High-definition Images Research Articles

Super-Resolution Semantic Segmentation of Droplet Deposition Image for Low-Cost Spraying Measurement

In-field in situ droplet deposition digitization is beneficial for obtaining feedback on spraying performance and precise spray control, the cost-effectiveness of the measurement system is crucial to its scalable application. However, the limitations of camera performance in low-cost imaging systems, coupled with dense spray droplets and a complex imaging environment, result in blurred and low-resolution images of the deposited droplets, which creates challenges in obtaining accurate measurements. This paper proposes a Droplet Super-Resolution Semantic Segmentation (DSRSS) model and a Multi-Adhesion Concave Segmentation (MACS) algorithm to address the accurate segmentation problem in low-quality droplet deposition images, and achieve a precise and efficient multi-parameter measurement of droplet deposition. Firstly, a droplet deposition image dataset (DDID) is constructed by capturing high-definition droplet images and using image reconstruction methods. Then, a lightweight DSRSS model combined with anti-blurring and super-resolution semantic segmentation is proposed to achieve semantic segmentation of deposited droplets and super-resolution reconstruction of segmentation masks. The weighted IoU (WIoU) loss function is used to improve the segmented independence of droplets, and a comprehensive evaluation criterion containing six sub-items is used for parameter optimization. Finally, the MACS algorithm continues to segment the remained adhesive droplets processed by the DSRSS model and corrects the bias of the individual droplet regions by regression. The experiments show that when the two weight parameters α and β in WIoU are 0.775 and 0.225, respectively, the droplet segmentation independence rate of DSRSS on the DDID reaches 0.998, and the IoU reaches 0.973. The MACS algorithm reduces the droplet adhesion rate in images with a coverage rate of more than 30% by 15.7%, and the correction function reduces the coverage error of model segmentation by 3.54%. The parameters of the DSRSS model are less than 1 M, making it possible to run it on embedded platforms. The proposed approach improves the accuracy of spray measurement using low-quality droplet deposition image and will help to scale-up of fast spray measurements in the field.

Deep learning-assisted light sheet holography.

In a novel approach to layer-based holography, we propose a machine learning-assisted light sheet holography-an optimized holography technique which projects a target scene onto sheets of light along the longitudinal planes (i.e. planes perpendicular to the plane of the hologram). Using a convolutional neural network in conjunction with superposition of Bessel beams, we generate high-definition images which can be stacked in parallel onto longitudinal planes with very high fidelity. Our holography system provides high axial resolution and excellent control over the light intensity along the optical path, which is suitable for augmented reality and/or virtual reality applications.

Surgical Significance of Variant Anatomy of Inferior Mesenteric Artery and Left Colic Artery: A Computed Tomography Angiographic Study

Introduction: A variation in vessel morphology determines flow dynamics and vascular disease pathogenesis. Definite information on the distinctive anatomical variations of the mesenteric vessels is extremely important for laparoscopic surgical procedures and interventional radiological procedures in the abdominal region. Three-dimensional-computed tomography angiography (3D-CTA) is a less invasive method to evaluate vascular anatomy using a visual tracking method on high-definition images, contrast-enhanced inferior mesenteric artery (IMA) can be traced to its terminal branches beside the colon wall. Subjects and Methods: Abdominal contrast-enhanced computed tomography scan data from 180 patients was retrospectively collected, reconstructed, and analyzed. The origin, length of IMA from origin to the first branch, branching patterns of IMA, and tracking patterns of the ascending branch of left colic artery (LCA), were examined, and their associations with clinical features were analyzed. Results: IMA displayed variations in the level of origin ranging from upper 1/3 of L3 to upper 1/3 of L4, mean length from origin to the first branch was 27.4 ± 7.8 mm. Observed branching patterns of IMA were grouped according to Yada classification: Type 1 (52.2%), Type 2 (15.5%), Type 3 (14.4%), and Type 4 (17.7%). Tracking of the ascending branch of LCA before anastomosis with marginal artery revealed four patterns: Type A LCA pattern in 43.9%; Type B LCA anatomy pattern was (19.4%); Type C LCA anatomy pattern was (20.0%); and Type D LCA anatomy pattern was observed (16.7%). Conclusion: Preoperative understanding of the vascular variations of IMA and LCA can be obtained by 3D-CTA. This may be helpful to surgeons in planning preoperative strategies to prevent iatrogenic injuries.

Unveiling the evolution of endoscopy

Abstract Endoscopy, also known as minimally invasive surgery (MIS), has dramatically transformed surgical practice since its inception in the early 20th century. Initially met with skepticism, it has now become fundamental in various surgical fields. This evolution is marked by significant milestones, including the pioneering work of Georg Kelling and Jacobaeus in the early 1900s, the technical innovations of Raoul Palmer and Kurt Semm from the 1960s to the 1980s, and the advent of video laparoscopy in the late 20th century. These advancements have facilitated the transition from diagnostic to surgical laparoscopy, improving safety, efficacy, and patient outcomes. Modern laparoscopy, enriched by high-definition imaging and robotics, allows for complex procedures with enhanced precision. Robotic-assisted surgery, introduced in 1994, has further expanded the capabilities of minimally invasive techniques, offering 3D visualization and improved dexterity. The continuous evolution in this field is driven by technological advancements and a commitment to improving surgical outcomes. This article explores the historical milestones, current practices, and future challenges of endoscopy, highlighting its revolutionary impact on medicine. As the field progresses, innovations such as artificial intelligence and augmented reality are poised to shape the future of laparoscopy, making it safer, more cost-effective, and accessible. The ongoing evolution of endoscopy underscores the relentless pursuit of excellence in surgical care and the enduring impact of human ingenuity and technological progress.

Accurate classification of wheat freeze injury severity from the color information in digital canopy images.

This paper explores whether it is feasible to use the RGB color information in images of wheat canopies that were exposed to low temperatures during the growth season to achieve fast, non-destructive, and accurate determination of the severity of any freeze injury it may have incurred. For the study presented in this paper, we compared the accuracy of a number of algorithmic classification models using either meteorological data reported by weather services or the color gradation skewness-distribution from high-definition digital canopy images acquired in situ as inputs against a reference obtained by manually assessing the severity of the freeze injury inflicted upon wheat populations at three experimental stations in Shandong, China. The algorithms we used to construct the models included in our study were based on either K-means clustering, systematic clustering, or naïve Bayesian classification. When analyzing the reliability of our models, we found that, at more than 85%, the accuracy of the Bayesian model, which used the color information as inputs and involved the use of prior data in the form of the reference data we had obtained through manual classification, was significantly higher than that of the models based on systematic or the K-means clustering, which did not involve the use of prior data. It was interesting to note that the determination accuracy of algorithms using meteorological factors as inputs was significantly lower than that of those using color information. We also noted that the determination accuracy of the Bayesian model had some potential for optimization, which prompted us to subject the inputs of the model to a factor analysis in order to identify the key independent leaf color distribution parameters characterizing wheat freeze injury severity. This optimization allowed us to improve the determination accuracy of the model to over 90%, even in environments comprising several different ecological zones, as was the case at one of our experimental sites. In conclusion, our naïve Bayesian classification algorithm, which uses six key color gradation skewness-distribution parameters as inputs and involves the use of prior data in the form of manual assessments, qualifies as a contender for the development of commercial-grade wheat freeze injury severity monitoring systems supporting post-freeze management measures aimed at ensuring food security.

Unmanned aerial vehicle digital image and hyperspectral data for estimating the comparison of leaf area index and biomass of potato at different growth stages

Abstract Leaf Area Index (LAI) and biomass (BIO) are essential agronomic parameters that reflect the growth of potatoes and are related to their biomass. Their precise estimation is capable of monitoring crop growth, guiding field management, and optimizing planting spatial patterns. Traditional potato leaf area indexing and biomass estimation primarily rely on field sampling surveys. This method is low in efficiency, high in cost, and limited by the number of samples. It cannot accurately reflect potato growth and meet the real-time estimation needs of large areas. Compared to the use of satellite remote sensing data (RSD) for estimating LAI and biomass, research on estimating these two phenotypic parameters using crewless aerial vehicle (UAV) RSD is relatively immature. Research on estimating crop growth index parameters by remote sensing primarily focuses on data obtained from specific types of sensors, targeting specific growth stages to compare and analyze the accuracy of different methods. However, there are few estimates of the impact of optimizing the best data types and optimal growth stage for LAI and biomass estimation by comparing and analyzing different sensor data and different growth stages. Multi-sensor integration technology has made it possible to study different crop phenotype information and estimate the best data type and optimal growth stage in crop phenotypic data estimation, establishing it as a new hot spot in the field. This paper integrates high-definition digital cameras and imaging hyperspectrometers on the UAV platform to obtain digital images and hyperspectral data simultaneously, along with ground-measured potato leaf area index and biomass data. Using the partial least squares regression (PLSR), random forest (RF), support vector machine (SVM), and backpropagation (BP) neural network methods, we got digital images and hyperspectral data from different stages of growth, put together a digital image index and a vegetation index, and looked at how they related to LAI and BIO. Then, we chose the index that had the strongest correlation. To establish LAI and biomass estimation models at various growth phases, this paper compared and analyzed the estimation impacts of various data types and models at various growth phases. It then selected the best data types for LAI estimation and biomass estimation at different growth stages, as well as the best growth phases for LAI and biomass estimation. The outcomes indicated that when potato LAI was estimated, the mean values of R 2 and RMSE of the four estimation models were 0.75 and 0.30 Kg/mu at the tuber growth stage, respectively, and the estimation effect was the best, indicating that this was the best growth phase for LAI estimation. The average values of R 2 and RMSE in the LAI estimation model using the hyperspectral vegetation index were 0.73 and 0.33 Kg/mu, respectively, indicating that hyperspectral data was the best data type for LAI estimation. When potato biomass was estimated, the mean values of R 2 and RMSE of the four methods were 0.67 and 15.25 Kg/mu, respectively, at the tuber growth stage, which were better than other growth phases, demonstrating that this was the best growth phase for biomass estimation. The average values of R 2 and RMSE of the biomass estimation model using the hyperspectral vegetation index were 0.67 and 20.08 Kg/mu, respectively, indicating that the hyperspectral data was the best data type for biomass estimation. The average values of R 2 of the LAI and biomass estimation model at the maturity stage were only 0.56 and 0.36, both of which indicated poor estimation effects. Our study can serve as a guide to selecting the most effective method for estimating parameters for essential indexes in crop growth monitoring.

Enhanced accuracy with Segmentation of Colorectal Polyp using NanoNetB, and Conditional Random Field Test-Time Augmentation.

Colonoscopy is a reliable diagnostic method to detect colorectal polyps early on and prevent colorectal cancer. The current examination techniques face a significant challenge of high missed rates, resulting in numerous undetected polyps and irregularities. Automated and real-time segmentation methods can help endoscopists to segment the shape and location of polyps from colonoscopy images in order to facilitate clinician's timely diagnosis and interventions. Different parameters like shapes, small sizes of polyps, and their close resemblance to surrounding tissues make this task challenging. Furthermore, high-definition image quality and reliance on the operator make real-time and accurate endoscopic image segmentation more challenging. Deep learning models utilized for segmenting polyps, designed to capture diverse patterns, are becoming progressively complex. This complexity poses challenges for real-time medical operations. In clinical settings, utilizing automated methods requires the development of accurate, lightweight models with minimal latency, ensuring seamless integration with endoscopic hardware devices. To address these challenges, in this study a novel lightweight and more generalized Enhanced Nanonet model, an improved version of Nanonet using NanonetB for real-time and precise colonoscopy image segmentation, is proposed. The proposed model enhances the performance of Nanonet using Nanonet B on the overall prediction scheme by applying data augmentation, Conditional Random Field (CRF), and Test-Time Augmentation (TTA). Six publicly available datasets are utilized to perform thorough evaluations, assess generalizability, and validate the improvements: Kvasir-SEG, Endotect Challenge 2020, Kvasir-instrument, CVC-ClinicDB, CVC-ColonDB, and CVC-300. Through extensive experimentation, using the Kvasir-SEG dataset, our model achieves a mIoU score of 0.8188 and a Dice coefficient of 0.8060 with only 132,049 parameters and employing minimal computational resources. A thorough cross-dataset evaluation was performed to assess the generalization capability of the proposed Enhanced Nanonet model across various publicly available polyp datasets for potential real-world applications. The result of this study shows that using CRF (Conditional Random Fields) and TTA (Test-Time Augmentation) enhances performance within the same dataset and also across diverse datasets with a model size of just 132,049 parameters. Also, the proposed method indicates improved results in detecting smaller and sessile polyps (flats) that are significant contributors to the high miss rates.

3D Trajectory Planning for Real-Time Image Acquisition in UAV-Assisted VR

Nowadays, unmanned aerial vehicles (UAVs), empowered with the capability of high-definition image transmission, are used to capture the rapidly changing physical environment by leveraging its high flexibility to reconstruct an immersive realistic virtual environment for metaverse users. In this paper, we consider a novel UAV-assisted image acquisition system where a UAV is dispatched to take off from an initial location to capture real-time images of multiple ground targets and then transfer the captured images back to the ground user for virtual environment reconstruction. We aim to minimize the time for the UAV to complete the image acquisition task by optimizing the three-dimensional UAV trajectory under the constraints of image quality, information causality and energy consumption. To this end, we first formulate the investigated scenario into a mixed integer optimization problem, which, however, is difficult to solve due to the infinite time-varying variables closely coupled with each other. Then, a three-stage progressive algorithm is proposed to obtain an efficient solution to the formulated mixed integer optimization problem, where the constraints of image quality, information causality and energy consumption can be sequentially satisfied. Finally, comprehensive performance evaluation is conducted to verify the effectiveness of the proposed three-stage progressive trajectory design algorithm, and the results show that the proposed algorithm significantly outperforms the benchmark schemes.

Accurate Detection and Tracking of Small-Scale Vehicles in High-Altitude Unmanned Aerial Vehicle Bird-View Imagery

Vehicle detection and tracking from unmanned aerial vehicles (UAVs) aerial images are among the main tasks of intelligent traffic systems. Especially in tasks with long distances, extensive backgrounds, and small objects, it increases the difficulty of localization and regression, which can easily lead to missed detections and false positives. This paper proposes a detection-based small-scale vehicle tracking framework that integrates an improved YOLOX network and the DeepSORT algorithm to address these issues. Based on the original YOLOX network, a shallow feature extraction network, 160 × 160 pixels, is added to enhance the ability to extract small-scale object features. A convolutional block attention module (CBAM) is inserted in front of the neck network to select crucial information for vehicle detection tasks while suppressing noncritical ones. EIoU_Loss is introduced as the bounding box regression loss function in training to speed up their convergence and improve the localization accuracy of the small objects. Furthermore, an image segmentation method is proposed to effectively reduce missed and false detection events. It divides the original high-definition image into multiple subimages, first detected and then reassembled. Finally, the improved YOLOX network is used as the detector of the DeepSORT to perform small-scale vehicle detection and tracking tasks in various traffic scenarios. Experiments show that the proposed method can significantly improve the detection accuracy of the network and effectively solve the problems of missed detection and false positives in small-scale vehicle tracking tasks in high-resolution aerial images captured by high-altitude UAVs. Significantly, the algorithm proposed in this paper has sufficient robustness for small-scale tracking tasks of aerial videos captured at different altitudes.

Radar-SR3: A Weather Radar Image Super-Resolution Generation Model Based on SR3

To solve the problems of the current deep learning radar extrapolation model consuming many resources and the final prediction result lacking details, a weather radar image super-resolution weather model based on SR3 (super-resolution via image restoration and recognition) for radar images is proposed. This model uses a diffusion model to super-resolve weather radar images to generate high-definition images and optimizes the performance of the U-Net denoising network on the basis of SR3 to further improve image quality. The model receives high-resolution images with Gaussian noise added and performs channel splicing with low-resolution images for conditional generation. The experimental results showed that the introduction of the diffusion model significantly improved the spatial resolution of weather radar images, providing new technical means for applications in related fields; when the amplification factor was 8, Radar-SR3, compared with the image super-resolution model based on the generative adversarial network (SRGAN) and the bicubic interpolation algorithm, the peak signal-to-noise ratio (PSNR) increased by 146% and 52% on average. According to this model, it is possible to train radar extrapolation models with limited computing resources with high-resolution images.

High-Definition SPAD Image Sensors: Technical Trends and Applications

High-Definition SPAD Image Sensors: Technical Trends and Applications

Line-Field Confocal Optical Coherence Tomography Evaluation of Eyelid Skin Lesions

Background: Periocular malignancies may be clinically different from the examples arising at other sites, with possible delayed diagnosis and greater challenges for treatment and repair. Line-field confocal optical coherence tomography (LC-OCT) is a recently developed technique characterized by an unprecedented capacity to acquire high-definition images in vertical and horizontal modes. In this study, we aimed to investigate the LC-OCT morphological features of a series of eyelid skin lesions, correlating them to histopathological findings. Methods: Patients with biopsy-proven equivocal skin lesion in the eyelid area, previously investigated by means of LC-OCT, were included in the study. Percentage overall agreement was estimated for LC-OCT and histopathological diagnosis for study cases. Results: A total of 51 patients (28 women, 23 men; mean age 66.4 years old), for a total of 51 skin lesions, were assessed. The histopathological diagnosis consisted of 30 malignant and 21 benign tumors. Different entities were characterized by peculiar findings in LC-OCT, alike to histopathological features, allowing for an accurate “in vivo” classification in almost all cases, with a diagnostic concordance with histopathology of 92.1% (47/51). Conclusions: By integrating this new imaging technique into the assessment of suspicious tumors in this area, diagnostic accuracy may increase, improving strategies adopted in multidisciplinary meetings and patient-centered care.

Automated Peripheral Smear Recognition and Classification of LGL Leukemia Using Machine Learning

Background: Machine learning can augment clinical decision support, especially in cases where complex diagnostic criteria are necessary. In making a diagnosis of LGL leukemia, a pathologist must review the peripheral smear thoroughly, which is a tedious process. Supervised machine learning has been successfully implemented in such cases by training on very large image-based datasets, followed by a corrective phase of updating the model weights and finally demonstrating a high degree of successful predictions in an independent dataset. We sought to assess whether a supervised machine learning model can automate histopathological image analysis and provide a degree of confidence in supporting a pathologist's manual review. Methods: We searched the published literature from 2016-2022 for high-definition peripheral smear images with confirmed cases. The samples were downloaded, pre-processed with the Segment Anything Model (SAM), and labeled as training, testing, or validation data. Non-pathological controls, defined as peripheral smears confirmed negative for LGLL, were also obtained from published literature and labeled manually. The neural network was written and trained on TensorFlow using the open-source Keras library with parallel threading and on a multicore GPU machine. The resulting predictive model had a binary classification task: Does the presented image have cells that resemble LGL cells, returning true or false. Every “true” label returned from the classifier also creates a stack of probable positives that need pathology review. Feedback from each pathologist review further updates the model weights to increase the probability of successful prediction in the future. Results: We screened 57 publications to obtain 163 high-definition peripheral smear images that had confirmed LGLL and 100 non-pathological smears. Two cohorts were created: 200 smears in a training cohort comprised of 100 LGLL smears and 100 non-pathological smears, and 63 smears in the validation cohort. To remain agnostic towards image orientation, we created 7 views of each smear, and 1400 total images underwent feature extraction using SAM. The resulting data was labeled and used for model training and our process is depicted visually below in Figure 1. During validation, our model accurately predicted LGL-cells in 56/63 cases with an 8% false positive rate, 2% false negatives and 2% unclassifiable. After updating model weights and re-training with Adaptive Boosting, the accuracy increased to 60/63 smears with a false-positive rate of 3%. Our trained classification model achieved an AUC of 0.8144. Conclusion: Supervised machine learning model with Adaptive Boosting can be used to label a peripheral smear image with a high likelihood of containing LGL cells and, in turn, help a pathologist review smears with higher priority in a timely fashion. Our initial training data was limited, which hindered the performance of our model; however, with a sufficiently large training dataset, we can drastically improve our accuracy. A more extensive training set with robust hardware can transform our image-recognition model into a foundational model where other clinical parameters can be added, and the final diagnostic probability will be based on multiple data streams. Our work takes a meaningful step in this direction, and future work at Karmanos Cancer Center will focus on extending our classifier to more sophisticated deep-learning algorithms. Our open-source platform will eventually support several niches in the future that are currently only served by commercial applications.

Defect Detection in Food Using Multispectral and High-Definition Imaging Combined with a Newly Developed Deep Learning Model

The automatic detection of defects (cortical fibers) in pickled mustard tubers (Chinese Zhacai) remains a challenge. Moreover, few papers have discussed detection based on the segmentation of the physical characteristics of this food. In this study, we designate cortical fibers in pickled mustard as the target class, while considering the background and the edible portion of pickled mustard as other classes. We attempt to realize an automatic defect-detection system to accurately and rapidly detect cortical fibers in pickled mustard based on multiple images combined with a UNet4+ segmentation model. A multispectral sensor (MS) covering nine wavebands with a resolution of 870 × 750 pixels and an imaging speed over two frames per second and a high-definition (HD), 4096 × 3000 pixel resolution imaging system were applied to obtain MS and HD images of 200 pickled mustard tuber samples. An improved imaging fusion method was applied to fuse the MS with HD images. After image fusion and other preprocessing methods, each image contained a target; 150 images were randomly selected as the training data and 50 images as the test data. Furthermore, a segmentation model called UNet4+ was developed to detect the cortical fibers in the pickled mustard tubers. Finally, the UNet4+ model was tested on three types of datasets (MS, HD, and fusion images), and the detection results were compared based on Recall, Precision, and Dice values. Our study indicates that the model can successfully detect cortical fibers within about a 30 ± 3 ms timeframe for each type of image. Among the three types of images, the fusion images achieved the highest mean average Dice value of 73.91% for the cortical fibers. At the same time, we compared the UNet4+ model with the UNet++ and UNet3+ models using the same fusion data; the results show that our model achieved better prediction performance for the Dice values, i.e., 9.72% and 27.41% higher than those of the UNet++ and UNet3+ models, respectively.

Feasibility and accuracy of real-time 3D-holographic graft length measurements.

Mixed reality (MR) holograms can display high-definition images while preserving the user's situational awareness. New MR software can measure 3D objects with gestures and voice commands; however, these measurements have not been validated. We aimed to assess the feasibility and accuracy of using 3D holograms for measuring the length of coronary artery bypass grafts. An independent core lab analyzed follow-up computer tomography coronary angiograms performed 30 days after coronary artery bypass grafting in 30 consecutive cases enrolled in the FASTTRACK CABG trial. Two analysts, blinded to clinical information, performed holographic reconstruction and measurements using the CarnaLife Holo software (Medapp, Krakow, Poland). Inter-observer agreement was assessed in the first 20 cases. Another analyst performed the validation measurements using the CardIQ W8 CT system (GE Healthcare, Milwaukee, Wisconsin). Seventy grafts (30 left internal mammary artery grafts, 31 saphenous vein grafts, and 9 right internal mammary artery grafts) were measured. Holographic measurements were feasible in 97.1% of grafts and took 3 minutes 36 s ± 50.74 s per case. There was an excellent inter-observer agreement [interclass correlation coefficient (ICC) 0.99 (0.97-0.99)]. There was no significant difference between the total graft length on hologram and CT [187.5 mm (157.7-211.4) vs. 183.1 mm (156.8-206.1), P = 0.50], respectively. Hologram and CT measurements are highly correlated (r = 0.97, P < 0.001) with an excellent agreement [ICC 0.98 (0.97-0.99)]. Real-time holographic measurements are feasible, quick, and accurate even for tortuous bypass grafts. This new methodology can empower clinicians to visualize and measure 3D images by themselves and may provide insights for procedural strategy.

Early-stage fertilization detection in pigeon eggs during incubation using improved lightweight model

Early detection of fertilized and unfertilized eggs in the early stage of poultry egg hatching can improve economic efficiency by saving space, reducing costs, and obtaining unfertilized egg values earlier. As such, detecting early hatching information of breeding eggs is crucial in the poultry industry. This paper proposes an improved Mov3-CapNet lightweight model combined with machine vision for accurate, fast and non-destructive discrimination of pigeon breeder egg fertilization. The model is based on the MobileNetV3, with some SE (Squeeze-and-Excitation) modules replaced by DFC (Coupled Full Connected) modules, the classification layer replaced with CapProNet (Capsule Projection Network) and part of the original channel extension layer censored. The experiments use an industrial camera to collect high-definition images of breeder eggs from day 1 to day 4, which are then preprocessed with data augmentation methods. The improved Mov3-CapNet lightweight network is trained and detected, achieving 95.54 % accuracy on day 1, 98.85 % accuracy on day 2, 99.73 % accuracy on day 3, and 100 % accuracy on day 4. The average detection time of a single sample using CPU and GPU is 30 ms and 0.7822 ms, respectively. This study has practical application significance for accurate, fast and nondestructive determination of pigeon breeder egg fertilization.

Research on a Blue–Green LED Communication System Based on an Underwater Mobile Robot

Underwater robots have been widely used in ocean exploration, deep-sea observation, seabed operations, marine scientific research, and other fields. Underwater low-latency, efficient, and safe communication modes are key to realizing the application of an underwater robot data transmission system. This paper mainly studies the optical communication between underwater mobile robots, including the large-dispersion-angle light-emitting diode (LED) design, large field of view receiving technology, weak light detector technology, etc. By designing a 120° large divergence angle underwater optical communication system in this study, the receiving field-of-view angle of the receiving end can reach 60°, which is suitable for the optical communication system of an underwater mobile platform. The high-power LED driver circuit is designed to drive the high-power LED and adopt weak light detection technology to ensure its stability and reliability. The experimental results show that, in the case of incomplete alignment between the transmitter and receiver, stable communication of underwater robots in motion is achieved through the design of a large divergence angle and a receiving field-of-view angle and the use of an underwater weak light detection technology. The communication distance is 30 m, and the communication rate remains above 10 Mbps. The information transmission content can include network data transmission, real-time video, high-definition video, high-definition images, and other data types. This equipment provides a solution for cableless data transmission of remotely operated vehicles (ROVs) and substantially enhances the application field of ROVs.

Drone network for early warning of forest fire and dynamic fire quenching plan generation

Wildfires are one of the most frequent natural disasters which significantly harm the environment, society, and the economy. Transfer learning algorithms and modern machine learning tools can help in early forest fire prediction, detection, and dynamic fire quenching. A group of drones that has high-definition image processing and decision-making capabilities are used to detect the forest fires in the very early stage. The proposed system generates a fire quenching plan using particle swarm optimization technique and alerts the fire and rescue department for a quick action, thereby stop the forest fire at an early stage. Also, the drone network plays a major role to track the live status of forest fire and quenches the fire. ResNet, VGGNet, MobileNet, AlexNet, and GoogLeNet are used to detect the forest fire hazards. The experimental results prove that the proposed technique GoogLeNet-TL provides 96% accuracy and 97% F1 score in comparison with the state-of-the-art deep learning models.

Exposición de la oliva bulbar mediante los abordajes farlateral y retrosigmoideo bajo. Análisis comparativo de la superficie expuesta y ángulo de ataque

ObjectivesThroughout neurosurgical history, the treatment of intrinsic lesions located in the brainstem has been subject of much controversy. The brainstem is the anatomical structure of the central nervous system (CNS) that presents the highest concentration of nuclei and fibers, and its simple manipulation can lead to significant morbidity and mortality. Once one of the safe entry points at the medulla oblongata has been established, we wanted to evaluate the safest approach to the olivary body (the most used safe entry zone on the anterolateral surface of the medulla oblongata). The proposed objective was to evaluate the working channel from the surface of each of the far lateral and retrosigmoid approaches to the olivary body: distances, angles of attack and channel content. Material and methodsTo complete this work, a total of 10 heads injected with red/blue silicone were used. A total of 40 approaches were made in the 10 heads used (20 retrosigmoid and 20 far lateral). After completing the anatomical study and obtaining the data referring to all the approaches performed, it was decided to expand the sample of this research study by using 30 high-definition magnetic resonance imaging of anonymous patients without cranial or cerebral pathology. The reference points used were the same ones defined in the anatomical study. After defining the working channels in each of the approaches, the working distances, angle of attack, exposed surface, and the number of neurovascular structures present in the central trajectory were analyzed. ResultsThe distances to the cranial and medial region of the olivary body were 52.71 mm (SD 3.59) from the retrosigmoid approach and 27.94 mm (SD 3.99) from the far lateral; to the most basal region of the olivary body, the distances were 49.93 (SD 3.72) from the retrosigmoid approach and 18.1 mm (SD 2.5) from the far lateral. The angle of attack to the caudal region was 19.44° (SD 1.3) for the retrosigmoid approach and 50.97° (SD 8.01) for the far lateral approach; the angle of attack to the cranial region was 20.3° (SD 1.22) for the retrosigmoid and 39.9° (SD 5.12) for the far lateral. Regarding neurovascular structures, the probability of finding an arterial structure is higher for the far lateral, whereas a neural structure will be more likely from a retrosigmoid approach. ConclusionsAs conclusions of this work, we can say that far lateral approach presents more favorable conditions for the microsurgical treatment of intrinsic bulbar and bulbomedullary lesions approached through the caudal half of the olivary body. In those cases of bulbar and pontine-bulbar lesions approached through the cranial half of the olivary body, the retrosigmoid approach can be considered for selected cases.

Value of micro-flow imaging and high-definition micro-flow imaging in differentiating malignant and benign breast lesions

To evaluate the value of non-contrast micro-flow imaging (MFI) and high-definition micro-flow imaging (HD-MFI) in differentiating malignant and benign breast lesions. One hundred and thirty-three patients with 138 breast lesions (80 benign and 58 malignant lesions) were examined using colour Doppler flow imaging (CDFI), MFI, and HD-MFI before biopsy, with blood flow signals graded into four types (grade 0, 1, 2, and 3) and penetrating vessels evaluated. The micro-vascular patterns of MFI and HD-MFI were evaluated and classified into five patterns: avascular, line-like, tree-like, root hair-like, and crab claw-like pattern. The diagnostic efficiency of micro-vascular patterns was analysed. Moreover, ultrasound Breast Imaging Reporting and Data System (BI-RADS) 4A lesions were also re-assessed according to the micro-vascular patterns of MFI or HD-MFI. The capability of detecting blood flow and penetrating vessels from high to low was HD-MFI, MFI, and CDFI, respectively (p<0.05). Rich blood flow signals, penetrating vessels, and root hair-like or crab claw-like pattern were more likely in malignant breast lesions, while few blood flow signals, tree-like pattern were mostly in benign lesions (p<0.05). The diagnostic efficiency of HD-MFI and MFI were higher than CDFI (p>0.05). MFI could reduce unnecessary biopsy of 52 US BI-RADS 4A lesions but with two malignancies missed, while 56 ultrasound BI-RADS 4A lesions could be downgraded by HD-MFI with none malignancies missed. MFI and HD-MFI can detect more blood flow in breast lesions than CDFI, and could help distinguish benign and malignant breast lesions. HD-MFI could reduce the unnecessary biopsy of US BI-RADS 4A lesions without missed malignancy.