Changes In Ambient Light Research Articles

SlCPK27 cross-links SlHY5 and SlPIF4 in brassinosteroid-dependent photo- and thermo-morphogenesis in tomato.

ELONGATED HYPOCOTOYL5 (HY5) and PHYTOCHROME INTERACTING FACTORs (PIFs) are two types of important light-related regulators of plant growth, however, their interplay remains elusive. Here, we report that the activated tomato (Solanum lycopersicum) HY5 (SlHY5) triggers the transcription of a Calcium-dependent Protein Kinase SlCPK27. SlCPK27 interacts with and phosphorylates SlPIF4 at Ser-252 and Ser-308 phosphosites to promote its degradation. SlPIF4 promotes hypocotyl elongation mainly by activating the transcription of SlDWF, a key gene in brassinosteroid (BR) biosynthesis. Such a SlHY5-SlCPK27-SlPIF4-BR cascade not only plays a crucial role in photomorphogenesis but also regulates thermomorphogenesis. Our results uncover a previously unidentified mechanism that integrates Ca2+ signaling with the light signaling pathways to regulate plant growth by modulating BR biosynthesis in response to changes in ambient light and temperature.

Catadioptric omnidirectional thermal odometry in dynamic environment

This paper presents a catadioptric omnidirectional thermal odometry (COTO) system that estimates the six degrees of freedom (DoF) pose of a camera using only omnidirectional thermal images in visually degraded, fast-motion, and dynamic environments. First, we design and fabricate a central hyperbolic catadioptric omnidirectional thermal camera that captures surrounding thermal images with 360° horizontal field of view (FoV), and improve the omnidirectional camera model and calibration method to obtain high-precision camera intrinsic parameter. Second, we propose the epipolar curve constraint combining with omnidirectional thermal object detection to significantly reduce the interference of moving objects on pose estimation. Third, the implemented COTO pipeline consists of photometric calibration, dynamic region removal, tracking and mapping to overcome the drawbacks of photometric inconsistency and large distortion in omnidirectional thermal images. Experiments have been conducted on a total of 17 sequences of Lab, Outdoor and Driving, amounting to more than 60,000 omnidirectional thermal images of real environments. The experimental results indicate that the proposed COTO system has excellent localization accuracy and unparalleled robustness over the current state-of-the-art methods. The average localization accuracy measured by the absolute trajectory error (ATE) is less than 15 cm from the ground truth in both Lab and Outdoor sequences. In addition, COTO was the only system with complete and successful tracking in all sequences. The system can be used as an innovative localization solution, particularly in challenging environments with changes in ambient light, rapid vehicle motion, and moving object interference, which can be a difficult problem for visual odometry to solve.

High-precision rail movement measurement under ambient-light changes via video image progressing

Abstract The advancement of train technology makes monitoring railroad lines increasingly important. The use of cameras to measure rail movement can be done in real-time monitoring. The camera, however, is easily influenced by variations in ambient light intensity. Therefore, this research proposes a method to address the issue. The system can calculate rail displacement without being affected by ambient light intensity and monitor rail displacement in real-time. By using a flexible field programmable gate array-based framework that uses parallel and pipelined architecture to process image data, the system’s efficiency is improved by 24.7%. The system can accurately locate and measure displacement in complex environments despite interference from light and surroundings. Following experimental validation, the detection system achieves 0.07 mm precision and 95.2% detection accuracy in varying light.

High-performance Smart Home System based on Optimization Algorithm

Background: With the recent COVID-19 pandemic, people have become increasingly concerned about their physical health. Therefore, the ability to monitor changes in the surrounding environment in real-time and automatically improve the environment has become a current hot topic to improve the overall health level. Objective: This article describes the design of a high-performance intelligent home system that can simultaneously perform monitoring and automatic adjustment functions. Methods: The ESP8266 was used as the core controller, and the DHT11 and G12-04 sensors were used to collect data, such as temperature, humidity, and ambient light intensity. The sampling frequency was increased and the sampled data were processed to improve data accuracy. The sampled data were wirelessly transmitted to a PC or mobile terminal for real-time display. When the sampled data underwent sudden changes, an alert message was sent via the mobile terminal. Based on the real-time changes in ambient light, an improved lighting brightness adjustment algorithm combining bang-bang and single neuron adaptive PID control was used to adjust the lighting brightness. Results: After testing the system designed in this paper and analyzing the errors compared to standard values, the temperature measurement error ranged from 0% to 0.01107%, and the humidity measurement error ranged from 0% to 0.03797%. The improved algorithm was simulated and tested using MATLAB software and compared with traditional PID algorithms and single-neuron adaptive PID algorithms. The improved algorithm did not overshoot during adjustment, and the system reached a steady state much faster than traditional algorithms. Conclusion: The system showed good performance in real-time, stability, and accuracy, fully demonstrating the effectiveness of the devices and algorithms used in the system. This provides ideas for the design and improvement of future smart homes.

Multi-camera joint calibration algorithm for guiding machining robot positioning considering ambient light and error uncertainty

Multi-camera guided robotic machining is widely used in the removal of flash and burrs on the complex automotive castings and forgings. The multi-camera calibration process, however, is easily affected by the ambient light and uncertain error, thereby resulting in the undesired robot positioning accuracy. To overcome the challenging problem, this paper proposes a multi-camera joint calibration (MCJC) algorithm by considering both the ambient light and error uncertainty to optimize and compensate the positioning error of the image-guided machining robot. The high dynamic range imaging is used at first to obtain stable calibration image data for countering the effects of ambient light changes. Then the uncertainty error in camera self-calibration process is removed by the confidence threshold filtering. The hand-eye calibration matrix is corrected finally using the deviation feedback optimization for positioning accuracy enhancement of the image-guided machining robot. Taking the automotive flywheel shell as the experimental object, the results demonstrate that the average absolute positioning error is controlled within 1.3 mm, the optimum can reach 0.43 mm, and the average relative positioning error is smaller than 0.5 mm. Compared with the existing state-of-the-art algorithms, the proposed algorithm shows high positioning accuracy and strong algorithm robustness, and can meet the requirements for the subsequent image-guided robotic machining.

Yarn Angle Detection of Glass Fiber Plain Weave Fabric Based on Machine Vision

To address the issue of low accuracy in the yarn angle detection of glass fiber plain weave fabrics, which significantly impacts the quality and performance of the final products, a machine vision-based method for the yarn angle detection of glass fiber fabrics is proposed. The method involves pre-processing the image with brightness calculation, threshold segmentation, and skeleton extraction to identify the feature region. Line segment detection is then performed on this region, using the Hough transform. The concept of a “line segment evaluation index” is introduced, and it was used as a criterion for assessing the quality and relevance of detected line segments. Moreover, the warp and weft yarn extrusion area contours refer to the reconstructed outlines of yarn areas, achieved by combining the center of mass extraction with morphological operations and used to accurately determine the yarn angle. Tested under a range of challenging scenarios, including varied lighting conditions, fabric densities, and levels of image noise, this method has demonstrated robust stability and maintained high accuracy. These tests mimic real-world manufacturing environments, where factors such as ambient light changes and material inconsistencies can affect the quality of image capture and analysis. The proposed method has high accuracy, as shown by MSE and a Pearson’s r of 0.931. By successfully navigating these complexities, the proposed machine vision-based approach offers a significant enhancement in the precision of yarn angle detection for glass fiber fabric manufacturing, thus ensuring improved quality and performance of the final products.

Dynamic Color Design Model Construction Considering Ambient Light Changes

Abstract The study takes dynamic color design as the main research object, firstly, based on the atmospheric scattering model and the improved dark primary color a priori algorithm, designing the image defogging algorithm based on the ambient light variation, which is used to carry out the optimization processing of images. Aiming at the lack of the user’s visual cognitive semantics of color matching in the process of color design, it is proposed to construct a dynamic color design model based on the theoretical foundation of the image translation model, combined with the visual aesthetics data flow. The model obtains the user’s visual aesthetic cognition of the optimized image samples through eye tracking technology and, at the same time, applies the image translation model to the color design process to generate a color design scheme that is more in line with the human visual characteristics by learning the color matching law of the samples and their visual cognitive semantics. The experimental analysis shows that the image-defogging algorithm based on ambient light variation has a better image-processing effect and operation efficiency, exhibiting a higher average gradient (11.83), information entropy (7.86) and visual fidelity (2.99). Meanwhile, the color design scheme generated by the method of this paper is highly recognized by the subjects, with evaluation scores of more than 4 points in each dimension. The dynamic color design model constructed in this paper, by considering ambient light changes, can generate color schemes that are both rational and comfortable.

Adaptive Unsupervised Learning-Based 3D Spatiotemporal Filter for Event-Driven Cameras.

In the evolving landscape of robotics and visual navigation, event cameras have gained important traction, notably for their exceptional dynamic range, efficient power consumption, and low latency. Despite these advantages, conventional processing methods oversimplify the data into 2 dimensions, neglecting critical temporal information. To overcome this limitation, we propose a novel method that treats events as 3D time-discrete signals. Drawing inspiration from the intricate biological filtering systems inherent to the human visual apparatus, we have developed a 3D spatiotemporal filter based on unsupervised machine learning algorithm. This filter effectively reduces noise levels and performs data size reduction, with its parameters being dynamically adjusted based on population activity. This ensures adaptability and precision under various conditions, like changes in motion velocity and ambient lighting. In our novel validation approach, we first identify the noise type and determine its power spectral density in the event stream. We then apply a one-dimensional discrete fast Fourier transform to assess the filtered event data within the frequency domain, ensuring that the targeted noise frequencies are adequately reduced. Our research also delved into the impact of indoor lighting on event stream noise. Remarkably, our method led to a 37% decrease in the data point cloud, improving data quality in diverse outdoor settings.

Illumination variation robust circular target based on digital image correlation method

Circular targets, as one of typical image features, are widely used in image-based measurement, such as photogrammetry, displacement sensing, and vibration monitoring. However, its positioning accuracy is usually sensitive to intensity changes in ambient lighting, particularly in outdoor applications and long-term monitoring scenarios. In this paper, a speckle-based circular target (SbCT) is introduced as a solution to the sensitivity of localization accuracy of traditional circular targets in illumination variation environment. By incorporating a speckle pattern on the circular target and utilizing digital image correlation method, the SbCT achieves insensitivity to illumination variations when an appropriate correlation function is chosen. This enables accurate and reliable measurements even in lighting varying environment. This paper initially outlines the design strategy of the SbCT and demonstrates its extraction procedure. Subsequently, simulations are conducted to analyze the positioning accuracy of SbCT under linearly varying illumination situations. Additionally, experiments are performed to study the accuracy and robustness of SbCT with the comparison to concentric circular targets under different lighting variation cases. The results from both simulation and experiment demonstrate the illumination robustness of SbCT, of which the stability is better than 0.01 pixel. According to the displacement tracking experiment, the relative error obtained by SbCT is smaller than 0.2%. Therefore, it can be concluded that SbCT is an illumination-robust feature, offering automatic recognition and accurate localization performance even in illumination varying situations.

Light-dependent changes in the outer plexiform layer of the mouse retina.

The ability of the visual system to relay meaningful information over a wide range of lighting conditions is critical to functional vision, and relies on mechanisms of adaptation within the retina that adjust sensitivity and gain as ambient light changes. Photoreceptor synapses represent the first stage of image processing in the visual system, thus activity-driven changes at this site are a potentially powerful, yet under-studied means of adaptation. To gain insight into these mechanisms, the abundance and distribution of key synaptic proteins involved in photoreceptor to ON-bipolar cell transmission were compared between light-adapted mice and mice subjected to prolonged dark exposure (72 hours), by immunofluorescence confocal microscopy and immunoblotting. We also tested the effects on protein abundance and distribution of 0.5-4 hours of light exposure following prolonged darkness. Proteins examined included the synaptic ribbon protein, ribeye, and components of the ON-bipolar cell signal transduction pathway (mGluR6, TRPM1, RGS11, GPR179, Goα). The results indicate a reduction in immunoreactivity for ribeye, TRPM1, mGluR6, and RGS11 following prolonged dark exposure compared to the light-adapted state, but a rapid restoration of the light-adapted pattern upon light exposure. Electron microscopy revealed similar ultrastructure of light-adapted and dark-adapted photoreceptor terminals, with the exception of electron dense vesicles in dark-adapted but not light-adapted ON-bipolar cell dendrites. To assess synaptic transmission from photoreceptors to ON-bipolar cells, we recorded electroretinograms after different dark exposure times (2, 16, 24, 48, 72 hours) and measured the b-wave to a-wave ratios. Consistent with the reduction in synaptic proteins, the b/a ratios were smaller following prolonged dark exposure (48-72 hours) compared to 16 hours dark exposure (13-21%, depending on flash intensity). Overall, the results provide evidence of light/dark-dependent plasticity in photoreceptor synapses at the biochemical, morphological, and physiological levels.

Micro-expression recognition based on EEG signals

Research into micro-expression recognition is increasingly motivated by its potential applications to mental-health diagnosis and policing. Thus far, micro-expression recognition research and applications have been based on computer vision processing technology, with notable limitations. For instance, changes in ambient lighting or in the head posture and facial occlusion can all affect micro-expression recognition. The high temporal resolution of EEG technology captures brain activity associated with micro-expressions, and hence objectively identifies micro-expressions from a neurophysiological perspective. Herein, we report a real-time supervision and emotional expression suppression (SEES) experimental paradigm. We collected micro-expression video and EEG data from 68 subjects under positive emotion induction. Thereafter, the EEG frequency-band power, statistical features, autoregressive (AR) model parameters, and wavelet entropy were extracted as features. We ranked all 128 channels based on the micro-expression recognition task classification accuracy using all the features extracted for each channel. Based on the channel ranking, different sizes of channel combinations were chosen. Subsequently, the optimal subset of these features from different combinations was selected using a sequential backward selection (SBS) algorithm and fed into different classifiers, including a support vector machine (SVM), K-nearest neighbor (KNN), gradient-boosted decision tree (GBDT), and back-propagation (BP) neuron network. The different classifiers achieve promising accuracy for micro-expression recognition, with GBDT exhibiting the highest accuracy of 85.96 % when 11 channels were selected. This demonstrates the feasibility of recognizing micro-expressions based on EEG signals.

ISEE: A Wearable Image-Sound Translation System for Partially Sighted People

As a growing number of blinds, many blind aid systems which use the way of converting image to sound or vibration have been designed. However, phonetic prompt and vibration are very limited in information expression. Therefore, a system called Image-Sound Eye-substitution Equipment (ISEE) is designed in this paper. This system consists of binocular camera, Raspberry Pi, and stereo headsets. It can convert image to sound by mapping each pixel into an independent sound source based on sensory substitution technology and enable blinds to browse pictures, appreciate scenery, and avoid obstacles for its high-density information expression. Its core of data processing design is the Sensory Substitution Technology-based Image-to-Sound Transform (SST-IST) algorithm. The SST-IST mainly consists of 3 steps, i.e., the image reshape, image-to-sound conversion matrix generation, and image-to-sound transmission. First, a single-frequency pitch of pixel is generated, followed by wave interception and localization to obtain independent sound clips to express pixel information. Second, the sound chips based on Head Related Transfer Functions (HRTF) are stereo processed. Third, a series of single-frequency sound chips are accumulated with weights to generate a complete sound. Extensive experiments show that our SST-IST can restore image information well and the sound restore rate can achieve 73.0%. Also, it is universally adaptive and robust to the changes in ambient light and noises within a certain range. The auditory discrimination experiments show that, with 4-5 hours training, the users can recognize basic graphics by using ISEE system.

Temporal effects on death by suicide: empirical evidence and possible molecular correlates

Popular culture and medical lore have long postulated a connection between full moon and exacerbations of psychiatric disorders. We wanted to empirically analyze the hypothesis that suicides are increased during the period around full moons. We analyzed pre-COVID suicides from the Marion County Coroner’s Office (n = 776), and show that deaths by suicide are significantly increased during the week of the full moon (p = 0.037), with older individuals (age ≥ 55) showing a stronger effect (p = 0.019). We also examined in our dataset which hour of the day (3–4 pm, p = 0.035), and which month of the year (September, p = 0.09) show the most deaths by suicide. We had blood samples on a subset of the subjects (n = 45), which enabled us to look at possible molecular mechanisms. We tested a list of top blood biomarkers for suicidality (n = 154) from previous studies of ours 7, to assess which of them are predictive. The biomarkers for suicidality that are predictive of death by suicide during full moon, peak hour of day, and peak month of year, respectively, compared to outside of those periods, appear to be enriched in circadian clock genes. For full moon it is AHCYL2, ACSM3, AK2, and RBM3. For peak hour it is GSK3B, AK2, and PRKCB. For peak month it is TBL1XR1 and PRKCI. Half of these genes are modulated in expression by lithium and by valproate in opposite direction to suicidality, and all of them are modulated by depression and alcohol in the same direction as suicidality. These data suggest that there are temporal effects on suicidality, possibly mediated by biological clocks, pointing to changes in ambient light (timing and intensity) as a therapeutically addressable target to decrease suicidality, that can be coupled with psychiatric pharmacological and addiction treatment preventive interventions.

Time-course analysis of change in display color-appearance based on ambient light change

This study investigates the time course of changes in display color appearance according to changes in ambient lighting. Psychophysical experiments were conducted to observe brightness under lighting transitions in illuminance and the perceived white point under lighting transitions in correlated color temperatures. The result shows that significant change in the color appearance occurs immediately after ambient lighting changes. In addition, the initial change in color appearance depends on the lighting transition characteristics. The continuous change in color appearance was observed when ambient lighting was changed to brighter or lower CCT. On the other hand, a significant abrupt change at the initial stage of adaptation was observed when ambient lighting was changed to darker or higher CCT.

Vision-based structural displacement measurement under ambient-light changes via deep learning and digital image processing

Structural displacement can provide critical information for structural safety assessment and maintenance. Although vision-based displacement measurement has significant advantages over traditional methods, it still faces challenges in long-term field applications owing to environmental uncertainties. This study proposes a novel displacement measurement method based on deep learning and digital image processing to mitigate the effects of ambient-light changes. The proposed method can automatically extract the calibration object in complex scenarios using You Only Look Once (YOLO) v5 and locate the calibration object under 24-h ambient-light changes precisely. Short- and long-term experiments were conducted in the laboratory to evaluate the performance of the method, and the short-term experimental results were compared with laser displacement sensor (LDS) data, which showed a maximum and minimum relative error of 0.4122% and 0.0024%. The long-term experimental results showed that the displacement responses were within ±1.0 mm. Hence, this method has good potential in structural displacement measurements.

Research on Instance Segmentation Algorithm of Greenhouse Sweet Pepper Detection Based on Improved Mask RCNN

The fruit quality and yield of sweet peppers can be effectively improved by accurately and efficiently controlling the growth conditions and taking timely corresponding measures to manage the planting process dynamically. The use of deep-learning-based image recognition technology to segment sweet pepper instances accurately is an important means of achieving the above goals. However, the accuracy of the existing instance segmentation algorithms is seriously affected by complex scenes such as changes in ambient light and shade, similarity between the pepper color and background, overlap, and leaf occlusion. Therefore, this paper proposes an instance segmentation algorithm that integrates the Swin Transformer attention mechanism into the backbone network of a Mask region-based convolutional neural network (Mask RCNN) to enhance the feature extraction ability of the algorithm. In addition, UNet3+ is used to improve the mask head and segmentation quality of the mask. The experimental results show that the proposed algorithm can effectively segment different categories of sweet peppers under conditions of extreme light, sweet pepper overlap, and leaf occlusion. The detection AP, AR, segmentation AP, and F1 score were 98.1%, 99.4%, 94.8%, and 98.8%, respectively. The average FPS value was 5, which can be satisfied with the requirement of dynamic monitoring of the growth status of sweet peppers. These findings provide important theoretical support for the intelligent management of greenhouse crops.

FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles

Abstract. The acclimative response of phytoplankton, which adjusts their nutrient and pigment content in response to changes in ambient light, nutrient levels, and temperature, is an important determinant of observed chlorophyll distributions and biogeochemistry. Acclimative models typically capture this response and its impact on the C : nutrient : Chl ratios of phytoplankton by explicitly resolving the dynamics of these constituents of phytoplankton biomass. The instantaneous acclimation (IA) approach only requires resolving the dynamics of a single tracer and calculates the elemental composition assuming instantaneous local equilibrium. IA can capture the acclimative response without substantial loss of accuracy in both 0D box models and spatially explicit 1D models. A major drawback of IA so far has been its inability to maintain mass balance for the elements with unresolved dynamics. Here we extend the IA model to capture both C and N cycles in a 0D setup, which requires analytical derivation of additional flux terms to account for the temporal changes in cellular N quota, Q. We present extensive tests of this model, with regard to the conservation of total C an N and its behavior in comparison to an otherwise equivalent, fully explicit dynamic acclimation (DA) variant under idealized conditions with variable light and temperature. We also demonstrate a modular implementation of this model in the Framework for Aquatic Biogeochemical Modelling (FABM), which facilitates modeling competition between an arbitrary number of different acclimative phytoplankton types. In a 0D setup, we did not find evidence for computational advantages of the IA approach over the DA variant. In a spatially explicit setup, performance gains may be possible but would require modifying the physical-flux calculations to account for spatial differences in Q between model grid cells.

Robust Reinforcement Learning Based Visual Servoing with Convolutional Features

Image-based visual servoing poses a significant challenge for robotic systems, as it involves detecting the object and controlling the robot arm based on image feedback. These tasks are further complicated by various interferences such as changes in ambient lighting, distractions, and background clutter. Recent research suggests that reinforcement learning is a promising approach to learning efficient control policies for such tasks. In this paper, we propose a data-driven approach for closed-loop visual servoing based on a reinforcement learning algorithm that does not require any prior knowledge of the task object or intrinsic camera parameters. Our method utilizes a convolutional neural network for object detection and a servoing strategy that enables the robot to determine the relative camera motion and position the camera at the desired pose. Our experimental results demonstrate that the proposed approach successfully steers the camera using only a single template image of the task object.

An Enhanced LBPH Approach to Ambient-Light-Affected Face Recognition Data in Sensor Network

Although combining a high-resolution camera with a wireless sensing network is effective for interpreting different signals for image presentation on the identification of face recognition, its accuracy is still severely restricted. Removing the unfavorable impact of ambient light remains one of the most difficult challenges in facial recognition. Therefore, it is important to find an algorithm that can capture the major features of the object when there are ambient light changes. In this study, face recognition is used as an example of image recognition to analyze the differences between Local Binary Patterns Histograms (LBPH) and OpenFace deep learning neural network algorithms and compare the accuracy and error rates of face recognition in different environmental lighting. According to the prediction results of 13 images based on grouping statistics, the accuracy rate of face recognition of LBPH is higher than that of OpenFace in scenes with changes in ambient lighting. When the azimuth angle of the light source is more than +/−25° and the elevation angle is +000°, the accuracy rate of face recognition is low. When the azimuth angle is between +25° and −25° and the elevation angle is +000°, the accuracy rate of face recognition is higher. Through the experimental design, the results show that, concerning the uncertainty of illumination angles of lighting source, the LBPH algorithm has a higher accuracy in face recognition.

PEMANFAATAN NODEMCU ESP8266 BERBASIS ANDROID (BLYNK) SEBAGAI ALAT ALAT MEMATIKAN DAN MENGHIDUPKAN LAMPU

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.