Brightness Changes Research Articles

Comprehensive Analysis of Pair Galaxy UGC 8335 Using Multiple Filters

The two galaxies, UGC 8335 (East) E and (West) W, were investigated through the utilization of photometric and spectroscopic approaches, employing g, r, i, and z filters to gather the maximum amount of details feasible; the observed data ware obtained using the data releases (DR7 and DR17) provided by "The Sloan Digital Sky Survey (SDSS)." Subsequently, the data were fitted to ellipsoids through the utilization of "the Images Reduction & Analysis Facilities (IRAF)" through implementation of the STSDAS ELLIPS tool. Multiple characteristics of the two galaxies UGC 8335 E and W, have been scrutinized, including surface brightness, magnitudes, both vertical and horizontal changes, accumulated flux, central axis position angles, ellipticities, isophotal shape parameters (B4), and star-forming rates (SFRs), together with their astronomical characteristics. The UGC 8335 galaxy pair has been confirmed to be an interacting pair that exhibits significant indications of interaction and is undergoing a merging process. Furthermore, this combination is categorized as an intermediate galaxy pair and represents a physical set with refined star formation rates.

Fourier‐Based Action Recognition for Wildlife Behavior Quantification with Event Cameras

Event cameras are novel bioinspired vision sensors that measure pixel‐wise brightness changes asynchronously instead of images at a given frame rate. They offer promising advantages, namely, a high dynamic range, low latency, and minimal motion blur. Modern computer vision algorithms often rely on artificial neural network approaches, which require image‐like representations of the data and cannot fully exploit the characteristics of event data. Herein, approaches to action recognition based on the Fourier transform are proposed. The approaches are intended to recognize oscillating motion patterns commonly present in nature. In particular, the approaches are applied to a recent dataset of breeding penguins annotated for “ecstatic display,” a behavior where the observed penguins flap their wings at a certain frequency. It is found that the approaches are both simple and effective, producing slightly lower results than a deep neural network (DNN) while relying just on a tiny fraction of the parameters compared to the DNN (five orders of magnitude fewer parameters). They work well despite the uncontrolled, diverse data present in the dataset. It is hoped that this work opens a new perspective on event‐based processing and action recognition.

High-performance surface defect detection of aluminum substrate based on event camera

Abstract Traditional industrial surface defect detection often employs CCD/CMOS cameras, but they are difficult to detect the minute defects on aluminum substrates in highly dynamic industrial scenes due to their nature. Event camera is a novel high-resolution vision sensor which measures per-pixel brightness changes in an asynchronous manner and outputs as event information flow (EIF). Small and weak defects on aluminum substrate can be captured by event camera effectively, but the EIF contain a large amount of noise, making it difficult to perform accurate and high-precision defect detection. To address this problem, we propose a frame aggregation method to realize good event information flow processing (EIFP), and then use an improved circle detection method to locate the aluminum substrate in each frame, removing abundant event information outside the aluminum substrate. Subsequently, we enhance the event signals under different frames based on optical flow tracking using multiple features, and construct a semi-supervised detector based on pseudo-labels to achieve high-precision defect localization. Finally, considering the small inter-class differences in defects on the surface of aluminum substrates, we construct a defect class corrector based on ensemble learning to enhance the ability to determine defect classes, achieving high-precision automatic quality inspection of aluminum substrate surfaces. The performance of our method is compared with other advanced methods based on event camera data of aluminum substrates in real industrial scenarios. The experimental results show that our method has improved the detection accuracy by about 10% and the classification accuracy by about 25% compared to the original state-of-the-art methods.

Relativistic reflection modeling in AGN and related variability from PCA: a brief review

X-ray observations of active galactic nuclei (AGNs) reveal relativistic reflections from the innermost regions of accretion disks, which contain general-relativistic footprints caused by spinning supermassive black holes (SMBH). We anticipate the spin of a SMBH to be stable over the human timeframe, so brightness changes in the high-energy corona above the SMBH should slightly alter relativistic reflection. In this brief review, we discuss the latest developments in modeling relativistic reflection, as well as the rapid small variation in relativistic emission disclosed by the principal component analysis (PCA) of X-ray variability in AGN. PCA studies of X-ray spectra from AGNs have shown that relativistically blurred reflection has negligible fluctuations over the course of observations, which could originate from rapid (intrahour) intrinsic variations in near-horizon accretion flows and photon rings. The PCA technique is an effective way to disclose relativistic reflection from X-ray observations of AGNs, simplifying the complexity of largely variable X-ray data for automated spectral analysis with machine learning algorithms.

A Search for Collisions and Planet–Disk Interactions in the Beta Pictoris Disk with 26 Years of High-precision HST/STIS Imaging

β Pictoris's well-studied debris disk and two known giant planets, in combination with the stability of the Hubble Space Telescope’s Space Telescope Imaging Spectrograph (HST/STIS) (and now also JWST), offers a unique opportunity to test planet–disk interaction models and to observe recent planetesimal collisions. We present HST/STIS coronagraphic imaging from two new epochs of data taken between 2021 and 2023, complementing earlier data taken in 1997 and 2012. This data set enables a temporal comparison with the longest baseline and highest precision of any debris disk to date, with sensitivity to variations in temporal surface brightness of sub-percent levels in the midplane of the disk. While no localized changes in surface brightness are detected, which would be indicative of a recent planetesimal collision, there is a tentative brightening of the southeast side of the disk over the past decade. We link the constraints on surface brightness variations to dynamical models of the planetary system’s evolution and to the collisional history of planetesimals. Using a coupled collisional model and injection/recovery framework, we estimate sensitivity to expanding collisional debris down to a Ceres mass per progenitor in the most sensitive regions of the disk midplane. These results demonstrate the capabilities of long-baseline, temporal studies with HST (and also soon with JWST) for constraining the physical processes occurring within debris disks.

Asynchronous Visual-Inertial Odometry for Event Cameras

Abstract. Event cameras are bio-inspired visual sensors that output changes in pixel-level brightness asynchronously instead of standard intensity frames at a fixed rate. These cameras offer reliable visual information in high-speed motion and high dynamic range (HDR) scenes, addressing the limitations of traditional cameras in such scenarios. Therefore, research of integrating event cameras into established visual algorithms holds significant value. In this study, based on traditional Visual-Inertial Odometry (VIO) frameworks, we proposed an innovative asynchronous monocular event-based inertial odometry method to fully exploit the benefits of event cameras. First, the corner features are extracted separately from the raw event stream and the time surface map, followed by uniform feature selection to accurately describe three-dimensional spatial geometry. Then, feature tracking is achieved by integrating these two event representation methods. In addition, our method obtains stable and high frequency state estimation by fusing event and IMU measurements through graph optimization. We validate the effectiveness of our proposed approach, comparing with several state-of-the-art EVIO systems and VIO systems.

Effects of simulated driving task screen background color combinations on visual performance and visual fatigue

ABSTRACT In-car smart devices, such as touchscreen control systems, internet interfaces and hands-free phone functions can cause problems affecting driver comfort, visual fatigue and performance, resulting in decreased reaction times, increasing the likelihood of accidents. This study utilizes a touchscreen display and driving simulator as the operational tools in experiments designed to investigate how changes in screen brightness and different color combinations affect the visual performance and the level of visual fatigue of the driver. Specifically, the aim is to explore the impact of changes in brightness and color combinations on visual fatigue and whether there are interactions between them. During the experimental process, relevant data, including the Critical Fusion Frequency (CFF), touchscreen offset, and the NASA Task Load Index (NASA-TLX), are collected and then analyzed using the SPSS statistical software to provide objective and reliable supporting data and valuable insights to related research fields. The experimental results show that the environmental illumination significantly affects the touchscreen offset, CFF and the NASA-TLX), while the color combination significantly affects touchscreen offset (movement and rotation) and the NASA-TLX.

Dark lens candidates from Gaia Data Release 3

Gravitational microlensing is a phenomenon that allows us to observe the dark remnants of stellar evolution, even if these bodies are no longer emitting electromagnetic radiation. In particular, it can be useful to observe solitary neutron stars or stellar-mass black holes, providing a unique window through which to understand stellar evolution. Obtaining direct mass measurements with this technique requires precise observations of both the change in brightness and the position of the microlensed star. The European Space Agency's satellite can provide both. Using publicly available data from different surveys, we analysed events published in the Data Release 3 ( ) microlensing catalogue. Here, we describe our selection of candidate dark lenses, where we suspect the lens is a white dwarf (WD), a neutron star (NS), a black hole (BH), or a mass-gap object, with a mass in the range between the heaviest NS and the least massive BH. We estimated the mass of the lenses using information obtained from the best-fitting microlensing models, source star, Galactic model, and the expected parameter distributions. We found eleven candidates for dark remnants: one WDs, three NSs, three mass-gap objects, and four BHs.

Sliding window based adaptative fuzzy measure for edge detection

AbstractIn this work, we explore the impact of adaptive fuzzy measures on edge detection, aiming to enhance how computers interpret images by identifying edges more accurately. Traditional methods rely on analysing changes in image brightness to locate edges, but they often use fixed rules that do not account for the unique characteristics of each image. Our approach differs by adjusting fuzzy measures based on the information within specific areas of an image under a sliding window approach, utilizing a variety of fusion functions and generalizations of the Choquet integral to analyse and combine pixel data. The proposed method is flexible, allowing for the adaptation of measures in response to the image's local features. We put our method to the test against the well‐established Canny edge detector to evaluate its effectiveness. Our experimental results suggest that by adapting fuzzy measures for each image section, we can improve edge detection results.

Effect of the negative-end discharge on the positive leader propagation in bidirectional leader discharges

Lightning strikes on aircraft are mainly due to self-triggered bidirectional leader discharges. Understanding the physical processes of lightning strikes is the basis for designing appropriate lightning protection measures. For this purpose, the development processes of bidirectional leaders triggered by a floating conductor were studied in a laboratory. In this paper, an experimental platform for the observation of bidirectional leader discharges is established through a reasonable gap arrangement. The discharge processes were recorded by a high-speed camera with up to 504 000 f/s. The effects of negative streamer discharges on the positive leader propagation speed as well as brightness during the bidirectional leader discharge at different electric field rise rates were mainly studied and quantitatively analyzed by introducing incremental coefficients. The experimental results show that the discharge development at the negative end of the floating conductor lags behind that at the positive end, and there is a coupling phenomenon between the positive and the negative discharge. At low electric field rise rates, when a negative streamer discharge occurs, it causes a positive corona burst, resulting in a sharp increase in brightness, while on the contrary the leader speed change is not as pronounced as the brightness change. As the rate of rise of the electric field increases, the effect of the discharge at the negative end on the positive end gradually decreases. This work could provide a theoretical basis for understanding the physical process of lightning strikes on aircraft.

Experimental Study of Turbulent Premixed Flames of Gas-to-Liquids (GTL) Fuel in a Fan-Stirred Combustion Bomb

This study experimentally investigates the turbulent flame speeds (St) of Gas-to-Liquids (GTL) fuel and a 50/50 blend with diesel across turbulence intensities (u') ranging from 0.5 to 3.0 m/s and equivalence ratios (Φ) from 0.7 to 1.3. Experiments were conducted using a cylindrical fan-stirred combustion bomb at an initial temperature (Ti) of 463 K and atmospheric pressure under near homogeneous and isotropic turbulence (HIT) conditions. A pressure transducer measured the St of the propagating GTL flame. High-speed imaging reveals that changes in flame brightness are associated with variations in flame temperature and soot incandescence. Compared to diesel, stoichiometric GTL and the 50/50 diesel-GTL blend showed peak combustion pressure reductions of 8.9% and 4.9%, respectively. Rich diesel fuel and lean GTL exhibited higher pressure rise rates, flame propagation, and St due to their Lewis numbers (Le) being less than one, enhancing flame-turbulence interaction. At u` of 0.5, 1.5, and 3.0 m/s, St for GTL increased by approximately 3.6%, 5.3%, and 2.8%, respectively, when compared to diesel, with these increases observed at Φ < 1.1. Transition from wrinkled to corrugated flamelets with increasing u` supports models predicting flame stability and potential industrial combustion efficiency improvements. Comparisons with numerical St results using the Zimont Turbulent Flame Speed Closure (Zimont TFC) model showed good agreement at lower (u' = 0.5 m/s) and mid-range (u' = 2.0 m/s) turbulence intensities but discrepancies at higher intensities (u' = 3.0 m/s), highlighting the need to refine numerical models for more accurate predictions across all turbulence levels.

Brightening and Fading in the Youngest Galactic Supernova Remnant G1.9+0.3: 13 Years of Monitoring with the Chandra X-Ray Observatory

We report 13 years of Chandra monitoring of the youngest Galactic supernova remnant G1.9+0.3, the only remnant known to be increasing in brightness. We confirm the spatially integrated rate of flux increase of (1.2 ± 0.2)% yr−1 (1–7 keV), but find large spatial variations, from −3% yr−1 to +7% yr−1, over length scales as small as 10″ or smaller. We observe relatively little change in spectral slope, though one region shows significant hardening as it brightens by 1% yr−1. Such rates of change can be accommodated by any of several explanations, including steady blast-wave evolution, expansion or compression of discrete plasma blobs, magnetic turbulence, or variations in magnetic-field aspect angle. Our results do not constrain the mean magnetic-field strength, but a self-consistent picture can be produced in which the maximum particle energies are limited by the remnant age (applying both to electrons and to ions) to about 20 TeV, and the remnant-averaged magnetic-field strength is about 30 μG. The deceleration parameter m (average shock radius varying as t m ) is about 0.7, consistent with estimates from overall expansion dynamics and confirming an explosion date of about 1900 CE. Shock-efficiency factors ϵ e and ϵ B (fractions of shock energy in relativistic electrons and magnetic field) are 0.003 and 0.0002 in this picture. However, the large range of rates of brightness change indicates that such a global model is oversimplified. Temporal variations of photon index, expected to be small but measurable with longer time baselines, can discriminate among possible models.

A novel application of laser speckle imaging technique for prediction of hypoxic stress of apples

BackgroundFruit storage methods such as dynamic controlled atmosphere (DCA) technology enable adjusting the level of oxygen in the storage room, according to the physiological state of the product to slow down the ripening process. However, the successful application of DCA requires precise and reliable sensors of the oxidative stress of the fruit. In this study, respiration rate and chlorophyll fluorescence (CF) signals were evaluated after introducing a novel predictors of apples' hypoxic stress based on laser speckle imaging technique (LSI).ResultsBoth chlorophyll fluorescence and LSI signals were equally good for stress detection in principle. However, in an application with automatic detection based on machine learning models, the LSI signal proved to be superior, due to its stability and measurement repeatability. Moreover, the shortcomings of the CF signal appear to be its inability to indicate oxygen stress in tissues with low chlorophyll content but this does not apply to LSI. A comparison of different LSI signal processing methods showed that method based on the dynamics of changes in image content was better indicators of stress than methods based on measurements of changes in pixel brightness (inertia moment or laser speckle contrast analysis). Data obtained using the near-infrared laser provided better prediction capabilities, compared to the laser with red light.ConclusionsThe study showed that the signal from the scattered laser light phenomenon is a good predictor for the oxidative stress of apples. Results showed that effective prediction using LSI was possible and did not require additional signals. The proposed method has great potential as an alternative indicator of fruit oxidative stress, which can be applied in modern storage systems with a dynamically controlled atmosphere.

Reversible Emission Color, Brightness, and Shape Changes of AIEgen‐containing Bulk Polymers

AbstractBulk polymers generally exhibit faster response, higher shape‐deformation speed, and stronger mechanical properties than the hydrogels. However, it still remains a formidable challenge to enable bulk polymers to exhibit reversible changes in shape and fluorescence simultaneously. Herein, for the first time, the temperature‐responsive reversible changes in fluorescence color, brightness, and shape of semi‐crystalline poly(ε‐caprolactone) (PCL) network doped with a D‐A based aggregation‐induced emission luminogen (AIEgen) is realized. The fabricated flower‐like and dragonfly‐shaped actuators show reversible blooming‐closing and wing fanning behaviors, respectively, as well as reversible fluorescence‐changing effects. Notably, the response and shape‐deformation speed as well as mechanical properties of the system is much better than the reported hydrogels. Moreover, a smart switch is developed to demonstrate the potential applications. The stick fabricated by the sample can lift and release an object 1000 times larger than its weight, serving as a color‐changing artificial muscle. This material design strategy may shine light on the design and preparation of other reversible shape‐deformation polymers in combination with stimuli‐responsive luminescence‐changing AIEgens, such as mechanochromic, thermochromic, and photochromic luminogens.

Heterochromatic brightness changes between monitors

Heterochromatic brightness changes between monitors

Monitoring the leaf damage by the rice leafroller with deep learning and ultra-light UAV.

Rice leafroller is a serious threat to the production of rice. Monitoring the damage caused by rice leafroller is essential for effective pest management. Owing to limitations in collecting decent quality images and high-performing identification methods to recognize the damage, studies recommending fast and accurate identification of rice leafroller damage are rare. In this study, we employed an ultra-lightweight unmanned aerial vehicle (UAV) to eliminate the influence of the downwash flow field and obtain very high-resolution images of the damaged areas of the rice leafroller. We used deep learning technology and the segmentation model, Attention U-Net, to recognize the damaged area by the rice leafroller. Further, a method is presented to count the damaged patches from the segmented area. The result shows that Attention U-Net achieves high performance, with an F1 score of 0.908. Further analysis indicates that the deep learning model performs better than the traditional image classification method, Random Forest (RF). The traditional method of RF causes a lot of false alarms around the edge of leaves, and is sensitive to the changes in brightness. Validation based on the ground survey indicates that the UAV and deep learning-based method achieve a reasonable accuracy in identifying damage patches, with a coefficient of determination of 0.879. The spatial distribution of the damage is uneven, and the UAV-based image collecting method provides a dense and accurate method to recognize the damaged area. Overall, this study presents a vision to monitor the damage caused by the rice leafroller with ultra-light UAV efficiently. It would also contribute to effectively controlling and managing the hazardous rice leafroller. © 2024 Society of Chemical Industry.

Touchless short-wave infrared imaging for dynamic rapid pupillometry and gaze estimation in closed eyes

BackgroundAssessments of gaze direction (eye movements), pupil size, and the pupillary light reflex (PLR) are critical for neurological examination and neuroscience research and constitute a powerful tool in diverse clinical settings ranging from critical care through endocrinology and drug addiction to cardiology and psychiatry. However, current bedside pupillometry is typically intermittent, qualitative, manual, and limited to open-eye cases, restricting its use in sleep medicine, anesthesia, and intensive care.MethodsWe combined short-wave infrared (SWIR, ~0.9-1.7μm) imaging with image processing algorithms to perform rapid (~30 ms) pupillometry and eye tracking behind closed eyelids. Forty-three healthy volunteers participated in two experiments with PLR evoked by visible light stimuli or directing eye movements towards screen targets. Imaging was performed simultaneously on one eye closed, and the other open eye serving as ground truth. Data analysis was performed with a custom approach quantifying changes in brightness around the pupil area or with a deep learning U-NET-based procedure.ResultsHere we show that analysis of SWIR imaging data can successfully measure stimulus-evoked PLR in closed-eye conditions, revealing PLR events in single trials and significant PLRs in nearly all individual subjects, as well as estimating gaze direction. The neural net-based analysis could successfully use closed-eye SWIR data to recreate estimates of open-eye images and assess pupil size.ConclusionsContinuous touchless monitoring of rapid dynamics in pupil size and gaze direction through closed eyes paves the way for developing devices with wide-ranging applications, fulfilling long-standing goals in clinical and research fields.

Self-Supervised Monocular Depth Estimation for Endoscopic Imaging.

Endoscopy holds a pivotal role in the early detection and treatment of diverse diseases, with artificial intelligence (AI)-assisted methods increasingly gaining prominence in disease screening. Among them, the depth estimation from endoscopic sequences is crucial for a spectrum of AI-assisted surgical techniques. However, the development of endoscopic depth estimation algorithms presents a formidable challenge due to the unique environmental intricacies and constraints within the dataset. This paper proposes a self-supervised depth estimation network to comprehensively explore the brightness changes in endoscopic images, and fuse different features at multiple levels to achieve an accurate prediction of endoscopic depth. First, a FlowNet is designed to evaluate the brightness changes of adjacent frames by calculating the multi-scale structural similarity. Second, a feature fusion module is presented to capture multi-scale contextual information. Experiments show that the average accuracy of the algorithm is 97.03% in the Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED dataset). Based on the training parameters of the SCARED dataset, the algorithm achieves superior performance on the other two datasets (EndoSLAM and KVASIR dataset), indicating that the algorithm has good generalization performance.

Study of stellar properties under TESS satellite observations

This study employs data from the TESS satellite to investigate the properties of stars. TESS, the Transiting Exoplanet Survey Satellite, offers insights into star activity, periodic variations, and planetary transits by monitoring changes in stellar brightness. Our analysis of TESS data explores critical star attributes, including mass, luminosity, activity levels, and dynamic changes. By examining the relationship between these stellar properties and the presence of planets, we aim to uncover the intricate interplay between celestial bodies and unveil the universe's mysteries. This research encompasses data collection methods, processing techniques, and data analysis approaches, addressing star classifications, parameters, and periodic stellar activity. We also delve into flare energy, which reveals the energy released during stellar flares, and photoperiodic analysis, a tool for understanding periodic light changes in celestial bodies. The study concludes by highlighting the significance of newly discovered planets and the diversity of stars observed by TESS. It underscores the importance of comprehending the connections between stars and planets, shaping our understanding of celestial habitability. In summary, this study enriches our knowledge of star and planet properties, contributing to a broader comprehension of the universe. Through our analysis, we seek to illuminate the complex relationships between celestial entities and unveil the universe's enigmas.

Eye Tracking Based on Event Camera and Spiking Neural Network

An event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, and addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye-tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of a spiking neural network (SNN) with an event camera, a near-range eye-tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081 ms, enhancing tracking stability while maintaining accuracy.