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The low noise amplifier (LNA) is a significant device in RF front-end. In this paper, a straight and efficient modeling method for LNA based on the Volterra series with recursive least squares (RLS) algorithm is proposed. Instead of calculating the high nonlinearity order of Volterra kernels, the proposed method extracts the first three order Volterra kernels characterizing the memory effect to construct...
We discuss the feasibility of an in-house Schrödinger equation solver on the Intel Broadwell Xeon processor with a built-in FPGA, with a particular focus on the performance of large-scale sparse matrix-vector multiplication (SpMV) that is the core numerical operation of electronic structure simulations for multi-million atomic systems. The double-precision SpMV section in our solver is offloaded to...
It is non-trivial to optimise computations of chaotic systems since slightly perturbed simulations diverge exponentially over time due to the well-known butterfly effect if bit-reproducible results are not achieved. Therefore, two model setups that show the same quality in the representation of a chaotic system will show uncorrelated behaviour if integrated long enough, hence it is challenging to...
A new method of constructing nonparametric dynamic model of the human oculomotor system on the basis of experimental data “input-output” is developed, considering nonlinear and inertial properties of the rectus muscles of the eye. A technology for tracking eye movement is based on the videos. It is possible to determine the dynamic characteristics of the oculomotor system functions as a transition...
Contrast of echographic images has been highly improved by the injection of microbubbles, due to their nonlinear behavior. However, this contrast enhancement is limited by the nonlinear acoustic propagation in tissue. To overcome this drawback, sub and ultra-harmonic contrast imaging can be used, since only microbubbles can generate these components. Nonlinear modeling is a primordial step in the...
A newly-invented, distributed, high-performance graphical processing framework that simulates complex radio frequency (RF) propagation has been developed and demonstrated. The approach uses an advanced computer architecture and intensive multi-core system to enable highperformance data analysis at the fidelity necessary to design and develop modern sensor systems. This widely applicable simulation...
Granger causality approaches have been widely used to estimate effective connectivity in complex dynamic systems. These techniques are based on the building of predictive models which not only depend on a proper selection of the predictive vectors size but also on the chosen class of regression functions. The question addressed in this paper is the estimation of the model order in the computation...
In this study we use triangular basis function set to solve second kind fuzzy integral equation that can be converted to a system of two integral equations in crisp case. We also consider collocation method for approximately solving the equation.
To satisfy growing computational demands of modern applications, significant enhancements have been introduced in the contemporary processor architectures with the aim to increase their attainable performance, such as increased number of cores, improved capability of memory subsystem and enhancements in the processor pipeline [1]. Therefore, the performance improvements are usually coupled with an...
In this paper a semi-supervised regression model based on co-training is applied on the soft sensor context, together with a feature ranking approach which has the purpose of removing irrelevant features. The description of both the methods of semi-supervised regression and feature ranking, as well as the theorethical foundation of the proposed feature ranking approach are also given. To evaluate...
For the mathematical model of tug handling simulator, the locally optimal locally weighted learning (LWL) is proposed. Firstly, samples space rearrangement is taken to diminish the one-to-many mapping and non-separable of ship motion states. Secondly, distance metric is learned by leave-one-out cross validation for every sample, and this approach improves the nonlinearity mapping ability and robustness...
The paper considers the class of discrete-time, single-input, single-output, nonlinear dynamical systems described by a polynomial difference equation. This class, call polynomial time-invariant, is a proper generalization of the linear time-invariant model class. The identification data is assumed to be generated in the errors-in-variables setting, where the input and the output noise is zero mean,...
The problem of preference functions model development for multiple criteria decision-making is considered based on machine-learning approach. It is assumed that the training sample for a plurality of objects, for which decisions are made, is formed from a set of measured features or the particular criteria and the matrix of pairwise comparisons. The problem of constructing a linear preference function...
The paper is concerned with an increase in the accuracy of the dynamic input-output systems modeling with Volterra polynomials owing to a fuller consideration of data on system outputs to the test inputs. The methodology applied to construct the non-stationary Volterra polynomials is based on a priori consideration of necessary conditions for solvability of special multi-dimensional integral Volterra...
In this paper, we present two high performing and efficient Smoothed Particle Hydrodynamics (SPH) methods used to physically simulate incompressible fluids: Implicit Incompressible SPH (IISPH) and Divergence-Free SPH (DFSPH). At the moment, the most stable SPH methods employ density correctors that reduce the density error of the fluid particles to achieve very low levels of compressibility. We analyze...
Classification of sparsely and irregularly sampled time series data is a challenging machine learning task. To tackle this problem, we present a learning in model space framework in which time-continuous dynamical system models are first inferred from individual time series and then the inferred models are used to represent these time series for the classification task. In contrast to the existing...
It is shown how differential invariance can be used to extract an underlying signal from its noisy measurement towards constructing a non-asymptotic state estimator for linear systems. While the model of the system is assumed known, the noise can have arbitrary characteristics. The differential invariance is rendered by the Cayley-Hamilton theorem and the system is represented in terms of a output...
Recent years have seen a growing interest in neural networks whose hidden-layer weights are randomly selected, such as Extreme Learning Machines (ELMs). These models are motivated by their ease of development, high computational learning speed and relatively good results. Alternatively, constructive models that select the hidden-layer weights as a subset of the data have shown superior performance...
In content-based image retrieval systems, visual content of the image is the criterion for measuring image similarity. We propose a method to solve the problem of loss of spatial information of objects when local descriptors from an image with multiple objects are aggregated to form a global representation. In our approach, after saliency-based spatial partitioning, local feature descriptors from...
Simulation of activated sludge model (ASM) including detailed biokinetic reaction network often requires the solution of a large system of ordinary differential equations (ODEs) at each time frame, which requires long computing times. In this study, an adaptive time step backward differentiation formula (BDF) is proposed to solve the ASM's system of ODEs that mainly contains a high degree of stiffness...
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