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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.

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.

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.

In the absence of vision, spatial representation may be altered. When asked to compare the relative distances between three sounds (i.e., auditory spatial bisection task), blind individuals demonstrate significant deficits and do not show an event‐related potential response mimicking the visual C1 reported in sighted people. However, we have recently demonstrated that the spatial deficit disappears if coherent time and space cues are presented to blind people, suggesting that they may use time information to infer spatial maps. In this study, we examined whether the modification of temporal cues during space evaluation altered the recruitment of the visual and auditory cortices in blind individuals. We demonstrated that the early (50–90 ms) occipital response, mimicking the visual C1, is not elicited by the physical position of the sound, but by its virtual position suggested by its temporal delay. Even more impressively, in the same time window, the auditory cortex also showed this pattern and responded to temporal instead of spatial coordinates.

In Hyperspectral image (HSI) classification, combining spectral information with spatial information has become an efficient measure to obtain good classification results, where spatial information is generally introduced in an unsupervised way or some complicated way. We introduce spatial coordinates as the spatial information in a simple supervised way and propose two HSI classification algorithms, where spatial coordinates of samples are regarded as the spatial features of samples. A spectral-spatial classification algorithm is proposed, named as HSI Classification Based on Spectral-Spatial Feature Fusion using Spatial Coordinates (SSFFSC). The HSI is divided into multiple small images in spatial dimension, and samples in each small image are randomly selected as training samples. Support vector machine (SVM) is used to classify the samples to obtain the probability of samples belonging to each class according to the spatial coordinate features and spectral features respectively. The probability features are further classified by SVM to achieve the final classification result. Considering that the performance of SSFFSC relies on the partition of HSI, SSFFSC is further combined with active learning (AL) as a new method named as HSI Classification Based on Active Learning and SSFFSC (SSFFSC-AL). Partition of HSI is omitted and the training samples are selected adaptively by AL’s sampling scheme. We find spatial coordinates are useful spatial information. SSFFSC and SSFFSC-AL run fast and improve the classification accuracy effectively by using the spatial coordinates as the spatial features. Experiments demonstrate that comparing with other algorithms, SSFFSC and SSFFSC-AL can obtain higher classification accuracy in less time.

Nowadays, with the incursion of low-cost GNSS receivers with modern characteristics, it is common to investigate and apply new methodologies and solutions with different receivers of this nature. Based on this fact, the performance of the solution obtained from the low-cost GNSS receiver is evaluated compared to a geodetic grade GNSS receiver at different sampling frequencies for the PPP-static and PPP-kinematic modes. For this, the original RINEX observation files were analyzed and decimated into different sampling rates as 0.1, 0.2, 1, 5, 15 and 30 s with TEQC software. All RINEX files were submitted to the Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) online service for processing with static and kinematic modes. The PPP-derived coordinates from the low-cost GNSS receiver were compared with the geodetic receiver to evaluate the obtained solution. The results reveal that the behavior of all studied sampling rates from the low-cost GNSS receiver are constant in achieved positioning. In addition, the achieved precision shows that it is recommendable to use a high sampling rate to obtain a cm level in PPP-static mode by using a low-cost GNSS receiver, this mode being the most accurate and potential alternative for structural health monitoring studies, mapping and positioning in urban areas.

Embryonic development leads to the reproducible and ordered appearance of complexity from egg to adult. The successive differentiation of different cell types that elaborate this complexity results from the activity of gene networks and was likened by Waddington to a flow through a landscape in which valleys represent alternative fates. Geometric methods allow the formal representation of such landscapes and codify the types of behaviors that result from systems of differential equations. Results from Smale and coworkers imply that systems encompassing gene network models can be represented as potential gradients with a Riemann metric, justifying the Waddington metaphor. Here, we extend this representation to include parameter dependence and enumerate all three-way cellular decisions realizable by tuning at most two parameters, which can be generalized to include spatial coordinates in a tissue. All diagrams of cell states vs. model parameters are thereby enumerated. We unify a number of standard models for spatial pattern formation by expressing them in potential form (i.e., as topographic elevation). Turing systems appear nonpotential, yet in suitable variables the dynamics are low dimensional and potential. A time-independent embedding recovers the original variables. Lateral inhibition is described by a saddle point with many unstable directions. A model for the patterning of the Drosophila eye appears as relaxation in a bistable potential. Geometric reasoning provides intuitive dynamic models for development that are well adapted to fit time-lapse data.

Skull registration is one of the important steps in craniofacial reconstruction, and its registration accuracy and efficiency have an important impact on the reconstruction results. To solve the problem of low accuracy and efficiency of existing skull registration methods, a hierarchical skull point cloud registration method is proposed in this paper. The whole registration process is divided into a rough registration stage and a fine registration stage. Firstly, feature points are extracted from the pre-processed skull point cloud model, and a local coordinate reference system is established according to the feature points and their neighbor points. The improved spin image is used to construct the local feature descriptor. The feature matching is carried out according to the nearest neighbor algorithm, and the k-means algorithm is used to eliminate the mismatching points to achieve skull rough registration. Then, based on rough registration, we use an improved ICP algorithm to achieve fine registration of the skull. In this process, we use random sampling to reduce the search scale of points and add geometric feature constraints to further eliminate mismatched points. Finally, the whole registration algorithm is applied to the skull point cloud data to verify. The experimental results show that, compared with other methods, the registration effect and efficiency of the proposed method are superior to those of other methods. In order to verify the universality of the method, we also use a common data set for verification. Experiments show that the method is also very effective.

Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization problem to reduce cycle skipping in full-waveform inversion (FWI). WRI is often implemented by solving for the frequency-domain representation of the wavefield using the finite-difference method. The approach requires matrix inversions and affords limited flexibility to accommodating irregular model geometries. On the other hand, physics-informed neural network (PINN) uses the underlying physical laws as loss functions to train the neural network (NN) to provide flexible continuous functional approximations of the solutions without matrix inversions. By including a data-constrained term in the loss function, the trained NN can reconstruct a wavefield that simultaneously fits the recorded data and satisfies the Helmholtz equation for a given initial velocity model. Using the predicted wavefields, we rely on a small-size NN to predict the velocity using the reconstructed wavefield. In this velocity prediction NN, spatial coordinates are used as input data to the network and the scattered Helmholtz equation is used to define the loss function. After we train this network, we are able to predict the velocity in the domain of interest. We develop this PINN-based WRI method and demonstrate its potential using a part of the Sigsbee2A model and a modified Marmousi model. The results show that the PINN-based WRI is able to invert for a reasonable velocity with very limited iterations and frequencies, which can be used in a subsequent FWI application.

The reconstruction of electrophysiological sources within the brain is sensitive to the constructed head model, which depends on the positioning accuracy of anatomical landmarks known as fiducials. In this work, we propose an algorithm for the automatic detection of fiducial landmarks of EEG electrodes on the 3D human head model. Our proposal combines a dimensional reduction approach with a perspective projection from 3D to 2D object space; the eye and ear automatic detection in a 2D face image by two cascades of classifiers and geometric transformations to obtain 3D spatial coordinates of the landmarks and to generate the head coordinate system, This is accomplished by considering the characteristics of the scanner information. Capturing the 3D model of the head is done with Occipital Inc. ST01 structure sensor and the implementation of our algorithm was carried out on MATLAB R2018b using the Computer Vision Toolbox and the FieldTrip Toolbox. The experimental results were aimed at recursively exploring the efficacy of the facial feature detectors as a function of the projection angle; they show that robust results are obtained in terms of false acceptance rate. Our proposal is an initial step of an approach for the automatic digitization of electrode locations. The experimental results demonstrate that the proposed method detects anatomical facial landmarks automatically, accurately, and rapidly.

Single hot-wire velocity measurements have been conducted along a three-dimensional measurement grid to capture the flow-field induced by a 45 $$^\circ$$ inclined slotted pulsed jet. Based on the periodic behavior of the flow, two different estimation methods have been implemented. The first one, considered as the reference baseline, is the conditional approach which consists in the redistribution of the experimental data into space- and time-resolved three-dimensional velocity fields. The second one uses a neural network to estimate 3D velocity fields given spatial coordinates and time. This paper compares the two methods for a complete flow-field estimation based on hot-wire measurements. Results suggest that the neural network is tailored to capture the phase-averaged dynamic response of the jet induced by the actuator, and identify the coherent structures in the flow field. Interesting performances are also observed when degrading the learning database, meaning that neural networks can be used to drastically improve the temporal or spatial resolution of a flow field estimation compared to the experimental data resolution.

This paper proposes an action recognition algorithm based on the capsule network and Kalman filter called “Reading Pictures Instead of Looking” (RPIL). This method resolves the convolutional neural network’s over sensitivity to rotation and scaling and increases the interpretability of the model as per the spatial coordinates in graphics. The capsule network is first used to obtain the components of the target human body. The detected parts and their attribute parameters (e.g., spatial coordinates, color) are then analyzed by Bert. A Kalman filter analyzes the predicted capsules and filters out any misinformation to prevent the action recognition results from being affected by incorrectly predicted capsules. The parameters between neuron layers are evaluated, then the structure is pruned into a dendritic network to enhance the computational efficiency of the algorithm. This minimizes the dependence of in-depth learning on the random features extracted by the CNN without sacrificing the model’s accuracy. The association between hidden layers of the neural network is also explained. With a 90% observation rate, the OAD dataset test precision is 83.3%, the ChaLearn Gesture dataset test precision is 72.2%, and the G3D dataset test precision is 86.5%. The RPILNet also satisfies real-time operation requirements (&gt, 30 fps).

Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image., Author summary We propose a two-step graph-based analyses for high-dimensional multiplexed datasets characterizing ROIs and their inter-relationships. The first step consists of extracting the steady state distribution of the random walk on the graph, which captures the mutual relations between the covariates of each ROI. The second step employs a nonlinear dimensionality reduction on the steady state distributions to construct a map that unravels the intrinsic geometric structure of the ROIs. We theoretically show that when the ROIs have a two-class structure, our method accentuates the distinction between the classes. Particularly, in a setting with Gaussian distribution it outperforms the MAP estimator, implying that the mutual relations between the covariates within the ROIs and spatial coordinates are well captured by the steady state distributions. We apply our method to imaging mass cytometry (IMC). Our analysis provides a representation that facilitates prediction of the sensitivity to PD-1 axis blockers treatment of lung cancer subjects. Particularly, our approach achieves state of the art results with average accuracy of 97.3% on two IMC datasets.

Power law distributions characterise several natural and social phenomena. Zipf’s law for cities is one of those. The study views the question of whether that global regularity is independent of different spatial distributions of cities. For that purpose, a typical Zipfian rank-size distribution of cities is generated with random numbers. This distribution is then cast into two different settings of spatial coordinates. For the estimation, the variables rank and size are supplemented by considerations of spatial dependence within a spatial econometric approach. Results suggest that distance potentially matters. This finding is further corroborated by four country analyses even though estimates reveal only modest effects.

Made available in DSpace on 2021-07-14T10:31:27Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-12-21. Added 1 bitstream(s) on 2021-07-14T11:32:07Z : No. of bitstreams: 1 S1806-11172021000100410.pdf: 460980 bytes, checksum: f2347d664650114c993e47a62cca41c9 (MD5) The Schrödinger equation for an isotropic three-dimensional harmonic oscillator is solved using ladder operators. The starting point is the shape invariance condition, obtained from supersymmetric quantum mechanics. Generalized ladder operators can be constructed for the three spherical spatial coordinates. Special emphasis is given to the adaptation made to each of these coordinates. The approach used is general and is indicated as an alternative method to solve the Schrödinger equation. Universidade Estadual Paulista, Instituto de Biociências, Letras e Ciências Exatas Universidade Estadual Paulista, Instituto de Biociências, Letras e Ciências Exatas

The angular elements of external orientation characterize the position of a shooting camera relative to the coordinate system in which the spatial coordinates of the points of the object under study are determined from the processing of its images. In many cases of aerial photography, e.g. shooting from an unmanned aerial vehicle, as well as in the case of space survey, the values of the orientation angles could be very significant. This paper presents the results of numerical experiments for five different systems of external orientation angles (Euler angles). The studies were performed using the condition of complanarity, which is the basis of space forward intersection. For a space forward intersection, a model of errors in determining spatial coordinates for five systems of shooting camera orientation angles has been developed. In the numerical experiments, the general case of aerial photography from an unmanned aerial vehicle and of space survey of the Earth were simulated. By comparing the root-mean-square errors (RMSE) in determining the spatial coordinates obtained using the studied systems of external orientation angles, the features of the use of these systems of orientation angles were revealed. The results of the research will allow to determine the spatial coordinates of the points of the studied objects with a greater degree of reliability by photogrammetry methods.

We assess the accuracy and the precision of the TanDEM-X digital elevation model (DEM) of the western Gulf of Corinth, Greece. We use a dense set of accurate ground coordinates obtained by kinematic Global Navigation Satellite Systems (GNSS) observations. Between 2001 and 2019, 148 surveys were made, at a 1 s sampling rate, along highways, roads, and tracks, with a total traveled distance of ∼25 000 km. The data are processed with the online Canadian Spatial Reference System precise point positioning software. From the output files, we select 885 252 coordinates from epochs with theoretical uncertainty below 0.1 m in horizontal and 0.2 m in vertical. Using specific calibration surveys, we estimate the mean vertical accuracy of the GNSS coordinates at 0.2 m. Resampling the DEM by a factor of 10 allows one to compare it with the GNSS in pixels of metric size, smaller than the width of the roads, even the small trails. The best fit is obtained by shifting the DEM by 0.47 ± 0.03 m upward, 0.10 ± 0.1 m westward, and 0.36 ± 0.1 m southward. Those values are 20 times below the nominal resolution of the DEM. Once the shift is corrected, the root mean square deviation between TanDEM-X DEM and GNSS elevations is 1.125 m. In forest and urban areas, the shift between the DEM and the GNSS increases by ∼0.5 m. The metric accuracy of the TanDEM-X DEM paves the way for new applications for long-term deformation monitoring of this area.

The safety of the gas transmission infrastructure is one of the main concerns for infrastructure operating companies. Common gas pipelines’ tightness control is tedious and time-consuming. The development of new methods is highly desirable. This paper focuses on the applications of air-borne methods for inspections of the natural gas pipelines. The main goal of this study is to test an unmanned aerial vehicle (UAV), equipped with a remote sensing methane detector, for natural gas leak detection from the pipeline network. Many studies of the use of the UAV with laser detectors have been presented in the literature. These studies include experiments mainly on the artificial methane sources simulating gas leaks. This study concerns the experiments on a real leakage of natural gas from a pipeline. The vehicle at first monitored the artificial source of methane to determine conditions for further experiments. Then the experiments on the selected section of the natural gas pipelines were conducted. The measurement data, along with spatial coordinates, were collected and analyzed using machine learning methods. The analysis enabled the identification of groups of spatially correlated regions which have increased methane concentrations. Investigations on the flight altitude influence on the accuracy of measurements were also carried out. A range of between 4 m and 15 m was depicted as optimal for data collection in the natural gas pipeline inspections. However, the results from the field experiments showed that areas with increased methane concentrations are significantly more difficult to identify, though they are still noticeable. The experiments also indicate that the lower altitudes of the UAV flights should be chosen. The results showed that UAV monitoring can be used as a tool for the preliminary selection of potentially untight gas pipeline sections.

Usually, previous studies on the future development pathway of coal power are based on the economic models to provide the administrative pathways, or the coal-fired power plants dataset to provide bottom-up pathways with the multi-scenario hypothesis. However, these two methods above are difficult to be combined: there is a gap between the comprehensive consideration of economic, policy, and environmental factors, with the high spatial resolution of technology and space. This study narrows the gap between regional projection and unit data, and also considers the uncertainty of the operating units with the Monte Carlo Method. Firstly, we evaluate the score of each unit according to its technical parameters and other attribute information, which is based on a sufficient dataset of coal-fired power units with their geographical spatial coordinates. And next, the probability distribution function is built according to the scores of the candidate units. Then, we do sampling from the candidate units until the total capacity reaches the regional projection of the coal power development goal. Based on this method, we could identify the spatial distribution probability of coal-fired power units in the future, and therefore it can help us explore the environmental impacts in high-resolution space.•The method calculates the probability of operating status of candidate units using technical and attribute information-base scores with Monte Carlo method.•This paper describes the uncertainties in determining the spatial distribution of future power plants, and verifies the robustness of the results.•This method narrows the scale gap between regional projection and unit-level data., Graphical abstract Image, graphical abstract

SPatio-temporal ENcoding (SPEN) MRI is a non-Fourier imaging technique that encodes the spatial information in such a way that there is a one-to-one correspondence between the signal intensity as a function of time and the spin density at the corresponding position. In current spatio-temporal encoding methods imparting a quadratic phase – that is the phase of the transverse magnetization depends as a quadratic function of the spatial coordinates – onto the transverse magnetization is the first crucial step. Usually, this is achieved by simultaneous application of a frequency-swept (chirp) pulse and a linear magnetic field gradient. In this work, we show that it can be advantageous to use quadratic encoding gradients for this purpose. By avoiding chirp pulses one can achieve much smaller specific absorption rates (SARs), and shorter echo times (TEs), while the spatial resolution, the field of view (FOV), and the signal-to-noise ratio (SNR) are the same as in SPEN if one uses similar parameters. In addition, the proposed sequence can readily be used for multi-slice applications.

Given the closeness of the two open clusters Cr 135 and UBC 7 on the sky, we investigate the possibility of the two clusters to be physically related. We aim to recover the present-day stellar membership in the open clusters Collinder 135 and UBC 7 (300 pc from the Sun), to constrain their kinematic parameters, ages and masses, and to restore their primordial phase space configuration. The most reliable cluster members are selected with our traditional method modified for the use of Gaia DR2 data. Numerical simulations use the integration of cluster trajectories backwards in time with our original high order Hermite4 code \PGRAPE. We constrain the age, spatial coordinates and velocities, radii and masses of the clusters. We estimate the actual separation of the cluster centres equal to 24 pc. The orbital integration shows that the clusters were much closer in the past if their current line-of-sight velocities are very similar and the total mass is more than 7 times larger the mass of the determined most reliable members. We conclude that the two clusters Cr 135 and UBC 7 might very well have formed a physial pair, based on the observational evidence as well as numerical simulations. The probability of a chance coincidence is only about $2\%$., Accepted for publication as a Letter in Astronomy and Astrophysics. 5 pages, 2 figures

To what extent does specific spatiotopic location accompany the remembered representation of a visual event? Feature integration theory suggests that identifying a multi-feature object requires focusing on its spatial location to integrate those features. Moreover, single unit data from anterior ventral stream neurons that fire preferentially to complex objects indicates that they have retinotopic receptive fields. It can, therefore, be predicted that location information of features of a complex stimulus is inherent in the memory of a perceived visual stimulus’ representation. To evaluate this prediction, we presented participants with a brief array of characters with instructions to identify and locate the solitary letter among a set of digits. Surprisingly, analysis of trials in which the target identity was accurately reported indicated that in more than 15% of trials (i.e., in Experiments 2b & 2c) participants were almost completely uninformed about the location of the letter that they had just identified. Further analysis showed that there were two main sources of these location errors; misbinding the target to the distractors’ locations and extremely poor spatial representation of the target’s location to an extent that was indistinguishable from guessing. The latter finding indicates that consciously accessible representations of visual events can form despite being untethered to robust and spatially-specific representations, implying that the specific location was either not quite encoded into working memory, or was rapidly forgotten. However, when the target was marked by a single feature (color), there was no evidence of remembering the target identity without remembering its location even with strong masking.

Several works have demonstrated that visual experience plays a critical role in the development of allocentric spatial coding. Indeed, while children with a typical development start to code space by relying on allocentric landmarks from the first year of life, blind children remain anchored to an egocentric perspective until late adolescence. Nonetheless, little is known about when and how visually impaired children acquire the ability to switch from an egocentric to an allocentric frame of reference across childhood. This work aims to investigate whether visual experience is necessary to shift from bodily to external frames of reference. Children with visual impairment and normally sighted controls between 4 and 9 years of age were asked to solve a visual switching-perspective task requiring them to assume an egocentric or an allocentric perspective depending on the task condition. We hypothesize that, if visual experience is necessary for allocentric spatial coding, then visually impaired children would have been impaired to switch from egocentric to allocentric perspectives. Results support this hypothesis, confirming a developmental delay in the ability to update spatial coordinates in visually impaired children. It suggests a pivotal role of vision in shaping allocentric spatial coding across development.

Consider the motion of a Brownian particle in three dimensions, whose two spatial coordinates are standard Brownian motions with zero drift, and the remaining (unknown) spatial coordinate is a standard Brownian motion with a (known) non-zero drift. Given that the position of the Brownian particle is being observed in real time, the problem is to detect as soon as possible and with minimal probabilities of the wrong terminal decisions, which spatial coordinate has the non-zero drift. We solve this problem in the Bayesian formulation, under any prior probabilities of the non-zero drift being in any of the three spatial coordinates, when the passage of time is penalised linearly. Finding the exact solution to the problem in three dimensions, including a rigorous treatment of its non-monotone optimal stopping boundaries, is the main contribution of the present paper. To our knowledge this is the first time that such a problem has been solved in the literature.

Three-dimensional (3D) reconstruction is a useful technique for the documentation, characterization, and evaluation of small archeological objects. In this research, a comparison among different photogrammetric setups that use different lenses (macro and standard zoom) and dense point cloud generation calibration processes for real specific objects of archaeological interest with different textures, geometries, and materials is raised using an automated data collection. The data acquisition protocol is carried out from a platform with control points referenced with a metrology absolute arm to accurately define a common spatial reference system. The photogrammetric reconstruction is performed considering a camera pre-calibration as well as a self-calibration. The latter is common for most data acquisition situations in archaeology. The results for the different lenses and calibration processes are compared based on a robust statistical analysis, which entails the estimation of both standard Gaussian and non-parametric estimators, to assess the accuracy potential of different configurations. As a result, 95% of the reconstructed points show geometric discrepancies lower than 0.85 mm for the most unfavorable case and less than 0.35 mm for the other cases.

Radio maps provide metrics such as power spectral density for every location in a geographic area and find numerous applications such as UAV communications, interference control, spectrum management, resource allocation, and network planning to name a few. Radio maps are constructed from measurements collected by spectrum sensors distributed across space. Since radio maps are complicated functions of the spatial coordinates due to the nature of electromagnetic wave propagation, model-free approaches are strongly motivated. Nevertheless, all existing schemes for radio occupancy map estimation rely on interpolation algorithms unable to learn from experience. In contrast, this paper proposes a novel approach in which the spatial structure of propagation phenomena such as shadowing is learned beforehand from a data set with measurements in other environments. Relative to existing schemes, a significantly smaller number of measurements is therefore required to estimate a map with a prescribed accuracy. As an additional novelty, this is also the first work to estimate radio occupancy maps using deep neural networks. Specifically, a fully convolutional deep completion autoencoder architecture is developed to effectively exploit the manifold structure of this class of maps., 15 pages, 19 figures. Accepted for publication in the IEEE Transactions on Wireless Communications. arXiv admin note: text overlap with arXiv:1911.12810

With the extensive development and utilization of urban underground space, coal mines, and other indoor areas, the indoor positioning technology of these areas has become a hot research topic. This paper proposes a robust localization method for indoor mobile platforms. Firstly, a series of coding graphics were designed for localizing the platform, and the spatial coordinates of these coding graphics were calculated by using a new method proposed in this paper. Secondly, two spatial resection models were constructed based on unit weight and Tukey weight to localize the platform in indoor environments. Lastly, the experimental results show that both models can calculate the position of the platform with good accuracy. The space resection model based on Tukey weight correctly identified the residuals of the observations for calculating the weights to obtain robust positioning results and has a high positioning accuracy. The navigation and positioning method proposed in this study has a high localization accuracy and can be potentially used in localizing practical indoor space mobile platforms.

Light detection and ranging (Lidar) spatial coordinates, especially height data, and the intensity data of point clouds are often used for strip adjustment in airborne Lidar. However, inconsistency in the intensity data and then intensity gradient data because of the variations in the incidence and reflection angles in the scanning direction and sunlight incident in the same areas of different strips may cause problems in the Lidar strip adjustment process. Instead of the Lidar intensity, a new type of data, termed surface feature strength data derived by using the tensor voting method, were introduced into the strip adjustment process using the partial least squares method in this study. These data are consistent in the same regions of different strips, especially on the roofs of buildings. Our experimental results indicated a significant improvement in the accuracy of strip adjustment results when both height data and surface feature strength data were used.

This study focuses on the measurement accuracy of Magnetic Resonance Velocimetry (MRV) in high-speed turbulent flows. One of the most prominent errors in MRV is the displacement error, which describes the misregistration of spatial coordinates and velocity components in moving fluids. Displacement errors are particularly critical for experiments with high flow velocity and high spatial resolution. The degree of displacement error also depends on the sequence structure of the MRV technique. In this study, two MRV sequence types are examined regarding their measurement capabilities in high-speed turbulent flows: a conventional MRV sequence based on the popular “4D FLOW” technique, and a newly developed sequence, named “SYNC SPI”. Compared to conventional MRV, SYNC SPI is designed for high measurement accuracy, and not for imaging speed, which limits its application to statistically stationary flows. Both sequence types are evaluated in a flow experiment with a converging–diverging nozzle. Time-averaged results are presented for velocities up to 12 m/s at the throat. Supported by Particle Imaging Velocimetry, it is shown that SYNC SPI is capable of acquiring accurate velocity data in these highly turbulent flows. In contrast, the data from the conventional MRV sequence exhibits substantial displacement errors with a maximum displacement of 21 mm. The long acquisition time is the main disadvantage of the SYNC SPI sequence. Therefore, it is examined if undersampling and non-linear reconstruction, known as Compressed Sensing, can be utilized to make data acquisition more efficient. In the presented measurements, Compressed Sensing is successfully applied to shorten the acquisition time by up to 70% with almost no reduction in measurement accuracy.

This document reports on an exercise involving several government organizations, private companies, and academics trying to harmonize various representations of boreholes into acommon model. Boreholes are widely used in geoscience and engineering to probe and assess the underground, and all those communities (custodians, vendors, distributors, etc.) model this information differently. This heterogeneity becomes a problem when information from various sources needs to be aggregated. On the other hand, different communities use boreholes,therefore, different models are expected to address different use cases. This experiment looked at solutions to harmonize core aspects of boreholes while recognizing domain specific models. This exercise resulted in a tentative model, examples and recommendations for next steps. Those steps include the recommendation for OGC to support the creation of a Borehole Standards Working Group (SWG) to formalize the core borehole model (BoreholeML (BhML)) as anofficial standard. Future work should include expanding the scope of the draft borehole model presented here and co-ordinate with ongoing OGC Geoscience standards for geology and hydrogeology to ensure that those standards can operate together. It also includes working with existing standard custodians to produce BhML-compliant version of their existing borehole standards. Generic information describing the Borehole (who, how, when, etc.) and the other aspects that describe a Borehole (e.g. construction, monitoring, management areas, etc.) are required butwere excluded from this IE. Many of these are already summarized in pre-existing models. This semantic enrichment should be undertaken by a second Borehole IE or a SWG. This report includes several XML Instance Documents that conform to a draft BhML XML Schema. These are contained in the various ER Annexes as well as from the Borehole IE GitHub repository (https://github.com/opengeospatial/boreholeie/). Where appropriate, the report references the corresponding GitHub content.

The object of our work is to test the possibility of using the method of multidimensional scaling to identify the features of EEG in order to classify subjects in patients with dyscirculatory encephalopathy and healthy people by establishing differences in the graphs of the dynamics of the location of vectors in the multidimensional coordinate space. For healthy subjects, in almost all tests, there is a coincidence or close location of the spatial coordinates of the multidimensional scaling. For patients with dyscirculatory encephalopathy, there is no marked coincidence of coordinates; however, in some cases it is possible to group the data into local areas according to individual tests. The use of the method of multidimensional scaling in the diagnosis of dyscirculatory encephalopathy allows the analysis of the patient's condition, makes it possible to identify the degree of the disease

I present the first public releases (v3.4 and v3.5) of the USINE code for cosmic-ray propagation in the Galaxy (https://lpsc.in2p3.fr/usine). It contains several semi-analytical propagation models previously used in the literature (leaky-box model, 2-zone 1D and 2D diffusion models) for the calculation of nuclei ($Z=1-30$), anti-protons, and anti-deuterons. For minimisations, the geometry, transport, and source parameters of all models can be enabled as free parameters, whereas nuisance parameters are enabled on solar modulation levels, cross sections (inelastic and production), and systematics of the CR data. With a single ASCII initialisation file to configure runs, its many displays, and the speed associated to semi-analytical approaches, USINE should be a useful tool for beginners, but also for experts to perform statistical analyses of high-precision cosmic-ray data., Comment: 19 pages, 8 figures, 5 tables, also describe V3.5 used in arXiv:1904.08210, arXiv:1904.08917, and arXiv:1906.07119

Objective The process of diagnosing many neurodegenerative diseases, such as Parkinson's and progressive supranuclear palsy, involves the study of brain magnetic resonance imaging (MRI) scans in order to identify and locate morphological markers that can highlight the health status of the subject. A fundamental step in the pre-processing and analysis of MRI scans is the identification of the mid-sagittal plane, which corresponds to the mid-brain and allows a coordinate reference system for the whole MRI scan set. Approach To improve the identification of the mid-sagittal plane we have developed an algorithm in Matlab® based on the k-means clustering function. The results have been compared with the evaluation of four experts who manually identified the mid-sagittal plane and whose performances have been combined with a cognitive decisional algorithm in order to define a gold standard. Main results The comparison provided a mean percentage error of 1.84%. To further refine the automatic procedure we trained a machine learning system using the results from the proposed algorithm and the gold standard. We tested this machine learning system and obtained results comparable to medical raters with a mean absolute error of 1.86 slices. Significance The system is promising and could be directly incorporated into broader diagnostic support systems.

Spectrum maps, which provide RF spectrum metrics such as power spectral density for every location in a geographic area, find numerous applications in wireless communications such as interference control, spectrum management, resource allocation, and network planning to name a few. Spectrum cartography techniques construct these maps from a collection of measurements collected by spatially distributed sensors. Due to the nature of the propagation of electromagnetic waves, spectrum maps are complicated functions of the spatial coordinates. For this reason, model-free approaches have been preferred. However, all existing schemes rely on some interpolation algorithm unable to learn from data. This work proposes a novel approach to spectrum cartography where propagation phenomena are learned from data. The resulting algorithms can therefore construct a spectrum map from a significantly smaller number of measurements than existing schemes since the spatial structure of shadowing and other phenomena is previously learned from maps in other environments. Besides the aforementioned new paradigm, this is also the first work to perform spectrum cartography with deep neural networks. To exploit the manifold structure of spectrum maps, a deep network architecture is proposed based on completion autoencoders., 11 pages, 6 figures, 1 table, submitted to IEEE International Conference on Communications ICC 2020

[Purpose] To describe the nature of multi-joint motor coordination during body rotation while in the standing position. [Participants and Methods] The participants were 22 healthy adults with no visual problems or history of diseases that could compromise their ability to execute body rotation. The position facing forward in an upright standing position was defined as 0°, and targets were placed at the following five points on concentric circles: 30°, 60°, 90°, 120°, and 150°. The participants always turned to the right. A three-dimensional motion analyzer consisting of six infrared cameras was used to measure the spatial coordinates of the infrared reflective markers. [Results] A main effect was found for all body segments. For all the target angles, the start of movement was approximately equal, and the angular change of the craniocervical joint was the largest. A nonlinear relationship was observed between the craniocervical and thoracolumbar joints for all target angles. However, a linear relationship was found between the thoracolumbar and pelvic joints. [Conclusion] The results of this study demonstrate that various regions such as the craniocervical and thoracolumbar junctions and the pelvis coordinate during such move to achieve optimal locomotive patterns.

The main purpose of this work is to confirm the possibility of making largescale orthophotomaps applying unmanned aerial vehicle (UAV) Trimble- UX5. A planned altitude reference of the studying territory was carried out before to the aerial surveying. The studying territory has been marked with distinctive checkpoints in the form of triangles (0.5 × 0.5 × 0.2 m). The checkpoints used to precise the accuracy of orthophotomap have been marked with similar triangles. To determine marked reference point coordinates and check-points method of GNSS in real-time kinematics (RTK) measuring has been applied. Projecting of aerial surveying has been done with the help of installed Trimble Access Aerial Imaging, having been used to run out the UX5. Aerial survey out of the Trimble UX5 UAV has been done with the help of the digital camera SONY NEX-5R from 200m and 300 m altitude. These aerial surveying data have been calculated applying special photogrammetric software Pix 4D. The orthophotomap of the surveying objects has been made with its help. To determine the precise accuracy of the got results of aerial surveying the checkpoint coordinates according to the orthophotomap have been set. The average square error has been calculated according to the set coordinates applying GNSS measurements. A-priori accuracy estimation of spatial coordinates of the studying territory using the aerial surveying data have been calculated: mx=0.11 m, my=0.15 m, mz=0.23 m in the village of Remeniv and mx=0.26 m, my=0.38 m, mz=0.43 m in the town of Vynnyky. The accuracy of determining checkpoint coordinates has been investigated using images obtained out of UAV and the average square error of the reference points. Based on comparative analysis of the got results of the accuracy estimation of the made orthophotomap it can be concluded that the value the average square error does not exceed a-priori accuracy estimation. The possibility of applying Trimble UX5 UAV for making large-scale orthophotomaps has been investigated. The aerial surveying output data using UAV can be applied for monitoring potentially dangerous for people objects, the state border controlling, checking out the plots of settlements. Thus, it is important to control the accuracy the got results. Having based on the done analysis and experimental researches it can be concluded that applying UAV gives the possibility to find data more efficiently in comparison with the land surveying methods. As the result, the Trimble UX5 UAV gives the possibility to survey built-up territories with the required accuracy for making orthophotomaps with the following scales 1: 2000, 1: 1000, 1: 500.

Precise Point Positioning (PPP) is a technique used to determine the position of receiver antenna without communication with the reference station. It may be an alternative solution to differential measurements, where maintaining a connection with a single RTK station or a regional network of reference stations RTN is necessary. This situation is especially common in areas with poorly developed infrastructure of ground stations. A lot of research conducted so far on the use of the PPP technique has been concerned about the development of entire day observation sessions. However, this paper presents the results of a comparative analysis of accuracy of absolute determination of position from observations which last between 1 to 7 hours with the use of four permanent services which execute calculations with PPP technique such as: Automatic Precise Positioning Service (APPS), Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP), GNSS Analysis and Positioning Software (GAPS) and magicPPP - Precise Point Positioning Solution (magicGNSS). On the basis of acquired results of measurements, it can be concluded that at least two-hour long measurements allow acquiring an absolute position with an accuracy of 2-4 cm. An evaluation of the impact on the accuracy of simultaneous positioning of three points test network on the change of the horizontal distance and the relative height difference between measured triangle vertices was also conducted. Distances and relative height differences between points of the triangular test network measured with a laser station Leica TDRA6000 were adopted as references. The analyses of results show that at least two hours long measurement sessions can be used to determine the horizontal distance or the difference in height with an accuracy of 1-2 cm. Rapid products employed in calculations conducted with PPP technique reached the accuracy of determining coordinates on a close level as in elaborations which employ Final products.

Deformation monitoring of the engineering structures such as buildings provides information about the health of the structures and their safety aspects as well as the safety of the public. This paper describes the development of a method for the monitoring and analysis of vertical and horizontal movements of structures. The proposed method is developed to add a new solution to traditional methods of the three-dimensional intersection for getting the spatial coordinates of points. A software was developed to provide a simultaneous solution to all observations and data in one step using a least-squares solution to improve the expected accuracy and to generate the necessary data for statistical analysis. The proposed method was used for monitoring and analyzing the movements of high-rise buildings in Cairo, Egypt. A traverse of four points and well-distributed twenty-three monitoring points were used. All measurements were taken using total station. The observations were carried out at four epochs of the two-month interval. The developed software was used for getting the adjusted ground coordinates of the monitoring points, observations and generating the necessary statistical data. The results of the first epoch were used as reference values such that the subsequent epochs values were compared with them to compute the horizontal and vertical movements of the monitoring points for each epoch. Furthermore, values of horizontal and vertical movements were compared with their corresponding computed 95% confidence intervals to determine the significance of the existing displacement. The results show the horizontal and vertical stability of the building during the monitoring period. The case study shows the efficiency of the proposed method for the monitoring of the deformation of the building structures. It is strongly recommended that engineering structures especially high-rise buildings should be monitored on a regular basis to check their stability and thereby increasing their safety.

Location extraction, also called “toponym extraction,” is a field covering geoparsing, extracting spatial representations from location mentions in text, and geotagging, assigning spatial coordinates to content items. This article evaluates five “best-of-class” location extraction algorithms. We develop a geoparsing algorithm using an OpenStreetMap database, and a geotagging algorithm using a language model constructed from social media tags and multiple gazetteers. Third-party work evaluated includes a DBpedia-based entity recognition and disambiguation approach, a named entity recognition and Geonames gazetteer approach, and a Google Geocoder API approach. We perform two quantitative benchmark evaluations, one geoparsing tweets and one geotagging Flickr posts, to compare all approaches. We also perform a qualitative evaluation recalling top N location mentions from tweets during major news events. The OpenStreetMap approach was best (F1 0.90+) for geoparsing English, and the language model approach was best (F1 0.66) for Turkish. The language model was best (F1@1km 0.49) for the geotagging evaluation. The map database was best (R@20 0.60+) in the qualitative evaluation. We report on strengths, weaknesses, and a detailed failure analysis for the approaches and suggest concrete areas for further research.

We consider a differential game of fisheries in a fan-like control structure of the type &ldquo, supervisor&mdash, several agents&rdquo, The dynamics of the controlled system is described by a non-linear differential equation model which is identified on the Azov Sea data. An averaging by two spatial coordinates is conducted. Different information structures of the game are generated by the control methods of compulsion (supervisor restricts the feasible strategies of agents) and impulsion (she exerts an impact to their payoff functionals). Both Stackerlberg and inverse Stackelberg games are considered. For the numerical investigation we use a discretization of the initial model and the method of qualitatively representative scenarios in simulation modeling.

International audience; Augmented Reality (AR) is already transforming many fields, from medical applications to industry, entertainment and heritage. In its most common form, AR expands reality with virtual 3D elements, providing users with an enhanced and enriched experience of the surroundings. Until now, most of the research work focused on techniques based on markers or on GNSS/INS positioning. These approaches require either the preparation of the scene or a strong satellite signal to work properly. In this paper, we investigate the use of visual-based methods, i.e., methods that exploit distinctive features of the scene estimated with Visual Simultaneous Localization and Mapping (V-SLAM) algorithms, to determine and track the user position and attitude. The detected features, which encode the visual appearance of the scene, can be saved and later used to track the user in successive AR sessions. Existing AR frameworks like Google ARCore, Apple ARKit and Unity AR Foundation recently introduced visual-based localization in their frameworks, but they target mainly small scenarios. We propose a new Mobile Augmented Reality (MAR) methodology that exploits OPEN-V-SLAM to extend the application range of Unity AR Foundation and better handle large-scale environments. The proposed methodology is successfully tested in both controlled and real-case large heritage scenarios. Results are available also in this video: https://youtu.be/Q7VybmiWIuI. a) b) c) Figure 1. The three large-scale scenarios used in the paper and the AR results based on markerless smartphone solution: (a) historical photographs of the city of Trento, (b) the remains of the underground roman city in Trento and (c) the pile dwelling site of Fiavè.

The paper developed the closed-form solution for the free vibration problem of inhomogeneous orthotropic rectangular plates (IHORPs) resting on the inhomogeneous viscoelastic foundation (IHVEF). The Young's moduli and density of the orthotropic plate vary continuously with respect to three spatial coordinates, while the characteristics of the viscoelastic foundation vary depending on the in-plane coordinates. The relevant motion equation is obtained using the classical plate theory (CPT) and solved using method of separation of variables. The influences of inhomogeneity of orthotropic materials, inhomogeneity of viscoelastic and elastic foundations on the non-dimensional frequencies (NDFs) of plates are studied in detail.

If a gabor pattern drifts in one direction while its internal texture drifts in the orthogonal direction, observers see a remarkable shift in its perceived direction when it is viewed in the periphery. The reported direction of the double-drift stimulus (also known as the infinite regress and curveball illusions) is some combination of the actual external motion of the gabor envelope and the internal motion of its texture (Tse & Hsieh, 2006). Here we find that if the observers track a fixation point that moves in tandem with the gabor, the illusion is undiminished. The pursuit of the moving fixation spot keeps the gabor roughly fixed at one location on the retina, cancelling its external motion, leaving only the internal motion. The gabor is seen to move in the world at roughly its actual speed as the motion of the eye is discounted at some point to recover velocities in world coordinates (e.g. Wallach, 1959). Our finding indicates that the combination of internal and external motion that produces the double drift illusion must happen after the eye movement signals have been factored into stimulus motions. We also test the double drift effect at various path lengths, durations, and speeds, with both mid-grey and black backgrounds, all with a static fixation. These results confirm that a simple vector combination of the two speeds alone accounts for virtually all the direction shifts on the grey background. On the black background, the illusion is eliminated. These results place constraints on where perceived spatial coordinates arise in the visual processing hierarchy to locations at or beyond where compensation for pursuit eye movements arise, specifically V3A, V6, MSTd, and VIP (e.g., Nau et al, 2018).

When people judge the temporal order (TOJ task) of two tactile stimuli at the two hands while their hands are crossed, performance is much worse than with uncrossed hands [1]. This crossed-hands deficit is widely considered to indicate interferences of external spatial coordinates with body-centered coordinates in the localization of touch [2]. Similar deficits have also been observed when people are only about to move their hands towards a crossed position [3]-[5], suggesting a predictive update of external spatial coordinates. Here, we extend the investigation of the dynamics of external coordinates during hand movement. Participants performed a TOJ task while they executed an uncrossing or a crossing movement, and during presentation of the TOJ stimuli the present posture of the hands was crossed, uncrossed or in-between. Present, future and past crossed-hands postures decreased performance in the TOJ task, suggesting that the update of external spatial coordinates of touch includes both predictive processes and processes that preserve the recent past. In addition, our data corroborate the flip model of crossed-hands deficits [1], and suggest that more pronounced deficits come along with higher time requirements to resolve interferences.

A dual-view polarization resolved digital-holographic system is presented. The necessary calibration for both polarization and spatial coordinates are outlined. As an example the system is is used to track spherical microparticles in a cuvette. ISBN för värdpublikation: 978-1-943580-59-0