The parsing of RGB-D indoor scenes is a significant hurdle in computer vision tasks. Indoor scenes, a blend of unordered elements and intricate complexities, have consistently challenged the efficacy of conventional scene-parsing methods that rely on manually extracted features. A feature-adaptive selection and fusion lightweight network (FASFLNet) is proposed in this study for efficient and accurate RGB-D indoor scene parsing. The feature extraction within the proposed FASFLNet architecture is predicated on a lightweight MobileNetV2 classification network. The efficiency and feature extraction performance of FASFLNet are both guaranteed by its lightweight backbone model. Depth images' supplementary spatial data, encompassing object shape and size, augments the feature-level adaptive fusion process in FASFLNet, combining RGB and depth streams. Furthermore, during the decoding phase, features from differing layers are merged from the highest to the lowest level, and integrated across different layers, ultimately culminating in pixel-level classification, producing an effect similar to hierarchical supervision, akin to a pyramid. From experiments using the NYU V2 and SUN RGB-D datasets, the results show that the FASFLNet model demonstrates a superior performance in efficiency and accuracy compared to leading existing models.
A strong market need for fabricating microresonators exhibiting precise optical characteristics has led to a range of optimized techniques focusing on geometric shapes, optical modes, nonlinear effects, and dispersion. Dispersion in these resonators, tailored to the application, counteracts their optical nonlinearities and thereby influences the intracavity optical processes. We, in this paper, utilize a machine learning (ML) algorithm to ascertain the geometric configuration of microresonators based on their dispersion profiles. Integrated silicon nitride microresonators were instrumental in experimentally validating the model trained on a finite element simulation-generated dataset of 460 samples. Two machine learning algorithms were assessed alongside their hyperparameter tuning, ultimately showing Random Forest to have the most favorable results. The simulated data's average error is substantially less than the 15% threshold.
Estimating spectral reflectance accurately relies heavily on the amount, scope of coverage, and representativeness of samples in the training data. Edralbrutinib A method for artificial data augmentation is presented, which utilizes alterations in light source spectra, while employing a limited quantity of actual training examples. Utilizing our enhanced color samples, the reflectance estimation process was then performed on frequently used datasets, including IES, Munsell, Macbeth, and Leeds. Ultimately, the research explores how altering the number of augmented color samples affects the outcome. Edralbrutinib Our research, as demonstrated by the results, shows that our proposed approach can artificially expand the color palette from the CCSG 140 initial sample set, increasing it to 13791 colors, and potentially more. The benchmark CCSG datasets are outperformed by augmented color samples in reflectance estimation across all evaluated datasets (IES, Munsell, Macbeth, Leeds, and a real-world hyperspectral reflectance database). The proposed augmentation of the dataset proves practical in boosting the accuracy of reflectance estimation.
Robust optical entanglement within cavity optomagnonics is achieved through a scheme where two optical whispering gallery modes (WGMs) engage with a magnon mode within a yttrium iron garnet (YIG) sphere. Beam-splitter-like and two-mode squeezing magnon-photon interactions are simultaneously achievable when external fields act upon the two optical WGMs. Their coupling to magnons then produces entanglement between the two optical modes. By capitalizing on the destructive quantum interference phenomenon between the bright modes of the interface, the effects of initial thermal magnon populations can be eliminated. In addition, the Bogoliubov dark mode's activation can protect optical entanglement from the damaging effects of thermal heating. Hence, the produced optical entanglement exhibits robustness against thermal noise, lessening the need for cooling the magnon mode. Our scheme has the potential for applications in the analysis of quantum information processing using magnons.
Amplifying the optical path length and improving the sensitivity of photometers can be accomplished effectively through the strategy of multiple axial reflections of a parallel light beam inside a capillary cavity. Conversely, an optimal balance between optical path length and light intensity is elusive; a smaller aperture in the cavity mirrors, for instance, might increase the multiple axial reflections (thereby lengthening the optical path) due to lower cavity losses, but simultaneously reduce coupling efficiency, light intensity, and the related signal-to-noise ratio. This optical beam shaper, featuring two lenses and an apertured mirror, was intended to focus the light beam, improving coupling efficiency without sacrificing beam parallelism or encouraging multiple axial reflections. The concurrent employment of an optical beam shaper and a capillary cavity produces a noteworthy amplification of the optical path (ten times the capillary length) and a high coupling efficiency (exceeding 65%). This outcome includes a fifty-fold enhancement in the coupling efficiency. A newly developed optical beam shaper photometer, equipped with a 7-centimeter capillary, was used for the detection of water in ethanol, yielding a detection limit of 125 ppm. This surpasses the sensitivity of existing commercial spectrometers (with 1 cm cuvettes) by a factor of 800, and previous reports by a factor of 3280.
For camera-based optical coordinate metrology, such as digital fringe projection, precise calibration of the system's cameras is essential. Locating targets—circular dots, in this case—within a set of calibration images is crucial for camera calibration, a procedure which identifies the intrinsic and distortion parameters defining the camera model. High-quality measurement results rely on the sub-pixel accuracy of feature localization, which in turn requires high-quality calibration results. For calibrating localized features, the OpenCV library provides a common solution. Edralbrutinib We employ a hybrid machine learning method in this paper, starting with OpenCV for initial localization, then refining the result with a convolutional neural network model built upon the EfficientNet architecture. Our localization methodology, as proposed, is subsequently juxtaposed with unrefined OpenCV locations, and contrasted with an alternative refinement technique rooted in traditional image processing. Our analysis reveals that both refinement methods achieve an approximate 50% reduction in mean residual reprojection error, given ideal imaging conditions. Under adverse imaging situations, especially those with high noise levels and specular reflections, our analysis shows that the conventional enhancement procedure diminishes the accuracy of the OpenCV-derived results. This degradation is quantified as a 34% increase in the mean residual magnitude, equal to 0.2 pixels. The EfficientNet refinement stands out by exhibiting robustness to non-ideal environments, decreasing the mean residual magnitude by 50% in comparison to OpenCV. Subsequently, the enhancement of feature localization within EfficientNet permits a more extensive range of imaging positions throughout the measurement volume. Consequently, this leads to more robust camera parameter estimations.
Breath analyzer models encounter a substantial challenge in detecting volatile organic compounds (VOCs), particularly due to their extremely low concentrations (parts-per-billion (ppb) to parts-per-million (ppm)) and the high humidity levels associated with exhaled breath. Gas detection capabilities arise from the refractive index of metal-organic frameworks (MOFs), an essential optical property, which is adjustable by variations in gas types and concentrations. Employing the Lorentz-Lorentz, Maxwell-Garnett, and Bruggeman effective medium approximation formulas, we, for the first time, quantitatively assessed the percentage change in refractive index (n%) of ZIF-7, ZIF-8, ZIF-90, MIL-101(Cr), and HKUST-1 upon ethanol exposure at various partial pressures. The storage capacity of MOFs and the selectivity of biosensors were evaluated by determining the enhancement factors of the designated MOFs, especially at low guest concentrations, through their guest-host interactions.
High data rates in visible light communication (VLC) systems reliant on high-power phosphor-coated LEDs are challenging to achieve due to the sluggish yellow light and the constrained bandwidth. In this paper, we propose a novel transmitter, utilizing a commercially available phosphor-coated LED, to accomplish a wideband VLC system that does not necessitate a blue filter. The transmitter's design incorporates a folded equalization circuit and a bridge-T equalizer. A significant bandwidth expansion of high-power LEDs is achieved by the folded equalization circuit, which is based on a novel equalization scheme. The bridge-T equalizer is a better choice than blue filters for reducing the impact of the slow yellow light generated by the phosphor-coated LED. With the implementation of the proposed transmitter, the VLC system's 3 dB bandwidth, using a phosphor-coated LED, saw an enhancement from a range of several megahertz to 893 MHz. The VLC system consequently facilitates real-time on-off keying non-return to zero (OOK-NRZ) data rates of 19 Gb/s at a span of 7 meters, achieving a bit error rate (BER) of 3.1 x 10^-5.
A high-average-power terahertz time-domain spectroscopy (THz-TDS) system, based on optical rectification in a tilted-pulse front geometry utilizing lithium niobate at room temperature, is demonstrated. This system is driven by a commercially available, industrial femtosecond laser that operates with a variable repetition rate ranging from 40 kHz to 400 kHz.