Weak Detection Research Articles

Weak signal detection signifying foreign metal objects in wireless power transfer systems

Weak signal detection signifying foreign metal objects in wireless power transfer systems

Recent Advances in Organic Photodetectors

Organic photodetectors (OPDs) have garnered significant attention in fields such as image sensing, health monitoring, and wearable devices due to their exceptional performance. This review summarizes recent research advancements in materials, structures, performance, and applications of narrowband organic photodetectors, hybrid organic–inorganic perovskite photodetectors, flexible organic photodetectors (FOPDs), and photomultiplication type organic photodetectors (PM-OPDs). Organic semiconductors offer substantial potential in optoelectronic devices owing to their low cost, ease of processing, and tunable spectral response. Hybrid perovskite materials extend the spectral response range, FOPDs meet the demands of wearable devices, and PM-OPDs enhance sensitivity, allowing for the detection of weak light signals. Through innovations in materials, structural optimization, and improvements in manufacturing processes, the performance of OPDs has seen significant enhancement. This article also explores the application prospects of these technologies in medical monitoring, optical communications, and image sensing.

Unveiling Weak Signals of Emergence in Underwater Sensing Research Trends

Detecting emerging research trends is crucial as it allows for the proactive identification and monitoring of novel and influential topics in the scientific community. Monitoring research trends aids researchers, institutions, and policymakers in allocating resources, fostering innovation, and staying competitive in rapidly changing scientific landscapes. The growing significance of underwater sensing technologies in various domains has propelled research endeavours aimed at understanding the characteristics of academic discourse in this field. In this work, we comprehensively analyzed the academic research topics related to underwater sensing technologies using advanced computational methodologies. Leveraging natural language processing, topic modelling, and weak signal detection techniques, and focusing on underwater sensing as the case technology, we dissect a large corpus of scholarly articles published between 2007 and 2021 to unveil underlying thematic patterns and emergent trends within this domain while shedding light on signals of emerging technologies. Among the eighty extracted topics, six research topics were identified and recognized as emerging weak signals, and validated by experts. Notably, deep learning for underwater imaging was the only topic that transitioned from being weak to a strong signal in the final period.

A novel memristor chaotic circuit and its application in weak signal detection of wind turbine fault

A novel memristor chaotic circuit and its application in weak signal detection of wind turbine fault

Adaptive Measurement and Parameter Estimation for Low-SNR PRBC-PAM Signal Based on Adjusting Zero Value and Chaotic State Ratio

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator’s behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB.

Wafer-Sized CsPbBr3/CsPbCl3 Heterojunction: Breaking the Trade-Off between Sensitivity and Dark Current for Efficient X-ray Detector.

Polycrystalline lead halide perovskite finds promising use in fabricating X-ray detectors with a large lateral size, adjustable thickness, and diverse synthesis processes. However, a large dark current hinders its development for weak signal detection. Herein, we propose a multistep pressing strategy for manufacturing a CsPbBr3/CsPbCl3 heterojunction wafer for a reduced dark current X-ray detector, and the device keeps a high sensitivity value after the insertion of a barrier by heterojunction; thus, the trade-off between sensitivity and dark current can be broken. The X-ray detector with a metal-semiconductor-metal structure yields a sensitivity of 6.32 × 104 μC Gyair-1 cm-2 at a bias of 12 V, a 1/f noise of 1.02 × 10-13 A/Hz-1/2, and a detection limit of 66.58 nGy s-1. These performance parameters are considerably better than those of a similar X-ray detector based on the single-structure wafer. The improved device performance of the heterostructure X-ray detector is ascribed to the suppressed carrier recombination, enhanced carrier transportation of the heterojunction, and strong X-ray attenuation of the CsPbCl3 layer. The pixel array device is further used in imaging applications. Hence, this study provides an efficient strategy for fabricating heterostructure polycrystalline lead halide perovskite wafers for use in high-performance wafer-based X-ray detectors.

Enhancement of single-photon level signal detection based on phase sensitive amplification strategy

Abstract We investigate the ability of phase-sensitive amplification (PSA) to noiselessly amplify the preferred quadrature components of single-photonsignals that are limited by phase fluctuations relative to the pump. We presenta PSA enhancement strategy that is more realistic for possible experimentalrealization and is expected to significantly improve the detection of weaksignals at the single-photon level and obtain more useful information. Our studyshows that with a large PSA gain, proper transmissivity allows both direct and
balanced homodyne detections to operate optimally simultaneously, and effectively improves the noise figure degradation owing to the internal losses and non-ideal detection efficiency.

Nonlinear coupled asymmetric stochastic resonance for weak signal detection based on intelligent algorithm optimization

Stochastic resonance has been extensively studied for detecting weak signals. To improve the diagnostic ability of weak signals, a novel nonlinear coupled asymmetric stochastic resonance (NCASR) system is investigated in this paper. Firstly, the NCASR system is established by coupling the asymmetric bistable system with the monostable system. Next, the expressions for the steady-state probability density (SPD) function, the mean first passage time (MFPT) and the signal-to-noise ratio (SNR) of the proposed system are derived based on the adiabatic approximation theory. Furthermore, the impact of system parameters on the SPD, the MFPT and the SNR is analyzed. Then, by simulation experiments, we verify the effectiveness of detecting weak signals for the NCASR system with Lévy noise. Finally, the NCASR system optimized by Adaptive Weighted Particle Swarm Optimization (AWPSO) algorithm is applied to detect the bearing fault signal. Compared with the optimized classical bistable stochastic resonance (CBSR) system, it is found that the detection performance of the NCASR system is superior to the CBSR system in detecting bearing fault signals.

Multi-frequency weak signal detection based on Liu-like chaotic synchronization system and its hardware circuit implementation

Considering the shortcomings of traditional chaotic systems in weak signal detection methods, such as the high threshold sensitivity requirement and the narrow detection frequency domain. This study proposes a novel three-dimensional chaotic synchronization system, and the dynamical of the system are exhaustively characterized using equilibrium points, phase diagrams, Lyapunov exponential spectra, and bifurcation diagrams. This method involves weak signal detection by means of chaotic synchronization control. Synchronization of a chaotic system using a backstepping synchronization method is used to detect weak signals by analyzing the synchronization error after the introduction of weak signals in a strong noise background. The chaotic system is implemented by hardware circuits, and the simulation of chaotic synchronization control and detection of weak signals from the perspective of circuits is carried out by circuit simulation software. Additionally, the frequency range within which the system is capable of weak signal detection is tested through extensive simulation experiments. Finally, multi-frequency signals detection experiments are performed. The experimental results demonstrate that the system can accurately detect the frequency of weak signals address the limitations of narrow-band detection and multi-frequency signal detection is possible. Meanwhile, the circuit structure proposed in this paper is simple and has some value for engineering applications.

Practical considerations for the amplification of electro-optically detected THz signals

Advances in THz methods and applications require the detection of weak THz pulsed signals. One solution to this problem is to amplify weak signals using optically biased electro-optical (EO) techniques. Several different EO amplification schemes are compared. The simplest of these involves using a non-polarizing beam splitter followed by two quarter-wave plates set to small positive and negative angles, respectively. The signals are then passed through polarizers perpendicular to the polarization of the 800 nm EO detector crystal gating beam. As long as sufficient intensity is present in the gate pulse for linear photodiode detection, amplification of up to 10 times with dynamic ranges for single sweep scans of >104 is obtained.

AEEFCSR: an adaptive ensemble empirical feed-forward cascade stochastic resonance system for weak signal detection

Abstract A novel adaptive ensemble empirical feed-forward cascade stochastic resonance (AEEFCSR) method is proposed in this study for the challenges of detecting target signals from intense background noise. At first, we create an unsaturated piecewise self-adaptive variable-stable potential function to overcome the limitations of traditional potential functions. Subsequently, based on the foundation of a feed-forward cascaded stochastic resonance method, a novel weighted function and system architecture is created, which effectively addresses the issue of low-frequency noise enrichment through ensemble empirical mode decomposition. Lastly, inspired by the spider wasp algorithm and nutcracker optimization algorithm, the spider wasp nutcracker optimization algorithm is proposed to optimize the system parameters and overcome the problem of relying on manual experience. In this paper, to evaluate its performance, the output signal-to-noise ratio (SNR), spectral sub-peak difference, and time-domain recovery capability are used as evaluation metrics. The AEEFCSR method is demonstrated through theoretical analysis. To further illustrate the performance of the AEEFCSR method, Validate the adoption of multiple engineering datasets. The results show that compared with the compared algorithms, the output SNR of the AEEFCSR method is at least 6.2801 dB higher, the spectral subpeak difference is more than 0.25 higher, and the time-domain recovery effect is more excellent. In summary, the AEEFCSR method has great potential for weak signal detection in complex environments.

Weak signal detection in chaotic noise background - based on VMD-EEMD and self-attention mechanisms

Abstract In this paper, a method for detection and estimation of weak harmonic signals in chaotic noise background is proposed. Firstly, the observed signal is smoothed by local weighted regression model to reduce the influence of noise. Then, the smoothed signals are decomposed into the intrinsic mode functions by Variational Mode Decomposition and Ensemble Empirical Mode Decomposition (VMD-EEMD). The components are reconstructed in the phase space, and the one-step prediction is carried out by adding a layer of the self-attention mechanism in the Long Short-Term Memory (LSTM). The one-step prediction error is obtained by summing up the prediction results of the components and the weak harmonic signals will be detected from the one-step prediction error. Finally, the periodogram method is used to detect whether the observed signals contain harmonic signals or not. If the observed signals contain harmonic signals, the amplitude and phase are estimated by the least squares method. Simulation experiments show that the method proposed in this paper has good performance. In detection of single harmonic signal, the Mean Absolute Error (MAE) and Mean Square Error (MSE) of the one-step prediction are 0.0212 and 0.00068 respectively; MAE and MSE of the signal estimation are 0.001977 and 0.0007 respectively. In detection of multiple harmonic signals, MAE and MSE of the one-step prediction are 0. 0248 and 0. 0006 respectively; MAE and MSE of the signal estimation are 0. 0035 and 1.681×10−5 respectively. The method proposed in this paper is able to efficiently detect weak harmonic signals from chaotic noise backgrounds and estimate the harmonic signals.

Self-bias Mo–Sb–Ga multilayer photodetector encompassing ultra-broad spectral response from UV–C to IR–B

Achieving an ultra-broad spectral response in a self-bias detector is a formidable challenge that persists in optoelectronics that necessitates innovative solutions. We propose a unique ultra-broadband photodetecting device, utilizing a multilayer structure comprising Molybdenum Di-sulfide (MoS2), Antimony Tri-selenide (Sb2Se3), and Gallium Nitride (GaN), which exhibits the unique capability of detecting photons without applied bias. The fabricated device demonstrates exceptional sensitivity to a wide range of illumination wavelengths, spanning from ultraviolet–C (UV–C) to infrared–B (IR–B). The design detector displays the highest photo-responsivity of 665 mAW−1 in photovoltaic mode and 3.89 × 105 mAW−1 in photoconductive mode. The designed detector also exhibits a minimal dark current of 90 nA and an extremely weak signal detection capability of ∼12 femto watt-hertz−1/2 at 6 V bias. Additionally, the thermal stability of the MoS2-Sb2Se3-GaN (Mo-Sb-Ga) multi-layer-based self-bias detector was explored. Under the self-bias conditions, the photodetector exhibits a stable behavior up to 250°C with a peak responsivity of 635 mAW−1. The thermal durability of the self-bias ultra-broadband photodetector indicates excellent potential for developing futuristic optoelectronic devices. Further, the performance of the developed detector was examined using Technology Computer-Aided Design (TCAD) simulations, providing valuable insights into the device behavior and the transport of photo-generated carriers, enhancing our understanding of the device operation and enabling performance optimization for diverse applications.

Weak signal detection capacity of type-II Morris–Lecar neuron system under presynaptic bombardments

The information encoding mechanism of the neuronal system is quite complex and is sensitive to environmental conditions. Neuronal noise, especially synaptic random inputs, is a natural part of the brain. Theoretical and experimental studies have revealed that noise plays a vital role in signal frequency selectivity and weak sensory input perception of the neuronal system. There is a consensus that sufficiently intense noise can trigger rhythmic brain activity, and these noise-induced oscillations could improve signal processing of the neuronal system, particularly when the external signal frequency aligns with the frequency of the noise-induced rhythm. Such behavior in response to noise in the signal-processing mechanism of biological systems is elucidated by stochastic resonance. In this regard, this paper systematically analyzes the stochastic resonance in a Type-II Morris–Lecar neuron under excitatory and inhibitory background spike bombardments. Our results indicate that Morris–Lecar Type II neurons enhance their signal coding capacity by exploiting background synaptic activity. In addition, it has been revealed that the biological properties of the background synaptic connections of neurons that oscillate at different frequencies, such as delta, theta, alpha, beta, and gamma bands, are different. On the other hand, it has been found that neurons adapt to the properties of pre-synaptic neurons to encode weak signals. This study ensures novel insights into the functional role of background synaptic input in neuronal coding mechanisms by demonstrating a biophysical factor of stochastic resonance via numerical analyses. Furthermore, our results show that the properties of the background synaptic inputs significantly determine what kind of signal to encode or not. These results are an important indicator of the signal frequency selectivity of neurons, such as auditory cells.

Stochastic Resonance Effect in Segmented Underdamped Asymmetric Tristable System and Its Application in Weak Signal Detection Research

Stochastic Resonance Effect in Segmented Underdamped Asymmetric Tristable System and Its Application in Weak Signal Detection Research

Dynamic potential stochastic resonance for weak signal detection

To suppress the low-frequency interference noise, a dynamic potential stochastic resonance (DPSR) model is proposed in this paper for weak signal detection. The DPSR model introduces a single dynamic parameter k that simplifies parameter optimization. Its dynamic potential function can adaptively adjust to match noisy input signals. This model provides a new nonlinear model for triggering the SR phenomenon. Experimental results indicate that compared to the traditional methods that rely on clear interference frequency distributions to filter low-frequency components directly, the SR model offers greater flexibility and convenience. Unlike classical SR models, the proposed DPSR model demonstrates a 1.5 dB improvement in output performance for suppressing low-frequency interference. Therefore, the DPSR model not only robustly suppresses interference but also effectively enhances and detects characteristic signals in variant-noise environments. Application to sea trial signals highlights the superior performance of the DPSR model in significantly reducing low-frequency interference and improving target signal recognizability compared to other models.

Enhanced detection of UV fluorescence from food products and tissue using lanthanide-doped polymer composite membranes

We demonstrate polymer composite membranes that can be coupled to conventional spectroscopic tools for enhancing the detection of weak UV fluorescence signals. The membranes incorporate a down-conversion phosphor with lanthanide ions allowing for the detection of UV emission from biomolecules commonly found in food products. We also explore their capability to discern between healthy and wounded tissue through measurements of the UV autofluorescence emission from skin. Our results show that the composite membranes have great potential to improve UV fluorescence detection in applications related to the biomedical, pharmaceutical and food areas.

Fourth-stable stochastic resonance and its application to weak fault signal detection of rotating machinery

Weak characteristic extraction is vital for weak fault signal detection of machinery. Stochastic resonance (SR) is able to transfer noise energy into weak fault characteristic frequency excited by a defect of machines. However, the potential function in SR is vital to enhance weak fault characteristic frequency and determines the capability of SR to improve the signal-to-noise ratio (SNR) of a noisy signal. Now, common potential functions include monostable, bistable and even tri-stable potentials but fourth-stable SR has not been studied and applied to detect early fault characteristic frequency. In this paper, thus, we would investigate the behaviors of SR with a fourth-stable potential subject to additive noise, in which the approximate theoretical expression of the power done by SR is derived to demonstrate the fourth-stable Sr Then, a SR method with the fourth-stable potential is proposed to enhance weak fault characteristic frequency, in which these system parameters are adjusted by using SNR as the objective function and using genetic algorithms adaptively. In this paper, thus, Finally, the proposed method is verified by using a simulated signal with noise and two early fault experiment of rolling element bearings with different levels of defects on the outer and inner races. Moreover, the proposed method is compared with wavelet denoising and fast kurtogram methods. The comparisons indicate that the proposed method has the better performance for enhancing weak fault characteristic frequency or weak useful signals than other two methods and is available to weak fault signal detection of machinery.

Genetic algorithm optimized microstructure to enhance waveguide light coupling efficiency at normal incidences

The recent trend to place well-designed photonic structures on waveguides is capable of effectively enhancing waveguides properties. One typical example is a nanostructure-empowered waveguide targeted for efficient light coupling. However, conceiving the high-freedom structures is a time-consuming and labor-intensive process, where an ineffective workflow limits the development of photonic microstructures. To address this issue, we deploy a genetic algorithm to customize structures in order to improve the coupling coefficients under predetermined situations (i.e., normal incidence combined with two polarizations). Three types of micropatterns are first conceived in the periodical model and then fully characterized on the real waveguide sizes. The simulated data reveal that the 550–1650 nm average coupling efficiencies of structure-enabled waveguides are raised by about 2% in contrast to the bare case, and the lineshapes are also flattened thanks to the grating modifications. In short, our solution underlines the role of an algorithm-developed nanostructure to lift waveguide coupling coefficients. By integrating well-engineering patterns, the waveguide-based probes may find a multitude of usages for weak signal detection and communication systems.

Mitigating Noise Current in 2D Perovskite Single Crystal Photodetectors for Imaging under Black‐Light Condition

AbstractWeak‐light imaging plays a pivotal role in various fields such as astronomical photography, military nighttime surveillance, and biomedical imaging. The capability of photodetectors (PDs) in detecting weak‐light relies heavily on minimizing their noise current. In this study, the weak‐light detection performances of PDs constructed from PEA2MAn‐1PbnI3n+1 (n = 1, 2, 3) 2D perovskite single crystals are presented. Among these, the n = 1 PD showcases incredibly low noise current that induces an ultra‐low detection limit of 14 pW cm−2 under 532 nm light illumination, and a high detectivity of 3.25 × 1015 Jones. The n = 1 PD also meets imaging requirements even under black‐level illumination conditions of 75 pW cm−2. The investigation reveals that decreasing n value corresponds to an increase in the PEA+ ratios of cations, resulting in reduced defects and enhanced ion migration activation energy and exciton binding energy. These reduce noise current of devices from electron/hole, ion, and exciton dynamic behaviors. Notably, inhibiting ion migration can significantly improve the stability of the noise current baseline and facilitate the stable detection of weak signals. This study underscores the potential of 2D perovskites for advancing weak‐light imaging technologies, offering valuable insights for future development in this field.