Light Variations Research Articles

The role of spirulina in food security: Nutritional and ecological implications in the context of climate change

Abstract In the context of a changing climate and the goal of achieving carbon neutrality by 2050, microalgae, particularly spirulina, are emerging as a versatile and sustainable solution to ensure food security, serving as an alternative protein source for human consumption. Spirulina can absorb carbon dioxide during photosynthesis, grows rapidly, does not require arable land, is cultivated year-round, and is rich in protein as well as other macro- and micronutrients. Spirulina stands out not only for its resilience and adaptability to a range of environmental conditions – including variations in light, temperature, pH, and salinity – but also for its low water requirements, making it a promising option for regions with limited water resources. It can address food security challenges by providing nutritional benefits as a dietary supplement and enhancing animal feed quality. Additionally, it supports sustainable agriculture as a biostimulant, improving crop productivity and soil health while reducing reliance on chemical inputs. Its potential as a raw material across various industries and the exploration of new applications make spirulina an attractive candidate for future research, which should focus on enhanced profitability and large-scale accessibility.

BRA-YOLOv7: improvements on large leaf disease object detection using FasterNet and dual-level routing attention in YOLOv7

Tea leaf diseases are significant causes of reduced quality and yield in tea production. In the Yunnan region, where the climate is suitable for tea cultivation, tea leaf diseases are small, scattered, and vary in scale, making their detection challenging due to complex backgrounds and issues such as occlusion, overlap, and lighting variations. Existing object detection models often struggle to achieve high accuracy in detecting tea leaf diseases. To address these challenges, this paper proposes a tea leaf disease detection model, BRA-YOLOv7, which combines a dual-level routing dynamic sparse attention mechanism for fast identification of tea leaf diseases in complex scenarios. BRA-YOLOv7 incorporates PConv and FasterNet as replacements for the original network structure of YOLOv7, reducing the number of floating-point operations and improving efficiency. In the Neck layer, a dual-level routing dynamic sparse attention mechanism is introduced to enable flexible computation allocation and content awareness, enhancing the model’s ability to capture global information about tea leaf diseases. Finally, the loss function is replaced with MPDIoU to enhance target localization accuracy and reduce false detection cases. Experiments and analysis were conducted on a collected dataset using the Faster R-CNN, YOLOv6, and YOLOv7 models, with Mean Average Precision (mAP), Floating-point Operations (FLOPs), and Frames Per Second (FPS) as evaluation metrics for accuracy and efficiency. The experimental results show that the improved algorithm achieved a 4.8% improvement in recognition accuracy, a 5.3% improvement in recall rate, a 5% improvement in balance score, and a 2.6% improvement in mAP compared to the traditional YOLOv7 algorithm. Furthermore, in external validation, the floating-point operation count decreased by 1.4G, FPS improved by 5.52%, and mAP increased by 2.4%. In conclusion, the improved YOLOv7 model demonstrates remarkable results in terms of parameter quantity, floating-point operation count, model size, and convergence time. It provides efficient lossless identification while balancing recognition accuracy, real-time performance, and model robustness. This has significant implications for adopting targeted preventive measures against tea leaf diseases in the future.

Development of AI Model for Robotic Vision Inspection of Sheet-Metal Components in Manufacturing

This paper presents an AI-based robotic vision inspection system designed to enhance quality control in the manufacturing of sheet-metal components. The system integrates a sequential AI model with advanced image processing techniques to automate real-time defect detection and classification. Utilizing a high-resolution camera, the system captures images of components on a conveyor, which are pre- processed using grayscale conversion, Gaussian blurring, and Canny edge detection to emphasize structural details. A deep learning model then classifies isolated regions of interest based on normalized, resized images. Feature matching through ORB (Oriented FAST and Rotated BRIEF) enables accurate alignment with reference templates, while automated measurements convert pixel dimensions to physical units, ensuring reliable detection of deviations. With an average accuracy of 88.3%, the system consistently identifies subtle and complex defects, such as scratches and dimensional deviations, under variable lighting and noise conditions. This AI-driven approach reduces the need for manual inspection, minimizes error, and enhances workflow efficiency, representing a major step toward robust, real-time quality assurance solutions in industrial environments.

YOLOv10-based Model for Player and Football Detection

This study presents an advanced YOLOv10n-based method for the automatic detection of football players and balls directly from match videos. We enhance the YOLOv10 architecture with several significant improvements, including additional detection heads, the integration of C2f_faster and C3_faster modules for enhanced processing speed and accuracy, and the inclusion of BotNet modules with self-attention mechanisms for managing complex visual scenes. Further, we incorporate GhostConv modules to reduce computational overhead while maintaining effective feature extraction. These architectural modifications ensure robust detection capabilities in real-time sports environments, addressing challenges such as high-speed movements, frequent occlusions, and variable lighting conditions typical of both indoor and outdoor stadiums. Validation on internet-sourced images from football matches demonstrates the practicality and effectiveness of our model.

Impact of light spectrum electromagnetic radiation variations on performance and hormonal profiles in laying hens

Avian photopic curves show energy peaks at different wavelengths than humans, particularly in the ultraviolet, yellow, and red range. Therefore, an illumination system with a spectrum tailored for laying hens, encompassing the entire visible spectrum, can enhance performance and welfare. The primary contribution of this study was the development of two Spectral Power Distributions (SPDs) specifically designed for laying hens, with different spectral proportions (S1 and S2), and the evaluation of their effects on productive performance, egg quality, and hormonal levels, compared to conventional white lighting at 3000 K (C). The SPD with a higher emission of red light to increased egg production and egg mass. It also had a lower melatonin concentration, suggesting an inverse relationship with the egg production rate. Regarding egg quality, SPDs specifically designed for laying hens resulted in eggs with greater weight (S1), shell strength (S2), and yolk height (S1 and S2) and diameter (S2). The study’s results indicate that lighting emitting wavelengths within the spectrum visible to poultry, with higher emissions at long wavelengths, appears to be more favorable for laying hens than conventional lighting. A spectrum with higher emissions at shorter wavelengths appears to impair the productive performance of laying hens.

Label Propagation Algorithm for Face Clustering using Shared Nearest Neighbor Similarity

Facial image datasets are particularly vulnerable to challenges such as lighting variations and occlusion, which can complicate data classification. Semi-supervised learning, using a limited amount of labeled facial data, offers a solution by enhancing face classification accuracy while reducing manual labeling efforts. The Label Propagation Algorithm (LPA) is a commonly used semi-supervised algorithm that employs Radial Basis Function (RBF) to measure similarities between data nodes. However, RBF struggles to capture complex nonlinear relationships in facial data. To address this, an improved LPA is proposed that integrates Shared Nearest Neighbor (SNN) to enhance the correlation measurement between facial data and RBF. Three known datasets were considered: FERET, Yale, and ORL. The experiments showed that in the case of insufficient label samples, the accuracy reached 89.76%, 92.46%, and 81.48%, respectively. The proposed LPA enhances clustering robustness by introducing 128 dimensional facial features and more complex similarity measurement. The parameter of similarity measurement can be adjusted based on the characteristics of different datasets to achieve better clustering results. The improved LPA achieved better performance and face clustering effectiveness by enhancing robustness and adaptability.

Online Multi-object Tracking with YOLOv9 and DeepSORT Optimized by Optical Flow

To ensure reliable environmental perception in the realm of autonomous driving, precise and robust multi-object tracking proves imperative. This study proposes an innovative approach to multi-object tracking by combining YOLOv9's sophisticated detection capabilities with an enhanced DeepSORT tracking algorithm, enriched through the integration of optical flow. In the proposed method, the YOLOv9 detector acutely identifies objects in input images, and these detected entities are subsequently transmitted to the optimized DeepSORT tracking algorithm. The principal contribution of this study lies in improving the Kalman filter measurement model within DeepSORT by incorporating robust local optical flow, thus adding a velocity dimension to the filter's update vector. This novel approach significantly improves tracking resilience in the face of occlusions, rapid movements, and appearance changes. Evaluations on MOT17 and KITTI show substantial improvement gains of 2.42%, 2.85%, and 1.84% for HOTA, MOTA, and IDF1, respectively, on MOT17, and 1.94% in MOTA and 2.09% in HOTA on KITTI. The proposed method particularly excels in managing scenarios involving dense traffic and light variations, which are recurrent problems in dynamic urban environments. This enhanced performance positions the proposed solution as an essential component of future perception architectures for autonomous vehicles, promising safer and more efficient navigation in the complex real world.

Model GHT-SVM for Traffic Sign Detection Using Support Vector Machine Algorithm Based On Gabor Filter and Top-Black Hat Transform

A factor that can hinder the detection and recognition of traffic signs is the variation in lighting in the image of traffic signs. This study aims to detect traffic symbols using Gabor Filter (GFT), Top Hat Transform (THT), and Black Hat Transform (BHT) methods on the Support Vector Machine (SVM) algorithm for traffic sign dataset images with data problems that tend to have dark backgrounds at night and bright backgrounds during the day. From the experimental results, GHT-SVM gets the highest accuracy compared to HSV-SVM, HSV-RF, HSV-KNN, and H2T-SVM models. Based on experimental results, H2T-SVM from HOG ⊕ ENT ⊕ BHT ⊕ SVM results get the best accuracy of 86.42%. The Gabor Filter (GFT) parameters used are the number of filters with a value of 16, ksize with a value of 30, sigma with a standard deviation value of 3.0, lambd with a sinusoidal factor value of 10.0, gamma with a spatial aspect ratio value of 0.5 and psi with a phase offset value of 0 while the Top Hat Transform (THT) and Black Hat Transform (BHT) methods use filterSize sizes with values (3, 3).

CSegNet: a hybrid transformer-CNN network for road crack image segmentation

Detecting cracks from optical images plays a crucial role in road maintenance but its good realisation has many challenges. Road cracks exhibit significant diversity and complexity in terms of shape, size and texture and road images may contain various types of noise and interference, such as lighting variations, shadows and different appearances, due to varying perspectives and scales. To address these challenges, a comprehensive road crack dataset called CRCrack has been constructed, which encompasses various crack characteristics. This study proposes a road crack segmentation network called CSegNet, which combines convolutional neural networks (CNNs) and transformers. The network adopts an encoder-decoder framework, namely DeepLabV3+. In the encoder, leveraging the transformers' flexibility in modelling long-term dependencies and the CNNs' ability to capture local contextual information through local receptive fields, weight sharing and spatial subsampling, a ResNeXt-Transformer (ResNeXTR) feature extraction module is designed as the backbone network to enhance the feature extraction capability for road crack images. To reduce the computational cost in the self-attention (SA) computation of the transformer, an average pooling layer is introduced to downsample the dimensions of the encoded features. In the decoder, to focus on the key information of road cracks under diverse environmental conditions and interferences, an efficient channel attention module (ECAM) and a spatial attention module (SAM) are combined to design an efficient convolutional block attention module (ECBAM) to optimise feature representation. Through comparative experiments on the CRCrack dataset, the results demonstrate that the proposed method outperforms classic networks such as U-Net and DeepLabV3+ in terms of intersection over union (IoU), Dice coefficient and area under the receiver operating characteristic (AUROC) curve evaluation metrics. It exhibits good adaptability to ground crack images from different sources, providing a basis for estimating the degree of road damage.

Revolutionizing agricultural efficiency with advanced coconut harvesting automation

<span style="vertical-align: inherit;"><span style="vertical-align: inherit;">The precision coconut harvesting system aims to develop an efficient system for accurately detecting coconuts in agricultural landscapes using advanced image processing techniques. Coconut cultivation is vital to many tropical economies and precise monitoring is essential for optimizing yield and resource utilization. Traditional methods of coconut detection are labor-intensive and time-consuming. The proposed computer vision-based approach automates and enhances coconut detection by analyzing high-resolution images of coconut plantations. Pre-processing techniques improve image quality and object detection algorithms such as convolutional neural networks (CNNs) identify coconut clusters. Challenges like lighting variations and background clutter are addressed using feature extraction and pattern recognition. A user-friendly interface visualizes detection results, aiding farmers in timely decision-making. Extensive testing on diverse datasets evaluates system effectiveness. This model aims to advance precision agriculture, enhancing productivity and informing coconut farmers' decision-making processes. Using a CNN model, the accuracy of coconut detection based on its ripeness was 98.8%.</span></span>

Determinants of spatial variation of ventilation quality and natural lighting in the residential environment of Dekernes city using GIS

Determinants of spatial variation of ventilation quality and natural lighting in the residential environment of Dekernes city using GIS

Evaluation and optimization of annual light variations for visual and non-visual effects within a ground-floor middle school classroom

Evaluation and optimization of annual light variations for visual and non-visual effects within a ground-floor middle school classroom

Radar-based elderly fall detection using SPWVD and ResNet Network

Abstract Falls in the elderly population present substantial health risks, often resulting in morbidity and a decline in quality of life. Conventional fall detection methods, including wearable devices and cameras, are hindered by issues such as variable lighting conditions and privacy considerations. Radarbased fall detection has emerged as a promising alternative, providing an unobtrusive method. This study aims to classify fall detection using Smoothed Pseudo Wigner-Ville distribution (SPWVD) images and ResNet-50 model. For this, an online publicly available radar database comprising 15 subjects is utilized. Radar signals is processed into SPWVD timefrequency representation images for analysis. The SPWVD images is fed into the ResNet-50 model. Experiments are performed and performance is evaluated using 10- fold cross validation. The proposed approach is able to distinguish elderly fall. Using ResNet-50 model, the approach yields a maximum average classification f-measure, area under curve score, precision and, sensitivity of 67.15%, 52.31%, 50.55% and, 99.88%, respectively. Hence, the proposed framework holds promise for accurately and efficiently detecting falls among the elderly population within their private environments.

Monocular thermal SLAM with neural radiance fields for 3D scene reconstruction

Visual simultaneous localization and mapping (SLAM) faces significant challenges in environments with variable lighting and smoke. Excelling in such visually degraded settings, thermal imaging captures scene radiance effectively. To address the limitations of traditional thermal SLAM in 3D scene reconstruction, we propose ThermalSLAM-NeRF, a novel integration of thermal SLAM with neural radiance fields (NeRF). This method significantly enhances the quality of high dynamic range thermal images by improving their signal-to-noise ratio, contrast, and detail. It also employs online photometric calibration to ensure grayscale consistency between consecutive frames. We utilize a sparse direct method for pose estimation, selecting keyframes based on photometric error and tracking quality. The NeRF map is reconstructed using a multi-view keyframe sequence. Our evaluations on datasets containing over 15,000 thermal images show that ThermalSLAM-NeRF achieves an average improvement of 59.30% in trajectory accuracy over existing state-of-the-art SLAM methods. This approach uniquely tracks all sequences and constructs comprehensive NeRF maps, enabling robust and precise pose estimation without the need for extensive pre-training.

Application of Improved Visual SLAM in Intraoperative Navigation of Robotic-Assisted Laparoscopic Surgery

In robot-assisted laparoscopic surgery, precise intraoperative navigation is essential to enhance the accuracy, safety, and efficiency of the procedure. By extracting feature points from the environment, Visual Simultaneous Localization and Mapping (V-SLAM) technology has been widely employed to accomplish self-localization and map generation. In laparoscopic surgery, V-SLAM offers high-precision real-time positional data for surgical robots, thereby improving the accuracy of operative navigation. Nonetheless, the performance of conventional V-SLAM algorithms is significantly compromised by the extremely dynamic characteristics of surgical environments. Widely used visual SLAM systems, such as ORB-SLAM, are susceptible to inaccurate feature point tracking in the presence of dynamic objects and variations in lighting, hence compromising navigation precision and surgical safety. This study assessed the efficacy of visual V-SLAM technology in enhancing intraoperative navigation during robot-assisted laparoscopic surgery, pointing out its contribution to surgical precision, safety, and operational efficiency. The research enhanced the ORB-SLAM2 algorithm to adapt to the dynamically changing conditions encountered during surgical procedures. The experiment validated the efficacy of the enhanced method within a simulated laparoscopic surgery setting and assessed the impact of three variables: light intensity, instrument occlusion, and dynamic tissue deformation, on trajectory error (ATE and RPE) and mapping accuracy. The findings indicated that under situations of low illumination, significant occlusion, and substantial deformation, ATE and RPE mistakes escalated by over 50%, while map accuracy diminished by approximately 40%, leading to heightened potential surgical risks.

영상처리 기법 기반 겹친 넙치 객체 탐지를 위한 자동 라벨링 방법

Detecting objects in underwater environments is challenging due to factors such as object overlap, noise, and lighting variations. In particular, labeling underwater organisms like Paralichthys olivaceus requires significant time and effort when done manually. To address these challenges, this paper proposes a method to automate the labeling of Paralichthys olivaceus using image processing techniques and to increase the size of the dataset for training. The proposed method applies morphological operations and contour detection to remove noise and accurately extract object boundaries. Additionally, the YOLOv8 model is used to analyze performance variations based on dataset size and training methods. The results demonstrate that expanding the dataset through automated image preprocessing improves object detection performance, even in cases of object overlap, and proves to be more time-efficient compared to manual labeling.

Spectroscopic and Photometric Analyses of the Trapezium Stars θ1 Ori A and θ1 Ori D

ABSTRACTIn this paper, we report on the spectroscopic and photometric observations of the two Trapezium stars, Ori A and Ori D. The spectra of these stars were extracted from the ESO and ESPaDOnSarchives. Both stars exhibit nebular emission features, particularly within the core of the Balmer profiles and He I lines at and . Additionally, the spectral energy distributions of these targets were constructed, revealing an infrared excess in both. These emission structures and infrared excesses are attributed to the H II region caused by Ori C. Furthermore, detailed spectroscopic analysis performed to Ori A and Ori D revealed similar abundance patterns, with slight differences in carbon, magnesium, and aluminum. The effective temperatures derived from the spectroscopic analysis were used to determine their positions on the Hertzsprung‐Russell (H‐R) diagram. Additionally, the evolutionary tracks and isochrones constructed on the H‐R diagram allowed for the estimation of their evolutionary masses and ages. Finally, TESS observations of the target stars were obtained to investigate their light variations.

Clonal Lineage and Gene Diversity Analysis of Paired Antibody Heavy and Light Chains.

Antibodies consist of unique variable heavy (VH) and variable light (VL) chains, and both are required to fully characterize an antibody. Methods to detect paired heavy and light chain variable regions (VH:VL) using high-throughput sequencing (HTS) have recently enabled large-scale analysis of complete functional antibody responses. Here, we describe an HTS computational pipeline to analyze paired VH:VL antibody sequences and obtain a comprehensive profile of immune diversity landscapes, including gene usage, antibody isotypes, and clonal lineage analysis. This protocol uses Illumina MiSeq 2 × 300-bp sequencing data and integrates with several different computational tools for flexible analyses of paired VH:VL gene repertoire data to enable efficient antibody discovery.

MSOAR-YOLOv10: Multi-Scale Occluded Apple Detection for Enhanced Harvest Robotics

The accuracy of apple fruit recognition in orchard environments is significantly affected by factors such as occlusion and lighting variations, leading to issues such as missed and false detections. To address these challenges, particularly related to occluded apples, this study proposes an improved apple-detection model, MSOAR-YOLOv10, based on YOLOv10. Firstly, a multi-scale feature fusion network is enhanced by adding a 160 × 160 feature scale layer to the backbone network, which increases the model’s sensitivity to small local features, particularly for occluded fruits. Secondly, the Squeeze-and-Excitation (SE) attention mechanism is integrated into the C2fCIB convolution module of the backbone network to improve the network’s focus on the regions of interest in the input images. Additionally, a Diverse Branch Block (DBB) module is introduced to enhance the performance of the convolutional neural network. Furthermore, a Normalized Wasserstein Distance (NWD) loss function is proposed to effectively reduce missed detections of densely packed and overlapping targets. Experimental results in orchards indicate that the proposed improved YOLOv10 model achieves precision, recall, and mean average precision rates of 89.3%, 89.8%, and 92.8%, respectively, representing increases of 3.1%, 2.2%, and 3.0% compared to the original YOLOv10 model. These results validate that the proposed network significantly enhances apple recognition accuracy in complex orchard environments, particularly improving the operational precision of harvesting robots in real-world conditions.

Meta-learning enhanced adaptive robot control strategy for automated PCB assembly

The assembly of printed circuit boards (PCBs) is one of the standard processes in chip production, directly contributing to the quality and performance of the chips. In the automated PCB assembly process, machine vision and coordinate localization methods are commonly employed to guide the positioning of assembly units. However, occlusion or poor lighting conditions can affect the effectiveness of machine vision-based methods. Additionally, the assembly of odd-form components requires highly specialized fixtures for assembly unit positioning, leading to high costs and low flexibility, especially for multi-variety and small-batch production. Drawing on these considerations, a vision-free, model-agnostic meta-method for compensating robotic position errors is proposed, which maximizes the probability of accurate robotic positioning through interactive feedback, thereby reducing the dependency on visual feedback and mitigating the impact of occlusions or lighting variations. The proposed method endows the robot with the capability to learn and adapt to various position errors, inspired by the human instinct for grasping under uncertainties. Furthermore, it is a self-adaptive method that can accelerate the robotic positioning process as more examples are incorporated and learned. Empirical studies show that the proposed method can handle a variety of odd-form components without relying on specialized fixtures, while achieving similar assembly efficiency to highly dedicated automation equipment. As of the writing of this paper, the proposed meta-method has already been implemented in a robotic-based assembly line for odd-form electronic components. Since PCB assembly involves various electronic components with different sizes, shapes, and functions, subsequent studies can focus on assembly sequence and assembly route optimization to further enhance assembly efficiency.