Weak Detection Research Articles

Multi frequency weak signals detection based on under-sampling bistable stochastic resonance

Signal detection has a wide range of practical applications. Compared with traditional weak signal detection, the nonlinear effect of noise in the bistable system is typical and easy to extend, so bistable stochastic resonance detection technique has a wider applicability. In this paper, the multi-frequency weak signals detection based on under-sampling bistable stochastic resonance is studied. Based on the theory of bistable stochastic resonance, a simulation model is established to simultaneously detect multiple low-frequency weak signals. The spectrum of input and output signals is obtained through numerical simulation calculation, and the system characteristics are analyzed. When the large parameter signal does not meet the adiabatic approximation theory, the appropriate sampling coefficient can be selected to directly under-sample the input signal to make it conform to the conditions, and then the scale inverse transformation can be carried out. Finally, combined with the simulation analysis, the correctness of the under-sampling bistable stochastic resonance system to detect the multi-frequency weak signals is verified, the sampling rate is reduced, and the complexity is effectively decreased.

High-performance broadband Si/ZnO heterojunction photodetector based on pyro-phototronic effect

ZnO-based photodetectors are of great significance to military early warning and medical health. However, it is challenging to detect weak signals in a broad spectral range due to the nature of wide band gap and high background carriers, seriously limiting further practical application. In this work, large-area of uniform C-axis oriented ZnO films were grown on Si substrates via sputtering technique, and Si/ZnO heterojunction photodetectors were fabricated. Because of the compact ZnO thin films structure, built-in electrical field and the enhanced pyro-phototronic coupling effect at the heterojunction interface, the fabricated Si/ZnO photodetectors exhibited excellent broadband photoresponse behaviors in the vast range from the ultraviolet to the near-infrared (355–1550 nm) under zero external bias. Especially, the device shows a significant enhancement ability to ultralow laser power (∼500 nW/cm2) with a high responsivity value of 550.6 mA/W and the great optical detectivity of 7.7 × 1012 cm Hz1/2 W−1, which is ∼1–2 orders of magnitude larger than others. These results supply an innovative strategy to develop high-performance ZnO-based photoelectric devices for weak light signal detection.

Weak signal extraction in non-stationary channel with weak measurement

An emerging challenge of integrated communication and sensing is the extraction of weak sensing signals transmitted through an unknown non-stationary channel. In this work, we propose a weak signal extraction method with weak measurement. Taking advantage of time division multiplexing, we preliminarily estimate the channel via adjustable finite impulse response filter, further suppressing the interfering signal caused by background noises via spectrum shift. By subsequently using the time-varying phase estimation method via weak measurement, the real-time detection of weak signals in the non-stationary channel is achieved. We demonstrate via theoretical analysis and confirmatory experiment that our method is able to amplify the phase shift, to suppress technical noise and to improve detection resolution limit, while proving robust against light source fluctuations, initial phase differences and detector saturation. The method hence enables weak sensing signal extraction with a low signal-to-noise ratio non-stationary channel. Furthermore, we interface our measurement method to squeezed light sources, offering the possibility of surpassing standard quantum limit.

Multi-dimensional hybrid potential stochastic resonance and application of bearing fault diagnosis

Stochastic resonance is a method to enhance weak signal by using noise, which has a wide range of applications in weak signal detection. In order to further investigate the application of multi-dimensional coupling stochastic resonance in practical engineering, a Multi-dimensional Double connection coupling Hybrid Potential Stochastic Resonance (MDHPSR) system is proposed in this paper. Firstly, the potential functions of bi-stable and tri-stable are briefly described, and the tri-stable system having a higher output amplitude. Secondly, the effect of different coupling methods on the output of the central and adjacent coupling ends are studied, and the output amplitude of double connection coupling is higher. Then, the double connection coupling of different potential functions is studied, and MDHPSR system effect is the best. Compared with Multi-dimensional Double connection Classical Bi-stable Stochastic Resonance (MDCBSR) system, MDHPSR system has better anti-noise performance. Finally, applying the two multi-dimensional coupling systems to bearing fault diagnosis, MDHPSR system output amplitude is higher and the Signal-to-Noise Ratio (SNR) is improved by more than 3 dB. This demonstrates the superior performance of MDHPSR system for weak signal detection and the value of the multi-dimensional coupling system for practical engineering applications.

Weak fault feature extraction of rolling bearing based on multi-system coupled cascaded stochastic resonance system

The accurate extraction of weak signal features under strong noise background plays a crucial role in the fault detection of rolling bearings. In order to promote the ability of stochastic resonance (SR) system to detect weak signals and improve the output performance of the system, a multi-system coupled cascaded SR (MCCSR) system is investigated and applied to the fault detection of rolling bearings. Firstly, a MCCSR system is constructed by exploiting the positive synergistic effect between multiple systems, which consists of a triangular-topology coupled system composed of three SR subsystems and a cascaded SR system with topology output as input. This system makes full use of the advantages of coupled system and cascaded system in weak signal detection. In terms of parameter optimization, a stepwise multi-parameter optimization strategy is proposed, which adopts different optimization methods for different parameters, and avoids the inconsistency between error and step factor by improving the least mean square algorithm. Finally, through the comparative analysis of numerical simulation and experimental signals, it is verified that the proposed method can effectively enhance the weak signal features and improve the system output signal-to-noise ratio, which can better serve for rolling bearing fault detection.

Line Spectrum Trajectory Detection Method of Underwater Acoustic Signal Based on Particle filter

Line spectrum detection is a significant task to detect, track, and classify weak target sources for passive sonar systems. In order to solve the problem of tracking time-varying frequency trajectories, a track-before-detect (TBD) algorithm based on the two-layer particle filter structure is proposed. The detect layer deals with the potential tracks by maintaining a low-threshold dynamic detection grid, which is used to determine the birth, death, and false alarm of targets. The tracking layer utilizes the particle filter to compute the joint multitarget probability density through a Bayesian framework. Simulation results show that the proposed algorithm can effectively detect and track time-varying frequency trajectories in a low signal-to-noise ratio situation, which has certain research and application value in weak signal detection.

Stochastic resonance induced weak signal enhancement in a second-order tri-stable system with single-parameter adjusting

Weak signal detection methods based on stochastic resonance (SR) have been extensively studied due to their capability to utilize noise energy for enhancing weak signals. Among various SR models, the second-order tri-stable SR models have demonstrated their superiority in weak-signal detection with better output performance compared to other SR models. To optimize the output performance of the second-order tri-stable systems, a variety of parameter optimization methods have been proposed to optimize the parameters of the system. However, these optimization methods often require to optimize multiple parameters, which leads to an increase in computational costs and reduces the real-time processing efficiency of signal processing. Such multi-parameter optimization methods cannot meet the demands for timely signal processing in the context of big data. To address this challenge, this paper proposes two single-parameter-adjusting SR models. The proposed models can attain an ideal output performance by adjusting a single parameter in the SR system. The spectral amplification as an indicator is derived to quantitatively analyze the effects of the proposed models on SR output. On this basis, the influences of the proposed models on the SR output under different potential well parameters, noise intensities, signal frequency, and damping ratio are fully investigated through numerical simulations. At last, the proposed models are employed to process an experimental signal with a weak fault feature, and the experimental results verify the feasibility of the proposed models in optimizing the SR output. The research results can guide the design of tri-stable SR models and support the application of the SR-based signal processing model in the context of big data.

Magnetic Anomaly Detection Based on a Compound Tri-Stable Stochastic Resonance System.

In the case of strong background noise, a tri-stable stochastic resonance model has higher noise utilization than a bi-stable stochastic resonance (BSR) model for weak signal detection. However, the problem of severe system parameter coupling in a conventional tri-stable stochastic resonance model leads to difficulty in potential function regulation. In this paper, a new compound tri-stable stochastic resonance (CTSR) model is proposed to address this problem by combining a Gaussian Potential model and the mixed bi-stable model. The weak magnetic anomaly signal detection system consists of the CTSR system and judgment system based on statistical analysis. The system parameters are adjusted by using a quantum genetic algorithm (QGA) to optimize the output signal-to-noise ratio (SNR). The experimental results show that the CTSR system performs better than the traditional tri-stable stochastic resonance (TTSR) system and BSR system. When the input SNR is -8 dB, the detection probability of the CTSR system approaches 80%. Moreover, this detection system not only detects the magnetic anomaly signal but also retains information on the relative motion (heading) of the ferromagnetic target and the magnetic detection device.

Dual-heterodyne Kelvin probe force microscopy.

We present a new open-loop implementation of Kelvin probe force microscopy (KPFM) that provides access to the Fourier spectrum of the time-periodic surface electrostatic potential generated under optical (or electrical) pumping with an atomic force microscope. The modulus and phase coefficients are probed by exploiting a double heterodyne frequency mixing effect between the mechanical oscillation of the cantilever, modulated components of the time-periodic electrostatic potential at harmonic frequencies of the pump, and an ac bias modulation signal. Each harmonic can be selectively transferred to the second cantilever eigenmode. We show how phase coherent sideband generation and signal demodulation at the second eigenmode can be achieved by using two numerical lock-in amplifiers configured in cascade. Dual-heterodyne KPFM (DHe-KPFM) can be used to map any harmonic (amplitude/phase) of the time-periodic surface potential at a standard scanning speed. The Fourier spectrum (series of harmonics) can also be recorded in spectroscopic mode (DHe-KPFM spectroscopy), and 2D dynamic images can be acquired in data cube mode. The capabilities of DHe-KPFM in terms of time-resolved measurements, surface photovoltage (SPV) imaging, and detection of weak SPV signals are demonstrated through a series of experiments on difference surfaces: a reference substrate, a bulk organic photovoltaic heterojunction thin film, and an optoelectronic interface obtained by depositing caesium lead bromide perovskite nanosheets on a graphite surface. The conclusion provides perspectives for future improvements and applications.

Pupil-engineered coherent anti-Stokes Raman scattering spectroscopy

Coherent anti-Stokes Raman scattering (CARS) spectroscopy suffers from non-resonant background (NB) that is several orders of magnitude stronger than CARS signals. NB occupies the camera sensor dynamic range and hampers quantitative detection of weak Raman signals at low molecular concentrations. Here, we propose a novel technique to reduce NB for heterodyne CARS spectroscopy, by independently controlling the parities of the amplitude point spread functions of CARS and NB. We realize the parity control by applying the pupil engineering of the excitation and detection optical systems. A proof-of-concept experiment shows that the signal-to-background ratio (SBR) of the CARS signal to the NB can be improved as designed.

Weak Signal Detection in the Hodgkin–Huxley Neural Network with Channel Blocks under Electromagnetic Stimulus

Neurons can detect weak signals in noisy cellular environments and complex backgrounds. Channel blocks have a great impact on the initiation and propagation of action potentials for neurons. The effects of channel blocks and electromagnetic stimulus on weak signal detection are studied in Hodgkin–Huxley neuronal network. The results suggest that the weak signal detection and the neural discharge behaviors of the neural network can be suppressed by the sodium channel blockage, whereas the block of potassium channels will have a positive impact on the signal detection and neuronal firing. For moderate membrane patch size, there is an optimal ratio of non-blocked potassium channels that maximize improvement in the detection of weak signals and the regularity of the neuronal activities. The combined influence of the two channel blocks on weak signal detection can be modulated by the ratio between the number of the sodium channels and potassium channels that function properly. The electromagnetic stimulus can significantly ameliorate the damage caused by the poisoning of sodium ion channels. In addition, synchronization of the neural network can promote weak signal detection.

Enhanced detection stability in femtosecond time resolution transient absorption system from a tight-focusing filamentation

Femtosecond time resolution transient absorption spectroscopy (FTRTAS) is a powerful tool to obtain ultrafast photoexcitation dynamics of matter. The rapid development of photosensitive materials such as bioprotein and advanced photoelectric nanocrystal put forward higher stability requirements for the technique of FTRTAS. In this paper, we propose a strategy to establish a tight-focusing stable femtosecond laser filamentation system to optimize the stability of probe pulses, and finally achieve highly sensitive detection of weak signals. It is found that intense geometric focusing with short-focus lens can effectively reduce the self-focusing effect and plasma defocusing effect in the process of filamentation, avoid multiple filamentation, and promote a stable and tight femtosecond laser filamentation in the crystal. The results indicate that the supercontinuum white light (WL) pulse generated from 50 mm lens tight-focusing filamentation displays a comprehensive high detection ability with an enhanced stability and appropriate power and width, which improves the performance of the FTRTAS system and increases the detection sensitivity from conventional mode of 0.75 mOD to 0.3 mOD. The optimized FTRTAS system shows a great performance in detecting weak signals and tracking carrier evolution of perovskite and carotenoid after photoexcitation. These results demonstrate that the optimization of the femtosecond laser filamentation system can effectively improve the signal-to-noise ratio, and provide a valuable strategy for optimizing the FTRTAS system.

A chaotic synchronization system based on memristor for weak signal detection and its circuit implementation

A chaotic synchronization system based on memristor for weak signal detection and its circuit implementation

Signal encoding performance of astrocyte-dressed Morris Lecar neurons

There are more support cells called glia in the central nervous system than neurons. Although previous studies have shown that one of these glial cells, astrocytes, can communicate with neurons bidirectionally, giving a silent neuron the ability to fire or changing the neuron’s firing frequency, understanding the role of astrocytes in neuroscience is still in its infancy. With this motivation, in this study, the weak signal encoding performance of different types of astrocyte-dressed Morris Lecar neurons in the presence of noise naturally found in biological systems is systematically investigated. The results showed that astrocyte-dressed Morris Lecar neurons had a subthreshold signal frequency suitable for optimal encoding and that Morris Lecar neurons resonated at this frequency. In addition, the rate of inositol trisphosphate production in the astrocyte has been shown to affect signal detection at different noise intensities and coupling strengths. Finally, simulations performed by including the internal noise of the astrocytes originating from calcium ion channels in the model have shown how the signal transmission changes depending on the calcium channel number, noise intensity, and coupling strength. In this context, the presented study also contains important information in terms of examining stochastic astrocyte dynamics that affect the weak signal detection performance of different types of astrocyte-dressed Morris Lecar neurons.

SNR enhancement of quasi-distributed weak acoustic signal detection by elastomers and MMF integrated Φ-OTDR.

We have proposed and demonstrated a weak acoustic signal detection technology based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). Non-contact acoustic signals transmitting through air gap between the sound source and the receiver are difficult to detect due to fast attenuation. In order to improve the detection ability of non-contact weak acoustic signals, we demonstrate that multi-mode fiber (MMF) is a better solution than single-mode fiber (SMF) benefiting from its larger core and higher Rayleigh backscattering (RBS) capture coefficient. The frequency signal-to-noise ratio (SNR) has been enhanced by 9.26 dB. Then, with the help of 3D printing technology, elastomers have been designed to further enhance the detection ability due to the high-sensitive response to acoustic signals. Compared with the previous reported "I" type elastomer, the location and frequency SNR enhancement caused by our new proposed "n" type elastomer are 8.39 dB and 11.02 dB in SMF based system. The values are further improved to 10.51 dB and 13.38 dB in MMF and "n" type elastomer integrated system. And a phase-pressure sensitivity of -94.62 dB re rad/µPa has been achieved at 2.5 kHz. This non-contact weak acoustic signal detection technique has great application potential in the quasi-distributed partial discharge (PD) detection of smart grid.

Fractional Order Derivative and Time-Delay Feedback Enabled Stochastic Resonance for Bearing Fault Diagnosis

The benefits of noise can be found in nonlinear systems where a type of resonances can inject the noise into systems to enhance weak signals of interest, including stochastic resonance, vibrational resonance, and chaotic resonance. Such benefits of noise can be improved further by adding some items into the nonlinear systems. Considering the time-dependent memory of fractional-order derivative and time-delay feedback which makes the nonlinear systems take advantage of their historical information and makes the output of nonlinear systems affect the input by feedback control, therefore, we attempt to design the model of stochastic resonance (SR) enhanced by both fractional-order derivative and time-delay feedback. Among them, fractional-order derivative and time delay would reinforce the memory of nonlinear systems for historical information and feedback would use the output of systems to control the systems precisely. Therefore, we hope that their advantages would be fused to improve the weak signal detection performance of SR further. Then, it would be applied to bearing fault diagnosis and compared with that without fractional-order derivative and time-delay feedback and even other diagnostic methods. The experimental results indicate that the SR enhanced by fractional-order derivative and time-delay feedback where a local signal-to-noise ratio is designed as the objective function to optimize these tuning parameters of the proposed method could enhance early fault signature of bearings and outperform that without fractional-order derivative and time-delay feedback and even infogram method.

Ultra-highly sensitive and self-healing flexible strain sensor with a wide measuring range based on a bilayer structure

Flexible sensors have attracted attention for their characteristics of high tensility, bendability and deformation recovery. This paper introduces a bilayer flexible strain sensor with ultra-high sensitivity, wide measurable range and self-healing character. The strain sensor comprises a fragile upper metal film and a flexible Ecoflex/graphene substrate. The specially designed dual-channel structure allows two-stage strain detection and realizes higher detection accuracy. The gauge factor reached 136,100 in a micro strain stage of 0%–1.18%, allowing the accurate detection of weak signals from the human body. The gauge factor reached 6145 in a strain range of 1.18%–50%. After 10,000 stretching cycles, the propagation of cracks on the sensor led to large residual resistance. The sensor could self-repair quickly through simple electrical heating. The sensor has potential in the monitoring of human movement, from tiny facial expressions, speech recognition and heart beats to large-scale finger and wrist bending, and is thus promising for application to wearable devices and human–machine interaction.

Signal response enhanced by partial time delay in anormal diffusive coupled bistable oscillators

The effect of a partial time delay on the response to external weak input signals in a bistable oscillator with anormal diffusive coupling was studied. Periodic resonance or anti-resonance in the signal response with time delay was observed, and the resonance period equals the period of the external input signal. Specifically, for the negative mean-field density parameter, the signal response can be improved through time delay, which is a resonance phenomenon. Conversely, for the positive mean-field density parameter, no such enhancement effect was observed, suggesting the presence of an anti-resonance phenomenon. As the probability of a partial time delay increases, the width of the time delay of the optimal signal response becomes narrower. When the probability of a partial time delay is large enough, the response of the system is optimal only when the time delay closely approximates integer or half-integer multiples of the external signal period. These numerical findings provide a new approach for weak signal detection that could be applied to the extraction of weak feature information within relevant fields.

Fabrication of highly sensitive visible photodetector based on SnS2 Terrazzo-like structure for weak signal detection

Chemically inert and van der Waal-structured tin-disulfide (SnS2) has become a potential candidate for optoelectronics due to its ultra-sensitivity and high performance resulting from high light absorption coefficient and thickness-dependent electrical properties. We report the facile synthesis of SnS2 Terrazzo-like structure using the CVD technique. A highly responsive MSM SnS2-based device is designed to detect optical radiation ranging from 300 to 800 nm. The fabricated device exhibits excellent responsivity (external quantum efficiency) of 2240 mAW−1 (522%) under 532 nm wavelengths for a 5 μW optical signal. The other merits, like detectivity and noise equivalent power, are 1.4 × 1010 Jones and 2.6 × 10−12 WHz−1/2, respectively, with a linear dynamic range (LDR) of ∼41 dB. The low LDR plays a vital role in evaluating light intensity from the corresponding generated photocurrent. The weak optical signal detection capability of SnS2-based photodetector paves the way for its application in astronomy, communication systems, and biomedical imaging.

Five-Step Phase-Shift-Based Multiwavelength Averaging for Extrinsic Fabry–Perot Interferometric Sensors

In order to address issues such as low demodulation accuracy, large demodulation errors, small dynamic range, and complex algorithms for the extrinsic Fabry–Perot interferometric (EFPI) sensor, a five-step phase-shift algorithm based on a multiwavelength (MW)-averaging method is proposed to improve demodulation speed, noise stability, dynamic range, and noise suppression. The proposed demodulation method utilizes white-light interferometry (WLI) technology to acquire the reflection spectra and extract the five-step phase-shift signals at Ns consecutive operating points. The demodulation results of Ns sets of five-step phase-shift signals are averaged to obtain the average demodulated phase and cavity length variation. Theoretical analysis demonstrates the significant effects of the MW demodulation method on demodulation parameter errors and noise suppression. Particularly, when the demodulation parameter θ = π/2 rad, the method exhibits excellent stability against demodulation parameter error-induced instability. Moreover, it greatly improves noise suppression and reduces noise fluctuations. Numerical simulations are conducted to validate the performance of the proposed demodulation method. Compared with the traditional single-wavelength (SW) five-step phase-shifting demodulation method, the MW demodulation method exhibits stronger noise- and harmonic-suppression capabilities as the number of averaging wavelengths Ns increases. The harmonic distortion of the MW demodulation method with Ns = 128 is 20 dB lower than that of the SW demodulation method, and the noise is 15 dB lower. Furthermore, the proposed method effectively suppresses the influence of demodulation parameter errors on signal demodulation. This proposed demodulation method has the potential for fast real-time dynamic demodulation. It has great significance and application in the field of weak signal detection in fiber-optic sensors with interferometer structures and has enormous advantages in noise-suppression in complex environments.