Security Authentication and Tracking of Unmanned Moving Vehicles with Optical ID Tags

A photonic frequency discrimination and phase identification system based on an on-chip dual Mach–Zehnder modulator (MZM) is proposed. By utilizing the power cancellation (PCD) condition, the system achieves high-precision frequency discrimination and phase identification of multi-frequency radio frequency (RF) signals. The system adopts an on-chip dual-MZM architecture, effectively reducing phase interference in signal transmission caused by environmental factors. This is achieved through precise bias control and the adjustment of the local oscillator (LO) signal’s optical path delay using a tunable optical delay line (TODL), ensuring that the dual MZM operates in the phase inversion condition. When the LO frequency matches that of an RF signal, a significant power attenuation is observed at the system output. The phase of the RF signal is extracted from the corresponding PCD. Experimental results demonstrate that the system achieves a bandwidth of 30 GHz, a frequency resolution of 700 kHz, and a frequency resolution error of less than 498 kHz, with a phase identification range from 0° to 65°. With high integration, the system demonstrates excellent accuracy in multi-frequency signal measurement and phase identification, offering a reliable solution for complex RF scenarios.

A distributed optical fiber event identification approach based on Fast-DTW algorithm

AIS, the global ship identification standard, is vulnerable to outages, coverage gaps, and deliberate deactivation, highlighting the need for independent ship identification methods. Optical imaging satellites offer a global, non-compliance-dependent solution. Paired with deep neural networks trained on satellite imagery of ships, it has become possible to determine the identity of specific vessels, based on their unique visual signatures. This enables re-identification, even when cooperative signals like AIS are unavailable or unreliable. Our paper builds on previous work with neural networks for ship identification, and presents an approach based on contrastive self-supervised learning. Self-supervised learning allows for existing, unlabeled, and freely available satellite imagery datasets with ships, to be leveraged for model training. Using these self-supervised models to initialize ship identification training results in almost 32% higher accuracy compared to baseline models. In one case equivalent to doubling the labeled training data. This lowers the threshold for optical ship identification from space by reducing dependence on large labeled datasets. This scalability is crucial for making space-based ship identification viable for global maritime situational awareness.

ABSTRACT Operando monitoring of internal multi‐physical processes is critical for analyzing battery failures and issuing risk warnings. However, existing approaches fall short in enabling operando internal measurement, particularly during the drastic failure stage of batteries. To address this challenge, we implant a Fiber Bragg Grating sensor into the battery, enabling concurrent monitoring of internal strain and temperature without compromising the cell performance. For the first time, we unveil the critical role of internal strain in elucidating the failure modes during the thermal runaway (TR) incubation, encompassing abnormal electrode expansion, gas generation, lithium plating, and “anode‐electrolyte‐cathode” chain reactions. Furthermore, we identify the internal strain derivative as a novel clue for early warning of TR, offering an extended warning window of up to 18.8 min compared with commonly‐used voltage clues. This study provides an innovative and scalable approach for operando failure stage identification and early TR warning of lithium‐ion batteries.

HighlightsWhat are the main findings?The proposed integration of polarization modulation with push-broom scanning reduces the system volume by approximately 70% and achieves a total weight of around 1.5 kg, compared with traditional technology.The developed Fourier transform-based demodulation algorithm achieves accurate reconstruction of polarimetric spectral image information, with a linear polarization measurement error of less than 2.87% verified by experiments.What are the implications of the main findings?Retrieval of Earth’s atmospheric composition and optical parametersThe identification of sensitive bands reduces the number of redundant experiments and guides polarization-based material discrimination.Polarimetric spectral imaging systems have unique application advantages in environmental remote sensing, military target recognition, astronomy, medicine, etc., because of their ability to acquire multidimensional information. However, traditional systems are constrained by complex structures and low spectral resolution, making them unlikely to achieve their full potential. This study proposes a novel polarimetric spectral imaging method for information acquisition to address these shortcomings. The method integrates a polarization modulator (composed of two retarders and one polarizer) into the incident optical path of a push-broom imaging spectrometer for hardware integration. The modulator statically encodes the full polarization spectral information of the measured light into output power spectra, which the spectrometer records as raw spectral image data. Target polarimetric spectral imaging information is then reconstructed from the raw data to realize sensor functions. The system structure, data reconstruction principles, laboratory experiments with typical polarized light sources, and preliminary outdoor experiments verified the system’s correctness and reliability. The results facilitate further expansion of the application scope of polarimetric spectral imaging systems.

Optical identification of liquid droplets, aerosols, or thin films is critical for many applications. While reference spectra are sometimes available for such measurements, they are not always applicable to the observed spectrum or the given sample morphology. Reference spectra for many forms can be modeled, however, if the n/k vectors (real and imaginary refractive indices) are available. In previous work we have reported protocols to determine the n/k vectors for dozens of liquids, primarily in the mid-infrared (MIR) spectral range from 7500 to 400 cm-1. In this work we extend the spectral range into the near-infrared (NIR) region, demonstrating a method to measure and merge the data sets to create composite n/k data ranging from 10 000 to 400 cm-1 (1.0 to 25 µm) with absorbance fidelity spanning over four orders of magnitude, and vastly improved signal-to-noise in the NIR. The precision of the composite data is evaluated for three different liquids, focusing primarily on the steps for converting the raw absorbance spectra to k values. The variability in both MIR and NIR data as well as in the final n/k vectors is also investigated for several liquids. For typical liquids, the overall variability (reported as 2σ) in the final n and k-vectors is determined to be ∼0.4% and 3%, respectively. Finally, the derived n/k data are used to calculate absorbance spectra for aerosol droplets, showing marginal variability due to the typical measurement errors in the final n/k vectors.

Optical tracking of unknown space objects requires both spatial accuracy and temporal stability to enable high-resolution identification through narrow field-of-view sensors. Traditional performance indices such as RMS error and persistence time (PT) have been used for controller tuning, but they each capture only a subset of the requirements for successful optical identification. This paper proposes a new composite metric, the Persistence-Weighted Tracking Index (PWTI), which combines spatial precision and segment-level continuity into a single measure. The metric assigns a frame-level score based on positional error and accumulates weighted scores over the longest continuous in-threshold segment. Using PWTI as the optimization objective, a genetic algorithm (GA) is employed to tune the PID gains of a frame-by-frame offset correction controller. Comparative simulations under various observation scenarios demonstrate that the PWTI-based approach outperforms RMS- and PT-based tuning methods in both alignment accuracy and consistency. The results validate the proposed metric as a more suitable performance indicator for optical identification tasks involving unknown or uncataloged targets.

Abstract We present the results of optical identifications for Very Large Array Sky Survey (VLASS) radio sources employing the Ultraviolet Near Infrared Optical Northern Survey (UNIONS). A crossmatch between UNIONS and VLASS Epoch 2 catalogs yields 146,212 radio galaxies over a wide area of ∼4200 deg 2 in the Northern Hemisphere. We perform g- dropout selections and find ≳200 sources at z ∼ 4 optimistically. Of 63,019 sources with valid photometric redshifts, 8692 are at z photo ≥ 1 and 1171 are at z photo ≥ 2. Based on spectral luminosities at 1.4 and 3 GHz using the valid UNIONS photo- z , we identify ∼49,000 radio-loud active galactic nuclei (RLAGNs) with L 1.4 GHz > 10 24 W Hz −1 , and all radio galaxies at z ≥ 1 are radio-loud (RL). Adopting radio-loudness instead, 138,266 out of 146,212 UNIONS-VLASS radio galaxies are RL. By virtue of the wide area and medium depth provided by UNIONS, our catalog greatly increases the number counts of RLAGNs at z > 1. We further crossmatch the UNIONS-VLASS catalog with the LOFAR Two-metre Sky Survey (LoTSS) at 144 MHz and VLA Faint Images of the Radio Sky at Twenty-cm (FIRST) at 1.4 GHz, yielding 101,671 UNIONS-VLASS-LoTSS, 79,638 UNIONS-VLASS-FIRST, and 64,672 UNIONS-VLASS-LoTSS-FIRST sources, respectively. This multifrequency radio data set reveals sources of various spectral shapes, including the steep spectrum of aged populations, the peaked spectrum of young populations, and the upturned spectrum that might be associated with transient sources. The UNIONS-VLASS radio galaxy candidates will be covered by the Euclid wide survey, bringing about legacy values to benefit multifaceted studies related to radio galaxies and beyond.

CrSBr is an air-stable magnetic van der Waals semiconductorwithstrong magnetic anisotropy, where the interaction of excitons withthe magnetic order enables the optical identification of differentmagnetic phases. Here, we study the magnetic anisotropy of multilayerCrSBr inside a three-axis vector magnet and correlate magnetic orderand optical transitions in emission and absorption. We identify layer-by-layerswitching of the magnetization through drastic changes in the opticalemission and absorption energy and strength as a function of the appliedmagnetic field. We correlate optical transitions in reflection spectrawith photoluminescence (PL) emission using transfer-matrix analysisand find that ferromagnetic and antiferromagnetic order between layerscan coexist in the same crystal. In the multipeak PL emission, theintensity of energetically lower-lying transitions reduces monotonouslywith increasing field strength, whereas energetically higher-lyingtransitions around the bright exciton XB brighten closeto the saturation field. Using this contrasting behavior, we can thereforecorrelate transitions with each other.

Optical recognition and identification of nanoplastics such as polystyrene nanobeads (PSbs), a widely used polymer and an actual source of environmental pollution, is a challenging task relying on knowledge of the PSbs' refractive index (RI) and its relation to the PSbs' morphology. This is, however, lacking for PSbs' sizes lower than 1 μm. Here, we bridge this gap by measuring UV-vis spectra of PSbs deposited on a sapphire substrate via spin coating and by connecting the experimental data to the RI, PSbs' morphology, and optical transitions through a new optical model based on the Mie theory. Specifically, the new model allows us to fit the total and diffuse light components considering the particles' size distribution, the superficial density, and the substrate effects, thus correlating the PSbs' wavelength-dependent RI features to the experimentally validated specific sample morphology. Two critical model assumptions, i.e., Mie theory and substrate effects, are also discussed employing discrete dipole approximation simulations. Moreover, we identify the optically allowed molecular electronic transitions in nanometric PSbs as potential fingerprints for nanoparticle characterization in more complex matrixes.

Optical communication-based identification for multi-UAV systems: theory and practice

A Rapid and Efficient Optical Transients Identification Framework Based on a Multimodal Neural Network and Machine Learning Operations

Dual-mode luminescent security inks based on Eu@Er core-shell nanocrystals for multiwavelength optical information encryption and identification

The Terracotta Army, an integral part of China’s cultural heritage, has suffered physical erosion like cracks and notches over time. Manual inspection methods are inefficient and subjective. This study proposes an automated defect detection system based on computer vision to enhance the efficiency and precision of detecting these defects. The system includes the following core modules: (1) high-resolution image acquisition, which ensures comprehensive and detailed data capture; (2) sophisticated image illumination processing, which compensates for varying lighting conditions and improves image quality; (3) advanced image data augmentation techniques, which enrich the dataset and improve the generalization ability of the detection model; and (4) accurate defect detection, which leverages state-of-the-art algorithms. In the experimental phase, the efficacy of the proposed approach was evaluated. Illumination-enhanced low-light images were used for data augmentation, and the generated images showed high similarity to the original images, as measured by PSNR and SSIM. The YOLOv10 algorithm was employed for defect detection and achieved average detection rates of 91.71% for cracks and 93.04% for abrasions. This research provides a scientific and efficient solution for cultural relic protection and offers a valuable reference for future research in heritage conservation.

This work focuses on the detection of X-ray supernova remnants (SNRs) in the galaxy NGC 7793 and the study of their properties. X-ray SNRs in galaxies beyond the Local Group are rare, mainly due to the limited sensitivity of current X-ray instruments. Additionally, their identification requires an optical counterpart, making incomplete optical identification methods an extra challenge. Detecting X-ray SNRs in other galaxies is crucial to understanding their feedback in different evolutionary phases and gaining insights into their local interstellar medium (ISM). In NGC 7793, only one X-ray SNR was previously known, while a recent study reported nearly 240 optical SNRs. The discovery of a new, larger optical SNR sample motivated a re-examination of the X-ray SNR population by comparing optical SNRs with X-ray sources. To identify X-ray SNRs, we utilised ̧handra’s spatial resolution and analyzed all available archival data of NGC 7793, totaling 229.9 ks over 19 years. After data reduction, we performed source detection and analysis, searching for X-ray sources coinciding with optical SNRs. We also used (1.1 Ms combined EPIC MOS) for a spectral analysis of the confirmed and candidate SNRs. We detected 58 X-ray sources down to an observed luminosity of ̊m ∼ 1.5 erg, s^ . Among them, five X-ray counterparts to optical SNRs were identified, all presenting soft emission (<1.2 keV) with no short- or long-term variability. One corresponds to the previously known X-ray SNR, while four are newly detected. Spectral modelling of two SNRs shows thermal spectra exceeding 2.5 million K, with strong O,VII, O,VIII, and Ne,IX emission lines. A correlation between density, X-ray luminosity, and source softness was observed. We also report X-ray emission from supernova 2008bk, refining its position, and suggest two candidate X-ray SNRs with soft, non-variable spectra, one resembling the identified X-ray SNRs.

Abstract The optically levitated mechanical system in vacuum is a powerful platform in physics. It has been displaying more extensive application prospects. This paper presents an experimental study of optical levitation, identification, motion measurement, and assembly of two-species photoluminescence nanoparticles. A laser trapping array simultaneously levitates nitrogen-vacancy (NV) nanodiamonds and Yb3+/Er3+:NaYF4 nanoparticles. The species of each nanoparticle can be individually identified by measuring the photoluminescence spectrum. We choose the single NV nanodiamond and Yb3+/Er3+:NaYF4 nanoparticle and assemble them into a Janus composite nanoparticle, which integrates the merits of the two components. This work demonstrates the potential advantages of a hybrid optically levitated system. It provides a practicable scheme for the study of macroscopic quantum phenomena and precision measurement, thanks to the spin manipulation or spin-mechanical coupling of an NV diamond and by simultaneously implementing laser refrigeration to the Yb3+/Er3+:NaYF4 nanoparticle in an optically levitated composite nanoparticle.

ABSTRACTUsing L‐cysteine capped gold nanoparticles (L‐Cys‐AuNPs) as a colorimetric sensor, an easy, low‐cost, and highly efficient strategy for the optical chiral identification of ketoprofen enantiomers was developed. R‐ketoprofen tends to rapidly adsorb onto L‐Cys‐AuNPs, resulting in a color change of the solution from red to purple; however, no significant change was observed upon the addition of S‐ketoprofen. The potential of L‐cysteine capped with AuNPs (L‐Cys‐AuNPs) as chiral selectors for the recognition of ketoprofen enantiomers has been investigated using UV–Vis, FESEM, FT‐IR, SERS, and zeta potential. The recognition process is easily identified by the naked eye or by using a UV–Vis spectrometer. Sensors L‐Cys‐AuNPs revealed a good linear response to ketoprofen enantiomers in the concentration range of 8.33–33.33 μM with a relative standard deviation of 3.08%. And detection limit of 2.35 μM. The proposed method was applied to determine the ketoprofen racemic mixture in water samples and commercial tablets. This method represents easy, inexpensive, simple, and very effective methods for the recognition of ketoprofen enantiomers.

Fluorescence characteristics are generally attributed to conjugated molecular structures. Substances exhibiting chromatic or fluorescent effects hold significant application value in functional coatings, biochemical detection, anti-counterfeiting technologies, and pharmaceutical tracking due to their distinctive optical identification properties. Although numerous fluorescent materials have been developed, research on fluorescence mechanisms in low-molecular-weight substances remains insufficient. This study serendipitously discovered that a simple thermal mixing reaction between caprolactam (CPL) and itaconic acid (ITA) can produce a fluorescent-colored metastable colloidal system. The colloid exhibits exceptional stability at ambient temperature, maintaining noncrystalline status or forming novel butterfly/leaf-like crystal structures upon cooling, with reversible colloidal characteristics through thermal cycling. Through comprehensive characterization using fourier transform infrared spectroscopy (FT-IR), nuclear magnetic resonance (NMR), high-performance liquid chromatography (HPLC), and polarized optical microscopy, we elucidated the chromatic and fluorescent mechanisms: intermolecular hydrogen bonding networks facilitate the construction of spatial conjugation systems containing unsaturated groups, achieving photoluminescence via π-electron orbital transitions. The reliability of fluorescence color generation mechanism was confirmed by 3D printing side. This material exhibits excellent solubility in common solvents and shows promising potential for developing fluorescent composites through polymer matrix incorporation.

All-optical matching systems that detect and localize designated target sequences in input all-optical data sequences have attracted significant attention in all-optical processing. They have various applications, including all-optical intrusion detection, optical frame alignment, and optical package identification. In real-world applications, noise is inevitable and can lead to incorrect matching results. In particular, noise accumulates in serial all-optical matching systems, rendering the systems useless after several cycles. In this study, we developed a scheme for suppressing noise in quadrature phase-shift-keying (QPSK)-oriented all-optical matching systems. First, we evaluated the impact of input and amplifier noise on a QPSK-oriented all-optical matching system at a transmission rate of 100 Gbaud. We then developed a second-order noise-suppression structure using a highly nonlinear fiber (HNLF). With an input optical signal-to-noise ratio (OSNR) of 6 dB and an amplifier noise figure (NF) of 4 dB, the QPSK-oriented all-optical matching system without the noise-suppression structure output incorrect results. However, when the system was optimized using the proposed noise-suppression structure, correct matching results were obtained. Furthermore, when the NF of the amplifiers was fixed at 4 dB, the optimized system could reduce the minimum input OSNR to 0 dB. With an input OSNR of 0 dB, the logarithm of the bit error rate (BER) of the output matching results of the optimized system tended to negative infinity. The extinction ratio (ER), contrast ratio (CR), and quality (Q) factor of the output of the optimized system were 154.9532, 166.94289, and 161.12 dB, respectively, indicating high noise resistance. These results demonstrate that the system optimized using the proposed noise-suppression scheme exhibits high stability and reliability in noisy environments.