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Добірка наукової літератури з теми "Spatiotemporal metrics"

Автор: Grafiati

Опубліковано: 10 січня 2023

Оновлено: 28 січня 2023

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Статті в журналах з теми "Spatiotemporal metrics"

Gonçalves, Bruno, Diogo Coutinho, Bruno Travassos, João Brito, and Pedro Figueiredo. "Match Analysis of Soccer Refereeing Using Spatiotemporal Data: A Case Study." Sensors 21, no. 7 (April 5, 2021): 2541. http://dx.doi.org/10.3390/s21072541.

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This case study explored how spatiotemporal data can develop key metrics to evaluate and understand elite soccer referees’ performance during one elite soccer match. The dynamic position of players from both teams, the ball and three elite referees allowed to capture the following performance metrics: (i) assistant referees: alignment with the second last defender; (ii) referee: referee diagonal movement—a position density was computed and a principal component analysis was carried to identify the directions of greatest variability; and (iii) referee: assessing the distance from the referee to the ball. All computations were processed when the ball was in-play and separated by 1st and 2nd halves. The first metric showed an alignment lower than 1 m between the assistant referee and the second last defender. The second metric showed that in the 1st half, the referee position ellipsis area was 548 m2, which increased during the 2nd half (671 m2). The third metric showed an increase in the distance from the referee to the ball and >80% of the distance between 5–30 m during the 2nd half. The findings may be used as a starting point to elaborate normative behavior models from the referee’s movement performance in soccer.

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Hacker, Kathryn P., Andrew J. Greenlee, Alison L. Hill, Daniel Schneider, and Michael Z. Levy. "Spatiotemporal trends in bed bug metrics: New York City." PLOS ONE 17, no. 5 (May 26, 2022): e0268798. http://dx.doi.org/10.1371/journal.pone.0268798.

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Bed bug outbreaks pose a major challenge in urban environments and cause significant strain on public resources. Few studies have systematically analyzed this insect epidemic or the potential effects of policies to combat bed bugs. Here we use three sources of administrative data to characterize the spatial-temporal trends of bed bug inquiries, complaints, and reports in New York City. Bed bug complaints have significantly decreased (p < 0.01) from 2014–2020, the absolute number of complaints per month dropping by half (875 average complaints per month to 440 average complaints per month); conversely, complaints for other insects including cockroaches and flies did not decrease over the same period. Despite the decrease of bed bug complaints, areas with reported high bed bug infestation tend to remain infested, highlighting the persistence of these pests. There are limitations to the datasets; still the evidence available suggests that interventions employed by New York City residents and lawmakers are stemming the bed bug epidemic and may serve as a model for other large cities.

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Shiu, Janice, Sarah Fletcher, and Dara Entekhabi. "Spatiotemporal monsoon characteristics and maize yields in West Africa." Environmental Research Communications 3, no. 12 (December 1, 2021): 125007. http://dx.doi.org/10.1088/2515-7620/ac3776.

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Abstract To assess the vulnerability of rainfed agriculture in West Africa (WA) to climate change, a detailed understanding of the relationship between food crop yields and seasonal rainfall characteristics is required. The highly seasonal rainfall in the region is expected to change characteristics such as seasonal timing, duration, intensity, and intermittency. The food crop yield response to changes in these characteristics needs greater understanding. We follow a data-driven approach based on historical yield and climate data. Such an approach complements model-based approaches. Previous data-driven studies use spatially and temporally averaged precipitation measures, which do not describe the high degree of spatial and temporal variability of the West African Monsoon (WAM), the primary source of water for agriculture in the region. This has led previous studies to find small or insignificant dependence of crop yields on precipitation amount. Here, we develop metrics that characterize important temporal features and variability in growing season precipitation, including total precipitation, onset and duration of the WAM, and number of non-precipitating days. For each temporal precipitation metric, we apply several unique spatial aggregation functions that allow us to assess how different patterns of high-resolution spatial variability are related to country-level maize yields. We develop correlation analyses between spatiotemporal precipitation metrics and detrended country-level maize yields based on findings that non-climatic factors, such as agricultural policy reform and increased investment, have driven the region’s long-term increase in maize yields. Results show that that the variability in the number of days without rain during the monsoon season and the lower bounds to the spatial rain pattern and end to the monsoon season are most strongly associated with maize yields. Our findings highlight the importance of considering spatial and temporal variability in precipitation when evaluating impacts on crop yields, providing a possible explanation for weak connections found in previous studies.

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Mandal, Somnath, Sanjit Kundu, Subrata Haldar, Subhasis Bhattacharya, and Suman Paul. "Monitoring and Measuring the Urban Forms Using Spatial Metrics of Howrah City, India." Remote Sensing of Land 4, no. 1-2 (November 22, 2020): 19–39. http://dx.doi.org/10.21523/gcj1.20040103.

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Monitoring and measurement of urban growth pattern with the help of urban-rural gradient and spatial metrics are gaining significant importance in recent times. Rapid and unplanned urban growth has a great impact on natural resources, local ecology, forestry and infrastructure. Temporal satellite data, gradient analysis and landscape metrics of urban landscapes will help to evolve appropriate strategies for integrated planning and sustainable management of natural resources. This communication focuses on spatiotemporal patterns of land use dynamics of Howrah Municipal Corporation (HMC), India and its surroundings with six buffer zones of 2kms. Analysis has been carried out on HMC using temporal remote sensing data. HMC has been used to identify the changes in the gradient of urban to peri-urban and rural regions. Further, the entire study area has been divided into eight zones radiated from city center based on directions. Different landscape metrics have been computed for each zone which helps to understand the spatiotemporal patterns and associated dynamics of the landscape at local levels.

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Paley, Derek A., and Artur Wolek. "Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 91–114. http://dx.doi.org/10.1146/annurev-control-073119-090634.

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The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.

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Parrott, Lael, Raphaël Proulx, and Xavier Thibert-Plante. "Three-dimensional metrics for the analysis of spatiotemporal data in ecology." Ecological Informatics 3, no. 6 (December 2008): 343–53. http://dx.doi.org/10.1016/j.ecoinf.2008.07.001.

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Zhao, Jie, Wenfu Yang, Junhuan Peng, Cheng Li, Zhen Li, and Xiaosong Liu. "ANALYZING AND MODELING THE SPATIOTEMPORAL DYNAMICS OF URBAN EXPANSION: A CASE STUDY OF HANGZHOU CITY, CHINA." Journal of Environmental Engineering and Landscape Management 27, no. 4 (November 28, 2019): 228–41. http://dx.doi.org/10.3846/jeelm.2019.11561.

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Understanding the spatiotemporal characteristics of urban expansion is increasingly important for assisting the decision making related to sustainable urban development. By integrating remote sensing (RS), spatial metrics, and the cellular automata (CA) model, this study explored the spatiotemporal dynamics of urban expansion and simulated future scenarios for Hangzhou City, China. The land cover maps (2002, 2008, and 2013) were derived from Landsat images. Moreover, the spatial metrics were applied to characterize the spatial pattern of urban land. The CA model was developed to simulate three scenarios (Business-As-Usual (BAU), Environmental Protection (EP), and Coordination Development (CD)) based on the various strategies. In addition, the scenarios were further evaluated and compared. The results indicated that Hangzhou City has experienced significant urban expansion, and the urban area has increased by 698.59 km2. Meanwhile, the spatial pattern of urban land has become more fragmented and complex. Hangzhou City will face unprecedented pressure on land use efficiency and coordination development if this historical trend continues. The CD scenario was regarded as the optimized scenario for achieving sustainable development. The findings revealed the spatiotemporal characteristics of urban expansion and provide a support for future urban development.

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Hornberger, Zachary, Bruce Cox, and Raymond R. Hill. "Analysis of the effects of spatiotemporal demand data aggregation methods on distance and volume errors." Journal of Defense Analytics and Logistics 5, no. 1 (May 10, 2021): 29–45. http://dx.doi.org/10.1108/jdal-03-2020-0003.

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Purpose Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors. Design/methodology/approach This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering. Findings As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands. Originality/value This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.

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Nouri, Milad, and Mehdi Homaee. "Spatiotemporal changes of snow metrics in mountainous data-scarce areas using reanalyses." Journal of Hydrology 603 (December 2021): 126858. http://dx.doi.org/10.1016/j.jhydrol.2021.126858.

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Adams, David K., Henrique M. J. Barbosa, and Karen Patricia Gaitán De Los Ríos. "A Spatiotemporal Water Vapor–Deep Convection Correlation Metric Derived from the Amazon Dense GNSS Meteorological Network." Monthly Weather Review 145, no. 1 (January 1, 2017): 279–88. http://dx.doi.org/10.1175/mwr-d-16-0140.1.

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Abstract Deep atmospheric convection, which covers a large range of spatial scales during its evolution, continues to be a challenge for models to replicate, particularly over land in the tropics. Specifically, the shallow-to-deep convective transition and organization on the mesoscale are often not properly represented in coarse-resolution models. High-resolution models offer insights on physical mechanisms responsible for the shallow-to-deep transition. Model verification, however, at both coarse and high resolution requires validation and, hence, observational metrics, which are lacking in the tropics. Here a straightforward metric derived from the Amazon Dense GNSS Meteorological Network (~100 km × 100 km) is presented based on a spatial correlation decay time scale during convective evolution on the mesoscale. For the shallow-to-deep transition, the correlation decay time scale is shown to be around 3.5 h. This novel result provides a much needed metric from the deep tropics for numerical models to replicate.

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Більше джерел

Дисертації з теми "Spatiotemporal metrics"

Mirambell, Alberto Benito. "Application of spatiotemporal techniques to estimate evapotranspiration in the Paraíba do Sul river watershed." Universidade Federal de Juiz de Fora (UFJF), 2018. https://repositorio.ufjf.br/jspui/handle/ufjf/6909.

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No dia de hoje, qualquer estudo relacionado aos recursos hídricos e seus usos, tais como irrigação, abastecimento de água e geração de energia, é de suma importância em função dos cenários que vivemos atualmente face às variabilidades climáticas. O uso eficiente desses recursos faz-se cada vez mais necessário, envolvendo fatores como a estimativa de algumas variáveis relacionadas ao ciclo hidrológico, notadamente a evapotranspiração. Há quase trinta anos a FAO recomendou o uso da equação de Penman-Monteith para a estimativa da evapotranspiração de referência. Desde então tem sido aplicada com sucesso em diferentes regiões e sob diferentes climas. No entanto, esta abordagem tem algumas desvantagens, entre elas, o fato de depender de medições de campo de parâmetros climáticos, tais como temperatura, humidade do ar, velocidade do vento e radiação solar. Além disso, essas medições são pontuais em referência ao local de operação da estação meteorológica e podem não representar de forma fidedigna as condições climáticas dos ambientes circundantes. Nos últimos tempos, com o avanço da tecnologia, o desenvolvimento de potentes linguagens de programação orientados à análise de dados, o surgimento das técnicas na área de inteligência artificial e do tratamento de grandes volumes de dados (“Big Data”), surgiram ferramentas com grande potencial para melhorar a forma como se tratam os eventos naturais ou antrópicos, permitindo maior eficiência e produtividade. Nessa linha, o objetivo principal do presente estudo é o uso desse conjunto de tecnologias para uma estimação confiável e robusta da evapotranspiração, na medida que constitui uma variável fundamental no fechamento do balanço hídrico no nível de uma bacia hidrográfica. Complementarmente, essa estimativa poderia ser também empregada como indicativo da perda água em uma cultura pelo agricultor. Em especial, dois procedimentos foram aplicados ao longo deste trabalho: redes neurais artificiais (RNA’s) e o algoritmo METRIC. O primeiro está associado a uma ferramenta com base em inteligência artificial, capaz de reproduzir o comportamento de certas variáveis com um alto nível de semelhança abrindo a possibilidade de gerar predições a curto-maio prazo que ajude no gerenciamento dos recursos hídricos por parte dos comités de bacia e outros entes responsáveis por eles. Por outro lado, METRIC permite usar imagens de satélite para estimar evapotranspiração em escala horária, capturando as abruptas mudanças que sofrem algumas variáveis climáticas ao longo do dia, sendo esta uma informação de vital importância para os agricultores determinarem a irrigação com maior confiabilidade. Os resultados obtidos após a aplicação de ambos os procedimentos, que compõem a abordagem metodológica deste trabalho, foram muito satisfatórios e com uma alta correlação com aqueles gerados pela metodologia considerada como referência. Assim sendo, pode-se concluir que ambos procedimentos formam um referencial apropriado na estimativa de valores de evapotranspiração que podem ser transferidos à prática agrícola com a certeza de uma melhora constante a tenor da rápida e imparável evolução da tecnologia na área da agricultura de precisão.
Nowadays, any study related to water resources and its usage, such as irrigation, water consumption and energy production is a central issue due to the climate change scenario we are currently living. The efficient use of such resources is a must and involves several factors, among them, the estimate of some hydrologic cycle-related variables, highlighting evapotranspiration, among them. About thirty years ago, the FAO recommended Penman-Monteith equation as the most trustworthy and representative methodology to estimate reference crop evapotranspiration. Since then, it has been applied successfully over different regions and under diverse weather conditions. However, this approach has some cons, such as its dependency on ground measurements of most common climatological parameters: temperature, relative air humidity, wind speed or solar radiation. In addition, these measurements are punctual on the weather station’s location and may not fully represent surrounding environments’ conditions. Lately, thanks to technological advances, the development of powerful programming data analysis-oriented languages, the rising of artificial intelligence, as well as big data, we have a wide variety of tools to improve the way we analyse natural phenomena, making it more efficient and productive. Therefore, the main objective of the present study is the use of such technologies aiming to estimate reliable evapotranspiration values, as a central parameter on water resources management at watershed basis, or even as an indicator of crop water loss, by farmers. Mainly, two different technology-based approaches have been applied along this dissertation pursuing the objective previously mentioned: artificial neural networks (ANN’s) and METRIC algorithm. The former is an artificial intelligence-based tool, capable of “recording” specific variables behaviour and succeed in “mimicking” them at a high resemblance level, favouring the possibility of short-half term forecasts to help watershed committees and other responsible bodies manage water resources. On the other hand, METRIC algorithm uses satellite imagery in order to estimate evapotranspiration hourly and able thus to catch the disrupting changes some of the climatological variables suffer along the day, turning into a vital piece of information for farmers, since they can design irrigation schedule more precisely. Results obtained after both procedures’ application, which compose the methodological approach throughout this study, fully satisfied our expectations and showed a high correlation to those results estimated by the methodology of reference. To sum up, we conclude that both approaches are reliable at estimating reference crop evapotranspiration and can be transferred to the agricultural management assuring a steady improvement due to the quick and unstoppable evolution in technology on the “agriculture of precision” field.

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Hengenius, James B. "Quantitative modeling of spatiotemporal systems| Simulation of biological systems and analysis of error metric effects on model fitting." Thesis, Purdue University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3687049.

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Understanding the biophysical processes underlying biological and biotechnological processes is a prerequisite for therapeutic treatments and technological innovation. With the exponential growth of computational processing speed, experimental findings in these fields have been complemented by dynamic simulations of developmental signaling and genetic interactions. Models provide means to evaluate "emergent" properties of systems sometimes inaccessible by reductionist approaches, making them test beds for biological inference and technological refinement.

The complexity and interconnectedness of biological processes pose special challenges to modelers; biological models typically possess a large number of unknown parameters relative to their counterparts in other physical sciences. Estimating these parameter values requires iterative testing of parameter values to find values that produce low error between model and data. This is a task whose length grows exponentially with the number of unknown parameters. Many biological systems require spatial representation (i.e., they are not well-mixed systems and change over space and time). Adding spatial dimensions complicates parameter estimation by increasing computational time for each model evaluation. Defining error for model-data comparison is also complicated on spatial domains. Different metrics compare different features of data and simulation, and the desired features are dependent on the underlying research question.

This dissertation documents the modeling, parameter estimation, and simulation of two spatiotemporal modeling studies. Each study addresses an unanswered research question in the respective experimental system. The former is a 3D model of a nanoscale amperometric glucose biosensor; the model was used to optimize the sensor's design for improved sensitivity to glucose. The latter is a 3D model of the developmental gap gene system that helps establish the bodyplan of Drosophila melanogaster; I wished to determine if the embryo's geometry alone was capable of accounting for observed spatial distributions of gap gene products and to infer feasible genetic regulatory networks (GRNs) via parameter estimation of the GRN interaction terms. Simulation of the biosensor successfully predicted an optimal electrode density on the biosensor surface, allowing us to fabricate improved biosensors. Simulation of the gap gene system on 1D and 3D embryonic demonstrated that geometric effects were insufficient to produce observed distributions when simulated with previously reported GRNs. Noting the effects of the error definition on the outcome of parameter estimation, I conclude with a characterization of assorted error definitions (objective functions), describe data characteristics to which they are sensitive, and end with a suggested procedure for objective function selection. Choice of objective function is important in parameter estimation of spatiotemporal system models in varied biological and biotechnological disciplines.

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Kim, Woojin. "The Persistent Topology of Dynamic Data." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587503336988272.

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Alexander, Jeremy P. "A framework for quantifying tactical team behaviour in Australian Rules Football." Thesis, 2020. https://vuir.vu.edu.au/40991/.

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The inception of tracking technologies has allowed for increased access to the positioning data of team sport athletes. This information assists in understanding collective team behaviour by measuring the continuous movement patterns of players. Assessing the efficacy of collective team behaviour research requires comprehension of the contextual factors that may influence movement behaviour, such as the match phase and field location of the ball. Limited studies that have analysed collective team behaviour have accounted for such contextual variables. Research on collective team behaviour in invasion sports has typically focused on football and basketball, while investigations in Australian football (AF) remain largely absent. Furthermore, collective team behaviour investigations to date have generally inferred performance through the positioning of players without directly determining the continuous influence on match play. Therefore, this thesis presents new methodologies for measuring collective team behaviour in AF. This information was used to understand the extent to which collective team behaviour influenced match play in a continuous manner. The findings provide a framework to quantify tactical team behaviour in Australian Rules football (AFL). Global positioning systems (GPS) spatiotemporal datasets were obtained from match simulation sessions and elite-level AFL matches. This information was aligned with match event data to provide contextual information, such as match phase and ball location. Initial chapters investigated the collective behaviour of AF teams using a macroscopic approach during match simulation and a competitive match. This was undertaken using a range of spatiotemporal metrics that summarise how certain players are positioned across a field of play. These chapters identified teams that were able to obtain increased possession of the ball covered greater spatial regions. Players also repositioned deeper towards their own goal when the ball was in their defensive half and relocated higher up the field when the ball was in their forward half. Subsequent chapters used a microscopic approach to model the position of every player to understand the spatial control of each team across a playing surface. The central findings from these chapters were that the total number of players increased based on where the ball was positioned and both teams obtained greater spatial control compared to the opposition when the ball was in their defensive half. Teams were also able to arrest spatial control when forcing a turnover in possession. The general findings from this thesis are spatiotemporal metrics can be used to infer tactical behaviour. A method that continuously represents how players occupy sub-areas of play may provide coaches and sport science practitioners with a more precise account of how tactical team behaviour influences ensuing match play. Finally, quantifying the resistive exchange in spatial control between teams and detecting the value placed on controlling specific regions may contribute to providing a more representative understanding of tactical team behaviour.

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LEE, YI-SHENG, and 李易陞. "No-reference Video Quality Metric Computation Using Spatial, Temporal, Transform, and Spatiotemporal Features." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/q8h52q.

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碩士
國立中正大學
資訊工程研究所
106
Nowadays, Internet is booming and the perception of video quality by video providers and users is becoming more important, but limit by the bandwidth of network transmission. No reference video quality computation is the best and well-known in three types of video quality assessment metrics. In this study, the proposed video quality computation metric is based on no reference and extracted spatial, temporal, transform, and spatiotemporal features as the basis for predicting quality scores. First, edge detection and blockiness are extracted as the spatial features and difference of luminance and motion are extracted as temporal features. The pairwise products of discrete cosine transform and wavelet transform are extracted to enhance the center point pixel and surrounding neighbor pixels, and are regarded as transform features. Considering that spatial and temporal information can extracted simultaneously, the statistical properties of trajectory and three-dimensional discrete cosine transform are taken as spatiotemporal features. Finally, support vector regression is utilized to predict the final quality score. This experiment using LIVE video quality assessment database and experimental results show that the results have better results than other existing metrics.

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CHUNG, KUO-CHUN, and 鍾國君. "No-reference Stereoscopic Video Quality Metric Computation Using Spatial, Depth, Transform, and Spatiotemporal Features." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/27s73g.

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碩士
國立中正大學
資訊工程研究所
106
In recent year, 3D technology application provides a new viewing experience and has become more and more widespread. Due to the reason mentioned above, humans will pay more attention on stereoscopic video quality. In other words, it is necessary to develop the stereoscopic video quality assessment approaches. Full-reference and reduced-reference stereoscopic video quality assessment methods usually obtain better performance since these approaches can make use of the information of original videos. However, it is hard to get original videos when transmitting. Hence, no-reference stereoscopic video quality assessment technology is mainly focused in this study. First, features from four domains, including spatial, depth, transform, and spatiotemporal features are extracted. On the spatial domain, blockiness, cyclopean view, binocular rivalry, cross entropy, and edge are extracted. On the depth domain, disparity saliency, depth structure, NSS, and depth entropy are extracted. On the transform domain, discrete wavelet transform (DWT) and contourlet transform information are extracted. On the spatiotemporal domain, depth motion and 3D-DCT information are extracted. The feature vectors from the left-view and right-view videos are averaged and represented as statistical feature and normalize to the same distribution. Then, support vector regression (SVR) is applied to measure the stereoscopic video quality score. Finally, experimental results show that the proposed approach is better than the other NR approach on NAMA3DS1_COSPAD1 database.

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Книги з теми "Spatiotemporal metrics"

Silberstein, Michael, W. M. Stuckey, and Timothy McDevitt. Resolving Puzzles, Problems, and Paradoxes from General Relativity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198807087.003.0004.

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The main thread of chapter 3 introduces general relativity (GR), Big Bang cosmology, and closed timelike curves, showing how the ant’s-eye view leads to the puzzle of the creation of the universe, the horizon problem, the flatness problem, the low entropy problem, and the paradoxes of closed time-like curves. All these puzzles, problems, and paradoxes of the dynamical universe are resolved using the God’s-eye view of the adynamical block universe. Accordingly, Einstein’s equations of GR are not understood dynamically, but rather adynamically, that is, as a global self-consistency constraint between the spacetime metric and stress–energy tensor throughout the spacetime manifold. This is “spatiotemporal ontological contextuality” as applied to GR. The philosophical nuances such as the status of the block universe argument in GR and debates about the Past Hypothesis have been placed in Philosophy of Physics for Chapter 3. The associated formalism and computations are in Foundational Physics for Chapter 3.

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Частини книг з теми "Spatiotemporal metrics"

Sharmiladevi, S., and S. Siva Sathya. "Evaluation Metrics of Spatial and Spatiotemporal Data Mining Techniques." In Emerging Technologies in Data Mining and Information Security, 449–63. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9774-9_42.

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Gholipour, Ali, Catherine Limperopoulos, Sean Clancy, Cedric Clouchoux, Alireza Akhondi-Asl, Judy A. Estroff, and Simon K. Warfield. "Construction of a Deformable Spatiotemporal MRI Atlas of the Fetal Brain: Evaluation of Similarity Metrics and Deformation Models." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, 292–99. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10470-6_37.

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Xu, Hengpeng, Yao Zhang, Jinmao Wei, Zhenglu Yang, and Jun Wang. "Spatiotemporal-Aware Region Recommendation with Deep Metric Learning." In Database Systems for Advanced Applications, 491–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18590-9_73.

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Christakos, George. "Space–Time Metrics." In Spatiotemporal Random Fields, 83–120. Elsevier, 2017. http://dx.doi.org/10.1016/b978-0-12-803012-7.00003-9.

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Joos, Fortunat, and Thomas L. Frölicher. "Impact of Climate Change Mitigation On Ocean Acidification Projections." In Ocean Acidification. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780199591091.003.0019.

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Ocean acidification caused by the uptake of carbon dioxide (CO2) by the ocean is an important global change problem (Kleypas et al. 1999; Caldeira and Wickett 2003; Doney et al. 2009). Ongoing ocean acidification is closely linked to global warming, as acidification and warming are primarily caused by continued anthropogenic emissions of CO2 from fossil fuel burning (Marland et al. 2008 ), land use, and land-use change (Strassmann et al. 2007). Future ocean acidification will be determined by past and future emissions of CO2 and their redistribution within the earth system and the ocean. Calculation of the potential range of ocean acidification requires consideration of both a plausible range of emissions scenarios and uncertainties in earth system responses, preferably by using results from multiple scenarios and models. The goal of this chapter is to map out the spatiotemporal evolution of ocean acidification for different metrics and for a wide range of multigas climate change emissions scenarios from the integrated assessment models (Nakićenović 2000; Van Vuuren et al. 2008b). By including emissions reduction scenarios that are among the most stringent in the current literature, this chapter explores the potential benefits of climate mitigation actions in terms of how much ocean acidification can be avoided and how much is likely to remain as a result of inertia within the energy and climate systems. The longterm impacts of carbon emissions are addressed using so-called zero-emissions commitment scenarios and pathways leading to stabilization of atmospheric CO 2. Discussion will primarily rely on results from the cost-efficient Bern2.5CC model (Plattner et al. 2008) and the comprehensive carbon cycle– climate model of the National Centre for Atmospheric Research (NCAR), CSM1.4-carbon (Steinacher et al. 2009; Frölicher and Joos 2010). The magnitude of the human perturbation of the climate system is well documented by observations (Solomon e t al. 2007). Carbon emissions from human activities force the atmospheric composition, climate, and the geochemical state of the ocean towards conditions that are unique for at least the last million years (see Chapter 2).

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Bigaj, Tomasz. "Radical Structural Essentialism for the Spacetime Substantivalist." In The Foundation of Reality, 217–32. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198831501.003.0013.

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Spacetime substantivalists insist that spatiotemporal points are fundamental entities and thus are ontologically independent from the physical objects occupying these points.This chapter argues that the best metaphysical option for the substantivalist is to adopt a radical version of structural essentialism, according to which participation in a certain relational structure is both a necessary and sufficient condition of identity for the elements characterized by this structure.It comparesthe author’sproposed variant of essentialism with some alternative conceptions, such as Tim Maudlin’s metric essentialism and David Glick’s Minimal Structural Essentialism. It discusses how the author’sRadical Structural Essentialism deals with the challenge of the hole argument as well as some less well-known challenges, such as the problem of contingent counterfactuals regarding the material contents of spacetime, and the ‘mole’ argument. Later the chapter explains why the structure consisting of the metric tensor is a genuinely relational structure, and itdefend the claim that in order to preserve the identity of its elements the actual structure may be merely embeddable in (and not necessarily isomorphic with) an alternative structure.

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Тези доповідей конференцій з теми "Spatiotemporal metrics"

Stapenhurst, Robert, Jinyun Lu, and Dimitris Agrafiotis. "Performance evaluation of objective video quality metrics on mixed spatiotemporal resolution content." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738014.

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Church, George. "Hunger for new technologies, metrics, and spatiotemporal models in functional genomic (abstract only)." In the fifth annual international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/369133.369179.

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Zhang, Zhenlong, Lingde Jiang, Rui Peng, and Yixing Yin. "The spatiotemporal change of urban form in Nanjing, China: Based on SLEUTH and spatial metrics analysis." In 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567753.

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Taubenböck, Hannes, Martin Wegmann, Michael Wurm, Tobias Ullmann, and Stefan Dech. "The global trend of urbanization: spatiotemporal analysis of megacities using multi-temporal remote sensing, landscape metrics, and gradient analysis." In Remote Sensing, edited by Ulrich Michel and Daniel L. Civco. SPIE, 2010. http://dx.doi.org/10.1117/12.864917.

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Arellano-González, Juan C., Hugo I. Medellín-Castillo, and J. Jesús Cervantes-Sánchez. "Identification and Analysis of the Biomechanical Parameters Used for the Assessment of Normal and Pathological Gait: A Literature Review." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10140.

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Abstract The analysis of human gait represents a valuable tool for an early and timely identification of diseases and pathologies, as well as to follow up treatments and rehabilitation programs. However, although several research works in the literature have addressed the assessment of human gait as a diagnostic tool, few works have focused on the biomechanical parameters and metrics needed for such practice. This work presents the results of an investigation carried out to identify and analyze the biomechanical parameters used in the literature to assess the human walking, both pathological and normal. For this purpose, a literature review was conducted to detect and analyze the biomechanical parameters. A classification of these parameters based on the application area is proposed and comprises clinical, sport and exploration. These parameters are also classified according to the origin of the problem into musculoskeletal, neurological and circulatory. The biomechanical parameters identified are analyzed and discussed using set theory. The results indicate that the analysis of the spatiotemporal parameters of the gait allows a detailed and economic study of this mode of locomotion. The most used gait parameters are: step length, stride length, step width, gait speed, gait phases, cadence, swing time and stance time. On the other hand, the study of gait in the clinical area makes use of nearly all the gait parameters reported in the literature, i.e. spatial, temporal, angular, force and other specific parameters according to the type of pathology being analyzed.

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Goroshin, Ross, Joan Bruna, Jonathan Tompson, David Eigen, and Yann LeCun. "Unsupervised Learning of Spatiotemporally Coherent Metrics." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.465.

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Jiang, Zhanhong, and Soumik Sarkar. "Understanding Wind Turbine Interactions Using Spatiotemporal Pattern Network." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9784.

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This paper presents a data-driven modeling framework to understand spatiotemporal interactions among wind turbines in a large scale wind energy farm. A recently developed probabilistic graphical modeling scheme, namely the spatiotemporal pattern network (STPN) is used to capture individual turbine characteristics as well as pair-wise causal dependencies. The causal dependency is quantified by a mutual information based metric and it has been shown that it efficiently and correctly captures both temporal and spatial characteristics of wind turbines. The causal interaction models are also used for predicting wind power production by one wind turbine using observations from another turbine. The proposed tools are validated using the Western Wind Integration data set from the National Renewable Energy Laboratory (NREL).

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Junyong You, Miska M. Hannuksela, and Moncef Gabbouj. "An objective video quality metric based on spatiotemporal distortion." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5413900.

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Gielniak, Michael J., and Andrea L. Thomaz. "Spatiotemporal correspondence as a metric for human-like robot motion." In the 6th international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1957656.1957676.

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Kwong, Ngai-Wing, Sik-Ho Tsang, Yui-Lam Chan, Daniel Pak-Kong Lun, and Tsz-Kwan Lee. "No-reference video quality assessment metric using spatiotemporal features through LSTM." In International Workshop on Advanced Image Technology 2021, edited by Wen-Nung Lie, Qian Kemao, Jae-Gon Kim, and Masayuki Nakajima. SPIE, 2021. http://dx.doi.org/10.1117/12.2590406.

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Звіти організацій з теми "Spatiotemporal metrics"

Perdigão, Rui A. P., and Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, November 2020. http://dx.doi.org/10.46337/201111.

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Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the non-recurrent emergence of fundamental new properties absent from the a priori kinematic geometric and statistical features. Unveiling causal mechanisms and eliciting system dynamic predictability under such challenging conditions is not only a fundamental problem in mathematical and statistical physics, but also one of critical importance to dynamic modelling, risk assessment and decision support e.g. regarding non-recurrent critical transitions and extreme events. In order to address these challenges, generalized metrics in non-ergodic information physics are hereby introduced for unveiling elusive dynamics, causality and predictability of complex dynamical systems undergoing far-from-equilibrium structural-functional coevolution. With these methodological developments at hand, hidden dynamic information is hereby brought out and explicitly quantified even beyond post-critical regime collapse, long after statistical information is lost. The added causal insights and operational predictive value are further highlighted by evaluating the new information metrics among statistically independent variables, where traditional techniques therefore find no information links. Notwithstanding the factorability of the distributions associated to the aforementioned independent variables, synergistic and redundant information are found to emerge from microphysical, event-scale codependencies in far-from-equilibrium nonlinear statistical mechanics. The findings are illustrated to shed light onto fundamental causal mechanisms and unveil elusive dynamic predictability of non-recurrent critical transitions and extreme events across multiscale hydro-climatic problems.

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.

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