Transformation Matrix Research Articles

Assessment of the accuracy of the modal-domain line models with real and frequency-independent transformation matrix for electromagnetic transient simulation in lines with underbuilt cables

This paper investigates the potential inaccuracies associated with using a modal-domain model featuring a real and frequency-independent transformation matrix in simulating electromagnetic transients on power overhead lines equipped with underbuilt wires. Three transmission line configurations are examined: one without underbuilt wires, one with a single underbuilt wire, and another with two underbuilt wires. Comparisons between the simulated voltages computed using the modal-domain approach and a more accurate phase-domain model as a reference reveal that the modal-domain approach leads to inaccuracies in the simulated voltages. These inaccuracies intensify with an increase in the number of underbuilt wires and the soil resistivity. Differences of up to approximately 24 % in the peak values of the simulated voltages were observed, considering the transmission line geometry analyzed in this study. Based on these findings, it is recommended that for simulating transients in overhead lines with installed underbuilt wires, phase-domain models, such as the Universal Line Model, should be employed.

Advanced stability assessment for control systems: Gelfand’s method with iterated Shanks transformation

ABSTRACT In this paper, we present an efficient method for evaluating the stability of both linear time-invariant (LTI) systems and continuous-time nonlinear systems. Our approach centres around the modulus of the spectral radius derived from a specific matrix transformation of the original system state matrix in the case of LTI systems, or the Jacobian of the nonlinear system. We enhance computational speed by employing the iterated Shanks transformation on Gelfand’s formula, eliminating the need to rely on the system’s characteristic equation. Notably, our stability criterion avoids the computation of all eigenvalues of the system matrix. Illustrative examples, including a fluidised bed reactor, a continuous stirred tank reactor and a hypothetical 20th-order dynamical system, are provided. Additionally, we conduct a CPU-time analysis, revealing that our method demonstrates several orders of magnitude faster computational speed compared to the classical Routh-Hurwitz stability test for systems ranging from two to 80 dimensions.

Colorimetric characterization of multispectral imaging systems for visualization of historical artifacts

Multispectral imaging has long been used in the study of cultural heritage artifacts, particularly manuscripts with low-contrast text. The goal of multispectral imaging systems has largely been to enhance the text by imaging outside the spectral response of the human visual system and by applying various image processing techniques. However, there is a desire to use multispectral images for high quality, true color rendering of the artifacts as well. We use two multispectral imaging systems in this work. One of them is the Multispectral Imaging System for the study of Historical Artifacts (MISHA) which has been demonstrated to have significant utility at a lower cost and better ease of use compared to commercially available systems. This system was specifically designed and built for the purpose of imaging artifacts, particularly manuscripts and works on paper with illegible text due to deterioration and other environmental causes. Given this goal of “discovering” low-contrast text (and other features) on planar objects, the system has not been optimized for color reproduction purposes. The second system is a commercially available MegaVision EVTM Spectral Imaging System. This system is also used for cultural heritage imaging. In this work, we develop an approach to colorimetrically characterize these cameras and illumination systems, adding an option for accurate color reproduction to their capabilities. To do this, we present two different approaches. The first approach we call the Color Transformation Method. Here we use a Macbeth ColorChecker to first linearize the camera photometric response, and then find a linear matrix transformation from the camera response to CIEXYZ tristimulus values. The second approach we call the Illuminant Method. Here we consider each output by the camera as a reflectance factor value at the known wavelength of a narrowband illuminator. We then convert these values to the color of the object in terms of CIEXYZ tristimulus values. We show that the Color Transformation Method using a Macbeth ColorChecker leads to a more accurate result. Meanwhile, the Illuminant Method is much simpler and produces acceptable results, particularly considering accurate color reproduction was not a goal in designing the MISHA camera system. The same methods are applied to both imaging systems to show the robustness and applicability of the approaches developed herein to other imaging systems. Results are shown for both quantitative calibration targets as well as artifacts of interest to the field of cultural heritage studies. It is worth noting that we are not comparing the two imaging systems overall or the color processing software available from MegaVision. Rather, we simply show that the two approaches developed here are applicable to different multispectral imaging systems, from a very cost-effective system, i.e., MISHA to a more expensive one, i.e., MegaVision.

Reconfigurable Intelligent Surface Assisted Wireless Network Rate Optimization Design

To enhance the communication rate, we deploy one active reconfigurable intelligent surface (RIS) and one passive RIS to cooperatively assist the downlink communication, while meeting the constraints of base station transmission power, total Active RIS power, and RIS unit modulus constraint. Due to the nonlinear coupling of multiple variables in the objective function, non-convexity is caused. Therefore, an alternating optimization algorithm (AO) is proposed, which uses semi-definite programming (SDP) based algorithm to optimize the transmit beamforming vector, ARIS reflection matrix, and PRIS diagonal phase shift matrix. The simulation results have verified the effectiveness of the proposed algorithm, which indicates that optimizing the coefficients can significantly improve the spectrum efficiency of wireless networks.

A new novel recognition and positioning system of black light-absorbing volute for automation depalletizing development

Abstract In the application of vision measurement, the black light-absorbing object is difficult to reflect the structured light from infrared emitter of the RGB-D camera. Therefore, an image recognition algorithm based on reference environment information is proposed to acquire the spatial positioning information of black volutes in the depalletizing system. The hardware system of the depalletizing system is mainly constructed of an upper computer, a six-axis industrial robot, an RGB-D camera and an end adsorption device. Firstly, the horizontal position information of each volute placed on the cardboard is obtained by the depth differences between the cardboard and the volute. Then, the depth information of the volute is obtained by the upper cardboard depth through collecting the position of the end vacuum suction cup triggered by feedback signal from vacuum generator. Secondly, a regional planar hand-eye calibration method is developed to improve the calibration accuracy in two-dimensional coordinates. The regional calibration method divides the robot working area into four regions: upper left, lower left, upper right, and lower right. The transformation matrix of each region is calculated separately. Finally, the depalletizing experiment is conducted on the three types of volutes. It is concluded that the average positioning error of the grasping center point of each volute obtained by our method is 3.795 mm, and its standard deviation is 1.769 mm. The average value of regional planar hand-eye calibration error is 4.044 mm, and its standard deviation is 1.501 mm. Under a stack of materials with dimensions of 1350 mm × 1350 mm × 1500 mm, the maximum error is controlled within 15 mm. Additionally, when combined with the end feedback compensation mechanism, the success rate for grasping all three volutes reaches 100%.

Model Establishment and Parameter Identification of Remotely Operated Vehicle (ROV)

Abstract To study the dynamic characteristics of remotely operated vehicle (ROV) underwater motion, a transformation matrix between the carrier coordinate system and the inertial coordinate system was established, and the rigid body dynamics model of the ROV was derived by using the Newton-Euler equation. According to the results of the ROV experimental prototype in this article, the dynamic model is simplified based on the force, motion characteristics, and structural characteristics. In the pool experiment, based on the propeller dynamic model, the least squares method was used to identify the parameters of the heading and vertical dynamic models, and through the experimental results under step control signals of different amplitudes, they were compared with the models under the same control signal. The output is subjected to error analysis. The experimental results show that the error between the theoretical value and the experimental value is small, verifying the accuracy of the ROV dynamics model established in this article.

Joint Anchor Graph Embedding and Discrete Feature Scoring for Unsupervised Feature Selection.

The success of existing unsupervised feature selection (UFS) methods heavily relies on the assumption that the intrinsic relationships among original high-dimensional (HD) data samples exist in the discriminative low-dimension (LD) subspace. However, previous UFS methods commonly construct pairwise graphs and employ l2,1 -norm regularization to severally preserve the local structure and calculate the score of features, which is computationally complex and easy to get stuck into local optimum, so that those approaches cannot be applied in dealing with large-scale datasets in practice. To overcome this challenge, we propose a novel UFS method, in which a novel anchor graph embedding paradigm is designed to extract the local neighborhood relationships among data samples by reducing the computational complexity of graph construction to be linear in the number of data. Moreover, to improve the optimality of selected features as well as the performance of downstream tasks, we propose a discrete feature scoring mechanism, which imposes orthogonal l2,0 -norm constraints on learned projections, in order to enhance the distinction of feature scores as well as reduce the probability of falling into local optimum. In addition, solving the proposed nonconvex and nonsmooth NP-hard problem is challenging, and we present an efficient optimization algorithm to address it and acquire a closed-form solution of the transformation matrix. Extensive experiments demonstrate the effectiveness and efficiency of the proposed UFS by comparison with several state-of-the-art approaches to clustering and image segmentation tasks.

Anomaly Detection in Dynamic Graphs: A Comprehensive Survey

This survey paper presents a comprehensive and conceptual overview of anomaly detection using dynamic graphs. We focus on existing graph-based anomaly detection (AD) techniques and their applications to dynamic networks. The contributions of this survey paper include the following: i) a comparative study of existing surveys on anomaly detection; ii) a D ynamic G raph-based A nomaly D etection ( DGAD ) review framework in which approaches for detecting anomalies in dynamic graphs are grouped based on traditional machine-learning models, matrix transformations, probabilistic approaches, and deep-learning approaches; iii) a discussion of graphically representing both discrete and dynamic networks; and iv) a discussion of the advantages of graph-based techniques for capturing the relational structure and complex interactions in dynamic graph data. Finally, this work identifies the potential challenges and future directions for detecting anomalies in dynamic networks. This DGAD survey approach aims to provide a valuable resource for researchers and practitioners by summarizing the strengths and limitations of each approach, highlighting current research trends, and identifying open challenges. In doing so, it can guide future research efforts and promote advancements in anomaly detection in dynamic graphs.

Aerodynamic modeling and performance analysis of model predictive controller for fixed wing vertical takeoff and landing unmanned aerial vehicle

This research focuses mainly on the aerodynamic modelling and performance analysis of a model predictive controller for a hybrid fixed-wing vertical takeoff and landing of unmanned aerial vehicle. The aerodynamics, which comprises several aerodynamic characteristics including the lift, drag, and thrust coefficients, is modelled using Newton’s second law of motion. The force and moment equations were obtained, and they were then converted into matrix and state equation form, together with the transformation matrices. The essential equations with six degrees of freedom (6 DoF) and 12 state matrices including the control input and manipulating variables were obtained. The proposed model predictive controller (MPC) was designed using the optimal model predictive controller design parameters, such as a sampling time of 0.1, a prediction horizon of 15, a control horizon of 3 and additional controller settings. With a settling period of 3 s, an overshoot of 0.5865, and a steady state inaccuracy of 0.00785, the MATLAB simulation demonstrates that the system variables, including roll, pitch, and yaw, are stabilized. This MPC control is more effective in anticipating and optimizing the UAV than other control strategies. Eventually, the controller’s simulation on MATLAB Simulink demonstrates the controller’s ability to stabilize and control the system in a real-time application. Autonomous vertical takeoff and landing operations depend heavily on the mathematical model and architecture of the flight controller. Among the uses are inspection, monitoring, and rescue.

Nonlinear dynamical modeling and response analysis of complex structures based on assumed mode weighting

The assumed mode method (AMM) is widely used for dynamical modeling but faces increasing challenges in developing low-dimensional and high-precision models of complex structures for investigations of nonlinear dynamical responses and active vibration control. This is mainly because the current complex structure has an increasing number of components, each of which needs to be discretized by multiple assumed modes in dynamical modeling, leading to increasing degrees of freedom for the system. To address this issue, this paper proposes a dimension-reduced method based on the assumed mode weighting method, referred to as Mode Weighting Method (MWM). The modeling process of a multi-beam structure is fully presented as an example to show the strategy of the MWM. During this process, the assumed mode (AM) model (the dynamic model derived in terms of a set of assumed modes) is developed first where the joints of the structure between its components are considered as artificial springs and each component is discretized by its vibration modes without constraints. The components of the eigenvector of the characteristic equation derived from the AM model are taken as the weighting coefficients to get the approximate analytical global modes of the structure by multiplying the corresponding assumed modes. Consequently, a nonlinear dynamic model with a few degree of freedom is obtained by discretizing the structure with the weighted modes. Moreover, a technique for simply obtaining the weighted mode (WM) models by directly processing the AM model through a transformation matrix is presented. To validate the proposed method, the simulations on the natural characteristics and the dynamical responses for both linear and nonlinear models, are performed respectively based on the WM and the finite element models, where both results are well matched each other. This work provides a new way of developing advanced dynamical modeling methods for structural dynamics design, optimization, and active vibration control.

Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19

The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the Markov transfer field and deep learning to predict the fault location, which can accurately predict the location of a single-phase ground fault in the distribution network. First, a new phase-mode transformation matrix is used to take the fault current of the distribution network as the modulus 1 component, avoiding complex calculations in the complex field; then, the extracted modulus 1 component of the current is transformed into a Markov transfer field and converted into an image using pseudo-color coding, thereby fully exploiting the fault signal characteristics; finally, the Darknet-19 network is used to automatically extract fault features and predict the distance of the fault occurrence. Through simulations on existing models and training and testing with a large amount of data, the experimental results show that this method has good stability, high accuracy, and strong anti-interference ability. This solution can effectively predict the distance of ground faults in distribution networks.

Performance of a vectorcardiographic deep learning algorithm compared to single-lead and 12-lead ECG for atrial flutter detection: implications for wearable devices

Abstract Background Wearable devices have developed the capacity to offer single-lead ECG analysis and are already widespread in use, particularly for detection of atrial fibrillation (1,2). Vectorcardiography entails the transformation of electrical vectors into X, Y, and Z axes to constitute a vectorcardiogram (VCG) (3). The VCG has diagnostic promise given the capacity to generate it without requiring clinical presentation for standard 12-lead ECG acquisition. Furthermore, a novel credit-card sized device capable of acquiring a VCG has recently been developed – raising the potential for arrhythmia detection and discrimination beyond wearable single-lead ECG systems. Objective To assess the performance of a deep learning algorithm trained on single lead and 12-lead ECGs and VCGs for detecting atrial flutter. Methods A convolutional neural network (AFL-CNN) was developed on a dataset of 4,000 ECGs and VCGs from a single US academic medical institution (80% training and 20% for testing). Validation was performed on a separate publicly available dataset (2223 ECGs). Model performance was assessed on single lead ECG analysis (lead I, obtained by removing the other leads) and standard 12-lead ECG. The gold standard for AFL diagnosis was physician-confirmed 12-lead ECG diagnoses in the dataset. The VCG was generated from the 12-lead ECG via Kors transformation matrix to which AFL-CNN was also applied. Model performance was assessed with sensitivity, specificity, positive and negative predictive values, accuracy, and F1 statistic. Results On single lead ECG analysis, AFL-CNN achieved 71.2% sensitivity and 96.9% specificity with PPV 84.0% and NPV 93.7% and 92.2% accuracy with F1 83.2%. When AFL-CNN was applied to the VCG, performance improved: 91.0% sensitivity and 98.7% specificity, PPV 94.24%, NPV 98.0%, 97.3% accuracy, and F1 95.31%. Lastly, the performance remained high when AFL-CNN was applied to the 12-lead ECG: 95.4% sensitivity, 98.63% specificity, PPV 94.0%, NPV 99.0%, and accuracy of 98.0%, with F1 97.2%. Conclusion A deep learning algorithm applied to vectorcardiography for the detection of atrial flutter showed improved performance over single lead analysis and comparable performance to standard 12-lead applications. The finding of improved model performance beyond single lead ECG tracings underscores both the limitations of current wearable technologies in arrhythmia detection beyond atrial fibrillation and the potential for VCG-based algorithm prediction to fill gaps in healthcare inequality when standard 12-lead acquisition is challenging to obtain.

The electrical substrate of vectorcardiographic QRS area in cardiac resynchronization therapy: insights from ECG-imaging

Abstract Background The QRS area shows promise as a vectorcardiographic (VCG) marker for electrical dyssynchrony in patient selection for cardiac resynchronization therapy (CRT). Nevertheless, little information is known about the underlying cardiac electrical substrate it may reflect. Purpose The aim of this study was to compare the VCG QRS area with measures of cardiac electrical dyssynchrony derived from electrocardiographic imaging (ECG-imaging). Methods QRS area from the synthesized VCG (Kors transformation matrix from the 12-lead ECG) was computed for 27 patients who underwent ECG-imaging (11 patients with no history of structural heart disease and 16 consecutive CRT patients). ECG-imaging measures for electrical dyssynchrony analyzed were: ventricular electrical uncoupling (VEU) computed as the difference between the average LV and RV activation times from ECG-imaging, standard deviation of activation times from the left ventricle (LV SDAT, and the maximal activation (Max AT). Pearson correlation analyses between VCG QRS area and ECG-imaging measures of dyssynchrony were carried out for the entire cohort and for patients with a wide-QRS only. Results ECG-imaging measures of dyssynchrony compared with QRS area in 11 patients with no structural heart disease with narrow-QRS (blue) and 16 candidates for CRT with heart failure (HF) and predominantly wide-QRS (LBBB [n=6], IVCD [n=8], RVpaced [n=1], and nQRS-HF [n=1]) are provided in Figure 1. QRS area were associated with all ECG-imaging measures of dyssynchrony. However, sub-analysis in only the wide-QRS patients revealed that QRS area exhibited the strongest association with VEU. Conclusion Vectorcardiographic QRS area is associated with cardiac electrical dyssynchrony on ECG-imaging, exhibiting a particularly strong association with ventricular electrical uncoupling.

Robust feature selection via central point link information and sparse latent representation

Before conducting unsupervised feature selection, it is usually assumed that these data are independent of each other. On the contrary, real data will influence each other. Therefore, traditional feature selection methods may lose information related to each other between data. This can lead to inaccurately generated pseudo-label information and may result in poor feature selection results. To find solutions to this issue, this paper proposes robust feature selection via central point link information and sparse latent representation (CPSLR). Firstly, structure a link graph by calculating the center matrix to store the distance information from the sample to the center point. If two samples have similar distances to the center point, it can be determined that they belong to the same class. Therefore, the similarity between samples is preserved, and more accurate pseudo-label information is obtained. Secondly, CPSLR uses data graph and link graph to form a dual graph structure. It can not only retain the link information between samples but also retain the manifold structures of the samples. Then, CPSLR saves the interconnection contents between samples by sparse latent representation. That is, the constraint l2,1-norm is exerted on the expression of latent representation, and sparse non-redundant interconnection information is preserved. And by combining central point link information with sparse latent representation makes the interconnections between data reserved more comprehensive. That is to say, the pseudo-labels obtained are more like the real labels of the classes. Finally, CPSLR constrains the feature transformation matrix by l2,1/2-norm constraint so as to select robust and sparse features. CPSLR uses l2,1/2-norm constraint to assure that the feature transformation matrix is sparse, selecting more discriminative features, thereby obtaining the feature selection that can improve its efficiency. The experiments demonstrate that the clustering result of CPSLR outperform six classical or latest compared algorithms on eight datasets.

Simulated augmented reality-based calibration of optical see-through head mound display for surgical navigation.

Calibration of an optical see-through head-mounted display is critical for augmented reality-based surgical navigation. While conventional methods have advanced, calibration errors remain significant. Moreover, prior research has focused primarily on calibration accuracy and procedure, neglecting the impact on the overall surgical navigation system. Consequently, these enhancements do not necessarily translate to accurate augmented reality in the optical see-through head mount due to systemic errors, including those in calibration. This study introduces a simulated augmented reality-based calibration to address these issues. By replicating the augmented reality that appeared in the optical see-through head mount, the method achieves calibration that compensates for augmented reality errors, thereby reducing them. The process involves two distinct calibration approaches, followed by adjusting the transformation matrix to minimize displacement in the simulated augmented reality. The efficacy of this method was assessed through two accuracy evaluations: registration accuracy and augmented reality accuracy. Experimental results showed an average translational error of 2.14mm and rotational error of 1.06° across axes in both approaches. Additionally, augmented reality accuracy, measured by the overlay regions' ratio, increased to approximately 95%. These findings confirm the enhancement in both calibration and augmented reality accuracy with the proposed method. The study presents a calibration method using simulated augmented reality, which minimizes augmented reality errors. This approach, requiring minimal manual intervention, offers a more robust and precise calibration technique for augmented reality applications in surgical navigation.

Integrated Multi-fidelity Structural Optimization for UAV Wings

The paper explores comprehensive Unmanned Aerial Vehicle (UAV) wing optimization, integrating aerodynamic and structural techniques. A detailed comparison between the base and optimized modules while considering specifications such as composite material orientation, spar and rib material, deformation, stress, strain, safety factor, and weight. The methodology utilizes material changes for wing weight reduction while maintaining structural integrity. The optimized module (Case 7) balances weight reduction, safety, and structural performance, notably shifting from Al 7075 to Al 2024. Structural optimization focuses on changes in Carbon Fiber/Epoxy orientation, leveraging material changes for weight reduction. Constitutive equations and transformation matrices calculate stiffness matrices for the laminate, resulting in a robust wing. This holistic optimization combines low and high-fidelity techniques, addressing UAV wing structural aspects. Outcomes include weight reduction, deformation minimization, fluttering modal deformation and buckling points, an increased factor of safety, and improved strength-to-weight ratio. The research significantly advances aerospace engineering, particularly in UAV design and optimization.

Formation mechanism and elimination method of joint defects in lattice structures printed by metal droplet deposition manufacturing

Benefiting from the dense inner microstructure and smooth bead-like surface morphology, metal droplet deposition manufacturing (MDDM) is advantageous in manufacturing small metallic lattice structures, which have great potential in advanced aerospace, energy, and robotics applications. However, the improper top-layer droplet distance leads to joint defects in the printed lattices and deteriorates the forming accuracy. Moreover, if the substrate temperature and inclined angle increase, joint defects may occur even if the top-layer droplets are at an ideal distance due to droplet oscillation. This work proposes a novel method to eliminate joint defects by re-placing the joint region droplets to an ideal center distance. To this end, the dynamics of top-layer droplets (impact, spread, recoil, and oscillation) were investigated by combining morphology analysis and high-speed photography (Ts = 673 K, θ = 45°). A theoretical model was established to obtain the parameter range of defect-free joints through a series of bifurcate lattice printing experiments. Then, the morphology and generation mechanism of cumulative joint defects in the lattices at higher temperatures and angles (Ts = 773 K, θ = 60°) were analyzed. A parameter window of joint morphology with different values of inclined angle, substrate temperature, and top-layer droplet distance was established for predicting potential defects in the printing trace. Next, a novel method was proposed to remove redundant droplets and re-place top-layer droplet coordinates into the ideal center distance. Furthermore, the printing strategy was expanded to multi-truss lattices using three-dimensional transformation matrixes. Finally, multiple cells bifurcate, triangular, and pyramid lattices were successfully printed with perfect joints. The height deviation of joint regions is <5 % of the droplet diameter, verifying the feasibility of the proposed strategy. This work represents a further step towards MDDM printing of high-quality truss lattice structures and other complex 3D architectures.

GraM: Geometric Structure Embedding into Attention Mechanisms for 3D Point Cloud Registration

3D point cloud registration is a crucial technology for 3D scene reconstruction and has been successfully applied in various domains, such as smart healthcare and intelligent transportation. With theoretical analysis, we find that geometric structural relationships are essential for 3D point cloud registration. The 3D point cloud registration method achieves excellent performance only when fusing local and global features with geometric structure information. Based on these discoveries, we propose a 3D point cloud registration method based on geometric structure embedding into the attention mechanism (GraM), which can extract the local features of the non-critical point and global features of the corresponding point containing geometric structure information. According to the local and global features, the simple regression operation can obtain the transformation matrix of point cloud pairs, thereby eliminating the semantics that ignores the geometric structure relationship. GraM surpasses the state-of-the-art results by 0.548° and 0.915° regarding the relative rotation error on ModelNet40 and LowModelNet40, respectively.

A novel calibration method based on fixed multi-plane targets for cameras with large field of view

For camera calibration with large field of view (FOV), the accuracy is impacted by the size of the calibration targets. Considering the challenges associated with producing and carrying large targets, existing methods resort to using small targets to achieve calibration without high accuracy. In order to solve this problem, a photogrammetric calibration method based on the fixed multi-plane targets (FMPT) is proposed in this paper for cameras with large FOV. The FMPT is composed by multiple identical small planar targets (PTs), with fixed pose transformation relationships among PTs. The proposed calibration method involves the following main steps. Firstly, the camera is moved several times to capture a series of images of FMPT. Secondly, the fixed pose transformation matrices of different small PTs in FMPT are calculated through the initial parameters of camera. Finally, calibration based on photogrammetry is conducted, with a continuous optimization of camera intrinsic and extrinsic parameters based on the multiple 2D and 3D constraint in the FMPT. Simulation and real data experiments show that the calibration accuracy using the proposed method is much higher than that using a small target, and sightly higher than that using a large target. Furthermore, the experiments demonstrate the robust stability of this method, maintaining high calibration accuracy even in the presence of increased noise and error in target production.

Cryptanalysis and construction of keyed strong S-Box based on random affine transformation matrix and 2D hyper chaotic map

As the only nonlinear module, S-Box (substitution box) is widely used in stream cipher, hash function, and so on. However, there exist some weaknesses in existing S-Boxes, such as fixed point, reverse fixed point and short iteration period rings. Furthermore, the S-Boxes in AES and SM4.0 were fixed, which makes them vulnerable to attack. To address these weaknesses, first, a non-degenerate 2D chaotic map (2D-NDCM) with ergodicity in phase space was constructed. Second, based on 2D-NDCM, the seed S-Boxes with high nonlinearity were constructed in batches by affine transformation and Boolean function, and then three possible weaknesses were eliminated to obtain strong keyed S-Boxes. Statistical analysis demonstrated the effectiveness and practicability of the proposed S-Box batch generating algorithm.