Hyperspectral image (HSI) classification constitutes the fundamental research in remote sensing fields. Convolutional Neural Networks (CNNs) and Transformers have demonstrated impressive capability in capturing spectral-spatial contextual dependencies. However, these architectures suffer from limited receptive fields and quadratic computational complexity, respectively. Fortunately, recent Mamba architectures built upon the State Space Models integrate the advantages of long-range sequence modeling and linear computational efficiency, exhibiting substantial potential in low-dimensional scenarios. Motivated by this, we propose a novel 3D-Spectral-Spatial Mamba (3DSS-Mamba) framework for HSI classification, allowing for global spectral-spatial relationship modeling with greater computational efficiency. Technically, a spectral-spatial token generation (SSTG) module is designed to convert the HSI cube into a set of 3D spectral-spatial tokens. To overcome the limitations of traditional Mamba, which is confined to modeling causal sequences and inadaptable to high-dimensional scenarios, a 3D-Spectral-Spatial Selective Scanning (3DSS) mechanism is introduced, which performs pixel-wise selective scanning on 3D hyperspectral tokens along the spectral and spatial dimensions. Five scanning routes are constructed to investigate the impact of dimension prioritization. The 3DSS scanning mechanism combined with conventional mapping operations forms the 3D-spectral-spatial mamba block (3DMB), enabling the extraction of global spectral-spatial semantic representations. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods on HSI classification benchmarks. The code is available at https://github.com/IIP-Team/3DSS-Mamba.

A novel parallel structured fiber-optic Fabry-Perot interferometer (FPI) based on Vernier-effect is theoretically proposed and experimentally demonstrated for ultrasensitive strain measurement. This proposed sensor consists of open-cavity and closed-cavity fiber-optic FPI, both of which are connected in parallel via a 3 dB coupler. The open-cavity is implemented for sensing, while the closed-cavity for reference. Experimental results show that the proposed parallel structured fiber-optic FPI can provide an ultra-high strain sensitivity of −43.2 pm/με, which is 4.6 times higher than that of a single open-cavity FPI. Furthermore, the sensor is simple in fabrication, robust in structure, and stable in measurement. Finally, the parallel structured fiber-optic FPI scheme proposed in this paper can also be applied to other sensing field, and provide a new perspective idea for high sensitivity sensing.

Existing wavefront sensorless (WFS-less) adaptive optics (AO) generally require a search algorithm that takes lots of iterations and measurements to get optimal results. So the latency is a serious problem in the current WFS-less AO system, especially in applications to free-space optics communication. To solve this issue, we propose a deep neural network (DNN)–based aberration correction method. The DNN model can detect the wavefront distortion directly from the intensity images, thereby avoiding time-consuming iterative processes. Since the tip-and-tilt mode of Zernike coefficients are considered, the tip-tilt correction system is not necessarily required in the proposed method. From our simulation results, the proposed method can effectively reduce the computation time and has an impressive improvement of root mean square (RMS) in different turbulence conditions.

Hyperspectral image (HSI) classification has garnered substantial attention in remote sensing fields. Recent Mamba architectures built upon the Selective State Space Models (S6) have demonstrated enormous potential in long-range sequence modeling. However, the high dimensionality of hyperspectral data and information redundancy pose challenges to the application of S6 in HSI classification, suffering from suboptimal performance and computational efficiency. In light of this, this paper investigates a lightweight Interval Group Spatial-Spectral Mamba framework (IGroupSS-Mamba) for HSI classification, which allows for multi-directional and multi-scale global spatial-spectral information extraction in a grouping and hierarchical manner. Technically, an Interval Group S6 Mechanism (IGSM) is developed as the core component, which partitions high-dimensional features into multiple non-overlapping groups at intervals, and then integrates a unidirectional S6 for each group with a specific scanning direction to achieve non-redundant sequence modeling. Compared to conventional applying multi-directional scanning to all bands, this grouping strategy leverages the complementary strengths of different scanning directions while decreasing computational costs. To adequately capture the spatial-spectral contextual information, an Interval Group Spatial-Spectral Block (IGSSB) is introduced, in which two IGSM-based spatial and spectral operators are cascaded to characterize the global spatial-spectral relationship along the spatial and spectral dimensions, respectively. IGroupSS-Mamba is constructed as a hierarchical structure stacked by multiple IGSSB blocks, integrating a pixel aggregation-based downsampling strategy for multiscale spatial-spectral semantic learning from shallow to deep stages. Extensive experiments demonstrate that IGroupSS-Mamba significantly outperforms the state-of-the-art methods in classification accuracy and achieves lower model parameters and floating point operations (FLOPs). The code is available at https://github.com/IIP-Team/IGroupSS-Mamba.

In this article we have enhanced the security of an orthogonal frequency division multiplexed passive optical network (OFDM-PON) based on four dimensional (4D) encryption, including constellation, subcarrier, symbol and time, which is proposed for the first time in this paper. 4D-hyperchaotic mapping is used to generate four masking factors to achieve ultra-high security encryption in four different dimensions. During the encryption, dimension coordination optimization is adopted, which effectively reduces the time cost of the system and improves the encryption efficiency by 3 times. At the same time, probabilistic shaping (PS) technology is used to further optimize the system that has effectively improved the bit error performance by about 1 dB. The proposed encryption technique for OFDM-PON has been demonstrated successfully with the help of experiments. The generated OFDM signal is modulated by the quadrature amplitude modulation (QAM) technique, which transmitted 16 Gb/s data rate across a 25 km fiber span of standard single-mode fiber. The values of bit error rate (BER) and peak-to-average-power ratio (PAPR) are analyzed during the experiments, and the obtained results show that the proposed security-enhanced OFDM-PON has high sensitivity and security and can be well compatible with PS and OFDM technologies. The proposed scheme has very reliable security performance and also has excellent benefit improvement, which is very promising in the future PS-OFDM-PON.

We propose and analyze an efficient scheme for realizing high-sensitive mass sensor in a quadratically coupled optomechanical system via nonlinear second-order sideband process. This is achieved by exploiting a well-established optomechanical circumstance, where a degenerate parametric amplifier (DPA) is embedded into a membrane-in-the-middle cavity driven by a strong control field and a weak probe pulse. Beyond the conventional linearized approximation, we derive analytical expressions for the efficiency of a second-order sideband and the sensitivity of a mass sensor by using a perturbation method. In this scheme, an added mass deposited on the dielectric membrane can be measured by monitoring the efficiency variation of second-order sideband generation. Using experimentally achievable parameters, we identify the conditions under which nonlinear gain of DPA allows us to enhance the efficiency of a second-order sideband and improve the sensitivity of a mass sensor beyond what is achievable in the linearly coupled optomechanical system based on the detection of mechanical frequency shift. More importantly, we also find that the maximum efficiency of a second-order sideband and the optimum sensitivity of a mass sensor simultaneously serve as an efficient detection for the added mass of a membrane when a control field and nonlinear gain of DPA become strong. The present proposal offers a practical opportunity to design an all-optical nonlinear mass sensor at the picogram level.