Microwave photonics has recently come to the forefront as a valuable approach to generating, processing, and measuring signals in high-performance domains such as communication, radar, and timing systems. Recent studies have introduced a range of photonics-based phase-noise analyzers (PNAs) that utilize a variety of architectures, including phase detection, frequency discrimination, and hybrid mechanisms that combine optical with electronic processing. This review focuses on microwave photonic techniques for phase-noise measurement based on the fiber-optic delay-line method, by exploring their fundamental principles, system design frameworks, and performance indicators. The fiber-optic delay-line method is examined as the core architecture, due to the exceptionally low loss and wide bandwidth of the optical fiber, which enable long delays and high measurement sensitivity. Through the integration of insights garnered from recent publications, our objective is to deliver a comprehensive understanding of the strengths and limitations associated with fiber-optic delay-line-based PNAs and to pinpoint new and promising areas for advancing research in the field of oscillator metrology.
The bidirectional propagation properties of partially coherent Laguerre–Gaussian (PCLG) beams under atmospheric turbulence and plasma were numerically investigated. The corresponding analytical formulas for the intensity distribution, effective beam width, and <i>M</i><sup>2</sup> factor of PCLG beams were derived by utilizing the generalized Collins integral formula, atmospheric turbulence theory, and second-order moments theory of the Wigner distribution function. The intensity distribution of the PCLG beams ultimately evolved into a Gaussian-like intensity distribution. Additionally, the effective beam width and <i>M</i><sup>2</sup> factor could be less affected by selecting appropriate parameter values for the beam order, transverse coherence width, and wavelength of the PCLG beam. The impact of parameters such as the beam order, transverse coherence width, and wavelength for reverse transmission on the PCLG beam propagation properties was greater than that for forward transmission. These results are beneficial for applications in free-space optical communications.
Emily D. Caldwell, Laura C. Sinclair, Jean-Daniel Deschenes
et al.
With the demonstration of quantum-limited optical time transfer capable of tolerating the losses associated with long ground-to-space links, two future applications of free-space time transfer have emerged: intercontinental clock comparisons for time dissemination and coherence transfer for future distributed sensing in the mm-wave region. In this paper, we estimated the projected performance of these two applications using quantum-limited optical time transfer and assuming existing low-size, low-weight, and low-power hardware. In both cases, we limit the discussion to the simplest case of a single geosynchronous satellite linked to either one or two ground stations. One important consideration for such future space-based operations is the choice of reference oscillator onboard the satellite. We find that with a modestly performing optical reference oscillator and low-power fiber-based frequency combs, quantum-limited time transfer could support intercontinental clock comparisons through a common-view node in geostationary orbit with a modified Allan deviation at the 10−16 level at 10-s averaging time, limited primarily by residual turbulence piston noise. In the second application of coherence transfer from ground-to-geosynchronous orbit, we find the system should support high short-term coherence with ∼10 millirad phase noise on a 300 GHz carrier at essentially unlimited integration times.
We investigate the utilization of advanced single photons produced by quantum dots (QDs) in a microcavity for quantum metrology. Through the integration of lateral excitation and the Purcell effect in an Fabry–Perot microcavity, we realized single-photon emission with an extraction efficiency of 46.39%, high purity of 96.91%, and high indistinguishability of 98.32%. Our QD-generated single photons enabled the creation of high-quality NOON states (N = 2) for phase measurement, yielding an interference contrast of 79.79% and surpassing the standard quantum limit (SQL) with phase super-sensitivity. Our results underscore the immense potential of QD-derived single photons for propelling quantum metrology forward, facilitating enhanced precision measurements across diverse applications.
This study proposes a differential wavelength measurement method based on the electromagnetic-induced photoacoustic effect. The differential method involves irradiating the sample with multiple wavelengths and utilizing differences in absorption characteristics across different materials to calculate and measure the excitation light wavelengths. Compared to traditional detection methods, this approach combines the unique properties of electromagnetic-induced photoacoustic effect, offering high sensitivity and a wider detection range from microwave to light. Furthermore, the system is structurally simple and stable, suitable for non-destructive testing of various materials, including wavelength-sensitive biological tissues. The experimental results demonstrate that combined with Polymers Benzodithiophene Triazole–Quinoxaline (PBTQ) and Single-Walled Carbon Nanotubes (SWCNTs) as absorbing media, this technique provides a rapid and cost-effective means of wavelength measurement, achieving an uncertainty of approximately 2.33 nm within the range of 680–800 nm, and it can be used for wavelength/frequency measurement of various electromagnetic waves.
Driven by emerging technologies such as the Internet of Things, 4K/8K video applications, virtual reality, and the metaverse, global internet protocol traffic has experienced an explosive growth in recent years. The surge in traffic imposes higher requirements for the data rate, spectral efficiency, cost, and power consumption of optical transceivers in short-reach optical networks, including data-center interconnects, passive optical networks, and 5G front-haul networks. Recently, a number of self-coherent detection (SCD) systems have been proposed and gained considerable attention due to their spectral efficiency and low cost. Compared with coherent detection, the narrow-linewidth and high-stable local oscillator can be saved at the receiver, significantly reducing the hardware complexity and cost of optical modules. At the same time, machine learning (ML) algorithms have demonstrated a remarkable performance in various types of optical communication applications, including channel equalization, constellation optimization, and optical performance monitoring. ML can also find its place in SCD systems in these scenarios. In this paper, we provide a comprehensive review of the recent progress in SCD systems designed for high-speed optical short- to medium-reach transmission links. We discuss the diverse applications and the future perspectives of ML for these SCD systems.
Convolutional neural networks (CNNs) are at the heart of several machine learning applications, while they suffer from computational complexity due to their large number of parameters and operations. Recently, all-optical implementation of the CNNs has achieved many attentions, however, the recently proposed optical architectures for CNNs cannot fully utilize the tremendous capabilities of optical processing, due to the required electro-optical conversions in-between successive layers. To implement an all-optical multi-layer CNN, it is essential to optically implement all required operations, namely convolution, summation of channels’ output for each convolutional kernel feeding the nonlinear unit, nonlinear activation function, and finally, pooling operations. Considering the lack of multi-layer photonic CNN implementation, in this paper, we explore a fully-optical design for implementing successive convolutional layers in an optical CNN. As a proof of concept, and without loss of generality, we considered two successive optical layers in the proposed network, named as 2L-OPCNN, for comparative studies against electrical counterpart and single optical layer CNN. Our simulation results confirm nearly the same accuracies for classifying images of Kaggle Cats and Dogs challenge, CIFAR-10, and MNIST datasets, compared to the electrical counterpart, as well as improved accuracies compared to single optical layer CNN.
Lanthanide ions act as excellent luminescence centers and good charge carrier traps. By selecting proper lanthanide ions, persistent phosphors with the desired luminescent color can be developed. In addition, an appropriate host material can give not only better persistent luminescence performance but also an additional function. Herein, bright white persistent phosphors of Pr3+–Tb3+–Eu3+ tridoped paramagnetic Gd3Ga5O12 (GGG) are successfully developed. The GGG phosphors singly doped with Pr3+, Tb3+, and Eu3+ show reddish‐white (3PJ–3HJ), blue (5DJ–7FJ), and red (5D0–7FJ) photoluminescence, respectively, by UV excitation. On the other hand, the GGG samples codoped with Pr3+–Eu3+ and with Tb3+–Eu3+ show only Pr3+ reddish‐white persistent luminescence and Tb3+ blue persistent luminescence, respectively. Based on the thermoluminescence glow curves, it is found that the Eu3+ ion acts only as an electron trap in the persistent luminescence mechanism and the trapped electrons are released at around 325 K. The cool‐white persistent luminescence is achieved by combining Pr3+ and Tb3+ persistent luminescence centers in the GGG:Pr3+–Tb3+–Eu3+ phosphors. It is demonstrated that the white persistent phosphor powder in water can be dragged around by a permanent magnet due to the paramagnetic property of GGG.
Tree species classification at individual tree level is a challenging problem in forest management. Deep learning, a cutting-edge technology evolved from Artificial Intelligence, was seen to outperform other techniques when it comes to complex problems such as image classification. In this work, we present a novel method to classify forest tree species through high resolution RGB images acquired with a simple consumer grade camera mounted on a UAV platform using Residual Neural Networks. We used UAV RGB images acquired over three years that varied in numerous acquisition parameters such as season, time, illumination and angle to train the neural network. To begin with, we have experimented with limited data towards the identification of two pine species namely red pine and white pine from the rest of the species. We performed two experiments, first with the images from all three acquisition years and the second with images from only one acquisition year. In the first experiment, we obtained 80% classification accuracy when the trained network was tested on a distinct set of images and in the second experiment, we obtained 51% classification accuracy. As a part of this work, a novel dataset of high-resolution labelled tree species is generated that can be used to conduct further studies involving deep neural networks in forestry.
Abstract This paper presents a theoretical study of the utilization of the shift in the reflection peak of the thin dielectric film with embedded metal nanoparticles (NPs) towards humidity and vapor applications. The presence of the NPs in the film results in a complex effective index. Hence, the reflected light at the superstrate-film interface causes a phase shift when the index of the surrounding is changed. This alters the reflected spectrum of the formed Fabry-Perot, for both the reflection peak wavelength and intensity. Here, the dynamic range of the proposed sensor is optimized through the variation of the film thickness and nanoparticle metal type, as well as the volume fraction.
AlGaN alloys have been widely used to make ultraviolet light-emitting diodes (UV-LEDs) because its energy bandgap covers 200–360 nm wavelength range. However, AlGaN shows strong transverse magnetic polarization in deep UV range, which severely prevents light extraction from top surface of UV-LEDs. In this paper, we propose a novel flip-chip AlGaN nanowire LED with top photonic crystals, for the purpose of improving light extraction efficiency (LEE) from top surface. Using three-dimensional finite-difference time domain simulation, we first investigate the LEE in vertical direction of nanowire LEDs. By carefully optimizing the size and density of nanowires, we demonstrate that nanowire structures can be designed to inhibit the emission of guided mode and promote light extraction from top surface. Based on the optimized nanowire structure, we also study the effect of top photonic crystals on the LEE of vertical emission. A high LEE up to 79.4% can be achieved by optimizing the height, spacing, and radius of top photonic crystals. Analyzing the lateral electric field distribution of AlGaN nanowire LEDs with and without top photonic crystals, we find that top photonic crystals can effectively improve the LEE of vertical emission by coupling the light trapped in epitaxial layers out of LEDs.
Feature matching is a fundamental technical issue in many applications of photogrammetry and remote sensing. Although recently developed local feature detectors and descriptors have contributed to the advancement of point matching, challenges remain with regard to urban area images that are characterized by large discrepancies in viewing angles. In this paper, we define a concept of local geometrical structure (LGS) and propose a novel feature matching method by exploring the LGS of interest points to specifically address difficult situations in matching points on wide-baseline urban area images. In this study, we first detect interest points from images using a popular detector and compute the LGS of each interest point. Then, the interest points are classified into three categories on the basis of LGS. Thereafter, a hierarchical matching framework that is robust to image viewpoint change is proposed to compute correspondences, in which different feature region computation methods, description methods, and matching strategies are designed for various types of interest points according to their LGS properties. Finally, random sample consensus algorithm based on fundamental matrix is applied to eliminate outliers. The proposed method can generate similar feature descriptors for corresponding interest points under large viewpoint variation even in discontinuous areas that benefit from the LGS-based adaptive feature region construction. Experimental results demonstrate that the proposed method provides significant improvements in correct match number and matching precision compared with other traditional matching methods for urban area wide-baseline images.
The present study aims to demonstrate the usefulness of GIS to support archive searches and historical studies (e.g. related to industrial archaeology), in the case of an ancient channel for mill powering near Cesena (Emilia-Romagna, Italy), whose history is weaved together with the history of the Compagnia dei Molini di Cesena mill company, the most ancient limited company in Italy.
Several historical maps (about 40 sheets in total) inherent the studied area and 80 archive documents (drawings, photos, specifications, administrative acts, newspaper articles), over a period of more than 600 years, were collected. Once digitized, historical maps were analysed, georeferenced and mosaicked where necessary. Subsequently, in all the maps the channel with its four mills and the Savio river were vectorized. All the additional archive documents were digitized, catalogued and stored. Using the QGIS open source platform, a Historical GIS was created, encompassing the current cartographic base and all historical maps, with their vectorized elements; each archive document was linked to the proper historical map, so that the document can be immediately retrieved and visualized.
<br><br>
In such a HGIS, the maps form the base for a spatial and temporal navigation, facilitated by a specific interface; the external documents linked to them complete the description of the represented elements. This simple and interactive tool offers a new approach to archive searches, as it allows reconstruction in space and time of the evolution of the ancient channel and the history of this important mill company.
We analyzed a great deal of high-resolution Synthetic Aperture Radar (SAR) data of dry-fallen intertidal flats in the German Wadden
Sea with respect to the imaging of sediments, macrophytes, and mussels. TerraSAR-X and Radarsat-2 images of five test areas along
the German North Sea coast acquired between 2008 and 2013 form the basis for the present investigation and are used to
demonstrate that pairs of SAR images, if combined through basic algebraic operations, can already provide useful indicators for
morphological changes and for bivalve (oyster and mussel) beds. Depending on the type of sediment, but also on the water level and
on environmental conditions (wind speed) exposed sediments may show up on SAR imagery as areas of enhanced, or reduced, radar
backscattering. The (multi-temporal) analysis of series of such images allows for the detection of mussel beds, and our results show
evidence that also single-acquisition, multi-polarization SAR imagery can be used for that purpose.
Photosynthetic Active Radiation (PAR) is the most important light source for plant photosynthesis. It is known that most of PAR
from solar radiation is well absorbed by the surface. The canopy is the surface in forest region, consists an aboveground portion of
plant community and formed by plant crowns. On the other hand, incident solar radiation is fluctuating at all times because of
fluctuating sky conditions. Therefore, qualitative light environmental measurements in forest are recommended to execute under
stable cloudy condition. In fact, it is quite a few opportunities to do under this sky condition. It means that the diffuse light condition
without the direct light is only suitable for this measurement.
<br><br>
In this study, we challenged the characterization the forest light environment as its representativeness under no consideration of sky
conditions through analysis huge quantities of instantaneous data which obtained under the different sky conditions. All examined
data were obtained under the different sky conditions at the tropical rainforest canopy as one of the typical fluctuating sky conditions
regions. An incident PAR is transmitted and scattered by different forest layers at different heights. Various PAR data were
measured with quantum units as Photosynthetic Photon Flux Density (PPFD) at different forest heights by the quantum sensors. By
comparing PPFDs at different heights with an incident PPFD, relative PPFDs were calculated, which indicate the degree of PPFD
decrease from the canopy top to lower levels. As the results of these considerations, daily averaging is confirmed to be cancelled sky
fluctuating influences.
The Dazu Thousand-hand Bodhisattva Statue is located at Baoding Mountain in Chongqing. It has the reputation as "the Gem of
World’s Rock Carving Art". At present，the Dazu Thousand-hand Bodhisattva Statue is basically well conserved, while the local
damage is already very serious. However, the Dazu Thousand-hand Bodhisattva Statue is a three-dimensional caved statue, the
present plane surveying and mapping device cannot reflect the preservation situation completely. Therefore, the documentation of
the Dazu Thousand-hand Bodhisattva Statue using terrestrial laser scanning is of great significance. This paper will introduce a new
method for superfine 3D modeling of Thousand-hand Bodhisattva based on the high-resolution 3D cloud points. By analyzing these
3D cloud points and 3D models, some useful information, such as several 3D statistics, 3D thematic map and 3D shape restoration
suggestion of Thousand-hand Bodhisattva will be revealed, which are beneficial to restoration work and some other application.
Due to the all-weather data acquisition capabilities, high resolution space borne Synthetic Aperture Radar (SAR) plays an important role in remote sensing applications like change detection. However, because of the complex geometric mapping of buildings in urban areas, SAR images are often hard to interpret. SAR simulation techniques ease the visual interpretation of SAR images, while fully automatic interpretation is still a challenge. This paper presents a method for supporting the interpretation of high resolution SAR images with simulated radar images using a LiDAR digital surface model (DSM). Line features are extracted from the simulated and real SAR images and used for matching. A single building model is generated from the DSM and used for building recognition in the SAR image. An application for the concept is presented for the city centre of Munich where the comparison of the simulation to the TerraSAR-X data shows a good similarity. Based on the result of simulation and matching, special features (e.g. like double bounce lines, shadow areas etc.) can be automatically indicated in SAR image.
As remote sensing technology advances, imagery becomes one of the most important data sources for GIS applications –as it can quickly
provide the most current and most detailed information with high resolution image data. Traditional image processing workflow
becomes inefficient in GIS applications especially in the case of disaster analysis. This paper introduces two ArcGIS data models that are
designed for on-demand image processing in managing imagery: raster product and mosaic dataset. A raster product allows you to access
single scene remote sensing image products dynamically such as a pan-sharpened and orthorectified GeoEye image or multi-spectral
Landsat scene with a single drag and drop to the map display. Mosaic datasets allow you to catalog a large collection of images from
many sensor platforms, process them on-the-fly and dynamically mosaic the images together. The key concept – the raster function that
is used to implement on-the-fly image processing capabilities – will also be discussed.