Articles de revues : « Space-time features »

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Zambotti, G., W. Guan, and J. Gest. "VISUALIZING HUMAN MIGRATION TRHOUGH SPACE AND TIME." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 155–61. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-155-2015.

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Human migration has been an important activity in human societies since antiquity. Since 1890, approximately three percent of the world’s population has lived outside of their country of origin. As globalization intensifies in the modern era, human migration persists even as governments seek to more stringently regulate flows. Understanding this phenomenon, its causes, processes and impacts often starts from measuring and visualizing its spatiotemporal patterns. This study builds a generic online platform for users to interactively visualize human migration through space and time. This entails quickly ingesting human migration data in plain text or tabular format; matching the records with pre-established geographic features such as administrative polygons; symbolizing the migration flow by circular arcs of varying color and weight based on the flow attributes; connecting the centroids of the origin and destination polygons; and allowing the user to select either an origin or a destination feature to display all flows in or out of that feature through time. The method was first developed using ArcGIS Server for world-wide cross-country migration, and later applied to visualizing domestic migration patterns within China between provinces, and between states in the United States, all through multiple years. The technical challenges of this study include simplifying the shapes of features to enhance user interaction, rendering performance and application scalability; enabling the temporal renderers to provide time-based rendering of features and the flow among them; and developing a responsive web design (RWD) application to provide an optimal viewing experience. The platform is available online for the public to use, and the methodology is easily adoptable to visualizing any flow, not only human migration but also the flow of goods, capital, disease, ideology, etc., between multiple origins and destinations across space and time.

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Kobro, Katarzyna, and Władysław Strzemiński. "Composing Space/Calculating Space-Time Rhythms." October 156 (May 2016): 12–74. http://dx.doi.org/10.1162/octo_a_00251.

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In this treatise, Katarzyna Kobro and Władysław Strzemiński distinguish between the condition of painting (which features a picture on a support with physical imits) and of sculpture (which involves space, which is limitless) and propose that the ways in which each medium determines its own essence must be fundamentally different. While painting relies on what would later be called “deductive structure,” in sculpture the issue is how to relate the object to space. After conducting a chronological examination of the different ways in which the sculptural object has related to space (in the Ancient, Medieval, Renaissance, and Baroque epochs), Kobro and Strzemiński propose various ways in which sculpture can not only relate to space but to “unite” with it via such solutions as polychrony and a disjunctive syntax through which the object itself eludes the perception of its identity.

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Mitroff, Stephen R., and George A. Alvarez. "Space and time, not surface features, guide object persistence." Psychonomic Bulletin & Review 14, no. 6 (2007): 1199–204. http://dx.doi.org/10.3758/bf03193113.

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Rebouças, M. J., and A. F. F. Teixeira. "Features of a relativistic space-time with seven isometries." Physical Review D 34, no. 10 (1986): 2985–89. http://dx.doi.org/10.1103/physrevd.34.2985.

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李, 宝芬. "Time-Space Distribution Features of Annual Precipitation in Kunming." Journal of Water Resources Research 05, no. 02 (2016): 182–89. http://dx.doi.org/10.12677/jwrr.2016.52024.

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Maneuf, S., A. Barthelemy, and C. Froehly. "Soliton beam propagation; space-time behaviour and spectral features." Journal of Optics 17, no. 3 (1986): 139–45. http://dx.doi.org/10.1088/0150-536x/17/3/004.

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Moore, Cathleen M., Teresa Stephens, and Elisabeth Hein. "Features, as well as space and time, guide object persistence." Psychonomic Bulletin & Review 17, no. 5 (2010): 731–36. http://dx.doi.org/10.3758/pbr.17.5.731.

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Chaplya, Tatyana V. "ARCHITECTURAL SPACE OF THE NEW TIME: DYNAMICS AND ORGANIZATIONAL FEATURES." Vestnik Tomskogo gosudarstvennogo universiteta. Kul'turologiya i iskusstvovedenie, no. 33 (March 2019): 120–32. http://dx.doi.org/10.17223/22220836/33/10.

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ZACHOS, COSMAS K. "UMBRAL DEFORMATIONS ON DISCRETE SPACE–TIME." International Journal of Modern Physics A 23, no. 13 (2008): 2005–14. http://dx.doi.org/10.1142/s0217751x08040548.

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Given a minimum measurable length underlying space–time, the latter may be effectively regarded as discrete, at scales of order of the Planck length. A systematic discretization of continuum physics may be effected most efficiently through the umbral deformation. General functionals yielding such deformations at the level of solutions are furnished and illustrated, and broad features of discrete oscillations and wave propagation are outlined.

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Wang, Yilin, and Baokuan Chang. "Extraction of Human Motion Information from Digital Video Based on 3D Poisson Equation." Advances in Mathematical Physics 2021 (December 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/1268747.

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Based on the 3D Poisson equation, this paper extracts the features of the digital video human body action sequence. By solving the Poisson equation on the silhouette sequence, the time and space features, time and space structure features, shape features, and orientation features can be obtained. First, we use the silhouette structure features in three-dimensional space-time and the orientation features of the silhouette in three-dimensional space-time to represent the local features of the silhouette sequence and use the 3D Zernike moment feature to represent the overall features of the silhouette sequence. Secondly, we combine the Bayesian classifier and AdaBoost classifier to learn and classify the features of human action sequences, conduct experiments on the Weizmann video database, and conduct multiple experiments using the method of classifying samples and selecting partial combinations for training. Then, using the recognition algorithm of motion capture, after the above process, the three-dimensional model is obtained and matched with the model in the three-dimensional model database, the sequence with the smallest distance is calculated, and the corresponding skeleton is outputted as the results of action capture. During the experiment, the human motion tracking method based on the university matching kernel (EMK) image kernel descriptor was used; that is, the scale invariant operator was used to count the characteristics of multiple training images, and finally, the high-dimensional feature space was mapped into the low-dimensional to obtain the feature space approximating the Gaussian kernel. Based on the above analysis, the main user has prior knowledge of the network environment. The experimental results show that the method in this paper can effectively extract the characteristics of human body movements and has a good classification effect for bending, one-foot jumping, vertical jumping, waving, and other movements. Due to the linear separability of the data in the kernel space, fast linear interpolation regression is performed on the features in the feature space, which significantly improves the robustness and accuracy of the estimation of the human motion pose in the image sequence.

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Motornyy, О. "TIME AND SPACE IN IVAN WERNISCH’S POETRY." Comparative studies of Slavic languages and literatures. In memory of Academician Leonid Bulakhovsky, no. 36 (2020): 198–209. http://dx.doi.org/10.17721/2075-437x.2020.36.16.

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The article examines the poetic world of the modern Czech prose writer, poet, translator from several European languages, a participant in the Prague Spring, whose works have not been printed for a long time and were banned, author of poetry collections “Kam letí nebe” (1961), “Zimohrádek” (1965), “Loutky” (1970), “Doupě latinářů” (1992), “Blbecká poezie” (2002), “Penthesilea” (2019), Ivan Wernisch. The writer has a great creative legacy that dates back to the sixties of the last century. During this long time, he managed to publish about thirty poetry books. Surrealism, interweaving of temporal and spatial indicators, interweaving of poetic and prosaic forms, rich poetic world are typical features of I. Wernisch’s poetry. The article explores the spatio-temporal relations of the Ivan Wernisch’s poetic world, the features of the image of the lyrical hero. Some poetry by Ivan Wernisch was used over time as lyrics of songs by Czech rock bands. The Ivan Wernisch’s son Michal Wernisch (also known as Ewald Murrer) followed his father’s footsteps and today is also known as a poet.

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PADMANABHAN, T. "PROBING THE QUANTUM MICROSTRUCTURE OF SPACE–TIME." Modern Physics Letters A 14, no. 24 (1999): 1667–72. http://dx.doi.org/10.1142/s0217732399001759.

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The question of how tightly one can constrain the microscopic theory of quantum gravity from the known features of low energy gravity is addressed. To begin with, from the very fact that our universe made a transition from a quantum regime to classical one, it is possible to conclude that infinite number of degrees of freedom had to be integrated out from the fundamental theory to obtain the low energy Einstein Lagrangian. Further constraints can be imposed from the fact that the quantum state describing a black hole has to possess certain universal form of density of states, in any microscopic description of space–time, which can be ascertained from general considerations. Since a black hole can be formed from the collapse of any physical system with a low energy (E ≪ Ep) Hamiltonian H, it is possible to obtain the form the effective high energy (E ≫ Ep) Hamiltonian from general consideration. These results provide the physical reasons for some of the mathematical features underlying string theories and other models for quantum gravity.

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Kryukov, Valery. "Subsoil Knowledge Economy in Time and in Space." Science Management: Theory and Practice 2, no. 4 (2020): 71–117. http://dx.doi.org/10.19181/smtp.2020.2.4.4.

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The article examines the features of natural resources in the context of the dynamics of their development as of changing of types, internal features as of quality. The author shows that over time, the qualitative characteristics of the developed natural resources (first of all, subsoil minerals) are change - the developed long-term objects pass into the stage of declining productivity, while new objects become smaller and more complex from the point in terms of the application of previously obtained and implemented approaches. According to the author’s opinion, which he develops and defends in this article, a distinctive featureof the modern stage of the development of mineral resources is a significant strengthening of the role of science and new knowledge and the growing dominance of an intensive way of solving emerging problems. This is reflected in the significant strengthening of scientific research and the growing role of high-tech service companies. At the same time, as a rule, these service companies are located in scientific and industrial centers and agglomerations located at a considerable distance from the places of development and extraction of minerals. The circumstances noted above determine and set new approaches to the spatial sideof interaction of the resource sector and the knowledge economy in modern conditions. The article presents examples of the creation and development of such approaches in various countries. At the same time, in the author’s opinion, Russia is still largely implementing a model based on the use of previously tested (in other countries) technologies and an emphasis on the development of traditional facilities located in new, more remote areas.

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Mahyudi, Johan, Djoko Saryono, Wahyudi Siswanto, and Yuni Pratiwi. "Construction of Visual Features of Indonesian Digital Poetry." International Journal of Linguistics, Literature and Culture 3, no. 5 (2017): 1. http://dx.doi.org/10.21744/ijllc.v3i5.526.

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In short time, Indonesian digital poetry attracts its audience through a series of visualization features of the digital art. This research uses a short segment analysis on Indonesian videography digital poetry to demonstrate the existence of visual conglomeration practices through the creation of objects, features, a feature of space, measuring distance in feature space, and dimension reduction. These five approaches are proposed by Manovich (2014) in grouping millions of visual artworks based on simple criteria. Of the three common objects are found, Indonesian animators, prefer individuals and texts as the main impression. The movement features are found in cinematic poetry and its rely depend on kinetic texts. Meanwhile, non-movement features can be found in the form of human imitation or part of them, portraits, silhouettes, and comics. Indonesian digital poetry of space features in form of textual space is prioritizing on the kinetics text, the space of time is prioritizing the presentation of objects association of words are spoken, the neutral space is prioritizing the use of computer technology application. The grouping of visual art composition is based on two criteria: the technique of creating and artistic impressions. The dimensional reducing is prominently practiced by Afrizal Malna.

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., M. Shillin Bella. "HUMAN ACTION RECOGNITION USING LOCAL SPACE TIME FEATURES AND ADABOOST SVM." International Journal of Research in Engineering and Technology 03, no. 04 (2014): 139–42. http://dx.doi.org/10.15623/ijret.2014.0304026.

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Alais, David, Randolph Blake, and Sang-Hun Lee. "Visual features that vary together over time group together over space." Nature Neuroscience 1, no. 2 (1998): 160–64. http://dx.doi.org/10.1038/414.

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Wei, Sha, Dong Wang, Hong Wang, and Zhike Peng. "Time-Varying Envelope Filtering for Exhibiting Space Bearing Cage Fault Features." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–13. http://dx.doi.org/10.1109/tim.2020.3033061.

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Pasanen, Leena, Ilkka Launonen, and Lasse Holmström. "A scale space multiresolution method for extraction of time series features." Stat 2, no. 1 (2013): 273–91. http://dx.doi.org/10.1002/sta4.35.

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SHEN, H. C., and R. PILKEY. "AN ADAPTIVE APPROACH TO FEATURE SELECTION IN PATTERN RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 04 (1990): 573–601. http://dx.doi.org/10.1142/s0218001490000332.

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Feature selection is an important phase in most pattern recognition problems, especially when the space of the extracted features is very large. Feature selection methods attempt to reduce the feature space to satisfy certain objectives. We propose the concept of defining a performance potential as a measure of the effectiveness of the set of selected features. This paper begins by outlining a ranking scheme for features based on a feature’s calculated “performance potential”. The performance potential is made up of a number of performance measures: extraction time, memory requirements, variance, covariance and classification success. An adaptive scheme is proposed to process a number of initial features and arrive at the “best” subset based on their performance potential. The approach is applied to a texture analysis problem. The results of the testing of the approach point to conclusions concerning its effectiveness.

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Wu, Songjiao. "Image Recognition of Standard Actions in Sports Videos Based on Feature Fusion." Traitement du Signal 38, no. 6 (2021): 1801–7. http://dx.doi.org/10.18280/ts.380624.

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Standard actions are crucial to sports training of athletes and daily exercise of ordinary people. There are two key issues in sports action recognition: the extraction of sports action features, and the classification of sports actions. The existing action recognition algorithms cannot work effectively on sports competitions, which feature high complexity, fine class granularity, and fast action speed. To solve the problem, this paper develops an image recognition method of standard actions in sports videos, which merges local and global features. Firstly, the authors combed through the functions and performance required for the recognition of standard actions of sports, and proposed an attention-based local feature extraction algorithm for the frames of sports match videos. Next, a sampling algorithm was developed based on time-space compression, and a standard sports action recognition algorithm was designed based on time-space feature fusion, with the aim to fuse the time-space features of the standard actions in sports match videos, and to overcome the underfitting problem of direct fusion of time-space features extracted by the attention mechanism. The workflow of these algorithms was explained in details. Experimental results confirm the effectiveness of our approach.

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CAPOZZIELLO, S., and C. STORNAIOLO. "SPACE-TIME DEFORMATIONS AS EXTENDED CONFORMAL TRANSFORMATIONS." International Journal of Geometric Methods in Modern Physics 05, no. 02 (2008): 185–95. http://dx.doi.org/10.1142/s0219887808002709.

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A definition of space-time metric deformations on an n-dimensional manifold is given. We show that such deformations can be regarded as extended conformal transformations. In particular, their features can be related to the perturbation theory giving a natural picture by which gravitational waves are described by small deformations of the metric. As further result, deformations can be related to approximate Killing vectors (approximate symmetries) by which it is possible to parameterize the deformed region of a given manifold. The perspectives and some possible physical applications of such an approach are discussed.

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Szekeres, Peter. "Signal spaces—an axiomatic approach to space-time." Bulletin of the Australian Mathematical Society 43, no. 3 (1991): 355–63. http://dx.doi.org/10.1017/s0004972700029191.

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An axiomatic approach to relativity theory is proposed which is based entirely on a single reflexive relation called signalling. This generalises and simplifies earlier schemes of Kronheimer and Penrose and of Carter which are based on causality relations. Many of the features of space-times, such as photon paths and topology, have their counterparts in general signal spaces.

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Hu, Jing, Xiang Qi, and Jian Feng Chen. "Fights Behavior Detection Based on Space-Time Interest Points." Applied Mechanics and Materials 577 (July 2014): 659–63. http://dx.doi.org/10.4028/www.scientific.net/amm.577.659.

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Human action recognition belongs to the senior visual analysis of computer vision, which involves image processing, artificial intelligence, pattern recognition and so on, is becoming one of the most hot research topic in recent years. In this paper, on the basis of comparative analysis and study towards current methods related to human action recognition, we propose a novel fights behavior detection method which is based on spatial-temporal interest point. Since most information of human action in video are indicated by the space-time interest points of video, we combine spatial-temporal features with motion energy image to describe information of video, and local spatial-temporal features are applied to extract fights behavior model by bags of words. Experimental results show that this method can achieve high accuracy and certain practical value.

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Baiasu, Sorin. "Space, Time and Mind-Dependence." Kantian Review 16, no. 2 (2011): 175–90. http://dx.doi.org/10.1017/s1369415411000045.

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AbstractThe interpretation of Kant's Critical philosophy as a version of traditional idealism has a long history. In spite of Kant's and his commentators’ various attempts to distinguish between traditional and transcendental idealism, his philosophy continues to be construed as committed (whether explicitly or implicitly and whether consistently or inconsistently) to various features usually associated with the traditional idealist project. As a result, most often, the accusation is that his Critical philosophy makes too strong metaphysical and epistemological claims.In his The Revolutionary Kant, Graham Bird engages in a systematic and thorough evaluation of the traditionalist interpretation, as part of perhaps the most comprehensive and compelling defence of a revolutionary reading of Kant's thought. In the third part of this special issue, the exchanges between, on the one hand, Graham Bird and, on the other, Gary Banham, Gordon Brittan, Manfred Kuehn, Adrian Moore and Kenneth Westphal focus on specific aspects of Bird's interpretation of Kant's first Critique. More exactly, the emphasis is on specific aspects of Bird's interpretation of the Introduction, Analytic of Principles and Transcendental Dialectic of Kant's first Critique.The second part of the special issue is devoted to discussions of particular topics in Bird's construal of the remaining significant parts of the first Critique, namely, of the Transcendental Aesthetic and the Analytic of Concepts. Written by Sorin Baiasu and Michelle Grier, these articles examine specific issues in these two remaining parts of the Critique, from the perspective of the debate between the traditionalist and revolutionary interpretation. The special issue begins with an Introduction by the guest co-editors. This provides a summary of the exchanges between Bird and his critics, with a particular focus on the debates stemming from the differences between traditional and revolutionary interpretations of Kant.

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Alais, David, Randolph Blake, and Sang-Hun Lee. "Erratum: Visual features that vary together over time group together over space." Nature Neuroscience 1, no. 4 (1998): 330. http://dx.doi.org/10.1038/1151.

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Yuan, Jie, Lidong Wang, Peng Wu, Chao Gao, and Lingqing Sun. "Detection of Wildfires along Transmission Lines Using Deep Time and Space Features." Pattern Recognition and Image Analysis 28, no. 4 (2018): 805–12. http://dx.doi.org/10.1134/s1054661818040168.

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Delmelle, Eric, Changjoo Kim, Ningchuan Xiao, and Wei Chen. "Methods for Space-Time Analysis and Modeling." International Journal of Applied Geospatial Research 4, no. 4 (2013): 1–18. http://dx.doi.org/10.4018/jagr.2013100101.

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With increasing availability of spatio-temporal data and the democratization of Geographical Information Systems (GIS), there has been a demand for novel statistical and visualization techniques which can explicitly integrate space and time. The paper discusses the nature of spatio-temporal data, the integration of time within GIS and the flourishing availability of spatial and temporal-explicit data over the Internet. The paper attempts to answer the fundamental question on how these large datasets can be analyzed in space and time to reveal critical patterns. The authors further elaborate on how spatial autocorrelation techniques are extended to deal with time, for point, linear, and areal features, and the impact of parameter selection, such as critical distance and time threshold to build adjacency matrices. The authors also discuss issues of space-time modeling for optimization problems.

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Zhao, Xiao Li, and Shu Jun Yin. "Color Image Retrieval Algorithm Based on Dual Tree Complex Wavelet Transform." Applied Mechanics and Materials 427-429 (September 2013): 1761–64. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1761.

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A color image retrieval algorithm was presented in order to retrieve image from massive images. This method firstly transformed image from RGB space to HSV space. V component of HSV space was decomposed into three levels by dual tree complex wavelet transform (DT-CWT) to extract high frequency components as texture features. Invariant moments of V component were extracted as shape features. Information of H component of HSV space was extracted as color features. Then three types of features were combined to form feature vector to judge similarity of images. Experiments show that this algorithm has not only high precision and recall but also low time consumption.

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Liu, Mengyuan, Fanyang Meng, Chen Chen, and Songtao Wu. "Joint Dynamic Pose Image and Space Time Reversal for Human Action Recognition from Videos." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8762–69. http://dx.doi.org/10.1609/aaai.v33i01.33018762.

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Human action recognition aims to classify a given video according to which type of action it contains. Disturbance brought by clutter background and unrelated motions makes the task challenging for video frame-based methods. To solve this problem, this paper takes advantage of pose estimation to enhance the performances of video frame features. First, we present a pose feature called dynamic pose image (DPI), which describes human action as the aggregation of a sequence of joint estimation maps. Different from traditional pose features using sole joints, DPI suffers less from disturbance and provides richer information about human body shape and movements. Second, we present attention-based dynamic texture images (att-DTIs) as pose-guided video frame feature. Specifically, a video is treated as a space-time volume, and DTIs are obtained by observing the volume from different views. To alleviate the effect of disturbance on DTIs, we accumulate joint estimation maps as attention map, and extend DTIs to attention-based DTIs (att-DTIs). Finally, we fuse DPI and att-DTIs with multi-stream deep neural networks and late fusion scheme for action recognition. Experiments on NTU RGB+D, UTD-MHAD, and Penn-Action datasets show the effectiveness of DPI and att-DTIs, as well as the complementary property between them.

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Kim, Seong-Eun, Michael K. Behr, Demba Ba, and Emery N. Brown. "State-space multitaper time-frequency analysis." Proceedings of the National Academy of Sciences 115, no. 1 (2017): E5—E14. http://dx.doi.org/10.1073/pnas.1702877115.

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Time series are an important data class that includes recordings ranging from radio emissions, seismic activity, global positioning data, and stock prices to EEG measurements, vital signs, and voice recordings. Rapid growth in sensor and recording technologies is increasing the production of time series data and the importance of rapid, accurate analyses. Time series data are commonly analyzed using time-varying spectral methods to characterize their nonstationary and often oscillatory structure. Current methods provide local estimates of data features. However, they do not offer a statistical inference framework that applies to the entire time series. The important advances that we report are state-space multitaper (SS-MT) methods, which provide a statistical inference framework for time-varying spectral analysis of nonstationary time series. We model nonstationary time series as a sequence of second-order stationary Gaussian processes defined on nonoverlapping intervals. We use a frequency-domain random-walk model to relate the spectral representations of the Gaussian processes across intervals. The SS-MT algorithm efficiently computes spectral updates using parallel 1D complex Kalman filters. An expectation–maximization algorithm computes static and dynamic model parameter estimates. We test the framework in time-varying spectral analyses of simulated time series and EEG recordings from patients receiving general anesthesia. Relative to standard multitaper (MT), SS-MT gave enhanced spectral resolution and noise reduction (>10 dB) and allowed statistical comparisons of spectral properties among arbitrary time series segments. SS-MT also extracts time-domain estimates of signal components. The SS-MT paradigm is a broadly applicable, empirical Bayes’ framework for statistical inference that can help ensure accurate, reproducible findings from nonstationary time series analyses.

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Ding, Qing Yan, Xi Fu Wang, Xiu Yuan Zhang, and Zhan Quan Sun. "Forecasting Traffic Volume with Space-Time ARIMA Model." Advanced Materials Research 156-157 (October 2010): 979–83. http://dx.doi.org/10.4028/www.scientific.net/amr.156-157.979.

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The paper proposes a space–time autoregressive integrated moving average (STARIMA) model to predict the traffic volume in urban areas. The methodological framework incorporates the historical traffic data and the spatial features of a road network. Moreover, the spatial characteristics in a way that reflects not only the distance but also the average travel speed on the links. In order to response the time-varying speed, six traffic modes are classified by level of service (LOS) which is updated in 5 minute interval. In the end, with the real traffic data in Beijing for experiments, the model achieves a very good accuracy on the 5 minute interval forecasting, it also provides a sufficient accuracy of 30 minute interval forecasting compared with ARIMA model.

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Brown, David. "The Cuneiform Conception of Celestial Space and Time." Cambridge Archaeological Journal 10, no. 1 (2000): 103–22. http://dx.doi.org/10.1017/s0959774300000044.

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The significance to the modern world of Mesopotamian celestial divination and astronomy cannot be overstated. The names and the ‘ominous’ values assigned to the heavenly bodies by the Mesopotamians underlie Western astrology, and have also influenced Indian astrology. Many of the key features in the astronomy of Hipparchus and Ptolemy, which later passed into the astronomy of the medieval world, were borrowed from the astronomers of Babylon and Uruk. The zodiac, the Metonic cycle, horoscopy, and a variety of astrological techniques are all first attested in Mesopotamia. The same goes for units, notably those divisions of space and time which are now used throughout the world (such as 60 minutes in an hour and 360° in a circle) which can be traced back to cuneiform antecedents.

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Savarimuthu, Charles, and Arockiam L. "Pairwise Fuzzy Ordered Weighted Average Algorithm-Gaussian Mixture Model for Feature Reduction." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 6, no. 1 (2013): 287–301. http://dx.doi.org/10.24297/ijct.v6i1.4457.

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Feature Reduction is a kind of dimensionality reduction of feature space. There are a number of approaches are used to identify the significant features but they are not using the weighing approach. The weighing approach is quite useful for obtaining the significant features and removing the insignificant and irrelevant features using OWA formulation. The aim of this approach is to obtain the significant features and removing insignificant features by using the pairwise approach. This approach is helpful to find the weights of pairwise features at the same time, which leads to remove the insignificant features from the feature space using OWA. The significance of the OWA formulation is that, the paired features are identified in priori and their sum of weights are equal to 1. OWA criterion is introduced to obtain the significant features that are useful for predicting the accuracy of the cluster in GMM.

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Sun, Ying, Xue-Ying Zhang, Jiang-He Ma, Chun-Xiao Song, and Hui-Fen Lv. "Nonlinear Dynamic Feature Extraction Based on Phase Space Reconstruction for the Classification of Speech and Emotion." Mathematical Problems in Engineering 2020 (April 9, 2020): 1–15. http://dx.doi.org/10.1155/2020/9452976.

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Due to the shortcomings of linear feature parameters in speech signals, and the limitations of existing time- and frequency-domain attribute features in characterizing the integrity of the speech information, in this paper, we propose a nonlinear method for feature extraction based on the phase space reconstruction (PSR) theory. First, the speech signal was analyzed using a nonlinear dynamic model. Then, the model was used to reconstruct a one-dimensional time speech signal. Finally, nonlinear dynamic (NLD) features based on the reconstruction of the phase space were extracted as the new characteristic parameters. Then, the performance of NLD features was verified by comparing their recognition rates with those of other features (NLD features, prosodic features, and MFCC features). Finally, the Korean isolated words database, the Berlin emotional speech database, and the CASIA emotional speech database were chosen for validation. The effectiveness of the NLD features was tested using the Support Vector Machine classifier. The results show that NLD features not only have high recognition rate and excellent antinoise performance for speech recognition tasks but also can fully characterize the different emotions contained in speech signals.

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Chekanov, Vsevolod Iu. "The formatting of space and time in totalitarian historical discourse." Linguistics and Culture Review 5, S4 (2021): 915–26. http://dx.doi.org/10.21744/lingcure.v5ns4.1773.

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The subject of this article is the influence of socio-political engagement in non-democratic societies on the formation of scientific and historical discourse and on its further functioning and use for non-historical – political and educational purposes. It is analyzed not only from the point of view of the unique features inherent exclusively to totalitarianism, but rather as a derivative of socio-political requests for history that arise and are realized in any society, constantly becoming more complex over time. For Soviet totalitarianism, a characteristic feature of such requests was the absolutization of revolutions, which were interpreted as pivotal, milestone events that signified the main content of the progress of social development at literally all its stages. Because of this, Soviet historiography and the historiography of countries dependent on the USSR was characterized by attempts to “conceptually update the status” of a number of historical events, even those that preceded revolutions in their generally accepted meaning. In addition, an in-depth study of revolutions was characterized by the introduction of new terminology into scientific circulation and the identification of new elements of the division of historical time and space within revolutions.

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Borodina, E. L., and Yu V. Petukhov. "Specific features of the space-time structure of acoustic signals in multimode waveguides." Acoustical Physics 47, no. 4 (2001): 384–91. http://dx.doi.org/10.1134/1.1385410.

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Aswinseshadri, K., and V. Thulasi Bai. "Time-Space Fusion Techniques for Obtaining Discriminative Electrocorticogram Features in Brain Computer Interface." Journal of Medical Imaging and Health Informatics 6, no. 8 (2016): 2024–29. http://dx.doi.org/10.1166/jmihi.2016.1968.

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Zhao, Dongye, Fengzhen Tang, Bailu Si, and Xisheng Feng. "Learning joint space–time–frequency features for EEG decoding on small labeled data." Neural Networks 114 (June 2019): 67–77. http://dx.doi.org/10.1016/j.neunet.2019.02.009.

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Crespi, Mattia, Vladimir Kossobokov, Giuliano F. Panza, and Antonella Peresan. "Space-Time Precursory Features within Ground Velocities and Seismicity in North-Central Italy." Pure and Applied Geophysics 177, no. 1 (2019): 369–86. http://dx.doi.org/10.1007/s00024-019-02297-y.

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Corcoran, Padraig, and Christopher B. Jones. "Modelling Topological Features of Swarm Behaviour in Space and Time With Persistence Landscapes." IEEE Access 5 (2017): 18534–44. http://dx.doi.org/10.1109/access.2017.2749319.

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Shi, Jinxuan, and Kun Wang. "Fatigue driving detection method based on Time-Space-Frequency features of multimodal signals." Biomedical Signal Processing and Control 84 (July 2023): 104744. http://dx.doi.org/10.1016/j.bspc.2023.104744.

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Hemalatha, C. Sweetlin, V. Vaidehi, K. Nithya, A. Annis Fathima, M. Visalakshi, and M. Saranya. "Multi-Level Search Space Reduction Framework for Face Image Database." International Journal of Intelligent Information Technologies 11, no. 1 (2015): 12–29. http://dx.doi.org/10.4018/ijiit.2015010102.

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In face recognition, searching and retrieval of relevant images from a large database form a major task. Recognition time is greatly related to the dimensionality of the original data and the number of training samples. This demands the selection of discriminant features that produce similar results as the entire set and a reduced search space. To address this issue, a Multi-Level Search Space Reduction framework for large scale face image database is proposed. The proposed approach identifies discriminating features and groups face images sharing similar properties using feature-weighted Fuzzy C-Means approach. A hierarchical tree model is then constructed inside every cluster based on the discriminating features which enables a branch based selection, thereby reducing the search space. The proposed framework is tested on three benchmark and two self-created databases. The experimental results show that the proposed method achieved an average accuracy of 93% and an average search time reduction of 66% compared to existing approaches for search space reduction of face recognition.

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Geethanjali, P., and K. K. Ray. "STATISTICAL PATTERN RECOGNITION TECHNIQUE FOR IMPROVED REAL-TIME MYOELECTRIC SIGNAL CLASSIFICATION." Biomedical Engineering: Applications, Basis and Communications 25, no. 02 (2013): 1350026. http://dx.doi.org/10.4015/s1016237213500269.

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The authors in this paper propose a statistical technique for pattern recognition of electromyogram (EMG) signals along with effective feature ensemble to achieve an improved classification performance with less processing time and memory space. In this study, EMG signals from 10 healthy subjects and two transradial amputees for six motions of hand and wrist is considered for identification of the intended motion. From four channels myoelectric signals, the extracted time domain features are grouped into three ensembles to identify the effectiveness of feature ensemble in classification. The three feature ensembles obtained from multichannel continuous EMG signals are applied to the new classifiers namely simple logistic regression (SLR), J48 algorithm for decision tree (DT), logistic model tree (LMT) and feature subspace ensemble using k-nearest neighbor (kNN). Novel classifiers SLR, DT and LMT, select only the dominant features during training to develop the model for pattern recognition. This selection of features reduces the processing time as well as memory space of the controller for real-time application. The performance of SLR, DT, LMT and feature subspace ensemble using kNN classifiers are compared with other conventional classifiers, such as neural network (NN), simple kNN and linear discriminant analysis (LDA). The average classification accuracy with SLR is found to be better with feature ensemble-1 compared to the other classifiers. Also, the statistical Kruscal–Wallis test shows, the classification performance of SLR is not only better but also takes less time and memory space compared to other classifiers for classification. Also the performance of the classifier is tested in real-time with transradial amputees for actuation of drive for two intended motions with TMS320F28335eZdsp controller. The experimental results show that the SLR classifier improves the controller response in real-time.

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Zhu, Cheng Hui, Wen Jun Xu, Jian Ping Wang, and Xiao Bing Xu. "Research on a Characteristic Extraction Algorithm Based on Analog Space-Time Process for Off-Line Handwritten Chinese Characters." Advanced Materials Research 433-440 (January 2012): 3649–55. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3649.

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On the absence of space-time information, it is difficult to extract the character stroke feature from the off-line handwritten Chinese character image. A feature extraction algorithm is proposed based on analog space-time process by the process neural network. The handwritten Chinese character image is transformed into geometric shape by different types, different numbers, different locations, different orders and different structures of Chinese character strokes. By extracting fault-tolerant features of the five kinds of the off-line handwritten Chinese characters, the data-knowledge table of features is constructed. The parameters of process neural networks are optimized by Particle Swarm optimization (PSO). The handwritten Chinese characters are used to carry out simulation experiment in SCUT-IRAC-HCCLIB. The experiment results show that the algorithm exhibits a strong ability of cognizing handwritten Chinese characters.

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Chen, Yanli, Zhipeng Chen, Weihua Gui, and Chunhua Yang. "Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features." Sensors 22, no. 14 (2022): 5412. http://dx.doi.org/10.3390/s22145412.

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Real-time, continuous and accurate blast furnace burden level information is of great significance for controlling the charging process, ensuring a smooth operation of a blast furnace, reducing energy consumption and emissions and improving blast furnace output. However, the burden level information measured by conventional mechanical stock rods and radar probes exhibit problems of weak anti-interference ability, large fluctuations in accuracy, poor stability and discontinuity. Therefore, a space-time fusion prediction and detection method of burden level based on a long-term focus memory network (LFMN) and an efficient structure self-tuning RBF neural network (ESST-RBFNN) is proposed. First, the space dimensional features are extracted by the space regression model based on radar data. Then, the LFMN is designed to predict the burden level and extract the time dimensional features. Finally, the ESST-RBFNN based on a proposed fast eigenvector space clustering algorithm (ESC) is constructed to obtain reliable and continuous burden level information with high accuracy. Both the simulation results and industrial verification indicate that the proposed method can provide real-time and continuous burden level information in real-time, which has great practical value for industrial production.

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Li, Li Min, and Zhong Sheng Wang. "Feature Selection of Sudden Failure Based on Affinity Propagation Clustering." Advanced Materials Research 586 (November 2012): 241–46. http://dx.doi.org/10.4028/www.scientific.net/amr.586.241.

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When diagnosing sudden mechanical failure, in order to make the result of classification more accurate, in this article we describe an affinity propagation clustering algorithm for feature selection of sudden machinery failure diagnosis. General methods of feature selection select features by reducing dimension of the features, at the same time changing the data in the feature space, which would result in incorrect answer to the diagnosis. While affinity propagation method is based on measuring similarity between features whereby redundancy therein is removed, and selecting the exemplar subset of features, while doesn't change the data in the feature space. After testing on clustering and taking the result of PCA and affinity propagation clustering as input of a same SVM classifier, we get the conclusion that the latter has lower error than the former.

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Sokolova, Galina Aleksandrovna. "The semantic connection of time and space in literary text." Interactive science, no. 8 (42) (August 19, 2019): 28–30. http://dx.doi.org/10.21661/r-497998.

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The article deals with the connection of time and space in literary text. It gives some definitions of the time-space concept, the chronotope; it presents different points of view of Russian linguists about the leading role of the chronotope components; it also lists the main ways of detecting the chronotope in literary work; it defines some features and characteristics of time and space in the chronotope.

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H. Borse, Janhavi, Dipti D. Patil, and Vinod Kumar. "Tracking Keypoints from Consecutive Video Frames Using CNN Features for Space Applications." Tehnički glasnik 15, no. 1 (2021): 11–17. http://dx.doi.org/10.31803/tg-20210204161210.

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Hard time constraints in space missions bring in the problem of fast video processing for numerous autonomous tasks. Video processing involves the separation of distinct image frames, fetching image descriptors, applying different machine learning algorithms for object detection, obstacle avoidance, and many more tasks involved in the automatic maneuvering of a spacecraft. These tasks require the most informative descriptions of an image within the time constraints. Tracking these informative points from consecutive image frames is needed in flow estimation applications. Classical algorithms like SIFT and SURF are the milestones in the feature description development. But computational complexity and high time requirements force the critical missions to avoid these techniques to get adopted in real-time processing. Hence a time conservative and less complex pre-trained Convolutional Neural Network (CNN) model is chosen in this paper as a feature descriptor. 7-layer CNN model is designed and implemented with pre-trained VGG model parameters and then these CNN features are used to match the points of interests from consecutive image frames of a lunar descent video. The performance of the system is evaluated based on visual and empirical keypoints matching. The scores of matches between two consecutive images from the video using CNN features are then compared with state-of-the-art algorithms like SIFT and SURF. The results show that CNN features are more reliable and robust in case of time-critical video processing tasks for keypoint tracking applications of space missions.

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Lazzari-Dean, Julia R., Anneliese M. M. Gest, and Evan W. Miller. "Measuring Absolute Membrane Potential Across Space and Time." Annual Review of Biophysics 50, no. 1 (2021): 447–68. http://dx.doi.org/10.1146/annurev-biophys-062920-063555.

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Membrane potential (Vmem) is a fundamental biophysical signal present in all cells. Vmem signals range in time from milliseconds to days, and they span lengths from microns to centimeters. Vmem affects many cellular processes, ranging from neurotransmitter release to cell cycle control to tissue patterning. However, existing tools are not suitable for Vmem quantification in many of these areas. In this review, we outline the diverse biology of Vmem, drafting a wish list of features for a Vmem sensing platform. We then use these guidelines to discuss electrode-based and optical platforms for interrogating Vmem. On the one hand, electrode-based strategies exhibit excellent quantification but are most effective in short-term, cellular recordings. On the other hand, optical strategies provide easier access to diverse samples but generally only detect relative changes in Vmem. By combining the respective strengths of these technologies, recent advances in optical quantification of absolute Vmem enable new inquiries into Vmem biology.

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Poznin, Vitaly Fyodorovich. "Stylistic Features Of Russian Arthouse." Journal of Flm Arts and Film Studies 7, no. 2 (2015): 41–51. http://dx.doi.org/10.17816/vgik7241-51.

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The article analyzes stylistic features of Russian arthouse films of the last decade. Contrary to mainstream these movies are designed for narrow, aesthetically advanced audience. It is commonly known, that the style of any film is primarily determined by interpretation of screen space, formed first of all through visual images. Every fresh artistic cinema current differs from others by the method of interpretation of artistic space. Proven practices show that most of the art films of the last decade have one thing in common. This is the mode of interpretation of the art space, characterized by realistic, but in the same time conventional environment, in which the characters act. Interpretation of spatial and temporal space in Russian arthouse is close to conventional theatrical style. This phenomenon is explained by the dominance of parabolic plots with abundance of symbols and allegories. Russian art films are also marked with negative perception of reality. Their action mostly takes place in the middle of nowhere with the minimum civilizational and cultural properties. The choice of location, conventional environment, slow-paced action, unambiguous characters are the dominant features defining the style of majority of art films.

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