Weak Detection Research Articles

Energetic and entropic vibrational resonance with a time-delayed feedback

Energetic and entropic vibrational resonance with a time-delayed feedback is studied by considering a Brownian particle confined in a two-dimensional boundary. Both the time delay and high-frequency signal can optimize the output of the system. The delay time can induce the multimodal resonance with a local optimum peak in the spectral amplification factor. Furthermore, the mechanism corresponded to dynamical transition from a multi-stable state to a monostable state caused by the high-frequency signal is elucidated. In addition, a quantity called the mean entropic Poisson intensity is employed, which can be used to assist in describing the constraint effects of uneven boundaries. It is worth mentioning that this quantity can also properly describe the vibrational resonance of the system. Finally, our work provides some theoretical references for the detection of weak signals in small-scale systems with ubiquitous fluctuation and time delay.

A Flexible, Transparent, Ultralow Detection Limit Capacitive Pressure Sensor

AbstractThe capacitive pressure sensor (CPS) with low detection limit and high sensitivity is crucial for the future development of electronic skins. Although introducing surface microstructures on dielectric layer may improve the sensitivity of CPS in some extent, it leads to a limited pressure‐response range due to the poor structural compressibility. Moreover, such microstructures deteriorate the transparency and flexibility of the final sensor and require costly microfabrication methods. Herein, a low‐cost high performance pressure sensor is developed by integrating the high permittivity and soft dielectric gel with the mechanically comparable ionic conductor. The device shows an unprecedented detection limit of 0.2 Pa, a high sensitivity of 4.2 kPa−1 in the low‐pressure regime of <1.2 kPa, and a fast response time of 13 ms. This ultralow detection limit allows for the efficient and accurate detection of weak pressure signals from acoustic waves to pulse and air‐flow, exhibiting promising application potentials in voice recognition, screening of cardiovascular diseases, and diagnosis of gas leakage. Furthermore, the combination of ionic conductor and dielectric gel also endows the capacitive pressure sensor with remarkable transparency, mechanical stability, and durability even under extreme environments. This study proposes a novel perspective for development of flexible and transparent electronic skins.

Performance comparison of some weak signal detection techniques

Performance comparison of some weak signal detection techniques is introduced. This comparison is very necessary since different applications require different operating conditions such as signal to noise ratio SNR, bandwidth, coherency, processing time and complexity. Three methods for detecting weak signals are considered. These are based on chaos theory, wavelet transform, and stocastic resonance. A detection algorithm based on a rectangular region in phase space plane is suggested in chaos method. The stocastic resonance method is considered in this research, as it is used for signal detection in underwater at a certain frequency. Simulation results obtained from MATLAB programs verify the studied methods giving an estimation of probability of detection and probability of false alarm versus SNR.

Weak Signal Detection of Pulse Waveform by Bistable Potential Circuit

4次関数ポテンシャルシステムなどの非線形システムにおいて適度なノイズにより微弱信号が強調される確率共鳴現象が，高感度な信号検出方法として注目されている。本研究では，高速な微弱パルス信号の高感度検出を目的に，4次関数ポテンシャルシステムのシステムパラメータと，SNR，応答性，信号再現率の関係を理論計算，数値解析および実測により評価した。システムの応答速度は，正帰還係数に比例，減衰係数に反比例して変化することを示し，高速なビットレートの信号検出性能を向上するには，正帰還係数と減衰係数で定義した応答速度係数を高くすることが有効であり，実測にて100 kb/sのパルス信号の信号検出性能の向上を確認した。

Ultra-Low-Noise MIMO Distributed Acoustic Sensor Using Few-Mode Optical Fibers

Distributed optical fiber acoustic sensing technology (DAS) based on Rayleigh backscattering (RBS) is experiencing a rapid development because of the unique capability of measuring acoustic waves along long-distance optical fiber with high spatial resolution, that has broad prospects of applications, including geophysical prospecting, seismology, pipeline security, and train tracking. Despite the numerous applications, DAS currently suffers from high noise level and insufficient signal-to-noise ratio (SNR) due to inherent low RBS coefficient (&#x223C; &#x2212;70 dB&#x002F;m) in fiber, and cannot meet the application requirements of weak signal detection. Here, an ultra-low-noise MIMO DAS system using few-mode fibers (FMF) is proposed. FMF is characterized by higher non-linear effect threshold and larger capture fraction of RBS light, and through MIMO technology, the system can realize higher diversity scale and obtain more SNR gain. In addition, &#x2018;frequency label&#x2019; method is proposed to address the signal aliasing problem in transmission modes. In preliminary experiments, a 4&#x00D7;4 MIMO &#x03A6;-OTDR system is realized, where the minimum noise floor is &#x2212;88 dB rad²&#x002F;Hz with corresponding strain resolution of 0.15 <inline-formula><tex-math notation="LaTeX">$p\varepsilon /\sqrt {Hz} $</tex-math></inline-formula>. These results pave the way for advanced acoustic applications of DAS, such as acoustic emission and marine acoustics.

Research on the Weak Signal Extraction Method with Adaptive Stochastic Resonance Based on the Time Grid Sensor for PMSM

Because the output signal of the time grid sensor is so weak and is easily affected by the disturbing noise, it is difficult to extract the induction signal containing the position information, when the time grid sensor is used to detect the rotor position of the permanent magnet synchronous motor (PMSM). In this paper, an adaptive monostable stochastic resonance method is proposed to realize the weak signal detection of the time grid sensor. The tunnel magnetoresistance effect sensor (TMR) is adopted to detect the change of the rotor magnetic field of the motor in this method, and the detection model of the time grid sensor about PMSM is constructed. The monostable stochastic resonance theory is applied to extract the induction signal of the time grid sensor. As to the problem of the tuning the parameters of the monostable stochastic resonance system, the genetic algorithm is adopted to optimize the parameters of the monostable stochastic resonance system. The experimental results show that the monostable stochastic resonance method based on the genetic algorithm can effectively detect the weak induction signal of the time grid sensor about PMSM. This method solves the problem of the weak signal extraction of the time grid sensor for PMSM, and the detection accuracy is also improved.

Advances in Graphene Based MEMS and Nems Devices: Materials, Fabrication, and Applications

Microelectromechanical systems (MEMS) are generally known as miniaturized mechanical and electro-mechanical systems, whereas NEMS stands for nanoelectromechanical systems. Graphene is an atomically thin material that features unique properties, such as high carrier mobility, high mechanical strength, and piezoresistive electromechanical transduction, which makes it an extremely promising material for future MEMS and NEMS devices. Design and fabrication of MEMS/NEMS devices using graphene process includes trench etching, wafer backside etching, graphene transfer, and mass release, which are described in a comprehensive manner. This review provides interesting perspectives for lock-in detection of weak fluorescent signals, NEMS position detection, electromechanical control of the on-chip transmitter, and single-photon-level fast electromechanical optical modulation. There are various applications of MEMS/NEMS devices, namely radio frequency devices, optic NEMS, pressure sensors, inertial sensors, which have been discussed in detail. The review mainly focusses on the devices made up of graphene (atom-layer distance of ~0.335 nm) as a main electronic/mechanical material due to its remarkable mechanical and electrical (Young’s modulus of up to ~1 TPa cm2Vs-1) and charge-carrier mobility of up to 200,000, which makes it an extremely promising membrane and transducer material for MEMS/NEMS system applications. Modern MEMS/NEMS experiments utilize mechanical resonators to push the bounds of force and mass sensing, demonstrate novel electromechanical circuit applications, measure the structural properties of materials are also covered in this review.

Experimental Investigation of Large Area Subwavelength Cavity Antennas With Planar Metamaterials

We experimentally investigated the large area subwavelength cavity antenna with artificial permeability-negative metamaterials in the GHz region. It is demonstrated that this new type of planar metamaterials has better directivity and higher gain with the radiation source using the large non-uniform distributed patch array than using a uniform distributed patch array, where the current distribution of the radiation source satisfies the Chebyshev distribution. The experimental values agreed well with simulated results. This new metamaterial antenna has potential applications in weak microwave signal detection and radio observation fields.

Weak Pulse Signal Detection Based on the Broad Learning Method under the Chaotic Background

Weak Pulse Signal Detection Based on the Broad Learning Method under the Chaotic Background

Weak signal detection for visible light communication in the pulse and transition regimes of an operational PMT detector via an SVM-based learning method

Photo-multiplier tube can be adopted for optical signal detection under weak signal and ambient light intensity, where the signals can be classified into three regimes, discrete-pulse regime, continuous waveform regime and the transition regime between the discrete-photon and continuous waveform regimes. While Poisson and Gaussian distributions can well characterize the discrete-photon and continuous waveform regimes, respectively, a statistical characterization and the related signal detection in the transition regime are difficult. In this work, we resort to a learning approach for the signal characterization and detection under pulse and transition regimes. We propose a support vector machine (SVM)-based approach for signal detection, which extracts eight key features on the received signal. We optimize the hyper-parameters to improve the SVM detection performance. The proposed SVM-based approach is experimentally evaluated under different symbol and sampling rates, and outperforms that of various statistics-based comparison benchmarks.

Six‐arm Stellat Dendritic‐PbS Flexible Infrared Photodetector for Intelligent Healthcare Monitoring

AbstractThe flexible photodetector plays an important role in improving human medical health status. However, the narrow spectral detection range, poor stress stability, and non‐degradation of traditional flexible photodetectors greatly hinder the further development of wearable medical devices. In this paper, a novel flexible infrared photodetector is proposed for intelligent healthcare monitoring using high purity lead sulfide (PbS) nanoparticles on paper‐based flexible substrate synthesized by hydrothermal method and physical friction. The excellent performance of the detector is attributed to the 1.01 eV band gap and six‐arm stellate dendritic structure of PdS, a good combination between PbS and paper substrate via physical friction. As a result, our photodetector demonstrates wide‐spectrum detection capabilities from 365 to 1550 nm. The photodetector at 980 nm (50.4 μW cm−2) shows responsivity of 6.45 mA W−1, detectivity of 6.4 × 1010 Jones, response recovery time of 0.36 s/0.41 s, with good mechanical stability. By comparison, our detector has a wider detection range, better weak signal detection performance, and shorter response time than the performance of the former paper‐based detector. Furthermore, the paper‐based PbS photodetector as a dual‐wavelength photoplethysmography sensor is applied to analyze the oxygen saturation and develop an intelligent bandage to monitor wound healing. This paper‐based PbS photodetector has tremendous potential in the field of wearable medical devices and intelligent medical applications are expected.

Accumulatively Increasing Sensitivity of Ultrawide Instantaneous Bandwidth Digital Receiver with Fine Time and Frequency Resolution for Weak Signal Detection

It is always an interesting research topic for digital receiver (DRX) designers to develop a DRX with (1) ultrawide instantaneous bandwidth (IBW), (2) high sensitivity, (3) fine time-of-arrival-measurement resolution (TMR), and (4) fine frequency-measurement resolution (FMR) for weak signal detection. This is because designers always want their receivers to have the widest possible IBW to detect far away and/or weak signals. As the analog-to-digital converter (ADC) rate increasing, the modern DRX IBW increases continuously. To improve the signal detection based on blocking FFT (BFFT) method, this paper introduces the new concept of accumulatively increasing receiver sensitivity (AIRS) for DRX design. In AIRS, a very large number of frequency-bins can be used for a given IBW in the time-to-frequency transform (TTFT), and the DRX sensitivity is cumulatively increased, when more samples are available from high-speed ADC. Unlike traditional FFT-based TTFT, the AIRS can have both fine TMR and fine FMR simultaneously. It also inherits all the merits of the BFFT, which can be implemented in an embedded system. This study shows that AIRS-based DRX is more efficient than normal FFT-based DRX in terms of using time-domain samples. For example, with a probability of false alarm rate of 10−7, for N=220 frequency-bins with TMR = 50 nSec, FMR = 2.4414 KHz, IBW &gt; 1 GHz and ADC rate at 2.56 GHz, AIRS-based DRX detects narrow-band signals at about −42 dB of input signal-to-noise ratio (Input-SNR), and just uses a little less than N/2 real-samples. However, FFT-based DRX have to use all N samples. Simulation results also show that AIRS-based DRX can detect frequency-modulated continuous wave signals with ±0.1, ±1, ±10 and ±100 MHz bandwidths at about −39.4, −35.1, −30.2, and −25.5 dB of Input-SNR using about 264.6 K, 104.7 K, 40.2 K and 18.3 K real-samples, respectively, in 220 frequency-bins for TTFT.

An Exploration of the Potential of Machine Learning Tools for Media Analysis to Support Sense-Making Processes in Foresight

In view of the many discussions about uncertainty regarding the further development of the coronavirus disease 2019 (COVID-19) pandemic and its effects on the economy and society, we observed that the crisis led to an increased presence of individual researchers and experts making forward-looking statements on the impacts of the COVID-19 pandemic or stating trends in mass media publications. From a strategic foresight research perspective, there is a need to further analyse an increase of future-oriented expert statements in public media in a context of high uncertainty like the impacts of the COVID-19 pandemic and related crises. Given the increasing amount of media texts available for web-based scanning and text analysis, Machine Learning (ML) is a promising approach for text analysis of big data, which also raises high expectations in the field of foresight, particularly in the context of scoping and scanning activities for weak signal detection and text analysis for sense-making processes. In this study, we apply a natural language processing (NLP)-based ML approach to analyse a large corpus of news articles from web sources to explore the potential of applied ML to support sense-making in the field of foresight, specifically for the analysis of future-related statements or predictive statements in media. The results underline the potential of ML approaches as a heuristic tool to support sense-making in foresight processes and research, particularly by pre-structuring large datasets (e.g., news articles around a particular topic of public debate). The ML can provide additional insights for actor analysis associated with a specific topic of public debate from a large data corpus. At the same time, our results show that ML models are limited in their ability to provide solid evidence and that they can also lead to fallacies. Therefore, an ML can only be considered as a heuristic tool supporting specific steps in a sense-making process and development of further research questions, as well as encouraging reflection on the application of ML-based approaches in foresight.

Self-Powered Sb2S3 Thin-Film Photodetectors with High Detectivity for Weak Light Signal Detection.

Photodetectors (PDs) for weak light signal detection have wide applications for optical communication and imaging. Antimony sulfide (Sb2S3) as a nontoxic and stable light-sensitive material becomes a promising candidate for weak light PDs, which are developing in the direction of high response, high speed, and low cost. Herein, a self-powered Sb2S3 PD with the structure of FTO/TiO2/Sb2S3/Au is developed to achieve weak light detection for 300-750 nm visible light. We control the Sb2S3 thickness with about 460 nm to match depletion region width (438 nm) and obtain an excellent photoresponsivity and 3 dB bandwidth. Furtherly, we prepare pyramid structure polydimethylsiloxane (PDMS) on the illuminating surface to enhance the performance of weak light detection by light-trapping effects. The photocurrent of Sb2S3 PD with 20 μm-sized PDMS texture achieves 13.6% improvement compared with the control one. Under weak 530 nm light illumination of 1 μW cm-2, the self-powered Sb2S3 PD with PDMS achieves high responsivity (3.41 A W-1), large detectivity (2.84 × 1013 Jones), and ultrafast speed (15 μs). The present Sb2S3 PD and light-trapping strategy are expected to provide an alternative to future commercial weak light detection applications.

Tri-stable stochastic resonance coupling system driven by dual-input signals and its application in bearing fault detection

Stochastic resonance is of great significance for extracting fault signals of bearings. A novel tri-stable stochastic resonance coupling system driven by dual-input signals(DTDTSR) is proposed in this paper, which significantly improve Spectral Amplification(SA) and amplitude of traditional two-dimensional tri-stable stochastic resonance system(TDTSR). Firstly, under the condition of adiabatic approximation theory, the Steady-state Probability Density(SPD), Mean First Pass Time(MFPT) and SA are derived, and the system parameters’ influence on them are analyzed. Then, using SA as the measurement index, numerical simulations are carried out and system parameters are optimized by adaptive genetic algorithm to achieve optimal performance. So DTDTSR, TDTSR and classical tri-stable stochastic resonance system(CTSR) are applied to weak periodic signals detection and compared with each other. The experimental results show that DTDTSR has a large SA and amplitude, which proves that the synergistic effect of coupled system and dual input signal drive can better promote the generation of stochastic resonance. Finally, the three systems and wavelet transform method are applied in two kinds of engineering bearing fault detection, and adaptive genetic algorithm is also used to optimize the system parameters. The experiments reveal are similar to the previous one, proving that DTDTSR is indeed optimal among the three systems. This system is therefore very adaptable and advanced in practical engineering applications.

A Novel Fault Diagnosis Approach for Rolling Bearing Based on CWT and Adaptive Sparse Representation

Extraction and enhancement of weak impulse signature is the key of rolling bearing fault prognostics in which case the features are often weak and covered by noise. Tunable Q-factor wavelet transform (TQWT), as an emerging wavelet construction theory developed in a frequency domain explicitly, has the advantages of matching with the specific oscillation behavior of signal components. In this article, an adaptive sparse representation (ASR) method is proposed, which integrates the sparse code shrinkage (SCS) and parameter optimization into TQWT. However, direct application of ASR is difficult to extract fault signatures at the early stage or low-speed operation due to weak fault symptoms and background noise. A novel fault diagnosis strategy based on continuous wavelet transform (CWT) and ASR is investigated. CWT owns significant advantages on multiscale subdivision and weak signal detection. The results of simulated and experimental vibration signal analyses verify the effectiveness of the proposed method in accurately extracting weak impulse features from the noise environment.

Characteristics of piecewise linear symmetric tri-stable stochastic resonance system and its application under different noises

Weak signal detection has become an important means of mechanical fault detections. In order to solve the problem of poor signal detection performance in classical tristable stochastic resonance system (CTSR), a novel unsaturated piecewise linear symmetric tristable stochastic resonance system (PLSTSR) is proposed. Firstly, by making the analysis and comparison of the output and input relationship between CTSR and PLSTSR, it is verified that the PLSTSR has good unsaturation characteristics. Then, on the basis of adiabatic approximation theory, the Kramers escape rate, the mean first-passage time (MFPT), and output signal-to-noise ratio (SNR) of PLSTSR are deduced, and the influences of different system parameters on them are studied. Combined with the adaptive genetic algorithm to synergistically optimize the system parameters, the PLSTSR and CTSR are used for numerically simulating the verification and detection of low-frequency, high-frequency, and multi-frequency signals. And the results show that the SNR and output amplitude of the PLSTSR are greatly improved compared with those of the CTSR, and the detection effect is better. Finally, the PLSTSR and CTSR are applied to the bearing fault detection under Gaussian white noise and Levy noise. The experimental results also show that the PLSTSR can obtain larger output amplitude and SNR, and can detect fault signals more easily, which proves that the system has better performance than other systems in bearing fault detection, and has good theoretical significance and practical value.

Cyclic correntropy: Properties and the application in symbol rate estimation under alpha-stable distributed noise

Over the past decades, cyclostationary signal processing has been considered one of the most critical theories in the field of nonstationary signal processing. Moreover, it has been widely adopted to handle practical problems, including signal parameter estimation, weak signal detection and pattern recognition. To improve the robustness of conventional methods under non-Gaussian noise, cyclic correntropy was recently proposed as the extension of correntropy in the cycle frequency domain. Due to the advantages inherited from both cyclostationary statistics and correntropy, cyclic correntropy has been employed in various applications involving co-channel interference and impulsive noise, such as carrier frequency estimation, direction of arrival (DOA) estimation, time difference of arrival (TDOA) estimation, automatic modulation classification and bearing fault diagnosis. Although cyclic correntropy has gained much attention, the related theoretical study is still insufficient, prohibiting further applications in other areas. To complete the theoretical framework, the existence condition of cyclic correntropy is first discussed in detail. Then, the relations among the cyclic correntropy spectrum, the symbol rate and the carrier frequency are addressed and proved mathematically. Besides, other properties of cyclic correntropy are also studied, involving symmetry, conjugation and time-shifting. These properties are studied for the first time and they fulfill the void of cyclic correntropy theory. Furthermore, a novel method is proposed for symbol rate estimation under non-Gaussian noise. Simulations are executed to validate the proposed method's superior robustness compared with other methods based on different cyclic spectra under the framework of cyclostationary signal processing. This method contributes to the application of cyclic correntropy, further highlighting the significance of the properties mentioned above.

A Quantum Weak Signal Detection Method for Strengthening Target Signal Features under Strong White Gaussian Noise

As the noise power increases, the target signal features become less obvious, which leads to the failure of weak signal detection methods. To address this problem, a quantum weak signal detection method, Local Semi-Classical Signal Analysis-Singular Value Decomposition (LSCSA-SVD), for strengthening target signal features under strong white Gaussian noise is proposed. Firstly, the time domain weak signal is quantized by the Schrodinger operator and its discrete spectrum formula. Then, in the quantum domain, the later eigenvalues are used to reconstruct the time domain signal, which can protect and enhance the target signal features. Finally, the difference between signal and noise in the singular value vector is used to further extract the reconstruction signal features. In simulation, the LSCSA-SVD can accurately extract target signals from white Gaussian noise signals with a signal-to-noise ratio (SNR) of −30 dB, which is better than the comparison methods. In the experiment, the weak acceleration sensor signal and the weak signal of the test circuit are successfully extracted. The results show that the LSCSA-SVD can suppress strong noise and improve the SNR.

Amplitude-Modulated Cavity-Enhanced Absorption Spectroscopy with Phase-Sensitive Detection: A New Approach Applied to the Fast and Sensitive Detection of NO2.

Accurate and sensitive measurements of NO2 play an extremely important role in atmospheric studies. Increasingly, studies require NO2 measurements with parts per trillion by volume (pptv-level) detection limits. Other desirable instrument attributes include ease of use, long-term stability, and low maintenance. In this work, we report the development of an amplitude-modulated multimode-diode-laser-based cavity-enhanced absorption spectroscopy (AM-CEAS) system operating at 406 nm that uses phase-sensitive detection for extremely sensitive NO2 detection. The laser was TTL-modulated at 35 kHz. The mirror reflectivity was determined to be 99.985% based on the ring-down time measurement. The cavity base length was 47.5 cm, giving an effective absorption pathlength of ∼3.26 km. AM-CEAS achieved a 1σ detection precision of 35 pptv in a 1 s data acquisition time (4.98 × 10-10 cm-1), over 4 times lower than that attained using a ring-down approach and the same optical system. The AM-CEAS precision improved to 8 pptv over a data acquisition time of 30 s (1.14 × 10-10 cm-1). The AM-CEAS method with the multimode diode laser integrates the advantages of high light injection efficiency like on-axis alignment cavity ring-down spectroscopy, low cavity-mode noise like off-axis alignment CEAS, and narrow-bandwidth high-sensitivity weak signal detection of modulation spectroscopy, providing a powerful, straightforward, and general method for ultrasensitive absorption and extinction measurements.