Weak Detection Research Articles

A Novel Underwater Location Beacon Signal Detection Method Based on Mixing and Normalizing Stochastic Resonance.

A flight data recorder (FDR) is an electronic recording device placed in an aircraft for the purpose of facilitating the investigation of aviation accidents. If an aircraft crashes into water, an underwater locator beacon (ULB), which is installed on the FDR, is triggered by water immersion, and emits an ultrasonic 10 ms pulse signal once per second at 37.5 kHz. This pulse signal can be detected by sonar equipment. However, the ULB signal only can be detectable 1–2 kilometers from the surface in normal conditions. Stochastic resonance (SR) is a rising theory in the field of weak signal detection. The classical stochastic resonance limits state that the input must be small-parameter and the sampling frequency must be 50 times higher than the signal frequency. It cannot be applied to the ULB signal detection. To resolve this problem, this paper presents a novel approach named mixing and normalizing stochastic resonance (MNSR). By mixing the ULB signal and normalizing SR system parameters, MNSR provides a new way to detect weak ULB signal. Meanwhile, we propose the parameters adjustment method of MNSR. We prove the effectiveness through numerical simulation. An experiment in a tank is employed to verify the practicability of this method.

Ultrahigh Sensitivity Graphene/Nanoporous GaN Ultraviolet Photodetectors.

Integration of graphene with three-dimensional semiconductors can introduce unique optical and electrical properties that overcome the intrinsic limitation of the materials. Here, we report on the high sensitivity ultraviolet (UV) photodetectors based on monolayer graphene/nanoporous GaN heterojunctions. By investigating the reflectivity, photoluminescence, and Raman spectral characteristics of nanoporous GaN, we find that the increase in the porosity can help to improve its optical properties. The device based on the highest-porosity nanoporous GaN demonstrates rapid and linear response to UV photons, with an ultrahigh detectivity of 1.0 × 1017 Jones and a UV-visible rejection ratio of 4.8 × 107 at V = -1.5 V. We attribute such high sensitivity to the combination of the significantly enhanced light harvesting of high-porosity nanoporous GaN and the unique UV absorption, high mobility, and finite density of states of the monolayer graphene. The high performance together with a simple and low-cost fabrication process endow these graphene/nanoporous GaN heterojunctions with great potential for future selective detection of weak UV optical signals.

High-sensitivity laser confocal tomography based on frequency-shifted feedback technique

Laser confocal tomography faces severe challenges in weak signal detection such as biological tissue imaging and low-reflection interfaces measurement. An ultra-high sensitive optical confocal tomography based on laser frequency-shifted feedback imaging technology is presented, whose theoretical magnification can reach 106 notably without using high-gain detectors. In addition, this achievement possesses the same tomographic resolution as conventional laser confocal tomography without the need for spatial filtering devices. In this work, supported by theories and experiments, the lateral and vertical resolution, optical sensitivity, and system repeatability of LFCT and traditional LCT are investigated and analyzed quantificationally. Finally, comparison on the results of measuring the microfluidic device and biological tissue sections is conducted.

NovelWoods–Saxon stochastic resonance system for weak signal detection**Project supported by the National Natural Science Foundation of China (Grant No. 61501525) and the National Natural Science Foundation of Hunan Province of China (Grant No. 2018JJ3680).

We propose a joint exponential function and Woods–Saxon stochastic resonance (EWSSR) model. Because change of a single parameter in the classical stochastic resonance model may cause a great change in the shape of the potential function, it is difficult to obtain the optimal output signal-to-noise ratio by adjusting one parameter. In the novel system, the influence of different parameters on the shape of the potential function has its own emphasis, making it easier for us to adjust the shape of the potential function. The system can obtain different widths of the potential well or barrier height by adjusting one of these parameters, so that the system can match different types of input signals adaptively. By adjusting the system parameters, the potential function model can be transformed between the bistable model and the monostable model. The potential function of EWSSR has richer shapes and geometric characteristics. The effects of parameters, such as the height of the barrier and the width of the potential well, on SNR are studied, and a set of relatively optimal parameters are determined. Moreover, the EWSSR model is compared with other classical stochastic resonance models. Numerical experiments show that the proposed EWSSR model has higher SNR and better noise immunity than other classical stochastic resonance models. Simultaneously, the EWSSR model is applied to the detection of actual bearing fault signals, and the detection effect is also superior to other models.

Improved Hydrometeor Detection Method: An Application to CloudSat

AbstractClouds play an important role in the climate system and are a principal source of uncertainty in climate projections. CloudSat has provided an unprecedented opportunity to study the vertical structure of clouds, and its observations are being widely used in scientific studies. However, some clouds are not detected or are only weakly detected by CloudSat. In most studies, the weakest detections, specifically those detected by the so‐called along‐track integration scheme, are typically ignored due to the high rate of false detections, namely, a significant probability that a detected cloud is actually a region of increased measurement noise, rather than a true cloud signal. False detections have been reduced in the latest version (called R05 for release 5) of the CloudSat cloud mask product but at a cost of a significant loss in the true weak signals (i.e., a higher false omission rate). In this study, the CloudSat hydrometeor detection algorithm used in R05 is modified by adding a bilateral filter scheme to improve the detection of weak signals. By comparing with the CALIPSO lidar vertical feature mask, it is shown that the new scheme largely reduces the false detection rate compared to the R04 version, while retaining a large fraction of the true weak signals that have been lost in the R05 version. Implementing this scheme in future CloudSat data processing is expected to lead to a better detection of thin clouds.

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.

Weak Signal Detection Based on Strongly Coupled Duffing-Van der Pol Oscillator and Long Short-term Memory

This study presents a weak-signal detection system based on a strongly coupled Duffing-Van der Pol (DVP) oscillator. The simulation shows that under all noise conditions with different noise varian...

Bearing Fault Diagnosis Based on Unsaturated Piecewise Non-linear Bistable Stochastic Resonance under Trichotomous Noise

The classical bistable stochastic resonance (CBSR) has the disadvantage of output saturation, which limits the enhancement capability for weak signal detection. To break the limitation of output saturation, a novel unsaturated piecewise non-linear bistable stochastic resonance (PNBSR) method is proposed. Because the trichotomous noise exists in practical engineering, the PNBSR under trichotomous noise is explored in this paper. The performance of PNBSR is evaluated by the index, i.e., the mean of [Formula: see text] times signal-to-noise ratio increase (MSNRI). The double-peak phenomenon of SR is observed under trichotomous noise. Experiments reveal that the proposed PNBSR method performs best on extracting characteristic components from a strong noise background, compared with the CBSR method and the traditional digital filter. Then, the PNBSR is applied to the fault diagnosis of rolling element bearings. The paper focuses on solving practical engineering problems with mathematical methods.

Design of weak optical signal detection system for measuring eye axial length

Since the reflectivity of two reflective surfaces of the corneal surface and the retinal pigment cortex is greatly different when measuring the axial length of eye, the intensity of the reflected optical signal belonges to the weak signal category under the premise of the incident light safety in the eye. In order to remove the noise and quickly extract the peak value of the signal for the calculation of the starting and ending point of the eye axial length, a weak optical signal detection system was designed. This system used PIN photodiode to receive weak optical signal and a weak light signal detection system was built coordinated with the I-V conversion circuit, automatic gain control circuit as well as the post-processing circuit. The experiment used ZIESS simulation eye to test. The results show that the designed detection system can detect the weak optical signal of about 0.77 nW at least, the maximum photoelectric conversion capacity can reach 4.5×108 V/W, the signal-to-noise ratio is more than 9 dB, and the maximum error between the measured eye length and the actual value can reach 0.05%. This system is characterized by low noise, high gain and large dynamic range, which provides an effective solution for accurate measurement of the eye axial length.

The design of computer controlled ground information transmission down platform in drilling

In order to improve the drilling efficiency and avoid the disadvantages of manual operation, the method of sending the surface intervention instruction with computer instruction is researched. Based on the similarity of liquid flow, a set of hydraulic circulation system is used to simulate the drilling process, selected the key components, designed and built the intelligent drilling system indoor experiment platform; Define different commands with the length of the time and decode them and carried out the sending command experiment. The results show that the platform can successfully send the command to the downhole under the condition of non-stop drilling, which is the attitude intervention, command coding and Weak signal detection in the intelligent drilling system provides a new platform.

A Adaptive Stochastic Resonance Method Based on Two-Dimensional Tristable Controllable System and Its Application in Bearing Fault Diagnosis

At present, most systems with good performance have more parameters. However, increasing the number of parameters lead to increasing the difficulties of parameter optimization, thus reducing the system’s feasibility. To solve this problem, a model of controllable coupled stochastic resonance system is proposed. Firstly, the formula of the output signal-to-noise ratio of the model is derived and analyzed. Then the accuracy of the formula’s derivation and results is proved by numerical simulation. It provides a theoretical basis for adjusting the parameters and coupled coefficients of the control system to induce a stochastic resonance in the controlled system or make it much stronger. Finally, better system parameters are obtained by genetic algorithm for the controlled system (tristable system), and then better system performance is obtained by adjusting the parameters and coupled coefficient of the control system (monostable system). The model is applied into the bearing fault detection and the results show that the model achieves better performance without increasing the complexity of parameter optimization, and has great practical value in weak signal detection.

Transient nanostrain detection in phi-OTDR using statistics-based signal processing

Phase-sensitive optical time-domain reflectometry (Φ-OTDR) is capable of detecting acoustic emission induced small strain with high sensitivity. However, there is a tradeoff between sensitivity, bandwidth and detection range, which makes the detection of a transient weak signal challenging. In this work, we focus on transient weak signal detection using Φ-OTDR. To achieve this aim, we propose a cascaded statistics-based signal-processing framework in a Φ-OTDR system to fetch the transient weak signal from a noisy background. Our framework is based on two key elements, including an estimator that is derived based on the probability characteristic of the Rayleigh backscattered light, and a time-frequency feature extraction process that maps the signal to the time-frequency domain. Using our statistics-based signal processing Φ-OTDR, we demonstrate experimentally the detections of, firstly a weak persistent signal with a magnitude down to 4 nε and a frequency up to 40 kHz, and then a weak transient acoustic tone-burst signal. Our proposed scheme is promising for Φ-OTDR performance improvement particularly in weak signal detections, and it will find new applications in the future systems.

Detection Algorithm of BPSK Signal of Parameter-Adjusted Bistable Stochastic Resonance Model Based on Scale Change

Given that signal is weakened to a certain extent in the process of noise suppression using mainstream method, and that new noise is introduced by signal processing system, causing the decrease of detection performance, to improve the performance of detection to BPSK signal under the condition of strong noise and no prior information, the detection algorithm of BPSK signal of bistable stochastic resonance model based on scale change is proposed in this study. Using classical bistable stochastic resonance (BSR) system, only low-amplitude and low-frequency periodic signal can be processed. Scale change is first made to BSR in this study, verifying that BSR can be applied to high-frequency BPSK signal under high sampling frequency condition, and nonlinear threshold detection system is designed following Neyman-Pearson criterion to deduce and quantitatively show error rate of detector. Besides, complete flow for signal detection was built by taking it as feedback quantity to adjust the system parameters adaptively. Scale change feasibility and applicability of algorithm proposed in this study were verified through simulation experiment, which lays the theoretical basis for the detection of weak BPSK signal under low signal-to-noise ratio (SNR).

Secondary Nuclear Spin Echo Signals in Thin Yttrium Iron Ferrite Garnet Films

The results are presented of experimental and theoretical study of the phenomenon of secondary nuclear spin echo in magnetically ordered materials in which the formation of additional echo signals is due to dynamic hyperfine coupling. Numerical simulation of the effect of the amplitude (ω1) and the durations of the first (t1) and the second (t2) exciting pulses on the echo signals is performed. It is found that the maximum amplitude of the secondary echo is formed under the conditions ω1t1 = 0.5π and ω1t2 ≈ 0.6π. It is shown that secondary echo signals can be observed upon inhomogeneous excitation of the spectral line ω1 ≤ Δω, where Δω is the inhomogeneous spectral line width. At a temperature of T = 4.2 K, additional double-pulse spin 3τ-echo signals from iron nuclei are experimentally observed in an epitaxial yttrium ferrite garnet film enriched with 57Fe magnetic isotope to 96%. The experimentally observed phase relationships between the primary and secondary echo signals, as well as the dependence of the echo signal amplitude on the amplitude and duration of the exciting pulses, are in good agreement with the results of numerical simulation of the dynamics of nuclear magnetization with regard to the dynamic hyperfine coupling. It is shown that the secondary echo exhibits the effect of spectral line narrowing, and the amplitude of the secondary echo is proportional to the nuclear magnetic resonance (NMR) enhancement factor in magnets, η. In the case of 57Fe NMR in an yttrium iron garnet (YIG) film, the amplitude of the 3τ-echo is two to three orders of magnitude smaller than the amplitude of the primary 2τ-echo, which corresponds to η ≈ 440. The detection of weak secondary echo signals proves to be possible due to the use of a phase-coherent NMR spectrometer with digital quadrature detection at the carrier frequency and signal accumulation.

Spectral and Thermal Properties of Rhodopsins: Closely Related but not Tightly Coupled

Rhodopsins, the primary molecules of vision in all seeing animals, can be activated not only by photon energy (light) but also by thermal energy (heat). Spectral absorbance is evolutionarily tuned by critical residues in the amino acid sequence of the protein part (opsin), which affect the energy needed for 11-cis → all-trans isomerization of the covalently bound chromophore. Already in the 1940’s it was suggested that high sensitivity to long-wavelength light, being indicative of a low energy barrier for activation, should correlate with high probability for thermal activation, and that randomly occurring thermal activations would constitute an irreducible noise setting absolute constraints for the detection of weak light signals. This idea has received strong experimental as well as theoretical support over the last 40 years. Most of the experimental evidence comes from physiological studies of light responses and dark noise in the light-sensitive current of vertebrate photoreceptor cells. Here I review this work, which has firmly established the correlation of spectral sensitivity and thermal noise and led to new theoretical insights. On the other hand, there remains significant freedom for independent adjustment of the two variables by tinkering with the opsin. This is a question of fundamental evolutionary as well as practical interest.

Intelligent Medical Rehabilitation Training Instrument Based on Movement Coordination

This study designed an intelligent medical rehabilitation training system based on motion coordination, which is mainly aimed at patients with balance functions and motion coordination disorders. The system consists of training beds, pedal components, manual control devices, main control computers and corresponding training software. Biofeedback control through the brain is combining physical and psychological treatment of patients, artificial intelligence judgment and adjustment, analysis and evaluation of biofeedback. The rehabilitation device adopts the structure mode of the upper and lower computers. The lower computers take DSP as the core and cooperate to complete the functions of weak EMG signal detection and feedback, stimulation signal output and data transmission. The weak electrical signals remaining in the limbs of stroke patients are processed by the host to form a closed-loop biological feedback electrical stimulation system. According to the intermittent distraction joint method and aerobic training method, an intelligent medical rehabilitation device is designed, including a rehabilitation device for training the rotation function of the wrist and a rehabilitation device for training the flexion and extension function of the wrist. In order to meet the rehabilitation needs of the spasm and recovery periods, both wrist rehabilitation devices have three rehabilitation modes, namely passive rehabilitation mode, assisted rehabilitation mode and damping rehabilitation mode. The intelligent medical rehabilitation training instrument system can realize the combination of patient adjustment force and hand control training to achieve the matching of lower limb, lower limb movements and visual information, so as to promote the patient's balance function and coordinate rehabilitation training. The system sets visual and auditory feedback, and increases the interest of training by adjusting the training speed and training difficulty, which has important clinical application value.

The Design of Focal Plane Splitting Unit in a Hyperspectral Lidar System

A hyperspectral lidar system with focal plane splitting unit is proposed, including an off-axis receiving telescope, a grating spectrometer, and a single-tube detector array. The spectrum of the system covers 380-930nm, and is separated by grating spectrometer. The microlens-fiber coupling system guides the echo signal of 50 channels into each detector. The system solves the data processing problem of the bandwidth and gain the line array and area array detector in traditional hyperspectral lidar. And it also meets the requirement of the high efficiency splitting coupling and weak signal acquisition and detection.

Magnetic field effect on the free induction decay of hydroxyl radicals (OH) in the terahertz region.

The effect of an external longitudinal magnetic field on the optical free induction decay from a free radical is observed for the first time. The experiments were performed on a rotational transition of the hydroxyl radical, OH (2Π3/2(J = 1) ← 2Π3/2(J = 0) at 83.8 cm-1), using a terahertz free electron laser. In contrast to the results of the experiments with a stable paramagnetic molecule, NO, the observed effect of an external magnetic field on the free induction decay from hydroxyl radicals is more complicated. A longitudinal magnetic field leads to the rotation of the polarization plane of the FID radiation and to an additional modulation of the signal intensity. The angle of the rotation of the plane of polarization is large, in agreement with the theoretical predictions. The observed FID kinetics in the time domain are in semi-quantitative agreement with the modeling. This observation opens an opportunity for the selective detection of weak signals of short-lived reactive paramagnetic free radicals from overwhelming signals that originate from stable non-paramagnetic species by polarization discrimination.

An Improved Underdamped Asymmetric Bistable Stochastic Resonance Method and its Application for Spindle Bearing Fault Diagnosis

High-precision spindle bearing is one of the most critical and vulnerable parts in a motorized spindle. Its unexpected failure may lead to production loss. Stochastic resonance (SR) is a weak signal detection method, which can obtain noise energy in strong background noise and enhance incipient fault characteristics of spindle bearing. Based on the fact that asymmetry can improve the enhancement ability of asymmetric bistable SR in weak feature extraction, we introduce an underdamped well-width asymmetric bistable SR (UABSR) method to the field of bearing fault diagnosis for the first time. However, the engineering application of UABSR can still be limited by two aspects. Firstly, the SNR index can take effect only when the actual fault frequency is obtained in advance, so the UABSR method is at high-cost in real practices. Secondly, an appropriate band-pass filter band range of the bearing faults can hardly be obtained due to the massive impulsive noise in operations. Here an improved UABSR method for spindle bearing fault diagnosis is proposed. Infogram method is used to process and analysis the original vibration signal for resisting the influence from the impulsive noise and obtaining more accurate frequency range of spindle bearing fault. In addition, time domain zero-crossing (TDZC) index, as the index of the improved UABSR method, can directly reflect the fault characteristics of spindle bearings without knowing the accurate fault characteristic frequency in advance. Besides, the Quantum Genetic Algorithms (QGAs) and the fourth-order Runge-Kutta algorithm are combined to simultaneously obtain the optimal system parameter, the asymmetric ratio, the damping factor and the rescaling factor of the improved UABSR model. Comparing the Infogram and original UABSR methods, the improved UABSR method performs better effect in incipient spindle bearing fault diagnosis.

Application of Weak Signal Denoising Based on Improved Wavelet Threshold

In this Study, the application of wavelet threshold denoising in weak signal detection of underwater targets under complex sea conditions is discussed. Firstly, the basic principle and steps of wavelet threshold denoising method based on Mallat decomposition are analyzed. Then a new threshold function is proposed to overcome the shortcomings of traditional Donoho hard and soft thresholds. The denoising effects of wavelet and wavelet packet threshold denoising methods under hard threshold, soft threshold and new threshold functions are simulated, and the signal-to-noise ratio (SNR) and mean square error (MSE) after denoising are taken as evaluation indexes. The results show that the improved threshold denoising method outperforms the traditional threshold denoising methods.