Nonlinear Interference Research Articles

Mid‐Infrared Sensing Using a Hollow–Core Fiber in a Nonlinear Interferometer

AbstractIncreasing the interaction path length is a well‐known method for enhancing the sensitivity of the optical detection system. Hollow–core fibers (HCFs) represent a viable alternative to the traditional multi‐path cells offering low optical losses and strong confinement of the optical field. Here, the incorporation of an Antiresonant Hollow–core Fiber (AR‐HCF) section into a nonlinear interferometer, where the AR‐HCF section serves as a gas‐sensing cell operating in the IR range is presented. By exploiting the effect of nonlinear interference, the detection is brought into the more operation‐friendly visible range. The detection of methane (CH4) gas at mid‐IR wavelengths within a half‐meter section of AR‐HCF, with an estimated concentration accuracy of 200 ppm·m is demonstrated. These results represent the combination of two research fields within a single instrument and pave the way for further advancement of quantum‐inspired gas sensing techniques.

Digital Post-Cancellation of Nonlinear Interference for Millimeter Wave and Terahertz Systems

Digital Post-Cancellation of Nonlinear Interference for Millimeter Wave and Terahertz Systems

Transformer-empowered receiver design of OFDM communication systems

With deep learning, we perform channel estimation and signal detection in massive Multiple Input Multiple Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) systems in this paper. Specifically, we design and extend the basic framework of receivers for MIMO-OFDM systems in an end-to-end approach. A Transformer-based MIMO-OFDM receiver called TCD-Receiver is proposed, which introduces a multi-attention mechanism to learn the channel characteristics by introducing a generic and flexible Transformer network structure. The network parameters are updated based on the relationship between the received signal and the original signal, where the final signal information is obtained without explicit channel estimation and the predicted transmit bits are directly output. The experimental results show that the TCD-Receiver proposed can effectively solve the channel distortion and detect the transmitted signals compared with the traditional communication receivers, and its performance can be comparable to that of the traditional OFDM receivers, and it also has obvious advantages in combating the complex and difficult-to-model channel environment as well as the nonlinear interference factors.

Generation of noise-like pulses in a linear-cavity Tm fiber mode-locked laser based on FMF saturable absorber

As a real saturable absorber (SA) based on the nonlinear multimode interference (NL-MMI) effect, the devices based on the few-mode fiber possess numerous exciting characteristics due to their all-fiber structure, straightforward manufacturing process. In this paper, by employing a SA with the SMF-FMF-SMF structure in a linear cavity, we demonstrate a stable mode-locked Tm-doped fiber laser. At the pump power of 1 W, a single noise-like pulse (NLP) with a pedestal pulse duration of 28 ps and coherent peak width of 0.77 ps is generated at a central wavelength of 1940 nm with a 3 dB bandwidth of 10.51 nm. The stable noise-like pulse can be sustained from the lasing pump threshold up to the maximum pump power of 2.2 W without experiencing pulse splitting. The experiment results in a peak average output power of 116 mW, along with a corresponding maximum pulse energy of 24.73 nJ at a repetition frequency of 4.69 MHz. The high stability of our fiber laser is confirmed by the signal-to-Noise Ratio (SNR) of 63 dB, and its enduring stability is additionally validated over an 8-hour period. The experimental findings suggest that the SMF-FMF-SMF structure has significant potential in the simple and robust all-fiber mode-locked lasers operating in the noise-like pulse regime.

In-fiber waveguide-based mode-locker for generating diverse ultrafast pulses

The pursuit of an optimal mode-locker in nonlinear photonics is crucial for advancing high-performance laser technologies. Addressing the market demands and technical complexities in creating highly integrated and robust saturable absorbers, we present a femtosecond laser-inscribed in-fiber straight waveguide, seamlessly integrating a few-mode fiber segment into a single-mode fiber. This design reduces costs, simplifies fabrication, and enhances system integration and stability. As a saturable absorber, it enables nonlinear polarization rotation and multimode interference, which are essential for ultrafast pulse generation. It exhibits slight polarization-dependent loss and significant modulation depth, effectively inducing mode-locking. Based on the waveguide, 1.36 ps soliton pulses with a 2.9 nm bandwidth at a central wavelength of 1573 nm are initially achieved in an anomalous dispersion regime. Transitioning to the normal dispersion regime, at 190 mW pump power, the system produces Q-switched mode-locked pulses at 1574 nm. Increasing the pump power to 295 mW results in 712 fs noise-like pulses at 1572 nm, featuring an 8 nm bandwidth and 4.57 MHz repetition rate. This advancement in fiber lasers, facilitated by hybrid nonlinear effects in the waveguide, represents a significant milestone, promising for nonlinear optics and photonics due to its simplicity, cost-effectiveness, compactness, robustness, and high damage threshold.

Video-based subtle vibration measurement in the presence of large motions

Recently, phase-based methods offer precise measurement of subtle vibrations from videos but face challenges when dealing with moving objects. This paper introduces a novel video-based method for measuring subtle vibrations in the presence of large motions. It employs complex steerable filters to capture phase variations at each pixel, extracting a composite motion signal. Based on the differences in acceleration characteristics between subtle vibrations and large motions, a band-passed acceleration filter is designed to separate them, relying only on limited prior knowledge of the frequency band. The filter design minimizes passband attenuation using a genetic algorithm for iterative parameter optimization. By further incorporating a Gaussian amplitude kernel, we suppress large-amplitude nonlinear motion interference and extract subtle vibrations with acceleration characteristics. Both simulation experiments and real-world tests demonstrate that this method outperforms state-of-the-art techniques in terms of accuracy, providing a more effective approach for measuring subtle vibrations in moving objects.

Reversing the magnetization of 50-nm-wide ferromagnets by ultrashort magnons in thin-film yttrium iron garnet.

Spin waves (magnons) can enable neuromorphic computing by which one aims at overcoming limitations inherent to conventional electronics and the von Neumann architecture. Encoding magnon signal by reversing magnetization of a nanomagnetic memory bit is pivotal to realize such novel computing schemes efficiently. A magnonic neural network was recently proposed consisting of differently configured nanomagnets that control nonlinear magnon interference in an underlying yttrium iron garnet (YIG) film [Papp et al., Nat. Commun., 2021, 12, 6422]. In this study, we explore the nonvolatile encoding of magnon signals by switching the magnetization of periodic and aperiodic arrays (gratings) of Ni81Fe19 (Py) nanostripes with widths w between 50 nm and 200 nm. Integrating 50-nm-wide nanostripes with a coplanar waveguide, we excited magnons having a wavelength λ of ≈100 nm. At a small spin-precessional power of 11 nW, these ultrashort magnons switch the magnetization of 50-nm-wide Py nanostripes after they have propagated over 25 μm in YIG in an applied field. We also demonstrate the magnetization reversal of nanostripes patterned in an aperiodic sequence. We thereby show that the magnon-induced reversal happens regardless of the width and periodicity of the nanostripe gratings. Our study enlarges substantially the parameter regime for magnon-induced nanomagnet reversal on YIG and is important for realizing in-memory computing paradigms making use of magnons with ultrashort wavelengths at low power consumption.

Fast-Activated Minimal Gated Unit: Lightweight Processing and Feature Recognition for Multiple Mechanical Impact Signals.

Multiple dynamic impact signals are widely used in a variety of engineering scenarios and are difficult to identify accurately and quickly due to the signal adhesion phenomenon caused by nonlinear interference. To address this problem, an intelligent algorithm combining wavelet transforms with lightweight neural networks is proposed. First, the features of multiple impact signals are analyzed by establishing a transfer model for multiple impacts in multibody dynamical systems, and interference is suppressed using wavelet transformation. Second, a lightweight neural network, i.e., fast-activated minimal gated unit (FMGU), is elaborated for multiple impact signals, which can reduce computational complexity and improve real-time performance. Third, the experimental results show that the proposed method maintains excellent feature recognition results compared to gate recurrent unit (GRU) and long short-term memory (LSTM) networks under all test datasets with varying impact speeds, while its metrics for computational complexity are 50% lower than those of the GRU and LSTM. Therefore, the proposed method is of great practical value for weak hardware application platforms that require the accurate identification of multiple dynamic impact signals in real time.

Feedback-linearization decoupling based coordinated control of air supply and thermal management for vehicular fuel cell system

The coordinated control of the coupled system of air supply and thermal management is one of the important ways to improve the efficiency of proton exchange membrane fuel cell (PEMFC) system. The novelty of this paper is to design a feedback-linearization decoupling based coordinated controller combining with active disturbance rejection control (ADRC) method. Aiming at strong coupling of air supply and thermal management system, we have proposed a three-dimensional particle swarm coordinated optimization algorithm to calculate the steady-state reference, then, a feedback-linearization decoupling controller is designed to achieve independent control of air flow, cathode pressure and temperature. Besides, aiming at time-varying, nonlinear interference, we have designed an ADRC based on the decoupled method to achieve better dynamic response and stability. The current step load test reveals that the overshoot and the average tracking error are much smaller under the air flow-pressure-temperature decoupling coordinated controller compared that under the proportional-integral-derivative (PID) controller and the air flow-pressure decoupling controller. The test results under different ambient temperatures indicate that the proposed coordinated strategy reduces the tracking error by over 10 %, the efficiency of fuel cell system is increased by 3.5%–4.5 %.

Fractionally spaced nonlinear Tomlinson-Harashima precoding with weight clustering for C-band 100 Gbaud/λ PAM-4 transmission.

A nonlinear Tomlinson-Harashima precoding (NTHP) scheme has been verified for its capability to effectively address both the linear and nonlinear inter-symbol interferences (ISIs) arising in the intensity-modulation direct-detection (IM-DD) fiber optics transmission. Nevertheless, the application of the NTHP scheme may significantly increase the number of levels for the intensity modulated signals, resulting in the reduction of both eye width and receiver sensitivity. Here, we propose a fractionally spaced NTHP with a weight clustering (FS-NTHP-WC) scheme. Consequently, an accurate ISI feedback can be obtained to enlarge the eye width; meanwhile a hardware-efficient implementation without the equalization penalty can be achieved by weight clustering and pruning. When the C-band 100 Gbaud/λ PAM-4 signals are transmitted, our proposed FS-NTHP-WC scheme not only can achieve 0.25 dB and 0.5 dB gains of receiver sensitivity under back-to-back (B2B) and 2-km standard single-mode fiber (SSMF) transmission conditions, respectively, but can also cut down the computational complexity by 90% and 76% in terms of the number of multiplications and additions, respectively, in comparison with the NTHP scheme.

A nonlinear noise active control algorithm based on deep neural network

To solve the problem that the traditional filter-x least mean square (FxLMS) algorithm and the correlation variable step FxLMS algorithms cannot handle nonlinear interference, a nonlinear noise active control algorithm based on deep neural network (DNN) is proposed. First, nonlinear interference between the acoustic channel and the secondary loudspeaker is introduced into the active noise control system. Second, the training objectives and loss functions of the deep neural network are set, and the network parameters are trained. Finally, a simulation of the active noise control in the local workspace of a tufted carpet loom is carried out to verify the performance of the DNN algorithm. The results show that the DNN algorithm is effective in dealing with nonlinear disturbances.

Nonlinear Multimodal Interference as Ultrafast Photonic Device for Dual-Wavelength Domain-Wall Dark Pulse Generation

Abstract In comparison to bright pulses, better stability that is not susceptible to loss makes dark pulses accessible for applications in such fields as signal processing, optics sensing, and quantum communication. Here we investigate the dual-wavelength domain-wall dark pulse generation in a graded-index multimode fiber (GIMF) based anomalous dispersion single-mode fiber (SMF) laser. By optimizing intra-cavity nonlinearity and pulse polarization, the mode-locked states can evolve each other between bright pulses, dark pulses, and bright-dark pulse pairs. The evolution mechanism among them may be relevant to the coherent mode superposition, spectral filtering, and mode selection in SMF-GIMF-SMF hybrid-fiber modulation devices that affect the pulse formation and evolution in temporal, frequency, and space domains. These results provide a valuable reference for promoting further development of nonlinear optics and ultrafast optics, in which ultrafast photonic devices, with low cost, simple manufacture as well as wide adaptability, as novel pulsed generation technique, play a vital role.

Tunable mode-locked holmium-doped fiber laser based on GIMF-SIMF-GIMF structure at 2.09 µm

In this study, a structure of graded-index multimode fiber-step index multimode fiber-graded index multimode fiber (GIMF-SIMF-GIMF) was used as a saturable absorber (SA) to generate mode-locking in a holmium-doped fiber laser (HDFL). The HDFL was pumped with a compact in-house thulium-doped fiber laser (TDFL) operating at 1943 nm with a maximum output power of 2.1 W. Mode-locked pulses with a pulse duration of 1.61 ps were successfully generated at the center wavelength of 2091.1 nm. The pulse had a repetition rate of 12.79 MHz with a bit of fluctuation of 2.09 %, indicating stable pulses. The mode-locked pulses were also tunable with a range from 2086.5 nm to 2091.1 nm by stretching the GIMF part of the SA. This first report demonstrated mode-locking using nonlinear multimode interference (NL-MMI) using GIMF-SIMF-GIMF structure as a saturable absorber in HDFL operating near 2.1 µm wavelength.

Machine Learning-Based Channel Estimation Techniques for ATSC 3.0

Channel estimation accuracy significantly affects the performance of orthogonal frequency-division multiplexing (OFDM) systems. In the literature, there are quite a few channel estimation methods. However, the performances of these methods deteriorate considerably when the wireless channels suffer from nonlinear distortions and interferences. Machine learning (ML) shows great potential for solving nonparametric problems. This paper proposes ML-based channel estimation methods for systems with comb-type pilot patterns and random pilot symbols, such as ATSC 3.0. We compare their performances with conventional channel estimations in ATSC 3.0 systems for linear and nonlinear channel models. We also evaluate the robustness of the ML-based methods against channel model mismatch and signal-to-noise ratio (SNR) mismatch. The results show that the ML-based channel estimations achieve good mean squared error (MSE) performance for linear and nonlinear channels if the channel statistics used for the training stage match those of the deployment stage. Otherwise, the ML estimation models may overfit the training channel, leading to poor deployment performance. Furthermore, the deep neural network (DNN)-based method does not outperform the linear channel estimation methods in nonlinear channels.

On nonlinear effects in multiphase WKB analysis for the nonlinear Schrödinger equation *

We consider the Schrödinger equation with an external potential and a cubic nonlinearity, in the semiclassical limit. The initial data are sums of WKB states, with smooth phases and smooth, compactly supported initial amplitudes, with disjoint supports. We show that like in the linear case, a superposition principle holds on some time interval independent of the semiclassical parameter, in several régimes in term of the size of initial data with respect to the semiclassical parameter. When nonlinear effects are strong in terms of the semiclassical parameter, we invoke properties of compressible Euler equations. For weaker nonlinear effects, we show that there may be no nonlinear interferences on some time interval independent of the semiclassical parameter, and interferences for later time, thanks to explicit computations available for particular phases.

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Hydraulic structures are the key national infrastructures, whose safety and stability are crucial for socio-economic development. Global Navigation Satellite System (GNSS) technology, as a high-precision deformation monitoring method, is of great significance for the safety and stability of hydraulic structures. However, the GNSS time series exhibits characteristics such as high nonlinearity, spatiotemporal correlation, and noise interference, making it difficult to model for prediction. The Neural Networks (CNN) model has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. The Gated Recurrent Units (GRU) model could improve the training effectiveness by introducing gate mechanisms, but its ability to model long-term dependencies is limited. This study proposes a combined model, using CNN to extract spatial features and GRU to capture temporal information, to achieve an accurate prediction. The experiment shows that the proposed CNN-GRU model has a better performance, with an improvement of approximately 45%, demonstrating higher accuracy and reliability in predictions for GNSS deformation monitoring. This provides a new feasible solution for the safety monitoring and early warning of hydraulic structures.

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

Dynamic Modeling and Robust Control for Slant-Axis Telescope

The slant-axis telescope has a novel structure, and its unique structural design is more suitable for extreme environment such as the South Pole. However, there is a lack of research on the dynamic modeling and the controller design of slant-axis telescopes at home and abroad. This paper proposes a dynamic modeling and robust control method for slant-axis telescope. Firstly, the dynamical analysis of the slant-axis telescope is carried out. The 2-degree-of-freedom rigid body model of the telescope is established by the Lagrange method. Next, combining the flexibility of the driver system and disturbance, a mathematical model of the rigid-flexible coupling system of the slant-axis telescope is completed. Then, according to the established mathematical model, a sliding mode controller based on the disturbance observer is designed to suppress the disturbance, and realize the robust control of the slant-axis telescope. Finally, the simulation results show that the disturbance observer based sliding mode controller gets better dynamic performance and anti-disturbance characteristics than the traditional Proportion-Integration-Differentiation (PID) controller in the case of considering nonlinear external interference of the model.

Nanosecond pulse generation mode-locked by an artificial saturable absorber based on SMF-MMF-SMF structure

We present a noteworthy demonstration of nanosecond pulse generation utilizing an all-fiber artificial saturable absorber (SA) structure, composed of single-mode multimode single-mode fiber (SMF-MMF-SMF), integrated into a passively mode-locked erbium-doped fiber laser (EDFL). The SMS-based SA effectively phase-locked the longitudinal modes within the elongated EDFL cavity, resulting in the production of mode-locked pulses operating at 1567 nm. Mode-locking is achieved through the Kerr effect of nonlinear multimode interference. The pulse repetition rate is synchronized with the fundamental frequency at 1.28 MHz. At a pump power of 269.1 mW, the laser achieves a remarkable peak average pulse energy of 6.0 nJ. Demonstrating outstanding stability, the laser operation exhibits a peak-to-pedestal extinction ratio of 70 dB for the fundamental frequency. We are confident in the substantial potential of this SMS structure, positioning it as a compelling candidate for integration into ultrafast fiber lasers, with promising prospects in the field.

Optimizing elastic optical networks quality of transmission with an empirical optical amplifier gain saturation model

In this paper, we revisit the problem of adjusting transceiver power, amplifier gain, and nodal gain in optical-bypass-enabled networks, taking into account an empirical model of erbium-doped fiber amplifier (EDFA) gain saturation. We assume that network demands have been routed via the shortest possible paths. A heuristic algorithm, which estimates the degradation of the signal-to-noise ratio (SNR) for each demand, is used to assign the modulation level and spectrum slots to the routed demands. Our optimization process aims to maximize the minimum SNR margins of demands, by adjusting EDFA small-signal gain, variable optical attenuator (VOA) attenuation, and transceiver power. Constraints and impairments imposed by the physical-layer include EDFA and VOA gain limits, transceiver power limits, receiver sensitivity, insertion loss of optical switch input ports, EDFA gain saturation, EDFA noise, and fiber non-linear interference. These are modeled using the Gaussian noise model. The proposed optimization problem is formulated as a geometric programming problem and then converted into an equivalent convex problem.Numerical results suggest that in the optimal solution of our optimization, all demands are served with typically low bit error rates (BER) (<10−4). We calculate the operational gains of the EDFA to set the spans to full compensation whenever possible. Pre-amplifiers and line amplifiers do not experience gain saturation due to relatively small input powers, while approximately 10% of boosters undergo more than a 1 dB gain reduction due to high input powers. We observe that overlooking the effect of EDFA gain saturation and physical-layer-imposed constraints such as nodal gain limits or transceiver power limits during network optimization leads to a considerable number of blocked demands due to SNR degradation or high BER (>10−3).