Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis:A Cross-Sectional Multicenter Study

In ophthalmology,the quality of fundus images is critical for accurate diagnosis,both in clinical practice and in artificial intelligence(AI)-assisted diagnostics.Despite the broad view provided by ultrawide-field(UWF)imaging,pseudocolor images may conceal critical lesions necessary for precise diagnosis.To address this,we introduce UWF-Net,a sophisticated image enhancement algorithm that takes disease characteristics into consideration.Using the Fudan University ultra-wide-field image(FDUWI)dataset,which includes 11294 Optos pseudocolor and 2415 Zeiss true-color UWF images,each of which is rigorously annotated,UWF-Net combines global style modeling with feature-level lesion enhancement.Pathological consistency loss is also applied to maintain fundus feature integrity,significantly improving image quality.Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization(CLAHE)and structure and illumination constrained generative adversarial network(StillGAN),delivering superior retinal image quality,higher quality scores,and preserved feature details after enhancement.In disease classification tasks,images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN,demonstrating a 4.62%increase in sensitivity(SEN)and a 3.97%increase in accuracy(ACC).In a multicenter clinical setting,UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors,and yielded a significant reduction in diagnostic time((13.17±8.40)s for UWF-Net enhanced images vs(19.54±12.40)s for original images)and an increase in diagnostic accuracy(87.71%for UWF-Net enhanced images vs 80.40%for original images).Our research verifies that UWF-Net markedly improves the quality of UWF imaging,facilitating better clinical outcomes and more reliable AI-assisted disease classification.The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.

A Novel Cross-section Interpolation Method for Medical Images

Image interpolation of cross-sections is one of the key steps of medical visualization, and the cubic convolution interpolation is usually employed due to its good tradeoff between computational cost and accuracy, however, sometimes its accuracy can still not meet the requirement. Aimed at the problem, in this paper, the interpolation principle based cubic convolution is firstly analyzed systematically, and then essential relationship among the different cubic convolution interpolation methods is clarified. Lastly, a novel cross-section interpolation method for medical images that is based on the optimal parameter of sharp control is presented. The method takes full advantage of the local characteristic of medical images, and the optimized sharp control parameter is obtained by the iterative computation, and then the cross-section interpolation is performed by the cubic convolution with the optimized parameter in one time.The experimental results show that the method presented in the paper not only can improve the interpolation accuracy effectively, but also is robust.

Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.

Background removal from global auroral images:Data-driven dayglow modeling

Global images of auroras obtained by cameras on spacecraft are a key tool for studying the near-Earth environment.However,the cameras are sensitive not only to auroral emissions produced by precipitating particles,but also to dayglow emissions produced by photoelectrons induced by sunlight.Nightglow emissions and scattered sunlight can contribute to the background signal.To fully utilize such images in space science,background contamination must be removed to isolate the auroral signal.Here we outline a data-driven approach to modeling the background intensity in multiple images by formulating linear inverse problems based on B-splines and spherical harmonics.The approach is robust,flexible,and iteratively deselects outliers,such as auroral emissions.The final model is smooth across the terminator and accounts for slow temporal variations and large-scale asymmetries in the dayglow.We demonstrate the model by using the three far ultraviolet cameras on the Imager for Magnetopause-to-Aurora Global Exploration(IMAGE)mission.The method can be applied to historical missions and is relevant for upcoming missions,such as the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.

Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.

Association between triglyceride-glucose index and colorectal polyps:A retrospective cross-sectional study

BACKGROUND Colorectal polyps(CPs)are frequently occurring abnormal growths in the colorectum,and are a primary precursor of colorectal cancer(CRC).The triglyceride-glucose(TyG)index is a novel marker that assesses metabolic health and insulin resistance,and has been linked to gastrointestinal cancers.AIM To investigate the potential association between the TyG index and CPs,as the relation between them has not been documented.METHODS A total of 2537 persons undergoing a routine health physical examination and colonoscopy at The First People's Hospital of Kunshan,Jiangsu Province,China,between January 2020 and December 2022 were included in this retrospective cross-sectional study.After excluding individuals who did not meet the eligibility criteria,descriptive statistics were used to compare characteristics between patients with and without CPs.Logistic regression analyses were conducted to determine the associations between the TyG index and the prevalence of CPs.The TyG index was calculated using the following formula:Ln[triglyceride(mg/dL)×glucose(mg/dL)/2].The presence and types of CPs was determined based on data from colonoscopy reports and pathology reports.RESULTS A nonlinear relation between the TyG index and the prevalence of CPs was identified,and exhibited a curvilinear pattern with a cut-off point of 2.31.A significant association was observed before the turning point,with an odds ratio(95% confidence interval)of 1.70(1.40,2.06),P<0.0001.However,the association between the TyG index and CPs was not significant after the cut-off point,with an odds ratio(95% confidence interval)of 0.57(0.27,1.23),P=0.1521.CONCLUSION Our study revealed a curvilinear association between the TyG index and CPs in Chinese individuals,suggesting its potential utility in developing colonoscopy screening strategies for preventing CRC.

Mental health and insomnia problems in healthcare workers after the COVID-19 pandemic:A multicenter cross-sectional study

BACKGROUND Healthcare workers(HCWs)are at increased risk of contracting coronavirus disease 2019(COVID-19)as well as worsening mental health problems and insomnia.These problems can persist for a long period,even after the pandemic.However,less is known about this topic.AIM To analyze mental health,insomnia problems,and their influencing factors in HCWs after the COVID-19 pandemic.METHODS This multicenter cross-sectional,hospital-based study was conducted from June 1,2023 to June 30,2023,which was a half-year after the end of the COVID-19 emergency.Region-stratified population-based cluster sampling was applied at the provincial level for Chinese HCWs.Symptoms such as anxiety,depression,and insomnia were evaluated by the Generalized Anxiety Disorder-7,Patient Health Questionnaire-9,and Insomnia Severity Index.Factors influencing the symptoms were identified by multivariable logistic regression.RESULTS A total of 2000 participants were invited,for a response rate of 70.6%.A total of 1412 HCWs[618(43.8%)doctors,583(41.3%)nurses and 211(14.9%)nonfrontline],254(18.0%),231(16.4%),and 289(20.5%)had symptoms of anxiety,depression,and insomnia,respectively;severe symptoms were found in 58(4.1%),49(3.5%),and 111(7.9%)of the participants.Nurses,female sex,and hospitalization for COVID-19 were risk factors for anxiety,depression,and insomnia symptoms;moreover,death from family or friends was a risk factor for insomnia symptoms.During the COVID-19 outbreak,most[1086(76.9%)]of the participating HCWs received psychological interventions,while nearly all[994(70.4%)]of them had received public psychological education.Only 102(7.2%)of the HCWs received individual counseling from COVID-19.CONCLUSION Although the mental health and sleep problems of HCWs were relieved after the COVID-19 pandemic,they still faced challenges and greater risks than did the general population.Identifying risk factors would help in providing targeted interventions.In addition,although a major proportion of HCWs have received public psychological education,individual interventions are still insufficient.

Cross-sectional imaging work-up of adrenal masses

Advances in medical imaging with current cross-section modalities enable non-invasive characterization of adrenal lesions. Computed tomography (CT) provides characterization with its non-contrast and wash-out features. Magnetic resonance imaging (MRI) is helpful in further characterization using chemical shift imaging (CSI) and MR spectroscopy. For differentiating between benign and malignant masses, positron emission tomography (PET) imaging is useful with its qualitative analysis, as well as its ability to detect the presence of extra-adrenal metastases in cancer patients. The workup for an indeterminate adrenal mass includes evaluation with a non-contrast CT. If a lesion is less than 10 Hounsfield Units on a non-contrast CT, it is a benign lipid-rich adenoma and no further work-up is required. For the indeterminate adrenal masses, a lipid-poor adenoma can be differentiated from a metastasis utilizing CT wash-out features. Also, MRI is beneficial with CSI and MR spectroscopy. If a mass remains indeterminate, PET imaging may be of use, in which benign lesions demonstrate low or no fluorodeoxyglucose activity. In the few cases in which adrenal lesions remain indeterminate, surgical sampling such as percutaneous biopsy can be performed.

Artificial Intelligence and Computer Vision during Surgery: Discussing Laparoscopic Images with ChatGPT4—Preliminary Results

Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce. Aim: To test a novel AI commercially available tool for image analysis on a series of laparoscopic scenes. Methods: The research tools included OPENAI CHATGPT 4.0 with its corresponding image recognition plugin which was fed with a list of 100 laparoscopic selected snapshots from common surgical procedures. In order to score reliability of received responses from image-recognition bot, two corresponding scales were developed ranging from 0 - 5. The set of images was divided into two groups: unlabeled (Group A) and labeled (Group B), and according to the type of surgical procedure or image resolution. Results: AI was able to recognize correctly the context of surgical-related images in 97% of its reports. For the labeled surgical pictures, the image-processing bot scored 3.95/5 (79%), whilst for the unlabeled, it scored 2.905/5 (58.1%). Phases of the procedure were commented in detail, after all successful interpretations. With rates 4 - 5/5, the chatbot was able to talk in detail about the indications, contraindications, stages, instrumentation, complications and outcome rates of the operation discussed. Conclusion: Interaction between surgeon and chatbot appears to be an interesting frontend for further research by clinicians in parallel with evolution of its complex underlying infrastructure. In this early phase of using artificial intelligence for image recognition in surgery, no safe conclusions can be drawn by small cohorts with commercially available software. Further development of medically-oriented AI software and clinical world awareness are expected to bring fruitful information on the topic in the years to come.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

Reconstruction of Knowledge and Medical Images in the Convergence of Chinese and Western Medicine:Taking “Sweet Meat” as an Example

The pancreas is neither part of the five Zang organs(五脏) nor the six Fu organs(六腑).Thus,it has received little attention in Chinese medical literature.In the late 19th century,medical missionaries in China started translating and introducing anatomical and physiological knowledge about the pancreas.As for the word pancreas,an early and influential translation was “sweet meat”(甜肉),proposed by Benjamin Hobson(合信).The translation “sweet meat” is not faithful to the original meaning of “pancreas”,but is a term coined by Hobson based on his personal habits,and the word “sweet” appeared by chance.However,in the decades since the term “sweet meat” became popular,Chinese medicine practitioners,such as Tang Zonghai(唐宗海),reinterpreted it by drawing new medical illustrations for “sweet meat” and giving new connotations to the word “sweet”.This discussion and interpretation of “sweet meat” in modern China,particularly among Chinese medicine professionals,is not only a dissemination and interpretation of the knowledge of “pancreas”,but also a construction of knowledge around the term “sweet meat”.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

Association between Residential Greenness and Cardiometabolic Risk Factors among Adults in Rural Xinjiang Uygur Autonomous Region,China:A Cross-Sectional Study

Objective This study aimed to explore the relationships between residential greenness and cardiometabolic risk factors among rural adults in Xinjiang Uygur Autonomous Region(Xinjiang)and thus provide a theoretical basis and data support for improving the health of residents in this region.Methods We recruited 9,723 adult rural residents from the 51st Regiment of the Third Division of the Xinjiang Production and Construction Corps in September 2016.The normalized difference vegetation index(NDVI)was used to estimate residential greenness.The generalized linear mixed model(GLMM)was used to examine the association between residential greenness and cardiometabolic risk factors.Results Higher residential greenness was associated with lower cardiometabolic risk factor prevalence.After adjustments were made for age,sex,education,and marital status,for each interquartile range(IQR)increase of NDVI500-m,the risk of hypertension was reduced by 10.3%(OR=0.897,95%CI=0.836-0.962),the risk of obesity by 20.5%(OR=0.795,95%CI=0.695-0.910),the risk of type 2 diabetes by 15.1%(OR=0.849,95%CI=0.740-0.974),and the risk of dyslipidemia by 10.5%(OR=0.895,95%CI=0.825-0.971).Risk factor aggregation was reduced by 20.4%(OR=0.796,95%CI=0.716-0.885)for the same.Stratified analysis showed that NDVI500-m was associated more strongly with hypertension,dyslipidemia,and risk factor aggregation among male participants.The association of NDVI500-m with type 2 diabetes was stronger among participants with a higher education level.PM10 and physical activity mediated 1.9%-9.2%of the associations between NDVI500-m and obesity,dyslipidemia,and risk factor aggregation.Conclusion Higher residential greenness has a protective effect against cardiometabolic risk factors among rural residents in Xinjiang.Increasing the area of green space around residences is an effective measure to reduce the burden of cardiometabolic-related diseases among rural residents in Xinjiang.

Epithelioid angiomyolipoma of the liver:Cross-sectional imaging findings of 10 immunohistochemically-verified cases

AIM： To retrospectively evaluate the computed tomography （CT）/magnetic resonance imaging （MRI） imaging features of epithelioid angiomyolipoma of the liver （Epi-HAML）, with pathology as a reference. METHODS： The CT/MRI findings （number, diameter, lobar location, and appearance of lesions） in a series of 10 patients with 12 pathologically proven epithelioid angiomyolipomas of the liver were retrospectively analyzed. The imaging features, including attenuation/ signal intensity characteristics, presence of fat, hypervascular, outer rim, and vessels within lesion, were evaluated and compared with that of non-Epi- HAML in 11 patients （13 lesions）. The Fisher exact test was used to compare difference in probability of imaging features between the two types. RESULTS： For 21 patients, CT images of 15 patients and MR images of six patients were available. No patient underwent two examinations. For the 15 patients with a CT scan, all HAML lesions in the two groups （10 Epi-HAML and seven non-Epi-HAML） manifested as hypoattenuation. For the six patients with MRI, all lesions （two Epi-HAML and six non-Epi- HAML） were hypointense on TlWI （fat suppression） and hyperintense on T2WI. There were 10 non-Epi-HAML, but only two Epi-HAML lesions showed the presence of fat, which significantly different between the two types （P = 0.005）. On the dynamic contrast enhancement （DCE） imaging, eight Epi-HAML, and 13 non-Epi lesions manifested as hypervascular. Punctate or curved vessels were displayed in 10 Epi-HAML as well as in nine non- Epi lesions and outer rim enhancement could be found with eight Epi-HAML as well as six non-Epi lesions. CONCLUSION： Little or no presence of adipose tissue was found to be an imaging feature of Epi- HAML, compared with the non-Epi type. In addition, hypervascularity with opacification of central punctiform or filiform vessels on DCE would be a characteristic enhancement pattern for Epi-HAML.

Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spinefractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picturesegmentation, feature reduction, and image classification. Two important elements are investigated to reducethe classification time: Using feature reduction software and leveraging the capabilities of sophisticated digitalprocessing hardware. The researchers use different algorithms for picture enhancement, including theWiener andKalman filters, and they look into two background correction techniques. The article presents a technique forextracting textural features and evaluates three picture segmentation algorithms and three fractured spine detectionalgorithms using transformdomain, PowerDensity Spectrum(PDS), andHigher-Order Statistics (HOS) for featureextraction.With an emphasis on reducing digital processing time, this all-encompassing method helps to create asimplified system for classifying fractured spine fractures. A feature reduction program code has been built toimprove the processing speed for picture classification. Overall, the proposed approach shows great potential forsignificantly reducing classification time in clinical settings where time is critical. In comparison to other transformdomains, the texture features’ discrete cosine transform (DCT) yielded an exceptional classification rate, and theprocess of extracting features from the transform domain took less time. More capable hardware can also result inquicker execution times for the feature extraction algorithms.

Improving the Transmission Security of Vein Images Using a Bezier Curve and Long Short-Term Memory

The act of transmitting photos via the Internet has become a routine and significant activity.Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced.This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images.The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression.This paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during transmission.By employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious actions.Furthermore,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been accomplished.For applications involving the transmission and sharing of images across a network.The suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture transfer.An effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of responsibilities.This scheme ensures a large reduction in computational and energy requirements while minimizing image quality loss.This approach employs multi-scale characteristics to improve the resistance of signatures against image deterioration.The proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.

Wideband radar cross-section reduction by a double-layer-plasma-based metasurface

Reduction of the radar cross-section(RCS) is the key to stealth technology. To improve the RCS reduction effect of the designed checkerboard metasurface and overcome the limitation of thinlayer plasma in RCS reduction technology, a double-layer-plasma-based metasurface—composed of a checkerboard metasurface, a double-layer plasma and an air gap between them—was investigated. Based on the principle of backscattering cancellation, we designed a checkerboard metasurface composed of different artificial magnetic conductor units;the checkerboard metasurface can reflect vertically incident electromagnetic(EM) waves in four different inclined directions to reduce the RCS. Full-wave simulations confirm that the doublelayer-plasma-based metasurface can improve the RCS reduction effect of the metasurface and the plasma. This is because in a band lower than the working band of the metasurface, the RCS reduction effect is mainly improved by the plasma layer. In the working band of the metasurface,impedance mismatching between the air gap and first plasma layer and between first and second plasma layers cause the scattered waves to become more dispersed, so the propagation path of the EM waves in the plasma becomes longer, increasing the absorption of the EM waves by the plasma. Thus, the RCS reduction effect is enhanced. The double-layer-plasma-based metasurface can be insensitive to the polarization of the incoming EM waves, and can also maintain a satisfactory RCS reduction band when the incident waves are oblique.