45 resultados para Amplify-and-Forward


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Finding the smallest eigenvalue of a given square matrix A of order n is computationally very intensive problem. The most popular method for this problem is the Inverse Power Method which uses LU-decomposition and forward and backward solving of the factored system at every iteration step. An alternative to this method is the Resolvent Monte Carlo method which uses representation of the resolvent matrix [I -qA](-m) as a series and then performs Monte Carlo iterations (random walks) on the elements of the matrix. This leads to great savings in computations, but the method has many restrictions and a very slow convergence. In this paper we propose a method that includes fast Monte Carlo procedure for finding the inverse matrix, refinement procedure to improve approximation of the inverse if necessary, and Monte Carlo power iterations to compute the smallest eigenvalue. We provide not only theoretical estimations about accuracy and convergence but also results from numerical tests performed on a number of test matrices.

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We evaluate a number of real estate sentiment indices to ascertain current and forward-looking information content that may be useful for forecasting the demand and supply activities. Our focus lies on sector-specific surveys targeting the players from the supply-side of both residential and non-residential real estate markets. Analyzing the dynamic relationships within a Vector Auto-Regression (VAR) framework, we test the efficacy of these indices by comparing them with other coincident indicators in predicting real estate returns. Overall, our analysis suggests that sentiment indicators convey important information which should be embedded in the modeling exercise to predict real estate market returns. Generally, sentiment indices show better information content than broad economic indicators. The goodness of fit of our models is higher for the residential market than for the non-residential real estate sector. The impulse responses, in general, conform to our theoretical expectations. Variance decompositions and out-of-sample predictions generally show desired contribution and reasonable improvement respectively, thus upholding our hypothesis. Quite remarkably, consistent with the theory, the predictability swings when we look through different phases of the cycle. This perhaps suggests that, e.g. during recessions, market players’ expectations may be more accurate predictor of the future performances, conceivably indicating a ‘negative’ information processing bias and thus conforming to the precautionary motive of consumer behaviour.

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The discourse surrounding the virtual has moved away from the utopian thinking accompanying the rise of the Internet in the 1990s. The Cyber-gurus of the last decades promised a technotopia removed from materiality and the confines of the flesh and the built environment, a liberation from old institutions and power structures. But since then, the virtual has grown into a distinct yet related sphere of cultural and political production that both parallels and occasionally flows over into the old world of material objects. The strict dichotomy of matter and digital purity has been replaced more recently with a more complex model where both the world of stuff and the world of knowledge support, resist and at the same time contain each other. Online social networks amplify and extend existing ones; other cultural interfaces like youtube have not replaced the communal experience of watching moving images in a semi-public space (the cinema) or the semi-private space (the family living room). Rather the experience of viewing is very much about sharing and communicating, offering interpretations and comments. Many of the web’s strongest entities (Amazon, eBay, Gumtree etc.) sit exactly at this juncture of applying tools taken from the knowledge management industry to organize the chaos of the material world along (post-)Fordist rationality. Since the early 1990s there have been many artistic and curatorial attempts to use the Internet as a platform of producing and exhibiting art, but a lot of these were reluctant to let go of the fantasy of digital freedom. Storage Room collapses the binary opposition of real and virtual space by using online data storage as a conduit for IRL art production. The artworks here will not be available for viewing online in a 'screen' environment but only as part of a downloadable package with the intention that the exhibition could be displayed (in a physical space) by any interested party and realised as ambitiously or minimally as the downloader wishes, based on their means. The artists will therefore also supply a set of instructions for the physical installation of the work alongside the digital files. In response to this curatorial initiative, File Transfer Protocol invites seven UK based artists to produce digital art for a physical environment, addressing the intersection between the virtual and the material. The files range from sound, video, digital prints and net art, blueprints for an action to take place, something to be made, a conceptual text piece, etc. About the works and artists: Polly Fibre is the pseudonym of London-based artist Christine Ellison. Ellison creates live music using domestic devices such as sewing machines, irons and slide projectors. Her costumes and stage sets propose a physical manifestation of the virtual space that is created inside software like Photoshop. For this exhibition, Polly Fibre invites the audience to create a musical composition using a pair of amplified scissors and a turntable. http://www.pollyfibre.com John Russell, a founding member of 1990s art group Bank, is an artist, curator and writer who explores in his work the contemporary political conditions of the work of art. In his digital print, Russell collages together visual representations of abstract philosophical ideas and transforms them into a post apocalyptic landscape that is complex and banal at the same time. www.john-russell.org The work of Bristol based artist Jem Nobel opens up a dialogue between the contemporary and the legacy of 20th century conceptual art around questions of collectivism and participation, authorship and individualism. His print SPACE concretizes the representation of the most common piece of Unicode: the vacant space between words. In this way, the gap itself turns from invisible cipher to sign. www.jemnoble.com Annabel Frearson is rewriting Mary Shelley's Frankenstein using all and only the words from the original text. Frankenstein 2, or the Monster of Main Stream, is read in parts by different performers, embodying the psychotic character of the protagonist, a mongrel hybrid of used language. www.annabelfrearson.com Darren Banks uses fragments of effect laden Holywood films to create an impossible space. The fictitious parts don't add up to a convincing material reality, leaving the viewer with a failed amalgamation of simulations of sophisticated technologies. www.darrenbanks.co.uk FIELDCLUB is collaboration between artist Paul Chaney and researcher Kenna Hernly. Chaney and Hernly developed together a project that critically examines various proposals for the management of sustainable ecological systems. Their FIELDMACHINE invites the public to design an ideal agricultural field. By playing with different types of crops that are found in the south west of England, it is possible for the user, for example, to create a balanced, but protein poor, diet or to simply decide to 'get rid' of half the population. The meeting point of the Platonic field and it physical consequences, generates a geometric abstraction that investigates the relationship between modernist utopianism and contemporary actuality. www.fieldclub.co.uk Pil and Galia Kollectiv, who have also curated the exhibition are London-based artists and run the xero, kline & coma gallery. Here they present a dialogue between two computers. The conversation opens with a simple text book problem in business studies. But gradually the language, mimicking the application of game theory in the business sector, becomes more abstract. The two interlocutors become adversaries trapped forever in a competition without winners. www.kollectiv.co.uk

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In this letter, we consider beamforming strategies in amplified-and-forward (AF) two-way relay channels, where two terminals and the relay are equipped with multiple antennas. Our aim is to optimize the worse end-to-end signal-to-noise ratio of the two links so that the reliability of both terminals can be guaranteed. We show that the optimization problem can be recast as a generalized fractional programing and be solved by using the Dinkelbach-type procedure combined with semidefinite programming. Simulation results confirm the efficiency of the proposed strategies.

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A new aerosol index for the Along-Track Scanning Radiometers (ATSRs) is presented that provides a means to detect desert dust contamination in infrared SST retrievals. The ATSR Saharan dust index (ASDI) utilises only the thermal infrared channels and may therefore be applied consistently to the entire ATSR data record (1991 to present), for both day time and night time observations. The derivation of the ASDI is based on a principal component (PC) analysis (PCA) of two unique pairs of channel brightness temperature differences (BTDs). In 2-D space (i.e. BTD vs BTD), it is found that the loci of data unaffected by aerosol are confined to a single axis of variability. In contrast, the loci of aerosol-contaminated data fall off-axis, shifting in a direction that is approximately orthogonal to the clear-sky axis. The ASDI is therefore defined to be the second PC, where the first PC accounts for the clear-sky variability. The primary ASDI utilises the ATSR nadir and forward-view observations at 11 and 12 μm (ASDI2). A secondary, three-channel nadir-only ASDI (ASDI3) is also defined for situations where data from the forward view are not available. Empirical and theoretical analyses suggest that ASDI is well correlated with aerosol optical depth (AOD: correlation r is typically > 0.7) and provides an effective tool for detecting desert mineral dust. Overall, ASDI2 is found to be more effective than ASDI3, with the latter being sensitive only to very high dust loading. In addition, use of ASDI3 is confined to night time observations as it relies on data from the 3.7 μm channel, which is sensitive to reflected solar radiation. This highlights the benefits of having data from both a nadir- and a forward-view for this particular approach to aerosol detection.

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Imitation is an important form of social behavior, and research has aimed to discover and explain the neural and kinematic aspects of imitation. However, much of this research has featured single participants imitating in response to pre-recorded video stimuli. This is in spite of findings that show reduced neural activation to video vs. real life movement stimuli, particularly in the motor cortex. We investigated the degree to which video stimuli may affect the imitation process using a novel motion tracking paradigm with high spatial and temporal resolution. We recorded 14 positions on the hands, arms, and heads of two individuals in an imitation experiment. One individual freely moved within given parameters (moving balls across a series of pegs) and a second participant imitated. This task was performed with either simple (one ball) or complex (three balls) movement difficulty, and either face-to-face or via a live video projection. After an exploratory analysis, three dependent variables were chosen for examination: 3D grip position, joint angles in the arm, and grip aperture. A cross-correlation and multivariate analysis revealed that object-directed imitation task accuracy (as represented by grip position) was reduced in video compared to face-to-face feedback, and in complex compared to simple difficulty. This was most prevalent in the left-right and forward-back motions, relevant to the imitator sitting face-to-face with the actor or with a live projected video of the same actor. The results suggest that for tasks which require object-directed imitation, video stimuli may not be an ecologically valid way to present task materials. However, no similar effects were found in the joint angle and grip aperture variables, suggesting that there are limits to the influence of video stimuli on imitation. The implications of these results are discussed with regards to previous findings, and with suggestions for future experimentation.

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The purpose of this paper is to investigate several analytical methods of solving first passage (FP) problem for the Rouse model, a simplest model of a polymer chain. We show that this problem has to be treated as a multi-dimensional Kramers' problem, which presents rich and unexpected behavior. We first perform direct and forward-flux sampling (FFS) simulations, and measure the mean first-passage time $\tau(z)$ for the free end to reach a certain distance $z$ away from the origin. The results show that the mean FP time is getting faster if the Rouse chain is represented by more beads. Two scaling regimes of $\tau(z)$ are observed, with transition between them varying as a function of chain length. We use these simulations results to test two theoretical approaches. One is a well known asymptotic theory valid in the limit of zero temperature. We show that this limit corresponds to fully extended chain when each chain segment is stretched, which is not particularly realistic. A new theory based on the well known Freidlin-Wentzell theory is proposed, where dynamics is projected onto the minimal action path. The new theory predicts both scaling regimes correctly, but fails to get the correct numerical prefactor in the first regime. Combining our theory with the FFS simulations lead us to a simple analytical expression valid for all extensions and chain lengths. One of the applications of polymer FP problem occurs in the context of branched polymer rheology. In this paper, we consider the arm-retraction mechanism in the tube model, which maps exactly on the model we have solved. The results are compared to the Milner-McLeish theory without constraint release, which is found to overestimate FP time by a factor of 10 or more.

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Recent coordinated observations of interplanetary scintillation (IPS) from the EISCAT, MERLIN, and STELab, and stereoscopic white-light imaging from the two heliospheric imagers (HIs) onboard the twin STEREO spacecraft are significant to continuously track the propagation and evolution of solar eruptions throughout interplanetary space. In order to obtain a better understanding of the observational signatures in these two remote-sensing techniques, the magnetohydrodynamics of the macro-scale interplanetary disturbance and the radio-wave scattering of the micro-scale electron-density fluctuation are coupled and investigated using a newly constructed multi-scale numerical model. This model is then applied to a case of an interplanetary shock propagation within the ecliptic plane. The shock could be nearly invisible to an HI, once entering the Thomson-scattering sphere of the HI. The asymmetry in the optical images between the western and eastern HIs suggests the shock propagation off the Sun–Earth line. Meanwhile, an IPS signal, strongly dependent on the local electron density, is insensitive to the density cavity far downstream of the shock front. When this cavity (or the shock nose) is cut through by an IPS ray-path, a single speed component at the flank (or the nose) of the shock can be recorded; when an IPS ray-path penetrates the sheath between the shock nose and this cavity, two speed components at the sheath and flank can be detected. Moreover, once a shock front touches an IPS ray-path, the derived position and speed at the irregularity source of this IPS signal, together with an assumption of a radial and constant propagation of the shock, can be used to estimate the later appearance of the shock front in the elongation of the HI field of view. The results of synthetic measurements from forward modelling are helpful in inferring the in-situ properties of coronal mass ejection from real observational data via an inverse approach.

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This paper critiques the approach taken by the Ghanaian Government to address mercury pollution in the artisanal and small-scale gold mining sector. Unmonitored releases of mercury-used in the gold-amalgamation process-have caused numerous environmental complications throughout rural Ghana. Certain policy, technological and educational initiatives taken to address the mounting problem, however, have proved marginally effective at best, having been designed and implemented without careful analysis of mine community dynamics, the organization of activities, operators' needs and local geological conditions. Marked improvements can only be achieved in this area through increased government-initiated dialogue with the now-ostracized illegal galamsey mining community; introducing simple, cost-effective techniques for the reduction of mercury emissions; and effecting government-sponsored participatory training exercises as mediums for communicating information about appropriate technologies and the environment. (c) 2006 Elsevier Inc. All rights reserved.

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This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

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The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.

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A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

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The next generation consumer level interactive services require reliable and constant communication for both mobile and static users. The Digital Video Broadcasting ( DVB) group has exploited the rapidly increasing satellite technology for the provision of interactive services and launched a standard called Digital Video Broadcast through Return Channel Satellite (DYB-RCS). DVB-RCS relies on DVB-Satellite (DVB-S) for the provision of forward channel. The Digital Signal processing (DSP) implemented in the satellite channel adapter block of these standards use powerful channel coding and modulation techniques. The investigation is concentrated towards the Forward Error Correction (FEC) of the satellite channel adapter block, which will help in determining, how the technology copes with the varying channel conditions and user requirements(1).

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An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.