Intensity Noise Research Articles

SESAM-assisted Kerr-lens mode-locked Cr:ZnS laser.

Mode-locking in Cr:ZnS/Se lasers typically rely on Kerr-lensing (KLM) or a semiconductor saturable absorber mirror (SESAM). The former allows generation of shorter pulses, but, unlike the latter, does not support self-starting mode-locking. Here, we combine the advantages of these two techniques and demonstrate the SESAM-assisted KLM Cr:ZnS laser. Our self-starting oscillator generates up to 1 W of average power with 54 fs pulses at a central wavelength of 2360 nm. We identify a general limitation for further pulse shortening in SESAM mode-locked Cr:ZnS/Se lasers, which is related to the finite operation bandwidth of the semiconductor absorbers. In our experiment, we fully exploit the potential of commercially available GaSb SESAMs and fill their entire reflection bands. Furthermore, we compare the performance of a SESAM-assisted KLM laser with a pure KLM oscillator producing broadband, yet not self-starting, 33 fs pulses with 780 mW power. We also show that the choice of saturable absorbers has a negligible impact on the laser intensity noise, which is exceptionally low with sub-0.005% integrated noise.

Polarization non-reciprocity error suppression in dual-polarization fiber optic gyroscopes

The dual-polarization interferometric fiber optic gyroscope is distinguished by the fact that both orthogonal polarized light beams in the fiber coil are simultaneously sensitive to the rotation rate. The relative intensity noise can be effectively suppressed due to the strong correlation between the two polarized light beams. However, the system exhibits significant polarization non-reciprocity errors, which worsen the bias instability. In this paper, we demonstrate how modulation depth affects the angular random walk and the bias instability. Polarization non-reciprocity errors can be efficiently mitigated while the angular random walk approaches optimal value by adjusting the modulation depth to π/2rad rather than 2.7 rad. It is confirmed that the bias instability and angular random walk can be simultaneously improved by optimizing the modulation depth without increasing the complexity of the optical path. Experimental results indicate that for a 500-m-long fiber coil, the bias instability is improved from 0.025∘/h (2.7 rad) to 0.010∘/h (π/2rad) while the angular random walk remains at 0.001∘/h through modulation depth optimization, which advances its application in navigation.

Adaptive strategy for achieving fast synchronization between two memristor chaotic circuits without and with noisy perturbation

This paper presents an innovative approach for achieving rapid synchronization between two memristor chaotic circuits (MCCs), both with and without noise perturbations. The proposed adaptive control strategy effectively handles the uncertainty in control gains by adhering to predesigned update law. Additionally, this protocol is non-chattering and differentiable, avoiding the use of conventional discontinuous functions such as signum and absolute value functions. This method successfully mitigates the tremors caused by discontinuous functions. We derive two sufficient criteria using finite-time Lyapunov and stochastic finite-time Lyapunov stability methods. Numerical results validate the theoretical analysis and demonstrate the influence of noise intensity on convergence speed. Furthermore, the results have an application in image encryption transmission.

Study on the impact of aging on the brain activity patterns in auditory speech comprehension dual-route regions

Objective: To elucidate the patterns of neural activity alterations associated with auditory speech comprehension across the lifespan and the impact of varying listening environments on these dynamics. Methods: Functional near-infrared spectroscopy (fNIRS) was employed to measure the concentration of oxygenated hemoglobin in the brains of 93 adults aged from 20 to 70 with normal hearing. These participants were recruited from Beijing Tongren Hospital, affiliated with Capital Medical University, between March 2021 and February 2023. Brain activity was recorded as subjects passively listened to sentences in both silent and noise conditions with varying signal-to-noise ratios (SNR). The alterations in brain activity were analyzed to delineate the age-related trends under different auditory conditions. Statistical analysis was performed using SPSS 22.0 software. Results: The bilateral primary auditory cortex, superior temporal gyrus, and Wernicke's area, critical for sound signal discrimination and perception, exhibited enhanced activity post-stimulus presentation. Broca's area, pivotal for speech production, demonstrated an initial decrease in activity followed by an increment after stimulus onset. The ventral middle temporal gyrus and dorsal postcentral gyrus showed augmented activity in later time windows. Furthermore, it was observed that in quiet conditions and at low noise levels (SNR=10 dB), auditory cortical activity diminished with age. With increasing noise levels (SNR=5 dB), compensatory brain regions (right ventral middle temporal gyrus and dorsal postcentral gyrus) showed enhanced activity with advancing age. As noise intensity further escalated (SNR=0, SNR=-5 dB), not only did auditory cortical activity decline, but also the activity in regions associated with semantic processing and motor functions reduced with age. Conclusion: During auditory speech comprehension, dual-pathway brain regions exhibit distinct activity patterns. With heightened noise exposure, an increasing number of brain regions are influenced by aging, manifesting as a general decline in activity in most dual-pathway regions, alongside a selective augmentation in some compensatory regions on the right hemisphere.

Suppression of intensity noise and phase noise for thulium-holmium co-doped fiber laser by self-injection locking

The present study proposes and demonstrates a narrow-linewidth thulium-holmium co-doped fiber laser based on self-injection locking. The laser with single-longitudinal-mode operation is realized using a fiber Bragg grating as a wavelength-selection component and a dual-ring compound cavity as a mode-selection filter. The linewidth was compressed by increasing the photon lifetime by utilizing delay fibers of different lengths in the feedback cavity. The results show that the optical signal-to-noise ratio of the output laser is 57.2 dB, and the relative intensity noise is -130.43 dB/Hz with self-injection locking. The intrinsic linewidth is compressed from 39.45 kHz to 3.21 kHz, a reduction of nearly an order of magnitude. The linewidth compression mechanism solves the problems associated with deep linewidth compression in fiber lasers with long ring cavities and fosters the development of practical and reliable all-fiber structures. The laser source is characterized by low cost, high coherence, and low noise, which are highly desirable features in coherent optical detection, high-resolution spectrometers, microwave photonics, and optical sensing.

Eight-wavelength-switchable single-frequency fiber laser with scattering-enhanced fiber-based wavelength self-adaptive linewidth compression

We demonstrate an eight-wavelength-switchable single-frequency (SF) ultra-narrow linewidth erbium-doped fiber laser (EDFL). A switchable polarization-dependent eight-channel filter (S-PDECF), fabricated using femtosecond laser direct-writing technology, determines the eight wavelengths of the laser output, with four channels concentrated near 1530 nm and the other four near 1550 nm. A dual-coupler ring-based compound-cavity filter is utilized to achieve single-longitudinal-mode selection for the EDFL. The introduction of a femtosecond laser direct-written scattering-enhanced fiber (SEF) effectively narrows the laser linewidth, enabling ultra-narrow linewidth outputs lasing at all eight wavelengths, and the wavelength self-adaptive linewidth compression of the SEF is verified for the first time. Furthermore, the incorporation of SEF significantly enhances the overall performance of the EDFL. The eight switchable single-wavelength laser outputs all exhibit exceptional characteristics, including spectral wavelength fluctuations of ≤0.011 nm, spectral peak power fluctuations of ≤0.54 dB, optical signal-to-noise ratios of ≥71 dB, linewidths of ≤248 Hz, output power fluctuations of ≤0.252 dB over a 10-min measurement period, and relative intensity noise of ≤−150.17 dB/Hz @≥ 3 MHz.

165 MHz highly stable femtosecond Er:ZBLAN fiber laser operating at 2.8 µm.

Three-micrometer mid-infrared (MIR) femtosecond pulse sources with a high repetition rate (HRR) have potential applications in a number of fields such as biological imaging, optical frequency combs, and gas detection. In this paper, by optimizing the fiber length and the cavity structure, we demonstrated a highly stable, self-starting mode-locked fluoride fiber laser (MLFFL) with a fundamental repetition rate of ∼165 MHz and a signal-to-noise ratio (SNR) of 90 dB. As far as we know, this stands as the highest fundamental repetition rate ever acquired directly from an ultrafast MLFFL in the >2.5 µm MIR region. Stable 352-fs pulses at 2795 nm with an average output power of 392 mW and a low integrated relative intensity noise (RIN) of 0.018% [10 Hz, 10 MHz] were generated. The root mean square (RMS) power fluctuation is 0.17% over 2 h, which indicates excellent oscillator stability. This high-performance laser offers a practicable scheme both for scaling the repetition frequency in MIR MLFFLs and high-precision ultrafast applications at longer wavelengths.

Machine Learning-Based Randomness Analysis For a Auantum Random Number Generator

Abstract Random numbers are critical to information security. Quantum random numbers are theoretically truly random and unpredictable. However, the measurement process of a quantum random number generator (QRNG) can be affected by environmental disturbances that compromise the integrity of the generated random numbers. A machine learning model is proposed to assess the stochasticity of the continuous variable QRNG under the influence of vacuum noise. The model is designed to detect the correlation between the randomness of the QRNG being corrupted under the influence of classical noise (electrical noise). In addition, our model detects a decrease in the randomness of the QRNG random numbers when the electrical noise intensity increases to a certain correlation. The results show that machine learning (ML) can be used as a measure of the quality of QRNG.

Self correction technique for amplitude and phase errors of underwater acoustic arrays based on variable decibel Bayesian inference

Abstract Direction of arrival (DOA) estimation of underwater targets is an important research direction in the field of underwater acoustic array signal processing. Due to factors such as assembly errors and component aging, amplitude and phase errors between each channel of the array are common, resulting in distortion of the array flow pattern matrix. At the same time, the non-uniform distribution of noise intensity at the positions of each array element further increases the difficulty of DOA for underwater targets. Based on the characteristics of sparse spatial distribution of underwater targets, this paper uses the flexibility of hyperparametric modeling to construct an array observation signal graph model. On this basis, the effects of amplitude and phase errors and non-uniform noise on the array model are added, and a comprehensive observation model with clear distribution types of amplitude and phase errors, signals, and noise is established. The variational Bayesian inference theory is used to obtain closed solutions of various variables and parameters, so as to achieve joint estimation of target azimuth and error parameters. Simulation results and sea trial data processing results show that the algorithm can achieve amplitude and phase error self correction while completing DOA estimation in the absence of prior information on the number of sources.

Stockwell transform spectral amplitude modulation method for rotating machinery fault diagnosis

Spectral amplitude modulation (SAM) method, as an automated and empirical nonlinear filtering approach, has shown great promise for rotating machinery fault diagnosis. However, due to the inherent shortcomings of Fourier transform in SAM, leading to significant errors in the edited amplitude, also it is easy to fail with selecting the optimal weight value manually under intense background noise. To solve the aforementioned drawbacks, the Stockwell transform spectral amplitude modulation (STSAM) method is proposed. The Stockwell transform (S-transform) is first utilized to obtain the phase and amplitude with time–frequency information. Then, the edited signals can be reconstructed by inverse S-transform with the above actual phase and the modified amplitudes under different weights. Hence, more comprehensive and accurate information about the amplitude can be computed. After that, their normalized square envelope spectra under each cyclic frequency are calculated to showcase the fault characteristics. Moreover, a novel indicator is proposed to automatically choose the optimal weight in STSAM, thus clearer characteristic frequencies can be represented by the optimal square envelope spectrum (OSES). Finally, the effectiveness and superiority of the STSAM and OSES methods are systematically demonstrated by the comparative studies with the SAM and time–frequency SAM approaches using simulated signals and real-world datasets.

Vocalization-induced middle ear muscle reflex and auditory fovea do not contribute to the unimpaired auditory sensitivity after intense noise exposure in the CF-FM bat, Hipposideros pratti.

Behaviors and auditory physiological responses of some species of echolocating bats remain unaffected after exposure to intense noise, but information on the underlying mechanisms remains limited. Here, we studied whether the vocalization-induced middle ear muscle (MEM) contractions (MEM reflex) and auditory fovea contributed to the unimpaired auditory sensitivity of constant frequency-frequency modulation (CF-FM) bats after exposure to broad-band intense noise. The vocalizations of the CF-FM bat, Hipposideros pratti, were inhibited through anesthesia to eliminate the vocalization-induced MEM reflex. First, the anesthetized bats were exposed to intense broad-band noise, and the findings showed that the bats could still maintain their auditory sensitivities. However, auditory sensitivities were seriously impaired in CBA/Ca mice exposed to intense noise under anesthesia. This indicated that the unimpaired auditory sensitivity in H. pratti after exposure to intense noise under anesthesia was not due to anesthetization. The bats were further exposed to low-frequency band-limited noise, whose passband did not overlap with echolocation call frequencies. The results showed that the auditory responses to sound frequencies within the noise spectrum and one-half octave higher than the spectrum were also unimpaired. Taken together, the results indicate that both vocalization-induced MEM reflex and auditory fovea do not contribute to the unimpaired auditory sensitivity in H. pratti after exposure to intense noise. The possible mechanisms underlying the unimpaired auditory sensitivity after echolocating bats were exposed to intense noise are discussed.

Lévy noise-induced effects in a long Josephson junction in the presence of two different spatial noise distributions

We analyze the impact of Lévy-distributed stochastic fluctuations on the average switching time and voltage drop across a current-biased long Josephson tunnel junction. We compare the system’s response for two spatial configurations of time-dependent noise, i.e., homogeneous and distributed along the junction length. The response of the Josephson junction is explored by varying the characteristic parameter of the Lévy source, i.e., the α stability index and the noise intensity. These findings offer an effective tool to characterize a Lévy component possibly embedded in an unknown noise signal.

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.

Effect of background noise characteristics on early warning indicators of thermoacoustic instability

In this work, we investigate the effects of background noise characteristics — specifically noise color (or correlation time) and intensity — arising from velocity-coupled and additive noise sources, on the early warning indicators (EWIs) of thermoacoustic instability. We employ a nonlinear reduced-order combustion dynamics model for our investigation. In the absence of noise, the nonlinear model undergoes a subcritical Hopf bifurcation, and our focus lies within the linearly stable region of the system (subthreshold region). The studied class of EWIs encompasses those derived from time series (variance), spectral analysis (coherence factor), Hurst exponent, and nonlinear methods (permutation entropy). We find that when the background noise is purely multiplicative, the trends in EWIs are primarily influenced by noise characteristics rather than the control parameter. Further, the EWIs cannot be estimated at low noise levels for large correlation times. In case of purely additive noise driven system, the coherence factor and variance are reliable EWIs across all range of investigated noise characteristics. The Hurst exponent can serve as effective EWI when the system features large noise correlation times, while permutation entropy is effective only when the system features small noise correlation time, i.e., where white noise assumption is acceptable. When the background noise includes contributions from both multiplicative and additive sources, coherence factor and variance emerges as the most reliable EWIs. These results provide insights for selecting appropriate EWIs to be employed in practical systems, considering potential variations in noise characteristics with simultaneous changes in combustor operating conditions.Novelty and significanceThis study conclusively shows that background noise (multiplicative and additive) characteristics – noise correlation time (color) and intensity – can significantly change the trends in early warning indicators (EWIs) for predicting the impending thermoacoustic oscillations. In gas turbine combustors, where thermoacoustic instability poses a critical challenge, inherent noise dynamics undergo variations with changing operating conditions and combustor designs. The development of effective EWIs to foresee the onset of thermoacoustic instability is crucial for preventing potential damage and ensuring the reliable operation of combustion systems. Previous works on stochastic dynamics of combustors simplify noise with the additive white noise assumption. Thus, our results demonstrated on a nonlinear combustion dynamics model advance the state-of-the-art with respect to the early prediction and control of thermoacoustic instability. The results provide valuable insights for selecting appropriate EWIs for engine monitoring in the absence of information on noise properties and their variation with operating conditions. This practical information is of direct relevance and interest to any gas turbine manufacturer/user.

Stationary distribution and mean extinction time in a generalist prey–predator model driven by Lévy noises

In this study, a delayed generalist prey–predator model incorporating the logistic-like growth and Holling type-III functional response for predator subjected to two Lévy noises is established. Such environmental fluctuations can describe abrupt change in population density. First, we conduct stability analysis and determine the critical value of time delay when Hopf bifurcation takes place. Then, the existence of the global positive solution and stability in distribution of stochastic model are studied. Numerical results are given to illustrate the analytical findings, and mainly dedicated to the consequences of time delay and Lévy noises on the stationary distribution, mean extinction time and reproduction rate of prey–predator populations, via stationary probability distribution function (PDF), mean first passage time (MFPT) and relaxation time, respectively. Our findings indicate that the decreases in time delay, noise intensities (Di,i=1,2), and stability indexes within the range of αi∈[1,2] are more likely to maintain the population in a high density state. Phase transition occurs when the system is in the suitable parameter regime. Additionally, there exist optimal noise intensities that slow down switching of prey species from a stable state to an extinction state. Smaller time delay τ and stability indexes αi inhibit population extinction, whereas smaller skewness parameter β speed up prey extinction. More importantly, two noises can tune the enhanced stability characteristic of the system.

Integrating PCA with deep learning models for stock market Forecasting: An analysis of Turkish stocks markets

Financial data such as stock prices are rich time series data that contain valuable information for investors and financial professionals. Analysis of such data is critical to understanding market behaviour and predicting future price movements. However, stock price predictions are complex and difficult due to the intense noise, non-linear structures, and high volatility contained in this data. While this situation increases the difficulty of making accurate predictions, it also creates an important area for investors and analysts to identify opportunities in the market. One of the effective methods used in predicting stock prices is technical analysis. Multiple indicators are used to predict stock prices with technical analysis. These indicators formulate past stock price movements in different ways and produce signals such as buy, sell, and hold. In this study, the most frequently used ten different indicators were analyzed with PCA (Principal Component Analysis. This study aims to investigate the integration of PCA and deep learning models into the Turkish stock market using indicator values and to assess the effect of this integration on market prediction performance. The most effective indicators used as input for market prediction were selected with the PCA method, and then 4 different models were created using different deep learning architectures (LSTM, CNN, BiLSTM, GRU). The performance values of the proposed models were evaluated with MSE, MAE, MAPE and R2 measurement metrics. The results obtained show that using the indicators selected by PCA together with deep learning models improves market prediction performance. In particular, it was observed that one of the proposed models, the PCA-LSTM-CNN model, produced very successful results.

Control and stochastic dynamic behavior of Fractional Gaussian noise-excited time-delayed inverted pendulum system

In this paper, we investigate the control and dynamic behavior of the inverted pendulum system with time delay under fractional Gaussian noise excitation. For H=1/2 and H∈(1/2,1), we analyze the stochastic dynamic characteristics of the system under Hopf bifurcation, utilizing time delay and noise intensity as bifurcation parameters, and validate the theoretical conclusions through numerical simulations. We also defined the engineering application range of angle and angular velocity under both asymptotically stable and periodic oscillation dynamic states. Furthermore, using the stochastic Itoˆ equation, we determined the values of time delay and noise intensity that satisfy the maximum engineering application range of angle and angular velocity, and verified their accuracy against the original equation. Additionally, we observed stochastic D-bifurcation and P-bifurcation arising from the combined effects of time delay and noise. Our results exhibit remarkable consistency between analytical and numerical findings, affirming the robustness of our approach and shedding light on the intricate dynamics of the system.

FDIA localization and classification detection in smart grids using multi-modal data and deep learning technique

False data injection attacks (FDIA) exploit the vulnerabilities of bad data detection in energy management systems to maliciously tamper with state estimation results, seriously jeopardizing the safe and reliable operation of power systems. In order to promptly detect FDIA, recent studies have used machine learning techniques to extract attack characteristics and detect FDIA based on changes in these characteristics. Current research predominantly focuses on detecting a single type of FDIA attack model and topology. However, the increasing diversification of FDIA construction methods and the variability of topological structures in power systems can reduce or even invalidate detection effectiveness. To cope with the abovementioned difficulties, this study introduces a multimodal deep learning detection model based on variational graph auto-encoders (VGAE), temporal convolutional networks (TCN), and gated recurrent units (GRU). The topological features of power systems are obtained through VGAE to adapt to the detection needs of various topological structures and performance enhancement. These features are then integrated with preprocessed measurement data via BDD to form multimodal data. Furthermore, this multimodal data is used to locate and classify FDIA with complete information, FDIA with incomplete information, and topology attack after applying a multi-label classification algorithm based on TCN-GRU for temporal feature extraction. Experiments carried out on the IEEE 14 and IEEE 118 bus systems show that the proposed method is robust and has high detection performance. The results indicate that the proposed detection method achieves AUC values that are, on average, 0.085, 0.145, and 0.04 higher than those of CNN, LSTM, and CNN-LSTM, respectively. Moreover, under various noise and attack intensities, recall, precision, F1 score, and RACC remain above 0.859, 0.877, 0.867, and 0.818, respectively, with a classification accuracy greater than 0.912. This study provides a unique perspective on detecting FDIA across various attack models and power system topologies.

Stochastic effects on plankton dynamics: Insights from a realistic 0-dimensional marine biogeochemical model

Marine ecosystems exist in a noisy and uncertain environment, not governed by deterministic laws. The development of ecological communities is significantly influenced by variability, and the interaction between nonlinearity and stochastic processes can lead to phenomena that deterministic models cannot explain. Plankton, forming the base of the marine food web, are highly affected by stochastic fluctuations due to their short reproductive timescales. Investigating the effects of noise on plankton growth is essential for accurately describing and predicting marine health. We present a realistic biogeochemical model where multiplicative white noise represents environmental stochasticity affecting plankton. The model suggests ergodic properties in the presence of stochastic fluctuations, with temporal and ensemble distributions being coherent. Analytical and numerical analyses reveal that, given sufficiently low noise intensity, dynamics near equilibrium resemble an Ornstein-Uhlenbeck additive process. With higher noise intensities, resonance occurs, particularly when endogenous dynamics are periodic. The results indicate that low noise intensity can positively influence plankton persistence with an higher number of species coexisting, while higher noise intensity can establish a new equilibrium in the system.

The Noise Exposure of Urban Rail Transit Drivers: Hazard Classification, Assessment, and Mitigation Strategies

Prolonged exposure to high-intensity noise environments in urban rail transit systems can negatively impact the health and work efficiency of drivers. However, there is a lack of comprehensive understanding of the noise pattern and, therefore, effective mitigation strategies. To control the noise in urban rail transit systems, this study proposes a comprehensive noise assessment framework, including metrics such as average sound pressure level, peak sound pressure level, percentile sound pressure levels, dynamic range, main frequency component, and cumulative time energy to evaluate the noise characteristics. We also employ a density-based spatial clustering of applications with noise (DBSCAN) method to identify the noise patterns with the evaluation of their hazard to urban rail transit drivers. The results have revealed that: (1) The equivalent continuous sound pressure level (Leq) in the cab of Lanzhou Urban Rail Transit Line 1 averages 87.12 dB, with a standard deviation of 8.52 dB, which reveals a high noise intensity with substantial fluctuations. (2) Ten noise patterns were identified, with frequencies varying from 14.47 Hz to 69.70 Hz and Leq varying from 60 dB to 115 dB. (3) The major noise sources from these patterns are inferred to be the train’s mechanical systems, wheel–rail interaction, aerodynamic effects, and braking systems. Combined with the noise patterns and urban rail transit’s operation environment, this study proposes tailored mitigation strategies for applications aimed at protecting drivers’ hearing health, enhancing work efficiency, and ensuring driving safety.