A Review on the Recent Trends of Image Steganography for VANET Applications

Image steganography is a technique of concealing confidential information within an image without dramatically changing its outside look.Whereas vehicular ad hoc networks(VANETs),which enable vehicles to communicate with one another and with roadside infrastructure to enhance safety and traffic flow provide a range of value-added services,as they are an essential component of modern smart transportation systems.VANETs steganography has been suggested by many authors for secure,reliable message transfer between terminal/hope to terminal/hope and also to secure it from attack for privacy protection.This paper aims to determine whether using steganography is possible to improve data security and secrecy in VANET applications and to analyze effective steganography techniques for incorporating data into images while minimizing visual quality loss.According to simulations in literature and real-world studies,Image steganography proved to be an effectivemethod for secure communication on VANETs,even in difficult network conditions.In this research,we also explore a variety of steganography approaches for vehicular ad-hoc network transportation systems like vector embedding,statistics,spatial domain(SD),transform domain(TD),distortion,masking,and filtering.This study possibly shall help researchers to improve vehicle networks’ability to communicate securely and lay the door for innovative steganography methods.

Advances in the application of auxiliary imaging techniques in parathyroid diseases

Hypoparathyroidism is one of the main complications after total thyroidectomy,severely affecting patients’quality of life.How to effectively protect parathyroid function after surgery and reduce the incidence of hypoparathyroidism has always been a key research area in thyroid surgery.Therefore,precise localization of parathyroid glands during surgery,effective imaging,and accurate surgical resection have become hot topics of concern for thyroid surgeons.In response to this clinical phenomenon,this study compared several different imaging methods for parathyroid surgery,including nanocarbon,indocyanine green,near-infrared imaging techniques,and technetium-99m methoxyisobutylisonitrile combined with gamma probe imaging technology.The advantages and disadvantages of each method were analyzed,providing scientific recommendations for future parathyroid imaging.In recent years,some related basic and clinical research has also been conducted in thyroid surgery.This article reviewed relevant literature and provided an overview of the practical application progress of various imaging techniques in parathyroid surgery.

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

A Preliminary Comparative Study on the Centering Algorithms for CassiniISS NAC Images

Obtaining high precision is an important consideration for astrometric studies using images from the Narrow Angle Camera(NAC)of the Cassini Imaging Science Subsystem(ISS).Selecting the best centering algorithm is key to enhancing astrometric accuracy.In this study,we compared the accuracy of five centering algorithms:Gaussian fitting,the modified moments method,and three point-spread function(PSF)fitting methods(effective PSF(ePSF),PSFEx,and extended PSF(x PSF)from the Cassini Imaging Central Laboratory for Operations(CICLOPS)).We assessed these algorithms using 70 ISS NAC star field images taken with CL1 and CL2 filters across different stellar magnitudes.The ePSF method consistently demonstrated the highest accuracy,achieving precision below 0.03 pixels for stars of magnitude 8-9.Compared to the previously considered best,the modified moments method,the e PSF method improved overall accuracy by about 10%and 21%in the sample and line directions,respectively.Surprisingly,the xPSF model provided by CICLOPS had lower precision than the ePSF.Conversely,the ePSF exhibits an improvement in measurement precision of 23%and 17%in the sample and line directions,respectively,over the xPSF.This discrepancy might be attributed to the xPSF focusing on photometry rather than astrometry.These findings highlight the necessity of constructing PSF models specifically tailored for astrometric purposes in NAC images and provide guidance for enhancing astrometric measurements using these ISS NAC images.

Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.

Lossless Compression Method for the Magnetic and Helioseismic Imager(MHI)Payload

The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity.However,one of the most significant challenges lies in ultra-long-distance data transmission,particularly for the Magnetic and Helioseismic Imager(MHI),which is the most important payload and generates the largest volume of data in SPO.In this paper,we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data.The background out of the solar disk is removed to decrease the pixel number of an image under compression.Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation(space,spectrum,and polarization)in data set,improving the compression ratio.Experimental results demonstrate that our method achieves an average compression ratio of 3.67.The compression time is also less than the general observation period.The method exhibits strong feasibility and can be easily adapted to MHI.

How to Coadd Images.Ⅱ.Anti-aliasing and PSF Deconvolution

We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function(PSF)deconvolution,resulting in enhanced restoration of extended sources,the highest peak signal-to-noise ratio,and reduced ringing artefacts.To test our method,we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/the VLT Survey Telescope(VST)and compared our results to those obtained using previous algorithms.The simulation showed that our method outperforms previous approaches in several ways,such as restoring the profile of extended sources and minimizing ringing artefacts.Additionally,because our method relies on the inherent advantages of least squares fitting,it is more versatile and does not depend on the local uniformity hypothesis for the PSF.However,the new method consumes much more computation than the other approaches.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Non-invasive evaluation of liver steatosis with imaging modalities:New techniques and applications

In the world,nonalcoholic fatty liver disease(NAFLD)accounts for majority of diffuse hepatic diseases.Notably,substantial liver fat accumulation can trigger and accelerate hepatic fibrosis,thus contributing to disease progression.Moreover,the presence of NAFLD not only puts adverse influences for liver but is also associated with an increased risk of type 2 diabetes and cardiovascular diseases.Therefore,early detection and quantified measurement of hepatic fat content are of great importance.Liver biopsy is currently the most accurate method for the evaluation of hepatic steatosis.However,liver biopsy has several limitations,namely,its invasiveness,sampling error,high cost and moderate intraobserver and interobserver reproducibility.Recently,various quantitative imaging techniques have been developed for the diagnosis and quantified measurement of hepatic fat content,including ultrasound-or magnetic resonancebased methods.These quantitative imaging techniques can provide objective continuous metrics associated with liver fat content and be recorded for comparison when patients receive check-ups to evaluate changes in liver fat content,which is useful for longitudinal follow-up.In this review,we introduce several imaging techniques and describe their diagnostic performance for the diagnosis and quantified measurement of hepatic fat content.

Transverse Velocity Field Measurements in High-resolution Solar Images Based on Deep Learning

To address the problem of the low accuracy of transverse velocity field measurements for small targets in highresolution solar images,we proposed a novel velocity field measurement method for high-resolution solar images based on PWCNet.This method transforms the transverse velocity field measurements into an optical flow field prediction problem.We evaluated the performance of the proposed method using the Hαand TiO data sets obtained from New Vacuum Solar Telescope observations.The experimental results show that our method effectively predicts the optical flow of small targets in images compared with several typical machine-and deeplearning methods.On the Hαdata set,the proposed method improves the image structure similarity from 0.9182 to0.9587 and reduces the mean of residuals from 24.9931 to 15.2818;on the TiO data set,the proposed method improves the image structure similarity from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 to17.0194.The optical flow predicted using the proposed method can provide accurate data for the atmospheric motion information of solar images.The code implementing the proposed method is available on https://github.com/lygmsy123/transverse-velocity-field-measurement.

Preliminary study of the electrospray DPE peculiarities from the liquid surface in the presence of the CSWs

The Ultrasonic Electric Propulsion(UEP)system is a cutting-edge propulsion technology that is mostly used on platforms for small satellites(less than 10 kg).The characteristics of droplet partial emissions(DPEs)in the UEP system are investigated using a high-speed imaging technique(an ultra-high speed camera(NAC HX-6)and a long-distance microscope)in this work.The experiments demonstrate that there are a few partial emission modes,including left-side emission,double-side emission,and right-side emission,that are present in the droplet emission process of the UEP system.These modes are primarily caused by the partial formation of capillary standing waves(CSWs)on the emission surface of the ultrasonic nozzle.The emission rate for single-and double-sided emissions varies at different times,indicating that there are different CSWs engaged in droplet emission due to variations in the liquid film thickness and charge state of the liquid cones.Additionally,as the droplets emit continuously,a raised area on the emission surface appears,with several droplets emitting there as a result of charge accumulation.Additionally,photos of the CSWs with emitting droplets are obtained,which highlights the CSWs'distinctive wave morphology.

E-Cervix imaging displaying cervical elasticity characteristics of healthy adult nulliparous women at different age groups and different menstrual cycle stages

Objective To observe the cervical elasticity of healthy adult nulliparous women at different age groups and different stages of menstrual cycle with E-Cervix imaging technology.Methods A total of 218 healthy adult nulliparous women who underwent transvaginal ultrasound examination for routine physical examination were retrospectively enrolled,including 103 in follicular phase,78 in ovulation phase and 37 in luteal phase.Cervical canal length(CL)and E-Cervix elasticity parameters were compared among different age groups and different stages of menstrual cycle,including elasticity contrast index(ECI),hardness ratio(HR),cervical internal and external orifice strain values(IOS and EOS)and IOS/EOS ratio.Results No significant difference of CL nor cervical elasticity parameters was detected among healthy adult nulliparous women at different age groups(all P>0.05).There were significant differences of ECI,HR and IOS among different menstrual cycle stages(all P<0.05),among which women in follicular phase had higher ECI and IOS but lower HR than those in luteal phase(all P<0.05).Conclusion No significant difference of cervical elasticity existed among healthy adult nulliparous women at different age groups.Meanwhile,cervical elasticity of healthy adult nulliparous women changed during menstrual cycle,in follicular phase had higher ECI and IOS but lower HR than in luteal phase.

Timely identification and treatment of uterine artery pseudoaneurysm after hysteroscopic procedures

Uterine artery pseudoaneurysm(UAP)is a rare but potentially life-threatening complication that can occur following hysteroscopic surgery for endometrial polyp resection.This article discusses the case study by Kakinuma et al,which highlights the successful diagnosis and treatment of UAP in a 48-year-old primiparous woman.Utilizing advanced imaging techniques such as ultrasound and computed tomography(CT),the medical team was able to promptly identify the UAP and subsequently perform a uterine artery embolization to treat the condition.The study underscores the critical need for rapid diagnosis and intervention to prevent severe outcomes and provides practical clinical recommendations for managing similar cases.This article aims to expand on the study’s findings,discuss the clinical implications,and suggest future research directions to optimize the management of UAP post-hysteroscopic surgery.

Super-resolution Imaging of Telescopic Systems based on Optical-neural Network Joint Optimization

Optical telescopes are an important tool for acquiring optical information about distant objects,and resolution is an important indicator that measures the ability to observe object details.However,due to the effects of system aberration,atmospheric seeing,and other factors,the observed image of ground-based telescopes is often degraded,resulting in reduced resolution.This paper proposes an optical-neural network joint optimization method to improve the resolution of the observed image by co-optimizing the point-spread function(PSF)of the telescopic system and the image super-resolution(SR)network.To improve the speed of image reconstruction,we designed a generative adversarial net(LCR-GAN)with light parameters,which is much faster than the latest unsupervised networks.To reconstruct the PSF trained by the network in the optical path,a phase mask is introduced.It improves the image reconstruction effect of LCR-GAN by reconstructing the PSF that best matches the network.The results of simulation and verification experiments show that compared with the pure deep learning method,the SR image reconstructed by this method is rich in detail and it is easier to distinguish stars or stripes.

Resolution Enhancement of SOHO/MDI Magnetograms

Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement frequency.The augmentation and assimilation of historical observational data timelines also play a significant role in understanding the patterns of solar magnetic field variation.Within the realm of astronomical data processing,super-resolution(SR)reconstruction refers to the process of using a substantial corpus of training data to learn the nonlinear mapping between low-resolution(LR)and high-resolution(HR)images,thereby achieving higherresolution astronomical images.This paper is an application study in high-dimensional nonlinear regression.Deep learning models were employed to perform SR modeling on SOHO/MDI magnetograms and SDO/HMI magnetograms,thus reliably achieving resolution enhancement of full-disk SOHO/MDI magnetograms and enhancing the image resolution to obtain more detailed information.For this study,a data set comprising 9717pairs of data from 2010 April to 2011 February was used as the training set,1332 pairs from 2011 March were used as the validation set and 1034 pairs from 2011 April were used as the test set.After data preprocessing,we randomly cropped 128×128 sub-images as the LR cases from the full-disk MDI magnetograms,and the corresponding 512×512 sub-images as HR ones from the HMI full-disk magnetograms for model training.The tests conducted have shown that the study successfully produced reliable 4×SR reconstruction of full-disk MDI magnetograms.The MESR model's results(0.911)were highly correlated with the target HMI magnetographs as indicated by the correlation coefficient values.Furthermore,the method achieved the best PSNR,SSIM,MAE and RMSE values,indicating that the MESR model can effectively reconstruct magnetograms.

Preparation for CSST:Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method

Most existing star-galaxy classifiers depend on the reduced information from catalogs,necessitating careful data processing and feature extraction.In this study,we employ a supervised machine learning method(GoogLeNet)to automatically classify stars and galaxies in the COSMOS field.Unlike traditional machine learning methods,we introduce several preprocessing techniques,including noise reduction and the unwrapping of denoised images in polar coordinates,applied to our carefully selected samples of stars and galaxies.By dividing the selected samples into training and validation sets in an 8:2 ratio,we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies.The results indicate that the GoogLeNet model is highly effective,achieving accuracies of 99.6% and 99.9% for stars and galaxies,respectively.Furthermore,by comparing the results with and without preprocessing,we find that preprocessing can significantly improve classification accuracy(by approximately 2.0% to 6.0%)when the images are rotated.In preparation for the future launch of the China Space Station Telescope(CSST),we also evaluate the performance of the GoogLeNet model on the CSST simulation data.These results demonstrate a high level of accuracy(approximately 99.8%),indicating that this model can be effectively utilized for future observations with the CSST.

On the Relation Between Symmetry of Radio Galaxies and Their Physical Parameters

Gravity as a fundamental force plays a dominant role in the formation and evolution of cosmic objects and leaves its effect in the emergence of symmetric and asymmetric structures.Thus,analyzing the symmetry criteria allows us to uncover mechanisms behind the gravity interaction and understand the underlying physical processes that contribute to the formation of large-scale structures such as galaxies.We use a segmentation process using intensity thresholding and the k-means clustering algorithm to analyze radio galaxy images.We employ a symmetry criterion and explore the relation between morphological symmetry in radio maps and host galaxy properties.Optical properties(stellar mass,black hole mass,optical size(R_(50)),concentration,stellar mass surface density(μ_(50)),and stellar age)and radio properties(radio flux density,radio luminosity,and radio size)are considered.We found that there is a correlation between symmetry and radio size,indicating larger radio sources have smaller symmetry indices.Therefore,size of radio sources should be considered in any investigation of symmetry.Weak correlations are also observed with other properties,such as R_(50)for FRI galaxies and stellar age.We compare the symmetry differences between FRI and FRII radio galaxies.FRII galaxies show higher symmetry in 1.4 GHz and 150 MHz maps.Investigating the influence of radio source sizes,we discovered that this result is independent of the sizes of radio sources.These findings contribute to our understanding of the morphological properties and analyses of radio galaxies.

Benign gallbladder diseases:Imaging techniques and tips for differentiating with malignant gallbladder diseases

Benign gallbladder diseases usually present with intraluminal lesions and localized or diffuse wall thickening.Intraluminal lesions of the gallbladder include gallstones,cholesterol polyps,adenomas,or sludge and polypoid type of gallbladder cancer must subsequently be excluded.Polyp size,stalk width,and enhancement intensity on contrast-enhanced ultrasound and degree of diffusion restriction may help differentiate cholesterol polyps and adenomas from gallbladder cancer.Localized gallbladder wall thickening is largely due to segmental or focal gallbladder adenomyomatosis,although infiltrative cancer may present similarly.Identification of Rokitansky-Aschoff sinuses is pivotal in diagnosing adenomyomatosis.The layered pattern,degree of enhancement,and integrity of the wall are imaging clues that help discriminate innocuous thickening from gallbladder cancer.High-resolution ultrasound is especially useful for analyzing the layering of gallbladder wall.A diffusely thickened wall is frequently seen in inflammatory processes of the gallbladder.Nevertheless,it is important to check for coexistent cancer in instances of acute cholecystitis.Ultrasound used alone is limited in evaluating complicated cholecystitis and often requires complementary computed tomography.In chronic cholecystitis,preservation of a two-layered wall and weak wall enhancement are diagnostic clues for excluding malignancy.Magnetic resonance imaging in conjunction with diffusion-weighted imaging helps to differentiate xathogranulomatous cholecystitis from gallbladder cancer by identifying the presence of fat and degree of diffusion restriction.Such distinctions require a familiarity with typical imaging features of various gallbladder diseases and an understanding of the roles that assorted imaging modalities play in gallbladder evaluations.

Imaging of the small bowel in Crohn's disease: A review of old and new techniques

The investigation of small bowel morphology is often mandatory in many patients with Crohn's disease. Traditional radiological techniques (small bowel enteroclysis and small bowel follow-through) have long been the only suitable methods for this purpose. In recent years, several alternative imaging techniques have been proposed. To review the most recent advances in imaging studies of the small bowel, with particular reference to their possible application in Crohn's disease, we conducted a complete review of the most important studies in which traditional and newer imaging methods were performed and compared in patients with Crohn's disease. Several radiological and endoscopic techniques are now available for the study of the small bowel; each of them is characterized by a distinct profile of favourable and unfavourable features. In some cases, they may also be used as complementary rather than alternative techniques. In everyday practice, the choice of the technique to be used stands upon its availability and a careful evaluation of diagnostic accuracy, clinical usefulness, safety and cost. The recent development ofinnovative imaging techniques has opened a new and exciting area in the exploration of the small bowel in Crohn's disease patients.

Use of a Digital Image Correlation Technique for Measuring the Material Properties of Beetle Wing

Beetle wings are very specialized flight organs consisting of the veins and membranes.Therefore it is necessary from a bionic view to investigate the material properties of a beetle wing experimentally.In the present study,we have used a Digital Image Correlation (DIC) technique to measure the elastic modulus of a beetle wing membrane.Specimens were prepared by carefully cutting a beetle hind wing into 3.0 mm by 7.0 mm segments (the gage length was 5 mm).We used a scanning electron microscope for a precise measurement of the thickness of the beetle wing membrane.The specimen was attached to a designed fixture to induce a uniform displacement by means of a micromanipulator.We used an ARAMISTM system based on the digital image correlation technique to measure the corresponding displacement of a specimen.The thickness of the beetle wing varied at different points of the membrane.The elastic modulus differed in relation to the membrane arrangement showing a structural anisotropy;the elastic modulus in the chordwise direction is approximately 2.65 GPa,which is three times larger than the elastic modulus in the spanwise direction of 0.84 GPa.As a result,the digital image correlation-based ARAMIS system was suc- cessfully used to measure the elastic modulus of a beetle wing.In addition to membrane's elastic modulus,we considered the Poisson's ratio of the membrane and measured the elastic modulus of a vein using an Instron universal tensile machine.The result reveals the Poisson's ratio is nearly zero and the elastic modulus of a vein is about 11 GPa.