains the vector sequence signal by reconstructing higher-dimensional phase space data matrix according to the Takens theorem. Then, a Jordan neural network- (JNN-) based model is designed, which can minimize the error squared sum by mixing the single-point jump model for targeting signal. Finally, based on short-term predictability of chaotic background, estimation of weak pulse signal from the chaotic background is achieved by a profile least square method for optimizing the proposed model parameters. The data generated by the Lorenz system are used as chaotic background noise for the simulation experiment. The simulation results show that Jordan neural network and profile least square algorithm are effective in estimating weak pulse signal from chaotic background. Compared with the traditional method, (1) the presented method can estimate the weak pulse signal in strong chaotic noise under lower error than ENN-based, BPNN-based, SVM-based, and -ased models and (2) the proposed method can extract the weak pulse signal under a higher output SNR than BPNN-based model.

Athlete’s respiratory frequency and physical energy consumption model based on speech recognition technology

Athlete’s respiratory frequency and the physical energy consumption model based on speech recognition technology is presented in this paper. We use the series of rotation angles reflects changes in the electrical axis of the heart caused by breathing, and then power spectrum analysis of the breathing signal is used to then estimate the breathing rate. The novelties of the paper are summarized as three aspects. (1) Gaussian mixture model is used to model the speaker. This system has the better noise robustness than the traditional feature parameters at low signal-to-noise ratio. (2) We use the photoelectric sensor technology to measure the heart rate of the human body, which can detect weak pulse signals. (3) The collected respiratory sound signals are processed by the dedicated data compression module and then displayed on the entire screen, as the system will show the analytic results. The experimental results have proven the effectiveness of the proposed methodology.

Statistical Detection of Weak Pulse Signal under Chaotic Noise Based on Elman Neural Network

Weak signal detection is a significant problem in modern detection such as mechanical fault diagnosis. The uniqueness of chaos and good learning ability of neural networks provide new ideas and framework for weak signal detection field. In this paper, Elman neural network is applied to detect and recover weak pulse signal in chaotic noise. For detection problem of weak pulse signal under chaotic noise, based on short-term predictability of chaotic observations, phase space reconstruction for observed signals is carried out. And Elman deep learning adaptive detection model (EDAD model) is established for weak pulse signal detection, and a hypothesis test is used to detect weak pulse signal from the prediction error. For the recovery of weak pulse signal under chaotic noise, a double-layer Elman deep neural network recovery model (DEDR model) is proposed, which is based on the Elman deep learning network model and single-point jump model for weak pulse signal, and it is optimized with goal of minimizing mean square prediction error of the Elman model. The profile least squares method is applied to estimate parameters of the DEDR model for difficult recovery of weak pulse signal because the DEDR model is essentially a semiparametric model with parametric and nonparametric parts. In the end, simulation experiments show that the model built in this paper can effectively detect and recover weak pulse signal in the background of chaotic noise.

Manipulation of a weak signal pulse by optical soliton via double electromagnetically induced transparency

We propose a scheme to realize the manipulation of a weak signal pulse by ultraslow optical soliton in a coherent inverted-Y-type atomic system via double electromagnetically induced transparency (EIT). Based on Maxwell-Bloch equations, we derive nonlinear equations governing the spatial-temporal evolution of the probe and signal pulse envelopes. We show the giant enhancement of optical Kerr nonlinearity can be obtained under the condition of the double EIT, which results in the generation of a (2+1)-dimension optical soliton and can realize the manipulation of a weak signal pulse. Applying a far-detuned laser field to the system, we find that a weak signal pulse can be trapped by a (3+1)-dimension light bullet. In particular, the trajectories of the light bullet and trapped signal pulse can be manipulated and controlled by introducing a Stern–Gerlach gradient magnetic field. The results predicted here may not only open a route for the study of weak-light nonlinear optics but also have potential applications in the precision measurements and optical information processing and transmission.

Detection and Extraction of Weak Pulse Signals in Chaotic Noise with PTAR and DLTAR Models

With the development in communications, the weak pulse signal is submerged in chaotic noise, which is very common in seismic monitoring and detection of ocean clutter targets, and is very difficult to detect and extract. Based on the threshold autoregressive model, pulse linear form, Markov chain Monte Carlo (MCMC), and profile least squares (PrLS) algorithm, phase threshold autoregressive (PTAR) model and double layer threshold autoregressive (DLTAR) model are proposed for detection and extraction of weak pulse signals in chaotic noise, respectively. Firstly, based on noisy chaotic observation, phase space is reconstructed according to Takens’s delay embedding theorem, and the phase threshold autoregressive (PTAR) model is presented to detect weak pulse signals, and then the MCMC algorithm is applied to estimate parameters in the PTAR model; lastly, we obtain one-step prediction error, which is used to realize adaptively detection of weak signals with the hypothesis test. Secondly, a linear form for the pulse signal and PTAR model is fused to build a DLTAR model to extract weak pulse signals. The DLTAR model owns two kinds of parameters, which are affected mutually. Here, the PrLS algorithm is applied to estimate parameters of the DLTAR model and ultimately extract weak pulse signals. Finally, accurate rate (Acc), receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) are used as the detector performance index; mean square error (MSE), mean absolute percent error (MAPE), and relative error (Re) are used as the extraction accuracy index. The presented scheme does not need prior knowledge of chaotic noise and weak pulse signals, and simulation results show that the proposed PTAR-DLTAR model is significantly effective for detection and extraction of weak pulse signals under chaotic interference. Specifically, in very low signal-to-interference ratio (SIR), weak pulse signals can be detected and extracted compared with support vector machine (SVM) class and neural network model.

Continuous-variable quantum key distribution based on high-rate phase reference

We propose a high-rate phase reference scheme for continuous-variable quantum key distribution (CV-QKD). In this scheme, a transmission mode has been employed in which several quantum signal pulses are accompanied by a pair of reference pulses, calibrating the measurement bases of the communicating parties. Within a certain range, and with the ratio between quantum signal pulses and reference pulses increased, the secret key rate approaches that of the traditional CV-QKD scheme (which is a scheme without phase noise). Compared with general CV-QKD schemes, which adopt the transmission mode of a quantum signal pulse plus a pair of reference pulses, this scheme has a higher spectral efficiency, enabling high-rate CV-QKD. In addition, we reduce the interference of strong reference pulses with weak quantum signal pulses by using a pair of polarization beam splitters with a dynamic polarization controller. Simulation results demonstrate that we achieve a higher secret key rate than the self-coherent phase reference scheme. This provides a pathway towards large-scale quantum communication networks.

Pulse signal restoration via stochastic resonance in a Fabry-Perot cavity with an intracavity nematic liquid crystal film.

We theoretically propose a method to restore weak pulse signals submerged in noise via stochastic resonance, which is based on the optical bistability induced by the molecule reorientation in a Fabry-Perot cavity with an intracavity nematic liquid-crystal film. The bistable properties of this cavity are analyzed with different reflectance of the mirrors, initial phase shift and initial angle between the phase propagation vector and the director. The cross-correlation coefficient between pure input pulses and output is calculated to quantitatively evaluate the influence of noise intensity on output. The simulation results show a cross-correlation gain of 3.2 and that the buried signal can be recovered effectively by this device. It proves the potential of using this structure to recover noise-hidden pulse signals in an all-optical system.

Detection and parameter estimation of weak pulse signal based on strongly coupled Duffing oscillators

Pulse signal detection is widely used in nuclear explosion electromagnetic pulse detection, lightning signal detection, power system partial discharge detection, electrostatic discharge detection, and other fields. The signal strength becomes weak with the increase of the detection distance and may be submerged in strong Gaussian noise for remote detection. Therefore, the detection and recovery of the weak signals, especially the weak pulse signals, have important applications in signal processing area. Some methods have been reported to detect and estimate weak pulse signals in strong background noise. Coupled Duffing oscillators are usually used in processing periodic signals, though it is still in an exploration stage for aperiodic transient signals. There remain some problems to be solved, for example, the system performance depends on some initial values, results are valid only for the period-doubling bifurcation state, the waveform time domain information cannot be accurately estimated, etc. In this paper, we explain the reasons why there exist these inherent defects in the current weakly coupled Duffing oscillators. In order to solve the above-mentioned problems, a new signal detection and recovery model is constructed, which is characterized by coupling the restoring force and damping force of the two oscillators simultaneously. A large coupling coefficient is applied to the two Duffing oscillators, and a generalized " in-well out-of-synchronization”phenomenon arises between the oscillators which conduces to detecting and recovering the weak pulse signals, and also overcoming the defects mentioned above. Using the metrics of signal-to-noise ratio improvement (SNRI) and waveform similarity, the effects of amplitude and period of periodic driving force, coupling coefficient, step size and damping coefficient on signal detection and waveform recovery are studied. Finally, experiments are performed to detect and recover the following three kinds of pulses: square wave pulses, double exponential pulses, and Gaussian derivative pulses. The input SNR thresholds of these three waveforms are –15, –12, and –16 dB, respectively, under the detection probabilities and waveform similarity all being greater than 0.9 simultaneously. The maximum error of the pulse amplitude and pulse width are both less than 5% of their corresponding true values. In summary, the strongly coupled Duffing system has advantages of being able to operate in any phase-space state and being no longer limited by the initial values. Especially, the time domain waveform of weak pulse signals can be well recovered in the low SNR case, and the error and the minimum mean square error are both very low.

Weak laser pulse signal amplification based on a fiber Brillouin amplifier

We propose a high-gain and frequency-selective amplifier for a weak optical signal based on stimulated Brillouin scattering in a single mode fiber. To be able to satisfy the needs of high gain and high signal-to-noise ratio laser pulse amplification, different fiber lengths and core diameters are used to fulfill this experiment. In the experiment, a 430 nW (peak power) pulsed signal is amplified by 70 dB with a signal-to-noise ratio of 14 dB. The small size, high gain, low cost, and low noise of the fiber Brillouin amplifier make it a promising weak signal amplification method for practical applications such as lidar.

Multiple InGaAs/InP single-photon avalanche detector scheme for wavelength-division-multiplexed quantum communications in a single transmission fiber

We propose a wavelength-division-multiplexed (WDM) scheme for high-speed and high-capacity quantum communications in a single transmission fiber. The scheme involves multiple weak coherent-state signal pulse sources (WCS-SPSs) and multiple single photon detectors (SPDs). Quantum signals encoded with WDM WCS-SPSs are transmitted through a standard single mode fiber to a receiver composed of WDM SPDs. This WDM scheme can provide high transmission capacity by avoiding the afterpulse effect of InGaAs/InP single-photon avalanche detectors (SPADs) but by adding up the total avalanche counts. We performed numerical calculations on the reduced afterpulse effect and the increased total quantum transmission capacity of the WDM scheme based on measured avalanche count data and previously reported afterpulse data from the InGaAs/InP SPADs. The results indicate that the WDM scheme provides increased quantum transmission capacity by avoiding increased afterpulse effects despite increased insertion loss and crosstalk due to the WDM devices.

Detection and estimation of weak pulse signal in chaotic background noise

As is well known, people has been suffering noise interference for a long time, and more and more researches show that a lot of weak signals such as pulse signal are embedded in the strong chaotic noise. The purpose of weak signal detection and recovery is to retrieve useful signal from strong noise. It is very difficult to detect and estimate the weak pulse signal which is mixed in the chaotic background interference. Therefore, the detection and recovery of weak signal are significant and have application value in signal processing area, especially for the weak pulse signal detection and recovery. By studying various methods of detecting and estimating the weak pulse signal in strong chaotic background noise, in this paper, we propose an efficient hybrid processing technique. First, based on the short-term predictability and sensitivity to the tiny disturbance, a new method is proposed, which can be used for detecting and estimating the weak pulse signals in chaotic background that the nonlinear mapping is unknown. We reconstruct a phase space according to Takens delay embedding theorem; then we establish the local linear autoregressive model to predict the short-term chaotic signal and obtain the fitting error, and judge whether there are weak pulse signals. Second, we establish a single-jump model for pulse signals, and combine the local linear autoregressive model with it to build a double local linear (DLL) model for estimating the weak pulse signal. DLL model contains two parameters, and the two parameters affect each other. We use the back-fitting algorithm to estimate model parameters and ultimately recover the weak pulse signals. Detecting and estimating the pulse signals in chaotic background turns into estimating the parameters of DLL model. The minimum fitting error criterion is used as the objective function to estimate the parameters of the DLL model. To make the estimation more exact, we can use the formula of mean square error. The new algorithm presented here in this paper does not need to know the prior knowledge of the chaotic background nor weak pulse signal, and this algorithm is also simple and effective. Finally, the simulation results show that the method is effective for detecting and estimating the weak pulse signals based on the chaotic background noise. Specifically, the weak pulse signal can be extracted well with low SNR and the minimum mean square error or the minimum normalized mean squared error is very low.

Developing a Wireless, High Precision and Processing Speed Pulse Monitoring Headset Using Photoplethysmography.

In this paper, thorough improvement of pulse monitoring and analysis equipment with a headset structure is presented. In order to study the most suitable infrared wavelength for the acquisition of the pulse wave at the earlobe, Monte Carlo simulation was adapted. Both high frequency noise and baseline drift, generated in the signal acquisition process, are considered. To further optimize the system design and improve accuracy, for the sensor’s dimensional drift, the corresponding compensation was carried on in the software. This paper introduced nonlinear quantization, especially in terms of very weak pulse signal, in the time domain analysis process. A quick extraction method named table look-up combing with interpolation was utilized to obtain frequency domain information whose processing speed can be increased by about 30 times compared with fast Fourier transformation setting the sampling point as 300. The results demonstrated the sensor’s excellent performance in pulse signal acquisition whose maximum residual is less than 0.004 mV. The test on a random sample of 300 people indicates that the system had high correlation with reference, validating the system accuracy is extremely high. Overall, this paper provides a practical pulse monitoring and analysis system with high precision and processing speed that can be widely applied in the field of health management or medical measurement.

Numerical analysis of pulse signal restoration by stochastic resonance in a buckled microcavity.

A novel scheme is proposed to restore weak pulse signals immersed in noise by stochastic resonance based on photothermal-effect-induced optical bistability in a buckled dome microcavity. The bistable properties of the dome microcavity are analyzed with different initial detuning wavelengths and effective cavity lengths, and bistable transmission can be obtained for input powers in submilliwatt range. A theoretical model is derived to interpret the nonlinear process of pulse signal recovery through double-well potential theory. The cross-correlation coefficient between output signals and pure input pulses is calculated to quantitatively analyze the influence of noise intensity on stochastic resonance. A cross-correlation gain of 7 is obtained, and the noise-hidden signal can be recovered effectively though the buckled dome microcavity with negligible distortion. The simulation results show the potential of using this structure to restore low-level or noise-hidden pulse signals in all-optical integrated systems.

Compression of ultra-short pulses due to cascaded second order nonlinearities in photonic bandgap structures

The cascaded second order nonlinearities in a 1-D photonic bandgap structure (1-D PBG) in the spectral domain have been explored. A weak signal pulse operating at frequency of interest is seeded with a strong pulse operating at its second harmonic (SH) frequency. The interaction of both pulses in the periodic structure takes place with a particular phase mismatch condition. The intensity of SH pulse controls the propagation of signal pulse and the signal pulse exhibits pulse compression at particular input SH intensity. Considering the parameter for GaInP/InAlP PBG structure we have demonstrated pulse compression from 290 fs to 155 fs. The dependency of pulse compression on the structural parameters, group velocity mismatch, group velocity dispersion and input intensity of pump has also been explored.