A universal multi-parameter sensing scheme based on a self-interference micro-ring resonator (SIMRR) is proposed. Benefit from the special intensity sensing mechanism, the SIMRR allows multimode sensing in a wide range of wavelengths but immune from frequency noise. To process the multiple mode spectra that are dependent on multiple parameters, we adopt the machine learning algorithm instead of massive asymptotic solutions of resonators. Employing the proposed multi-mode sensing approach, a two-parameter SIMRR sensor is designed. Assuming that two gases have different wavelength dependence of refractive indices, the feasibility and effectiveness of the two-parameter sensing strategy are verified numerically. Moreover, the dependence of parameter estimation accuracy on the laser intensity noises is also investigated. The numerical results indicate that our scheme of multi-parameter sensing in a multimode SIMRR holds great potential for practical high-sensitive sensing platforms compared with the single-mode sensing based on whispering gallery mode (WGM) resonators.

A dissipative sensing scheme based on a self-interference microring resonator (SIMRR), which is robust against lasing and microcavity frequency noise in the detecting system with a low noise level, is systematically investigated. The dependence of the performance of an SIMRR sensor in dispersive and dissipative sensing schemes on the physical structural parameters, e.g., the waveguide loss coefficient, the power coupling coefficient, the microring radius, and the initial sensing arm waveguide length in the SIMRR sensor, is studied. Based on the Cramer–Rao lower bound for parameter estimation, the detection limits of dispersive and dissipative sensing are theoretically and numerically analyzed, which demonstrate that the dissipative approach is immune from the frequency noises with a low noise level. The results show that the detection limit of dissipative sensing has great potential to achieve better performance than that of dispersive sensing for a practical commercial tunable laser scanning system.

A multimode microcavity sensor based on a self-interference microring resonator is demonstrated experimentally. The proposed multimode sensing method is implemented by recording wide band transmission spectra that consist of multiple resonant modes. It is different from previous dissipative sensing scheme, which aims at measuring the transmission depth changes of a single resonant mode in a microcavity. Here, by combining the dissipative sensing mechanism and the machine learning algorithm, the multimode sensing information extracted from a broadband spectrum can be efficiently fused to estimate the target parameter. The multimode sensing method is immune to laser frequency noises and robust against system imperfection, thus our work presents a great step towards practical applications of microcavity sensors outside the research laboratory. The voltage applied across the microheater on the chip was adjusted to bring its influence on transmittance through the thermo-optic effects. As a proof-of-principle experiment, the voltage was detected by the multimode sensing approach. The experimental results demonstrate that the limit of detection of the multimode sensing by the general regression neural network is reduced to 6.7% of that of single-mode sensing within a large measuring range.

We propose and experimentally demonstrate a simple, blind digital modulation format identification scheme for a quadrature amplitude modulation (QAM) coherent optical communication system. In the proposed scheme, we utilize a density-peak clustering algorithm to determine the number of amplitude levels of the signal as the feature for modulation format identification. Simulations are carried out to investigate the application of the proposed scheme to various modulation signals. Experiments are performed to verify the robustness of the method to amplified spontaneous emission (ASE) noise and fiber nonlinearities. We experimentally achieved a 95% identification accuracy for a quadrature phase shift keying (QPSK) signal with a lowest optical-signal-noise-ratio (OSNR) of 13.2 dB, a 16-QAM signal with a lowest OSNR of 13.3 dB and a 64-QAM signal with a lowest OSNR of 19.7 dB.

A high-accuracy unsupervised classification model is developed in a multimode self-interference microring resonator (SIMRR). For the SIMRR, there are many whispering gallery modes (WGMs) present. Each of these resonance modes supported by the SIMRR has a different response to different target parameters, so that the SIMRR-based sensor has the super capability to distinguish between multiple components. In the classification model, principal component analysis (PCA) is firstly used to reduce the dimensionality of this multimode sensing information from the SIMRR-based sensor. When the original higher-dimensional data points are projected onto the lower-dimensional data with only the first few principal components, they can be easily categorized into several different types by using density-based spatial clustering of application with noise (DBSCAN) algorithm. As an example, the unsupervised classification method is numerically validated based on a designed three-gas SIMRR-based sensor. The numerical results prove that the classification model can achieve an ultra high classification accuracy for the designed three-gas sensor with more than 60 dB in signal-to-noise ratio.