Intensity Correlation Research Articles

Correlated Photon Lidar Based on Time-Division Multiplexing

Single-photon lidar (SPL) exhibits high sensitivity, making it particularly suitable for detecting weak echoes over long distances. However, its susceptibility to background noise necessitates the implementation of advanced filtering techniques and complex algorithms, which can significantly increase system cost and complexity. To address these challenges, we propose a time-division-multiplexing-based correlated photon lidar system that employs a narrowband pulsed laser with stable time delays and variable pulse intensities, thereby establishing temporal and intensity correlations. This all-fiber solution not only simplifies the system architecture but also enhances operational efficiency. An adaptive cross-correlation method incorporating time slicing has been developed to extract histogram signals, enabling successful 1.5 km distance measurements under intense daytime noise conditions, using a 1 s accumulation time and a 20 mm receiving aperture. The experimental results demonstrate a 38% (from 1.11 to 1.52) improvement in the signal-to-noise ratio (SNR), thereby enhancing the system’s anti-noise capability, facilitating rapid detection, and reducing overall system costs.

Phase-dependent Hanbury-Brown and Twiss effect for the complete measurement of the complex coherence function

Hanbury-Brown and Twiss (HBT) effect is the foundation for stellar intensity interferometry. However, it is a phase insensitive two-photon interference effect. Here we extend the HBT interferometer by mixing intensity-matched reference fields with the input fields before intensity correlation measurement. With the freely available coherent state serving as the reference field, we experimentally demonstrate the phase sensitive two-photon interference effect when the input fields are thermal fields in either continuous wave or non-stationary pulsed wave and measure the complete complex second-order coherence function of the input fields without bringing them together from separate locations. Moreover, we discuss how to improve the signal level by using the more realistic continuous wave broadband anti-bunched light fields as the reference field. Our investigations pave the way for developing new technology of remote sensing and interferometric imaging with applications in long baseline high-resolution astronomy.

Elastic constant analysis of orthotropic steel sheets using multitask machine learning and the impulse excitation technique

Abstract This work presents a novel multitask learning approach featuring a dual convolutional neural network (CNN) system for determining the elastic constants of orthotropic rolled steel sheets. In the proposed model, resonance frequency spectra from the impulse excitation technique are converted into 2D image data. The first CNN focuses on detecting and predicting missing peak intensities, while the second CNN utilizes features from the entire spectrum image to predict elastic constants, including E11, E22, and G12. The input features include raw pixel data alongside three key categories for enhanced analysis: image-based features (such as the mean, median, mode, and skewness of pixel intensity distributions), spatial relations (including spatial frequency, pixel intensity correlations, and local contrast), and geometric features (such as shape descriptors and pixel connectivity). The results reveal that the optimal number of peaks (14), image resolution (121 pixels), and sample size (20 × 20 × 0.3 cm) maximize the model’s efficiency. Under these conditions, the model achieves R2 values of 0.952, 0.902, and 0.913, and RMSE values of 1.89 GPa, 3.09 GPa, and 1.92 GPa for E11, E22, and G12, respectively. It is suggested that the superior prediction accuracy for E11 is attributed to the stability of the Young’s modulus along the rolling direction, which is less variable in orthotropic materials. Furthermore, the study finds a dependency between input weight functions—including image-based features, spatial relations, and geometric features—as the material’s anisotropy changes, underscoring the importance of accounting for process variability in predictive modeling.

Investigation of Dislocations Introduced in Si Wafer during Flash Lamp Annealing by Photoluminescence Spectroscopy

Dislocations are generated in Si wafers during flash lamp annealing for 20 ms. The samples have been etched to different depths and macro‐photoluminescence (PL) spectra have been recorded for different dislocation densities. A micro‐PL investigation is also carried out on a cross section of a sample. Four characteristic emission peaks are found, which are the well‐known D1, D2, D3, and D4 lines. The findings demonstrate a significant influence of the defect densities on the PL spectra of the D lines by using both the micro‐ and the macro‐PL setups, and show a correlation of the PL intensities with etch pit density measured against the depth of the wafer. Additionally, the D lines dependency on temperature is explored, offering insights into the underlying mechanisms. The D lines exhibit a pronounced temperature dependence, which can be attributed to various factors including phonon interactions and thermal expansion effects. The influence of nickel contamination is also examined.

Correlations between spectrum-based and other ground-motion intensity measures considering various damping ratios

Spectral acceleration at a given period, SA( T), is usually combined with other intensity measures (IMs) for vector-valued ground-motion selection and seismic hazard assessment. The correlations between SA( T) and other IMs have been extensively studied, based on the SA-related IM pairs with a damping ratio ( ξ) of 5%. Yet, ξ varies notably with various types of structures. This article thus investigates the correlations of SA( T, ξ) with 10 commonly used non-SA IMs (e.g. Arias intensity), and the correlations of damping-specific acceleration spectrum, spectrum, and displacement spectrum intensities, namely ASI( ξ), SI( ξ), and DSI( ξ), with the other IMs. Comprehensive correlation analyses are conducted to derive the correlations for the IM pairs considered based on the NGA-West2 database and compatible ground-motion models. The results indicate that increasing ξ generally yields stronger correlations, and the difference between the ξ-specific and 5%-damped correlation coefficients could be larger than 0.2. The neural network and polynomial regressions are subsequently used to develop parametric models for estimating the correlations of SA( T, ξ)-containing pairs and the other IM pairs, respectively. All the models developed exhibit good performance in terms of predictive accuracy. The application of the proposed models is demonstrated within the generalized conditional IM approach. The models proposed could be useful for ground-motion selection and seismic demand or risk assessment for structures with various ξ values.

Nonlinear optical characterization of opaque and scattering media: from rough surfaces to powder

We introduce the reflection intensity correlation scan (RICO-scan), a nonlinear (NL) optical technique designed to characterize opaque and scattering media, where traditional transmittance methods fail. By analyzing variations in the intensity correlation functions of speckle patterns generated from backscattered light, the RICO-scan was applied to an unpolished silicon surface and silicon powders, providing information on the intensity dependence of the complex refractive index. Numerical simulations based on Fresnel equations and speckle propagation corroborated the experimental results, demonstrating RICO-scan’s robustness and versatility. The RICO-scan is the first, to the best of our knowledge, NL optical technique capable of characterizing the third-order nonlinearity of powdered materials, offering a reliable tool for studying disordered complex systems.

Effects of Primary Energy Consumption and Alternative Energy Patents on CO2 Emissions in China

China’s significant carbon emissions have attracted global attention, and the country has committed to reaching a peak in carbon emissions before 2030 and achieving carbon neutrality by 2060. It is crucial to achieve this goal by effectively controlling the combustion of primary fuels and developing alternative energy technologies. The existing literature has studied the effects of primary energy consumption on CO2 emissions, alternative energy technology on CO2 emissions, and energy patents on CO2 emissions. However, there are few studies on the effects of the relationship between primary energy consumption and alternative energy technology patents. This study analyzes the effects of primary energy consumption and alternative energy patents on CO2 emission intensity and CO2 emissions per capita, and their relationship using canonical correlation analysis. Our results are as follows. First, CO2 emissions from natural gas and liquefied petroleum gas have positive effects (correlation coefficients of 0.102 and 0.275, respectively), while CO2 emissions from gasoline, fuel oil, diesel, and kerosene have negative effects on CO2 emission intensity (correlation coefficients of −0.767, −0.420, −0.138, and −0.035, respectively). Second, patents for devices for producing mechanical power from muscle energy have large positive effects on total CO2 emissions (correlation coefficient of 0.533). Finally, the more the patents utilize waste heat, geothermal energy, hydro energy, and wind energy, the higher the CO2 emissions from liquefied petroleum gas, gasoline, and crude oil, and the lower the CO2 emissions from diesel, which are conducive to controlling CO2 emissions. Therefore, energy policies will be more effective, improve the living environment, and promote sustainable development based on the CO2 emissions level from primary energy consumption and the control degree of CO2 emissions by alternative energy.

Intensity correlations of light waves scattered by random media having parity-time symmetry

Light waves scattered by random media with parity-time (PT) symmetry exhibit unique second-order statistical characteristics in the far field [Phys. Rev. A 105, 023510 (2022)PLRAAN1050-294710.1103/PhysRevA.105.023510]; however, the nature of its higher-order statistics remains unexplored. This paper aims to analyze the intensity correlation (IC), i.e., fourth-order statistics, of a normally incident plane wave scattered by PT-symmetric random media. By utilizing the first-order Born approximation, we analytically show that this type of PT symmetry causes the spatial profile of the IC function of scattered light to split into three parts in the far zone. Moreover, this IC profile rotates with respect to its reference spatial point as the gain and loss of scatterers vary. These findings suggest an effective approach to detect the random medium’s gain or loss properties by measuring the IC distribution of scattered light in the far zone.

Hanbury Brown and Twiss-type optical secret sharing

Towards growing challenges of information security and authentication, various optical techniques based on holography, diffraction, interference, metasurfaces, etc., deliver promising solutions with low energy consumption and parallel high-speed information processing. Here, we report on a new dimension–second-order coherence found in the well-known Hanbury Brown and Twiss (HBT) effect–for performing optical authentication and secret sharing. We develop a method to generate a pair of correlated phase-only masks, each of which is distributed to a shareholder and can produce a specific pattern as authentication under coherent illumination, while two secret images are encrypted in the mutual information of two masks. By combining two masks in two configurations, two secret images can be extracted through spatially cascaded display under coherent illumination and intensity correlation under incoherent illumination, respectively. Conspicuously, two extremes of coherence–spatially coherent or incoherent–will enable the encoding and decoding of two different images with the same phase masks, indicating that the first-order and second-order coherence can be two independent channels for optical cryptography just like other degrees of freedom of light (e.g., polarization). Moreover, we demonstrate a polarization-multiplexing scheme to achieve polarization-selective HBT-type optically secret-sharing with increased capacity, and this type of polarization-phase masks can be readily replaced with metasurfaces.

Residual Pix2Pix networks: streamlining PET/CT imaging process by eliminating CT energy conversion.

Objective
Attenuation correction of PET data is commonly conducted through the utilization of a secondary imaging technique to produce attenuation maps. The customary approach to attenuation correction, which entails the employment of CT images, necessitates energy conversion. However, the present study introduces a novel deep learning-based method that obviates the requirement for CT images and energy conversion.
Methods
This study employs a residual Pix2Pix network to generate attenuation-corrected PET images using the 4033 2D PET images of 37 healthy adult brains for train and test. The model, implemented in TensorFlow and Keras, was evaluated by comparing image similarity, intensity correlation, and distribution against CT-AC images using metrics such as PSNR and SSIM for image similarity, while a 2D histogram plotted pixel intensities. Differences in standardized uptake values (SUV) demonstrated the model's efficiency compared to the CTAC method.
Results
The residual Pix2Pix demonstrated strong agreement with the CT-based attenuation correction, the proposed network yielding MAE, MSE, PSNR, and MS-SSIM values of 3×10-3, 2×10-4, 38.859, and 0.99, respectively. The residual Pix2Pix model's results showed a negligible mean SUV difference of 8×10-4(P-value = 0.10), indicating its accuracy in PET image correction. The residual Pix2Pix model exhibits high precision with a strong correlation coefficient of R2 = 0.99 to CT-based methods. The findings indicate that this approach surpasses the conventional method in terms of precision and efficacy.
Conclusions
The proposed residual Pix2Pix framework enables accurate and feasible attenuation correction of brain F-FDG PET without CT. However, clinical trials are required to evaluate its clinical performance. The PET images reconstructed by the framework have low errors compared to the accepted test reliability of PET/CT, indicating high quantitative similarity.&#xD.

Experimental Research on the Correction of Vortex Light Wavefront Distortion

Wavefront distortion occurs when vortex beams are transmitted in the atmosphere. The turbulence effect greatly affects the transmission of information, so it is necessary to use adaptive optical correction technology to correct the wavefront distortion of the vortex beam at the receiving end. In this paper, a method of vortex wavefront distortion correction based on the deep deterministic policy gradient algorithm is proposed; this is a new correction method that can effectively handle high-dimensional state and action spaces and is especially suitable for correction problems in continuous action spaces. The entire system uses adaptive wavefront correction technology without a wavefront sensor. The simulation results show that the deep deterministic policy gradient algorithm can effectively correct the distorted vortex beams and improve the mode purity, and the intensity correlation coefficient of single-mode vortex light can be increased to about 0.88 and 0.69, respectively, under weak turbulence and strong turbulence, and the intensity coefficient of weak-turbulence multi-mode vortex light can be increased to about 0.96. The experimental results also show that the adaptive correction technology based on the deep deterministic policy gradient algorithm can effectively correct the wavefront distortion of vortex light.

Molecular Oxygen (O2) Artifacts in Tandem Mass Spectra.

Peak annotation plays an important role in mass spectral evaluation of the NIST 2023 tandem mass spectral library. While most fragment ions are formed by neutral losses, there are peaks that represent adduct ions from these fragments. Previously, we have reported two main types of addition reactions in the collision cell, namely addition of H2O and N2. Here we report a different reaction in the collision cell, with addition of O2 leading to a small peak that could only be assigned to a peroxyl radical ion. For example, some protonated iodoaromatics lose an iodine atom to form a radical cation [M+H-I]+•, which reacts with O2 to generate a peroxyl radical ion [M+H-I+O2]+•. Higher concentrations of O2 result in higher peroxyl radical peaks, which become dominant while the precursor ions are consumed, as examined by five compounds under different concentrations of O2. The correlation of the peroxyl radical peak intensities to the concentration of O2 provides a tool to estimate trace amounts of O2 within the instrument. In the NIST 2023 tandem mass spectral library, the peaks for [M+H-X+O2]+· are most abundant in numbers and in intensity for X = NO2 or I, are much less abundant for X = Br, and are rare for X = Cl. Other leaving groups in this library are SO3H, SO2NH2, CSNH2, CO2C6F5, SO2CH3, and COCH3. The O2 addition reaction is also observed with negative ions in this library. While adducts of H2O and N2 often constitute major peaks, the peaks of the peroxyl radicals under standard conditions are mostly very small and may be mistaken for noise, but their correct annotation improves the quality of the spectra and is important when comparing spectra from different instruments or conditions.

Humans can use positive and negative spectrotemporal correlations to detect rising and falling pitch.

To discern speech or appreciate music, the human auditory system detects how pitch increases or decreases over time. However, the algorithms used to detect changes in pitch, or pitch motion, are incompletely understood. Here, using psychophysics, computational modeling, functional neuroimaging, and analysis of recorded speech, we ask if humans can detect pitch motion using computations analogous to those used by the visual system. We adapted stimuli from studies of vision to create novel auditory correlated noise stimuli that elicited robust pitch motion percepts. Crucially, these stimuli are inharmonic and possess no persistent features across frequency or time, but do possess positive or negative local spectrotemporal correlations in intensity. In psychophysical experiments, we found clear evidence that humans can judge pitch direction based only on positive or negative spectrotemporal intensity correlations. The key behavioral result-robust sensitivity to the negative spectrotemporal correlations-is a direct analogue of illusory "reverse-phi" motion in vision, and thus constitutes a new auditory illusion. Our behavioral results and computational modeling led us to hypothesize that human auditory processing may employ pitch direction opponency. fMRI measurements in auditory cortex supported this hypothesis. To link our psychophysical findings to real-world pitch perception, we analyzed recordings of English and Mandarin speech and found that pitch direction was robustly signaled by both positive and negative spectrotemporal correlations, suggesting that sensitivity to both types of correlations confers ecological benefits. Overall, this work reveals how motion detection algorithms sensitive to local correlations are deployed by the central nervous system across disparate modalities (vision and audition) and dimensions (space and frequency).

Spatial correlation-based quadratic cost function for wavefront shaping through scattering media.

The feedback-based wavefront shaping emerges as a promising method for deep tissue microscopy, energy control in bio-incubation, and re-configurable structural illuminations. The cost function plays a crucial role in the feedback-based wavefront optimization for focusing light through scattering media. However, popularly used cost functions, such as intensity ( ) and peak-to-background ratio (PBR) struggle to achieve precise intensity control and uniformity across the focus spot. We have proposed an -norm-based quadratic cost function (QCF) for establishing both intensity and position correlations between image pixels, which helps to advance the focusing light through scattering media, such as biological tissue and ground glass diffusers. The proposed cost function has been integrated into the genetic algorithm, establishing pixel-to-pixel correlations that enable precise and controlled contrast optimization, while maintaining uniformity across the focus spot and effectively suppressing the background intensity. We have conducted both simulations and experiments using the proposed QCF, comparing its performance with the commonly used and PBR-based cost functions. The results evidently indicate that the QCF achieves superior performance in terms of precise intensity control, uniformity, and background intensity suppression. By contrast, both the and PBR cost functions exhibit uncontrolled intensity gain compared with the proposed QCF. The proposed QCF is most suitable for applications requiring precise intensity control at the focus spot, better uniformity, and effective background intensity suppression. This method holds significant promise for applications where intensity control is critical, such as photolithography, photothermal treatments, dosimetry, and energy modulation within and outside bio-incubation systems.

Quantum interferences and gates with emitter-based coherent photon sources

Quantum emitters such as quantum dots, defects in diamond or in silicon have emerged as efficient single-photon sources that are progressively exploited in quantum technologies. In 2019, it was shown that the emitted single-photon states often include coherence with the vacuum component. Here we investigate how such photon-number coherence alters quantum interference experiments that are routinely implemented both for characterizing or exploiting the generated photons. We show that it strongly modifies intensity correlation measurements in a Hong–Ou–Mandel experiment and leads to errors in indistinguishability estimations. It also results in additional entanglement when performing partial measurements. We illustrate the impact on quantum protocols by evidencing modifications in heralding efficiency and fidelity of two-qubit gates.

Reciprocity-assisted ghost imaging through dynamic random media.

Ghost imaging enables the imaging of an object using intensity correlations between a single-pixel detector placed behind the object and a camera that records the light that did not interact with the object. The object and the camera are often placed at conjugate planes to ensure correlated illumination patterns. Here, we show how the combined effect of optical reciprocity and the memory effect in a random medium gives rise to correlations between two beams that traverse the random medium in opposite directions. In a proof-of-principle experiment, we observe such correlations when the beams traverse two thin diffusers separated by a variable distance. We find that the angular width of the region over which the beams are correlated decreases as the distance between the diffusers is increased. We then utilize these correlations to demonstrate a ghost imaging scheme in which the object and camera are placed at opposite ends of the random medium and illuminated by counter-propagating beams that can potentially be emitted by two different sources.

Rapid estimation of carbon footprints in agrochemical development: correlation of process mass intensity with CO2 emissions.

The agricultural sector faces a challenge in balancing increasing food demand while minimizing environmental impacts. Crop protection products are crucial for achieving high crop yields and ensuring food security. However, life cycle assessment (LCA), the standard framework for evaluating environmental impact, is time-consuming and costly, especially during early product development. To address this, a novel tool correlating process mass intensity (PMI) with greenhouse gas (GHG) emissions has been developed as a streamlined alternative. A strong linear correlation (R2 = 0.95) was identified between PMI and product GHG emissions, enabling rapid carbon footprint estimation using simplified PMI data. The model was validated using 13 small molecule active ingredients (AIs), showing a mean absolute error (MAE) of 55 g CO₂/kg AI and a root mean square error (RMSE) of 64 kg CO₂/kg AI. Residual analysis demonstrated random distribution, suggesting reliable predictions. The PMI-based tool provides rapid, accurate estimates of product carbon footprint (PCF), supporting early-stage decision-making in research and development for agrochemical processes. Its simplicity makes it applicable across various chemical sectors and valuable for sustainability efforts. © 2024 Society of Chemical Industry.

Versatile VCSEL source of thermal and super-thermal light.

We have built highly controllable sources of thermal and super-thermal states on the basis of a single-mode (SM) and a multi-mode (MM) vertical cavity surface emitting laser (VCSEL) with noised driving current operating above the threshold. Varying the average driving current and the amplitude and bandwidth of noise, one can robustly obtain light with the temporal second-order intensity correlation function reaching 2.5 and correlation times from 1 µs to 10 ns. For the SM VCSEL, generated thermal lights retain its single-mode quality negating a need for a spectral filtering. For the MM VCSEL, one can still find a near-threshold regime when the generated thermal radiation is close to a single-mode one.

Correlation Hyperspectral Imaging.

Hyperspectral imaging aims at providing information on both the spatial and the spectral distribution of light, with high resolution. However, state-of-the-art protocols are characterized by an intrinsic trade-off imposing to sacrifice either resolution or image acquisition speed. We address this limitation by exploiting light intensity correlations, which are shown to enable overcoming the typical downsides of traditional hyperspectral imaging techniques, both scanning and snapshot. The proposed approach also opens possibilities that are not otherwise achievable, such as sharper imaging and natural filtering of broadband spectral components that would otherwise hide the spectrum of interest. The enabled combination of high spatial and spectral resolution, high speed, and insensitivity to undesired spectral features shall lead to a paradigm change in hyperspectral imaging devices and open up new application scenarios.

The Effects of Air Quality and the Impact of Climate Conditions on the First COVID-19 Wave in Wuhan and Four European Metropolitan Regions

This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O3), nitrogen dioxide (NO2), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO2), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO2), AQI, and AOD but a high increase in ground-level O3 in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises.