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The rapid development of renewable energy presents challenges to the security and stability of power systems. Aiming at addressing the power system scheduling problem with load demand and wind power uncertainty, this paper proposes the establishment of different error fuzzy sets based on the Wasserstein probability distance to describe the uncertainties of load and wind power separately. Based on these Wasserstein fuzzy sets, a distributed robust chance-constrained scheduling model was established. In addition, the scheduling model was transformed into a linear programming problem through affine transformation and CVaR approximation. The simplex method and an improved differential evolution algorithm were used to solve the model. Finally, the model and algorithm proposed in this paper were applied to model and solve the economic scheduling problem for the IEEE 6-node system with a wind farm. The results show that the proposed method has better optimization performance than the traditional method. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the Zuker RNA folding algorithm, a bioinformatics task with non-serial polyadic dynamic programming and non-uniform loop dependencies. The intricate dependence pattern is represented using affine formulas, enabling the automatic application of tiling strategies via the polyhedral method. Three source-to-source compilers—PLUTO, TRACO, and DAPT—are employed, utilizing techniques such as affine transformations, the transitive closure of dependence relation graphs, and space–time tiling to generate cache-efficient codes, respectively. A dedicated transpose code technique for non-serial polyadic dynamic programming codes is also examined. The study evaluates the performance of these optimized codes for speed-up and scalability on multi-core machines and explores energy efficiency using RAPL. The paper provides insights into related approaches and outlines future research directions within the context of bioinformatics algorithm optimization. [ABSTRACT FROM AUTHOR]

To study the fruit image fusion technology in the case of thermal infrared and visible heterogeneous sources and the method of online defect detection on fruit fusion images, this paper takes apple as the research object and proposes a registration algorithm for thermal infrared and visible images of apple based on the registration of feature points with an active contour model. First, by designing a thermal infrared and visible image acquisition system, thermal infrared and visible images of apple in the same scene are obtained simultaneously. Then, the improved Chan-vese model is adopted to obtain the active contour segmentation curves of the thermal infrared and visible images of apple respectively. Next, the average Euclidean distance of all adjacent edge points on the active contour segmentation curve is calculated, and the alignment feature point set is constructed by the linear interpolation method based on the obtained average distance, and the optimal scale transformation factor and the optimal horizontal transformation factor are obtained by calculating the partial Hausdorff distance between the two feature point sets. Finally, the registered visible image is acquired based on the obtained affine transformation matrix, thus realizing the registration of the thermal infrared and visible images of apple. The experimental results on the self-built image dataset indicate that the algorithm proposed in this paper can accurately match the heterogeneous images of intact fruit, calyx/stems, and defective fruit, and it performs excellently in terms of precise matching rate and root mean square error, and the high alignment success rate of 96%. Also, it has much better performance than other methods. The proposed registration algorithm can accurately match thermal infrared and visible images of apple, and lays the foundation for further research on the image fusion of thermal infrared and visible, apple surface defect detection, as well as the construction of online dual-light apple grading systems. [ABSTRACT FROM AUTHOR]

With the three-dimensional (3D) geological information system development, 3D geological cross-sections (GCs) have become the primary data for geological work and scientific research. Throughout past geological surveys or research works, a lot of two-dimensional (2D) geological cross-section maps have been accumulated, which struggle to meet the scientific research and application needs of 3D visual expression, 3D geological analysis, and many other aspects. Therefore, this paper proposes an automatic generation method for 3D GCs by increasing the dimensions based on a digital elevation model (DEM) and 2D geological cross-section maps. By matching corresponding nodes, generating topographic feature lines, constructing an affine transformation matrix, and inferring the elevation value of each geometric node on the GC, the 3D transformation of the 2D GCs is realized. In this study, fourteen 2D GCs within Nanjing City, Jiangsu Province, are transformed into 3D GCs using the proposed method. The transformed results and quantitative error show that: (1) the proposed method applies to both straight and bent GCs; (2) each transformed GC can fit seamlessly with the ground and maintain minimal geometric deformation, and the geometric shape is consistent with the original GC in non-mountains area. This paper corroborated the proposed method's effectiveness by comparing it with the other two 3D transformation strategies. In addition, the transformed GCs can be subjected to 3D geological modeling and digital Earth presentation, achieving positive effects in both 3D application and representation. [ABSTRACT FROM AUTHOR]

Automatic target recognition technology is an important research direction in the field of machine vision. Artificial ground targets, such as bridges, airports and houses, are mostly composed of straight lines. The ratio of geometric primitive lines to the triangle area formed by their combination is used as the feature quantity to describe the group of lines, so as to characterize the artificial ground target. In view of the shortcomings of traditional edge detection methods, such as background suppression, non-prominent targets, missing positions, etc., this paper proposed an image edge detection method based on depth learning. By combining the traditional edge detection algorithm with the edge detection algorithm based on an improved HED network, the real-time target image edge detection was completed. An automatic target recognition method based on template matching was proposed. This method solved the problem of both homologous template matching and heterogeneous template matching, which has important theoretical value. First, the lines were combined to form the geometric primitives of the line group, and then the relationship of the lines in the group was determined by using the characteristic quantity of the line group. The best line group matching the target template was found in the image edge, and the homonymous points in the real-time image and the target template were calculated. The affine transformation matrix between the two images was obtained according to the homonymous points, and then the accurate position of the target in the real-time image was found. [ABSTRACT FROM AUTHOR]

Hysteresis loops constitute the source of important information for the designers of magnetic circuits in power transformers. The paper focused on the possibility to interpret the phenomenological T(x) model in terms of effective field vs. magnetization dependence. The interdependence of anhysteretic curve and hysteresis loops was emphasized. The concept of the anhysteretic plane introduced at the end of the last century by Sablik and Langman was subject to a tangible interpretation within the hyperbolic model framework. A novel geometric interpretation of the "effective field" related to the concept of affine transformation was introduced. It was shown in the paper that minor hysteresis loops of grain-oriented electrical steel may be described with the proposed formalism. [ABSTRACT FROM AUTHOR]

Local features of an image provide a robust way of image matching if they are invariant to large variations in scale, viewpoint, illumination, rotation, and affine transformations. In this paper, we propose a novel feature descriptor based on circular and elliptical local sampling of image pixels to attain fast and robust results under varying imaging conditions. The proposed descriptor is tested on a standard benchmark dataset comprising of images with varying imaging conditions and compression quality. Results show that the proposed method generates sufficient or more number of stable and correct matches between an image pair (original image and distorted image) as compared to SIFT with a speedup of 1.6 on average basis. The paper also discusses the reason of choosing SIFT descriptor for comparison and its efficacy in different scenarios. The paper also reasons the robustness of hand crafted feature descriptors and why they hold an upper hand among many other deep learning methods. [ABSTRACT FROM AUTHOR]

The novelty of this work is that it is the first to introduce complex-valued suprametric spaces and apply it to Fractal Generation and mixed Volterra–Fredholm Integral Equations. In the realm of fuzzy logic, complex-valued suprametric spaces provide a robust framework for quantifying the similarity between fuzzy sets; for instance, utilizing a complex-valued suprametric approach, we compared the similarity between fuzzy sets represented by complex-valued feature vectors, yielding quantitative measures of their relationships. Thereafter, we establish related fixed point results and their applications in algorithmic and numerical contexts. The study then delves into the generation of fractals, exemplified by the Barnsley Fern fractal, utilizing sequences of affine transformations within complex-valued suprametric spaces. Moreover, this article presents two algorithms for soft computing and fractal generation. The first algorithm uses complex-valued suprametric similarity for fuzzy clustering, iteratively assigning fuzzy sets to clusters based on similarity and updating cluster centers until convergence. The distinctive pattern of the Barnsley Fern fractal is produced by the second algorithm's repetitive affine transformations, which are chosen at random. These techniques demonstrate how well complex numbers cluster and how simple procedures can create complicated fractals. Moving beyond fractal generation, the paper addresses the solution of mixed Volterra–Fredholm integral equations in the complex plane using our results, demonstrating numerical illustrations of complex-valued integral equations. [ABSTRACT FROM AUTHOR]

In this paper, we examined the codimension-two bifurcation analysis of a two-dimensional discrete epidemic model. More precisely, we examined the codimension-two bifurcation analysis at an endemic equilibrium state associated with 1: 2, 1: 3 and 1: 4 strong resonances by bifurcation theory and series of affine transformations. Finally, theoretical results were carried out numerically. [ABSTRACT FROM AUTHOR]

Sugarcane stem node identification is the core technology required for the intelligence and mechanization of the sugarcane industry. However, detecting stem nodes quickly and accurately is still a significant challenge. In this paper, in order to solve this problem, a new algorithm combining YOLOv3 and traditional methods of computer vision is proposed, which can improve the identification rate during automated cutting. First, the input image is preprocessed, during which affine transformation is used to correct the posture of the sugarcane and a rotation matrix is established to obtain the region of interest of the sugarcane. Then, a dataset is built to train the YOLOv3 network model and the position of the stem nodes is initially determined using the YOLOv3 model. Finally, the position of the stem nodes is further located accurately. In this step, a new gradient operator is proposed to extract the edge of the image after YOLOv3 recognition. Then, a local threshold determination method is proposed, which is used to binarize the image after edge extraction. Finally, a localization algorithm for stem nodes is designed to accurately determine the number and location of the stem nodes. The experimental results show that the precision rate, recall rate, and harmonic mean of the stem node recognition algorithm in this paper are 99.68%, 100%, and 99.84%, respectively. Compared to the YOLOv3 network, the precision rate and the harmonic mean are improved by 2.28% and 1.13%, respectively. Compared to other methods introduced in this paper, this algorithm has the highest recognition rate. [ABSTRACT FROM AUTHOR]

Aiming at the operation and maintenance requirements of the fault location of high-temperature superconducting cables, a fault location method of high-temperature superconducting cables based on the improved time-frequency domain reflection method and EEMD noise reduction is proposed. Considering the cross-term interference problem in the traditional time-frequency domain reflection method, this paper introduces the affine transformation to project the time-frequency distribution of the self-term and the cross term and further highlights the characteristic differences between the two through coordinate transformation, and the particle swarm algorithm is employed to solve the optimal stagger angle of the affine transformation. The unscented particle filter is adopted to separate the cross term, and EEMD noise reduction is introduced to solve the signal noise problem. Finally, two software programs, PSCAD and MATLAB, are employed for joint simulation to build a model of high-temperature superconducting cable. The simulation example shows that the proposed method in this paper can eliminate the cross-term interference of the traditional time-frequency domain reflection method, effectively locate the fault of the high-temperature superconducting cable, and improve the positioning accuracy. [ABSTRACT FROM AUTHOR]

Distributed time-varying formation technology for multi-agent systems is recently become a research hotspot in formation control field. However, the formation reconfiguration control technology for agents that randomly appeared to fail during maneuvers is rarely studied. In this paper, the topological relations between intelligence are designed by graph theory to simplify the cooperative interaction between multi-agent systems. Moreover, this paper constructs the time-varying configuration of the target formation based on the rigidity graph theory and leader–follower strategy. Drawing on the establishment of the expert experience database in a collaborative process, we innovatively propose the establishment of a graphic library to help the multi-agent system quickly form an affine transformation as soon as it is disabled. Secondly, the improved Hungarian algorithm is adopted to allocate the target point when the first failure occurs. This algorithm incorporates a gradient weighting factor from the auction algorithm to improve the speed of system reconfiguration with minimum path cost. On this basis, a distributed multi-agent control law based on consistency theory is established, and the system's stability can be guaranteed via Lyapunov functions. Finally, the simulation results demonstrate the feasibility and effectiveness of the proposed formation reconfiguration control algorithm in a collaborative environment. [ABSTRACT FROM AUTHOR]

Due to the inherent periodicity of the angle, the direction of the object detected by the current rotating object detection algorithm is fuzzy. In order to solve this problem, this paper proposes a rotating object direction estimation method based on a neural network, which determines the unique direction of the object by predicting the direction vector of the object. Firstly, we use the two components (sin θ , cos θ) of the direction vector and the length and width parameters of the object to express the object model. Secondly, we construct a neural network model to predict the parameters used to express the object model. However, there is a constraint that the sum of the squares of the two components of the direction vector of the object is equal to 1. Because each output element of the neural network is independent, it is difficult to learn the constrained data between such neurons. Therefore, the function transformation model is designed, and the network transformation layer is added. Finally, affine transformation is used to transform the object parameters and carry out regression calculation, so as to detect the object and determine the direction of the object at the same time. This paper uses three sets of data to carry out the experiment, which are DOTA 1.5, HRSC, and UCAS-AOD data sets. It can be seen from the experimental results that for the object with correct ground truth, the proposed method can not only locate the object but also estimate the direction of the object accurately. [ABSTRACT FROM AUTHOR]

Collaborative robots face barriers to widespread adoption due to the complexity of programming them to achieve human-like movement. Learning from demonstration (LfD) has emerged as a crucial solution, allowing robots to learn tasks directly from expert demonstrations, offering versatility and an intuitive programming approach. However, many existing LfD methods encounter issues such as convergence failure and lack of generalization ability. In this paper, we propose: (1) a generative adversarial network (GAN)-based model with multilayer perceptron (MLP) architecture, coupled with a novel loss function designed to mitigate convergence issues; (2) an affine transformation-based generalization method aimed at enhancing LfD tasks by improving their generalization performance; (3) a data preprocessing method tailored to facilitate deployment on robotics platforms. We conduct experiments on a UR5 robotic platform tasked with handwritten digit recognition. Our results demonstrate that our proposed method significantly accelerates generation speed, achieving a remarkable processing time of 23 ms, which is five times faster than movement primitives (MPs), while preserving key features from demonstrations. This leads to outstanding convergence and generalization performance. [ABSTRACT FROM AUTHOR]

One of the classical problems of optimization theory in a finite-dimensional space is to find a minimum of a function on a nonempty set. Usually, finding the precise solution to this task analytically requires a lot of computational resources or is even impossible at all. So, approximate methods are used most often in practical cases. One of the simplest and the most well-known among such approximate methods for unconditional optimization is the method of gradient descent; its generalization for conditional optimization was found in 1964, the method of projected gradient. For some simple sets (line segment, parallelepiped, ball), the projection of the point on the set can be easily found by an explicit formula. However, for more complicated sets (e.g., an ellipse), projecting becomes a separate task. Nevertheless, sometimes computing projection can be simplified by affine transform; e.g., an ellipse can be transformed into a ball by affine (moreover, by linear) transformation. The paper aims to simplify the problem of minimizing function on the set by changing the condition set by affine transform F(x)= Ax+b, where A is a non-degenerated square matrix, and b is a fixed vector of proper dimension. [ABSTRACT FROM AUTHOR]

In this paper, we determine the set S of straight lines L 0 that have intersections with four given distinct lines L 1 , ... , L 4 in R 3. If any two of the four given lines are skew, i.e., not co-planar, Bielinski and Lapinska used techniques in projective geometry to show that there are either zero, one, or two elements in the set S. Using linear algebra techniques, we determine S and show that there are no, one, two or infinitely many elements L 0 in S , where the last case was overlooked in the earlier paper. For the sake of completeness, we provide a comprehensive determination of all the elements L 0 in S if at least two of the four given lines are co-planar. In this scenario, there may also be zero, one, two, or infinitely many solutions. [ABSTRACT FROM AUTHOR]

In recent years, augmented reality has emerged as an emerging technology with huge potential in image-guided surgery, and in particular, its application in brain tumor surgery seems promising. Augmented reality can be divided into two parts: hardware and software. Further, artificial intelligence, and deep learning in particular, have attracted great interest from researchers in the medical field, especially for the diagnosis of brain tumors. In this paper, we focus on the software part of an augmented reality scenario. The main objective of this study was to develop a classification technique based on a deep belief network (DBN) and a softmax classifier to (1) distinguish a benign brain tumor from a malignant one by exploiting the spatial heterogeneity of cancer tumors and homologous anatomical structures, and (2) extract the brain tumor features. In this work, we developed three steps to explain our classification method. In the first step, a global affine transformation is preprocessed for registration to obtain the same or similar results for different locations (voxels, ROI). In the next step, an unsupervised DBN with unlabeled features is used for the learning process. The discriminative subsets of features obtained in the first two steps serve as input to the classifier and are used in the third step for evaluation by a hybrid system combining the DBN and a softmax classifier. For the evaluation, we used data from Harvard Medical School to train the DBN with softmax regression. The model performed well in the classification phase, achieving an improved accuracy of 97.2%. [ABSTRACT FROM AUTHOR]

Political and ideological online teaching is a new thing integrating political and ideological theory teaching and artificial intelligence information technology. In order to effectively improve the quality of political and ideological online teaching, this paper aims to make an in-depth study on the application of artificial intelligence technology in the political and ideological online classroom system under the background of big data. This paper first expounds the overall architecture of the political and ideological online system from the system hardware architecture and software architecture and then discusses in detail the functional design of the online system and the structure of the database. This paper proposes a data mining algorithm based on scale affine transformation and curriculum data information space reconstruction, constructs the data structure model of the political and ideological online classroom system, uses scale affine transformation to fuse the curriculum data, carries out spatial reconstruction of the fused curriculum data, extracts the high-order features of the curriculum data in the political and ideological online classroom system in the reconstructed space, and completes the data mining of political and ideological courses. Experiments show that the political and ideological online classroom system under the proposed method is better than the political and ideological online classroom system under the traditional method in terms of user satisfaction and teacher operation proficiency and can effectively improve the quality of political and ideological online teaching. [ABSTRACT FROM AUTHOR]

Diffuse reflection occurs when light, further waves, or particles are reflected off a surface in such a way that a ray encountered on the surface is dispersed at several angles instead of one as in specular reflection. With the rapid development of modern industry, irregular diffuse reflection may appear due to the particles on the surface, such as frosted paint surfaces, making the defect information easy to be covered up by scattered light, preventing the defect from being detected, and reducing detection accuracy. Therefore, this paper aims to deeply explore the diffuse reflection surface defect detection approach based on machine learning. The Gray code and four-step phase shift technique are used to resolve the absolute phase of a reflection image while the image defect is determined by converting the absolute phase's gradient. The automatic edge finding algorithm is then used to obtain the image vertex of the sample to be measured, the affine transformation is used for attitude correction, and the module matching approach is used to locate the diffuse reflection surface defect. The gray morphological opening and closing operation is used for the original image to obtain the morphology and position information of the defect. Finally, Adam optimizer is chosen as the gradient descent algorithm's optimizer. Experimental results show that the proposed approach can effectively improve the accuracy of diffuse reflection surface defect detection and reduce the economic cost of defect detection, which has a certain practical value. [ABSTRACT FROM AUTHOR]

Due to the increasing progress of digital technology in recent years, the protection, inheritance, development, and utilization of historical and cultural resources have become more and more integrated into it, and it has become an ideal way. At present, many countries and regions have carried out relevant research projects. With the change of the living conditions of cultural heritage and the rapid development of sci & tech, many traditional protection methods are no longer suitable and need to be replaced by new protection methods. Digital protection technology is one of the fastest developing and most concerned. With the help of virtual reality (VR) technology, it presents the essence of Chinese culture for thousands of years and comprehensively combines the characteristics of all viewpoint with traditional Chinese culture. Select the representative Chinese traditional culture as the research direction, and analyze the cultural characteristics contained in the Chinese traditional culture. In this paper, Harris operator is used to extract the feature points of the images, and two registered images are calculated according to the affine transformation coefficient. The images after affine transformation are bilinear interpolated and weighted smoothed according to the adjacent position of the overlapping area to realize the seamless mosaic design of the images and the forward and backward projection algorithm of cylindrical panoramic images. And study the artistic expression of 3D panoramic image acquisition. Based on the analysis and experiment, it can be obtained that after calculation, the average spatial data acquisition efficiency can reach 61.8%. In practice, the average display effect can reach 60.4%. [ABSTRACT FROM AUTHOR]

In computer vision, pollutant detection is a highly concerning issue, and it has been widely used in the fields of pollutant identification, tracking, and precise positioning. In the ocean, oil tends to disperse into the water column as droplets under breaking waves, and it is called sunken and submerged oil. Aiming at the most difficult issue of identifying liquid submerged oil pollution, this paper proposes a method of synthesized data containing specific markers for oil detection. The Canny operator was used to remove the background of the liquid submerged oil. Then, affine transformation was applied to simulate the real situation of oil deformation. Linear mapping was presented by matrix multiplication, and translation was represented by vector addition. At last, bilinear interpolation was used to integrate the oil into the image of the laboratory pictures. In addition, this research randomly added interference information, so that the probability distribution of synthesized data was closer to the probability distribution of the real data. Then, this paper combined various methods to improve the accuracy of liquid oil detection, such as Feature Pyramid Networks, RoIAlign, difficult sample mining. Based on the above methods, 1838 images were synthesized in this paper and combined into a training set. The results show that the average accuracy of the oil detection is increased by 79.72%. The accuracy of the synthesized data method for labeled oil detection was 18.56% higher than that of oil detection without labeling. This research solves the difficulty of obtaining sunken and submerged oil images and the high cost of image annotation. [ABSTRACT FROM AUTHOR]

It is efficient to replace human eyes with underwater vehicles equipped with visual sensors to carry out underwater inspections. However, the inability of monocular vision to provide accurate depth information highlights the importance of binocular vision in underwater target detection and measurement. In this paper, an ROV (Remotely Operated Vehicles) based binocular vision system incorporating a specially designed underwater robot is developed to carry out underwater structure detection in real-time. The system is designed to adapt to long-distance and long-duration underwater missions in various underwater environments. Taking cracks as inspection targets, a crack detection and measurement approach is proposed after the robot's surface cleaning function is applied. Firstly, an affine transformation model is used to enhance the color-distorted underwater images effectively. Then, the multi-directional gray-level fluctuation analysis is applied to acquire an accurate crack segmented result. Finally, the computed disparity map is combined with the segmentation map to determine the crack width quickly. A group of experiments is performed and the validity and effectiveness of the system and crack measurement algorithm are demonstrated. [ABSTRACT FROM AUTHOR]

The Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites have acquired many GF-1 and GF-6 wide-field-view (WFV) images. These images have been made available for free use globally. The GF-1 WFV (GF-1) and GF-6 WFV (GF-6) images have rational polynomial coefficients (RPCs). In practical applications, RPC corrections of GF-1 and GF-6 images need to be completed using the rational function model (RFM). However, can the accuracy of the rational function model satisfy practical application requirements? To address this issue, a geometric accuracy method was proposed in this paper to evaluate the accuracy of the RFM of GF-1 and GF-6 images. First, RPC corrections were completed using the RFM and refined RFM, respectively. The RFM was constructed using the RPCs and Shuttle Radar Topography Mission (SRTM) 90 m DEM. The RFM was refined via affine transformation based on control points (CPs), which resulted in a refined RFM. Then, an automatic matching method was proposed to complete the automatic matching of GF-1/GF-6 images and reference images, which enabled us to obtain many uniformly distributed CPs. Finally, these CPs were used to evaluate the geometric accuracy of the RFM and refined RFM. The 14th-layer Google images of the corresponding area were used as reference images. In the experiments, the advantages and disadvantages of BRIEF, SIFT, and the proposed method were first compared. Then, the values of the root mean square error (RSME) of 10,561 Chinese, French, and Brazilian GF-1 and GF-6 images were calculated and statistically analyzed, and the local geometric distortions of the GF-1 and GF-6 images were evaluated; these were used to evaluate the accuracy of the RFM. Last, the accuracy of the refined RFM was evaluated using the eight GF-1 and GF-6 images. The experimental results indicate that the accuracy of the RFM for most GF-1 and GF-6 images cannot meet the actual use requirement of being better than 1.0 pixel, the accuracy of the refined RFM for GF-1 images cannot meet practical requirement of being better than 1.0 pixel, and the accuracy of the refined RFM for most GF-6 images meets the practical requirement of being better than 1.0 pixel. However, the RMSE values that meet the requirements are between 0.9 and 1.0, and the geometric accuracy can be further improved. [ABSTRACT FROM AUTHOR]

With the rapid development of network and communication technology, the interaction of various information data is more and more frequent, and people pay more and more attention to information security. The information encryption algorithm is a research hotspot in the field of information security. The Advanced Encryption Standard (AES) algorithm has been widely used in the field of information security with its high security and encryption efficiency. This paper mainly introduces the optimization of the AES-128 encryption algorithm of the security layer in ZigBee networking of the Internet of Things. By analyzing the principles of ZigBee networking and the AES encryption algorithm, the changes are optimized. In this paper, the new S-box cryptographic properties are used after analysis and calculation. The affine transformation period, the number of iteration cycles, and the algebraic expression of the S-box are improved. Its cryptographic properties are better than the S-box of the original algorithm, and the security of the algorithm is improved. In the theory of column hybrid algorithm, the computational complexity is reduced by changing the fixed polynomial, and the efficiency of the column hybrid algorithm is improved. In this paper, the performance of the improved AES algorithm is tested. The results show that, in the power consumption curve experiment, the recovery success rate of the original algorithm is about 42%, and the recovery success rate of the improved algorithm is nearly 60%. The improved algorithm is faster than the original algorithm in achieving a recovery success rate of 100%. Experimental results show that the design can accurately complete the encryption and decryption of the AES algorithm, and the performance is better than the original algorithm, which proves the overall superiority of the algorithm. [ABSTRACT FROM AUTHOR]

An efficient sign language recognition system (SLRS) can recognize the gestures of sign language to ease the communication between the signer and non-signer community. In this paper, a computer-vision based SLRS using a deep learning technique has been proposed. This study has primary three contributions: first, a large dataset of Indian sign language (ISL) has been created using 65 different users in an uncontrolled environment. Second, the intra-class variance in dataset has been increased using augmentation to improve the generalization ability of the proposed work. Three additional copies for each training image are generated in this paper, by using three different affine transformations. Third, a novel and robust model using Convolutional Neural Network (CNN) have been proposed for the feature extraction and classification of ISL gestures. The performance of this method is evaluated on a self-collected ISL dataset and publicly available dataset of ASL. For this total of three datasets have been used and the achieved accuracy is 92.43, 88.01, and 99.52%. The efficiency of this method has been also evaluated in terms of precision, recall, f-score, and time consumed by the system. The results indicate that the proposed method shows encouraging performance compared with existing work. [ABSTRACT FROM AUTHOR]

Before neural networks, image style transfer procedures had a common idea: analyze images with a certain style, build a mathematical or statistical model for the style, and then change the image to be transferred, so that it better fits the established model. In this paper, k-means and semantic closed natural matting algorithm is combined for image segmentation, the style and content in the image are extracted based on neural network, and the resulting image is synthesized by image reconstruction technology to realize the migration of national costume styles. Due to the serious artifacts of the output image, an improved image style transfer algorithm is adopted to constrain the transformation from the input image to the output image in the local affine transformation of the color space, and this constraint is expressed as a completely differentiable parameter term, image distortion is suppressed effectively. In the process of real photo style transfer, there is also space inconsistency. Smoothing is done to ensure that the space style is consistent after style processing, it greatly speeds up the operation speed. On NVIDIA GTX1080TI graphics card, algorithm is tested with 256×256 resolution images. It includes three indicators of average running time, memory usage and the number of styles generated by a single model, which are 0.06 s, 136.06 MB and 1 respectively. These indicators can reflect the efficiency and flexibility of the algorithm. [ABSTRACT FROM AUTHOR]

The primary source of food is extracted from the plant. Take care of and maintain the plants in real-time to enhance human survival. Diseases in plants can directly lead to a reduction in the industrial economy. Automatic detection of plant diseases is crucial for effective disease control. Vitis vinifera (Grapes) is an essential crop with rich vitamin C nutrients. This paper targets to classify the major diseases in Vitis vinifera leaves using Capsule Network (CapsNet), that prevails over the significant CNN limitations by discarding the pooling layers and adding capsule layers. Dynamic routing techniques of CapsNet make are more robust for the affine transformation of the leaves dataset. It is capable of learning large datasets effectively with vital image transformations such as rotations and transitions. Implementing CapsNet for Vitis vinifera leaf disease classification, which utilizes dynamic routing between capsules, is a novel method. The proposed CapsNet for disease classification is trained with augmented and non-augmented datasets. The performance metrics highlight that the proposed method can effectively classify the Vitis vinifera plant leaves with 98.7% of validation accuracy. The results highlight that the proposed model performs well in detecting and classifying the diseases of Vitis vinifera plant leaves on two benchmark datasets. [ABSTRACT FROM AUTHOR]

Diagnosing some eye pathologies, such as diabetic retinopathy (DR), depends on accurately detecting retinal eye lesions. Automatic lesion-segmentation methods based on deep learning involve heavy-weight models and have yet to produce the desired quality of results. This paper presents a new deep learning method for segmenting the four types of DR lesions found in eye fundus images. The method, called LezioSeg, is based on multi-scale modules and gated skip connections. It has three components: (1) Two multi-scale modules, the first is atrous spatial pyramid pooling (ASPP), which is inserted at the neck of the network, while the second is added at the end of the decoder to improve the fundus image feature extraction; (2) ImageNet MobileNet encoder; and (3) gated skip connection (GSC) mechanism for improving the ability to obtain information about retinal eye lesions. Experiments using affine-based transformation techniques showed that this architecture improved the performance in lesion segmentation on the well-known IDRiD and E-ophtha datasets. Considering the AUPR standard metric, for the IDRiD dataset, we obtained 81% for soft exudates, 86% for hard exudates, 69% for hemorrhages, and 40% for microaneurysms. For the E-ophtha dataset, we achieved an AUPR of 63% for hard exudates and 37.5% for microaneurysms. These results show that our model with affine-based augmentation achieved competitive results compared to several cutting-edge techniques, but with a model with much fewer parameters. [ABSTRACT FROM AUTHOR]

This paper presents a novel S-box generation scheme to enhance the security of the Advanced Encryption Standard（AES） algorithm. After analyzing the existing issues with S-boxes, a new S-box is constructed based on the principles of S-box construction and its cryptographic properties. This is achieved by selecting new irreducible polynomials and affine transformations while adjusting the order of affine transformations and multiplicative inversion operations. The newly generated S-box is compared with the S-box of AES and other improved S-boxes in terms of algebraic terms and strict avalanche criteria distance. The results demonstrate that the new S-box exhibits superior algebraic properties and effectively defends against algebraic attacks. Furthermore, the new S-box is subjected to hardware design and optimization. The DC synthesis results reveal that the optimized implementation of the new S-box in the finite field consumes 12% fewer resources compared to traditional finite field implementations and 41% fewer resources compared to lookup table methods. This paper substantiates the superiority of the new S-box in terms of security over the existing S-boxes. It also applies the new S-box to AES software and hardware designs, with the correctness of the implementation verified through simulation testing. [ABSTRACT FROM AUTHOR]

Capsule networks (CapsNets) are new neural networks that classify images based on the spatial relationships of features. By analyzing the pose of features and their relative positions, it is more capable of recognizing images after affine transformation. The stacked capsule autoencoder (SCAE) is a state-of-the-art CapsNet that achieved unsupervised classification of CapsNets for the first time. However, the security vulnerabilities and the robustness of the SCAE have rarely been explored. In this paper, we propose an evasion attack against SCAE, where the attacker can generate adversarial perturbations by reducing the contribution of the object capsules related to the original category of the image in the SCAE. Adversarial perturbations are then applied to the original images, and the perturbed images are misclassified with a high probability. For such an evasion attack, we further propose a defense method called hybrid adversarial training (HAT), which makes use of adversarial training and adversarial distillation to achieve better robustness of SCAE against the evasion attack. We evaluate the defense method and the experimental results show that the SCAE trained with HAT ensures that the model can maintain relatively high classification accuracy under the evasion attack and achieve similar classification accuracy to that of the original SCAE model on clean samples. The source code is available at https://github.com/FrostbiteXSW/SCAE_Defense. [ABSTRACT FROM AUTHOR]

This paper investigates the accuracy of convolutional neural network recognition of Laguerre and Hermite-Gauss optical modes with geometric distortions in the form of affine transformations. The influence of training data sampling on the accuracy is analyzed. The ability of a convolutional neural network to recognize Laguerre and Hermite-Gaussian optical modes with geometric distortions caused by environmental distortions and described by affine transformations was also examined. [ABSTRACT FROM AUTHOR]

This paper presents a deep architecture, called pyramidal semantic correspondence networks (PSCNet), that estimates locally-varying affine transformation fields across semantically similar images. To deal with large appearance and shape variations that commonly exist among different instances within the same object category, we leverage a pyramidal model where the affine transformation fields are progressively estimated in a coarse-to-fine manner so that the smoothness constraint is naturally imposed. Different from the previous methods which directly estimate global or local deformations, our method first starts to estimate the transformation from an entire image and then progressively increases the degree of freedom of the transformation by dividing coarse cell into finer ones. To this end, we propose two spatial pyramid models by dividing an image in a form of quad-tree rectangles or into multiple semantic elements of an object. Additionally, to overcome the limitation of insufficient training data, a novel weakly-supervised training scheme is introduced that generates progressively evolving supervisions through the spatial pyramid models by leveraging a correspondence consistency across image pairs. Extensive experimental results on various benchmarks including TSS, Proposal Flow-WILLOW, Proposal Flow-PASCAL, Caltech-101, and SPair-71k demonstrate that the proposed method outperforms the lastest methods for dense semantic correspondence. [ABSTRACT FROM AUTHOR]

In close-range or unmanned aerial vehicle (UAV) photogrammetry, Schneider concentric circular coded targets (SCTs), which are public, are widely used for image matching and as ground control points. GSI point-distributed coded targets (GCTs), which are only mainly applied in a video-simultaneous triangulation and resection system (V-STARS), are non-public and rarely applied in UAV photogrammetry. In this paper, we present our innovative detailed solution to identify GCTs. First, we analyze the structure of a GCT. Then, a special 2D P2-invariant of five coplanar points derived from cross ratios is adopted in template point registration and identification. Finally, the affine transformation is used for decoding. Experiments indoors—including different viewing angles ranging from 0° to 80° based on 6 mm-diameter GCTs, smaller 3 mm-diameter GCTs, and different sizes mixed—and outdoors with challenging scenes were carried out. Compared with V-STARS, the results show that the proposed method can preserve the robustness and achieves a high accuracy rate in identification when the viewing angle is not larger than 65° through indoor experiments, and the proposed method can achieve approximate or slightly weaker effectiveness than V-STARS on the whole. Finally, we attempted to extend and apply the designed GCTs in UAV photogrammetry for a preliminary experiment. This paper demonstrates that GCTs can be designed, printed, and identified easily through our method. It is expected that the proposed method may be helpful when applied to image matching, camera calibration, camera orientation, or 3D measurements or serving as control points in UAV photogrammetry for scenarios with complex structures in the future. [ABSTRACT FROM AUTHOR]

Invariant relations are used to analyze while loops; while their primary application is to derive the function of a loop, they can also be used to derive loop invariants, weakest preconditions, strongest postconditions, sufficient conditions of correctness, necessary conditions of correctness, and termination conditions of loops. In this paper we present two generic invariant relations that capture the semantics of loops whose loop body applies affine transformations on numeric variables. [ABSTRACT FROM AUTHOR]

In this paper, we take an axiomatic approach to the design of ecological footprint indices. Our focus is put on the heterogeneity of land with respect to types and regions, at the core of an inherent aggregation problem. We propose an axiomatic characterization of the ecological footprint index with two fundamentally new axioms, symmetry and independence, which can resolve the problem of land heterogeneity. It is shown that a unique index, up to an affine transformation, exists meeting the axiom system. This index simplifies the aggregation procedure considerably and avoids the need for a synthetic unit of measurement, like global hectares, as well as complex transformations of variables by means of weighting schemes. Our findings reveal differences with the Global Footprint Network (GFN) index, in particular with regard to the treatment of land heterogeneity. Finally, the axiomatic methodology employed may open up perspectives for the development of ecological measures in general, and especially of measures for sustainability and tipping points. [ABSTRACT FROM AUTHOR]

This document is a preface to a special issue of the Journal of Multiple-Valued Logic & Soft Computing dedicated to the memory of Professor Ivo G. Rosenberg. The issue includes five contributions that cover various research interests of Professor Rosenberg, including topics in universal algebra, graph theory, order theory, and theoretical physics. The preface expresses gratitude to the authors and reviewers of the volume and pays tribute to Professor Rosenberg as a knowledgeable researcher and wonderful human being. [Extracted from the article]

This paper introduces DCT-Net, a novel image translation architecture for few-shot portrait stylization. Given limited style exemplars (~100), the new architecture can produce high-quality style transfer results with advanced ability to synthesize high-fidelity contents and strong generality to handle complicated scenes (e.g., occlusions and accessories). Moreover, it enables full-body image translation via one elegant evaluation network trained by partial observations (i.e., stylized heads). Few-shot learning based style transfer is challenging since the learned model can easily become overfitted in the target domain, due to the biased distribution formed by only a few training examples. This paper aims to handle the challenge by adopting the key idea of "calibration first, translation later" and exploring the augmented global structure with locally-focused translation. Specifically, the proposed DCT-Net consists of three modules: a content adapter borrowing the powerful prior from source photos to calibrate the content distribution of target samples; a geometry expansion module using affine transformations to release spatially semantic constraints; and a texture translation module leveraging samples produced by the calibrated distribution to learn a fine-grained conversion. Experimental results demonstrate the proposed method's superiority over the state of the art in head stylization and its effectiveness on full image translation with adaptive deformations. Our code is publicly available at https://github.com/menyifang/DCT-Net. [ABSTRACT FROM AUTHOR]

Thermal imaging cameras and infrared (IR) temperature measurement devices act as state-of-the-art techniques for non-contact temperature determination of the skin surface. The former is cost-intensive in many cases for widespread application, and the latter requires manual alignment to the measuring point. Due to this background, this paper proposes a new method for automated, non-contact, and area-specific temperature measurement of the facial skin surface. It is based on the combined use of a low-cost thermopile sensor matrix and a 2D image sensor. The temperature values as well as the 2D image data are fused using a parametric affine transformation. Based on face recognition, this allows temperature values to be assigned to selected facial regions and used specifically to determine the skin surface temperature. The advantages of the proposed method are described. It is demonstrated by means of a participant study that the temperature absolute values, which are achieved without manual alignment in an automated manner, are comparable to a commercially available IR-based forehead thermometer. [ABSTRACT FROM AUTHOR]

The property of covariance, also referred to as equivariance, means that an image operator is well-behaved under image transformations, in the sense that the result of applying the image operator to a transformed input image gives essentially a similar result as applying the same image transformation to the output of applying the image operator to the original image. This paper presents a theory of geometric covariance properties in vision, developed for a generalised Gaussian derivative model of receptive fields in the primary visual cortex and the lateral geniculate nucleus, which, in turn, enable geometric invariance properties at higher levels in the visual hierarchy. It is shown how the studied generalised Gaussian derivative model for visual receptive fields obeys true covariance properties under spatial scaling transformations, spatial affine transformations, Galilean transformations and temporal scaling transformations. These covariance properties imply that a vision system, based on image and video measurements in terms of the receptive fields according to the generalised Gaussian derivative model, can, to first order of approximation, handle the image and video deformations between multiple views of objects delimited by smooth surfaces, as well as between multiple views of spatio-temporal events, under varying relative motions between the objects and events in the world and the observer. We conclude by describing implications of the presented theory for biological vision, regarding connections between the variabilities of the shapes of biological visual receptive fields and the variabilities of spatial and spatio-temporal image structures under natural image transformations. Specifically, we formulate experimentally testable biological hypotheses as well as needs for measuring population statistics of receptive field characteristics, originating from predictions from the presented theory, concerning the extent to which the shapes of the biological receptive fields in the primary visual cortex span the variabilities of spatial and spatio-temporal image structures induced by natural image transformations, based on geometric covariance properties. [ABSTRACT FROM AUTHOR]

This paper proposes a new method of deriving 9-parameter affine 3D datum transformations by ordinary least-squares. Unlike previous methods, it covers all versions of the transformation. Initially, an 'average' scale factor is computed by distance analysis. Removing the scaling effect, the 'RIGOPT' subroutine is applied to optimise the rigid transformation that consists of 3 translations and 3 rotations. Using an equivalent enlargement hypothesis, the number of scale factors is increased to 3 by a short series of single-search-direction optimisations. The minimisation of residuals is verified by enclosing-interval analysis. The case studies cover datasets in Western Australia, Great Britain and Sweden. [ABSTRACT FROM AUTHOR]

In recent years, the pandemic situation has forced the education system to shift from traditional teaching to online teaching or blended learning. The ability to monitor remote online examinations efficiently is a limiting factor to the scalability of this stage of online evaluation in the education system. Human Proctoring is the most used common approach by either asking learners to take a test in the examination centers or by monitoring visually asking learners to switch on their camera. However, these methods require huge labor, effort, infrastructure, and hardware. This paper presents an automated AI-based proctoring system- 'Attentive system' for online evaluation by capturing the live video of the examinee. Our Attentive system includes four components to estimate the malpractices such as face detection, multiple person detection, face spoofing, and head pose estimation. Attentive Net detects the faces and draws bounding boxes along with confidences. Attentive Net also checks the alignment of the face using the rotation matrix of Affine Transformation. The face net algorithm is combined with Attentive-Net to extract landmarks and facial features. The process for identifying spoofed faces is initiated only for aligned faces by using a shallow CNN Liveness net. The head pose of the examiner is estimated by using the SolvePnp equation, to check if he/she is seeking help from others. Crime Investigation and Prevention Lab (CIPL) datasets and customized datasets with various types of malpractices are used to evaluate our proposed system. Extensive Experimental results demonstrate that our method is more accurate, reliable and robust for proctoring system that can be practically implemented in real time environment as Automated proctoring System. An improved accuracy of 0.87 is reported by authors with the combination of Attentive Net, Liveness net and head pose estimation. [ABSTRACT FROM AUTHOR]

In this paper the problem of tomographic reconstruction of states is investigated within the so-called Schwinger’s picture of quantum mechanics in which a groupoid is associated with every quantum system. The attention is focussed on spin tomography: in this context the groupoid of interest is the groupoid of pairs over a finite set. In a nutshell, this groupoid is made up of transitions between all possible pairs of outcomes belonging to a finite set. In addition, these transitions possess a partial composition rule, generalizing the notion of groups. The main goal of the paper consists in providing a reconstruction formula for states on the groupoid-algebra associated with the observables of the system. Using the group of bisections of this groupoid, which are special subsets in one-to-one correspondence with the outcomes, a frame is defined and it is used to prove the validity of the tomographic reconstruction. The special case of the set of outcomes being the set of integers modulo n, with n odd prime, is considered in detail. In this case the subgroup of discrete affine linear transformations, whose graphs are linear subspaces of the groupoid, provides a quorum in close analogy with the continuous case. [ABSTRACT FROM AUTHOR]

The traditional template matching strategy of optical and synthetic aperture radar (SAR) is sensitive to the nonlinear transformation between two images. In some cases, the optical and SAR image pairs do not conform to the affine transformation condition. To address this issue, this study presents a novel template matching strategy which uses the One-Class Support Vector Machine (SVM) to remove outliers. First, we propose a method to construct the similarity map dataset using the SEN1-2 dataset for training the One-Class SVM. Second, a four-step strategy for optical and SAR image registration is presented in this paper. In the first step, the optical image is divided into some grids. In the second step, the strongest Harris response point is selected as the feature point in each grid. In the third step, we use Gaussian pyramid features of oriented gradients (GPOG) descriptor to calculate the similarity map in the search region. The trained One-Class SVM is used to remove outliers through similarity maps in the fourth step. Furthermore, the number of improve matches (NIM) and the rate of improve matches (RIM) are designed to measure the effect of image registration. Finally, this paper designs two experiments to prove that the proposed strategy can correctly select the matching points through similarity maps. The experimental results of the One-Class SVM in dataset show that the One-Class SVM can select the correct points in different datasets. The image registration results obtained on the second experiment show that the proposed strategy is robust to the nonlinear transformation between optical and SAR images. [ABSTRACT FROM AUTHOR]

An important part of the well-known iterative closest point algorithm (ICP) is the variational problem. Several variants of the variational problem are known, such as point-to-point, point-to-plane, generalized ICP, and normal ICP (NICP). This paper proposes a closed-form exact solution for orthogonal registration of point clouds based on the generalized point-to-point ICP algorithm. We use points and normal vectors to align 3D point clouds, while the common point-to-point approach uses only the coordinates of points. The paper also presents a closed-form approximate solution to the variational problem of the NICP. In addition, the paper introduces a regularization approach and proposes reliable algorithms for solving variational problems using closed-form solutions. The performance of the algorithms is compared with that of common algorithms for solving variational problems of the ICP algorithm. The proposed paper is significantly extended version of Makovetskii et al. (CCIS 1090, 217–231, 2019). [ABSTRACT FROM AUTHOR]

Seafloor unconstrained environment video is shot in uncontrolled open sea area. There are multiple backgrounds, complex illumination and weather changes, and rapid growth of algae and attached lenses, which affect the stability of video shooting quality, resulting in difficulty in image recognition. At present, there is still no algorithm that is superior to other methods generally, and it is necessary to build a model combined with specific scenes and applications. In this paper, a fish identification method in tropical waters based on transfer learning under unconstrained environment is proposed. Firstly, the image is pre-processed by affine transformation to realize data enhancement. Furthermore, RestNet50 deep convolutional neural network is constructed based on transfer learning to compare the effect of fish recognition before and after transfer learning. The results show that, the accuracy and loss indicators are better than those of non-transfer learning when the trained model of imagenet is introduced as the initial weight of the network. When the model is trained to 150 epochs, the indicators begin to converge, which can better complete the fish identification task in tropical waters under unconstrained environment. [ABSTRACT FROM AUTHOR]

To address the lack of fast and high-precision in situ measurement of large-sized pipes in current industrial applications, a pipe-diameter-measurement method based on stereo vision is designed in this paper. By using multiple sets of binocular cameras to perform 3D reconstruction and integration of multiple laser markers projected on measured cross sections of pipes, the pipe diameter can be estimated. In this method, a measurement algorithm is adopted to enable automatic matching of feature points through affine distance transformation, and an optimized point-cloud-registration algorithm with normal-vector constraints is used to ensure measurement robustness. To verify the feasibility of the method, an experimental system was built under laboratory conditions, and three types of pipes with outer diameters from 285 mm to 325 mm were measured. The experimental results show that the relative error is within ±0.570% and the maximum repeatability standard deviation is 0.551 mm. The experimental results basically meet industrial standards, and the proposed method therefore has good application prospects. [ABSTRACT FROM AUTHOR]

Image based Localization (IbL) uses both Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM) data for accurate pose estimation. However, under conditions where there is a large perspective difference between the SfM images and SLAM keyframes, the SfM-SLAM co-visibility graph becomes sparse. As a result, the scale drift can increase especially when using monocular SLAM as part of the IbL framework. The drift rarely gets corrected at loop closure due to its large magnitude. We propose a split affine transformation approach that uses SfM-SLAM information along with Sim(3) optimization to minimize the scale drift. Experiments are performed using an image dataset collected in a campus environment with different trajectories, showing the improvement in scale drift correction with the proposed method. The SLAM data was collected close to plainly textured structures like buildings while SfM images were captured from a larger distance from the building facade which leads to a challenging navigation scenario in the context of IbL. Localizing mobile platforms moving close to buildings is an example of such a case. The paper positively impacts the widespread use of small autonomous robotic platforms, which is to perform an accurate outdoor localization under urban conditions using only a monocular camera. [ABSTRACT FROM AUTHOR]

Optimization tasks in diagnostic radiological imaging require objective quantitative metrics that correlate with the subjective perception of observers. However, although one such metric, the structural similarity index (SSIM), is popular, it has limitations across various aspects in its application to medical images. In this study, we introduce a novel image quality evaluation approach based on keypoints and their associated unique image feature values, focusing on developing a framework to address the need for robustness and interpretability that are lacking in conventional methodologies. The proposed index quantifies and visualizes the distance between feature vectors associated with keypoints, which varies depending on changes in the image quality. This metric was validated on images with varying noise levels and resolution characteristics, and its applicability and effectiveness were examined by evaluating images subjected to various affine transformations. In the verification of X-ray computed tomography imaging using a head phantom, the distances between feature descriptors for each keypoint increased as the image quality degraded, exhibiting a strong correlation with the changes in the SSIM. Notably, the proposed index outperformed conventional full-reference metrics in terms of robustness to various transformations which are without changes in the image quality. Overall, the results suggested that image analysis performed using the proposed framework could effectively visualize the corresponding feature points, potentially harnessing lost feature information owing to changes in the image quality. These findings demonstrate the feasibility of applying the novel index to analyze changes in the image quality. This method may overcome limitations inherent in conventional evaluation methodologies and contribute to medical image analysis in the broader domain. [ABSTRACT FROM AUTHOR]