Representing gazetteers and period thesauri in four-dimensional space–time

Gazetteers, i.e., lists of place-names, enable having a global vision of places of interest through the assignment of a point, or a region, to a place name. However, such identification of the location corresponding to a place name is often a difficult task. There is no one-to-one correspondence between the two sets, places and names, because of name variants, different names for the same place and homonymy; the location corresponding to a place name may vary in time, changing its extension or even the position; and, in general, there is the imprecision deriving from the association of a concept belonging to language (the place name) to a precise concept (the spatial location). Also for named time periods, e.g., early Bronze Age, which are of current use in archaeology, the situation is similar: they depend on the location to which they refer as the same period may have different time-spans in different locations. The present paper avails of a recent extension of the CIDOC CRM called CRMgeo, which embeds events in a spatio-temporal 4-dimensional framework. The paper uses concepts from CRMgeo and introduces extensions to model gazetteers and period thesauri. This approach enables dealing with time-varying location appellations as well as with space-varying period appellations on a robust basis. For this purpose a refinement/extension of CRMgeo is proposed and a discretization of space and time is used to approximate real space–time extents occupied by events. Such an approach solves the problem and suggests further investigations in various directions.     

Mathematical modeling in the geoinformation problem of the dynamics of geomigration under space–time nonlocality

A mathematical model is set up to analyze the dynamics of locally time- and space-nonequilibrium migration–consolidation processes in a porous earth saturated with salt solutions under mass transfer. The corresponding nonlinear boundary-value problem is stated, an algorithm of its approximate solution is presented, and the results of the numerical implementation of the algorithm are given.     

Multigrid methods with space–time concurrency

Computing and Visualization in Science - We consider the comparison of multigrid methods for parabolic partial differential equations that allow space–time concurrency. With current trends in...     

Characterizing implications of two-dimensional space–time orientations for principal component analysis of geographic time series

This article introduces four new modes of principal component analysis (PCA) to investigate space–time variability in an image time series. Using the concept of tensors, an image time series can be understood as a space–time cube and can be analysed using six different orientations by grouping the basic elements (voxels) of the cube across different dimensions. Voxels grouped across columns or rows of the cube to produce vectors result in profiles. Voxels grouped across different planes to produce matrices result in slices. The traditional S-mode and T-mode PCA are thus the profile modes and slice modes across time and across space, respectively. This research introduces two profile-mode orientations across longitude and latitude and two slice-mode orientations across longitude–time and latitude–time. The research shows that a more complete understanding of the spatio-temporal variability in the data set can be achieved by investigating these different orientation modes, as individual modes have the capability of capturing variability in a particular dimension of a spatio-temporal data set. A case study was carried out using weekly anomalies of the AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data) sea surface height product filtered for tropical instability waves (TIWs) for a three-year time period from 1997 to 1999 in the tropical Pacific region. The results show that PCA with longitude as the dimension of variability and latitude–time as the dimension of variability were able to capture the TIW and barotropic Rossby wave propagation across the equatorial Pacific. The other two orientation modes were able to detect dominant latitudinal locations for TIW.     

Transport of pollutant in shallow flows: A space–time CE/SE scheme

This paper focuses on implementation of space–time CE/SE scheme for computing the transport of a passive pollutant by a flow. The flow model comprises of the Saint-Venant system of shallow water equations and the pollutant propagation is described by a transport equation. The one-dimensional and two-dimensional flow equations are numerically investigated using the CE/SE scheme. A number of test problems are presented to check the accuracy and efficiency of the proposed scheme. The results of CE/SE scheme are compared with the central scheme. Both the schemes are found to be in close agreement. However, our proposed CE/SE scheme accurately captures shocks and discontinuous profiles.     

A new method of speech transmission over space time block coded co-operative MIMO–OFDM networks using time and space diversity

Reliable and good quality of service for speech transmission over wireless network has been a major challenge for the communication engineers and researchers. In this paper a new technique of speech compression and transmission using different Daubechies wavelets in a space time block coded co-corporative MIMO–OFDM networks using time and space diversity has been proposed. The main focus has been laid on design and development of wavelet based compression of multimedia signals for cooperative MIMO–OFDM system. We tried to find out various major issues regarding the wavelet compression of a speech signal. These issues include choice of a wavelet, decomposition level and thresholding criteria suitable for speech compression and transmission in co-operative MIMO–OFDM systems. A wavelet based speech compression technique using hard and soft thresholding algorithm has been proposed. The work shows that wavelet compression with QPSK modulation is a promising compression technique in a cooperative MIMO–OFDM system which makes use of the elegant theory of wavelets. The performance has been evaluated using mean square error, peak signal to noise ratio, compression ratio, bit error rate, and retained signal energy. It has been found that the transmitted speech signal is retrieved well under noisy conditions at higher order Daubechies wavelets. From the results it is clear that proposed technique aims at a radio access technology that can provide service performance comparable to that of current fixed Line accesses. To evaluate the performance of the proposed method, various performance parameters have been compared with previously implemented techniques and it has been found that the proposed work shows better performance as compared to the already existing techniques.     

Embodied cognition of information visualization: Human–computer interaction with six degrees of freedom in movement of an information space

Mental healthcare has been identified as one of the major social and economic challenges that society will face in the coming decades. In this paper, we explore the potential of technology to improve people’s access to and engagement in, as well as the effectiveness and affordability of, mental healthcare services while interacting with computers. Information visualization plays a crucial role in people’s interaction with computers. Specifically, information visualization represents data or concepts graphically and helps people construct cognitive maps i.e., mental representations of the information space. Well-designed information visualization methods enable users to employ their mental capabilities to manipulate an information space and perceive it based on good mental health. According to the latest study of mental capability, movement will guide insight in problem solving situations. Movement in the 3D world is a crucial part of our mental capability, but traditional information visualization approaches fail to consider it. In an attempt to fill this research gap, we propose a new information visualization method that integrates a user’s visual perception and his/her ergonomic perception. Specifically, the approach enables users to manipulate an information space to follow the behavior of human movement in the 3D world with six degrees of freedom. Users can move an information space along the x-, y-, and z-axes as well as rotate it-giving six degrees of freedom. Manipulating the information space in this way provides users with a more comprehensive understanding of the space, and integrates their visual capability and ergonomic perception so that they can fully utilize their mental capabilities.     

Error estimates of the space–time spectral method for parabolic control problems

This work is devoted to investigate the spectral approximation of optimal control of parabolic problems. The space–time method is used to boost high-order accuracy by applying dual Petrov–Galerkin spectral scheme in time and spectral method in space. The optimality conditions are derived, and the a priori error estimates indicate the convergence of the proposed method. Numerical tests confirm the theoretical results, and show the efficiency of the method.     

Asymptotic properties of the space–time adaptive numerical solution of a nonlinear heat equation

We consider the fully adaptive space–time discretization of a class of nonlinear heat equations by Rothe’s method. Space discretization is based on adaptive polynomial collocation which relies on equidistribution of the defect of the numerical solution, and the time propagation is realized by an adaptive backward Euler scheme. From the known scaling laws, we infer theoretically the optimal grids implying error equidistribution, and verify that our adaptive procedure closely approaches these optimal grids.     

Refinement of flexible space–time finite element meshes and discontinuous Galerkin methods

In this paper we present an algorithm to refine space–time finite element meshes as needed for the numerical solution of parabolic initial boundary value problems. The approach is based on a decomposition of the space–time cylinder into finite elements, which also allows a rather general and flexible discretization in time. This also includes adaptive finite element meshes which move in time. For the handling of three-dimensional spatial domains, and therefore of a four-dimensional space–time cylinder, we describe a refinement strategy to decompose pentatopes into smaller ones. For the discretization of the initial boundary value problem we use an interior penalty Galerkin approach in space, and an upwind technique in time. A numerical example for the transient heat equation confirms the order of convergence as expected from the theory. First numerical results for the transient Navier–Stokes equations and for an adaptive mesh moving in time underline the applicability and flexibility of the presented approach.     

The experience of enchantment in human–computer interaction

Improving user experience is becoming something of a rallying call in human–computer interaction but experience is not a unitary thing. There are varieties of experiences, good and bad, and we need to characterise these varieties if we are to improve user experience. In this paper we argue that enchantment is a useful concept to facilitate closer relationships between people and technology. But enchantment is a complex concept in need of some clarification. So we explore how enchantment has been used in the discussions of technology and examine experiences of film and cell phones to see how enchantment with technology is possible. Based on these cases, we identify the sensibilities that help designers design for enchantment, including the specific sensuousness of a thing, senses of play, paradox and openness, and the potential for transformation. We use these to analyse digital jewellery in order to suggest how it can be made more enchanting. We conclude by relating enchantment to varieties of experience.An earlier version of this paper was presented at Chi’2004 Fringe.     

Culture of sedimentation in the human–technology interaction

New technologies inspire new interface paradigms. Promising utility of new interfaces continues attracting their modification. It is argued that in order for human users to share phenomenological experiences through multimodal systems, they need to deal with embedded computers. This paper discusses the embodied nature of communication and a need for the development of a postphenomenology of technology, which plays a vital role in the material culture.     

An optimal time–space algorithm for dense stereo matching

The increasing demand for higher resolution images and higher frame rate videos will always pose a challenge to computational power when real-time performance is required to solve the stereo-matching problem in 3D reconstruction applications. Therefore, the use of asymptotic analysis is necessary to measure the time and space performance of stereo-matching algorithms regardless of the size of the input and of the computational power available. In this paper, we survey several classic stereo-matching algorithms with regard to time–space complexity. We also report running time experiments for several algorithms that are consistent with our complexity analysis. We present a new dense stereo-matching algorithm based on a greedy heuristic path computation in disparity space. A procedure which improves disparity maps in depth discontinuity regions is introduced. This procedure works as a post-processing step for any technique that solves the dense stereo-matching problem. We prove that our algorithm and post-processing procedure have optimal O(n) time–space complexity, where n is the size of a stereo image. Our algorithm performs only a constant number of computations per pixel since it avoids a brute force search over the disparity range. Hence, our algorithm is faster than “real-time” techniques while producing comparable results when evaluated with ground-truth benchmarks. The correctness of our algorithm is demonstrated with experiments in real and synthetic data.     

Non-parametric regression for space–time forecasting under missing data

As more and more real time spatio-temporal datasets become available at increasing spatial and temporal resolutions, the provision of high quality, predictive information about spatio-temporal processes becomes an increasingly feasible goal. However, many sensor networks that collect spatio-temporal information are prone to failure, resulting in missing data. To complicate matters, the missing data is often not missing at random, and is characterised by long periods where no data is observed. The performance of traditional univariate forecasting methods such as ARIMA models decreases with the length of the missing data period because they do not have access to local temporal information. However, if spatio-temporal autocorrelation is present in a space–time series then spatio-temporal approaches have the potential to offer better forecasts. In this paper, a non-parametric spatio-temporal kernel regression model is developed to forecast the future unit journey time values of road links in central London, UK, under the assumption of sensor malfunction. Only the current traffic patterns of the upstream and downstream neighbouring links are used to inform the forecasts. The model performance is compared with another form of non-parametric regression, K-nearest neighbours, which is also effective in forecasting under missing data. The methods show promising forecasting performance, particularly in periods of high congestion.     

A BSS-based space–time multi-channel algorithm for complex-jamming suppression

This paper considers the problem of suppressing complex-jamming, which contains sidelobe blanket jammings (SLJs), multiple near-mainlobe blanket jammings (multiple-NMLJs) and self-defensive false target jamming (SDJ). We propose a blind source separation (BSS)-based space–time multi-channel algorithm for complex-jamming suppression. The space–time multi-channel consists of spatial multiple beams and temporal multiple adjacent pulse repetition intervals (PRIs). The source signals can be separated by the BSS, owing to their statistical independence. The real target and SDJ can then be obtained by the pulse compression approach, distinguished by echo identification simultaneously. A remarkable feature of the proposed approach is that it does not require prior knowledge about real target or jammings, and it is easy to implement for engineering applications.     

The EM algorithm in a distributed computing environment for modelling environmental space–time data

Statistical models for spatio-temporal data are increasingly used in environmetrics, climate change, epidemiology, remote sensing and dynamical risk mapping. Due to the complexity of the relationships among the involved variables and dimensionality of the parameter set to be estimated, techniques for model definition and estimation which can be worked out stepwise are welcome. In this context, hierarchical models are a suitable solution since they make it possible to define the joint dynamics and the full likelihood starting from simpler conditional submodels. Moreover, for a large class of hierarchical models, the maximum likelihood estimation procedure can be simplified using the Expectation–Maximization (EM) algorithm.In this paper, we define the EM algorithm for a rather general three-stage spatio-temporal hierarchical model, which includes also spatio-temporal covariates. In particular, we show that most of the parameters are updated using closed forms and this guarantees stability of the algorithm unlike the classical optimization techniques of the Newton–Raphson type for maximizing the full likelihood function. Moreover, we illustrate how the EM algorithm can be combined with a spatio-temporal parametric bootstrap for evaluating the parameter accuracy through standard errors and non-Gaussian confidence intervals.To do this a new software library in form of a standard R package has been developed. Moreover, realistic simulations on a distributed computing environment allow us to discuss the algorithm properties and performance also in terms of convergence iterations and computing times.     

Channel estimation based on neural network in space time block coded MIMO–OFDM system

In this study, we propose feed-forward multilayered perceptron (MLP) neural network trained with the Levenberg–Marquardt algorithm to estimate channel parameters in MIMO–OFDM systems. Bit error rate (BER) and mean square error (MSE) performances of least square (LS) and least mean square error (LMS) algorithms are also compared to our proposed neural network to evaluate the performances. Neural network channel estimator has got much better performance than LS and LMS algorithms. Furthermore it doesn?t need channel statistics and sending pilot tones, contrary to classical algorithms.     

Operational semantics of a domain specific language for real time musician–computer interaction

With the advent and availability of powerful personal computing, the computer music research and industry have been focusing on real-time musical interactions between musicians and computers; delegating human-like actions to computers who interact with a musical environment. One common use-case of this kind is Automatic Accompaniment where the system is comprised of a real-time machine listening system that in reaction to recognition of events in a score from a human performer, launches necessary actions for the accompaniment section. While the real-time detection of score events out of live musicians’ performance has been widely addressed in the literature, score accompaniment (or the reactive part of the process) has been rarely discussed. This paper deals with this missing component in the literature from a formal language perspective. We show how language considerations would enable better authoring of time and interaction during programming/composing and how it addresses critical aspects of a musical performance (such as errors) in real-time. We sketch the real-time features required by automatic musical accompaniment seen as a reactive system. We formalize the timing strategies for musical events taking into account the various temporal scales used in music. Various strategies for the handling of synchronization constraints and the handling of errors are presented. We give a formal semantics to model the possible behaviors of the system in terms of Parametric Timed Automata.     

Enhanced knowledge-aided space–time adaptive processing exploiting inaccurate prior knowledge of the array manifold

The accuracy of the prior knowledge of the clutter environments is critical to the clutter suppression performance of knowledge-aided space–time adaptive processing (KA-STAP) algorithms in airborne radar applications. In this paper, we propose an enhanced KA-STAP algorithm to estimate the clutter covariance matrix considering inaccurate prior knowledge of the array manifold for airborne radar systems. The core idea of this algorithm is to incorporate prior knowledge about the range of the measured platform velocity and the crab angle, and other radar parameters into the assumed clutter model to obtain increased robustness against inaccuracies of the data. It first over-samples the space–time subspace using prior knowledge about the range values of parameters and the inaccurate array manifold. By selecting the important clutter space–time steering vectors from the over-sampled candidates and computing the corresponding eigenvectors and eigenvalues of the assumed clutter model, we can obtain a more accurate clutter covariance matrix estimate than directly using the prior knowledge of the array manifold. Some extensions of the proposed algorithm with existing techniques are presented and a complexity analysis is conducted. Simulation results illustrate that the proposed algorithms can obtain good clutter suppression performance, even using just one snapshot, and outperform existing KA-STAP algorithms in presence of the errors in the prior knowledge of the array manifold.