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Abstract: The quasi-linear viscoelastic (QLV) theory of Fung has been widely used for the modeling of viscoelastic properties of soft tissues. The essence of Fung’s approach is that the stress relaxation can be expressed in terms of the instantaneous elastic response and the reduced relaxation function. Using the Boltzmann superposition principle, the constitutive equation can be written as a convolution integral of the strain history and reduced relaxation function. In the appropriate models, QLV theory usually consists of five material parameters (two for the elastic response and three for the reduced relaxation function), which must be determined experimentally. However, to be consistent with the assumptions of QLV theory, the material functions should be obtained based on a step change in strain which is not possible to be performed experimentally. It is known that this may result in regression algorithms that converge poorly and yield non-unique solutions with highly variable constants, especially for long ramp times. In this paper, we use the genetic algorithm approach, which is an adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetics, and simultaneously fit the ramping and relaxation experimental data (on ligaments) to the QLV constitutive equation to obtain the material parameters. [Copyright &y& Elsevier]

Abstract: This paper considers the restoration problem of images which are affected by multiple degradations. Under the assumption that the state–space model of the signal to be estimated is unknown, we propose an algorithm for the filtering problem of images which are corrupted by white plus coloured additive noises and multiplicative noise. Using the fact that the autocovariance functions of the signal and coloured noise are known and expressed in semi-degenerated kernel form, and the fact that the first and second-order moments of the multiplicative and white additive noises are also known, the least mean-squared error linear estimator is obtained. The proposed algorithm is applied to an image which has been corrupted by multiplicative and additive noises. [Copyright &y& Elsevier]

In this paper, based on the application of the sum-product (SP) algorithm to factor graphs (FGs) representing the joint a posteriori probability (APP) of the transmitted symbols, we propose new iterative soft-input soft-output (SISO) detection schemes for intersymbol interference (ISI) channels. We have verified by computer simulations that the SP algorithm converges to a good approximation of the exact marginal APPs of the transmitted symbols if the FG has girth at least 6. For ISI channels whose corresponding FG has girth 4, the application of a stretching technique allows us to obtain an equivalent girth-6 graph. For sparse 151 channels, the proposed algorithms have advantages in terms of complexity over optimal detection schemes based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. They also allow a parallel implementation of the receiver and the possibility of a more efficient complexity reduction. The application to joint detection and decoding of low-density parity-check (LDPC) codes is also considered and results are shown for some partial-response magnetic channels. Also in these cases, we show that the proposed algorithms have a limited performance loss with respect to that can be obtained when the optimal "serial" BCJR algorithm is used for detection. Therefore, for their parallel implementation, they represent a favorable alternative to the modified "parallel" BCJR algorithm proposed in the literature for the application to magnetic channels. [ABSTRACT FROM AUTHOR]

In this paper, we propose a unified approach for video summarization based on the analysis of video structures, and video highlights. Two major components in our approach are scene modeling and highlight detection. Scene modeling Is achieved by normalized cut algorithm and temporal graph analysis, while high-light detection is accomplished by motion attention modeling. In our proposed approach, a video is represented as a complete undirected graph and the normalized cut algorithm is carried out to globally and optimally partition the graph into video clusters. The resulting clusters form a directed temporal graph and a Shortest path algorithm is proposed to efficiently detect video scenes. The attention values are then computed and attached to the scenes, clusters, shots, and subshots in a temporal graph. As a result, the temporal graph can inherently describe the evolution and perceptual importance of a video. In our application, video summaries that emphasize both content balance and perceptual quality can be generated directly from a temporal graph that embeds both the structure and attention information. [ABSTRACT FROM AUTHOR]

Mobile agent technology has been applied to develop the solutions for various kinds of parallel and distributed computing problems. However, performance evaluation of mobile agent algorithms remains a difficult task, mainly due to the characteristics of mobile agents such as distributed and asynchronous execution, autonomy and mobility. This paper proposes a general approach based on direct execution simulation for evaluating the performance of mobile agent algorithms by collecting and analyzing the information about the agents during their execution. We describe the proposed generic simulation model, named MADES, the architecture of a software environment based on MADES, and a prototype implementation. A mobile agent-based distributed load balancing algorithm has been used for experiments with the prototype. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic analysis of the delayed least-mean-Square (DLMS) adaptive algorithm with leakage. This analysis is obtained taking into account that mismatches between the system delay and its estimate may occur. Such an approach is not considered in previous models. In addition, it is shown that the introduction of a leakage factor into the adaptive algorithm keeps the adaptive algorithm stable under an imperfect delay estimate condition. Recursive difference equations for the first and second moments of the adaptive filter weights are derived. An expression for the critical value of the step size is also determined. Results of Monte Carlo simulations present excellent agreement with the proposed model for both white and colored Gaussian inputs. [ABSTRACT FROM AUTHOR]

Various control algorithms have been proposed for the speed-sensorless control of an induction motor. These sensorless algorithms are mainly based on the speed feedback with the flux and speed estimations. This paper proposes a new scheme for the speed-sensorless control of an induction motor. The proposed scheme is based on the current estimation without the flux and speed estimations, in which the controlled stator voltage is applied to the induction motor so that the difference between stator currents of the mathematical model and motor may be forced to decay to zero. The performance of the proposed scheme is verified through simulation and experiment. [ABSTRACT FROM AUTHOR]

Abstract-Subspace-based estimation of multiple real-valued sine wave frequencies is considered in this paper. A novel data covariance model is proposed. In the proposed model, the dimension of the signal subspace equals the number of frequencies present in the data, which is half of the signal. subspace dimension for the conventional model. Consequently, an ESPRIT-like algorithm using the proposed data model is presented. The proposed algorithm is then extended for the case of complex-valued sine waves. Performance analysis of the proposed algorithms are also carried out. The algorithms are tested in numerical simulations. When compared with ESPRIT, the newly proposed algorithm results in a significant reduction in computational burden without any compromise in the accuracy. [ABSTRACT FROM AUTHOR]

In this paper we introduce a novel reinforcement learning algorithm called event-learning. The algorithm uses events, ordered pairs of two consecutive states. We define event-value function and we derive learning rules. Combining our method with a well-known robust control method, the SDS algorithm, we introduce Robust Policy Heuristics (RPH). It is shown that RPH, a fast-adapting non-Markovian policy, is particularly useful for coarse models of the environment and could be useful for some partially observed systems. RPH may be of help in alleviating the ‘curse of dimensionality’ problem. Event-learning and RPH can be used to separate time scales of learning of value functions and adaptation. We argue that the definition of modules is straightforward for event-learning and event-learning makes planning feasible in the RL framework. Computer simulations of a rotational inverted pendulum with coarse discretization are shown to demonstrate the principle. [Copyright &y& Elsevier]

The harmonic voltage issue becomes a challenge for a distributed generation system. Considering that droop control is the most common control algorithm used in the distributed system, a virtual harmonic power droop strategy which aims to mitigate the harmonic voltage is proposed in this paper. First, the conventional droop control is analyzed. Based on that concept, the virtual power algorithm is introduced. Second, the output harmonic voltage issue and the mathematical model of the inverter are presented. In addition, the second-order generalized integrator is briefly discussed. Third, taking into consideration the algorithms and models presented, a virtual harmonic power droop strategy is proposed to implement the harmonic voltage mitigation. In this algorithm, signals in fundamental frequency and harmonic frequency are separated with the help of second-order generalized integrators. Unlike the conventional voltage–current dual loop structure which is used to mitigate system harmonics, this method only needs the virtual power feedback to mitigate the harmonic voltage. Based on these features, the system's control structure is simplified. Simulation and experimental results verified the harmonic voltage mitigation ability of the proposed strategy. [ABSTRACT FROM AUTHOR]

A conference paper about CaliBayes is presented. CaliBayes is an integration of GRID based simulation and data resources for Bayesian calibration of biological models. The goal is to find a combination of parameters that give the best match to available experimental data, together with some idea of the uncertainty in the parameter value. The calibration algorithm selects parameter combinations at which the simulator will be run.