A geometric approach for adaptive estimation of unknown growth kinetics in bioreactors

This paper proposes a new approach for the estimation of unknown and time-varying specific growth rate in fed-batch bioprocess. A novel adaptive estimation technique based on the concept of invariant manifold is proposed as an effective approach to estimate growth kinetic parameters. An asymptotic nonlinear observer is used to provide simultaneous on-line estimation of biomass concentration and growth kinetic. The method is easy to implement and requires only one tuning parameter. The effectiveness of the proposed algorithm is illustrated with representative bioreactor simulation examples.     

Neural,fuzzy and neuro-fuzzy approach for concentration estimation of volatile organic compounds by surface acoustic wave sensor array

Present study evaluates application of adaptive neuro-fuzzy inference system (ANFIS) for concentration estimation of volatile organic compounds (VOCs) by analyzing response matrix of polymer-functionalized surface acoustic wave (SAW) sensor array. The performance of ANFIS is compared with that of subtractive clustering based fuzzy inference system (SC-FIS) and backpropagation artificial neural network (BP-ANN). For analysis, the raw SAW sensor array data is preprocessed by logarithmic scaling followed by dimensional autoscaling and the feature extraction by principal component analysis (PCA). For concentration prediction, the extracted feature vectors were fed as input to the three methods (ANFIS, SC-FIS and BP-ANN) independently. The performance of the three methods were evaluated on the basis of root mean square error (RMSE) and correlation value involving actual and estimated values of concentration. Five sets of SAW sensor array responses are analyzed. The analysis includes both experimental and synthetic (sensor model generated) data sets. It is found that the ANFIS has the least value of RMSE and highest value of correlation compared to SC-FIS and BP-ANN. This signifies the relative superiority of ANFIS method.     

Mapping average equivalized income using robust small area methods

Measures of economic well‐being are often needed for geographically small areas, as economic indicators may be distributed unevenly among the subsets of relatively small regions. We consider small area estimation of average equivalized income. Disposable household income data are usually available only for a sample of households, typically too small to provide reliable estimates for small regions. We consider a small area estimation technique that is robust to outliers, produces results consistent with design weighted estimates obtained for larger areas and yield maps with approximately no shrinkage. The proposed methodology is applied to the Local Labour Systems in Tuscany (Italy).     

自适应连续多级分区与初始阈值估计的快速模板匹配方法

归一化互相关测度在光照改变时比采用绝对差之和测度（SAD）要稳定，但是归一化互相关测度的缺陷在于它的计算量非常大。为此，提出了一种结合自适应连续多级分区和初始阈值估计的基于归一化互相关（NCC）的快速模板匹配算法。根据模板图像中不同模块的梯度值，将模板图像进行逐级分区，通过分区顺序将互相关之和分为不同的层，得到各层互相关的上界，运用柯西-施不等式得到上界间的关系，形成自适应连续多级分区淘汰方法。同时，为了加快匹配速度，利用初始阈值估计产生一个较大的边界阈值，以淘汰初始搜索时的大量非匹配点，减少搜索点数目。实验结果表明:所提出的算法具有较好的鲁棒性，且算法的执行速度优于传统算法。     

On the problem of optimal estimation of balanced and symmetric three-phase signals

This paper revisits the fundamental problem of optimal estimation of the magnitude and phase of balanced and symmetric three-phase voltage or current signals. We analyze and compare various setups for the corresponding optimal Kalman filter, including the direct use of three-phase measurements, as well as measurements subjected to the Clarke transform in real or complex form. One contribution is to show that the standard practice of disregarding the transformed zero-component of the Clarke transformed three-phase signal almost always leads to a sub-optimal performance of the Kalman estimator. Our analysis extends to show that the closely related complex Kalman estimator is also sub-optimal and that optimal performance can be recovered if the zero-component is made available to the filter provided that the noises are properly characterized. These results are illustrated by means of simple numerical examples, which also highlight the importance of correctly modeling the noise characteristics if a real or complex form of the Clarke transformation is to be used. We conclude the paper with a unified set of guidelines or best practices regarding the use of optimal Kalman estimators for balanced and symmetric three-phase signals.     

Port reduction in parametrized component static condensation: approximation and a posteriori error estimation

We introduce a port (interface) approximation and a posteriori error bound framework for a general component‐based static condensation method in the context of parameter‐dependent linear elliptic partial differential equations. The key ingredients are as follows: (i) efficient empirical port approximation spaces—the dimensions of these spaces may be chosen small to reduce the computational cost associated with formation and solution of the static condensation system; and (ii) a computationally tractable a posteriori error bound realized through a non‐conforming approximation and associated conditioner—the error in the global system approximation, or in a scalar output quantity, may be bounded relatively sharply with respect to the underlying finite element discretization. Our approximation and a posteriori error bound framework is of particular computational relevance for the static condensation reduced basis element (SCRBE) method. We provide several numerical examples within the SCRBE context, which serve to demonstrate the convergence rate of our port approximation procedure as well as the efficacy of our port reduction error bounds. Copyright © 2013 John Wiley & Sons, Ltd.     

Design of a low cross‐talk micro‐coil array for micro‐scale MRI

Design of magnetic resonance micro‐coil arrays with low cross‐talk among the coils can be the main challenge to improve the effectiveness of magnetic resonance micro‐imaging because the electrical cross‐talk which is mainly due to the inductive coupling perturbs the sensitivity profile of the array and causes image artifacts. In this work, a capacitive decoupling network with N(M ? 1) + (N ? 1)(M ? 2) capacitors is proposed to reduce the inductive coupling in an N × M array. A 3 × 3 array of optimized micro‐coils is designed using the finite element simulations and all the needed elements for the array equivalent circuit are extracted in order to evaluate the effectiveness of the proposed decoupling method by assessing the reduction of the coupled signals after employing the capacitive network on the circuit. The achieved results for the designed array show that the high cross‐talk level is reduced by the factor of 2.2–3.4 after employing the capacitive network. By employing this method of decoupling, the adjacent coils in each row and inner columns can be decoupled properly while the minimum decoupling belongs to the outer columns because of the lack of all necessary decoupling capacitances for these columns. The main advantages of the proposed decoupling method are its efficiency and design easiness which facilitates the design of dense arrays with the properly decoupled coils, especially the inner coils which are more coupled due to their neighbors. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 353–359, 2013     

A probabilistic framework for next best view estimation in a cluttered environment

In this article, we present an information gain-based variant of the next best view problem for occluded environment. Our proposed method utilizes a belief model of the unobserved space to estimate the expected information gain of each possible viewpoint. More precise, this belief model allows a more precise estimation of the visibility of occluded space and with that a more accurate prediction of the potential information gain of new viewing positions. We present experimental evaluation on a robotic platform for active data acquisition, however due to the generality of our approach it also applies to a wide variety of 3D reconstruction problems. With the evaluation done in simulation and on a real robotic platform, exploring and acquiring data from different environments we demonstrate the generality and usefulness of our approach for next best view estimation and autonomous data acquisition.     

Iteratively Reweighted Partial Least Squares Estimation for Generalized Linear Regression

I extend the concept of partial least squares (PLS) into the framework of generalized linear models. A spectroscopy example in a logistic regression framework illustrates the developments. These models form a sequence of rank 1 approximations useful for predicting the response variable when the explanatory information is severely ill-conditioned. Iteratively reweighted PLS algorithms are presented with various theoretical properties. Connections to principal-component and maximum likelihood estimation are made, as well as suggestions for rules to choose the proper rank of the final model.     

Unsupervised segmentation of hidden semi-Markov non-stationary chains

The Bayesian segmentation using Hidden Markov Chains (HMC) is widely used in various domains such as speech recognition, acoustics, biosciences, climatology, text recognition, automatic translation and image processing. On the one hand, hidden semi-Markov chains (HSMC), which extend HMC, have turned out to be of interest in many situations and have improved HMC-based results. On the other hand, the case of non-stationary data can pose an important problem in real-life situations, especially when the model parameters have to be estimated. The aim of this paper is to consider these two extensions simultaneously: we propose using a particular triplet Markov chain (TMC) to deal with non-stationary hidden semi-Markov chains. In addition, we consider a recent particular HSMC having the same computation complexity as the classical HMC. We propose a related parameter estimation method and the resulting unsupervised Bayesian segmentation is validated through experiments; in particular, a real radar image segmentations are provided.