Six distinct types of marine particles, distributed within a large volume of seawater, are assessed through a simultaneous holographic imaging and Raman spectroscopy procedure. Convolutional and single-layer autoencoders are the methods chosen for unsupervised feature learning, applied to the images and spectral data. Non-linear dimensional reduction of combined learned features leads to a noteworthy macro F1 score of 0.88 for clustering, dramatically surpassing the maximum score of 0.61 achieved using image or spectral features. The application of this method to the ocean allows long-term monitoring of particles without the need for any sample acquisition process. Moreover, data from diverse sensor measurements can be used with it, requiring minimal alterations.

Angular spectral representation enables a generalized approach for generating high-dimensional elliptic and hyperbolic umbilic caustics via phase holograms. To scrutinize the wavefronts of umbilic beams, the diffraction catastrophe theory, determined by the potential function dependent on the state and control parameters, is applied. We observe that hyperbolic umbilic beams are reducible to classical Airy beams if and only if the two control parameters are simultaneously zero, and elliptic umbilic beams demonstrate an engaging self-focusing trait. Data from numerical experiments indicates that these beams manifest distinct umbilics within the 3D caustic, serving as links between the two disjoined sections. The observed dynamical evolutions substantiate the significant self-healing properties of both. Moreover, our results demonstrate that hyperbolic umbilic beams follow a curved trajectory as they propagate. Due to the intricate numerical computation of diffraction integrals, we have devised a highly effective method for generating these beams, leveraging the phase hologram representation of the angular spectrum. Our experiments are in perfect agreement with the theoretical simulations. The application of beams with intriguing properties is anticipated in burgeoning fields, including particle manipulation and optical micromachining.

Extensive study has focused on horopter screens because their curvature diminishes parallax between the eyes, and immersive displays incorporating horopter-curved screens are renowned for their profound representation of depth and stereopsis. The horopter screen projection unfortunately results in difficulties focusing the image evenly across the whole screen, and the magnification varies from point to point. A warp projection, devoid of aberrations, holds considerable promise in resolving these issues, altering the optical path from the object plane to the image plane. A freeform optical element is indispensable for a warp projection devoid of aberrations, given the substantial variations in the horopter screen's curvature. The hologram printer's method of manufacturing free-form optical devices is more rapid than traditional techniques, achieving this by encoding the desired wavefront phase onto the holographic medium. Our research, detailed in this paper, implements aberration-free warp projection for a specified arbitrary horopter screen, leveraging freeform holographic optical elements (HOEs) fabricated by our tailored hologram printer. The experimental data conclusively supports the effective correction of distortion and defocus aberrations.

Optical systems have been instrumental in a multitude of applications, such as consumer electronics, remote sensing, and biomedical imaging. The specialized and demanding nature of optical system design has stemmed from the intricate interplay of aberration theories and the less-than-explicit rules-of-thumb; neural networks are only now gaining traction in this area. This study introduces a generic, differentiable freeform ray tracing module, designed for use with off-axis, multiple-surface freeform/aspheric optical systems, which paves the way for deep learning-driven optical design. With minimal pre-existing knowledge as a prerequisite for training, the network can infer several optical systems after a singular training process. This research highlights the potential of deep learning in freeform/aspheric optical systems, and the resulting trained network could serve as a unified and practical tool for the creation, documentation, and replication of beneficial initial optical layouts.

Photodetection employing superconductors boasts a broad spectral scope, encompassing microwaves to X-rays. In the high-energy portion of the spectrum, it enables single-photon detection. Nonetheless, the system's detection efficacy diminishes in the infrared region of longer wavelengths, stemming from reduced internal quantum efficiency and a weaker optical absorption. We exploited the properties of the superconducting metamaterial to significantly enhance light coupling efficiency, resulting in near-perfect absorption at dual infrared wavelengths. Due to the hybridization of the metamaterial structure's local surface plasmon mode and the Fabry-Perot-like cavity mode of the metal (Nb)-dielectric (Si)-metamaterial (NbN) tri-layer, dual color resonances emerge. At a working temperature of 8K, slightly below TC 88K, our infrared detector displayed peak responsivities of 12106 V/W and 32106 V/W at resonant frequencies of 366 THz and 104 THz, respectively. The peak responsivity, in comparison to the non-resonant frequency (67 THz), experiences an enhancement of 8 and 22 times, respectively. Our efforts in developing a method for efficiently harvesting infrared light enhance the sensitivity of superconducting photodetectors across the multispectral infrared spectrum, potentially leading to advancements in thermal imaging and gas detection, among other applications.

To enhance the performance of non-orthogonal multiple access (NOMA) within passive optical networks (PONs), this paper proposes the use of a 3-dimensional (3D) constellation and a 2-dimensional inverse fast Fourier transform (2D-IFFT) modulator. MI-503 nmr In order to produce a three-dimensional non-orthogonal multiple access (3D-NOMA) signal, two types of 3D constellation mapping have been developed. By pairing signals of varying power levels, higher-order 3D modulation signals can be created. The receiver's implementation of the successive interference cancellation (SIC) algorithm addresses interference from different users. genetic syndrome The proposed 3D-NOMA method, in comparison to the existing 2D-NOMA approach, shows a significant 1548% improvement in the minimum Euclidean distance (MED) of constellation points, thereby enhancing the overall bit error rate (BER) performance of NOMA. Reducing the peak-to-average power ratio (PAPR) of NOMA by 2dB is possible. The 1217 Gb/s 3D-NOMA transmission over a 25km stretch of single-mode fiber (SMF) has been experimentally verified. At a bit error rate of 3.81 x 10^-3, the high-power signals of both 3D-NOMA schemes exhibit a sensitivity enhancement of 0.7 dB and 1 dB respectively, compared to the performance of 2D-NOMA, given identical data rates. There is an improvement in the performance of low-power level signals, corresponding to 03dB and 1dB enhancements. As an alternative to 3D orthogonal frequency-division multiplexing (3D-OFDM), the 3D non-orthogonal multiple access (3D-NOMA) scheme potentially accommodates more users with no significant impact on overall performance. Due to its outstanding performance characteristics, 3D-NOMA is a potential solution for future optical access systems.

Multi-plane reconstruction is an essential element in producing a truly three-dimensional (3D) holographic display system. In conventional multi-plane Gerchberg-Saxton (GS) algorithms, inter-plane crosstalk is a significant concern. This arises from the omission of the interference from other planes during the amplitude replacement procedure at each object plane. The time-multiplexing stochastic gradient descent (TM-SGD) optimization algorithm, presented in this paper, seeks to reduce the interference from multi-plane reconstructions. Utilizing the global optimization aspect of stochastic gradient descent (SGD), the inter-plane crosstalk was initially reduced. Conversely, the effectiveness of crosstalk optimization decreases with a larger number of object planes, because the input and output data are not balanced. Accordingly, we extended the time-multiplexing strategy to encompass both the iteration and reconstruction steps of multi-plane SGD, thereby increasing the volume of input data. Sub-holograms, produced via multi-loop iteration in TM-SGD, are sequentially applied to the spatial light modulator (SLM). The optimization dynamics between holographic planes and object planes transition from a one-to-many arrangement to a many-to-many configuration, resulting in enhanced optimization of the crosstalk phenomenon between these planes. During the persistence of sight, multiple sub-holograms collaboratively reconstruct the crosstalk-free multi-plane images. The TM-SGD approach, as validated by simulations and experiments, effectively minimizes inter-plane crosstalk and improves the quality of displayed images.

We report on the development of a continuous-wave (CW) coherent detection lidar (CDL) system that is capable of detecting micro-Doppler (propeller) signatures and generating raster-scanned images of small unmanned aerial systems/vehicles (UAS/UAVs). Utilizing a narrow linewidth 1550nm CW laser, the system benefits from the established and affordable fiber-optic components readily available in the telecommunications market. By using lidar, the periodic motions of drone propellers, observable from a remote distance up to 500 meters, have been identified, utilizing either collimated or focused beam configurations. Two-dimensional images of flying UAVs, within a range of 70 meters, were obtained by raster-scanning a focused CDL beam with a galvo-resonant mirror-based beamscanner. Raster-scan image pixels are data points that contain both the amplitude of the lidar return signal and the target's radial speed. Bioprocessing The ability to discriminate various UAV types, based on their distinctive profiles, and to determine if they carry payloads, is afforded by the raster-scanned images captured at a rate of up to five frames per second.