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Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO2), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO2 requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO2 sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO2 detectors. The methodology was showcased using a purpose-built platform housing NO2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO2 data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO2 readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 µg/m3. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO2 monitoring. [ABSTRACT FROM AUTHOR]

The limitation of medical image data in open source is a big challenge for medical image processing. Medical data is closed because of confidential and ethical issues, also manual labeling of medical data is an expensive process. We propose a new augmentation method named MetaLung (Meticulous affine-transformation-based lung cancer augmentation method) for lung CT image augmentation. The key feature of the proposed method is the ability to expand the training dataset while preserving clinical and instrumental features. MetaLung shows a stable increase in image segmentation quality for three CNN-based models with different computational complexity (U-Net, DeepLabV3, and MaskRCNN). Also, the method allows in reduce the number of False Positive predictions. [ABSTRACT FROM AUTHOR]

Currently, digital communication generates a considerable amount of data from digital images. Preserving the confidentiality of these images during transmission through network channels is of crucial importance. To ensure the security of this data, this article proposes an image encryption approach based on enhancing the Hill cipher by constructing pseudo-random matrices operating in the ring Z/212Z injected into a controlled affine transformation. This approach relies on the use of chaotic maps for generating matrices used in the encryption process. The use of the ring Z/212Z aims to expand the key space of our cryptosystem, thus providing increased protection against bruteforce attacks. Moreover, to enhance security against differential attacks, a matrix of size (4×4), not necessarily invertible, is also integrated into a diffusion phase. The effectiveness of our technique is evaluated through specific tests, such as key space analysis, histogram analysis, entropy calculation, negative pixel count rate (NPCR) and unified average changing intensity (UACI) values, correlation analysis, as well as avalanche effect assessment. [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]

The soil three-dimensional (3D) pore structure information model has become a new hotspot in the research of cross-scale disposal of information such as ecology, engineering, and geology. It can solve problems such as the intelligent evaluation of ecological water–soil–air–plant feedback, intelligent risk control of foundation compression deformation, and multiscale intelligent disaster prediction and prevention. However, the current soil 3D pore structure model primarily draws on the global one-time static generation in simulation software, leading to low reliability, low stability of multiple generations, poor variability, difficulty in real-time transformation, high consumption of computing resources, and long generation time, making it challenging to meet the requirements of the rapid processing of information models. Therefore, this study adopts the 3D Drawing Protocol (WebGL) technology and implements the scaling, rotation, and translation transformation of 3D pore structures based on the principle of dynamic affine transformation. The random generation and distribution of pores are realized using the improved Monte Carlo method, and the loading response efficiency of the system is optimized. The dynamic interactive operation of arbitrary increasing and decreasing of pore model is realized using the ray and plane intersection detection principle. Finally, this information system established a visual correlation between the content and reaction time of water-retaining agents and soil porosity and pore distribution through tests on the impact of water-retaining agents on soil pore changes. The information system achieved the real-time tracking of soil pore changes under the action of water-retaining agents. This research solves the problems of static and low generation efficiency of the soil mesopore structure in traditional models and realizes the real-time characterization and visualization of dynamic changes and the distribution of the soil pore structure under the influence of external conditions, which can provide an intuitive visualization platform for mesogeological structure research.

The 2019 election process used the Vote Counting Information System, also known as Sistem Informasi Penghitungan Suara (Situng), to provide transparency in the recapitulation process. The data displayed in Situng is from document C1 for 813,336 voting stations in Indonesia. The data collected from the C1 document is entered and uploaded into Situng by the officers of the Municipal General Election Commission (GEC). Since this process is performed by humans, it is not immune to errors. In the recapitulation process of the 2019 election results, there were 269 data entry errors, and the data entry process also did not run according to the specified target, resulting in delays. Furthermore, there were cases of C1 document modification, raising concerns about the data's authenticity. To avoid human errors and increase data entry speed, automatic data entry is a plausible option. The data entered are text data in image documents with the same template format, so that optical character recognition (OCR) can be used to read the text while improving image quality and alignment, resulting in a more accurate OCR reading area. In this study, we developed a C1 document data extraction application using the waterfall SDLC method, which has undergone a systematic and thorough process. The application was developed using Tesseract optical character recognition. Tesseract is an open-source OCR engine and command-line program that allows for the recognition of text characters within a digital image. The accuracy obtained by using this method is still not optimal as a substitute for Situng's data entry officer. To guarantee the integrity of the C1 document, we use the RSA-2048 digital signature scheme. The use of the Tesseract-OCR Engine for character recognition, combined with digital signature capabilities, provides a comprehensive solution to reduce the human error factor that can lead to miscalculations and inaccurate processes.

In Indonesian traditional music, specifically Gamelan, overlapping fundamental frequencies occur among different instruments due to certain tones being tuned in the same octaves. This issue is challenging when the instruments are played simultaneously in the musical orchestras, resulting in mixed frequencies. This study utilizes Gamelan music dataset to address this issue by extracting deep semantic features to capture the distinctive characteristics of each instrument in the orchestras. We propose the fusion of Multi-Task Learning Autoencoder (MTL-AE) with Affine Transformation (AFT) to extract deep semantic features by investigating the optimal input derived from the Log Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). MTL-AE simultaneously extracts deep semantic features from eight instruments in the orchestras. AFT preserves these features according to the instrument class. The optimal extraction method was investigated by comparing the proposed method with baseline methods from MHU-Net and MHU-Net enhanced with Feature-wise Linear Modulation (FiLM). Subsequently, arranging deep semantic features from all instruments aims to obtain the structured feature patterns of eight instrument sources in the orchestras. Machine learning classifiers utilize structured deep semantic features for instrument recognition in the orchestras. Performance comparisons were executed against features derived from vanilla Log Mel Spectrogram, MFCC, Principal Component Analysis (PCA), Modified ResNet-50, MobileNet V3, and YAMNET. The results show that the deep semantic features, extracted using the proposed method with input from MFCC, contribute to the structured deep semantic feature to achieve superior accuracy up to 99%. Hence, these features effectively overcome the issue of overlapping frequencies in musical orchestras.

The objective of the image retargeting operator is to reduce the size of the image in a content-aware fashion. To strengthen the process of single operator-based image retargeting different hybrid sequences are being investigated. The visual quality of fractal images can be improved by utilizing efficient retargeting operators. In this paper, three frameworks are developed and combined to minimize the deficiencies of image retargeting operations. In framework-1, a seam diversion-based image retargeting technique is developed to overcome the drawbacks of the existing seam carving (SC) technique. In framework-2, the technique developed in framework-1 is combined with other retargeting operators to produce an efficient hybrid sequence of image retargeting operators. The objective of framework-2 is to overcome the limitation of single operator based image retargeting. In framework-3, an improved affine transformation function is developed that incorporates hybrid sequence of operator proposed in the framework-2. The main objective of framework-3 is to replace the scaling operator from the existing affine transformation. The structural similarity index measure (SSIM) metric is used to measure the performance of framework-1. The comparative analysis reveals that the performance of the retargeting technique proposed in framework-1 is 7% higher as compared to the existing SC technique. The superiority of framework-2 and 3 is analyzed using an optimization function in which Image Euclidian Distance (IMED) and Dominant Color Descriptor (DCD) techniques are incorporated. Finally, the performance analysis of framework-2 and 3 discloses their higher efficacy as compared to existing state-of-the-art. [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 the last several decades, application of affine transformation and optimization approaches have received enough attention in the domain of monomodal/multimodal image registration. A novel and robust image registration method is proposed which leverages teaching learning-based optimization (TLBO) to obtain the optimal value of rigid transformation parameters. Further, speeded up robust features (SURF) framework is employed to extract features along with Random sample consensus (RANSAC) algorithm. Afterwards, projective transformation is used to obtain more accurate registered image. To remove noise, gaussian filter is used during pre-processing and then normalization is carried out. TLBO is used to identify the optimal geometric transformation parameters by considering the mutual information (MI) as an objective function. This method detects keypoints (features) using SURF then K-nearest neighbour (KNN) is employed to match the detected features. Furthermore, RANSAC eliminates false matches. The registered image with optimal value of rigid transformation parameters is obtained and then enhanced by using SURF-RANSAC followed by projective transformation. Experiments are conducted on Whole Brain ATLAS and KAGGLE datasets. Robustness of proposed approach is verified by the improvement in evaluation metric, such as structure similarity index measure (SSIM) by 8%, MI by 12.71% on monomodal and root mean square error (RMSE) by 41.44% on multimodal images as compared to recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A set of algorithms with high positioning accuracy and recognition stability is proposed to meet the requirements of accurate positioning and recognition of ring-code targets. A method based on concentric projection transformation to solve the circle center is proposed for the precise positioning of annular coded targets. In the recognition of coded targets, the ellipse is changed into a standard circle by affine transformation, and then the internal coded band is decoded to get its unique decoded value. The experimental results show that the average error of the circle center extraction method is 0.225 pixels with the ideal circle center, 0.501 pixels with the ellipse fitting to extract the outer circle center, and 0.294 pixels with the inner circle center, and its accuracy is improved by 122.05% and 30.26% respectively. The angle between the camera and the coding target needs to be controlled within 75°, when the angle is less than 75°, the recognition rate of the coding target can reach 100%. The study finds that the coded target recognition and positioning method meets the photogrammetry requirements and has practical significance because it is more accurate than the ellipse fitting algorithm. [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]

Complex distortions on calcium imaging often impair image registration accuracy. Here, we developed a registration algorithm, PatchWarp, to robustly correct slow image distortion for calcium imaging data. PatchWarp is a two-step algorithm with rigid and non-rigid image registrations. To correct non-uniform image distortions, it splits the imaging field and estimates the best affine transformation matrix for each of the subfields. The distortion-corrected subfields are stitched together like a patchwork to reconstruct the distortion-corrected imaging field. We show that PatchWarp robustly corrects image distortions of calcium imaging data collected from various cortical areas through glass window or gradient-index (GRIN) lens with a higher accuracy than existing non-rigid algorithms. Furthermore, it provides a fully automated method of registering images from different imaging sessions for longitudinal neural activity analyses. PatchWarp improves the quality of neural activity analyses and is useful as a general approach to correct image distortions in a wide range of disciplines.

Deformable medical image registration plays an essential role in clinical diagnosis and treatment. However, due to the large difference in image deformation, unsupervised convolutional neural network (CNN)-based methods cannot extract global features and local features simultaneously and cannot capture long-distance dependencies to solve the problem of excessive deformation. In this paper, an unsupervised end-to-end registration network is proposed for 3D MRI medical image registration, named AEAU-Net, which includes two-stage operations, i.e., an affine transformation and a deformable registration. These two operations are implemented by an affine transformation subnetwork and a deformable registration subnetwork, respectively. In the deformable registration subnetwork, termed as EAU-Net, we designed an efficient attention mechanism (EAM) module and a recursive residual path (RSP) module. The EAM module is embedded in the bottom layer of the EAU-Net to capture long-distance dependencies. The RSP model is used to obtain effective features by fusing deep and shallow features. Extensive experiments on two datasets, LPBA40 and Mindboggle101, were conducted to verify the effectiveness of the proposed method. Compared with baseline methods, this proposed method could obtain better registration performance. The ablation study further demonstrated the reasonability and validity of the designed architecture of the proposed method. [ABSTRACT FROM AUTHOR]

Global motion greatly increases the number of false alarms for object detection in video sequences against dynamic backgrounds. Therefore, before detecting the target in the dynamic background, it is necessary to estimate and compensate the global motion to eliminate the influence of the global motion. In this paper, we use the SURF (speeded up robust features) algorithm combined with the MSAC (M-Estimate Sample Consensus) algorithm to process the video. The global motion of a video sequence is estimated according to the feature point matching pairs of adjacent frames of the video sequence and the global motion parameters of the video sequence under the dynamic background. On this basis, we propose an inverse transformation model of affine transformation, which acts on each adjacent frame of the video sequence in turn. The model compensates the global motion, and outputs a video sequence after global motion compensation from a specific view for object detection. Experimental results show that the algorithm proposed in this paper can accurately perform motion compensation on video sequences containing complex global motion, and the compensated video sequences achieve higher peak signal-to-noise ratio and better visual effects. [ABSTRACT FROM AUTHOR]

This paper investigates the affine formation maneuver control for multi-heterogeneous unmanned surface vessels (USV), aiming to enable them to navigate through narrow channels in the near-sea environment. The approach begins with implementing an affine transformation to facilitate flexible configuration adjustments within the formation system. The affine transformation of the entire formation is achieved by controlling the leaders' positions. Second, this article introduces an anti-perturbation formation tracking controller for the underactuated vessels, which assume the role of leaders, to accurately follow the arbitrary formation transformation. Third, the followers consist of fully actuated vessels with the same kinematic model as the leaders but different dynamic models. This paper utilizes the affine localizability theorem to derive an expected virtual time-varying trajectory based on the leaders' trajectory. The followers achieve the desired formation maneuver control by tracking this expected virtual time-varying trajectory through an anti-perturbation formation tracking controller. Finally, the efficacy of the introduced control law is confirmed and supported by the results obtained from rigorous simulation experiments. [ABSTRACT FROM AUTHOR]

OpenStreetMap (OSM), recognised for its current and readily accessible spatial database, frequently serves regions lacking precise data at the necessary granularity. Global collaboration among OSM contributors presents challenges to data quality and uniformity, exacerbated by the sheer volume of input and indistinct data annotation protocols. This study presents a methodological improvement in the spatial accuracy of OSM datasets centred over Baghdad, Iraq, utilising data derived from OSM services and satellite imagery. An analytical focus was placed on two geometric correction methods: a two-dimensional polynomial affine transformation and a two-dimensional polynomial conformal transformation. The former involves twelve coefficients for adjustment, while the latter encompasses six. Analysis within the selected region exposed variances in positional accuracy, with distinctions evident between Easting (E) and Northing (N) coordinates. Empirical results indicated that the conformal transformation method reduced the Root Mean Square Error (RMSE) by 4.434 meters in the amended OSM data. Contrastingly, the affine transformation method exhibited a further reduction in total RMSE by 4.053 meters. The deployment of these proposed techniques substantiates a marked enhancement in the geometric fidelity of OSM data. The refined datasets have significant applications, extending to the representation of roadmaps, the analysis of traffic flow, and the facilitation of urban planning initiatives. [ABSTRACT FROM AUTHOR]

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Feature matching is determining true correspondences between image pairs, important for many computer vision applications. It is challenging to determine true correspondences quickly under scene changes in viewpoint, rotation, scaling and illumination. Higher accuracy and efficiency are required for feature matching. While other methods determine true correspondences by treating the images independently, we instead condition on image pairs to take account of the affine information between them.To achieve this, we propose AAM-ORB, an efficient and robust algorithm for feature matching in the scene-shift. The key to our approach is an affine attention module (AAM), which can condition the affine features on both images to boost robustness. AAM is integrated into the well-known ORB feature matching pipeline, resulting in a significant improvement. Although remarkably matching accuracy, AAM can reduce computation efficient. To overcome this, we select a grid-based motion statistics for separating true correspondences from false ones at high speed. Extensive experiments show that AAM-ORB surpasses state-of-the-art approaches for feature matching on benchmark datasets. Moreover, the proposed AAM-ORB has less time consumption. Finally, AAM-ORB achieves better performance and efficiency of feature matching under scene changes. [ABSTRACT FROM AUTHOR]

Ice load is one of the main loads that needs to be considered in the design and construction of wading bridges (bridges whose piers are submerged in water) in cold regions, and ice velocity, area, and thickness are the main parameters that affect ice load. A noncontact measurement technology based on the " scale method" was proposed to realize small-range monitoring of river ice by laying calibration grids on the ice surface. The "bounding rectangle method" and Harris corner detection were used to accurately identify the area and velocity of a single ice drift. Also, the thickness of ice drift was estimated by statistical analysis. Considering the Huma River basin in the Daxing'an Mountains as the research object, field tests were performed to determine the flow ice parameters. The results indicate that noncontact measurement technology can accurately identify the velocity and area of ice drift and has wide application prospects, thereby providing a reference for the structural design and safety evaluation of wading bridges in cold areas. [ABSTRACT FROM AUTHOR]

In this paper, we present methods for identifying an image from a given set of Radon projections. Given a suitably regular 2-D or 3-D function, we form a new function $g$ from $f$ using a linear transformation. We show how the Radon projections of $f$ and $g$ can be used to determine the transformation. The proposed algorithms introduce three major contributions: 1) Improvements on the 2-D setting using the moments of the Radon projections with only two orthogonal projections. 2) A natural extension of the 2-D setting to work with the 3-D setting. In particular, reducing the 3-D problem to a 2-D problem so that we can recover a translation, a scaling, or a rotation transformation of a 3-D object in the Radon domain. 3) An efficient method of recovering a rotation of a 3-D image around an arbitrary axis and an angle of rotation.

Image moment is an important technique for pattern recognition. But, invariants constructed with high-order moments are sensitive to noise. Only a few invariants with low-order moments can be used in practice. In this paper, the definition of traditional moment is reviewed in polar coordinate system. Polar radius moment (PRM), which is defined by a linear combination of integrals on the symmetrical polar radiuses in an image, is proposed. Traditional moment is only a special case of PRM. Algorithm is developed to construct affine invariants with PRMs. In particular, the first-degree PRM is considered as the generalization of the zero-order moment. It can be used to construct affine invariants directly. The third-degree PRM can be viewed as the generalization of the second-order moment. Consequently, more invariants can be constructed with low-order (degree) moments. The experimental results also show that invariants with low-degree PRMs are more robust to noise. [ABSTRACT FROM AUTHOR]

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), launched in September 2018, has been widely used in forestry and surveying. A high-accuracy digital elevation model (DEM)/digital surface model (DSM) for terrain matching can effectively evaluate the ICESat-2 design requirements and provide essential data support for further study. The conventional terrain-matching methods regard the laser ground track as a whole, ignoring the individual differences caused by the interaction of photons during flight. Therefore, a novel terrain-matching method using a two-dimensional affine transformation model was proposed to describe the deformation of laser tracks. The least-square optimizes the model parameters with the high-accuracy terrain data to obtain the best matching result. The results in McMurdo Dry Valley (MDV), Antarctica, and Zhengzhou (ZZ), China, demonstrate that the proposed method can verify geolocation accuracy and indicate that the average horizontal accuracy of ICESat-2 V5 data is about 3.86 m in MDV and 4.67 m in ZZ. It shows that ICESat-2 has good positioning accuracy, even in mountainous areas with complex terrain. Additionally, the random forest (RF) model was calculated to analyze the influence of four factors on geographic location accuracy. The slope and signal-to-noise ratio (SNR) are considered the crucial factors affecting the accuracy of ICESat-2 data. [ABSTRACT FROM AUTHOR]

Binary image boundary vectorization is the process of converting raster images into vector images represented with a sequence of Bézier curves. Two main factors in reconstructing parametric curves are to approximate the underlying structure of the boundaries as much as possible while using as few curves as possible. Existing methods do not perform well when considering both of these two main factors. In this article, we mimic the process of human vectorizing image boundaries by first segmenting the boundary points into multiple segments with the corner points. For the boundary points in each segment, we adopt the bisection method to find the largest number of points, which a single curve can fit. More curves will be added if the fitting error is larger than a predefined threshold. The process is repeated until all the points in the segment are fitted, thus minimizing the number of Bézier curves. Besides, symmetric image boundaries can be detected and used to further decrease the number of curves required. Our method can also choose the optimal parameterization method case by case to further reduce the fitting error. We make a comparison with both new and classical methods and show that our method outperforms them. [ABSTRACT FROM AUTHOR]

Uncertainties are widely present in the design and simulation of aero-engine combustion systems. Common non-probabilistic convex models are only capable of processing independent or correlated uncertainty variables, while conventional precise probabilistic sensitivity analysis based on ideal conditions also fails due to the presence of uncertainties. Given the above-described problem, an imprecise p-box sensitivity analysis method is proposed in this study in accordance with a multi-dimensional parallelepiped model, comprising independent and correlated variables in a unified framework to effectively address complex hybrid uncertainty problems where the two variables co-exist. The concepts of the correlation angle and correlation coefficient of any two parameters are defined. A multi-dimensional parallelepiped model is built as the uncertainty domain based on the marginal intervals and correlation characteristics of all parameters. The correlated variables in the initial parameter space are converted into independent variables in the affine space by introducing an affine coordinate system. Significant and minor variables are filtered out through imprecise sensitivity analysis using pinching methods based on p-box characterization. The feasibility and accuracy of the method are verified based on the analysis of the numerical example and the outlet temperature distribution factor. As indicated by the results, the coupling between the variables can be significantly characterized using a multi-dimensional parallelepiped model, and a notable difference exists in the sensitivity ranking compared with considering only the independence of the variables, in which input parameters (e.g., inlet and outlet pressure, density, and reference flow rate) are highly sensitive to changes in the outlet temperature distribution factor. Furthermore, the structural parameters of the flame cylinder exert a secondary effect. [ABSTRACT FROM AUTHOR]

Besides descriptor designing, factoring out the categorical effects of affine transformations is also an effective way to match features. This paper proposed and demonstrated a hypothesis that any coplanar orientations have limited geometrical changes when observed in the camera screen under given ranges of 3D affine transformations. Confirmatory experiments are accomplished by Matlab simulations focusing on specific influences of 3D affine transformations on similarity biases between concerned orientation changes. Statistical analyses and results show that rotations about plane axes mainly affect the geometrical biases of orientation changes. A set of fit limits standing for the biggest influences of most affine transformations in specified ranges are numerically approximated by total sample space distribution and experimental extreme values. We use these Similarity Limits of Orientation Changes(SLOC) as optimization constraints in matching problems, and an application example of planar pattern matching is given. The effectiveness and efficiency of SLOC are proved by the experimental results in Mikolajczyk and HPatches testbed.

The problem of describing affinely homogeneous hypersurfaces in the space that possess exactly three-dimensional algebras of affine symmetries is discussed. For three types of solvable three-dimensional Lie algebras, their linearly homogeneous three-dimensional orbits in this space that differ from the second-order and cylindrical surfaces in (which are of no interest in the problem involved) are studied. The fact that there are two nontrivial commutation relations in each of the algebras involved makes the situation with their orbits in differ significantly from the case of a three-dimensional Abelian algebra with a big family of affinely distinct (linearly homogeneous) orbits in the same space. One of the studied types of Lie algebras is proved to not allow for nontrivial four-dimensional linear representations at all, with a great number of three-dimensional orbits of representations of the two other types having rich symmetry algebras. At the same time, for one of the three types of Lie algebras, a new family of linearly homogeneous orbits that have precisely three-dimensional algebras of affine symmetries is obtained. Proofs of the propositions significantly rely on symbolic calculations associated with -matrices from the matrix Lie algebras involved. [ABSTRACT FROM AUTHOR]

This paper investigates the affine formation maneuver control for multi-heterogeneous unmanned surface vessels (USV), aiming to enable them to navigate through narrow channels in the near-sea environment. The approach begins with implementing an affine transformation to facilitate flexible configuration adjustments within the formation system. The affine transformation of the entire formation is achieved by controlling the leaders’ positions. Second, this article introduces an anti-perturbation formation tracking controller for the underactuated vessels, which assume the role of leaders, to accurately follow the arbitrary formation transformation. Third, the followers consist of fully actuated vessels with the same kinematic model as the leaders but different dynamic models. This paper utilizes the affine localizability theorem to derive an expected virtual time-varying trajectory based on the leaders’ trajectory. The followers achieve the desired formation maneuver control by tracking this expected virtual time-varying trajectory through an anti-perturbation formation tracking controller. Finally, the efficacy of the introduced control law is confirmed and supported by the results obtained from rigorous simulation experiments.

Global motion greatly increases the number of false alarms for object detection in video sequences against dynamic backgrounds. Therefore, before detecting the target in the dynamic background, it is necessary to estimate and compensate the global motion to eliminate the influence of the global motion. In this paper, we use the SURF (speeded up robust features) algorithm combined with the MSAC (M-Estimate Sample Consensus) algorithm to process the video. The global motion of a video sequence is estimated according to the feature point matching pairs of adjacent frames of the video sequence and the global motion parameters of the video sequence under the dynamic background. On this basis, we propose an inverse transformation model of affine transformation, which acts on each adjacent frame of the video sequence in turn. The model compensates the global motion, and outputs a video sequence after global motion compensation from a specific view for object detection. Experimental results show that the algorithm proposed in this paper can accurately perform motion compensation on video sequences containing complex global motion, and the compensated video sequences achieve higher peak signal-to-noise ratio and better visual effects.

This study contributes to creating an unbreakable S-Box based on a strong bent function expanded by DNA sequences and investigates and analyzes the strength of the proposed S-Box against major standard criteria and benchmarks, such as interpolation attacks, algebraic attacks, avalanche effect, nonlinearity, and period. The outcome of the tests shows that the proposed S-box has good security, as well as it is passed all the randomness tests. On an average, the results after the tests applied have been come with SAC = 0.50122, NL = 112, BIC = 103.40625, and an iterative period with a maximum value of 256. The complexity of the proposed S-Box increased with an algebraic expression of 255 terms, which implies an algebraic attack resistance of $2^{160}$ . Based on the proposed S-Box, a candidate image-enciphering scheme is suggested to prove the strength of the S-Box. The analysis of the experiments that applied two modes of images, grey and RGB images, supports the scheme’s robustness against different differential and statical attacks using standard criteria such as correlation coefficient analysis, information entropy, histogram analysis, unified average change intensity, number of pixels change rate and many others. This enforces its capability for use in modern-day cryptosystems that are utilized in multimedia data exchange.

This paper proposes triangular region representations based on keypoints detected in images with viewpoint changes. The strongest keypoints in the reference and query images are allocated individually using a previously published contourlet-based approach to determine the keypoints. These selected keypoints serve as vertices of the triangular regions to be transformed into rectangular representations as simple numeric matrices. The suggested representation methods used to form rectangular matrices are full triangle representation (FTR) and a lighter representation called triangle medians and sides representation (TMSR). For the former, the intensity values along the lines connecting the anchor keypoint to the points between the other two triangular vertices form the rows of the representation matrix. These two triangle vertices are located in an allotted window around the anchor keypoint from the nearby keypoints. For the latter, the intensity values on the six triangular lines, medians, and sides formed the resulting rectangular matrix. The proposed representations are validated for image-matching applications using a descriptor-less matching method. Moreover, the performances of these algorithms are compared with those of traditional algorithms. The results confirmed the superiority of the proposed method over these algorithms.

The analysis of spectral reflectance data is an important tool for obtaining relevant information about the mineral composition of objects and has been used for research in chemistry, geology, biology, archaeology, pharmacy, medicine, anthropology, and other disciplines. In archaeology, the use of spectroscopic data allows us to characterize and classify artifacts and ecofacts, to analyze patterns, and to study the exchange of materials, etc., as well as to explain some properties, such as color or post-depositional processes. The spectroscopic data are of the so-called "big data" type and must be analyzed using multivariate statistical techniques, usually principal component analysis and cluster analysis. Although there are different transformations of the raw data, in this paper, we propose preprocessing by means of an affine transformation. From a mathematical point of view, this process modifies the values of reflectance for each spectral signature scaling them into a [0, 1] interval using minimum and maximum values of reflectance, thus highlighting the features of spectral curves. This method optimizes the characteristics of amplitude and shape, reduces the influence of noise, and improves results by highlighting relevant features as peaks and valleys that may remain hidden using the raw data. This methodology has been applied to a case study of prehistoric chert (flint) artifacts retrieved in archaeological excavations in the Andévalo area located in the Archaeological Museum of Huelva (Huelva, Andalusia). The use of transformed data considerably improves the results obtained with raw data, highlighting the peaks, valleys, and the shape of spectral signatures. [ABSTRACT FROM AUTHOR]

In order to further research the relationship between fractals and complicated networks in terms of self-similarity, the uniform convergence property of the sequence of fractal interpolation functions which can generate self-similar graphics through iterated function system defined by affine transformation is studied in this paper. The result illustrates that it is can be proved that the sequence of fractal interpolation functions uniformly converges to its limit function and its limit function is continuous and integrable over a closed interval under the uniformly convergent condition of the sequence of fractal interpolation functions. The following two conclusions can be indicated. First, both the number sequence limit operation of the sequence of fractal interpolation functions and the function limit operation of its limit function are exchangeable over a closed interval. Second, the two operations of limit and integral between the sequence of fractal interpolation functions and its limit function are exchangeable over a closed interval. [ABSTRACT FROM AUTHOR]