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Fires caused by DC series arcs are one of the primary threats to the safety of photovoltaic (PV) systems. Accurate fault location is extremely helpful in protecting PV plants, but existing research on locating DC series arc faults is limited. This paper proposes a novel DC series arc spatial location method. The proposed method estimates the distance based on the received signal strength indicator (RSSI) of electromagnetic radiation (EMR) emitted by the arc. The experimental system uses four antennas that form a tetrahedral topology to receive arc EMR signals. Some pretests are implemented before an arc occurs to obtain some known-location arc EMR signals, which are processed to be the candidate data of the arc EMR model. When the arc fault occurs, the candidate data are selected through comparison with the unknown-location signals. The arc EMR model is set up by the Bayesian regularized neural network (BRNN) algorithm. Then, the distances between the arc and antennas are estimated by the BRNN. The spatial coordinates are calculated with the antenna array topology and estimated distances. Finally, the verification results show that the algorithm has a 0.5 m average spatial location error, which is proven to be active and feasible.

International audience; Objectives: Convolutional neural networks (CNNs) have established state-of-the-art performance in computer vision tasks such as object detection and segmentation. One of the major remaining challenges concerns their ability to capture consistent spatial and anatomically plausible attributes in medical image segmentation. To address this issue, many works advocate to integrate prior information at the level of the loss function. However, prior-based losses often suffer from local solutions and training instability. The CoordConv layers are extensions of convolutional neural network wherein convolution is conditioned on spatial coordinates. The objective of this paper is to investigate CoordConv as a proficient substitute to convolutional layers for medical image segmentation tasks when trained under prior-based losses.Methods: This work introduces CoordConv-Unet which is a novel structure that can be used to accommodate training under anatomical prior losses. The proposed architecture demonstrates a dual role relative to prior constrained CNN learning: it either demonstrates a regularizing role that stabilizes learning while maintaining system performance, or improves system performance by allowing the learning to be more stable and to evade local minima.Results: To validate the performance of the proposed model, experiments are conducted on two well-known public datasets from the Decathlon challenge: a mono-modal MRI dataset dedicated to segmentation of the left atrium, and a CT image dataset whose objective is to segment the spleen, an organ characterized with varying size and mild convexity issues.Conclusion: Results show that, despite the inadequacy of CoordConv when trained with the regular dice baseline loss, the proposed CoordConv-Unet structure can improve significantly model performance when trained under anatomically constrained prior losses.

This paper analyzes the simulation of special effects animation through fluid equations and data-driven methods. This paper also considers the needs of computer fluid animation simulation in terms of computational accuracy and simulation efficiency, takes high real-time, high interactivity, and high physical accuracy of simulation algorithm as the research focus and target, and proposes a solution algorithm and acceleration scheme based on deep neural network framework for the key problems of simulation of natural phenomena including smoke and liquid. With the deep development of artificial intelligence technology, deep neural network models are widely used in research fields such as computer image classification, speech recognition, and fluid detail synthesis with their powerful data learning capability. Its stable and efficient computational model provides a new problem-solving approach for computerized fluid animation simulation. In terms of time series reconstruction, this paper adopts a tracking-based reconstruction method, including target tracking, 2D trajectory fitting and repair, and 3D trajectory reconstruction. For continuous image sequences, a linear dynamic model algorithm based on pyramidal optical flow is used to track the feature centers of the objects, and the spatial coordinates and motion parameters of the feature points are obtained by reconstructing the motion trajectories. The experimental results show that in terms of spatial reconstruction, the matching method proposed in this paper is more accurate compared with the traditional stereo matching algorithm; in terms of time series reconstruction, the error of target tracking reduced. Finally, the 3D motion trajectory of the point feature object and the motion pattern at a certain moment are shown, and the method in this paper obtains more ideal results, which proves the effectiveness of the method.

Based on binocular stereo vision measurement and wireless sensing theory, this paper studies the measurement and analysis methods of athletes' movement displacement parameters. The main purpose is to explore the relationship between sports biomechanical parameters. The analysis reveals the laws of athletes 'movements, and provides theoretical guidance on athletes' attitude recognition and physical training. Based on the principle of binocular stereo vision, a non-contact displacement measurement system was developed. The displacement measurement system uses template matching to extract the image coordinates of the measurement points and restore the spatial coordinates of the measurement points through European 3D reconstruction, which improves the practicality of the measurement system. In the cantilever beam vibration test, the traditional displacement sensor and binocular image displacement measurement system are used to simultaneously measure the cantilever beam displacement. The experimental results show that the integrated displacement measurement system in this paper has higher accuracy and better stability.

Mineral resource estimation is an integral part of making informed decisions while evaluating a mining operation’s feasibility. Geostatistical tools estimate geological features with the assumptions of first and second-order stationarity. Kriging is considered the best linear unbiased estimation technique for modelling geological features; however, in domains where data is non-Gaussian, and features are complex, the assumption of stationarity and the linearity of kriging can lead to suboptimal estimates. This manuscript presents a hybrid machine learning and geostatistical algorithm to improve estimation in complex domains. Elliptical radial basis function neural networks (ERBFN) take advantage of non-stationary functions to generate geological estimates. An ERBFN does not require the assumption of stationarity, and the only input features required are the spatial coordinates of the known data. The proposed hybrid estimation considers the machine learning estimate as exhaustive secondary data in ordinary intrinsic collocated cokriging, taking advantage of kriging’s exactitude while including the non-stationary features modelled in the ERBFN. The principle results of integrating geostatistics and machine learning indicate an improved estimation technique in domains with complex features, poorly defined domains, or non-Gaussian data. The major conclusion from this paper is that using the proposed hybrid algorithm can improve mineral resource estimations.

A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

Bio-logging devices equipped with inertial measurement units—particularly accelerometers, magnetometers, and pressure sensors—have revolutionized our ability to study animals as necessary electronics have gotten smaller and more affordable over the last two decades. These animal-attached tags allow for fine scale determination of behavior in the absence of direct observation, particularly useful in the marine realm, where direct observation is often impossible, and recent devices can integrate more power hungry and sensitive instruments, such as hydrophones, cameras, and physiological sensors. To convert the raw voltages recorded by bio-logging sensors into biologically meaningful metrics of orientation (e.g., pitch, roll and heading), motion (e.g., speed, specific acceleration) and position (e.g., depth and spatial coordinates), we developed a series of MATLAB tools and online instructional tutorials. Our tools are adaptable for a variety of devices, though we focus specifically on the integration of video, audio, 3-axis accelerometers, 3-axis magnetometers, 3-axis gyroscopes, pressure, temperature, light and GPS data that are the standard outputs from Customized Animal Tracking Solutions (CATS) video tags. Our tools were developed and tested on cetacean data but are designed to be modular and adaptable for a variety of marine and terrestrial species. In this text, we describe how to use these tools, the theories and ideas behind their development, and ideas and additional tools for applying the outputs of the process to biological research. We additionally explore and address common errors that can occur during processing and discuss future applications. All code is provided open source and is designed to be useful to both novice and experienced programmers.

In molecular-stream separation (MSS), a stream of a multi-component mixture is separated into multiple streams of individual components. Quantitative evaluation of MSS data has been a bottleneck in MSS for decades as there was no conventional way to present the data in a reproducible and uniform fashion. The roots of the problem were in the multi-dimensional nature of MSS data; even in the ideal case of steady-state separation, the data is three-dimensional: intensity and two spatial coordinates. We recently found a way to reduce the dimensionality via presenting the MSS data in a polar coordinate system and convoluting the data via integration of intensity along the radius axis. The result of this convolution is an angulagram — a simple 2D plot presenting integrated intensity vs angle. Not only does an angulagram simplify the visual assessment, but it also allows the determination of three quantitative parameters characterizing the quality of MSS: stream width, stream linearity, and stream deflection. Reliably converting an MSS image into an angulagram and accurately determining the stream parameters requires an advanced and user-friendly software tool. In this technical note, we introduce such a tool: the open-source software Topino available at https://github.com/Schallaven/topino. Topino is a stand-alone program with a modern graphical user interface that allows processing an MSS image in a fast ( p { margin-bottom: 0.25cm; direction: ltr; line-height: 115%; text-align: left; orphans: 2; widows: 2 }a:link { color: #0000ff }

The coordinate measuring machine (CMM) is one of the most widely used precision measurement machines in the machinery industry. In this study, the analysis and compensation of structural deformation in spatial coordinates effectively depicts the improvement of the spatial accuracy of the motion of a three-dimensional measuring machine in a measurement area. In addition to accuracy, it is also projected that the measurement can be carried out more efficiently. Therefore, the measurement speed of the CMM and dynamic characteristics of the structure are becoming increasingly important. Consequently, the measuring machine has better dynamic characteristics and meets high precision requirements. During measurement, the geometric errors generated by the machine are mainly because of its moving structure in the spatial position. In this study, the CMM is driven by aerostatic bearings; the boundary conditions and parameter settings of each component are established to obtain analysis results that are highly consistent with the actual machine characteristics. The finite element method is used as an analysis tool, including static deformation, modal, spectrum, and transient analyses. Because of the structural deformation and spatial geometric errors caused by the moving structure of the measuring machine, the relative deformation errors can be evaluated through the analysis of the spatial position error compensation to improve the measurement accuracy of the machine.

With the rapidly increasing scale and complexity, millimeter-wave communication systems are getting more powerful, but are also less reliable. Due to hardware characteristics and connecting structures, millimeter-wave communication systems may experience device faults which result in performance degradation or even failure. Therefore, system diagnosis techniques for fault detection and localization are essential to system monitoring and maintenance. In this paper, two compressed sensing based diagnosis techniques are proposed, which can jointly detect and locate faulty antennas, phase shifters, and RF chains. The first technique is applicable to the diagnosis in a multipath-free environment where the channel state information can be obtained based on spatial coordinates. Furthermore, we propose the second technique that does not require the channel state information, which is suitable for outdoor online diagnosis in a multipath scattering environment. Finally, numerical simulations show that the proposed techniques can jointly detect all faulty devices with high probability.

The systems of short-range radiolocation and multistatic radio holography based on multielement MIMO antenna arrays (AAs) with recirculation of orthogonal signals in the spatial feedback circuits of AA transmitting element–​​target–AA receiving element–AA transmitting element are investigated. The generalized ambiguity function in spatial coordinates for an ensemble of coherent orthogonal sounding signals with allowance for the partial propagation constants of the feedback circuits is obtained. The characteristics of the range, azimuth, and elevation resolution in MIMO retrodirective systems are determined based on the analysis of two-dimensional and one-dimensional sections of the generalized ambiguity function (GAF). For the centimeter wavelength range, the GAF cross sections of the azimuth–elevation and range–elevation are compared with and without recirculation as well as at different ranges to the target. The results show the presence of super-resolution effects in MIMO systems with spatial signal recirculation and a nontrivial dependence of the resolution on the target range.

For an equation of mixed parabolic-hyperbolic type in a rectangular parallelepiped, the inverse problem of finding the factors of the right-hand side that depend on spatial variables is investigated. A criterion for the uniqueness of a solution is established. The solution is constructed as a sum of orthogonal series. When justifying the convergence of the series, the problem of small denominators of two natural arguments arose. Estimates on the separation of small denominators from zero with the corresponding asymptotics are established. These estimates made it possible to substantiate the convergence of the constructed series in the class of regular solutions of this equation.

Precise point positioning (PPP) uses precise satellite orbits, clock corrections and biases derived from a global network of reference stations to enable accurate positioning worldwide. Natural Resources Canada’s Canadian Spatial Reference System (CSRS) PPP is a free Web service offering automated PPP processing. A critical factor limiting the adoption of PPP in many applications is the convergence time needed to reach centimeter-level accuracies. To address this issue, CSRS-PPP now implements PPP with ambiguity resolution (PPP-AR). This feature required the development of new algorithms, such as sequential normal stacking for least-squares filtering/smoothing, and the weighted integer decision concept for ambiguity validation. New satellite product lines (ultra-rapid, rapid, final) also have been deployed to enable PPP-AR processing with various latencies. This analysis demonstrates that sub-centimeter horizontal accuracies can be obtained in less than one hour for both static and kinematic modes. Using product lines with longer latencies is beneficial, although improvements are typically within the reported uncertainties.

During waveguide sound propagation in the marine environment, one of the main characteristics is the speed of sound. In hydrophysical waveguides, the speed of sound is mainly a function of depth; the radial coordinate introduces second-order corrections of smallness. Nevertheless, in some cases, significant changes in the characteristics of the waveguide along the radial coordinate are possible, for example, as a result of the influence of anthropogenic factors, sea currents, etc. In the present paper, an analytical representation is constructed for the sound field of a point source in a plane-layered sea waveguide that has an inhomogeneous speed of sound in depth and waveguide path. The waveguide model has radial symmetry, while the sound source is located at an arbitrary point on the waveguide, which leads to a solution that substantially depends on all three spatial coordinates. This solution is based on the decomposition of the waveguide region into cylindrical regions, allowing the construction of an analytical solution of the Helmholtz equation. It is shown that in the case of a constant profile of sound velocity and density in each of the waveguide decomposition regions, the solution of the problem can be obtained explicitly. The results of numerical modeling are presented, the influence of heterogeneity on the characteristics of the sound field is analyzed.

The perspective n-point problem (PnP), or a three-point photogrammetric resection in space, consists of the problem of finding the position and the orientation of the model of the camera of the central projection by the assigned spatial coordinates of the given points and by the corresponding coordinates of their projections on the image. Due to the available applications in robotics and machine vision, direct noniterative methods for solving such a problem are of interest. The well known general method of the direct noniterative solution of the PnP problem in the case of four control points (n = 4) uses double linearization and seems to be rather cumbersome. This paper presents an alternative simpler solution for the considered problem in the case of n = 4. The proposed method reduces the computing requirements for solving this problem. In the case of more control points, the solution obtained for four points can be used as the initial approximation for the Gauss–Newton iterations or in the RANSAC calculation scheme.

Background Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. Results We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user’s computer or through a web application. Conclusions spatialLIBD is available at https://bioconductor.org/packages/spatialLIBD. It is fully compatible with SpatialExperiment and the Bioconductor ecosystem. Its functionality facilitates analyzing and interactively exploring spatially-resolved data from the Visium platform.

Despite the impressive performance of some recent state-of-the-art detectors, small target detection, scale variation, and label ambiguities remain challenges. To tackle these issues, we present a coordinate-based anchor-free (CBAF) module for object detection. It can be used as a branch of a single-shot detector (e.g., RetinaNet or SSD) or predict the output probabilities and coordinates directly. The main idea of the CBAF module is to predict the category and the adjustments to the box of the object by part feature and its contextual part features, which are based on feature maps divided by spatial coordinates. This is inspired by the fact that human beings can infer an entire object by observing the part of the surrounding environment. The CBAF module will encode and decode boxes in the anchor-free manner per feature map with different resolutions during training and testing. During training, we first use the proposed spatial coordinate partition layer to divide feature maps into several parts of size n × n and then propose a contextual building layer to fuse the part and its contextual parts together. We will demonstrate the CBAF module through a concrete implementation. The CBAF module improves AP scores of object detection with nearly no additional computation when working in conjunction with the anchor-based RetinaNet. Furthermore, experimental results on the MS-COCO dataset show that the mAP of the CBAF module has increased by 1.1%, compared with RetinaNet. When the CBAF module works in conjunction with the anchor-based RetinaNet, the mAP increased by 2.2%.

In the present study, bending analysis of the functionally graded (FG) with spatial coordinates cylindrical nanoshells in a thermal environment is studied for the first time. The mechanical propert...

In the late eighteenth to early twentieth centuries Qing officials in China periodically instructed their Mongolian subordinates to make maps of the regions they governed. The Qing were aiming ther...

In this paper we present a new numerical technique for the dual-phase-lag model appearing in the heating tissue. The problems are described as general linear partial differential equations with mixed temporal and spatial derivatives defined in irregular 2D domains. We consider heat transfer problems in the inhomogeneous medium, i.e., the coefficients of the governing partial differential equations depend on the spatial coordinates. To solve the problem, we use the combination of the time-stepping method for temporal variables with the meshless semi-analytical technique for the spatial approximation. The effectiveness of the proposed scheme is demonstrated by solving dual-phase-lag heat conduction problems including the generalized bio-heat problem in single and multi-connected irregular domains.

A dynamic modeling method for a multi-panel structure connected with flexible hinges is proposed in this paper. For the solar array composed of several flexible rectangular panels, the global explicit modal functions of spatial coordinates are obtained by employing the Rayleigh-Ritz method, which are in favor of deriving the lower-dimension discrete dynamic equations and the design of controllers for the system in the further research. The Gram-Schmidt characteristic orthogonal polynomials are adopted to construct the flexible deformation expressions of the solar panel. The Lagrange multipliers are introduced to describe the constraint at flexible hinges. The eigen equation of the multi-panel structure is derived to obtain the natural frequencies and corresponding global modes of the system through the Rayleigh-Ritz procedure. Considering the natural frequency calculated from the finite element method as a reference value, the correctness of the modeling process is validated by comparing results with those obtained from the finite element method. Moreover, a high accuracy of the proposed method are confirmed by a very good agreement between results from ANSYS and the theoretical method proposed here. The proposed method derives analytical global modes of the whole system which can be conveniently used to design the controller and work out the nonlinear dynamic model and with a few degree-of-freedoms directly. Finally, the interesting mode interchanged phenomenon is observed with the variation of the stiffness of the rotation spring at flexible hinges.

The authors present the results of numerical experiments aimed to determine the mean square error, which can be used to obtain the spatial coordinates of the object’s under study points through processing images obtained from UAVs using only on-Board global satellite and inertial navigation equipment on drones. At those experiments, aerial photography from the UAVs was modeled for mapping and solving the engineering task of creating a three-dimensional building (structure) model. A pair of images was simulated for the research. The results using the characteristics of the GNSS inertial solutions Trimble АРХ-15 UAV and Trimble АРХ-20 UAV are presented. The experiments show that at solving engineering tasks under conditions corresponding to an arbitrary case of aerial photography, in order to determinie the spatial coordinates with the mean square error not more than 15 mm, the same error of determining the linear elements of external orientation should be 2–5 mm, and the angular elements 10–20ʺ. This significantly exceeds the characteristics corresponding to the post-processing mode for the specified GNSS-inertial solutions. The authors show the effect of angular elements’ external orientation errors to resulting the mean square error of each coordinate and a possible approach to reducing it.

– We use a new approach to combine magnetic and electrical geophysical data from two different archaeological areas in Greece (Europos & Doriskos). – Using the curvelet transform we express the feature of the images as a function of spatial coordinates, scale, and orientation. – A combined image is created in the curvelet domain through the objective selection of the most salient features. – The results show several potential archaeological structures.

The locativity category has philosophical and linguistic interpretations. Linguistics regards locativityas a category encompassing the entire spectrum of spatial meanings represented by such main variations as static, dynamics, transitivity. The category under consideration has been reflected on the sentence level of the structural submodel Sub + Praed + Adv loc with its attached invariant semantics ‘carrier of procedural characteristic + procedural characteristic + spatial boundaries of procedural characteristic’. Formation of differential meanings as specific representatives of invariant content plane is done through all constituent positions –subject, predicate, and near-predicate. The complex locative mechanism is reflected by sentences with the following denotative situations: ‘somebody (something) is residing somewhere’, ‘something (somebody) is heading some-where with registering spatial coordinates –start, finish, both start and finish simultaneously’, ‘something (somebody) is heading somewhere without registering spatial coordinates’. Locative conceptualization mechanism is complex, with its important marker being a locative reference point related to the real roundabouts of somebody or something.

Multicamera systems have important applications in industrial online measurement, attracting wide interest due to their encouraging performance. However, how to develop a convenient and high-accuracy multicamera calibration model is a big challenge. Traditional calibration methods based on 2-D objects require the preparation of high-precision and large-scale calibration objects and have poor adaptability to the spatial distribution of cameras. To address these issues, a convenient and accurate multicamera calibration method is proposed based on an imperfect spherical object. Specifically, a special calibration object, i.e., an imperfect sphere with many coded targets is designed for calibrating the multicamera system, where the optical axes of the cameras are different in orientations and converge into the measurement field. Then, a Euclidean reconstruction method is employed to calculate the camera poses and the spatial coordinates of feature points in the local coordinate system after the adaptive grouping of cameras is completed. Moreover, a graph theory-based optimal path transformation algorithm is developed to obtain the camera poses and spatial point coordinates in the global coordinate system (GCS), and a spherical projection optimization algorithm is designed to spheroidize the spatial coordinates of feature points. In the end, the camera poses and the spatial coordinates of feature points in the GCS are optimized by a bundle adjustment algorithm. We build a multicamera system and conduct extensive experiments to demonstrate the superiority of the proposed method.

In the last years, autonomous vehicle navigation has had considerable improvements; however, this technology has not yet been able to effectively be integrated with mobile systems that work inside buildings, houses or without the influence of absolute references such as GPS. It is for this reason that this paper provides a methodological proposal that allows the integration of the use of object detection methods such as YOLO (You Only Look Once) or R-CNN (Region Convolutional Neural Networks), with laser scanning systems for the measurement of spatial coordinates and an INS system to obtain spatial positioning through relative references. This method integrates the principle of dynamic triangulation and the methodological proposal IKZ (Inertial Navigation with Kalman and Zero Update) that allow to obtain acceptable mobile autonomy times in which the sector of interest is re-scanned.

Physical approach to biological movement is based on the idea of control with referent spatial coordinates for effectors, from the whole body to single muscles. Within this framework, neural control signals induce changes in parameters of corresponding biology-specific laws of nature, and motor performance emerges as a result of interaction with the external force field. This approach is naturally compatible with the principle of abundance and the uncontrolled manifold hypothesis, which offer the framework for analysis of movement stability. The presence of two basic commands, reciprocal and co-activation, makes even single-effector tasks abundant and allows stabilizing their performance at the control level. Kinesthetic perception can be viewed as the process of estimating afferent signals within a reference system provided by the efferent process. Percepts are reflections of stable iso-perceptual manifolds in the combined afferent-efferent multi-dimensional space. This approach offers new, logical and based on laws of nature, interpretations for such phenomena as muscle co-activation, unintentional drifts in performance, and vibration-induced kinesthetic illusions. It also allows predicting new phenomena such as counter-intuitive effects of muscle co-activation of force production and perception, vibration-induced force illusions, performance drifts at two different speeds, and high variability in matching the contribution of individual elements in multi-element tasks. This approach can be developed for various subfields of movement studies including studies of athletics, movement disorders, and movement rehabilitation.

The development of the theory and technology of radio location indicates the need to solve problems of radar recognition of targets by spatial parameters. This is due to the need to provide in promising radar stations (complexes) with a software overview of the required resolution to obtain three-dimensional images in all spatial coordinates. The task of radar targets recognition, which is to assign the observed objects to the appropriate classes and types, is of considerable and growing interest. Different classes (types) of targets make up a certain alphabet, the choice of which determines not only the effectiveness of the use of recognition, but also the difficulties that arise in its implementation. Currently, there are a large number of radar recognition algorithms. They differ in the stages of decision-making, the degree and nature of accounting for statistics of signs, obstacles and signals. Due to the fact that the secondary emission pattern of extended targets is multi-lobed, statistical algorithms are preferred. The information used for radar recognition is contained in the set of received radar signals. However, most often for target recognition certain measured target features are used, which are compared in accordance with the recognition algorithms with known (reference) features. The choice of recognition features is usually made heuristically. This set of parameters does not always allow providing the required quality of recognition. The synthesized algorithms work unstable or require unreasonably large computational costs due to a significant increase in the dimensionality of the feature space. Analysis of known radar recognition algorithms of extended targets shows that they were developed under significant constraints. At present, the tasks of point targets radar surveillance are most fully solved. The tasks of processing signals reflected from bodies of complex shape, given the difficulties of their formulation and solution are not fully explored. It is promising to consider a set of radar surveillance tasks and the criteria used in them as a multicriteria task, the solution of which is associated with vector optimization of the location system as a whole.

The two-point problem for a partial differential equation of the second order in the time and of the infinite order, in general, in spatial coordinates with time-dependent coefficients is investigated. A class of time-analytic functions which are quasipolynomials of a special type at every fixed time is separated. In this selected class of functions, there is a unique solution of the problem. This solution is constructed with the use of the differential-symbol method. Examples of solving the two-point problems for specific partial differential equations with quasipolynomials on the right-hand sides of the conditions are given.

In the last years Autonomous vehicle navigation has had considerable improvements; however, this technology has not yet been able to effectively be integrated with mobile systems that work inside buildings, houses or without the influence of absolute references such as GPS. It is for this reason that this paper provides a methodological proposal that allows the integration of the use of object detection methods such as YOLO (You Only Look Once) or R-CNN (Region Convolutional Neural Networks), with laser scanning systems for the measurement of spatial coordinates and an INS system to obtain spatial positioning through relative references. This method integrates the principle of dynamic triangulation and the methodological proposal IKZ (Inertial Navigation with Kalman and Zero Update) that allow to obtain acceptable mobile autonomy times in which the sector of interest is re-scanned.

A model of distributed information carriers in the form of stable spatially inhomogeneous structures in optical and fiber-optical communication systems is considered. We study the conditions for the occurrence of such stable spatially inhomogeneous structures of the light wave of the generator of the optical radiation. The formation of inhomogeneous structures that occur in a plane orthogonal to the direction of the wave propagation is provided by a thin layer of a nonlinear medium and a two-dimensional lagging feedback loop with the rotation operator of the spatial coordinates of the light wave in the emission plane of the optical generator. In the space of the main parameters of the generator (a control parameter, the angle of rotation of the spatial coordinates, and the magnitude of the delay), the areas of the generation of stable spatially inhomogeneous structures are constructed and the mechanisms of their occurrence are analyzed.

Purpose To quantify and verify the dosimetric impact of high-dose rate (HDR) source positional uncertainty in brachytherapy, and to introduce a model for three-dimensional (3D) position tracking of the HDR source based on a two-dimensional (2D) measurement. This model has been utilized for the development of a comprehensive source quality assurance (QA) method using radiochromic film (RCF) dosimetry including assessment of different digitization uncertainties. Methods An algorithm was developed and verified to generate 2D dose maps of the mHDR-V2 192 Ir source (Elekta, Veenendaal, Netherlands) based on the AAPM TG-43 formalism. The limits of the dosimetric error associated with source (0.9 mm diameter) positional uncertainty were evaluated and experimentally verified with EBT3 film measurements for 6F (2.0 mm diameter) and 4F (1.3 mm diameter) size catheters at the surface (4F, 6F) and 10 mm further (4F only). To quantify this uncertainty, a source tracking model was developed to incorporate the unique geometric features of all isodose lines (IDLs) within any given 2D dose map away from the source. The tracking model normalized the dose map to its maximum, then quantified the IDLs using blob analysis based on features such as area, perimeter, weighted centroid, elliptic orientation, and circularity. The Pearson correlation coefficients (PCCs) between these features and source coordinates (x, y, z, θy , θz ) were calculated. To experimentally verify the accuracy of the tracking model, EBT3 film pieces were positioned within a Solid Water® (SW) phantom above and below the source and they were exposed simultaneously. Results The maximum measured dosimetric variations on the 6F and 4F catheter surfaces were 39.8% and 36.1%, respectively. At 10 mm further, the variation reduced to 2.6% for the 4F catheter which is in agreement with the calculations. The source center (x, y) was strongly correlated with the low IDL-weighted centroid (PCC = 0.99), while the distance to source (z) was correlated with the IDL areas (PCC = 0.96) and perimeters (PCC = 0.99). The source orientation θy was correlated with the difference between high and low IDL-weighted centroids (PCC = 0.98), while θz was correlated with the elliptic orientation of the 60-90% IDLs (PCC = 0.97) for a maximum distance of z = 5 mm. Beyond 5 mm, IDL circularity was significant, therefore limiting the determination of θz (PCC ≤ 0.48). The measured positional errors from the film sets above and below the source indicated a source position at the bottom of the catheter (-0.24 ± 0.07 mm). Conclusions Isodose line features of a 2D dose map away from the HDR source can reveal its spatial coordinates. RCF was shown to be a suitable dosimeter for source tracking and dosimetry. This technique offers a novel source QA method and has the potential to be used for QA of commercial and customized applicators.

A method for calculating a cylindrical linear valve motor is proposed. The method is divided into formalized successive stages. Within this method, the electromagnetic field distribution in the motor along all three spatial coordinates is taken into account. The main purpose of creating the method is to reduce the calculation time. This is achieved by reducing the three-dimensional formulation of the problem of the motor electromagnetic field to the two-dimensional formulation. The calculation accuracy is improved as compared to similar axisymmetric problems by considering the electromagnetic field distortion due to the angular coordinate. The calculation method allows reducing the time of motor calculation and achieves acceptable accuracy.

Community ecologists and macroecologists have long sought to evaluate the importance of environmental conditions in determining species distributions, community composition, and diversity across sites. Different methods have been used to estimate species-environment relationships, but their differences to jointly fit and disentangle spatial autocorrelation and structure remain poorly studied. We compared how methods in four broad families of statistical models estimated the contribution of the environment and space to variation in species binary occurrence and abundance. These methods included distance-based regression, generalized linear models (GLM, and the special case of RDA), generalized additive models (GAM), and tree-based machine learning (ML): regression trees, boosted regression trees (BRT), and random forests. The spatial component of the model consisted of spatial distance (in distance-based regression), Moran’s Eigenvector Maps (MEM; in GLM and ML), smooth spatial splines (in GAM), or tree-based non-linear modelling of spatial coordinates (in ML). We simulated typical site-by-species data to assess the methods’ performance in (1) fitting environmental and spatial models, and (2) partitioning the variation explained by environmental and spatial predictors. We observed marked differences in performance mostly caused by imbalanced performance in estimating environmental and spatial effects. Such differences also manifested when analyzing eight different empirical datasets. GLM and BRT with MEMs were generally the most reliable methods for partitioning the variation explained by environmental and spatial effects across a wide range of simulated scenarios. The remaining methods tended to underfit simulated spatial structures, causing underestimation of spatial fractions of variation. Our results suggest that previously overlooked methods for performing variation partitioning, especially tree-based ML, offer flexible approaches to analyze site-by-species matrices. We provide general guidelines on the usefulness of different models under different ecological and sampling scenarios, for species distribution modelling, community ecology, and macroecology.

For practical use, the relevance of indoor surveillance from multiple cameras to track the movement of people and reidentify them in video sequences is constantly increasing. This is a complex task due to the effect of uneven illumination, background inhomogeneity, overlap, uncertainty of the trajectories of people, and the similarity of their visual features. The article presents an approach to track people by video sequences and reidentify them in multicamera video surveillance systems that are used indoors. At the first step, people are detected using a YOLO v4 convolution neural network (CNN) and described by a rectangular area. Further, the search for the face area and the calculation of its features are carried out, which in the developed method are used when accompanying a person in a video sequence and during his intercamera reidentification. This approach improves the accuracy of tracking with a complex movement trajectory and multiple intersections of people with similar characteristics. The search for faces is carried out on the detected areas based on the multitasking MTCNN, and the MobileFaceNetwork model is used to form the vector of the features of the face. Human features are generated using a modified CNN based on ResNet34 and an HSV color tone channel histogram. The correspondence between people on different frames is established based on the analysis of the spatial coordinates of faces and people, as well as their CNN features, using the Hungarian algorithm. To ensure the accuracy of intercamera tracking, reidentification is performed based on the facial features. Five test video sequences of different numbers of people captured indoors with a fixed video camera were used to test and compare different approaches. The obtained experimental results confirmed the strength of the characteristics of the proposed approach.

Standard anatomical atlases are common in neuroimaging because they facilitate data analyses and comparisons across subjects and studies. The purpose of this study was to develop a standardized human brain atlas based on the physical mechanical properties (i.e., tissue viscoelasticity) of brain tissue using magnetic resonance elastography (MRE). MRE is a phase contrast‐based MRI method that quantifies tissue viscoelasticity noninvasively and in vivo thus providing a macroscopic representation of the microstructural constituents of soft biological tissue. The development of standardized brain MRE atlases are therefore beneficial for comparing neural tissue integrity across populations. Data from a large number of healthy, young adults from multiple studies collected using common MRE acquisition and analysis protocols were assembled (N = 134; 78F/ 56 M; 18–35 years). Nonlinear image registration methods were applied to normalize viscoelastic property maps (shear stiffness, μ, and damping ratio, ξ) to the MNI152 standard structural template within the spatial coordinates of the ICBM‐152. We find that average MRE brain templates contain emerging and symmetrized anatomical detail. Leveraging the substantial amount of data assembled, we illustrate that subcortical gray matter structures, white matter tracts, and regions of the cerebral cortex exhibit differing mechanical characteristics. Moreover, we report sex differences in viscoelasticity for specific neuroanatomical structures, which has implications for understanding patterns of individual differences in health and disease. These atlases provide reference values for clinical investigations as well as novel biophysical signatures of neuroanatomy. The templates are made openly available (github.com/mechneurolab/mre134) to foster collaboration across research institutions and to support robust cross‐center comparisons., Tissue mechanical properties provide a macroscopic representation of the microstructural constituents of soft biological tissue. In the work, we have produced the first standard‐space atlas description of the stiffness and damping ratio of the healthy human brain. The detailed nature of the new atlas has revealed that neuroanatomical structures possess distinct mechanical characteristics and that sex differences exist in a range of brain structures. Our results provide a novel biophysical signature of the brain and have implications for understanding individual differences in both health and disease.

In time-delay two-dimensional (2-D) systems, the variables not only depend on time but also on spatial coordinates, moreover, more than one input data are subjected to time-delays at each sampling time. In order to overcome these difficulties, this paper develops an expectation maximization (EM) identification algorithm for estimating the 2-D systems. Compared with the traditional compressed sensing recovery algorithm and the redundant rule based estimation algorithm, the EM algorithm in this paper has three integrated key functions, (1) to estimate the parameters and the unknown time-delays simultaneously, (2) to keep the number of the unknown parameters unchanged, (3) to identify the 2-D systems with varying time-delays. The simulations made further guarantees the usefulness of the proposed algorithm.

This article studies and analyzes the results of applying numerical iterative methods for solving nonlinear equation systems (Newton, modified Newton's method, gradient descent, sequential iterations, Levenberg – Marquardt), compiled and used to calculate the rectangular spatial coordinates of radio emission sources in range-difference passive radars of various configurations (incorporating from 3 to 4 receiving points). The aim of the research was to determine the optimal number of receiving points and to select the most effective algorithm for coordinate transformations of the vector of observed parameters (a set of range difference estimates from radio emission sources to the corresponding pairs of receiving points) into the vector of measured parameters (rectangular spatial coordinates). The following parameters were used as comparison criteria: passive radar working area (a part of space where the deviation of target coordinate estimates from their true values does not exceed the maximum tolerable values); average error in calculating spatial coordinates in the working area; iterations number of coordinate calculation in the analyzed part of space. Upon completing a comparative analysis of obtained characteristics and dependencies, we concluded that it is optimal to include four receiving points in a range-difference passive radar and use the Levenberg – Marquardt method to calculate the spatial coordinates of radio emission sources.