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A diffractive deep neural network (D2NN) is proposed to distinguish the inverse nonlinear Fourier transform (INFT) symbols. Different from other recently proposed D2NNs, the D2NN is fiber based, and it is in the time domain rather than the spatial domain. The D2NN is composed of multiple cascaded dispersive elements and phase modulators. An all-optical back-propagation algorithm is proposed to optimize the phase. The fiber-based time domain D2NN acts as a powerful tool for signal conversion and recognition, and it is used in a receiver to recognize the INFT symbols all optically. After the symbol conversion by the D2NN, simple phase and amplitude measurement will determine the correct symbol while avoiding the time-consuming NFT. The proposed device can not only be implemented in the NFT transmission system, but also in other areas which require all optical time domain signal transformation and recognition, like sensing, signal coding and decoding, beam distortion compensation and image recognition.All-dielectric metasurfaces have been attracting attention in the terahertz spectral range for low-loss planar optical elements such as lenses, beam splitters, waveplates, vortex plates, and magnetic mirrors. Various shapes of meta-atoms have been used in many studies; however, no systematic comparative study of each shape has been reported. The optical properties of various shapes of metasurfaces are reported in this work using finite difference time domain simulation. The phase of a pillar-type all-dielectric metasurface is mainly determined by the cross-sectional area, rather than its detailed shape. Consequently, in the square lattice geometry, the square shape meta-atom performs best in terms of full phase control at the lowest pillar height with negligible polarization dependence. Furthermore, we compare the transmission, phase, and polarization dependence of the hexagonal and square lattices. Square-shape metasurface successfully realizes subwavelength focusing metalens and vortex plate.In the study of chemical reactions, it is desirable to have a diagnostic strategy that can detect multiple species simultaneously with high sensitivity, selectivity, and fast time response. Bisindolylmaleimide I research buy Laser-based selective detection of benzene, toluene, ethylbenzene, and xylenes (BTEX) has been challenging due to the similarly broad absorbance spectra of these species. Here, a mid-infrared laser sensor is presented for selective and simultaneous BTEX detection in high-temperature shock tube experiments using deep neural networks (DNN). A shock tube was coupled with a non-intrusive mid-infrared laser source, scanned over 3038.6-3039.8 cm-1, and an off-axis cavity enhanced absorption spectroscopy (OA-CEAS) setup of ∼ 100 gain to enable trace detection. Absorption cross-sections of BTEX species were measured at temperatures of 1000-1250 K and pressures near 1 atm. A DNN model with five hidden layers of 256, 128, 64, 32, and 16 nodes was implemented to split the composite measured spectra into the contributing spectra of each species. Several BTEX mixtures with varying mole fractions (0-600 ppm) of each species were prepared manometrically and shock-heated to 1000-1250 K and 1 atm, and the composite measured absorbance were split into contributions from each BTEX species using the developed DNN model, and thus make selective determinations of BTEX species. Predicted and manometric mole fractions were in good agreement with an absolute relative error of ∼ 11%. We obtained a minimum detection limit of 0.73-1.38 ppm of the target species at 1180 K. To the best of our knowledge, this work reports the first successful implementation of multispecies detection with a single narrow wavelength-tuning laser in a shock tube with laser absorption spectroscopy.We investigate the self-interference characterization, achievable rate, signal detection and parameter estimation for bi-directional ultra-violet (UV) communication. We firstly characterize and experimentally demonstrate the self-interference of UV communication, which is non-negligible as the angle between the transmission and receiver directions is blow 60°. Then, we present the achievable rate and symbol detection under self-interference, which show that the offset between self-interference and desirable symbols can increase the achievable rate and decrease the symbol detection error probability. We propose the practical system design with parameter estimation under self-interference. Finally, we experimentally evaluate the receiver-side signal detection with self-interference generated by Field Programmable Gate Array, and the signal detection of a real bidirectional UV communication system. Lower symbol detection error probability can also be observed as the offset between desirable symbols and self-interference symbols increases to half-symbol length from both system-level simulation and real experiments, which further validates the theoretical results.In atmospheric aerosol remote sensing and data assimilation studies, the Jacobians of the optical properties of non-spherical aerosol particles are required. Specifically, the partial derivatives of the extinction efficiency factor, single-scattering albedo, asymmetry factor, and scattering matrix should be obtained with respect to microphysical parameters, such as complex refractive indices, shape parameters and size parameters. When a look-up table (LUT) of optical properties of particles is available, the Jacobians traditionally can be calculated using the finite difference method (FDM), but the accuracy of the process depends on the resolution of microphysical parameters. In this paper, a deep learning scheme was proposed for computing Jacobians of the optical properties of super-spheroids, which is a flexible model of non-spherical atmospheric particles. Using the neural networks (NN), the error of the Jacobians in the FDM can be reduced by more than 60%, and the error reduction rate of the Jacobians of the scattering matrix elements can be more than 90%. We also tested the efficiency of the NN for computing the Jacobians. The computation takes 30 seconds for one million samples on a host with an NVIDIA GeForce RTX 3070 GPU. The accuracy and efficiency of the present NN scheme proves it is promising for applications in remote sensing and data assimilation studies.Graphene has unique advantages in ultrabroadband detection. However, nowadays graphene-based photodetectors cannot meet the requirements for practical applications due to their poor performance. Here, we report a graphene-silicon-graphene Schottky junction photodetector assisted by field effect. Two separate graphene sheets are located on both sides of the n-doped silicon to form two opposite lateral series heterojunctions with silicon, and a transparent top gate is designed to modulate the Schottky barrier. Low doping concentration of silicon and negative gate bias can significantly raise the barrier height. Under the combined action of these two measures, the barrier height increases from 0.39 eV to 0.77 eV. Accordingly, the performance of the photodetector has been greatly improved. The photoresponsivity of the optimized device is 2.6 A/W at 792 nm, 1.8 A/W at 1064 nm, and 0.42 A/W at 1550 nm, and the on/off photo-switching ratio reaches 104. Our work provides a feasible solution for the development of graphene-based optoelectronic devices.Study of exciton recombination process is of great significance for the optoelectronic device applications of two-dimensional transition metal chalcogenides (TMDCs). This research investigated the decoupling MoS2 structures by photoluminescence (PL) measurements. First, PL intensity of the bilayer MoS2 (BLM) is about twice of that of the single layer MoS2 (SLM) at low temperature, indicating no transition from direct bandgap to indirect bandgap for BLM due to the decrease of interlayer coupling which can be shown by Raman spectra. Then, the localized exciton emission appears for SLM at 7 K but none for BLM, showing different exciton localization characteristics. The PL evolution with respect to the excitation intensity and the temperature further reveal the filling, interaction, and the redistribution among free exciton states and localized exciton states. These results provide very useful information for understanding the localized states and carrier dynamics in BLM and SLM.The processes leading to the N2 + lasing are rather complex and even the population distribution after the pump laser excitation is unknown. In this paper, we study the population distribution at electronic and vibrational levels in N2 + driven by ultra-short laser pulse at the wavelengths of 800 nm and 400 nm by using the quantum-mechanical time-domain incoherent superposition model based on the time-dependent Schrödinger equation and the quasi-classical model assuming instantaneous ionization injection described by density matrix. It is shown that while both models provide qualitatively similar results, the quasi-classical instantaneous ionization injection model underestimates the population inversions corresponding to the optical transitions at 391 nm, 423 nm and 428 nm due to the assumption of quantum mixed states at the ionization time. A fast and accurate correction to this error is proposed. This work solidifies the theoretical models for population at vibrational states in N2 + and paves the way to uncover the mechanism of the N2 + lasing.The avalanche photodiode (APD) chip is the core component of the transistor outline (TO). The concentricity between the inner circle (IC) of the APD active area and the outer circle (OC) of the TO base will directly affect a component’s key performance indicators, such as external quantum efficiency, receiving sensitivity and responsivity, thereby impacting quality assurance, performance improvement, and stable operation. Nevertheless, as the surge in demand for components increases, the traditional visual inspection relying on manual and microscope has been unable to meet the requirements of mass manufacturing for real-time quality and efficiency. Thus, a Concentricity Microscopic Vision Measurement System (CMVMS) mainly composed of a microscopic vision acquisition unit and an intelligent concentricity measurement unit has been proposed, designed, and implemented. On the basis of analyzing the 3D complex environment of TO components, a coaxial illumination image acquisition scheme that can take into account the characteristics of the OC and IC has been proposed. Additionally, a concentricity image measurement method based on dynamic threshold segmentation has been designed to reduce the interference of complex industrial environment changes on measurement accuracy. The experiment results show that the measurement accuracy of the CMVMS system is over 97%, and with a single measurement time of less than 0.2s, it can better meet the real-time and accuracy requirements. To the best of our knowledge, this is the first report on the realization of real-time concentricity measurement in optical component packaging, and this technology can be extended to other fields of concentricity measurement.We found that temperature-dependent infrared spectroscopy measurements (i.e., reflectance or transmittance) using a Fourier-transform spectrometer can have substantial errors, especially for elevated sample temperatures and collection using an objective lens. These errors can arise as a result of partial detector saturation due to thermal emission from the measured sample reaching the detector, resulting in nonphysical apparent reduction of reflectance or transmittance with increasing sample temperature. Here, we demonstrate that these temperature-dependent errors can be corrected by implementing several levels of optical attenuation that enable convergence testing of the measured reflectance or transmittance as the thermal-emission signal is reduced, or by applying correction factors that can be inferred by looking at the spectral regions where the sample is not expected to have a substantial temperature dependence.