Advanced Camera Research Articles

An Empirical Calibration of the Tip of the Red Giant Branch Distance Method in the Near Infrared. I. Hubble Space Telescope WFC3/IR F110W and F160W Filters

The tip of the red giant branch (TRGB) based distance method in the I band is one of the most efficient and precise techniques for measuring distances to nearby galaxies (D ≲ 15 Mpc). The TRGB in the near-infrared (NIR) is 1–2 mag brighter relative to the I band, and has the potential to expand the range over which distance measurements to nearby galaxies are feasible. Using Hubble Space Telescope (HST) imaging of 12 fields in eight nearby galaxies, we determine color-based corrections and zero-points of the TRGB in the Wide Field Camera 3 IR (WFC3/IR) F110W and F160W filters. First, we measure TRGB distances in the I band equivalent Advanced Camera System (ACS) F814W filter from resolved stellar populations with the HST. The TRGB in the ACS F814W filter is used for our distance anchor and to place the WFC3/IR magnitudes on an absolute scale. We then determine the color dependence (a proxy for metallicity/age) and zero-point of the NIR TRGB from photometry of WFC3/IR fields that overlap with the ACS fields. The new calibration is accurate to ∼1% in distance relative to the F814W TRGB. Validating the accuracy of the calibrations, we find that the distance modulus for each field using the NIR TRGB calibration agrees with the distance modulus of the same field as determined from the F814W TRGB. This is a JWST preparatory program, and the work done here will directly inform our approach to calibrating the TRGB in JWST NIRCam and NIRISS photometric filters.

Exploring the Mpc Environment of the Quasar ULAS J1342+0928 at z = 7.54

Theoretical models predict that z ≳ 6 quasars are hosted in the most massive halos of the underlying dark matter distribution and thus would be immersed in protoclusters of galaxies. However, observations report inconclusive results. We investigate the 1.1 proper-Mpc2 environment of the z = 7.54 luminous quasar ULAS J1342+0928. We search for Lyman-break galaxy (LBG) candidates using deep imaging from the Hubble Space Telescope (HST) in the Advanced Camera for Surveys (ACS)/F814W, Wide Field Camera 3 (WFC3)/F105W/F125W bands, and Spitzer/Infrared Array Camera at 3.6 and 4.5 μm. We report a zphot=7.69−0.23+0.33 LBG with magF125W = 26.41 at 223 projected proper kpc (pkpc) from the quasar. We find no HST counterpart to one [C ii] emitter previously found with the Atacama Large millimeter/submillimeter Array (ALMA) at 27 projected pkpc and z [C II]=7.5341 ± 0.0009 (Venemans et al. ). We estimate the completeness of our LBG candidates using results from Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey/GOODS deep blank field searches sharing a similar filter setup. We find that >50% of the z ∼ 7.5 Lyman-break galaxies (LBGs) with magF125W > 25.5 are missed due to the absence of a filter redward of the Lyman break in F105W, hindering the UV color accuracy of the candidates. We conduct a QSO-LBG clustering analysis revealing a low LBG excess of 0.46−0.08+1.52 in this quasar field, consistent with an average or low-density field. Consequently, this result does not present strong evidence of an LBG overdensity around ULAS J1342+0928. Furthermore, we identify two LBG candidates with a z phot matching a confirmed z = 6.84 absorber along the line of sight to the quasar. All these galaxy candidates are excellent targets for follow-up observations with JWST and/or ALMA to confirm their redshift and physical properties.

Young Metal-Rich Stars in the Halo of M82: a tidal dwarf galaxy?

Abstract We present Hubble Space Telescope Advanced Camera for Surveys photometry of an arc structure of stars in the southern halo of M82. The structure is located at a projected height of ≈5 kpc from the disk of M82 but its stellar content exhibits a younger mean age than that of typical halo stars. We measured the distance, mean age, and peak metallicity of the stars in the arc and used them to determine the structure’s origin. The arc color-magnitude diagram yields a tip of red giant branch (RGB) distance of 3.63 ± 0.08 Mpc, similar to that of M82, confirming its association with the galaxy. The color-magnitude diagram also reveals a population of predominantly young stars with a mean age of ≈100 - 160 Myr, which is similar to that of the young stars formed in the tidal debris between M81, M82, and NGC 3077. We derived the mean metallicity of the halo stars surrounding the arc to be [Fe/H] ≈ -1.0, similar to that of the RGB population of the halo of M81. The RGB stars in the arc, while they are slightly more metal-rich than the halo RGB, show no spatial correlation with the young stars, suggesting that the arc is consist of mainly young stellar population. Furthermore, the observed age gradient along the structure of the arc indicates that the arc began forming from the closer side to M82, then moving outward. Taken together, the results suggest that the formation of the young stars which gives rise to the arc structure could have been triggered by the interactions between M82’s southern outflow and the HI gas clouds surrounding it, representing a snapshot of a tidal dwarf galaxy in the process of forming.

TREASUREHUNT: Transients and Variability Discovered with HST in the JWST North Ecliptic Pole Time-domain Field

The James Webb Space Telescope (JWST) North Ecliptic Pole (NEP) Time-domain Field (TDF) is a >14′ diameter field optimized for multiwavelength time-domain science with JWST. It has been observed across the electromagnetic spectrum both from the ground and from space, including with the Hubble Space Telescope (HST). As part of HST observations over three cycles (the “TREASUREHUNT” program), deep images were obtained with the Wide Field Camera on the Advanced Camera for Surveys in F435W and F606W that cover almost the entire JWST NEP TDF. Many of the individual pointings of these programs partially overlap, allowing an initial assessment of the potential of this field for time-domain science with HST and JWST. The cumulative area of overlapping pointings is ∼88 arcmin2, with time intervals between individual epochs that range between 1 day and 4+ yr. To a depth of m AB ≃ 29.5 mag (F606W), we present the discovery of 12 transients and 190 variable candidates. For the variable candidates, we demonstrate that Gaussian statistics are applicable and estimate that ∼80 are false positives. The majority of the transients will be supernovae, although at least two are likely quasars. Most variable candidates are active galactic nuclei (AGNs), where we find 0.42% of the general z ≲ 6 field galaxy population to vary at the ∼3σ level. Based on a 5 yr time frame, this translates into a random supernova areal density of up to ∼0.07 transients arcmin−2 (∼245 deg−2) per epoch and a variable AGN areal density of ∼1.25 variables arcmin−2 (∼4500 deg−2) to these depths.

OPTICAL CHARACTER RECOGNITION SYSTEM

Optical character acknowledgment (OCR) is the method concerning the classification of optical designs contained in a advanced picture. OCR accomplishes character acknowledgment through division, highlight extraction, and classification. OCR could be a interesting innovation that changes over different sorts of records, such as checked paper reports, PDF records, or pictures captured by a advanced camera into editable and searchable information. Here's a brief diagram of the OCR technique. The distinctive applications are OCR frameworks highlighted and examined subsequent to taken after by the current status of OCR frameworks. Thus, long run of OCR systems is displayed. Upon recognizing the dialect of the content, the OCR framework can interpret the text into the pointed target dialect employing a interpretation API or benefit like Google Decipher, Microsoft Interpreter, or DeepL. And the deciphered content replaces the first content extricated from the image. Text-to-Speech Transformation: After interpretation, the OCR framework can change over the deciphered content into discourse utilizing a text-to-speech motor. This highlight permits clients to tune in to the deciphered content rather than perusing it, which can demonstrate to be especially supportive for people with visual disabilities or in scenarios where hands-free operation gets to be vital!!! Key Words: text, image, scan, speech, language translation, conversion, extract, character

The Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey

We present the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey, a deep slitless spectroscopic and imaging Cycle 1 JWST treasury survey designed to constrain feedback mechanisms in low-mass galaxies across cosmic time. NGDEEP targets the Hubble Ultra Deep Field (HUDF) with NIRISS slitless spectroscopy ( flim,line,5σ≈1.2× 10−18 erg s−1 cm−2) to measure metallicities and star formation rates (SFRs) for low-mass galaxies through the peak of the cosmic SFR density (0.5 < z < 4). In parallel, NGDEEP targets the HUDF-Par2 parallel field with NIRCam ( mlim,5σ=30.6−30.9 ) to discover galaxies to z > 12, constraining the slope of the faint end of the rest-ultraviolet luminosity function. NGDEEP overlaps with the deepest HST Advanced Camera for Surveys optical imaging in the sky, F435W in the HUDF ( mlim,F435W=29.6 ) and F814W in HUDF-Par2 ( mlim,F814W=30 ), making this a premier HST+JWST deep field. As a treasury survey, NGDEEP data are public immediately, and we will rapidly release data products and catalogs in the spirit of previous deep-field initiatives. In this paper we present the NGDEEP survey design, summarize the science goals, and detail plans for the public release of NGDEEP reduced data products.

Development of an AI-based image/ultrasonic convergence camera system for accurate gas leak detection in petrochemical plants

Outdoor pipeline leaks are difficult to accurately measure using existing concentration measurement systems installed in petrochemical plants owing to external air currents. Besides, leak detection is only possible for a specific gas. The purpose of this study was to develop an image/ultrasonic convergence camera system that incorporates artificial intelligence (AI) to improve pipe leak detection and establish a real-time monitoring system. Our system includes an advanced ultrasonic camera coupled with a deep learning-based object-detection algorithm trained on pipe image data from petrochemical plants. The collected data improved the accuracy of detected gas leak localization through deep learning. Our detection model achieves an mAP50 (Mean average precision calculated at an intersection over union (IoU) threshold of 0.50)score of 0.45 on our data and is able to detect the majority of leak points within a system. The petrochemical plant environment was simulated by visiting petrochemical plants and reviewing drawings, and an outdoor experimental demonstration site was established. Scenarios such as flange connection failure were set under medium-/low-pressure conditions, and the developed product was experimented under gas leak conditions that simulated leakage accidents. These experiments enabled the removal of potentially confounding surrounding noise sources, which led to the false detection of actual gas leaks using the AI piping detection technique.

Imaging Flow Cytometry: Development, Present Applications, and Future Challenges.

Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the detailed imaging characteristics of microscopy. In our comprehensive review, we adopt a historical perspective to chart the development of ImFC, highlighting its origins and current state of the art and forecasting potential future advancements. The genesis of ImFC stemmed from merging the hydraulic system of a flow cytometer with advanced camera technology. This synergistic coupling facilitates the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. Nevertheless, ImFC's implementation has encountered hurdles, particularly in developing software capable of managing its sophisticated data acquisition and analysis needs. The scale and complexity of the data generated by ImFC necessitate the creation of novel analytical tools that can effectively manage and interpret these data, thus allowing us to unlock the full potential of ImFC. Notably, artificial intelligence (AI) algorithms have begun to be applied to ImFC, offering promise for enhancing its analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from the high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories, but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.

Discovery of Globular Cluster Candidates in the Dwarf Irregular Galaxy IC 2574 Using HST/ACS Imaging

ABSTRACT We report the discovery of 23 globular cluster (GC) candidates around the relatively isolated dwarf galaxy IC 2574 within the Messier 81 (M81) group, at a distance of 3.86 Mpc. We use observations from the HST Advanced Camera for Surveys (ACS) to analyse the imaging in the F814W and F555W broad-band filters. Our GC candidates have luminosities ranging from −5.9 ≥ MV ≥ −10.4 and half-light radii of 1.4 ≤ rh ≤ 11.5 pc. We find the total number of GCs (NGC) = 27 ± 5 after applying completeness corrections, which implies a specific frequency of SN = 4.0 ± 0.8, consistent with expectations based on its luminosity. The GC system appears to have a bimodal colour distribution, with 30 percent of the GC candidates having redder colours. We also find five objects with extremely blue colours that could be young star clusters linked to an intense star formation episode that occurred in IC 2574 ∼1 Gyr ago. We make an independent measurement of the halo mass of IC 2574 from its kinematic data, which is rare for low mass galaxies, and find log M200 = 10.93 ± 0.08. We place the galaxy on the well-known GC system mass–halo mass relation and find that it agrees well with the observed near-linear relation. IC 2574 has a rich GC population for a dwarf galaxy, which includes an unusually bright ω Cen-like GC, making it an exciting nearby laboratory for probing the peculiar efficiency of forming massive GCs in dwarf galaxies.

The Cosmos in Its Infancy: JADES Galaxy Candidates at z > 8 in GOODS-S and GOODS-N

We present a catalog of 717 candidate galaxies at z > 8 selected from 125 square arcmin of NIRCam imaging as part of the JWST Advanced Deep Extragalactic Survey (JADES). We combine the full JADES imaging data set with data from the JWST Extragalactic Medium Survey and First Reionization Epoch Spectroscopic COmplete Survey (FRESCO) along with extremely deep existing observations from Hubble Space Telescope (HST)/Advanced Camera for Surveys (ACS) for a final filter set that includes 15 JWST/NIRCam filters and five HST/ACS filters. The high-redshift galaxy candidates were selected from their estimated photometric redshifts calculated using a template-fitting approach, followed by visual inspection from seven independent reviewers. We explore these candidates in detail, highlighting interesting resolved or extended sources, sources with very red long-wavelength slopes, and our highest-redshift candidates, which extend to z phot ∼ 18. Over 93% of the sources are newly identified from our deep JADES imaging, including 31 new galaxy candidates at z phot > 12. We also investigate potential contamination by stellar objects, and do not find strong evidence from spectral energy distribution fitting that these faint high-redshift galaxy candidates are low-mass stars. Using 42 sources in our sample with measured spectroscopic redshifts from NIRSpec and FRESCO, we find excellent agreement to our photometric redshift estimates, with no catastrophic outliers and an average difference of 〈Δz = z phot − z spec〉 = 0.26. These sources comprise one of the most robust samples for probing the early buildup of galaxies within the first few hundred million years of the Universe’s history.

Imaging Spectropolarimetry—A New Observing Mode on the Hubble Space Telescope’s Advanced Camera for Surveys

Imaging spectropolarimetry is a new observing mode on the Advanced Camera for Surveys (ACS) aboard the Hubble Space Telescope (HST) that was commissioned in Cycle 30 and is available to HST observers starting in Cycle 31 (i.e., from 2023). It is a technique that is accessible from ground-based observatories, but the superb spatial resolution afforded by HST/ACS combined with the slitless nature of HST/ACS grism spectroscopy opens up the possibility of studying polarized extended emission in a way that is not currently possible even with Adaptive Optics facilities on the ground. This mode could help to study interesting targets including (but not limited to) QSOs, active galactic nucleus and Radio Galaxies, ISM Dust Properties, Pre-Planetary Nebulae, Proto-Planetary and Debris Disks, Supernovae/Supernova Remnants, and solar system objects. This research note presents the preliminary results from the calibration programs used to calibrate imaging spectropolarimetry on HST/ACS.

The Hubble Deep Hydrogen Alpha (HDHα) Project. I. Catalog of Emission-line Galaxies

We present the first results of the Hubble Deep Hydrogen Alpha (HDHα) project, which analyzes the space-borne deep Hα narrowband imaging data in the GOODS-S region. The HDHα data comprises 72 orbits’ images taken with the Hubble Space Telescope (HST) Advanced Camera for Surveys/Wide Field Channel F658N filter. The exposure time varies across a total area of ∼76.1 arcmin2, adding up to a total exposure time of 195.7 ks, among which 68.8 ks are spent in the deepest region. These images are aligned, reprojected, and combined to have the same pixel grid as the Hubble Legacy Fields. The scientific goals of the HDHα include establishing a sample of emission-line galaxies (ELGs) including [O iii] emitters at z ∼ 0.3, [O ii] emitters at z ∼ 0.8, and Lyα emitters (LAEs) at z ∼ 4.4, studying the line morphology of ELGs with high resolution imaging data, and statistically analyzing the line luminosity functions and line equivalent-width distributions of ELGs selected with HST. Furthermore, the HDHα project enhances the legacy value of the GOODS-S field by contributing the first HST-based narrowband image to the existing data sets, which includes the HST broadband data and other ancillary data from X-ray to radio taken by other facilities. In this paper, we describe the data reduction process of the HDHα, select ELGs based on HST's F658N and broadband data, validate the redshifts of the selected candidates by crossmatching with the public spectroscopic catalogs in the GOODS-S, and present a final catalog of the confirmed [O iii] emitters at z ∼ 0.3, [O ii] emitters at z ∼ 0.8, and LAEs at z ∼ 4.4.

The 3D Kinematics of the Orion Nebula Cluster. II. Mass-dependent Kinematics of the Inner Cluster

We present the kinematic analysis of 246 stars within 4′ from the center of Orion Nebula Cluster (ONC), the closest massive star cluster with active star formation across the full mass range, which provides valuable insights in the formation and evolution of star cluster on an individual-star basis. High-precision radial velocities and surface temperatures are retrieved from spectra acquired by the NIRSPEC instrument used with adaptive optics (NIRSPAO) on the Keck II 10 m telescope. A 3D kinematic map is then constructed by combining with the proper motions previously measured by the Hubble Space Telescope Advanced Camera for Surveys/WFPC2/WFC3IR and Keck II NIRC2. The measured root-mean-squared velocity dispersion is 2.26 ± 0.08 km s−1, significantly higher than the virial equilibrium’s requirement of 1.73 km s−1, suggesting that the ONC core is supervirial, consistent with previous findings. Energy equipartition is not detected in the cluster. Most notably, the velocity of each star relative to its neighbors is found to be negatively correlated with stellar mass. Low-mass stars moving faster than their surrounding stars in a supervirial cluster suggests that the initial masses of forming stars may be related to their initial kinematic states. Additionally, a clockwise rotation preference is detected. A weak sign of inverse mass segregation is also identified among stars excluding the Trapezium stars, although it could be a sample bias. Finally, this study reports the discovery of four new candidate spectroscopic binary systems.

Sediment imaging and factorized Haar wavelet transformation techniques for characterizing particle size distributions of granular soils

The particle size distribution (PSD) significantly influences various mechanical behaviors of granular soils. Advances in automation and camera technology allow for a rapid, green, and accurate PSD determination. This study develops a new sediment imaging system, mainly consisting of a sediment column filled with water and a line-scan camera driven by programmable logic controller, which can automatically capture high-resolution soil segregation images. The soil particles settle through the sediment column and segregate by size, which is scanned to generate sediment images. Naturally, the coarsest particles settle at the bottom of the image, while the finest particles are located at the top. Therefore, the analysis of the sediment image enables the determination of PSD. The higher sediment column, despite of poor operationality, yields better particle segregation quality and thus the more accurate PSD result. This study proposed a factorized Haar wavelet transformation (FHWT) algorithm that can account for less perfect segregation of particles by size, enabling to shorten the height of sediment column. The proposed hardware and FHWT are evaluated by six natural sands with different colors, internal textures, and geological origins. An excellent match between image-based PSDs and sieving-based PSDs are observed. Based on FHWT, four sediment columns with different heights of 2.1 m, 1.5 m, 1.0 m, 0.5 m are tested. The results indicate that columns higher than 1.0 m yield acceptable particle segregation results. Furthermore, the proposed FHWT is also adaptable to process high-resolution images captured by advanced cameras.

Assessing the Impacts of Merge and Speed Control Strategies on Highway Work Zone Safety and Operations Using Artificial Intelligence and Advanced Sensors

Speed management and merge control are two critical factors influencing highway work zone safety and operations. This study adopts artificial intelligence (AI) and advanced radar and thermal camera sensors to generate detailed vehicle trajectory and speed data, which are used to quantify the impacts of various Traffic control strategies on driver behavior as well as on work zone safety and operations. The strategies considered include transverse rumble strips, flashing speed limit signs, dynamic message signs, and different lane closure taper lengths. Among these strategies, flashing speed limit signs are found to significantly reduce vehicle speeds when approaching the lane closure point.

Estimating photometric redshift from mock flux for CSST survey by using weighted Random Forest

ABSTRACT Accurate estimation of photometric redshifts (photo-z) is crucial in studies of both galaxy evolution and cosmology using current and future large sky surveys. In this study, we employ Random Forest (RF), a machine learning algorithm, to estimate photo-z and investigate the systematic uncertainties affecting the results. Using galaxy flux and colour as input features, we construct a mapping between input features and redshift by using a training set of simulated data, generated from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and COSMOS catalogue, with the expected instrumental effects of the planned China Space Station Telescope (CSST). To improve the accuracy and confidence of predictions, we incorporate inverse variance weighting and perturb the catalogue using input feature errors. Our results show that weighted RF can achieve a photo-z accuracy of $\rm \sigma _{NMAD}=0.025$ and an outlier fraction of $\rm \eta =2.045\ \hbox{per cent}$, significantly better than the values of $\rm \sigma _{NMAD}=0.043$ and $\rm \eta =6.45\ \hbox{per cent}$ obtained by the widely used Easy and Accurate Zphot from Yale (EAZY) software, which uses template-fitting method. Furthermore, we have calculated the importance of each input feature for different redshift ranges and found that the most important input features reflect the approximate position of the break features in galaxy spectra, demonstrating the algorithm’s ability to extract physical information from data. Additionally, we have established confidence indices and error bars for each prediction value based on the shape of the redshift probability distribution function, suggesting that screening sources with high confidence can further reduce the outlier fraction.

People Tracking in Video Surveillance Systems Based on Artificial Intelligence

As security is one of the basic human needs, we need security systems that can prevent crimes from happening. In general, surveillance videos are used to observe the environment and human behavior in a given location. However, surveillance videos can only be used to record images or videos, without additional information. Therefore, more advanced cameras to obtain other additional information such as the position and movement of people. This research extracted this information from surveillance video footage using a person tracking, detection and identification algorithm. The framework for these is based on deep learning algorithms, a popular branch of artificial intelligence. In the field of video surveillance, a person tracking is considered a challenging task. Many computer vision, machine learning and deep learning techniques have been developed in recent years. The majority of these techniques are based on frontal view images or video sequences. In this work, we will compare some previous work related to the same topic.

A novel IoT based machine vision system for on-machine diameter measurement and optimization

The use of machine vision systems has been made user-friendly, cost-effective, and flawless by the rapid development in the fields of advanced electro-optical and camera systems, electronics systems, and software systems. One such application of machine vision systems in the field of manufacturing is the inspection of a semi-finished or finished component during an ongoing manufacturing process. In this study, the camera’s intrinsic and extrinsic parameters were maintained constant, while red, green, and blue light sources were employed to measure the component diameter using pixel analysis. A novel approach was used in an IoT-based machine vision system where, on the same image, the smartphone camera was calibrated and the image diameter of the component under study was measured, which was found to be quite accurate. Four different cases were used in the error analysis of image diameter, in which experimental results show that under blue light, the percentage pixel error span is the largest at 0.2624% followed by 0.1422% under green light and 0.0903% under red light. Further, the use of four different cases was followed by the ‘Weighted Sum Model’, which optimized the percentage errors in estimated actual diameter precisely and effectively, where outcome results showed that the approximate percentage errors were determined within 0.8% for blue light, 0.5% for a red light, and 0.1% for a green light. The proposed IoT-based machine vision system was found to be robust and effective for on-machine measurement.

Smart Parking System using Image Detection Algorithms &amp; Optical Character Recognition (OCR)

Abstract: The Smart Parking System is an innovative and effective solution designed to make parking more efficient in urban areas, using advanced technology to help both vehicle owners and parking operators automate and optimize the parking process. When a vehicle enters the parking lot, it is successfully identified using an advanced sensor and camera system, with a highresolution camera that records the license plate number and optical character recognition (OCR) technology. Get accurate license plate numbers. The data is then stored in the MongoDB database along with the input timestamp and when the vehicle moves, the license plate is re-identified using the same technique and the system stores the input timestamp associated with the detected license plate in the database. he does. Reading from Parking time is calculated from the time difference between entering and leaving the parking lot, and the parking fee is calculated per hour. To facilitate payment, a QR code is generated and displayed at the exit. Vehicle owners can use their mobile device to scan this QR code and connect to the secure payment gateway. After the payment is completed, the exit door will open automatically and the car can leave the parking lot. This smart parking system offers many benefits, including fewer numbers, increased accuracy, reduced waiting time, and efficient payment processing. It reduces congestion in urban areas and improves traffic flow while providing users with convenient and troublefree parking

Environmental Impacts on the Rest-frame UV Size and Morphology of Star-forming Galaxies at z ∼ 2

We report the measurement of rest-frame UV size and morphology of Hα-emission-selected star-forming galaxies (HAEs) in four protoclusters at z ∼ 2 (PKS 1138-262, USS 1558-003, PHz G237.0+42.5, and CC 2.2) using archival Hubble Space Telescope Advanced Camera Survey (HST/ACS) F814W data. We compare the measurement of 122 HAEs in protoclusters detected by HST/ACS to a coeval comparison field sample of 436 HAEs. We find the size distributions of protocluster and field HAEs are similar with a typical half-light radius of ∼2.5 kpc. At fixed stellar mass, there is no significant difference between HAEs in the protocluster and in the field, which is also supported by stacking analyses. This result suggests that the environment does not significantly affect the size of galaxies during the star-forming phase at this epoch. Based on the Sérsic index and nonparametric morphologies, HAE morphologies in both environments at z ∼ 2 in rest-frame UV are consistent with disk-like star-forming galaxies, although we also find 29% ± 4% HAEs showing disturbed morphologies. The fraction of disturbed galaxies is higher in the protocluster environment, with 39% ± 8% protocluster HAEs showing disturbed morphologies, compared to 26% ± 4% in the comparison field. The apparent disturbed morphologies are correlated with higher star formation activity and may be caused by either in situ giant clumps or mergers.