Modified iterative method for augmented system

The successive overrelaxation-like （SOR-like） method with the real param- eters ω is considered for solving the augmented system. The new method is called the modified SOR-like （MSOR-like） method. The functional equation between the parameters and the eigenvalues of the iteration matrix of the MSOR-like method is given. Therefore, the necessary and sufficient condition for the convergence of the MSOR-like method is derived. The optimal iteration parameter ω of the MSOR-like method is derived. Finally, the proof of theorem and numerical computation based on a particular linear system are given, which clearly show that the MSOR-like method outperforms the SOR-like （Li, C. J., Li, B. J., and Evans, D. J. Optimum accelerated parameter for the GSOR method. Neural, Parallel ＆ Scientific Computations, 7（4）, 453-462 （1999）） and the modified sym- metric SOR-like （MSSOR-like） methods （Wu, S. L., Huang, T. Z., and Zhao, X. L. A modified SSOR iterative method for augmented systems. Journal of Computational and Applied Mathematics, 228（4）, 424-433 （2009））.

Virtual and augmented reality systems and three-dimensional printing of the renal model—novel trends to guide preoperative planning for renal cancer

Objective:This study aimed to explore the applications of three-dimensional (3D) technology, including virtual reality, augmented reality (AR), and 3D printing system, in the field of medicine, particularly in renal interventions for cancer treatment.Methods:A specialized software transforms 2D medical images into precise 3D digital models, facilitating improved anatomical understanding and surgical planning. Patient-specific 3D printed anatomical models are utilized for preoperative planning, intraoperative guidance, and surgical education. AR technology enables the overlay of digital perceptions onto real-world surgical environments.Results:Patient-specific 3D printed anatomical models have multiple applications, such as preoperative planning, intraoperative guidance, trainee education, and patient counseling. Virtual reality involves substituting the real world with a computer-generated 3D environment, while AR overlays digitally created perceptions onto the existing reality. The advances in 3D modeling technology have sparked considerable interest in their application to partial nephrectomy in the realm of renal cancer. 3D printing, also known as additive manufacturing, constructs 3D objects based on computer-aided design or digital 3D models. Utilizing 3D-printed preoperative renal models provides benefits for surgical planning, offering a more reliable assessment of the tumor's relationship with vital anatomical structures and enabling better preparation for procedures. AR technology allows surgeons to visualize patient-specific renal anatomical structures and their spatial relationships with surrounding organs by projecting CT/MRI images onto a live laparoscopic video. Incorporating patient-specific 3D digital models into healthcare enhances best practice, resulting in improved patient care, increased patient satisfaction, and cost saving for the healthcare system.

Single-center experience with Knee+^(TM) augmented reality navigation system in primary total knee arthroplasty

BACKGROUND Computer-assisted systems obtained an increased interest in orthopaedic surgery over the last years,as they enhance precision compared to conventional hardware.The expansion of computer assistance is evolving with the employment of augmented reality.Yet,the accuracy of augmented reality navigation systems has not been determined.AIM To examine the accuracy of component alignment and restoration of the affected limb’s mechanical axis in primary total knee arthroplasty(TKA),utilizing an augmented reality navigation system and to assess whether such systems are conspicuously fruitful for an accomplished knee surgeon.METHODS From May 2021 to December 2021,30 patients,25 women and five men,under-went a primary unilateral TKA.Revision cases were excluded.A preoperative radiographic procedure was performed to evaluate the limb’s axial alignment.All patients were operated on by the same team,without a tourniquet,utilizing three distinct prostheses with the assistance of the Knee+™augmented reality navigation system in every operation.Postoperatively,the same radiographic exam protocol was executed to evaluate the implants’position,orientation and coronal plane alignment.We recorded measurements in 3 stages regarding femoral varus and flexion,tibial varus and posterior slope.Firstly,the expected values from the Augmented Reality system were documented.Then we calculated the same values after each cut and finally,the same measurements were recorded radiolo-gically after the operations.Concerning statistical analysis,Lin’s concordance correlation coefficient was estimated,while Wilcoxon Signed Rank Test was performed when needed.RESULTS A statistically significant difference was observed regarding mean expected values and radiographic mea-surements for femoral flexion measurements only(Z score=2.67,P value=0.01).Nonetheless,this difference was statistically significantly lower than 1 degree(Z score=-4.21,P value<0.01).In terms of discrepancies in the calculations of expected values and controlled measurements,a statistically significant difference between tibial varus values was detected(Z score=-2.33,P value=0.02),which was also statistically significantly lower than 1 degree(Z score=-4.99,P value<0.01).CONCLUSION The results indicate satisfactory postoperative coronal alignment without outliers across all three different implants utilized.Augmented reality navigation systems can bolster orthopaedic surgeons’accuracy in achieving precise axial alignment.However,further research is required to further evaluate their efficacy and potential.

Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.

Integrated guidance and control for damping augmented system via convex optimization

In this paper,an integrated guidance and control approach is presented to improve the performance of the missile interception.The approach includes damping augmented system with attitude rate feedback to decrease the oscillation during the homing phase for missiles with low damping.In addition,physical constraints,which can affect the performance of the missile interception,such as acceleration limit,seeker’s look angle,and look angle rate constraints are considered.The integrated guidance and control problem is formulated as a convex quadratic optimization problem with equality and inequality constraints,and the solution is obtained by a primal–dual interior point method.The performance of the proposed method is verified through several numerical examples.

Synchronization of the fractional-order generalized augmented L u¨system and its circuit implementation

In this paper, the synchronization of the fractional-order generalized augmented Lti system is investigated. Based on the predictor--corrector method, we obtain phase portraits, bifurcation diagrams, Lyapunov exponent spectra, and Poincar6 maps of the fractional-order system and find that a four-wing chaotic attractor exists in the system when the system pa- rameters change within certain ranges. Further, by varying the system parameters, rich dynamical behaviors occur in the 2.7-order system. According to the stability theory of a fractional-order linear system, and adopting the linearization by feedback method, we have designed a nonlinear feedback controller in our theoretical analysis to implement the synchro- nization of the drive system with the response system. In addition, the synchronization is also shown by an electronic circuit implementation for the 2.7-order system. The obtained experiment results accord with the theoretical analyses, which further demonstrate the feasibility and effectiveness of the proposed synchronization scheme.

Rectification of magnetic force tracker using neural network inaugmented reality system

Nonlinear errors always exist in data obtained from tracker in augmented reality （AR）, which badly influence the effect of AR. This paper proposes to rectify the errors using BP neural network. As BP neural network is prone to getting into local extrema and convergence is slow, genetic algorithm is employed to optimize the initial weights and threshold of neural network. This paper discusses how to set the crucial parameters in the algorithm. Experimental results show that the method ensures that the neural network achieves global convergence quickly and correctly. Tracking precision of AR system is improved after the tracker is rectified, and the third dimension of AR system is enhanced.

Registration Based on ORB and FREAK Features for Augmented Reality Systems

This paper proposes a novel registration method for augmented reality (AR) systems based on Oriented FAST and Rotated BRIEF (ORB) and Fast Retina Keypoint (FREAK) natural features. In the proposed ORB-FREAK method, feature extraction is implemented based on the combination of ORB and FREAK, and the feature points are matched using Hamming distance. To get good matching points, cross-checks and least median squares are used to perform outlier filtration, and camera pose is estimated using the matched points. Finally, AR is rendered. Experiments show that the proposed method improves the speed of registration to be in real time; the proposed method can accurately register the target object under the circumstances of partial occlusion of the object; and it also can overcome the effects of rotation, scale change, ambient light and distance. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

Augmented reality-based visual-haptic modeling for thoracoscopic surgery training systems

Background Compared with traditional thoracotomy,video-assisted thoracoscopic surgery(VATS)has less minor trauma,faster recovery,higher patient compliance,but higher requirements for surgeons.Virtual surgery training simulation systems are important and have been widely used in Europe and America.Augmented reality(AR)in surgical training simulation systems significantly improve the training effect of virtual surgical training,although AR technology is still in its initial stage.Mixed reality has gained increased attention in technology-driven modern medicine but has yet to be used in everyday practice.Methods This study proposed an immersive AR lobectomy within a thoracoscope surgery training system,using visual and haptic modeling to study the potential benefits of this critical technology.The content included immersive AR visual rendering,based on the cluster-based extended position-based dynamics algorithm of soft tissue physical modeling.Furthermore,we designed an AR haptic rendering systems,whose model architecture consisted of multi-touch interaction points,including kinesthetic and pressure-sensitive points.Finally,based on the above theoretical research,we developed an AR interactive VATS surgical training platform.Results Twenty-four volunteers were recruited from the First People's Hospital of Yunnan Province to evaluate the VATS training system.Face,content,and construct validation methods were used to assess the tactile sense,visual sense,scene authenticity,and simulator performance.Conclusions The results of our construction validation demonstrate that the simulator is useful in improving novice and surgical skills that can be retained after a certain period of time.The video-assisted thoracoscopic system based on AR developed in this study is effective and can be used as a training device to assist in the development of thoracoscopic skills for novices.

Capacity Optimization Using Augmented Lagrange Method in Intelligent Reflecting Surface-Based MIMO Communication Systems

In this paper,we present an algorithm for capacity optimization in intelligent reflecting surface(IRS)-based multiple-input multiple-output(MIMO)communication systems.To maximize the capacity of elements in IRS,we use augmented Lagrange method with the equivalent transformations on the covariance matrix and reflection matrix constraints.This results an adjustable phase shift on the incident signal.Furthermore,we reshape the complex-valued covariance matrix and reflection matrix to a vector for the ease of calculating partial derivatives to find the search direction.Then,the quasi-Newton updates and modified Broyden-Fletcher-Goldfarb-Shano(BFGS)method in the complex domain form are used to find the local minimum.Finally,numerical simulation results demonstrate that our proposed IRS-aided system using the algorithm performs better than the state-of-the-art and the conventional communication systems.

Systematic review and meta-analysis of augmented reality in medicine, retail, and games

This paper presents a detailed review of the applications of augmented reality(AR)in three important fields where AR use is currently increasing.The objective of this study is to highlight how AR improves and enhances the user experience in entertainment,medicine,and retail.The authors briefly introduce the topic of AR and discuss its differences from virtual reality.They also explain the software and hardware technologies required for implementing an AR system and the different types of displays required for enhancing the user experience.The growth of AR in markets is also briefly discussed.In the three sections of the paper,the applications of AR are discussed.The use of AR in multiplayer gaming,computer games,broadcasting,and multimedia videos,as an aspect of entertainment and gaming is highlighted.AR in medicine involves the use of AR in medical healing,medical training,medical teaching,surgery,and post-medical treatment.AR in retail was discussed in terms of its uses in advertisement,marketing,fashion retail,and online shopping.The authors concluded the paper by detailing the future use of AR and its advantages and disadvantages in the current scenario.

Outdoor guide system based on the mobile augmented reality technology

This paper proposes an outdoor guide system using vision-based augmented reality(AR) on mobile devices.Augmented reality provides a virtual-real fusion display interface for outdoor guide.Vision-based methods are more accurate than GPS or other hardware-based methods.However,vision-based methods require more resources and relatively strong computing power of mobile devices.A C/S framework for vision based augmented reality system is introduced in this paper.In a server,a vocabulary tree is used for location recognition.In a mobile device,BRISK feature is combined with optical flow methods to track the offline keyframe.The system is tested on UKbench datasets and in real environment.Experimental results show that the proposed vision-based augmented reality system works well and yields relatively high recognition rate and that the mobile device achieves realtime recognition performance.

Realization of Mobile Augmented Reality System Based on Image Recognition

With the development of computation technology,the augmented reality(AR)is widely applied in many fields as well as the image recognition.However,the AR application on mobile platform is not developed enough in the past decades due to the capability of the mobile processors.In recent years,the performance of mobile processors has changed rapidly,which makes it comparable to the desktop processors.This paper proposed and realized an AR system to be used on the Android mobile platform based on the image recognition through EasyAR engine and Unity 3D development tools.In this system,the image recognition could be done locally and/or using cloud recognition.Test results show that the cloud-based recognition is more efficient and accuracy than the local recognition for the mobile AR when there are more images to be recognized at the same time.

Experimental study of the influencing factors and mechanisms of the pressure-reduction and augmented injection effect by nanoparticles in ultra-low permeability reservoirs

Nanoparticles(NPs)have gained significant attention as a functional material due to their ability to effectively enhance pressure reduction in injection processes in ultra-low permeability reservoirs.NPs are typically studied in controlled laboratory conditions,and their behavior in real-world,complex environments such as ultra-low permeability reservoirs,is not well understood due to the limited scope of their applications.This study investigates the efficacy and underlying mechanisms of NPs in decreasing injection pressure under various injection conditions(25—85℃,10—25 MPa).The results reveal that under optimal injection conditions,NPs effectively reduce injection pressure by a maximum of 22.77%in core experiment.The pressure reduction rate is found to be positively correlated with oil saturation and permeability,and negatively correlated with temperature and salinity.Furthermore,particle image velocimetry(PIV)experiments(25℃,atmospheric pressure)indicate that the pressure reduction is achieved by NPs through the reduction of wall shear resistance and wettability change.This work has important implications for the design of water injection strategies in ultra-low permeability reservoirs.

Ultracompact and high-efficiency liquid-crystalon-silicon light engines for augmented reality glasses

In lightweight augmented reality(AR)glasses,the light engines must be very compact while keeping a high optical efficiency to enable longtime comfortable wearing and high ambient contrast ratio.“Liquid-crystal-on-silicon(LCoS)or micro-LED,who wins?”is recently a heated debate question.Conventional LCoS system is facing tremendous challenges due to its bulky illumination systems;it often incorporates a bulky polarizing beam splitter(PBS)cube.To minimize the formfactor of an LCoS system,here we demonstrate an ultracompact illumination system consisting of an in-coupling prism,and a light guide plate with multiple parallelepiped extraction prisms.The overall module volume including the illumination optics and an LCoS panel(4.4-μm pixel pitch and 1024x1024 resolution elements),but excluding the projection optics,is merely 0.25 cc(cm3).Yet,our system exhibits an excellent illuminance uniformity and an impressive optical efficiency(36%–41%for a polarized input light).Such an ultracompact and high-efficiency LCoS illumination system is expected to revolutionize the next-generation AR glasses.

Synergy Model of Artificial Intelligence and Augmented Reality in the Processes of Exploitation of Energy Systems

The article presents a fragment of research and development, which objective was to develop technical tools and methodology to improve exploitation processes of energy systems. The author＇s model includes synergy of artificial intelligence and augmented reality. This solution, which combines modem technologies in order to improve the activities related to the continuity of energy supply, and reduce costs associated with the time needed to carry out exploitation activities and employment of qualified staff, is presented. This paper presents both theoretical foundations as well as the development of technical systems. The characteristics of exploitation processes of energy systems and possible technical conditions, as well as factors characterizing them, are discussed. The physical and software structures of the system and individual modules, as well as dependencies connecting them are demonstrated. The dependencies between physical and logical elements during the exploitation processes of energy systems, that determine decisions related to the evaluation of technical states and related activities are described. The advantages and limitations of the developed model which connects methods of data processing and analysis, interactive visualization processes and possible areas of application are as well discussed in detailed.

Investigation of Inside-Out Tracking Methods for Six Degrees of Freedom Pose Estimation of a Smartphone in Augmented Reality

Six degrees of freedom(6DoF)input interfaces are essential formanipulating virtual objects through translation or rotation in three-dimensional(3D)space.A traditional outside-in tracking controller requires the installation of expensive hardware in advance.While inside-out tracking controllers have been proposed,they often suffer from limitations such as interaction limited to the tracking range of the sensor(e.g.,a sensor on the head-mounted display(HMD))or the need for pose value modification to function as an input interface(e.g.,a sensor on the controller).This study investigates 6DoF pose estimation methods without restricting the tracking range,using a smartphone as a controller in augmented reality(AR)environments.Our approach involves proposing methods for estimating the initial pose of the controller and correcting the pose using an inside-out tracking approach.In addition,seven pose estimation algorithms were presented as candidates depending on the tracking range of the device sensor,the tracking method(e.g.,marker recognition,visual-inertial odometry(VIO)),and whether modification of the initial pose is necessary.Through two experiments(discrete and continuous data),the performance of the algorithms was evaluated.The results demonstrate enhanced final pose accuracy achieved by correcting the initial pose.Furthermore,the importance of selecting the tracking algorithm based on the tracking range of the devices and the actual input value of the 3D interaction was emphasized.

Augmented Reality Based on Object Recognition for Piping System Maintenance

Augmented Reality(AR)applications can be used to improve tasks and mitigate errors during facilities operation and maintenance.This article presents an AR system for facility management using a three-dimensional(3D)object tracking method.Through spatial mapping,the object of interest,a pipe trap underneath a sink,is tracked and mixed onto the AR visualization.From that,the maintenance steps are transformed into visible and animated instructions.Although some tracking issues related to the component parts were observed,the designed AR application results demonstrated the potential to improve facility management tasks.

Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ahead.However,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and fatalities.In this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image segmentation.This article explores the potential of seeing-through vehicles as a solution to enhance overtaking safety.See-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of sight.To address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both cars.The server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front car.Our see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other vehicles.Our network was trained and tested on the Cityscape dataset using semantic segmentation.This transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has obscured.For our findings,we have achieved 97.1% F1-score.The article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

A multichannel human-swarm robot interaction system in augmented reality

Background A large number of robots have put forward the new requirements for human robot interaction.One of the problems in human-swarm robot interaction is how to naturally achieve an efficient and accurate interaction between humans and swarm robot systems.To address this,this paper proposes a new type of human-swarm natural interaction system.Methods Through the cooperation between three-dimensional(3D)gesture interaction channel and natural language instruction channel,a natural and efficient interaction between a human and swarm robots is achieved.Results First,A 3D lasso technology realizes a batch-picking interaction of swarm robots through oriented bounding boxes.Second,control instruction labels for swarm-oriented robots are defined.The instruction label is integrated with the 3D gesture and natural language through instruction label filling.Finally,the understanding of natural language instructions is realized through a text classifier based on the maximum entropy model.A head-mounted augmented reality display device is used as a visual feedback channel.Conclusions The experiments on selecting robots verify the feasibility and availability of the system.