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Multiple scattering poses a fundamental limitation in deep imaging, especially for high-resolution optical imaging methods. The amalgamation of reflection matrix measurements and optical coherence tomography (OCT) has afforded significant advantages for deep imaging through highly scattering media. To empirically exhibit the superior performance of reflection matrix OCT (RMOCT), this study proposes a unique method to ascertain the actual resolutions at each imaging point. In contrast to conventional theoretical lateral resolutions, these resolutions are derived by applying time-reversal decomposition to the time-gated reflection matrix. Moreover, the concept of contribution rate, which quantifies the imaging contributions for each point, is introduced by considering the local imaging point itself and its neighboring points. The contribution rate provides a quantitative evaluation of the imaging quality afforded by a system. To the best of our knowledge, this study represents the comprehensive assessment of the practical performance of RMOCT in terms of actual resolving power and imaging quality. [ABSTRACT FROM AUTHOR]

Background: To increase skin permeability, various transdermal delivery techniques have been developed. However, due to the stratum corneum as a skin barrier, transdermal delivery remains limited. Aims: In this study, we evaluated efficacy and safety of arc‐poration as a novel technique disrupting the stratum corneum. Results: Optical images and histological analysis using reconstituted human skin and porcine skin showed that the treatment of arc‐poration created micropores with an average diameter of approximately 100 μm only to the depth of the stratum corneum, but not viable epidermis. In addition, the Franz diffusion cell experiment using reconstituted human skin showed a remarkable increase in permeability following pretreatment with arc‐poration. Clinical results clearly demonstrated the enhancement of the skin‐improving effect of cosmetics by pretreatment of arc‐poration in terms of gloss, hydration, flakiness, texture, tone, tone evenness, and pigmentation of skin, without causing abnormal skin responses. The concentration of ozone and nitrogen oxides generated by arc‐poration was below the permissible value for the human body. Conclusions: Arc‐poration can increase skin permeability by creating stratum corneum‐specific micropores, which can enhance the skin‐improving effect of cosmetics without adverse responses. [ABSTRACT FROM AUTHOR]

Landslide event inventories are one of the most critical datasets to increase knowledge on landslide occurrences. However, they are rarely available in various regions, especially in countries of the Global South. This study aims to generate rainfall-induced landslide event inventories and define the rainfall thresholds responsible for landslide occurrence at the national scale of Malawi, Africa. We mainly followed a three-step methodology to generate landslide inventories. First, we went through media reports to identify documented landslide events. Second, we used Sentinel-2 images to identify possible areas affected by landslides using automated change detection algorithms based on vegetation indices. Third, we manually went through optical images provided by Planet Lab and Google Earth and mapped landslides via visual image interpretation. Overall, we mapped 27 rainfall-induced landslide inventories between 2003 and 2022, with a total of 4709 individual landslides. We then analysed the Malawian terrain and identified two different landscape clusters (i.e. Cluster 1 and Cluster 2) showing similar morphometric and climatic conditions. Ultimately, we calculated the rainfall threshold for each landscape cluster. The minimum rainfall amounts responsible for landsliding correspond to 66 mm/two-day and 51 mm/day in Clusters 1 and 2, respectively. In this context, our paper not only presents and shares the first national-scale, digital rainfall-induced landslide event inventory database of Malawi but also suitable rainfall thresholds to be potentially exploited for a national scale landslide early warning system. A similar framework could be applied to generate landslide inventories for other data scarce regions. [ABSTRACT FROM AUTHOR]

In the context of the hierarchical model of galaxy evolution, polar ring galaxies (PRGs) are considered the intermediate phase between ongoing mergers and quiescent galaxies. This study explores the globular cluster system (GCS) and its properties in the nearest PRG, NGC4262, serving as a pilot investigation to study GCS in nearby PRGs. We utilize wide and deep-field observations of the CFHT as part of the NGVS to investigate the GCS of NGC4262. We presented the first optical image of NGC4262 with an optically faint ring component. The photometric analysis of the GCS displays a distinct colour bimodality. We estimate the total number of GCs for NGC4262 to be 266 |$\pm$| 16 GCs with a specific frequency of 4.2 |$\pm$| 0.8 and a specific mass of 0.23 |$\pm$| 0.01, which is relatively high compared to other galaxies of similar mass and environmental conditions. The spatial and azimuthal distributions of subpopulations reveal strong evidence of previous interactions within the host galaxy. The colour distribution of GCS in NGC4262 shows a gradient of –0.05 |$\pm$| 0.01 within 5.5 |$^\prime$|⁠ , supporting the notion of past interactions and evolutionary transitions. PRG NGC4262 conforms to the overall trend of the GCS mass with respect to the halo mass. Furthermore, our investigation of the global scaling relations between GCS and host galaxy parameters provides further support for the hypothesis that PRGs are an intermediate phase connecting ongoing mergers and quiescent galaxies. [ABSTRACT FROM AUTHOR]

Optical imaging through dynamic and complex scattering media has attracted various applications, e.g., ranging from scene imaging to cell imaging. Nevertheless, imaging through complex media is full of challenges attributed to the inhomogeneous scattering, leading to nonlinear effects. Although ghost imaging (GI) has proven effective in solving some scattering problems, dynamic and complex scattering still requires an efficient solution. In this Letter, we report a model based on adaptive moving average (AMA) to correct the influence of dynamic scattering media from a statistical perspective for high-resolution GI. The developed AMA correction method selects an appropriate time window based on the changing trend of measured single-pixel light intensities to accurately correct a series of dynamic scaling factors. Then, the corrected single-pixel light intensities are used for ghost reconstruction using a second-order correlation algorithm. A series of optical experiments are conducted to verify superiority of the proposed method. Moreover, the proposed method can be applied with other algorithms to enhance the quality of the reconstructed ghost images. By leveraging a statistical model based on the measured data, the proposed scheme offers an enhanced solution to solving dynamic and complex scattering problems in GI. [ABSTRACT FROM AUTHOR]

[Display omitted] • It elucidates the advantages and disadvantages of three main clearing methods. • Integrating clearing with others enables single-cell analysis of intact tissues. • Tissue clearing provides perspective in therapeutics. The techniques of tissue clearing have been proposed and applied in anatomical and biomedical research since the 19th century. As we all know, the original study of the nervous system relied on serial ultrathin sections and stereoscopic techniques. The 3D visualization of the nervous system was established by software splicing and reconstruction. With the development of science and technology, microscope equipment had constantly been upgraded. Despite the great progress that has been made in this field, the workload is too complex, and it needs high technical requirements. Abundant mistakes due to manual sections were inescapable and structural integrity remained questionable. According to the classification of tissue transparency methods, we introduced the latest application of transparency methods in central and peripheral nerve research from optical imaging, molecular markers and data analysis. This review summarizes the application of transparent technology in neural pathways. We hope to provide some inspiration for the continuous optimization of tissue clearing methods. [ABSTRACT FROM AUTHOR]

[Display omitted] Near-infrared (NIR)-emitting persistent luminescence nanoparticles (PLNPs) are ideal optical imaging contrast reagents characterized by autofluorescence-free optical imaging for their frontier applications in long-term bioimaging. Preparation of uniform small-sized PLNPs with excellent luminescence performance is crucial for biomedical applications, but challenging. Here, we report a facile magnesium doping strategy to achieve size-independent boost of NIR persistent luminescence in typical and most concerned ZnGa 2 O 4 :Cr3+ PLNPs. This strategy relies on the doping of Mg2+ ions that with similar size of Zn2+ ions in the host lattice matrix, and concomitant to the electron traps tailoring tuned by varying the feed ratio of Mg2+. The optimum Mg2+-doped PLNPs give a long afterglow time (signal-to-noise ratio (SNR) = 31.6 at 30 d) without changing the desirable uniform sub-10 nm size of the original nanocrystals. The appropriate increase of the depth and concentration of electron trap contribute jointly to the enhancement of lifetime (488 % longer, 20.57 s) and afterglow time for 700 nm persistent luminescence. Meanwhile, these PLNPs keep the original excellent rechargeability and promote over 60 times increase of SNR in renewable in vivo imaging. This simple strategy provides a basis for new opportunities to address the critical challenge of effective optical performance boost in small-sized PLNPs. [ABSTRACT FROM AUTHOR]

The emerging optoelectronic neuromorphic devices are widely concerned due to their capability to integrate the functions of signal sensing, memory, and processing. Although significant advancements have been made in the study of individual optoelectronic synaptic devices, the development of hardware‐level image recognition systems based on photo‐synapse arrays remains a challenge. In this study, a crosstalk‐free, easy‐to‐integrate, and scalable 8 × 8 crossbar array for optical image sensing and storage is demonstrated using vertical two‐terminal ZnO photo‐synapses with the self‐denoising function. By designing peripheral circuits, a complete hardware‐level artificial visual system is constructed that successfully implements the real‐time pattern recognition tasks for 8 × 8 pixel images. The excellent performance of the photo‐synapse array shows its remarkable ability in highly efficient optic neuromorphic computing. Additionally, an in‐sensor reservoir computing (RC) system is constructed for image recognition of handwritten digits. The system achieves a high classification accuracy of 95.1%. [ABSTRACT FROM AUTHOR]

Background: Identifying remission is of high importance in rheumatoid arthritis (RA) because remission is associated with less structural progression. We investigated the efficacy of a new optical imaging device, HandScan, to identify RA remission, as defined by ultrasound (US). Methods: 61 RA patients were included. Disease activity was evaluated by clinical assessment and US, using gray-scale (GS) and Power Doppler (PD). HandScan determined unitary optical spectral transmission (OST) values for wrists, metacarpophalangeal and proximal interphalangeal joints. At the patient level, three composite HandScan (HS) scores were calculated: total HS score; disease activity score OST (DAS-OST) and DAS-OST without patient global assessment (PtGA). Using ROC curves, we determined HS cut-offs to identify US-defined remission. Results: At the joint level, unitary OST values significantly correlated with GS synovitis [odds ratio (OR) 2.43, p < 0.0001] and PD positivity (OR 3.72, p = 0.0002). At the patient level, total HS score and DAS-OST were significantly associated with all gray-scale US (GSUS) and power doppler US (PDUS) parameters evaluated (synovitis number and grade, synovial thickness, PD grade) (p < 0.05). The cut-off to identify US-defined remission at the joint level was of 0.92, giving an 81% sensitivity and a 96% positive predictive value (PPV). At the patient level, ROC-curves failed to identify a robust cut-off for the total HS score, but did identify a cut-off (3.68) for DAS-OST to identify US-defined remission, but with lower sensitivity (75%), specificity (56%) and PPV (67%). Conclusions: HandScan is a non-invasive optical imaging technique providing OST values that correlate with GSUS and PDUS parameters. In addition, HandScan is able to reliably identify US-defined remission in RA at the joint level, with a good sensitivity and high PPV. At the patient level, HandScan DAS-OST can also determine US remission (while total HS score failed to do so), but with lower performance. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) low-frequency lightning mapping system (LF-LMS) in north China has been updated. The lightning electric field derivative (dE/dt) sensor and continuous acquisition mode has been newly designed to ensure a capability of entire lightning processes detection, especially weak discharges during lightning the initiation process. The twice cross-correlation delay estimation and the grid iteration nested optimization location algorithm are used to realize the 3D location of the discharge channel, and the location resolution and calculation speed are balanced consequently. The location results of the rocket-triggered lightning demonstrated that the system achieved a high-resolution mapping of lightning discharge channels, which coincided well with the optical images. The horizontal and vertical location error for rocket triggered lightning was less than 40 m in both horizontal and vertical. Intracloud (IC) lightning flashes were observed to be initiated by three different discharge processes, initial breakdown pulse (IBP), narrow bipolar event (NBE), and initial E-change (IEC). The corresponding initial height was 10.5 km, 6.9 km, and 9.2 km, respectively. The upward negative leader was initially located, followed by scatter radiation sources and negative recoil leaders in the lower negative charge region for all cases. The electric field characteristics of the IEC and subsequent IBPs indicated that they are different discharge processes with the same current direction. The IEC process might correspond to the discharge process with continuous current and less noticeable current changes. [ABSTRACT FROM AUTHOR]

Optical approaches to modulating near work may be beneficial in arresting the progression of myopia To explore whether a long-distance optical image workbench (LOIW) can replace traditional near-distance reading and has beneficial influences on accommodation and choroidal parameters. This self-control study included two sessions. In session 1, an open-field autorefractor measured sustained accommodative response and pupil diameter when participants viewed virtual images from 3.42 m away through LOIW with + 0.50 D over-correction lenses (3.42 m/+0.50 D), LOIW with full-correction lenses (3.42 m/FC), or the visual target at a given distance of 0.33 m with + 0.50 D over-correction lenses (0.33 m/+0.50 D), with full-correction lenses (0.33 m/FC). In session 2, swept-source optical coherence tomography angiography assessed subfoveal choroidal thickness (SFChT), choroidal volume (CV), and choriocapillaris flow void percentage (FV%) at baseline and before and after reading via LOIW with + 0.50 D over-correction lenses (Far/+0.50 D) or in habitual near distance with full-correction (Near/FC) for a sustained 30-min period. Thirty-five myopic (mean spherical equivalent refraction: −1.11 ± 0.78 D) children (median [range] age: 8.36 [8–10] years) were analysed. Children viewing virtual images in 3.42 m/+0.50 D exhibited mild-degree accommodative leads (−0.54 ± 0.17 D), much smaller pupil diameter (3.39 ± 0.63 mm) and microfluctuations (0.13 ± 0.04 D); accommodative lags (0.88 ± 0.04 D), larger pupil diameter (5.05 ± 0.75 mm) and microfluctuations (0.88 ± 0.16 D) were observed in 0.33 m/+0.50 D, and the microfluctuations were unstable (F = 2.185, p = 0.038). Compared with baseline data, SFChT, CV, and FV% changed by 13.40 ± 4.71 μm, 0.26 ± 0.21 mm3, and − 0.14%±3.66% in Far/+0.50 D, respectively, while changes under Near/FC were − 10.19 ± 7.48 μm, −0.15 ± 0.22 mm3 and 1.95%±2.92%, respectively. All changes differed significantly between these two conditions (all p < 0.001). The long-distance working mode may exert beneficial effects on accommodative and choroidal parameters in myopic children compared with traditional near work. [ABSTRACT FROM AUTHOR]

Unsupervised learning, a branch of machine learning that can operate on unlabelled data, has proven to be a powerful tool for data exploration and discovery in astronomy. As large surveys and new telescopes drive a rapid increase in data size and richness, these techniques offer the promise of discovering new classes of objects and of efficient sorting of data into similar types. However, unsupervised learning techniques generally require feature extraction to derive simple but informative representations of images. In this paper, we explore the use of self-supervised deep learning as a method of automated representation learning. We apply the algorithm Bootstrap Your Own Latent to Galaxy Zoo DECaLS images to obtain a lower dimensional representation of each galaxy, known as features. We briefly validate these features using a small supervised classification problem. We then move on to apply an automated clustering algorithm, demonstrating that this fully unsupervised approach is able to successfully group together galaxies with similar morphology. The same features prove useful for anomaly detection, where we use the framework astronomaly to search for merger candidates. While the focus of this work is on optical images, we also explore the versatility of this technique by applying the exact same approach to a small radio galaxy data set. This work aims to demonstrate that applying deep representation learning is key to unlocking the potential of unsupervised discovery in future data sets from telescopes such as the Vera C. Rubin Observatory and the Square Kilometre Array. [ABSTRACT FROM AUTHOR]

We present 6 GHz Very Large Array radio images of 70 gravitational lens systems at 300 mas resolution, in which the source is an optically selected quasar, and nearly all of which have two lensed images. We find that about in half of the systems (40/70, with 33/70 secure), one or more lensed images are detected down to our detection limit of 20  μ Jy beam−1, similar to previous investigations and reinforcing the conclusion that typical optically selected quasars have intrinsic GHz radio flux densities of a few μ Jy (∼1023 W Hz−1 at redshifts of 1–2). In addition, for 10 cases it is likely that the lensing galaxies are detected in the radio. Available detections of, and limits on the far-infrared luminosities from the literature, suggest that nearly all of the sample lie on the radio-FIR correlation typical of star-forming galaxies, and that their radio luminosities are at least compatible with the radio emission being produced by star formation processes. One object, WISE2329−1258, has an extra radio component that is not present in optical images, and is difficult to explain using simple lens models. In-band spectral indices, where these can be determined, are generally moderately steep and consistent with synchrotron processes either from star formation/supernovae or AGNs. Comparison of the A/B image flux ratios at radio and optical wavelengths suggests a 10 per cent level contribution from finite source effects or optical extinction to the optical flux ratios, together with sporadic larger discrepancies that are likely to be due to optical microlensing. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate choroidal parameters in patients with systemic sclerosis (SSc) using enhanced depth imaging spectral-domain optical coherence tomography (EDI-SD-OCT) and to determine their relationships with clinical variables and ocular features. Methods: Thirty-three patients with SSc and 40 controls were enrolled. The groups did not differ with regard to age, sex, and axial length. The mean choroidal thickness and volume were obtained in each conventional Early Treatment of Diabetic Retinopathy Study grid subfield. The choroidal vascularity index (CVI), which provides a quantitative analysis of vasculature by calculating the proportion of the luminal area (LA) to the total choroidal area (TCA), was determined. Results: Lower choroidal thickness and volume were observed in the SSc group. The CVI was significantly higher in SSc patients, whereas the TCA, LA, and stromal area were significantly lower in the SSc group; however, the significant difference of the stromal component was more pronounced than that of the luminal component. Regression analyses did not identify any clinical factors associated with the CVI (except Ca-blocker use), central macular thickness, or volume. No significant differences in choroidal parameters were found within the SSc subtypes (diffuse cutaneous systemic sclerosis (dcSSc) vs. limited cutaneous systemic sclerosis (lcSSc)), or between eyes stratified according to SSc pattern (early, active, or late) using nailfold capillaroscopy (p > 0.05 for all). Conclusion: Our results, with notably higher CVI values, may shed new light on choroidal impairment in patients with SSc. Stromal involvement appeared to dominate the vascular component. [ABSTRACT FROM AUTHOR]

Empty large volume parenteral (LVP) bottle has irregular shape and narrow opening, and its detection accuracy of the foreign substances at the bottom is higher than that of ordinary packaging bottles. The current traditional detection method for the bottom of LVP bottles is to directly use manual visual inspection, which involves high labor intensity and is prone to visual fatigue and quality fluctuations, resulting in limited applicability for the detection of the bottom of LVP bottles. A geometric constraint-based detection model (GCBDM) has been proposed, which combines the imaging model and the shape characteristics of the bottle to construct a constraint model of the imaging parameters, according to the detection accuracy and the field of view. Then, the imaging model is designed and optimized for the detection. Further, the generalized GCBDM has been adopted to different bottle bottom detection scenarios, such as cough syrup and capsule medicine bottles by changing the target parameters of the model. The GCBDM, on the one hand, can avoid the information at the bottom being blocked by the narrow opening in the imaging optical path. On the other hand, by calculating the maximum position deviation between the center of visual inspection and the center of the bottom, it can provide the basis for the accuracy design of the transmission mechanism in the inspection, thus further ensuring the stability of the detection. [ABSTRACT FROM AUTHOR]

Aims. In this work we study the striking case of a narrow blue stream with a possible globular cluster-like progenitor around the NGC 7241 galaxy and its foreground dwarf companion. We want to figure out if the stream was generated by tidal interaction with NGC 7241 or if it first interacted with the foreground dwarf companion and later both fell together toward NGC 7241. Methods. We used four sets of observations, including a follow-up spectroscopic study of this stream based on data taken with the MEGARA instrument at the 10.4 m Gran Telescopio Canarias using the integral field spectroscopy mode, the Mount Lemmon 0.80 m telescope, the Telescopio Nazionale Galileo, the DESI Imaging Legacy surveys, and GALEX archival data. We also used high-resolution zoomed-in cosmological simulations. Results. Our data suggest that the compact object we detected in the stream is a foreground Milky Way halo star. Near this compact object we detect emission lines overlapping a less compact, bluer, and fainter blob of the stream that is clearly visible in both ultraviolet and optical deep images. From its heliocentric systemic radial velocity derived from the [O III]λ5007 Å lines (Vsyst = 1548.58 ± 1.80 km s−1) and new UV and optical broadband photometry, we conclude that this overdensity could be the actual core of the stream, with an absolute magnitude of Mg ∼ −10 and a g − r = 0.08  ±  0.11, consistent with a remnant of a low-mass dwarf satellite undergoing a current episode of star formation. From the width of the stream and assuming a circular orbit, we calculate that the progenitor mass can be typical of a dwarf galaxy, but it could also be substantially lower if the stream is on a very radial orbit or if it was created by tidal interaction with the companion dwarf instead of with NGC 7241. These estimates also suggest that this is one of the lowest mass streams detected to date beyond the Local Group. Finally, we find that blue stellar streams containing star formation regions are commonly predicted by high-resolution cosmological simulations of galaxies lighter than the Milky Way. This scenario is consistent with the processes explaining the bursty star formation history of some dwarf satellites, which are followed by a gas depletion and a fast quenching once they enter within the virial radius of their host galaxies for the first time. Thus, it is likely that the stream's progenitor is undergoing a star formation burst comparable to those that have shaped the star formation history of several Local Group dwarfs in the last few gigayears. [ABSTRACT FROM AUTHOR]

Transient absorption, or pump–probe microscopy is an absorption-based technique that can explore samples ultrafast dynamic properties and provide fluorescence-free contrast mechanisms. When applied to graphene and its derivatives, this technique exploits the graphene transient response caused by the ultrafast interband transition as the imaging contrast mechanism. The saturation of this transition is fundamental to allow for super-resolution optical far-field imaging, following the reversible saturable optical fluorescence transitions (RESOLFT) concept, although not involving fluorescence. With this aim, we propose a model to numerically compute the temporal evolution under saturation conditions of the single-layer graphene molecular states, which are involved in the transient absorption. Exploiting an algorithm based on the fourth order Runge–Kutta (RK4) method, and the density matrix approach, we numerically demonstrate that the transient absorption signal of single-layer graphene varies linearly as a function of excitation intensity until it reaches saturation. We experimentally verify this model using a custom pump–probe super-resolution microscope. The results define the intensities necessary to achieve super-resolution in a pump–probe nanoscope while studying graphene-based materials and open the possibility of predicting such a saturation process in other light-matter interactions that undergo the same transition. [ABSTRACT FROM AUTHOR]

Super-resolution imaging has rapidly emerged as an optical microscopy technique, offering advantages of high optical resolution over the past two decades; achieving improved imaging resolution requires significant efforts in developing super-resolution imaging agents characterized by high brightness, high contrast and high sensitivity to fluorescence switching. Apart from technical requirements in optical systems and algorithms, super-resolution imaging relies on fluorescent dyes with special photophysical or photochemical properties. The concept of aggregation-induced emission (AIE) was proposed in 2001, coinciding with unprecedented advancements and innovations in super-resolution imaging technology. AIE probes offer many advantages, including high brightness in the aggregated state, low background signal, a larger Stokes shift, ultra-high photostability, and excellent biocompatibility, making them highly promising for applications in super-resolution imaging. In this review, we summarize the progress in implementation methods and provide insights into the mechanism of AIE-based super-resolution imaging, including fluorescence switching resulting from photochemically-converted aggregationinduced emission, electrostatically controlled aggregation-induced emission and specific bindingregulated aggregation-induced emission. Particularly, the aggregation-induced emission principle has been proposed to achieve spontaneous fluorescence switching, expanding the selection and application scenarios of super-resolution imaging probes. By combining the aggregation-induced emission principle and specific molecular design, we offer some comprehensive insights to facilitate the applications of AIEgens (AIE-active molecules) in super-resolution imaging. [ABSTRACT FROM AUTHOR]

Background: Hydradermabrasion, also known as "HydraFacial," is an exfoliative cosmetic procedure for skin rejuvenation that has gained popularity. Despite its increasing popularity, clinical studies validating its efficacy with non‐invasive assessment of histological changes to the skin, are scarce. In this study, we used Line‐Field Confocal Optical Coherence Tomography (LC‐OCT), an optical imaging device, to non‐invasively visualize microscopic changes to skin anatomy after hydradermabrasion treatment. Materials/Methods: Eight volunteers (Fitzpatrick skin types II–V) were recruited for this study. Images, using LC‐OCT (DeepLive, DAMAE medical) were obtained before and after hydradermabrasion and at 2 weeks post‐treatment. A commercially available hydradermabrasion device was utilized to perform the dermabrasion. Results: In the epidermis, initially, a decrease in the average thickness of the stratum corneum, from 9.42 to 6.67 µm was visualized in LC‐OCT images after hydradermabrasion. However, at 2 weeks of follow‐up, the average stratum corneum thickness was 9.75 µm, resulting in an overall increase in the average thickness after treatment. Improved homogenization of the stratum corneum and decreased number of undulations in the epidermis post‐treatment were also visualized. In all the subjects, the superficial dermis appeared stretched, which returned to baseline by the 2‐week follow‐up. At the 2‐week follow‐up, there were no visible differences in the quality and quantity of collagen fibers in the dermis. Conclusion: In our study, LC‐OCT images of the epidermis and dermis demonstrated microscopic features of skin rejuvenation when treated with hydradermabrasion. Thus, not only highlighting the efficacy of hydradermabrasion but also the potential of LC‐OCT to serve as a tool for visualizing the microscopic effects of cosmetic procedures on skin anatomy. [ABSTRACT FROM AUTHOR]

More than 2 million implantable collamer lenses (ICLs) have been implanted worldwide. With a central port to improve aqueous flow through the ICL, the latest iteration of this phakic intraocular lens (pIOL) has been shown to have stable outcomes with very low rates of adverse events. However, correct planning and ICL size selection continue to be important to achieve an optimal vault. Shallow or excessive vaults are not complications in and of themselves but may increase the risk of complications. Historically, surgeons have relied on measurements of anterior chamber depth (ACD) and manual, caliper-measured white-to-white (WTW) distance to select the ICL size. New diagnostic and imaging technologies such as optical coherence tomography (OCT) and ultrasound biomicroscopy (UBM) provide additional opportunities for visualization and measurement of the intraocular dimensions involved in phakic intraocular lens implantation, including sulcus-to-sulcus (STS) and angle-to-angle (ATA) diameters. This paper reviews various approaches to ICL planning and sizing that have been published in the peer-reviewed literature, all of which produce acceptable results for predicting vault and size selection. Surgeons may also want to identify a methodology for patient evaluation and ICL size selection that best aligns with their personal preferences, diagnostic technology, and familiarity with analytical optimization tools. Plain Language Summary: Phakic intraocular lenses (pIOLs) are one method for correcting nearsightedness, with or without astigmatism. This category of refractive surgery has been growing rapidly in the US and around the world. Implantation of the implantable collamer lens (ICL), one type of pIOL, is safe and effective, with stable outcomes and low adverse event rates. When complications do occur, they are typically associated with an inappropriate vault, or distance between the implant and the natural lens. Preoperative planning and accurate ICL sizing are required to achieve an optimal vault and varies, depending on the diagnostic technology available to the surgeon. This paper reviews the current approaches to ICL planning and sizing in order to provide guidance to surgeons implanting this pIOL. [ABSTRACT FROM AUTHOR]

Summary Segmentation of neural somata is a crucial and usually the most time-consuming step in the analysis of optical functional imaging of neuronal microcircuits. In recent years, multiple auto-segmentation tools have been developed to improve the speed and consistency of the segmentation process, mostly, using deep learning approaches. Current segmentation tools, while advanced, still encounter challenges in producing accurate segmentation results, especially in datasets with a low signal-to-noise ratio. This has led to a reliance on manual segmentation techniques. However, manual methods, while customized to specific laboratory protocols, can introduce variability due to individual differences in interpretation, potentially affecting dataset consistency across studies. In response to this challenge, we present ViNe-Seg: a deep-learning-based semi-automatic segmentation tool that offers (i) detection of visible neurons, irrespective of their activity status; (ii) the ability to perform segmentation during an ongoing experiment; (iii) a user-friendly graphical interface that facilitates expert supervision, ensuring precise identification of Regions of Interest; (iv) an array of segmentation models with the option of training custom models and sharing them with the community; and (v) seamless integration of subsequent analysis steps. Availability and implementation ViNe-Seg code and documentation are publicly available at https://github.com/NiRuff/ViNe-Seg and can be installed from https://pypi.org/project/ViNeSeg/. [ABSTRACT FROM AUTHOR]

To objectively quantify the level of visual interference induced by lasers, we developed a biomimetic optical system designed to emulate human vision. This system is based on an optical model of the eye and synthetic imaging principles, allowing it to generate biomimetic optical images that closely mimic human visual perception. Upon exposure to a 532 nm laser, biomimetic optical images were captured under various ambient lighting conditions. By employing a contrast threshold model for human visual target detection and grayscale hierarchy analysis, we devised an evaluation model to quantify the levels of laser-induced visual interference. The bionic images obtained from our experiments, in conjunction with the constructed model, enabled us to assess the degree of laser-induced visual interference. Our results indicate that this system can effectively substitute the human eye when testing laser imaging effects, with the generated bionic images achieving up to 90% concordance with human vision. The proposed evaluation model facilitates the quantitative analysis of laser-induced visual impairment. This apparatus and evaluation model hold significant promise for the precise quantification of laser-induced visual interference levels. [ABSTRACT FROM AUTHOR]

Nonmelanoma skin cancers remain the most widely diagnosed types of cancers globally. Thus, for optimal patient management, it has become imperative that we focus our efforts on the detection and monitoring of cutaneous field carcinogenesis. The concept of field cancerization (or field carcinogenesis), introduced by Slaughter in 1953 in the context of oral cancer, suggests that invasive cancer may emerge from a molecularly and genetically altered field affecting a substantial area of underlying tissue including the skin. A carcinogenic field alteration, present in precancerous tissue over a relatively large area, is not easily detected by routine visualization. Conventional dermoscopy and microscopy imaging are often limited in assessing the entire carcinogenic landscape. Recent efforts have suggested the use of noninvasive mesoscopic (between microscopic and macroscopic) optical imaging methods that can detect chronic inflammatory features to identify pre‐cancerous and cancerous angiogenic changes in tissue microenvironments. This concise review covers major types of mesoscopic optical imaging modalities capable of assessing pro‐inflammatory cues by quantifying blood haemoglobin parameters and hemodynamics. Importantly, these imaging modalities demonstrate the ability to detect angiogenesis and inflammation associated with actinically damaged skin. Representative experimental preclinical and human clinical studies using these imaging methods provide biological and clinical relevance to cutaneous field carcinogenesis in altered tissue microenvironments in the apparently normal epidermis and dermis. Overall, mesoscopic optical imaging modalities assessing chronic inflammatory hyperemia can enhance the understanding of cutaneous field carcinogenesis, offer a window of intervention and monitoring for actinic keratoses and nonmelanoma skin cancers and maximise currently available treatment options. [ABSTRACT FROM AUTHOR]

Increase in viscosity under increasing shear stress, known as shear thickening (ST), is one of the most striking properties of dense particulate suspensions. Under appropriate conditions, they exhibit discontinuous shear thickening (DST), where the viscosity increases dramatically and can also transform into a solid-like state due to shear-induced jamming (SJ). The microscopic mechanism giving rise to such interesting phenomena is still a topic of intense research. A phenomenological model proposed by Wyart and Cates shows that the proliferation of stress-activated interparticle frictional contacts can give rise to such striking flow properties. Building on this model, recent work proposes and verifies a universal scaling relation for ST systems where two different power-law regimes with a well-defined crossover point are obtained. Nonetheless, the difference in the nature of the flow in these two scaling regimes remains unexplored. Here, using rheology in conjugation with high-speed optical imaging, we study the flow and local deformations in various ST systems. We observe that with increasing applied stress, the smooth flow changes into a spatio-temporally varying flow across the scaling crossover. We show that such fluctuating flow is associated with intermittent dilatancy, shear-band plasticity, and fracture induced by system-spanning frictional contacts. [ABSTRACT FROM AUTHOR]

This research explores the detection of flame front evolution in spark-ignition engines using an innovative neural network, the autoencoder. High-speed camera images from an optical access engine were analyzed under different air excess coefficient λ conditions to evaluate the autoencoder's performance. This study compared this new approach (AE) with an established method used by the same research group (BR) across multiple combustion cycles. Results revealed that the AE method outperformed the BR in accurately identifying flame pixels and significantly reducing overestimations outside the flame boundary. AE exhibited higher sensitivity levels, indicating its superior ability to identify pixels and minimize errors compared to the BR method. Additionally, AE's accuracy in representing combustion evolution was notably improved, offering a more detailed depiction of the process. AE's strength lies in its independence from specific threshold searches, a requirement in the BR method. By relying on learned representations within its latent space, AE eliminates laborious threshold exploration, ensuring reliability and reducing workload pressures. Comparative analyses consistently confirmed AE's superior performance in accurately reproducing and delineating combustion evolution compared to BR. This study highlights AE's potential as a promising technique for precise flame front detection in combustion processes. Its ability to autonomously extract features, minimize errors, and enhance overall accuracy signifies a significant step forward in analyzing flame fronts. AE's reliability, reduced need for manual intervention, and adaptability across various conditions suggest a promising future for improving combustion analysis techniques in spark-ignition engines with optical access. [ABSTRACT FROM AUTHOR]

In this study, a set of 316 L stainless steel test specimens was additively manufactured by laser-based Powder Bed Fusion. The process parameters were varied for each specimen in terms of laser scan speed and laser power. The objective was to use a narrow band of parameters well inside the process window, demonstrating detailed parameter engineering for specialized additive manufacturing cases. The process variation was monitored using Optical Tomography to capture light emissions from the layer surfaces. Process emission values were stored in a statistical form. Micrographs were prepared and analyzed for defects using optical microscopy and image manipulation. The results of two data sources were compared to find correlations between lack of fusion, porosity, and layer-based energy emissions. A data comparison of Optical Tomography data and micrograph analyses shows that Optical Tomography can partially be used independently to develop new process parameters. The data show that the number of critical defects increases when the average Optical Tomography grey value passes a certain threshold. This finding can contribute to accelerating manufacturing parameter development and help meet the industrial need for agile component-specific parameter development. [ABSTRACT FROM AUTHOR]

Geometric complexities play an important role in the nucleation, propagation, and termination of strike-slip earthquake ruptures. The 2021 Mw7.4 Maduo earthquake rupture initiated at a large releasing stepover with a complex fault intersection. In the epicentral region, we conducted detailed mapping and classification of the surface ruptures and slip measurements associated with the earthquake, combining high-resolution uncrewed aerial vehicle (UAV) images and optical image correlation with field investigations. Our findings indicate that the coseismic ruptures present discontinuous patterns mixed with numerous lateral spreadings due to strong ground shaking. The discontinuous surface ruptures are uncharacteristic in slip to account for the large and clear displacements of offset landforms in the epicentral region. Within the releasing stepovers, the deformation zone revealed from the optical image correlation map indicates that a fault may cut diagonally across the pull-apart basin at depth. The left-lateral horizontal coseismic displacements from field measurements are typically ≤0.6 m, significantly lower than the 1–2.7 m measured from the optical image correlation map. Such a discrepancy indicates a significant proportion of off-fault deformation or the possibility that the rupture stopped at a shallow depth during its initiation phase instead of extending to the surface. The fault network and multi-fault junctions west and south of the epicenter suggest a possible complex path, which retarded the westward propagation at the initial phase of rupture growth. A hampered initiation might enhance the seismic ground motion and the complex ground deformation features at the surface, including widespread shaking-related fissures. [ABSTRACT FROM AUTHOR]

The incomplete construction of optical image time series caused by cloud contamination is one of the major limitations facing the application of optical satellite images in crop monitoring. Thus, the construction of a complete optical image time series via image reconstruction of cloud-contaminated regions is essential for thematic mapping in croplands. This study investigates the potential of multi-temporal conditional generative adversarial networks (MTcGANs) that use a single synthetic aperture radar (SAR) image acquired on a prediction date and a pair of SAR and optical images acquired on a reference date in the context of early-stage crop monitoring. MTcGAN has an advantage over conventional SAR-to-optical image translation methods as it allows input data of various compositions. As the prediction performance of MTcGAN depends on the input data composition, the variations in the prediction performance should be assessed for different input data combination cases. Such an assessment was performed through experiments using Sentinel-1 and -2 images acquired in the US Corn Belt. MTcGAN outperformed existing SAR-to-optical image translation methods, including Pix2Pix and supervised CycleGAN (S-CycleGAN), in cases representing various input compositions. In particular, MTcGAN was substantially superior when there was little change in crop vitality between the reference and prediction dates. For the SWIR1 band, the root mean square error of MTcGAN (0.021) for corn was significantly improved by 54.4% and 50.0% compared to Pix2Pix (0.046) and S-CycleGAN (0.042), respectively. Even when there were large changes in crop vitality, the prediction accuracy of MTcGAN was more than twice that of Pix2Pix and S-CycleGAN. Without considering the temporal intervals between input image acquisition dates, MTcGAN was found to be beneficial when crops were visually distinct in both SAR and optical images. These experimental results demonstrate the potential of MTcGAN in SAR-to-optical image translation for crop monitoring during the early growth stage and can serve as a guideline for selecting appropriate input images for MTcGAN. [ABSTRACT FROM AUTHOR]

Traditional synthetic-aperture radar (SAR) imaging, while adept at capturing stationary scenes with clarity, often results in the blurring of moving targets due to Doppler spectrum disparities. To overcome this, this study introduces an innovative optical approach for imaging moving targets. By employing a spatial light modulator (SLM), the phase information of SAR data can be modulated onto a light beam. Then, the light propagation path is meticulously designed to take advantage of the free propagation of light to compensate for the phase differences in SAR data, thereby achieving focal clarity. Simulations and experimental validations have demonstrated that this method surpasses traditional digital imaging techniques in terms of focusing precision. Additionally, the processing delay is only as long as the time taken for light propagation. The optical path is simple, avoiding complex assembly and alignment. This novel approach opens up new possibilities for the SAR imaging of moving targets, offering potential applications in moving target extraction, separation, and velocity estimation. [ABSTRACT FROM AUTHOR]

Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future. [ABSTRACT FROM AUTHOR]

Underwater detection faces uncomfortable illumination conditions, and traditional optical images sensitive to intensity often cannot work well in these conditions. Polarization imaging is a good solution for underwater detection under adverse lighting conditions. However, the process of obtaining polarization information causes it to be more sensitive to noise; serious noise reduces the quality of polarized images and subsequent performance in advanced visual tasks. Unfortunately, the flourishing low-light image enhancement methods applied to intensity images have not demonstrated satisfactory performance when transferred to polarized images. In this paper, we propose a low-light image enhancement paradigm based on the antagonistic properties of polarization parameters. Furthermore, we develop a dual-branch network that relies on a gradient residual dense feature extraction module (GRD) designed for polarized image characteristics and polarization loss, effectively avoiding noise introduced during the direct amplification of brightness, and capable of restoring target contour details. To facilitate a data-driven learning method, we propose a simulation method for underwater low-light polarized images. Extensive experimental results on real-world datasets demonstrate the effectiveness of our proposed approach and its superiority against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated by the distance between the upper and lower boundaries of the epidermal layer in cross-sectional optical coherence tomography (OCT) images, which is segmented using CNN, and the internal fingerprint at the epidermis–dermis junction (DEJ) is extracted based on the maximum intensity projection (MIP) algorithm. The experimental results indicate that the internal fingerprint-guided epidermal thickness is insensitive to pressure due to normal correlation coefficients and the encryption process between epidermal thickness maps of fingertip skin under different pressures. In addition, the result of the numerical simulation demonstrates the feasibility and security of the encryption scheme by structural similarity index matrix (SSIM) analysis between the original image and the recovered image with the correct and error keys decryption, respectively. The robustness is analyzed based on the SSIM value in three aspects: different pressures, noise attacks, and data loss. Key randomness is valid by the gray histograms, and the average correlation coefficients of adjacent pixelated values in three directions and the average entropy were calculated. This study suggests that the epidermal thickness of fingertip skin could be seen as important biometric information for information encryption. [ABSTRACT FROM AUTHOR]

Metalens, a metasurface with a focusing phase, has been the focus of research due to its immense potential for use in imaging and display technology. Traditional lens and optical imaging systems rely on phase accumulation, however metalens with subwavelength structures provide a disruptive path for miniaturized optical imaging systems by allowing unfettered modulation of incident light's phase and amplitude. Recently, extensive efforts have been devoted to exploring new design strategies, new functionalities, and possible applications. This paper reviews the development, principle, classification, and research status of metalens. In particular, this review focuses on the progress and challenges of improving imaging quality and expanding imaging diversity, including improvements of resolution, enhancement of depth of field, extension of field of view, and achromatism. Lastly, the prospects of metalens in the future display are summarized, and the application potentials of metalens in novel 3D display, intelligent and bionic display, as well as nano‐pixelate light‐emitting display (NLED) are emphasized. [ABSTRACT FROM AUTHOR]

Metascreens are remarkable optical materials that have attracted significant research interest owing to their ability to manipulate electromagnetic waves by selectively transmitting, reflecting, and absorbing specific wavelengths. The present study introduces a new metascreen concept called visibly transparent multifunctional metascreen (VTMM), which is capable of simultaneously transmitting visible light, selectively transmitting microwaves, and modulating infrared (IR) emissions, all through a single surface. The VTMM is fabricated by patterning a thin metal film in a square‐loop pattern on a transparent substrate, resulting in frequency‐selective transmission characteristics in the X‐band and visible transparency. Before demonstrating independent control of the visible and IR coloration of the VTMM by varying the metal film thickness and using color backgrounds, the visible and IR coloration characteristics of the ultrathin metal film on glass (MFG) are analyzed. Based on the findings, the VTMM preserves optical images behind the metascreen while reducing the average IR signature from 89.0 to 69.3 W m−2 sr‐1, with up to 23.5% reduction compared to a glass substrate, as the metal film thickness increases from 40 to 100 nm is confirmed. Furthermore, the designed selective transmission characteristics are shown to remain consistent across varying metal film thicknesses. [ABSTRACT FROM AUTHOR]

The challenge of image reconstruction from very-low-resolution images is made exceedingly difficult by multiple degradation factors in practical applications. Traditional methods do not consider the interactions between these degradation factors, so the results are often insufficient. To reconstruct low-resolution blurry images, both super-resolution and deblurring processes must be applied. In this paper, we propose a joint super-resolution and a deblurring model with integrated processing of the degradation factors to obtain better image quality. The joint model includes two branches, a super-resolution module and deblurring module, and both of them share the same feature extraction module. The super-resolution module consists of multiple layers of residual blocks. The deblurring module supports the robustness of the super-resolution module through feature feed-back in the learning process, by introducing an image blurring feature description into the feature representation. To create modules with high magnification, the base two-branch model is also used in two stages with scale recursion. A second-stage deblurring module receives the output of the first-stage super-resolution module and improves the deblurring capability when the image is further magnified. The modules enhance each other, significantly improve the quality of very-low-resolution text images, and maintain a low model complexity. A step-by-step training strategy is applied to reduce second-stage training difficulty. Experiments show that our approach significantly outperforms state-of-the-art methods in terms of image quality and optical character recognition accuracy, and with a lower computational cost. [ABSTRACT FROM AUTHOR]

Background: In brain tumor surgery a personalized surgical approach is crucial to achieve a maximum safe tumor resection. The extent of resection decisively depends on the histological diagnosis. Stimulated Raman histology (SRH), a fiber laser-based optical imaging method, offers the possibility for evaluation of an intraoperative diagnosis in a few minutes. Objective: To provide an overview on the applications of SRH in neurosurgery and transference of the technique to other surgical disciplines. Methods: Description of the technique and review of the current literature on SRH. Results: The SRH technique was successfully used in multiple neuro-oncological tumor entities. Initial pilot projects showed the potential for analysis of extracranial tumors. Conclusion: The use of SRH provides a near real-time diagnosis with high diagnostic accuracy and provides further developmental potential to improve personalized tumor surgery. [ABSTRACT FROM AUTHOR]

Styrene–butadiene rubber (SBR) composites reinforced with two different local Libyan grasses; Stipa tenacissima (locally known as Halfa) and Stipagrostis pungens (locally known as Esbat); were prepared. The effect of filler type and content (10, 20, 30 and 40 wt%) on the mechanical properties of the composites including stress at break, elongation at break, impact strength and microhardness were evaluated. The results illustrated that the stress at break of SBR increased with the addition of 20% and 30% of Halfa or Esbat. In addition, the results revealed that the highest values of stress at break were obtained by composites with 20% filler content. Composites made with Halfa showed higher stress at break than composites made with Esbat. The incorporation of Halfa and Esbat into SBR caused a significant decrease in the elongation at break. The elongation at break of the composites made with Halfa was slightly higher than that of composites made with Esbat. In both types of composites, the impact strength was increased with increasing the filler content up to 30%, and then decreased. Composites made with Esbat showed marginally better impact strength than did composites made with Halfa. The incorporation of Halfa and Esbat caused an increase in the hardness properties in comparison to pure SBR. In the all composite compositions, Halfa composites had higher hardness values than Esbat composites. The optical microscope results of SBR composites were in good agreement with the mechanical properties results. The formation of filler aggregation was observed in SBR composites with 10% and 40% Halfa and Esbat. On the other hand, no filler aggregation was observed in the optical images of SBR composites with 20% and 30% Halfa and Esbat. In brief, these grasses can be used as effective filler for reinforcing polymers to develop low density composites with improved properties. [ABSTRACT FROM AUTHOR]

With the rapid advancements in information technology and communication technology, the utilization of moving image visual communication has become widespread across various domains. In this context, optical sensing technology plays a pivotal role due to its remarkable attributes including high speed, vast capacity, and minimal delay. Thus, this research aims to enhance the optical sensing effect and edge detection accuracy in moving image visual communication by leveraging AI recognition and an optical sensing image edge detection algorithm. For this purpose, an AI recognition algorithm is implemented to train the model to identify the objects present in the moving images, enabling the localization of target objects within the optical sensing images. Consequently, the edge information of the target object is extracted from the photosensing image by employing an edge detection algorithm. Subsequently, the edge information of the optical sensing image undergoes processing and optimization to enhance the accuracy of edge detection. Through experimental results, it is demonstrated that the proposed approach, which integrates AI recognition and an optical sensor image edge detection algorithm, effectively identifies and localizes target objects within the optical sensor image, consequently improving the accuracy of edge detection outcomes. Furthermore, in comparison to conventional methods, the proposed methodology exhibits superior performance in optical image edge detection. [ABSTRACT FROM AUTHOR]

Nutritional status is an important foundation for the growth and development, physical health, and athletic ability of university athletes. Athletes usually require higher amounts of energy and nutrients to support training and competition, as exercise consumes a lot of energy. In order to accurately evaluate heat consumption and nutritional needs during exercise, this paper designs a method based on optical imaging technology and temperature sensors. Optical imaging technology provides information about blood conditions by capturing the reflection and scattering of light on the body surface during movement. A temperature sensor can measure changes in body temperature to reflect the energy metabolism in the body. Place these two measuring tools at specific positions on the surface of the athlete's body to obtain accurate data. By continuously monitoring changes in body temperature and blood flow, various indicators of energy consumption and nutritional needs are obtained for evaluating energy consumption and nutritional needs in sports. The advantage of this method is that it is non-invasive, non-invasive, and can monitor and record physiological indicators of athletes in real-time. This provides researchers, athletes, and health managers with more precise and personalized methods for assessing calorie consumption and nutritional needs. [ABSTRACT FROM AUTHOR]

With the rapid development of fashion industry, the requirements for the design and manufacture of clothing fabrics are getting higher and higher. The traditional fabric design method has the problems of long time consumption, high cost and difficult evaluation, so it is necessary to introduce new technologies to improve the fabric design process. Based on optical imaging technology and big data intelligence, this study aims to develop a new method to realize 3D digital design of clothing fabrics, so as to improve design efficiency and quality. In this paper, optical imaging technology is used to scan and measure different types of fabrics with high precision, and the texture and color information of fabrics are obtained. Then the obtained data are processed and analyzed, and combined with big data technology, a three-dimensional digital model of fabric is established. Finally, computer-aided design software is used for simulation and adjustment to further improve the design effect of fabrics. The experimental results show that this method can effectively transform the physical characteristics of fabrics into digital models and realize the personalized design of fabrics. This method can also significantly improve the design efficiency and quality, making the fabric design process faster and more accurate. [ABSTRACT FROM AUTHOR]

As one of the most important parts in the field of architectural environment research, outdoor scene environment design is closely related to people's daily life. Deeply exploring it can further improve the quality of living environment and the overall efficiency of buildings. Driven by this positive impact, this article conducted a series of research and analysis on outdoor scene environment design simulation through a multi-channel optical imaging classification automation model. This article first analyzes the principle of multi-channel optical imaging, using the classification process of imaging as the foundation. Then, it delves into the automation of optical imaging classification and systematically analyzes its working principle. Finally, a classic feature based dimensionality reduction algorithm, Principal Component Analysis (PCA), was proposed to extract potential information with the same structure from the dataset. This article not only analyzes image conversion in outdoor scene environments, but also conducts a comprehensive study of two environmental simulation processes and tests their core functions. The results of research and testing have found that multi-channel optical imaging automatic classification methods can effectively assist in the environmental design of outdoor scenes, providing a new reference path for the sustainable development of this field. [ABSTRACT FROM AUTHOR]

This study aims to explore the potential value of light imaging detection based on Internet of Things devices in the simulation application of dance sports data monitoring. In this context, the aim of this study is to use iot devices and optical imaging technology to monitor dance sport movement, and to provide a more comprehensive and accurate motion monitoring scheme through data processing and simulation analysis. We adopt the optical imaging detection technology based on Internet of Things devices to realize the real-time monitoring of dance sports. By installing optical sensors and camera devices, the technology is able to capture key movements and postures during movement and transmit the data to a monitoring system for analysis. We use deep learning algorithm to process and recognize the acquired image data, so as to realize motion detection and posture analysis. Through experiments and data analysis, we get satisfactory results. By monitoring and analyzing the optical image data during the dancesport movement, we are able to accurately capture and record the movement characteristics of athletes, providing real-time movement monitoring and feedback. We have also developed simulation applications that enable the simulation evaluation of an athlete's posture and technical level by converting optical image data into a motion model. [ABSTRACT FROM AUTHOR]

In the AI sports training system, the traditional optical imaging technology limits the resolution of the image. Therefore, the use of optical super-resolution imaging technology to improve image resolution can promote the further development of AI motion training systems. In this study, high-resolution images and corresponding low-resolution images are collected as training data, and an optical superresolution imaging model based on deep learning is established. The GPU parallel computing technology is used to accelerate the training and inference process of the model. Finally, the optimized high-resolution image is applied to the AI sports training system, and the experimental evaluation is carried out. The experimental results show that the optical superresolution imaging technology based on GPU parallel computing can significantly improve the resolution and clarity of the image. Compared with the traditional optical imaging technology, the image processed by optical superresolution imaging has better performance in detail and edge. Through the test of the motion capture system, it is observed that the images processed by optical super-resolution imaging can detect and analyze the motion details more accurately. [ABSTRACT FROM AUTHOR]

With the development of tourism, it is necessary to understand and evaluate the status of tourist resources in forest parks in order to improve visitor experience and manage forest parks. This paper presents an embedded edge computing method, which uses optical imaging technology to monitor and evaluate tourism resources in forest parks. Select the appropriate optical imaging equipment and embed it into the forest park monitoring system. Then the optical image data of each corner of the forest park is collected by the equipment, and these data are preprocessed and analyzed. A computer vision algorithm is used to detect, extract and recognize tourist resources in optical images, such as scenic spots, plants and animals. By combining embedded edge computing technology with optical imaging, accurate and detailed evaluation results of forest park tourism resources are obtained. These results can provide valuable information to forest park managers to help them understand the utilization of tourism resources, hot spots and room for improvement, so as to enhance the tourism experience and sustainable development of forest parks. [ABSTRACT FROM AUTHOR]

Over the past few decades, the field of sports training has used traditional sensor technology to collect motion data, but sensor technology is limited by its installation location and number, and cannot provide accurate and comprehensive data acquisition. The traditional data acquisition system cannot realize real-time data acquisition and analysis because of the delay in data transmission and processing. Therefore, the goal of this study is to develop a real-time data acquisition system based on optical imaging technology, through the use of optical imaging technology, real-time acquisition of the image of the movement during the movement process, and extract the movement data. By connecting the camera device to the embedded processor, the data can be transmitted and processed quickly. Through the image processing algorithm, the key motion data can be extracted from the images of the movers. This data will be stored and analyzed by an embedded processor. The experimental results show that the optical imaging technology developed in this study has good performance in the acquisition of sports training data. [ABSTRACT FROM AUTHOR]

The precise classification of kidneys and kidney tumors is of great guiding significance for the diagnosis and surgical planning of clinical doctors. Artificial segmentation of kidneys and renal tumors not only increases the workload of doctors, but also has a certain degree of subjectivity. Due to the varying morphology and pathological characteristics of various kidney tumors, conventional imaging analysis methods have certain limitations in their application. This study aims to propose a novel optical imaging device based on deep learning to achieve better results in kidney tumor image segmentation and recognition. The study collected a batch of image data on kidney tumors and trained the data using deep learning algorithms. After many experiments and optimization, a model with excellent performance in renal tumor segmentation and recognition was obtained. Compared with several existing methods, the method proposed in this paper has better segmentation accuracy for kidney tumors. This can not only avoid mutual interference between the network and the resolution network, but also improve the recognition efficiency between the two networks. In addition, the system can also provide reliable data feedback for clinical doctors, helping them continuously correct problems in subsequent visual examinations, thereby achieving higher diagnostic accuracy. The experimental results show that the neural network is effective in image segmentation and quantitative analysis, and can be applied in clinical practice. [ABSTRACT FROM AUTHOR]

There are some limitations in traditional basketball training simulation methods, such as space limitation and manpower scheduling. Therefore, this study designed and developed a deep neural network-based optical imaging device to provide a more realistic and efficient simulation experience of basketball training games. With the help of this device, players can receive more accurate, real-time feedback to better understand and respond to situations in the game. An optical imaging device based on deep neural network is designed, which combines advanced optical technology and computer vision algorithm. The device senses motion and position information from different areas within the court and is analyzed and processed by deep learning algorithms. Using these data, we can generate the simulated game scene in real time, which helps the players to better deal with the complex game situation in training. The results show that the device can accurately simulate match scenarios and provide real-time feedback and guidance to players. The players showed better technical and tactical ability when using the equipment for training, proving the effectiveness of the equipment in training. [ABSTRACT FROM AUTHOR]

The aim of this study is to improve the image quality in physical education simulation by using intelligent image denoising technology based on optical imaging equipment. At present, the image equipment used in physical education is often disturbed by noise, which leads to the decline of image quality and affects the teaching effect. Therefore, developing an intelligent image denoising method based on optical principle is of great significance for improving the effect of physical education. In this study, optical technology is used to collect images in physical education simulation with optical imaging equipment. Through the analysis of the collected images, it is found that there is noise interference in the images. In order to reduce the effect of noise on image quality, an intelligent image denoising algorithm based on optical principle is proposed. Firstly, the algorithm uses the optical imaging principle to model the noise in the image and analyze its characteristics. Then the image noise is removed by a series of mathematical operations and filtering operations. Using the image quality evaluation index, the image quality after denoising is evaluated. The experimental results show that the intelligent image denoising method based on optical principle can effectively reduce the noise interference in physical education teaching images and improve the teaching effect. [ABSTRACT FROM AUTHOR]

The existing basketball teaching system can only capture images through the camera, and can not get the subtle movements of the players in real time. The combination of wearable sensor technology and moving image processing technology has injected new vitality into the field of sports, improved the training efficiency and level of athletes, and realized the comprehensive monitoring and analysis of sports. The aim of this research is to develop a wearable sensor based on infrared optical imaging technology for real-time capture of basketball players' movements and image processing. By embedding infrared sensors into the sports equipment of basketball players, the key movement data of the players can be obtained in real time. The collected infrared image is analyzed and recognized by image processing algorithm, and the athlete's joint position and movement trajectory are extracted. Through experimental verification, the wearable sensor proposed in this paper can capture the movement of athletes with high precision and carry out effective image processing, which has a good application prospect in basketball teaching. The system can obtain the athletes' action data in real time, and analyze and evaluate it accurately through image processing, which provides a strong support for basketball teaching. [ABSTRACT FROM AUTHOR]

Coronary Artery Disease (CAD) persists as a dominant cause of global morbidity and mortality, frequently maintaining its silent course until reaching advanced stages. This pioneering study investigates the potential of Intravascular Optical Imaging (IOI) as an innovative tool for the early detection of CAD in asymptomatic patients. IOI leverages advanced optical technologies to visualize coronary artery plaques and assess their vulnerability, presenting a unique opportunity for timely intervention and prevention. The research demonstrates IOI's capability to identify subclinical atherosclerotic lesions, characterize plaque composition, and quantify luminal stenosis. By integrating cutting-edge imaging modalities such as Optical Coherence Tomography (OCT) and Near-Infrared Spectroscopy (NIRS), unmatched accuracy and sensitivity in detecting CAD-related changes are achieved. The results of this study underline the clinical significance of IOI in identifying high-risk patients who may benefit from early therapeutic interventions. Moreover, IOI facilitates risk stratification and personalized treatment strategies, thus minimizing the burden of CAD-related events and enhancing patient outcomes. [ABSTRACT FROM AUTHOR]