Light Source Intensity Adjustment for Enhanced Feature Extraction

A Performance Test for a Fringe Projection Scanner in Various Ambient Light Conditions

The ambient light is changed by the factor of nature such as a weather, season and time. The glossy object reflects an ambient light and cause blown out highlights. Size and the position of the blown out highlights are changed by the change of the ambient light. It is difficult to use a general image processing system on the condition that an ambient light changes. In the last year, we described the development of the glossy sealing component inspection system using a blue lighting. In this paper, we describe the study of better illumination color.

In low-light environments, the scarcity of visual information makes feature extraction and matching challenging for traditional visual simultaneous localization and mapping (SLAM) systems. Changes in ambient lighting can also reduce the accuracy and recall of loop closure detection. Most existing image enhancement methods tend to introduce noise, artifacts, and color distortions when enhancing images. To address these issues, we propose an innovative low-light visual-inertial (LL-VI) SLAM system, named LL-VI SLAM, which integrates an image enhancement network into the front end of the SLAM system. This system consists of a learning-based low-light enhancement network and an improved visual-inertial odometry. Our low-light enhancement network, composed of a Retinex-based enhancer and a U-Net-based denoiser, enhances image brightness while mitigating the adverse effects of noise and artifacts. Additionally, we incorporate a robust Inertial Measurement Unit initialization process at the front end of the system to accurately estimate gyroscope biases and improve rotational estimation accuracy. Experimental results demonstrate that LL-VI SLAM outperforms existing methods on three datasets, namely LOLv1, ETH3D, and TUM VI, as well as in real-world scenarios. Our approach achieves a peak signal-to-noise ratio of 22.08 dB. Moreover, on the TUM VI dataset, our system reduces localization error by 22.05% compared to ORB-SLAM3, proving the accuracy and robustness of the proposed method in low-light environments.

Flowers of some Phlox (Phlox x paniculata L.) varieties undergo daily colour shifts, being blue in the early morning, turning red during the day, and returning to blue in the evening. The colour shift, which occurs only in the upper (adaxial) petal surfaces, is due to the daily changes in ambient light. In the laboratory, colour shifts could be induced by 2.5 h of ultraviolet, visible or far‐red light and recorded by reflectance spectrophotometry.There are indications that irradiations with different kinds of light cause qualitatively different colour shifts, and that thus more than one photoreceptor pigment and more than one primary light reaction may be involved. The presence of phytochrome was demonstrated in petals of white Phlox flowers by in vivo transmission spectrophotometry. It is therefore possible that colour shifts in coloured Phlox flowers are mediated by phytochrome. Possibly the movement of ions (e.g. hydrogen ions) into or out of the vacuole (where the visible pigments are located) is affected by light absorption in a pigment in the tonoplast.

In order to make quantitative statements regarding behavior patterns in animals, it is important to establish whether new observations are statistically consistent with the animal's equilibrium behavior. For example, traumatic stress from the presence of a telemetry transmitter may modify the baseline behavior of an animal, which in turn can lead to a bias in results. From the perspective of information theory such a bias can be interpreted as the amount of information gained from a new measurement, relative to an existing equilibrium distribution. One important concept in information theory is the relative entropy, from which we develop a framework for quantifying time-dependent differences between new observations and equilibrium. We demonstrate the utility of the relative entropy by analyzing observed speed distributions of Pacific bluefin tuna, recorded within a 48-hour time span after capture and release. When the observed and equilibrium distributions are Gaussian, we show that the tuna's behavior is modified by traumatic stress, and that the resulting modification is dominated by the difference in central tendencies of the two distributions. Within a 95% confidence level, we find that the tuna's behavior is significantly altered for approximately 5 hours after release. Our analysis reveals a periodic fluctuation in speed corresponding to the moment just before sunrise on each day, a phenomenon related to the tuna's daily diving pattern that occurs in response to changes in ambient light.

Background Pupillometry has been studied as a physiological marker for quantifying cognitive load since the early 1960s. It has been established that small changes in pupillary size can provide an index of the cognitive load of a participant as he/she performs a mental task. The utility of pupillometry as a measure of expertise is less well established, although recent research in the fields of education, medicine and psychology indicates that differences in pupillary size during domain-specific tasks allows differentiation between experts and novices in appropriately designed experiments. Purpose The goal of this review is to explore the existing body of evidence for the use of pupillometry as a measure of expertise and to identify its strengths and constraints within the context of expertise research in the medical sciences. Results Pupillometry is a robust metric that allows researchers to better understand cognitive load in medical practitioners with varying levels of expertise. In medical expertise research, it has been used to study surgeons, anesthetists and emergency physicians. Its strengths include its ability to provide quantitative and objective outputs, to be measured unobtrusively with new technology and to be precisely computed as cognitive load changes over the course of completion of a task. Constraints associated with this methodology include its potential inaccuracy with changes in ambient light and pupillary accommodation as well as the need for relatively expensive equipment. Conclusion With recent technological advances, pupillometry has become a simple and robust method for quantifying physiological changes attributable to cognitive load and is increasingly being utilized in medical education. It can be used as a reliable marker of mental effort and has been shown to differentiate levels of expertise in medical practitioners.

Classification of soil aggregates: A novel approach based on deep learning

Internet of Things (IoT) is a concept that aims to expand the benefits of continuously connected internet connectivity. This study aims to utilize IoT in home automation and remote light control systems that can be operated with a smartphone application via an internet connection (WiFi). This system uses the NodeMCU ESP8266 module as a microcontroller, a light emitting diode (LDR) sensor as a controller for automating lights according to environmental conditions, and the Blynk smartphone application as a remote control for lights. The light control process can be carried out specifically on certain lamps and can be controlled by changes in ambient light in the morning and evening. The results show that when the LDR sensor gets minimal light, the light will be ON and vice versa the light will be OFF when more light intensity is received by the LDR. In addition, the Blynk application is able to control the lights remotely when connected to the internet network and, in this study, has been tested up to a distance of 2.7 km. It can be concluded that as long as the system is connected to WiFi stably and continuously, this control system can perform the task of turning on and off the lights independently when the owner is not at home.

A common CNS architecture is observed in all chordates, from vertebrates to basal chordates like the ascidian Ciona. Ciona stands apart among chordates in having a complete larval connectome. Starting with visuomotor circuits predicted by the Ciona connectome, we used expression maps of neurotransmitter use with behavioral assays to identify two parallel visuomotor circuits that are responsive to different components of visual stimuli. The first circuit is characterized by glutamatergic photoreceptors and responds to the direction of light. These photoreceptors project to cholinergic motor neurons, via two tiers of cholinergic interneurons. The second circuit responds to changes in ambient light and mediates an escape response. This circuit uses GABAergic photoreceptors which project to GABAergic interneurons, and then to cholinergic interneurons. Our observations on the behavior of larvae either treated with a GABA receptor antagonist or carrying a mutation that eliminates photoreceptors indicate the second circuit is disinhibitory.

Optical health analysis of visual comfort for bright screen display based on back propagation neural network

BackgroundThe symptoms and associated characteristics of attention-deficit/hyperactivity disorder (ADHD) are typically assessed in person at a clinic or in a research lab. Mobile health offers a new approach to obtaining additional passively and continuously measured real-world behavioral data. Using our new ADHD remote technology (ART) system, based on the Remote Assessment of Disease and Relapses (RADAR)–base platform, we explore novel digital markers for their potential to identify behavioral patterns associated with ADHD. The RADAR-base Passive App and wearable device collect sensor data in the background, while the Active App involves participants completing clinical symptom questionnaires.ObjectiveThe main aim of this study was to investigate whether adults and adolescents with ADHD differ from individuals without ADHD on 10 digital signals that we hypothesize capture lapses in attention, restlessness, or impulsive behaviors.MethodsWe collected data over 10 weeks from 20 individuals with ADHD and 20 comparison participants without ADHD between the ages of 16 and 39 years. We focus on features derived from (1) Active App (mean and SD of questionnaire notification response latency and of the time interval between questionnaires), (2) Passive App (daily mean and SD of response time to social and communication app notifications, the SD in ambient light during phone use, total phone use time, and total number of new apps added), and (3) a wearable device (Fitbit) (daily steps taken while active on the phone). Linear mixed models and t tests were employed to assess the group differences for repeatedly measured and time-aggregated variables, respectively. Effect sizes (d) convey the magnitude of differences.ResultsGroup differences were significant for 5 of the 10 variables. The participants with ADHD were (1) slower (P=.047, d=1.05) and more variable (P=.01, d=0.84) in their speed of responding to the notifications to complete the questionnaires, (2) had a higher SD in the time interval between questionnaires (P=.04, d=1.13), (3) had higher daily mean response time to social and communication app notifications (P=.03, d=0.7), and (4) had a greater change in ambient (background) light when they were actively using the smartphone (P=.008, d=0.86). Moderate to high effect sizes with nonsignificant P values were additionally observed for the mean of time intervals between questionnaires (P=.06, d=0.82), daily SD in responding to social and communication app notifications (P=.05, d=0.64), and steps taken while active on the phone (P=.09, d=0.61). The groups did not differ in the total phone use time (P=.11, d=0.54) and the number of new apps downloaded (P=.24, d=0.18).ConclusionsIn a novel exploration of digital markers of ADHD, we identified candidate digital signals of restlessness, inconsistent attention, and difficulties completing tasks. Larger future studies are needed to replicate these findings and to assess the potential of such objective digital signals for tracking ADHD severity or predicting outcomes.

In this paper, we present a new 3D reconstruction approach based on local surface curvature analysis. Its integration can make a normal active stereoscopic system intelligent, and capable to produce directly optimized 3D model. The iterative 3D reconstruction process begins with a sparse and regular point pattern. Based on the reconstructed 3D point cloud, the local surface curvature around each 3D point is estimated. Those 3D points located in flat areas are removed from the 3D model, and a new pattern is created to project more points onto the object where there is high surface curvature. The 3D model is thus refined progressively during the acquisition process, and finally an optimized 3D model is obtained. Our numerous experiments showed that compared to the 3D models generated by commercial system, the loss of morphological quality is negligible, and the gain by the simplification of the model is considerable.KeywordsGaussian CurvatureCamera CalibrationPattern PointMesh SimplificationLocal Surface CurvatureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Artificial light at night is one of the most apparent environmental changes accompanying anthropogenic habitat change. The global increase in light pollution poses new challenges to wild species, but we still have limited understanding of the temporal and spatial pattern of exposure to light at night. In particular, it has been suggested by several studies that animals exposed to light pollution, such as songbirds, perceive a longer daylength compared with conspecifics living in natural darker areas, but direct tests of such a hypothesis are still lacking. Here, we use a combination of light loggers deployed on individual European blackbirds, as well as automated radio-telemetry, to examine whether urban birds are exposed to a longer daylength than forest counterparts. We first used activity data from forest birds to determine the level of light intensity which defines the onset and offset of daily activity in rural areas. We then used this value as threshold to calculate the subjective perceived daylength of both forest and urban blackbirds. In March, when reproductive growth occurs, urban birds were exposed on average to a 49-min longer subjective perceived daylength than forest ones, which corresponds to a 19-day difference in photoperiod at this time of the year. In the field, urban blackbirds reached reproductive maturity 19 day earlier than rural birds, suggesting that light pollution could be responsible of most of the variation in reproductive timing found between urban and rural dwellers. We conclude that light at night is the most relevant change in ambient light affecting biological rhythms in avian urban-dwellers, most likely via a modification of the perceived photoperiod.

Recent research advances on non-linear phenomena in various biosystems

Wildfires are mainly considered to be CO2-releasing events, while their long-term impact on biogeochemical carbon sequestration remains a major source of uncertainty. We analysed five years of ecosystem-scale eddy covariance data in a South African Fynbos shrubland that experienced a wildfire in the middle of the measurement period and combined it with leaf-scale ecophysiological measurements to quantify the ecosystem-scale carbon feedbacks and energy flux shifts following wildfire.Unexpectedly, wildfire doubled the annual net carbon sink from 5.36 to 10.55 tC ha-1 yr-1. This increase was driven by a ca. 50% suppression of ecosystem respiration while ecosystem energy exchange remained stable. These findings reveal a significant missing carbon pool of ca. 110 tC ha-1 over the course of the fire return interval of 15-20 years. Likely explanations for this discrepancy are either a below-ground carbon pool protected from volatilization through fire or a potential sink into dissolved carbon, potentially leading to eventual long-term ocean storage.To identify the biological drivers of this carbon sequestration, we measured gas exchange in the two main regeneration plant types of this fire-dominated ecosystem, i.e. obligate reseeders, whose seedlings must achieve reproduction before the next fire to persist, and resprouting species that invest into fire tolerance traits at the cost of slower growth. Stomatal conductance (gsw) was the primary trait distinguishing the two strategies. Reseeders initiated photosynthesis earlier in spring and exhibited gsw that was highly responsive to changes in ambient CO2 and light, while resprouters exhibited stronger resilience to drought but no response to ambient CO2 fluctuations. This difference in response to CO2 suggests that current climate trends may preferentially boost reseeders, potentially partially offsetting the impacts of shortened fire return intervals. Conversely, resprouter resilience may prove crucial under a higher drought intensity and duration scenario.Our unexpected findings for this Mediterranean-climate shrubland (typically considered to be a low carbon sink ecosystem) underscore the necessity for ground-based ecophysiological data to constrain Earth system models, and challenge biomass-centric climate policies, particularly in fire-prone, naturally tree-free ecosystems.