Exploiting 4D non-degenerate chaotic system and adaptive diffusion based on the finite field for 3D model encryption

Developing a three-dimensional (3D) data model for Geographic Information Systems (GIS) is an essential and complex issue. 3D modelling in GIS is becoming ever more important for the development of cyber cities and digital earth, which have recently become feasible. A competent 3D model forms an efficient foundation for 3D visualization, query and spatial analysis. As a development of the existing 3D models, this study proposes particular improvements in handling complex 3D objects. We present an object-oriented data model for handling complex 3D objects in GIS. First, the conceptual data model is developed based on the principle of object-oriented (OO) data modelling. This model is designed based on the following three basic geometric elements: node, segment and triangle. Accordingly, the abstract geometric objects are defined: including points, lines, surfaces and volumes. Second, the corresponding 3D logical model is designed based on the defined abstract objects and the relationships between them. Third, a formal representation of the 3D spatial objects is described in detail. Fourth, a prototype 3D GIS is developed based on the proposed 3D data model. Finally, we describe the results of an experimental study to reconstruct 3D objects using this 3D GIS and a comparison with the performance of other 3D data models. The proposed model is able to handle complex objects, such as complex buildings and TV towers, which is an essential functionality for building large-scale cyber cities, such as for Hong Kong. The proposed data model proves to be very efficient, particularly in visualization and rendering. The experimental results show which the data volume of the proposed model is compacted and the visualization speed for 3D objects is improved, compared with the existing models.

3D data model is an indispensable component to any 3D GIS, and forms the basis of 3D spatial analysis and representation. At present, plenty of representative 3D data models are proposed. However, existing models neglect the display result and the consumption of storage space. Based on the analysis of existing 3D GIS data model, a 3D surface model is proposed for fast visualization in this paper, which is composed of node, segment and triangle. The data structure and formal representation of the proposed 3D surface model is developed to organize and store data of 3D model. Finally, an experiment is made to compare this 3D surface model with other 3D data model, and the result demonstrates that the 3D surface model proposed in this paper is superior to the existing data model in terms of data volume, moreover, it can acquire fast visualization speed.

With the rise of technologies of VR technology, AR technology, and 3D printing, the application of 3D models has become more and more extensive. The data of the 3D model is the floating point and has a unique storage format, and the traditional 2D image encryption algorithms are unsuitable for 3D models. Therefore, based on 1D Sine-Map-Coupling-Logistic-Map (1D-SMCLM), a 3D model encryption algorithm is designed in this paper. The 1D-SMCLM is a new chaotic system with large parameter space and good chaotic characteristics. The keystream generated by the 1D-SMCLM has good randomness and is very suitable for cryptographic systems. In the new encryption algorithm (SMCLM-3ME), the vertices of the 3D models are divided into integer and decimal vertices. The integer part is encrypted by the strategy of simultaneous scrambling and diffusion. The 3D ciphertext model is obtained by combining the integer and fractional parts. Experimental results show that the SMCLM-IE exhibits excellent performance.

This chapter presents the implementation of a system that visualizes the construction process using 3D modeling data and schedule data to analyze construction planning. Previous papers have emphasized the benefits of visual 4D planning that combines 3D modeling data and process schedule data for work progress control. The proposed methodology offers rapid visualization of work performance with scheduled activity and facilitates construction planning and schedule inspection. Consequently, it should increase productivity and reduce rework. However, even major construction companies will not adopt such a work style, because the existing, well-organized way of working would not be readily changed unless the new style is proven to afford benefits that outweigh the effort and cost required to adapt to the style. The advanced CAD system, for example, is able to simulate the assembly process, and the advanced 3D graphic designer is able to animate the arrangement of objects. Even though each software provides multiple functionalities, the applications in practical use are all independent and specific, such as CAD for designing 3D models, and a project manager for scheduling and analyzing. Therefore, a system that integrates all outputs from each application is required to move from the conventional work style to the new one. This chapter, thus, aims to develop a system that integrates several types of data and enables the simulation of the construction progress by gradually showing 3D models according to the activity schedule. It is also possible to attach material data to each object and to display related information like cost and object properties. The system assumes the following requirements: (1) to import and display the 3D modeling data, (2) to import the project schedule, (3) to link each model and activity, (4) to give the material data on each object to enhance reality, and (5) to show cost accumulation. These functions are supposed to be realized such that the system utilizes the resources previously reserved. Therefore, the system should be able to import a DXF format file for 3D modeling data and access the MDB format database for the project schedule, including costs. The MDB file is originally a database that Microsoft Access creates. Microsoft Project, which is probably the most widely used software for project management, is also able to export the project data in this format. These functions are implemented with Microsoft Visual C++ and DirectX SDK. Although the system displays inaccurate models partially because of the misinterpretation of the DXF file, all of the demands listed above are satisfied currently. The authors of this chapter are now at the stage of implementation of further functions, that is, to display not only structures but also other elements such as the temporal space on the site, the route of delivery vehicles, and the work area of the temporally used heavy machinery, all for the sake of the visualization and analysis of the entire construction site.

Objective To explore the application of the renal calculus 3D model based on CT in percutameous nephrolithotomg (PCNL). Methods From July 2016 to September 2016, twelve patients were diagnosed with renal calculus and underwent PCNL in Shenzhen Baoan District People's Hospital. The mean age was 39 (range: 28-46) years old. Among them, there were 5 patients with left renal calculus and 7 patients with right renal calculus. The maximum diameter of the renal calculus was between 2.3 to 4.2 cm, and the mean diameter was 3.2 cm according to the CT scan. The CT image data collected from these patients would be divided and rebuilt its 3D model by using the 3D rebuilding software MIMICS 17.0, to build a personalized 3D model of the renal calculus. Before the surgery, 4 surgeons would design a specific surgery strategy according to the 3D model of the renal calculus and combine it with the iconography materials. During the surgery, they would record the practical operating data, and fill the appraisal questionnaire of the 3D models after the surgery to appraise the availability of the 3D model. As for the patients and their families, the 3D renal calculus model was used as a tool for communication before the surgery, and they were invited to fill the appraisal questionnaire. Results The 3D renal calculus models of these 12 patients were successfully constructed. The models clearly showed the size and location of the renal calculus, the inner renal collecting system, renal pelvis, vein and the interrelationship with the adjacent anatomical structures. The virtual puncture point and the renal calyx of puncture were basically corresponded to the actual operation. The puncture depth of virtual operations was (6.6±0.8) cm, while the puncture depth of actual operations was (7.1±0.7) cm, and there was no significant difference (P>0.05). The mean evaluation score of the models given by surgeons was (8.5±0.6). And the mean score of the doctor-patient conversation before operation given by the patients or their family members was (8.4±0.7). Conclusions The 3D renal calculus models based on CT can clearly reflect not only the size and location of renal calculus, but also the relationship between the kidney and adjacent anatomical structures.The models also play a very important role in preoperative planning and operative process. Meanwhile, the models could be useful in the doctor-patient consultation. Key words: Renal calculi; 3D technology; Percutaneous nephrostomy; Preoperative planning

Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Japan Organization of Occupational Health and Safety onbehalf J3D Introduction Improvements in cardiovascular imaging have contributed to the accurate diagnosis and treatment of cardiovascular diseases. In recent years, we can find a new demand from surgical departments, i.e., 3D heart models of patients for preoperative simulations. However, a 3D surgical model needs a detailed segmentation of heart structures from clinical images because these details are essential for achieving accurate polygon data of an exact 3D model. Thus, in most cases, manual segmentation is required for complicated heart shapes such as the chamber wall, valves, and papillary muscles, which causes a prolonged duration time. Purpose We aim to achieve an automated heart segmentation using a convolutional neural network (CNN) trained using deep learning techniques for the rapid creation of 3D surgical heart models of individual patients. Methods We constructed our original CNN program based on the latest artificial intelligence techniques and trained it to extract shapes of the heart from cardiac computed tomography (CT) images. The training data was 361 slices selected from CT scans of 10 patients. We used data augmentation to increase the amount and diversity of the data into 24,052 slices. The training result with the best Intersection over Union (IoU), one of the evaluation metrics used in deep learning, was saved. Finally, we used the best-trained CNN to construct 3D polygon data of two surgical cases of hypertrophic obstructive cardiomyopathy (HOCM) for preoperative assessments. Results The IoU attained 0.85 after deep learning. The time required to complete the 3D polygon data for the first HOCM case was 5 minutes for segmentation by the trained CNN and 3 hours for data correction by a human operator. Similarly, the time required for the second case was 5 minutes for segmentation without manual correction. We had to correct the segmentation for the first case because we needed an exact 3D model for the preoperative assessment (Fig. 1). According to our records of the other eight 3D heart models in the lab, the work for a 3D polygon shape from CT images needs a median 30 hours (quartiles 23-50 hours) when the procedure is fully manual and non-continuous with breaks in between. Conclusion The CNN-based segmentation aided the constructing heart shapes from cardiac CT images of preoperative patients. Although the performance, reaching IoU of 0.85, was insufficient for fully automatic segmentation, the methodology can shorten the process duration from several hours to several minutes for detailed segmentation of heart structures. We previously applied the CNN-based segmentation to the aorta, aortic stenosis valves, and atheromatous plaque in clinical images, demonstrating adequate segmentation performance. The proposed methodology can be applied as a fundamental technology of cardiovascular imaging for obtaining the actual structures of a target object as 3D coordinate values or a 3D model within a reasonable duration time. Abstract Figure 1

The 3D image construction for the 2D images is a longstanding problem, explored by the number of computer graphics, computer vision, and machine learning research organizations from decades. After the evolution of deep learning architectures, scholars have shown their interest in developing 3D images from the 2D greyscale or RGB images because this perspective shows a significant influence in the discipline of computer vision. The applications of this conversion found in various fields of medical image analysis, robotic vision, game design, lunar explorations, 3D modelling, military, geographical structuring, physics, support models, etc. Potentially, when a 2D image converted to 3D representation, the same image can be viewed from different angles and directions. The 3D structure, which in turn generated, is much more informative than 2D images as it contains information about distance from the camera to a particular object. In this paper, we discussed various exiting methods for generating 3D representations from 2D images, using 3D representational data as well as without 3D representational data and proposing a novel approach for the construction of 3D models from existing 2D images using GAN. Generative adversarial networks (GANs) have shown tremendous results in generating new fake data from existing data, where we cannot detect the false data. Various other architectures of GAN, like HOLO-GAN, IG-GAN, have also been proposed to meet the need to convert 2D to 3D representation, which produced excellent results. After analysing, we provide an extensive comparative review on methods and architectures, which can convert 2D images to 3D objects and express our thoughts on the ideas proposed. Further, the concept of GAN extended to represent 360 view images, panorama images in 3D structures, which plays a vital role in spherical view analysis and synthesis, virtual reality design, augmented reality design, 3D modelling of data, etc.

Restricted by the environment and hardware equipment resources, existing chaotic systems have shortcomings such as low complexity, low randomness, and chaotic degradation phenomena, which in turn cause the security risks of chaotic image encryption algorithms. To overcome these issues, this paper proposes a method for the construction of a LE-controlled four-dimensional (4D) non-degenerate discrete chaotic system. Numerical analysis has demonstrated that the developed system possess high complexity and unpredictability. Based on the developed chaotic system, an image compression encryption algorithm is proposed. Wherein, semi-tensor product compressed sensing is applied to allow data compression sampling in different dimensions resulting in reducing the data transfer load and storage cost. Subsequently, the positions and values of the image pixels are secretly altered during the algorithmic encryption process using two-dimensional cat confusion and finite field diffusion. The simulation results show that the proposed encryption algorithm effective enough to offer great encryption quality. The performance comparison analysis indicates that the proposed encryption algorithm has good furnishes better security, compression, as well as resistance to diverse data attacks.

Audio encryption has gained popularity in a variety of fields including education, banking over the phone, military, and private audio conferences. Data encryption algorithms are necessary for processing and sending sensitive information in the context of secure speech conversations. In recent years, the importance of security in any communications system has increased. To transfer data securely, a variety of methods have been used. Chaotic system-based encryption is one of the most significant encryption methods used in the field of security. Chaos-based communication is a promising application of chaos theory and nonlinear dynamics. In this research, a chaotic algorithm for the new chaotic chameleon system was proposed, studied, and implemented. The chameleon chaotic system has been preferred to be employed because it has the property of changing from self-excited (SA) to hidden-attractor (HA) which increases the complexity of the system dynamics and gives strength to the encryption algorithm. A chaotic chameleon system is one in which, depending on the parameter values, the chaotic attractor alternates between being a hidden attractor and a self-excited attractor. This is an important feature, so it is preferable to use it in cryptography compared to other types of chaotic systems. This model was first implemented using a Field Programmable Gate Array (FPGA), which is the first time it has been implemented in practical applications. The chameleon system model was implemented using MATLAB Simulink and the Xilinx System Generator model. Self-excited, hidden, and coexisting attractors are shown in the proposed system. Vivado software was used to validate the designs, and Xilinx ZedBoard Zynq-7000 FPGA was used to implement them. The dynamic behavior of the proposed chaotic system was also studied and analysis methods, including phase portrait, bifurcation diagrams, and Lyapunov exponents. Assessing the quality of the suggested method by doing analyses of many quality measures, including correlation, differential signal-to-noise ratio (SNR), entropy, histogram analysis, and spectral density plot. The numerical analyses and simulation results demonstrate how well the suggested method performs in terms of security against different types of cryptographic assaults.

The analysis of potentially chaotic behavior in biological and biomedical phenomena has attracted increased attention in recent years. Chaotic systems exhibit a trademark “sensitive dependence on initial conditions” (SDIC), meaning that very small perturbations will cause the resulting trajectories to separate exponentially fast. Lyapunov exponents directly measure SDIC by quantifying the exponential rates at which neighboring orbits on an attractor diverge (or converge) as the system evolves in time. Dissipative deterministic systems that exhibit at least one positive Lyapunov exponent are by definition “chaotic.” Hence, Lyapunov exponents are considered one of the most useful diagnostic tools available for analyzing potentially chaotic dynamical systems. However, Lyapunov exponents are also notoriously difficult to estimate reliably from experimental data and one should always be very skeptical about accepting any claims of “chaos” based solely on findings of positive Lyapunov exponents alone. This article reviews the basic definitions of Lyapunov exponents and outlines several of the more widely disseminated algorithms available to estimate them from experimental time series data. Critical issues related to finding spurious exponents, analyzing noisy signals, and validating against surrogate data are reviewed. A brief discussion of how Lyapunov exponents have been applied to study a wide variety of biomedical and biological phenomena is also presented. Finally, some thoughts on appropriate interpretations of such findings and the future directions for using Lyapunov exponents to study potentially chaotic systems are presented.

Digital images can be large in size and contain sensitive information that needs protection. Compression using compressed sensing performs well, but the measurement matrix directly affects the signal compression and reconstruction performance. The good cryptographic characteristics of chaotic systems mean that using one to construct the measurement matrix has obvious advantages. However, existing low-dimensional chaotic systems have low complexity and generate sequences with poor randomness. Hence, a new six-dimensional non-degenerate discrete hyperchaotic system with six positive Lyapunov exponents is proposed in this paper. Using this chaotic system to design the measurement matrix can improve the performance of image compression and reconstruction. Because image encryption using compressed sensing cannot resist known- and chosen-plaintext attacks, the chaotic system proposed in this paper is introduced into the compressed sensing encryption framework. A scrambling algorithm and two-way diffusion algorithm for the plaintext are used to encrypt the measured value matrix. The security of the encryption system is further improved by generating the SHA-256 value of the original image to calculate the initial conditions of the chaotic map. A simulation and performance analysis shows that the proposed image compression-encryption scheme has high compression and reconstruction performance and the ability to resist known- and chosen-plaintext attacks.

Three-dimensional (3D) laser scanning is a nondestructive and versatile technique that provides archaeologists with 3D models of archaeological and ethnographic objects. We have previously shown that 3D models facilitate shape analysis of archaeological bones and stone tools, due to the high measurement accuracy inherent in the latest generation of 3D laser scanners. Here, we explore the utility of 3D modeling as a tool for analyzing Viking Age metal artefacts with complex morphologies. Four highly ornate Viking Age brooches from Scandinavia and Russia were digitized with a portable laser scanner, and the resulting 3D models were used in three case studies of (a) artefact reconstruction, (b) tool mark analysis, and (c) motif documentation. The results revealed both strengths and limitations of the employed techniques. 3D modeling proved to be very well suited for artefact reconstruction and was helpful also in the stylistic and motif analysis. The tool mark analysis was only partially successful, due to the resolution limits of the laser scanner used. 3D-based motif analysis of a grandiose Scandinavian-style brooch from Yelets, Russia, identified an anthropomorphic figure with a bird-like body that previously has been overlooked. This figure may be a Rurikid coat of arms, possibly linking the object to a princely household and providing further evidence for a connection between Scandinavia and the Rurikids. As 3D technology keeps improving, we expect that additional applications for 3D modeling in archaeology will be developed, likely leading to many new findings when old objects are re-analyzed with modern techniques. However, our results indicate that 3D modeling cannot completely replace traditional artefact analysis—instead, we argue that the two approaches are best used in combination.

Background: The scarcity of 3D facial models presents a significant hurdle for researchers and educators. Gathering such data demands substantial resources. Objective: To introduce an open-source 3D morphing software to generate 3D facial data sets for research and to provide a large sample data set that is based on synthetically generated 3D models. Methods: Software is developed to morph 3D facial models in bulk by altering landmark locations. Twenty synthetic 3D facial models are generated utilizing deep learning tools and 28 landmarks located on each. The measurements of synthetic models are confirmed to be realistic by comparing them with facial statistics. Several facial deformities and types are simulated at various magnitudes on 3D models to generate a large data set. Results: An open-source software and an open-access data set of 980 3D facial models, each with 28 landmark locations, are provided. Since the data set is based on synthetically generated 3D models, no institutional review board approval is required. Conclusion: The 3D morphing software and the large 3D data set are expected to benefit researchers and educators in the field of facial surgery and facial landmark detection.

Chaotic sequences and chaotic dynamical systems are attractive candidates for use in signal synthesis and analysis as well as in communications applications. In previous works, various methods for the estimation of chaotic sequences under noise were developed. However, although the methods were different, their qualitative performance was the same: for high signal-to-noise ratio (SNR) the performance was good, but below some threshold SNR, a sharp degradation in performance occurred. We prove and quantify the existence of this threshold effect and derive lower bounds on the value of the threshold SNR. Using information-theoretic tools, we prove that for any ergodic chaotic system, there is a certain threshold SNR level below which the ratio between the mean-square error obtained by any estimator of the system's initial state at the output of additive white Gaussian noise (AWGN) channel and the Cramer-Rao bound increases exponentially fast as the number of observations N grows without bound. We explain the connection between the existence of a threshold effect in the estimation of chaotic sequences and the converse to the joint source-channel coding theorem. We derive lower bounds on SNR/sub th/, the value of the threshold SNR, as a function of the system's Lyapunov exponent. Our bounds have two versions, one for a finite number of observations, and one for the asymptotic regime as N/spl rarr//spl infin/. For the asymptotic regime, the bound we develop on the threshold SNR incorporates the key parameters of chaotic systems, the Lyapunov exponent, and the chaotic sequence power spectrum into a simple formula. We demonstrate our results on the chaotic system governed by the r-diadic map.

Background/Objectives: The possibilities of endovascular and minimally invasive treatment of visceral and renal artery aneurysms require precise procedure planning. Accurate visualization of vascular pathologies is crucial in this regard. Expanding diagnostic imaging with real 3D models is one of these methods. The objective of our study was to evaluate the utility of 3D printing and virtual 3D models in visualizing visceral and renal artery aneurysms. Methods: A group of 30 patients with true aneurysms of the visceral and renal arteries was selected based on computed tomography angiography (CTA). Aneurysm morphology, diameters, arterial diameters, and anatomical vessel variants were analyzed. Imaging data were processed and 3D-printed using Fused Filament Fabrication (FFF) technology. The resulting 3D models were measured, and dimensional deviations were compared to radiological images. Results: The cohort included 51 aneurysms across arteries supplying abdominal organs, with splenic artery aneurysms (49%) and renal artery aneurysms (25.5%) predominating. Half of the patient group had multiple aneurysms, and 36.7% exhibited anatomical arterial variants. Forty-three 3D models of visceral and renal artery aneurysms were generated, accurately depicting isolated vascular pathologies and the course of visceral arteries in regions of interest. Measurement analysis confirmed that the 3D-printed models showed a mean dimensional deviation of 0.24 mm compared to radiological images. Conclusions: 3D-printed and virtual models enhance the analysis of vascular pathologies, significantly improving the assessment of pathological changes and visualization of anatomical details, especially in hilar aneurysms and aneurysm branches.