Varying Lighting Conditions Research Articles

Molecular dynamics simulations of the interfacial behaviors and photo-oxidation of phytosterol under different emulsion oil content

Phytosterol, recognized for its health benefits, is predominantly extracted from plants and exhibits significantly reduced stability under varying light conditions. Their photooxidation is significantly influenced by emulsion interfaces. This study examined the mechanism of interface structure on phytosterol photooxidation with unparalleled molecular precision, utilizing molecular dynamics simulations and experimental procedures. Hydrogen bonding between the hydroxyl group at the C3 position of phytosterols and water molecules, coupled with van der Waals forces between the hydrophobic regions and the oil phase, induced phytosterol molecules to disperse toward the interface. The elevated polarity of the oil phase, specifically in tributyrin, facilitated the permeation of water molecules into the oil phase. This was achieved by diminishing the emulsion's interfacial tension, thereby fostering the development of more interface or micelles, and accelerating the photooxidation process of phytosterols. These simulations unraveled that the preponderance of phytosterol distribution is localized and oxidized at the oil-water interface.

Movie Character Recognition from Video and Image using AI

Recognition of movie characters using video and images An innovative research study called Using AI explores the use of Artificial Intelligence (AI) for recognizing and identifying movie characters using still photos and video clips. The study offers a new AI system that analyses visual input, extracts attributes, and correctly identifies movie characters using machine learning and deep learning methods. This system is able to identify characters in a single frame and follow their actions over several frames, giving it a thorough understanding of the character's function in the story. The technology's potential uses in a number of industries, including film creation, advertising, and audience analytics. The suggested AI system can automate video editing, help with character recognition-based targeted advertising, and offer insightful data on audience preferences and habits. The implications of this technology are enormous, creating new opportunities for the film industry to grow technologically. The challenges and limitations of implementing AI in movie character recognition. It highlights issues such as handling low-resolution images, varying lighting conditions, and changes in character appearance over time. Despite these challenges, the AI system demonstrates a high degree of accuracy and efficiency. The concludes by suggesting future enhancements to the system.

ProLiF: Progressively-connected Light Field network for efficient view synthesis

This paper presents a simple yet practical network architecture, ProLiF (Progressively-connected Light Field network), for the efficient differentiable view synthesis of complex forward-facing scenes in both the training and inference stages. The progress of view synthesis has advanced significantly due to the recent Neural Radiance Fields (NeRF). However, when training a NeRF, hundreds of network evaluations are required to synthesize a single pixel color, which is highly consuming of device memory and time. This issue prevents the differentiable rendering of a large patch of pixels in the training stage for semantic-level supervision, which is critical for many practical applications such as robust scene fitting, style transferring, and adversarial training. On the contrary, our proposed simple architecture ProLiF, encodes a two-plane light field, which allows rendering a large batch of rays in one training step for image- or patch-level losses. To keep the multi-view 3D consistency of the neural light field, we propose a progressive training strategy with novel regularization losses. We demonstrate that ProLiF has good compatibility with LPIPS loss to achieve robustness to varying light conditions, and NNFM loss as well as CLIP loss to edit the rendering style of the scene.

Sensorless Maximum Power Point Control for Single-stage Grid Connected PV Systems

In this paper, a novel approach for implementing the maximum power point that could be generated from a photovoltaic (PV) panel while eliminating the need for current sensors through the application of the Hill Climbing algorithm is proposed. The active power generated by the PV panel is injected into the grid via a three-phase inverter using voltage-oriented voltage control with Spatial Vector Modulation (SVM). The developed strategy ensures minimal ripples for both active and reactive power and produces a sinusoidal alternating current waveform, even under varying lighting conditions. A comprehensive description of the adopted control strategy is provided and validated through numerical simulations conducted in MATLAB/Simulink environment. Furthermore, the performance of the proposed method is assessed by analyzing the simulation results. In an attempt to validate the effectiveness of the proposed approach, an implementation of the inverter control was conducted with the DSpace 1104 board, and the results underscored the feasibility and effectiveness of the employed approach for grid-connected PV systems.

Indoor photovoltaics: A numerical model of dye-sensitized solar cells based on indoor illumination for the Internet of Things applications

Since dye-sensitized solar cells (DSSC) have a suitable efficiency in the invisible region, they present a promising avenue for sustainable energy generation, particularly in the context of indoor environments where ambient lighting prevails. This paper proposes a novel numerical model tailored to assess the performance of DSSCs under indoor lighting conditions, with a specific focus on their potential application in powering Internet of Things (IoT) devices. By considering factors such as radiation intensity and spectral composition, particularly from common indoor light sources like Light Emitting Diodes (LEDs) and Fluorescent lights, the model offers insights into the efficiency and viability of DSSCs in indoor settings. Since empirical analyses are difficult and expensive, the existence of a numerical model that investigates cell behavior under indoor illumination can be beneficial for developing such cells. In conducting verifying analyses under varying lighting conditions, the study demonstrates notable efficiencies of DSSCs, with LED 3000 K illumination at 1000 lux intensity yielding the highest performance. While DSSCs may appear less economically viable in environments with readily available electricity, their potential significance lies in providing sustainable energy solutions for IoT devices, thus reducing dependency on conventional power sources and contributing to environmental sustainability. The scientific significance of this research lies in its contribution to understanding the behavior of DSSCs under indoor lighting, offering insights into their potential applications in powering IoT devices. By providing a numerical model for evaluating DSSC performance under realistic indoor conditions, this study opens avenues for further research in optimizing DSSC technology for indoor energy harvesting applications, thereby advancing the field of renewable energy and sustainable technology integration.

Investigating the Effectiveness of Maximum Power Point Tracking (MPPT) with Perturb and Observe (P&O) Algorithm in Solar Power Battery Charging System

Solar energy has emerged as one of the most promising and crucial renewable energy sources in our efforts to reduce reliance on fossil fuels and mitigate their adverse impact on the environment. One of the primary applications of solar energy is in the solar power battery charging system. This system harnesses solar energy emitted by the sun through solar panels to charge batteries used as energy storage. At the heart of the solar power battery charging system lies the technology of Maximum Power Point Tracking (MPPT). MPPT is a method used to achieve maximum efficiency from solar panels by adjusting their voltage and current to ensure they constantly operate at their maximum power point, irrespective of varying light conditions. In this article, the solar power battery charging system with the use of the MPPT method employing the Perturb and Observe (P&O) algorithm will be explored. The proposed of modeling the solar battery charging system with the implementation of a DC-DC buck converter and lead-acid battery incorporated with P&O algorithm will be elaborated through MATLAB/Simulink simulation software. Additionally, the performance analysis of the system will be investigated by considering the state of charge (SOC), power (W), current (A), and voltage (V) variables. The simulation results indicate the effectiveness of the proposed P&O algorithm in maintaining the panel’s operating maximum power in multi-stage charging system.

Evaluation of Enhanced Resnet-50 Based Deep Learning Classifier for Tomato Leaf Disease Detection and Classification

This research presents a comprehensive assessment of a sophisticated approach for the accurate detection and classification of various tomato leaf diseases using an improved ResNet-50 based deep learning classifier. The alarming increase in plant diseases has prompted the need for advanced technologies that can promptly identify and categorize these ailments to ensure agricultural sustainability. The proposed method harnesses the potential of deep convolutional neural networks (CNNs) and builds upon the ResNet-50 architecture, renowned for its depth and performance. However, the approach's innovation lies in the incorporation of enhancements such as advanced data augmentation techniques and transfer learning from a vast plant disease dataset. These modifications empower the model to learn intricate disease-specific features and patterns, leading to heightened accuracy and robustness. The evaluation of the approach is conducted on an extensive dataset encompassing high-resolution images of tomato leaves affected by a range of diseases. The dataset is meticulously preprocessed to ensure consistency and quality, followed by a rigorous training regimen that fine-tunes the improved ResNet-50 model.The results underscore the efficacy of the proposed method in accurate disease detection and classification. The improved ResNet-50 based classifier demonstrates exceptional performance, achieving an impressive accuracy exceeding 95%. Notably, the model showcases resilience against variations in lighting conditions, angles, and disease severity, highlighting its applicability in real-world agricultural scenarios. The implications of this research are significant, offering an efficient and reliable tool for early detection and classification of tomato leaf diseases. The integration of advanced deep learning techniques and the enhancements introduced in this work signify a substantial advancement in precision agriculture and sustainable crop management practices. As future work, this approach can be extended to address diseases in other plant species, contributing to a versatile framework that can safeguard global food production and alleviate the challenges posed by plant diseases.

Boosting Vehicle Classification with Augmentation Techniques across Multiple YOLO Versions

In recent years, computer vision has experienced a surge in applications across various domains, including product and quality inspection, automatic surveillance, and robotics. This study proposes techniques to enhance vehicle object detection and classification using augmentation methods based on the YOLO (You Only Look Once) network. The primary objective of the trained model is to generate a local vehicle detection system for Malaysia which have the capacity to detect vehicles manufactured in Malaysia, adapt to the specific environmental factors in Malaysia, and accommodate varying lighting conditions prevalent in Malaysia. The dataset used for this paper to develop and evaluate the proposed system was provided by a highway company, which captured a comprehensive top-down view of the highway using a surveillance camera. Rigorous manual annotation was employed to ensure accurate annotations within the dataset. Various image augmentation techniques were also applied to enhance the dataset's diversity and improve the system's robustness. Experiments were conducted using different versions of the YOLO network, such as YOLOv5, YOLOv6, YOLOv7, and YOLOv8, each with varying hyperparameter settings. These experiments aimed to identify the optimal configuration for the given dataset. The experimental results demonstrated the superiority of YOLOv8 over other YOLO versions, achieving an impressive mean average precision of 97.9% for vehicle detection. Moreover, data augmentation effectively solves the issues of overfitting and data imbalance while providing diverse perspectives in the dataset. Future research can focus on optimizing computational efficiency for real-time applications and large-scale deployments.

Portable Head-Mounted System for Mobile Forearm Tracking.

Computer vision (CV)-based systems using cameras and recognition algorithms offer touchless, cost-effective, precise, and versatile hand tracking. These systems allow unrestricted, fluid, and natural movements without the constraints of wearable devices, gaining popularity in human-system interaction, virtual reality, and medical procedures. However, traditional CV-based systems, relying on stationary cameras, are not compatible with mobile applications and demand substantial computing power. To address these limitations, we propose a portable hand-tracking system utilizing the Leap Motion Controller 2 (LMC) mounted on the head and controlled by a single-board computer (SBC) powered by a compact power bank. The proposed system enhances portability, enabling users to interact freely with their surroundings. We present the system's design and conduct experimental tests to evaluate its robustness under variable lighting conditions, power consumption, CPU usage, temperature, and frame rate. This portable hand-tracking solution, which has minimal weight and runs independently of external power, proves suitable for mobile applications in daily life.

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.

Growth of spring- and autumn-spawned larvae of Atlantic herring Clupea harengus: a long-term experiment mimicking seasonal light conditions

Atlantic herring Clupea harengus populations differ in their spawning time, and spring- and autumn-spawning populations are genetically distinct. Offspring of these populations encounter seasonal variations in productivity. We conducted a fertilization experiment using spring-spawning Atlantic herring. Offspring were reared for 3 yr with seasonal varying light cycles starting either in spring or autumn, using 2 fixed temperature levels and food provided in excess. Such long-term experiments from hatching to maturation in small pelagic fish are very rare. We hypothesized that longer daylengths early in life would provide an overall growth advantage resulting in larger size after 1 yr (same amount of light) compared to those experiencing prolonged daylight later in life due to higher size-dependent growth rates at smaller sizes. Larvae with initial spring conditions initially grew faster. However, contrary to our expectations, offspring with initial autumn conditions had caught up to similar size after 1 yr. Herring at higher temperatures grew faster, even when correcting for the amount of degree-days. After the first year, individuals hatched in spring showed higher growth at the higher temperature while herring hatched under autumn light conditions consistently had higher growth rates at lower temperatures. The somatic condition of herring followed the daylength, with best conditions during summer and poorest during winter. This was the first long-term experiment conducted on herring with varying light conditions from hatching to maturation. Our novel results indicate that herring display considerable growth plasticity, reflecting the wide range of environmental conditions and life histories sustaining herring populations.

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking.

Few-Shot Neural Radiance Fields under Unconstrained Illumination

In this paper, we introduce a new challenge for synthesizing novel view images in practical environments with limited input multi-view images and varying lighting conditions. Neural radiance fields (NeRF), one of the pioneering works for this task, demand an extensive set of multi-view images taken under constrained illumination, which is often unattainable in real-world settings. While some previous works have managed to synthesize novel views given images with different illumination, their performance still relies on a substantial number of input multi-view images. To address this problem, we suggest ExtremeNeRF, which utilizes multi-view albedo consistency, supported by geometric alignment. Specifically, we extract intrinsic image components that should be illumination-invariant across different views, enabling direct appearance comparison between the input and novel view under unconstrained illumination. We offer thorough experimental results for task evaluation, employing the newly created NeRF Extreme benchmark—the first in-the-wild benchmark for novel view synthesis under multiple viewing directions and varying illuminations.

Efficient networks for textureless feature registration via free receptive field

In the real-world scenarios, challenges such as changes in viewpoint, variations in lighting conditions, and the presence of blur effects are pervasive issues faced in the field of image registration, making the rapid and accurate establishment of correspondences between images exceedingly difficult. Presently, encoder-based methods often entail larger parameter sizes and longer execution times. Additionally, when dealing with low-texture images, reliable keypoint registration becomes particularly challenging, leading to further increases in registration costs. To address such issues, this study proposes a novel Efficient Feature Registration Network (EFRNet) that integrates low-frequency texture information with high-frequency contour information to achieve efficient and stable feature registration. The proposed EFRNet possesses several significant advantages. Firstly, the introduced Free Receptive Field (FreeRF) module dynamically adapts to allocate extractors for each feature map and discards irrelevant feature information, thereby facilitating the extraction of features at different frequencies and achieving model lightweighting. Secondly, the Add Edge Margin Global Feature Registrant (AemGFR), incorporating a cross-attention architecture, flexibly adjusts feature interaction processes to meet the requirements of accurate registration. Moreover, by introducing a Gaussian distribution of edge values, the AemGFR effectively accelerates model convergence and overall efficiency. Throughout the entire algorithm, the FreeRF module is fully utilized to further enhance feature extraction capability while reducing the parameter count of the network during the testing phase, achieving lightweight feature registration. Extensive experimentation has demonstrated that our EFRNet exhibits significant advantages over state-of-the-art feature registration methods on three challenging datasets. Importantly, our method achieves faster inference speeds across different sensor devices.

An effective cross-scenario remote heart rate estimation network based on global-local information and video transformer.

Remote photoplethysmography (rPPG) technology is a non-contact physiological signal measurement method, characterized by non-invasiveness and ease of use. It has broad application potential in medical health, human factors engineering, and other fields. However, current rPPG technology is highly susceptible to variations in lighting conditions, head pose changes, and partial occlusions, posing significant challenges for its widespread application. In order to improve the accuracy of remote heart rate estimation and enhance model generalization, we propose PulseFormer, a dual-path network based on transformer. By integrating local and global information and utilizing fast and slow paths, PulseFormer effectively captures the temporal variations of key regions and spatial variations of the global area, facilitating the extraction of rPPG feature information while mitigating the impact of background noise variations. Heart rate estimation results on the popular rPPG dataset show that PulseFormer achieves state-of-the-art performance on public datasets. Additionally, we establish a dataset containing facial expressions and synchronized physiological signals in driving scenarios and test the pre-trained model from the public dataset on this collected dataset. The results indicate that PulseFormer exhibits strong generalization capabilities across different data distributions in cross-scenario settings. Therefore, this model is applicable for heart rate estimation of individuals in various scenarios.

Chemical Composition and Bioactivity of Dill Seed (Anethum graveolens L.) Essential Oil from Plants Grown under Shading

This study determined the content and composition of dill seed (Anethum graveolens L.) essential oil under varying light conditions: non-shaded plants in open fields and plants covered with pearl shade nets (40% shade index). Essential oil was extracted using Clevenger hydrodistillation. The essential oil content was 4.63% for non-shaded plants and 4.81% for shaded plants. GC/MS analysis revealed twenty-one and twenty-two components in dill seed from non-shaded and shaded plants, respectively. The terpenic fraction of essential oil from non-shaded plants consisted mainly of oxygen-containing monoterpene derivatives (53.6%), with carvone (46.1%) as the primary component, followed by monoterpene hydrocarbons (46.4%), predominantly limonene (43.8%). Essential oil from shaded plants contained a higher content of carvone (49.8%) and a lower content of limonene (37.8%) compared to essential oil from non-shaded plants. Non-shaded plant essential oil exhibited stronger antioxidant activity (EC50 value: 26.04 mg mL−1) than shaded plant essential oil (54.23 mg mL−1). Dill seed essential oil showed the most potent antimicrobial activity (disc diffusion method) against Escherichia coli (inhibition zone: 15–18 mm). Shaded plants demonstrated a positive influence of essential oil against Klebsiella pneumoniae. Carvone and its derivatives, as the main components, hold significant potential in the food industry and alternative medicines. A practical implication of this study could be higher plant densities or intercropping of dill, as it thrives with minimal light.

Repeated Divergence in Opsin Gene Expression Mirrors Photic Habitat Changes in Rapidly Evolving Crater Lake Cichlid Fishes.

AbstractSelection pressures differ along environmental gradients, and traits tightly linked to fitness (e.g., the visual system) are expected to track such variation. Along gradients, adaptation to local conditions might be due to heritable and nonheritable environmentally induced variation. Disentangling these sources of phenotypic variation requires studying closely related populations in nature and in the laboratory. The Nicaraguan lakes represent an environmental gradient in photic conditions from clear crater lakes to very turbid great lakes. From two old, turbid great lakes, Midas cichlid fish (Amphilophus cf. citrinellus) independently colonized seven isolated crater lakes of varying light conditions, resulting in a small adaptive radiation. We estimated variation in visual sensitivities along this photic gradient by measuring cone opsin gene expression among lake populations. Visual sensitivities observed in all seven derived crater lake populations shifted predictably in direction and magnitude, repeatedly mirroring changes in photic conditions. Comparing wild-caught and laboratory-reared fish revealed that 48% of this phenotypic variation is genetically determined and evolved rapidly. Decreasing intrapopulation variation as environments become spectrally narrower suggests that different selective landscapes operate along the gradient. We conclude that the power to predict phenotypic evolution along gradients depends on both the magnitude of environmental change and the selective landscape shape.

Robust hyperspectral image classification using generative adversarial networks

This paper introduces Sill-Rgan, a novel Generative Adversarial Network (GAN) designed to improve hyperspectral image (HSI) classification under varying lighting conditions. Sill-Rgan uniquely maps different light condition domains, enhancing sample classification robustness and generating new virtual samples. Addressing challenges like high spectral dimensionality and noise in HSI classification, our approach utilizes a deep proxy-based learning framework. It integrates and improves advanced GAN models and multitask networks for optimal training stability and loss function optimization. The model's mapping network is adept at generating domain-specific latent codes, enabling the transformation of original hyperspectral data into enhanced versions. Extensive experiments conducted on hyperspectral datasets of agricultural products under diverse indoor and outdoor lighting conditions confirm the effectiveness of Sill-Rgan. The results highlight the model's adaptability in both supervised and semi-supervised learning scenarios, yielding exceptional classification accuracy and enhanced data quality. The versatile potential of Sill-Rgan extends its applicability to a broad range of spectral data classifications, underlining its significant contribution to hyperspectral imaging. This advancement opens new avenues in machine vision systems, particularly in scenarios with dynamic lighting challenges.

Light and Light Signals Regulate Growth and Development in Woody Plants

This review synthesizes the current understanding on the dynamic influence of light on the developmental morphology of woody plants. It explores the regulatory effects of photosynthesis and photomorphogenesis in response to varying light conditions including intensity, quality, and photoperiodicity, and their subsequent impact on plant growth and architecture. Additionally, this review elucidates the role of the circadian system in synchronizing internal rhythms with external light cycles, a process mediated by photoreceptors such as PHYTOCHROME A (PHYA) and PHYTOCHROME B (PHYB), which are pivotal for seasonal growth and dormancy in species like poplar. The molecular perspective is provided on the light-regulated transcription of genes, along with their influence on the plant’s growth cycles and seasonal adaptions. Furthermore, the interactive role of plant hormones, including auxin, ethylene, and abscisic acid (ABA), is explored in the context of light signal transduction and its subsequent effect on plant physiology. By providing a comprehensive view of the light-dependent mechanisms that govern woody plant growth, this review contributes to our understanding of plant adaptation strategies and informs approaches to enhance forestry production and biodiversity conservation in the face of climate change.

Improving the Performance of Automatic Lip-Reading Using Image Conversion Techniques

Variation in lighting conditions is a major cause of performance degradation in pattern recognition when using optical imaging. In this study, infrared (IR) and depth images were considered as possible robust alternatives against variations in illumination, particularly for improving the performance of automatic lip-reading. The variations due to lighting conditions were quantitatively analyzed for optical, IR, and depth images. Then, deep neural network (DNN)-based lip-reading rules were built for each image modality. Speech recognition techniques based on IR or depth imaging required an additional light source that emitted light in the IR range, along with a special camera. To mitigate this problem, we propose a method that does not use an IR/depth image directly, but instead estimates images based on the optical RGB image. To this end, a modified U-net was adopted to estimate the IR/depth image from an optical RGB image. The results show that the IR and depth images were rarely affected by the lighting conditions. The recognition rates for the optical, IR, and depth images were 48.29%, 95.76%, and 92.34%, respectively, under various lighting conditions. Using the estimated IR and depth images, the recognition rates were 89.35% and 80.42%, respectively. This was significantly higher than for the optical RGB images.