Calcium phosphates grown on bacterial cellulose template

Bacterial cellulose membranes were employed as templates for calcium phosphates deposition by successive immersion in solutions of Ca(NO₃)₂·4H₂O and (NH₄)₂HPO₄, under ultrasonication. During the wet chemical reaction, mineral phases were loaded on bacterial cellulose fibrils, leading to precursor hybrid composites. These were subjected to a lyophilisation procedure in order to preserve the 3D porous aspect and afterwards to a thermal treatment with the aim of removing the polymeric phase and generating well crystallized structures. Different types of morphologies were achieved by varying the heating rate, as well as the calcination temperature and period. The as-prepared samples and the final ones were investigated from compositional and structural point of view through X-ray diffraction and Fourier-transform infrared spectroscopy and morphologically concerning by scanning electron microscopy. The magnetic properties were also evaluated in order to demonstrate the suitability of the obtained materials for the development of magnetic scaffolds dedicated to hard tissue applications.     

System identification from noisy measurements by using instrumental variables and subspace fitting

This paper considers the estimation of the parameters of a linear discrete-time system from noise-perturbed input and output measurements. The conditions imposed on the system are fairly general. In particular, the input and output noises are allowed to be auto-correlated and they may be cross-correlated as well. The proposed method makes use of an instrumental variable (IV)-vector to produce a covariance matrix that is pre- and postmultiplied by some prechosen weights. The singular vectors of the so-obtained matrix possess complete information on the system parameters. A weighted subspace fitting (WSF) method is then applied to the aforementioned singular vectors to consistently estimate the parameters of the system. The IV-WSF technique suggested herein is noniterative and easy to implement, and has a small computational burden. The asymptotic distribution of its estimation errors is derived and the result is used to motivate the choice of the weighting matrix in the WSF step and also to predict the estimation accuracy. Numerical examples are included to illustrate the performance achievable by the method.This work has been supported by the Swedish Research Council of Engineering Sciences under contract 93-669 and by the Göran Gustafsson Foundation.     

An asymptotically efficient ARMA estimator based on sample covariances

An asymptotically efficient autoregressive moving-average (ARMA) spectral estimator is presented, based on the sample covariances of observed time series. The estimate of the autoregressive (AR) part is shown to be identical to the optimal instrumental variable (IV) estimator in [7] although derived here using a different approach. The moving-average (MA) spectral parameter estimate is new.     

MODE-type algorithm for estimating damped, undamped, or explosive modes

We propose a new algorithm for estimating the parameters of damped, undamped, or explosive sinusoidal signals. The algorithm resembles the MODE algorithm, which is commonly used for direction of arrival estimation in the array signal processing field. It is derived as a natural approximation to an asymptotically (high-SNR) optimal parameter estimator and has excellent statistical accuracy. Nevertheless, it is computationally simple and easy to implement. Numerical examples are included to illustrate the performance of the proposed method.This work has been supported by the Swedish Research Council for Engineering Sciences (TFR).     

Hidden-Action in Network Routing

In communication networks, such as the Internet or mobile ad-hoc networks, the actions taken by intermediate nodes or links are typically hidden from the communicating endpoints; all the endpoints can observe is whether or not the end-to-end transmission was successful. Therefore, in the absence of incentives to the contrary, rational (i.e., selfish) intermediaries may choose to forward messages at a low priority or simply not forward messages at all. Using a principal-agent model, we show how the hidden-action problem can be overcome through appropriate design of contracts in both the direct (the endpoints contract with each individual router directly) and the recursive (each router contracts with the next downstream router) cases. We further show that, depending on the network topology, per-hop or per-path monitoring may not necessarily improve the utility of the principal or the social welfare of the system.     

Complex amplitude estimation in the known steering matrix and generalized waveform case

We consider a special growth-curve (SGC) model with a known steering matrix and generalized waveform in the presence of unknown interference and noise. Several estimators of the complex amplitude based on this model are derived, including the methods of approximate maximum likelihood (AML), minimum variance distortionless response (MVDR), and amplitude and phase estimation (APES). We analyze the statistical properties of these estimators and show that in the presence of temporally white but spatially correlated noise and interference, AML is asymptotically statistically efficient for a large snapshot number while MVDR and APES are asymptotically equivalent but not statistically efficient. Via several numerical examples, we also show that when the noise and interference are both spatially and temporally correlated, the APES estimator can achieve better estimation accuracy and exhibit greater robustness than the other methods.     

Orthogonal space-time block codes: maximum likelihood detection for unknown channels and unstructured interferences

Space-time coding (STC) schemes for communication systems employing multiple transmit and receive antennas have been attracting increased attention. The so-called orthogonal space-time block codes (OSTBCs) have been of particular interest due to their good performance and low decoding complexity. In this paper, we take a systematic maximum-likelihood (ML) approach to the decoding of OSTBC for unknown propagation channels and unknown noise and interference conditions. We derive a low-complexity ML decoding algorithm based on cyclic minimization and assisted by a minimum amount of training data. Furthermore, we discuss the design of optimal training sequences and optimal information transfer to an outer decoder. Numerical examples demonstrate the performance of our algorithm.     

On LP-MUSIC

Studies the consistency properties of a method recently proposed for temporal or spatial frequency estimation from noisy data. The method in question is a MUSIC technique that makes use of a linear prediction algorithm to determine the signal subspace. It is shown that the signal subspace determined by the subject linear prediction-MUSIC (LP-MUSIC) algorithm can collapse in certain scenarios and. Hence, that the LP-MUSIC frequency estimates are not always consistent. The difficulties LP-MUSIC may encounter in some cases are illustrated by means of numerical examples     

On Forward–Backward MODE for Array Signal Processing

We apply the MODE (method of direction estimation) principle to the forward–backward (FB) covariance of the output vector of a sensor array to obtain what we call the FB-MODE procedure. The derivation of FB-MODE is an interesting exercise in matrix analysis, the outcome of which was somewhat unexpected: FB-MODE simply consists of applying the standard MODE approach to the eigenelements of the FB sample covariance matrix. By using an asymptotic expansion technique we also establish the surprising result that FB-MODE is outperformed, from a statistical standpoint, by the standard MODE applied to the forward-only sample covariance (F-MODE). We believe this to be an important result that shows that the FB approach, which proved quite useful for improving the performance of many suboptimal array processing methods, shouldnotbe used with a statistically optimal method such as F-MODE.