Hasil untuk "Applied optics. Photonics"

Haixia Cui, Wanjiao Li, Qianxi Li et al.

Abstract Real-time dynamic and three-dimensional (3D) X-ray imaging are the most challenging types of X-ray imaging technology, placing more rigorous standards on scintillators. Lead-based (Pb2+) organic-inorganic hybrid halide (OIHH) scintillators with high X-ray absorption coefficients have been demonstrated to exhibit excellent scintillation performance. However, their toxicity and instability hindered further development, and it is necessary to explore novel low-toxic metal-based OIHHs possessing excellent scintillation performance. Antimony-based (Sb3+) OIHHs are not only environmentally friendly, but also show good stability compared to Pb2+-based OIHHs, which make them promising candidates as excellent scintillators. Currently, the understanding of Sb3+-based OIHH scintillators for X-ray detection and imaging is still in infancy and requires further exploration. Herein, we designed two Sb3+-based OIHH crystals of (BPP)2SbCl5 (CP1) and (BPP)2SbCl5 0.5 H2O (CP2), which have very similar crystal structures except the introduction of water molecules in CP2. Experimental and theoretical results reveal that CP2 has larger lattice distortion and smaller freedom of motion, which can promote the self-trapped excitons emissions. A flexible scintillator screen based on CP2 crystals was prepared and applied for real-time dynamic and 3D X-ray imaging, which is the first time for Sb3+-based OIHH scintillators and significantly broadens the potential of Sb3+-based OIHH scintillators.

G. Antuono, V. Cera, D. Ciarlo

The increasing adoption of point clouds in the digital documentation of Cultural Heritage (CH) has made three-dimensional semantic segmentation a key step for data interpretation and analysis. In this context, Deep Learning (DL) approaches have demonstrated high performance, albeit at the cost of substantial computational requirements and the need for large annotated datasets. Within this framework, the present study investigates the potential of leveraging a traditional supervised Machine Learning (ML) approach - Random Forest (RF) - through targeted optimization of training and validation procedures. To this end, the RF_CHC (Random Forest for Cultural Heritage Classification) model is proposed. Aimed at improving accuracy and, in particular, generalization capability in the semantic classification of architectural CH point clouds, RF_CHC integrates statistical hyperparameter calibration through the adoption of cross-validation procedures. The performance of RF_CHC was evaluated and compared with literature models (RF4PCC and optimized RF4PCC), demonstrating improved classification consistency and greater robustness across heterogeneous datasets, while highlighting the potential of an optimized ML-based approach as a competitive or complementary solution to currently prevalent DL models in the CH domain.

Qi Liu, Yu Tian, Zhaohua Tian et al.

Abstract For the requirement of quantum photonic integration in on-chip quantum information, we propose a scheme to realize quantum controlled-Z (CZ) gates through single gradient metasurface. Using its parallel beam-splitting feature, i.e., a series of connected beamsplitters with the same splitting ratio, one metasurface can support a polarization encoding CZ gate or path encoding CZ gate, several independent CZ gates, and cascade CZ gates. Taking advantage that the path of output state is locked by the polarization of input state, path encoding CZ gates can efficiently filter out bit-flip errors coming from beam-splitting processes. These CZ gates also have the potential to detect quantum errors and generate high-dimensional entanglement through multi-degree-of-freedom correlation on metasurfaces. By integrating quantum CZ gates into a single metasurface, our results open an avenue for high-density and multifunctional integration of quantum devices.

S. S. Fatehpur, T. P. Singh

Urban areas worldwide are experiencing increase in temperatures due to urbanisation and, leading to the effect of Urban Heat Islands (UHIs), which threaten urban sustainability. Global research aims to identify UHIs and develop mitigation measures. Most existing studies rely on coarse-resolution satellite imagery, limiting the detection and characterization of heterogeneous urban surfaces and localized UHI effects. Advances in drone technology with multi-payload thermal sensors now allows LST mapping at finer spatial resolutions (<1 m), enabling detailed analysis of temperature variations across urban surfaces. Assessing the accuracy of these measurements is essential and typically involves comparing UAV-derived LST with ground-based or in situ temperature observations collected simultaneously during UAV flights. Proper calibration of the TIR sensors is necessary to minimize systematic errors. Accuracy is commonly quantified using statistical quantification like Mean Absolute Error (MAE), R squared and Root Mean Square Error (RMSE). UAVs offer much finer spatial resolution (<1 m) than satellites, enabling detection of localized UHI hotspots that coarse-resolution imagery may miss. Combining UAV, ground, and satellite data enhances confidence in LST estimates and supports precise analysis of urban heat patterns, providing critical insights for mitigation strategies and urban planning. These high-resolution datasets can support machine-learning tools for urban planners to predict localized UHI impacts, adopt mitigation strategies, and advance Sustainable Development Goals.

Musa N. Hamza, Mohammad Tariqul Islam, Sunil Lavadiya et al.

This paper addresses the challenge of early-stage cancer diagnosis using microwave imaging (MWI) techniques by targeting circulating exosomes, recently identified as promising cancer biomarkers. We introduce an innovative nano-photonic perfect absorber (NPA) operating in the terahertz (THz) range, offering a significant improvement over existing MWI-based approaches in terms of simplicity, sensitivity, and specificity. Unlike previous THz absorbers, the proposed NPA achieves an exceptionally wide operating bandwidth from 100 GHz to 50 THz with an absorption efficiency exceeding 97.5%, while featuring an ultra-compact nanoscale footprint (100 × 100 nm², thickness 30 nm). The design integrates a silver (Ag) resonator and a nickel (Ni) ground plane on a silicon dioxide (SiO₂) substrate, with meticulously tuned geometries to create multiple resonance modes, enabling continuous broadband absorption. Full-wave electromagnetic simulations validate the structure’s performance, including electric and magnetic field distributions, surface currents, and scattering parameters. Comparative analysis with state-of-the-art absorbers demonstrates the superior bandwidth, absorption stability, and angular robustness of our device. Furthermore, we demonstrate the NPA’s unique ability to act as a label-free biosensor for exosome detection, where cancerous exosomes consistently induce stronger electric field responses than normal exosomes due to their distinct molecular compositions. These results confirm the proposed NPA as a novel, highly effective platform for non-invasive, early-stage cancer diagnostics via MWI.

F. Geniet, E. Séguin, E. Le Bihan et al.

The CityGML norm proposes specifications for the 3D representation of most urban objects, and in particular defines levels of details (LoD) for building models. The LoD2 corresponds to polyhedral roof structures representing the main roof slopes, but not superstructures as dormers and chimneys. Nowadays, several countries have a nationwide lidar program with densities well adapted to LoD2 building modeling and open topographic databases containing the building footprints, so automating LoD2 reconstruction from lidar and footprints allows the production of nationwide LoD2 models. This paper proposes a new benchmark to evaluate the quality, scalability and robustness of state of the art LoD2 building reconstruction from lidar and footprints. It is based on a subset of LiDAR HD data freely provided by IGN, the French mapping agency, along with building footprints derived from a high-quality, manually produced ground-truth dataset created by IGN. Results from various state-of-the-art algorithms are evaluated using the open source PyScoring tool that compares the results with the ground truth.

Gang Deng, Qiaochu Yang, Silun Du et al.

A novel mode-locking method based on nonlinear multimode interference (NLMI) using a distributed large-core (105 μm) graded-index multimode fiber (GIMF)-based saturable absorber (SA) capable of generating four pulse modes is proposed. The distributed SA geometry consists of two GIMFs located at different positions in the resonant cavity. The coupling and joint operation not only facilitate resistance to pulse fragmentation but also provide a sophisticated and widely tunable transmission with saturable and reverse saturable absorption phenomena. Based on this, dissipative soliton (DS), dissipative soliton resonance (DSR), wedge-shaped, and staircase pulses are achieved without additional filters. The DS has accessible output power, pulse energy, bandwidth, and duration of up to 15.33 mW, 2.02 nJ, 22.63 nm, and ~1.68 ps. The DSR has an achievable pulse duration and energy of ~32.39 ns, 30.3 nJ. The dispersion range that allows DS operation is studied, and the dynamics of the evolution from DS to DSR are observed. The versatility, flexibility, and simplicity of the SA device, combined with the possibility of scaling the pulse energy, make it highly attractive for ultrafast optics and nonlinear dynamics.

Qianke Wang, Jun Liu, Dawei Lyu et al.

Abstract While the spatial mode of photons is widely used in quantum cryptography, its potential for quantum computation remains largely unexplored. Here, we showcase the use of the multi-dimensional spatial mode of photons to construct a series of high-dimensional quantum gates, achieved through the use of diffractive deep neural networks (D2NNs). Notably, our gates demonstrate high fidelity of up to 99.6(2)%, as characterized by quantum process tomography. Our experimental implementation of these gates involves a programmable array of phase layers in a compact and scalable device, capable of performing complex operations or even quantum circuits. We also demonstrate the efficacy of the D2NN gates by successfully implementing the Deutsch algorithm and propose an intelligent deployment protocol that involves self-configuration and self-optimization. Moreover, we conduct a comparative analysis of the D2NN gate’s performance to the wave-front matching approach. Overall, our work opens a door for designing specific quantum gates using deep learning, with the potential for reliable execution of quantum computation.

A. M. Reda, A. M. Reda, N. El-Sheimy et al.

Recently, Deep learning algorithms are becoming increasingly instrumental in autonomous driving by identifying and acknowledging road entities to ensure secure navigation and decision-making. Autonomous car datasets play a vital role in developing and evaluating perception systems. Nevertheless, the majority of current datasets are acquired using Light Detection and Ranging (LiDAR) and camera sensors. Utilizing deep neural networks yields remarkable outcomes in object recognition, especially when applied to analyze data from cameras and LiDAR sensors which perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This paper aims to evaluate the ability to use RADAR dataset for detecting objects in adverse weather conditions, when LiDAR and Cameras may fail to be effective. This paper presents two experiments for object detection using Faster-RCNN architecture with Resnet-50 backbone and COCO evaluation metrics. Experiment 1 is object detection over only one class, while Experiment 2 is object detection over eight classes. The results show that as expected the average precision (AP) of detecting one class is (47.2) which is better than the results from detecting eight classes (27.4). Comparing my results from experiment 1 to the literature results which achieved an overall AP (45.77), my result was slightly better in accuracy than the literature mainly due to hyper-parameters optimization. The outcomes of object detection and recognition based on RADAR indicate the potential effectiveness of RADAR data in automotive applications particularly in adverse weather conditions, where vision and LiDAR may encounter limitations.

S. Bhadra, S. Bhadra, V. Sagan et al.

Calculating solar-sensor zenith and azimuth angles for hyperspectral images collected by UAVs are important in terms of conducting bi-directional reflectance function (BRDF) correction or radiative transfer modeling-based applications in remote sensing. These applications are even more necessary to perform high-throughput phenotyping and precision agriculture tasks. This study demonstrates an automated Python framework that can calculate the solar-sensor zenith and azimuth angles for a push-broom hyperspectral camera equipped in a UAV. First, the hyperspectral images were radiometrically and geometrically corrected. Second, the high-precision Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data for the flight path was extracted and corresponding UAV points for each pixel were identified. Finally, the angles were calculated using spherical trigonometry and linear algebra. The results show that the solar zenith angle (SZA) and solar azimuth angle (SAA) calculated by our method provided higher precision angular values compared to other available tools. The viewing zenith angle (VZA) was lower near the flight path and higher near the edge of the images. The viewing azimuth angle (VAA) pattern showed higher values to the left and lower values to the right side of the flight line. The methods described in this study is easily reproducible to other study areas and applications.

Anton Losev, Vladimir Zavodilenko, Andrey Koziy et al.

In this paper, we investigateda self-developed sine wave gated single-photon detector (SPD) for 1550 nm wavelength primary for quantum key distribution (QKD) usage. We studied the influence of DC bias voltage and AC gate amplitude on the SPD’s functional parameters and presented a simple and effective algorithm for its optimization. Such optimization showed practical benefits while SPD was set up on the QKD device. We admitted that the dark count rate decreases with an increase in gating voltage with fixed photon detection efficiency. We observed the charge persistence effect in sine-gated SPDs, which previously had been observed only at square-pulses gated SPDs, and showed that this effect is limiting for infinity increasing gate amplitude.

Siti Sulikhah, San Liang Lee, Hen Wai Tsao

DFB lasers with ultrashort cavities or integrated DFB lasers with passive waveguide reflectors are frequently proposed to realize directly modulated lasers (DMLs) for carrying ultrahigh data rate signals. The former suffers from poor heatsink and laser cleavage yield, while the single-mode stability of the latter scheme is seldom addressed. The mode selection and device performance of a DFB laser is well known to be very sensitive to the facet reflection and external optical feedback. The DFB lasers with partial corrugated gratings and passive feedback (PCG-PFL) are proposed here to overcome the challenging issues for the aforementioned two schemes. With PCG structure, the DFB lasers can maintain high single-mode yield (SMY) even with a high-reflection-coating on the rear facet and strong reflection from the integrated passive section. This provides the robustness in applying the integrated passive reflector to reshape the modulation response or to enhance the 3-dB bandwidth by using the photon-photon resonance (PPR) effect. By designing the PCG-PFL to have 150-μm long laser section, 50-μm long passive section, and 30% front-facet reflectivity, it can have about 86% of SMY, reduced resonant peak in the intensity modulation response, and reduced waveform overshoot and undershoot for transmitting 56-Gbaud/s PAM-4 signals. By using a longer passive reflector, enhanced bandwidth can be achieved by the PPR effect.

Pinjing He, Aiying Yang, Peng Guo et al.

A layer-reduced neural network based digital backpropagation algorithm called smoothing learned digital backpropagation (smoothing-LDBP), is proposed in this paper. The smoothing-LDBP smooths the power terms in nonlinear activation functions to limit the bandwidth. The limited bandwidth of the power terms generates fewer in-band distortions, thus reduces the required layer for a given equalization performance. Simulation results show that the required layers of smoothing-LDBP are reduced by approximately 62% at 6.7% HD-FEC compared with learned digital backpropagation. Owing to the layer reduction, the latency and the complexity are reduced by 69% and 51%, respectively. The layer-reduced property of smoothing-LDBP is also validated by a proof-of-concept experiment.

elham tazikeh, fatemeh azimi, fariborz kaveh et al.

Abstract: The opteoelectronical properties of B12N12 nano-cage was investigated in thepresent of some metals by density functional theory (DFT). After the adsorption of atoxic molecule with all complexes, the electronic properties in B11GeN12 nano-cagewere significantly increased. The UV-Vis adsorption and Infrared spectroscopy ofcyanogen chloride over the B11GeN12 have been performed by the time-dependentdensity functional theory (TD-DFT). The increasing of λmax values from the pristineB12N12 to B11GeN12, reveals that B11GeN12 nano-cages can be a suitable structure asoptic sensor for this gas detection. Overall, Because of the crystalline defect, Symmetrydisruption and the changes in the degree of polarization, the semiconductor propertyaffects these nano-cage systems. Finally, the changes of energy of gap (Eg) with asignificant charge transfer from this gas to Ge-doped nano-cage, which lead to changesof conductance of it and render this kind of system sufficient for gas detection.

N. Hedley, I. Lochhead

This paper reports on <i>Intertidal</i> – a collaborative project to demonstrate integrated workflows to 3D spatial data infrastructure (SDI), simulations and geovisual interfaces - as integrated approaches to support the 3D characterization of coastal morphology, intertidal dynamics, potential sea level rise, and mitigation responses to them. Specifically, this project emphasized the potential of emerging 3D data, new analytical visualization methods, and emerging 3D interface technologies as ingredients of emerging and future environmental data science and visualization practice of coastal/intertidal environments.

C. L. Kang, C. L. Kang, T. N. Lu et al.

In point cloud data processing, smooth sampling and surface reconstruction are important aspects of point cloud data processing. In view of the current point cloud sampling method, the point cloud distribution is not uniform, the point cloud feature information is incomplete, and the reconstructed model surface is not smooth. This paper proposes a method of smoothing sampling processing and surface reconstruction using point cloud using moving least squares method. This paper first introduces the traditional moving least squares method in detail, and then proposes an improved moving least squares method for point cloud smooth sampling and surface reconstruction. In this paper, the algorithm is designed for the proposed theory, combined with C++ and point cloud library PCL programming, using voxel grid sampling and uniform sampling and moving least squares smooth sampling comparison, after sampling, using greedy triangulation algorithm surface reconstruction. The experimental results show that the improved moving least squares method performs point cloud smooth sampling more uniformly than the voxel grid sampling and the feature information is more prominent. The surface reconstructed by the moving least squares method is smooth, the surface reconstructed by the voxel grid sampling and the uniformly sampled data surface is rough, and the surface has a rough triangular surface. Point cloud smooth sampling and surface reconstruction based on moving least squares method can better maintain point cloud feature information and smooth model smoothness. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of point cloud sampling and surface reconstruction.

S. K. M. Abujayyab, I. R. Karaş

Development of landslides susceptibility (LS) predictors based on 3D data is an active area of research in the recent years. Predicting landslides susceptibility maps help to secure human lives and maintaining infrastructures from this risk. Several advanced frameworks proposed with high input data to improve the predictors. The aim of this paper is to develop low data requirement framework for LS predictors development. This framework is only using one input 3D ALOS PALSAR image. The framework has three stages. (A) data pre-processing, (B) deriving explanatory factors, and (C) neural networks training and testing. Exactly. 22 input spatial factors were extracted from ALOS PALSAR image. Extracted factors were utilized to develop the FFNN predictor. The structure of the predictor is 22 factors (input layer) &times; 150 neurons (hidden layer) &times; 1 (output layer). Furthermore, 5829 sample points utilized during the training stage, while 745810 points sent to the trained predictor to create LS map. Based on confusion matrix metric, performance accuracy (89.3% training and 82.3 testing), While (95.22% training and 84.7% testing) based on Receiver Operating Characteristic curve. Out of the study area in Karabuk, 3.53&thinsp;km² (3.03%) were located in very high susceptibility category. Lastly, the application of the proposed framework showed that it is capable develop low data requirement predictors with high accuracy. Framework provide guideline data for future development in taxing topographic circumstances and large scale of data coverage. In addition, the framework handled the inconsistency in data quality and data updating problem.

张丽娟, 李维民, 张淳民 et al.

综合考虑多种因素,对突发的偏置时间进行改进,提出了一种新的解决资源竞争并能提供服务质量（QoS）保证的机制。结合3种信道调度算法（LRU、FF和PS）,对该机制进行了仿真,并与PPJET机制进行了对比,结果表明,在突发丢失方面该机制的性能明显优于后者。

蒋培培, 刘三阳

针对波分复用（WDM）网络中的路由与波长分配问题,提出了一种支持服务质量（QoS）的约束搜索算法。基于多目标规划模型,这种搜索算法可为网络各节点创建路由表,根据路由表信息求出非支配路径集合,从而一次性完成寻找路由和分配波长两项任务。仿真实例证明了该算法的有效性。