280 resultados para Linear equations


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A unique code (called Hensel's code) is derived for a rational number by truncating its infinite p-adic expansion. The four basic arithmetic algorithms for these codes are described and their application to rational matrix computations is demonstrated by solving a system of linear equations exactly, using the Gaussian elimination procedure.

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We report a circuit technique to measure the on-chip delay of an individual logic gate (both inverting and non-inverting) in its unmodified form using digitally reconfigurable ring oscillator (RO). Solving a system of linear equations with different configuration setting of the RO gives delay of an individual gate. Experimental results from a test chip in 65nm process node show the feasibility of measuring the delay of an individual inverter to within 1pS accuracy. Delay measurements of different nominally identical inverters in close physical proximity show variations of up to 26% indicating the large impact of local or within-die variations.

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We report the design and characterization of a circuit technique to measure the on-chip delay of an individual logic gate (both inverting and noninverting) in its unmodified form. The test circuit comprises of digitally reconfigurable ring oscillator (RO). The gate under test is embedded in each stage of the ring oscillator. A system of linear equations is then formed with different configuration settings of the RO, relating the individual gate delay to the measured period of the RO, whose solution gives the delay of the individual gates. Experimental results from a test chip in 65-nm process node show the feasibility of measuring the delay of an individual inverter to within 1 ps accuracy. Delay measurements of different nominally identicall inverters in close physical proximity show variations of up to 28% indicating the large impact of local variations. As a demonstration of this technique, we have studied delay variation with poly-pitch, length of diffusion (LOD) and different orientations of layout in silicon. The proposed technique is quite suitable for early process characterization, monitoring mature process in manufacturing and correlating model-to-hardware.

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One of the important developments in rotary wing aeroelasticity in the recent past has been the growing awareness and acceptance of the fact that the problem is inherently non-linear and that correct treatment of aeroelastic problems requires the development of a consistent mathematical model [l]. This has led to a number of studies devoted to the derivation of a consistent set of “second order” non-linear equations, for example, those of Hodges and Dowel1 [2], of Rosen and Friedmann [3], and of Kvaternik, White and Kaza [4], each of which differs from the others on the question of the inclusion of certain terms in the equations of motion. The final form of the equations depends first upon the ordering scheme used for characterizing the displacements and upon the consistency with which this is applied in omitting terms of lower order. The ideal way of achieving this would be to derive the equations of motion with all the terms first included regardless of their relative orders of magnitude and then to apply the ordering scheme.

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The simultaneous state and parameter estimation problem for a linear discrete-time system with unknown noise statistics is treated as a large-scale optimization problem. The a posterioriprobability density function is maximized directly with respect to the states and parameters subject to the constraint of the system dynamics. The resulting optimization problem is too large for any of the standard non-linear programming techniques and hence an hierarchical optimization approach is proposed. It turns out that the states can be computed at the first levelfor given noise and system parameters. These, in turn, are to be modified at the second level.The states are to be computed from a large system of linear equations and two solution methods are considered for solving these equations, limiting the horizon to a suitable length. The resulting algorithm is a filter-smoother, suitable for off-line as well as on-line state estimation for given noise and system parameters. The second level problem is split up into two, one for modifying the noise statistics and the other for modifying the system parameters. An adaptive relaxation technique is proposed for modifying the noise statistics and a modified Gauss-Newton technique is used to adjust the system parameters.

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An energy method is used in order to derive the non-linear equations of motion of a smart flapping wing. Flapping wing is actuated from the root by a PZT unimorph in the piezofan configuration. Dynamic characteristics of the wing, having the same size as dragonfly Aeshna Multicolor, are analyzed using numerical simulations. It is shown that flapping angle variations of the smart flapping wing are similar to the actual dragonfly wing for a specific feasible voltage. An unsteady aerodynamic model based on modified strip theory is used to obtain the aerodynamic forces. It is found that the smart wing generates sufficient lift to support its own weight and carry a small payload. It is therefore a potential candidate for flapping wing of micro air vehicles.

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Diffuse optical tomographic image reconstruction uses advanced numerical models that are computationally costly to be implemented in the real time. The graphics processing units (GPUs) offer desktop massive parallelization that can accelerate these computations. An open-source GPU-accelerated linear algebra library package is used to compute the most intensive matrix-matrix calculations and matrix decompositions that are used in solving the system of linear equations. These open-source functions were integrated into the existing frequency-domain diffuse optical image reconstruction algorithms to evaluate the acceleration capability of the GPUs (NVIDIA Tesla C 1060) with increasing reconstruction problem sizes. These studies indicate that single precision computations are sufficient for diffuse optical tomographic image reconstruction. The acceleration per iteration can be up to 40, using GPUs compared to traditional CPUs in case of three-dimensional reconstruction, where the reconstruction problem is more underdetermined, making the GPUs more attractive in the clinical settings. The current limitation of these GPUs in the available onboard memory (4 GB) that restricts the reconstruction of a large set of optical parameters, more than 13, 377. (C) 2010 Society of Photo-Optical Instrumentation Engineers. DOI: 10.1117/1.3506216]

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This paper deals with the direct position kinematics problem of a general 6-6 Stewart platform, the complete solution of which is not reported in the literature until now and even establishing the number of possible solutions for the general case has remained an unsolved problem for a long period. Here a canonical formulation of the direct position kinematics problem for a general 6-6 Stewart platform is presented. The kinematic equations are expressed as a system of six quadratic and three linear equations in nine unknowns, which has a maximum of 64 solutions. Thus, it is established that the mechanism, in general, can have up to 64 closures. Further reduction of the system is shown arriving at a set of three quartic equations in three unknowns, the solution of which will yield the assembly configurations of the general Stewart platform with far less computational effort compared to earlier models.

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A new linear algebraic approach for identification of a nonminimum phase FIR system of known order using only higher order (>2) cumulants of the output process is proposed. It is first shown that a matrix formed from a set of cumulants of arbitrary order can be expressed as a product of structured matrices. The subspaces of this matrix are then used to obtain the parameters of the FIR system using a set of linear equations. Theoretical analysis and numerical simulation studies are presented to characterize the performance of the proposed methods.

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We give a simple linear algebraic proof of the following conjecture of Frankl and Furedi [7, 9, 13]. (Frankl-Furedi Conjecture) if F is a hypergraph on X = {1, 2, 3,..., n} such that 1 less than or equal to /E boolean AND F/ less than or equal to k For All E, F is an element of F, E not equal F, then /F/ less than or equal to (i=0)Sigma(k) ((i) (n-1)). We generalise a method of Palisse and our proof-technique can be viewed as a variant of the technique used by Tverberg to prove a result of Graham and Pollak [10, 11, 14]. Our proof-technique is easily described. First, we derive an identity satisfied by a hypergraph F using its intersection properties. From this identity, we obtain a set of homogeneous linear equations. We then show that this defines the zero subspace of R-/F/. Finally, the desired bound on /F/ is obtained from the bound on the number of linearly independent equations. This proof-technique can also be used to prove a more general theorem (Theorem 2). We conclude by indicating how this technique can be generalised to uniform hypergraphs by proving the uniform Ray-Chaudhuri-Wilson theorem. (C) 1997 Academic Press.

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This paper presents a set of linear equations describing the temperature dependence of the saturated liquid thermal conductivity covering the region of engineering importance for the new hydrofluorocarbons (HFC) 32, 125, 134a, 143a, 152a and hydrochlorofluorocarbons (HCFC) 123, 124, 141b and 142b. Available experimental data in the literature have been considered to arrive at a correlation of the form lambda = A - BT. It is observed that there exists an appreciable discrepancy between various sources of data in spite of the same purity of samples used and the same measurement technique being adopted. The correlations obtained here could be useful in engineering design applications. (C) 1998 John Wiley & Sons, Ltd.

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This paper discusses the parallel implementation of the solution of a set of linear equations using the Alternative Quadrant Interlocking Factorisation Methods (AQIF), on a star topology. Both the AQIF and LU decomposition methods are mapped onto star topology on an IBM SP2 system, with MPI as the internode communicator. Performance parameters such as speedup, efficiency have been obtained through experimental and theoretical means. The studies demonstrate (i) a mismatch of 15% between the theoretical and experimental results, (ii) scalability of the AQIF algorithm, and (iii) faster executing AQIF algorithm.

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Microwave (MW) thawing of 2D frozen cylinders exposed to uniform plane waves from one face, is modeled using the effective heat capacity formulation with the MW power obtained from the electric field equations. Computations are illustrated for tylose (23% methyl cellulose gel) which melts over a range of temperatures giving rise to a mushy zone. Within the mushy region the dielectric properties are functions of the liquid volume fraction. The resulting coupled, time dependent non-linear equations are solved using the Galerkin finite element method with a fixed mesh. Our method efficiently captures the multiple connected thawed domains that arise due to the penetration of MWs in the sample. For a cylinder of diameter D, the two length scales that control the thawing dynamics are D/D-p and D/lambda(m), where D-p and lambda(m) are the penetration depth and wavelength of radiation in the sample respectively. For D/D-p, D/lambda(m) much less than 1 power absorption is uniform and thawing occurs almost simultaneously across the sample (Regime I). For D/D-p much greater than 1 thawing is seen to occur from the incident face, since the power decays exponentially into the sample (Regime III). At intermediate values, 0.2 < D/D-p, D/lambda(m) < 2.0 (Regime II) thawing occurs from the unexposed face at smaller diameters, from both faces at intermediate diameters and from the exposed and central regions at larger diameters. Average power absorption during thawing indicates a monotonic rise in Regime I and a monotonic decrease in Regime III. Local maxima in the average power observed for samples in Regime II are due to internal resonances within the sample. Thawing time increases monotonically with sample diameter and temperature gradients in the sample generally increase from Regime I to Regime III. (C) 2002 Elsevier Science Ltd. All rights reserved.

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An isothermal section of the phase diagram for (silver + rhodium + oxygen) at T = 1173 K has been established by equilibration of samples representing twelve different compositions, and phase identification after quenching by optical and scanning electron microscopy (s.e.m.), X-ray diffraction (x.r.d.), and energy dispersive analysis of X-rays (e.d.x.), Only one ternary oxide, AgRhO2, was found to be stable and a three phase region involving Ag, AgRhO2 and Rh2O3 was identified. The thermodynamic properties of AgRhO2 were measured using a galvanic cell in the temperature range 980 K to 1320 K. Yttria-stabilized zirconia was used as the solid electrolyte and pure oxygen gas at a pressure of 0.1 MPa was used as the reference electrode. The Gibbs free energy of formation of the ternary oxide from the elements, ΔfGo (AgRhO2), can be represented by two linear equations that join at the melting temperature of silver. In the temperature range 980 K to 1235 K, ΔfGo(AgRhO2)/(J . mol-1) = -249080 + 179.08 T/K (±120). Above the melting temperature of silver, in the temperature range 1235 K to 1320 K, ΔfGo(AgRhO2)/(J . mol-1) = -260400 + 188.24 T/K (±95). The thermodynamic properties of AgRhO2 at T = 298.15 K were evaluated from the high temperature data. The chemical potential diagram for (silver + rhodium + oxygen) at T = 1200 K was also computed on the basis of the results of this study.

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In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, ``fast learning fully complex-valued neural (FLFCN) classifier'' is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier. (c) 2012 Elsevier B.V. All rights reserved.