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Endogenous norepinephrine (NE) release in cerebral cortex slices taken from normal and morphine-tolerant guinea pigs was measured by HPLC. In normal slices, a linear relationship was found between electrically evoked NE release and the log of the frequency of stimulation in the range of 1-20 Hz. The efficiency of the alpha 2-mediated autofeedback was tested by adding the alpha 2-agonist clonidine and the alpha 2 agonist idazoxan. NE release was dose-dependently reduced by clonidine (1 nmol/L-1 mumol/L) and increased by idazoxan (10-100 nmol/L). The inhibition by clonidine was significantly greater at 1 Hz than at 3 Hz, whereas the absolute increase in NE release induced by idazoxan was greater at 3 Hz than at 1 Hz. Morphine at 1 mumol/L (a concentration per se ineffective) shifted to the left the clonidine concentrations able to inhibit NE release at 3 and 1 Hz (1-10 nmol/L), but at both frequencies, the opiate reduced the maximal inhibition induced by clonidine at 1 mumol/L. In slices taken from morphine-tolerant guinea pigs (in the presence of morphine at 1 mumol/L), clonidine (1 nmol/L-1 mumol/L) was ineffective at the stimulation rate of 3 Hz, but it was more active than in normal slices at 1 Hz. Such a response pattern suggests a reduced availability of alpha 2 receptors and an increase in their sensitivity to clonidine. However, chronic morphine treatment did not influence the physiological autoinhibition because the increase in NE release elicited by idazoxan (10-100 nmol/L) at 1 and 3 Hz was the same in normal and in "morphine-tolerant" slices.(ABSTRACT TRUNCATED AT 250 WORDS)

The inhibition induced by clonidine (CL) on electrically-evoked release of endogenous norepinephrine (NE) from guinea pig neocortical slices depends on the stimulation rate and is affected by acute and chronic morphine (Mo) treatment in a complex manner. The effect of CL is more evident at the low stimulation rates (1 Hz) and acute Mo potentiates the low alpha-2 agonist concentrations while it antagonizes the higher one. In tolerant slices the potentiation of CL inhibition is evident only at 1 Hz while no antagonism is found at 3 Hz, suggesting that, in the biophase, the endogenous transmitter plays a crucial role in CL-Mo interactions. Idazoxan (Id) increases NE release at 3 Hz more than at 1 Hz and displays the same effect in normal and tolerant slices. Naloxone does not affect NE release in tolerant slices at all. These findings suggest that the noradrenergic nerve endings are provided with two subsets of alpha-2 autoreceptors: the first one is stimulated by the endogenous transmitter while the latter is more accessible to CL and is modulated by acute and chronic Mo treatment.

This paper deals with the identification of errors–in–variables models where the additive input and output noises are mutually correlated white processes. The proposed solution is based on the extension of the dynamic Frisch scheme introduced in (Beghelli et al., 1990). First, a geometric characterization of the whole set of admissible solutions in the noise space is described. Then, a criterion that allows to select the solution of the identification problem inside the locus is proposed. This criterion relies on the properties of a set of high–order Yule–Walker equations. The effectiveness of this identification approach is tested by means of Monte Carlo simulations.

This paper deals with the identification of multi–input multi–output errors–in–variables (EIV) models in the Frisch scheme context. The extension of the Frisch scheme to MIMO models introduces some problems not present in the SISO case. The approach proposed in this paper relies on the association of EIV models to directions in the noise space and on the statistical properties of the residual of the EIV model. In particular, a selection criterion based on the comparison of the theoretical statistical properties of the residual with those computed from the data is introduced. The performance of the proposed identification algorithm is evaluated by means of numerical simulations.

ARMAX models are widely used in identification and are a standard tool in control engineering for both system description and control design. These models, however, can be non realistic in many practical contexts because of the presence of measurement errors that play an important role in applications like fault diagnosis and optimal filtering. ARMAX models can be enhanced by introducing also additive error terms on the input and output observations. This scheme, that can be denoted as “ARMAX + noise”, belongs to the errors–in–variables family and allows taking into account the presence of both process disturbances and measurement noise. This paper proposes a three-step procedure for identifying “ARMAX + noise” processes. The first step of the identification algorithm in based on an iterative search procedure while the second and third ones rely on simple least–squares formulas. The paper reports also the results of some Monte Carlo simulations that underline the effectiveness of the proposed approach.

The extension of the Frisch scheme from the original algebraic case to the dynamic one leads to the use of errors–in–variables models where the measurements of the input and output are affected by additive white and independent noises. This problem admits a single solution when the assumptions of the scheme are exactly fulfilled but its application to real processes requires the introduction of specific model selection criteria. This paper analyzes the additional problems encountered in the extension of Frisch identification to the multivariable case and introduces a geometric approach for its solution.

ARMAX models constitute an excellent compromise between performance and complexity and can model in an effective way the presence of disturbances acting on the process state. These models, however, do not take into account the observation errors on the output of the process to be identified and this can be particularly important in applications like filtering and fault diagnosis. This paper concerns extended ARMAX models that consider also the presence of additive white noise on the output observation and describes an approach for their identification that takes advantage of both the errors–in–variables framework and the instrumental variable properties. The paper reports also the results of Monte Carlo simulations that underline the effectiveness of the proposed approach.

This paper describes an identification procedure for minimally parametrized multivariable models in the Errors–in–Variables (EIV) context of the Frisch scheme that considers additive white observation noise on the process inputs and outputs. This procedure relies on the geometric approach described in (Guidorzi and Diversi, 2009) that associates EIV models to directions in the noise space. The proposed procedure has been tested by means of a Monte Carlo simulation that confirms its effectiveness.

The identification of dynamic processes can be performed by means of different classes of models relying on different stochastic environments to describe the misfit between the model and process observations. This paper introduces a new class of models by considering additive error terms on the observations of the input and output of ARARX models and proposes a three–step identification procedure for their identification. ARARX + noise models extend the traditional ARARX or ARMAX ones and can be seen as errors–in–variables models where both measurement errors and process disturbances are taken into account. The results of Monte Carlo simulations show the good performance of the proposed identification procedure.

A common approach in modeling signals in many engineering applications consists in adopting autoregressive (AR) models, consisting in filters with transfer functions having a unitary numerator, driven by white noise. Despite their wide application, these models do not take into account the possible presence of errors on the observations and cannot prove accurate when these errors are significant. AR plus noise models constitute an extension of AR models that consider also the presence of an observation noise. This paper describes a new algorithm for the identification of AR plus noise models that is characterized by a very good compromise between accuracy and efficiency. This algorithm, taking advantage of both low and high-order Yule–Walker equations, also guarantees the positive definiteness of the autocorrelation matrix of the estimated process and allows to estimate the equation error and observation noise variances. It is also shown how the proposed procedure can be used for estimating the order of the AR model. The new algorithm is compared with some traditional algorithms by means of Monte Carlo simulations. Copyright © 2007 John Wiley & Sons, Ltd.

In the framework of speech enhancement, several parametric approaches based on an a priori model for a speech signal have been proposed. When using an autoregressive (AR) model, three issues must be addressed. (1) How to deal with AR parameter estimation? Indeed, due to additive noise, the standard least squares criterion leads to biased estimates of AR parameters. (2) Can an estimation of the variance of the additive noise for each speech frame be obtained? A voice activity detector is often used for its estimation. (3) Which estimation rules and techniques (filtering, smoothing, etc.) can be considered to retrieve the speech signal? Our contribution in this paper is threefold. First, we propose to view the identification of the noisy AR process as an errors-in-variables problem. This blind method has the advantage of providing accurate estimations of both the AR parameters and the variance of the additive noise. Second, we propose an alternative algorithm to standard Kalman smoothing, based on a constrained minimum variance estimation procedure with a lower computational cost. Third, the combination of these two steps is investigated. It provides better results than some existing speech enhancement approaches in terms of signal-to-noise-ratio (SNR), segmental SNR, and informal subjective tests.

This work deals with the identification of errors-in-variables models corrupted by white and uncorrelated Gaussian noises. By introducing an auxiliary process, it is possible to obtain a maximum likelihood solution of this identification problem, by means of a two-step iterative algorithm. This approach allows also to estimate, as a byproduct, the noise-free input and output sequences. Moreover, an analytic expression of the finite Cramer-Rao lower bound is derived. The method does not require any particular assumption on the input process, however, the ratio of the noise variances is assumed as known. The effectiveness of the proposed algorithm has been verified by means of Monte Carlo simulations.

This paper introduces an extended environment for Kalman filtering that considers also the presence of additive noise on input observations in order to solve the problem of optimal (minimal variance) estimation of noise-corrupted input and output sequences. This environment includes as subcases both errors-in-variables filtering (optimal estimate of inputs and outputs from noisy observations) and traditional Kalman filtering (optimal stimate of state and output in presence of state and output noise). A Monte Carlo simulation shows that the performance of this extended filtering technique leads to the expected minimal variance estimates.

This paper considers the problem of estimating the parameters of an autoregressive (AR) process in presence of additive white noise and proposes a new identification method, based on theoretical results originally developed in errors-in-variables contexts. This approach allows to estimate the AR parameters, the driving noise variance and the variance of the additive noise in a congruent way in that these estimates assure the positive definiteness of the autocorrelation matrix. The performance of the proposed algorithm is compared with that of bias-compensated least-squares methods by means fo Monte Carlo simulations. The results show the effectivenesss of the new method also in presence of high amounts of noise.

Blind identification is a very significant problem in many contexts where only the output of transmission channels can be observed. The solutions that can be found in the literature are limited to the case of equal amounts of additive noise on the observations; this paper proposes new identification procedures that can be applied to the case of two FIR channels affected by unknown and unbalanced amounts of additive noise. The identified models are then used for the minimal variance deconvolution of the unknown input signal. Several Monte Carlo simulations confirm the good performance of these procedures also in severe SNR conditions.

This paper proposes a two-step identification procedure for the direction-of-arrival estimation problem in the presence of nonuniform white noise. The first step consists in estimating the unknown sensor noise variances by exploiting the properties of the Frisch scheme. Once that the noise covariance matrix has been identified, the angles of arrival are computed by using the classical ESPRIT algorithm. The effectiveness of the whole procedure is tested by means of Monte Carlo simulations.

Structural health monitoring (SHM) is a field that relies on different methodologies to develop procedures that characterize the dynamic properties of physical structures to identify possible deteriorations of their behaviors. SHM systems include usually a data acquisition subsystem suitable for recording the structure response to ambient or external excitations. The recorded data are then analyzed in order to characterize the dynamic properties of the considered structure. This paper describes some tests performed by means of a new advanced SHM system, the Teleco SHM602, on a truncated middle-age tower presently included in an ancient palace of the XVI century located in the central part of Bologna. These tests rely on models obtained by means of standard and advanced identification techniques.

Errors-in-Variables (EIV) models consider the presence of additive errors on the measures of all measurable attributes of a process. Traditional identification procedures for these processes rely on the properties of the covariance matrix of the observations and on its decomposition into the sum of a matrix associated with the (unknown) noiseless sequences and of the observation noise covariance matrix. This paper makes reference, in a behavioural framework, to the observed noisy trajectories and performs a decomposition of these trajectories into a regular part, defininig the associated behaviour of the process, and a noise part. The Monte Carlo simulations that have been performed show that the proposed approach leads to accurate estimates of both the system parameters and the noise variances.

Structural Health Monitoring (SHM) methodologies are taking advantage of the development of the new families of MEMS sensors and of the progress in network technologies; future systems will rely on intelligent sensors performing locally data filtering, elaboration and model identification, connected over suitable buses. This paper describes some families of multivariate models that can be used in SHM–oriented identification procedures and, in particular, the extension of AR models known as AR+noise. It describes also the implementation of a new advanced SHM system, the Teleco SHM602, in the tower of the Engineering School of Bologna University and the multivariate models identified from the data collected by this system.

Multivariable output-error identification does not constitute, in any way, a straightforward extension of the scalar case. The aim of this paper is twofold: 1) Introduction of a new minimal parametrization for multivariable output error models leading to an easily implementable prediction error identification procedure; 2) Comparison of PEM and errors-in-variables approaches based on the dynamic Frisch scheme in the identification of MIMO output error processes.

Nella memoria si descrivono le caratteristiche e le potenzialità di un sistema di monitoraggio dinamico basato su tecnologia MEMS per l’installazione su sistemi strutturali, sviluppata in collaborazione tra l’Università di Bologna, l’Università di Modena e Reggio Emilia e la TELECO. Il sistema è composto da una unità centrale e da unità periferiche sulle quali sono presenti un accelerometro biassiale e un sensore di temperatura oltre ad un microchip programmabile in grado di elaborare il segnale acquisito, di risolvere algoritmi di identificazione e di comunicare all’unità centrale i risultati. Il sistema è stato istallato sulla torre della Facoltà di Ingegneria dell’Università di Bologna e, per poterne validare le prestazioni, è stato installato un secondo, di tipo tradizionale, in parallelo al primo. Si descrivono i risultati in termini di accelerazioni e PSD delle accelerazioni quantificando l’entità del rumore delle misure, oltre al confronto tra i modelli Autoregressivi ottenuti da entrambi i sistemi. Infine, vengono descritte le caratteristiche modali della torre, dedotte dal processo di identificazione.

This paper proposes some considerations on the role played by information and communication technologies in the evolution of educational systems and describes the design philosophy and the realization of a basic course on dynamic system identification that relies on constructivist methodologies and on the use of e–learning environments. It reports also some of the opinions formulated by the students on the effectiveness of the available tools and on their role in acquiring proficiency in the application of identification techniques in modeling real processes.

Errors–in–Variables (EIV) models, i.e. models whose stochastic environment considers measurement errors on both inputs and outputs are intrinsically more realistic than representations assuming an exact knowledge of the input but are also more difficult to estimate. The difficulties increase in a non trivial way passing from the SISO and MISO cases to the MIMO one. This paper proposes a procedure for EIV identification of MIMO processes based on the Frisch scheme that assumes additional white noises on all inputs and outputs and shows its effectiveness by means of Monte Carlo simulations.

Structural Health Monitoring (SHM) is a filed that take advantage of the unprecedented development of sensor, microelectronics and microprocessor technologies. The new generation of SHM systems are based on MEMS (Micro Electro-Mechanical Systems) and relies on designs that integrate advanced sensor technologies with distributed computational power as well as efficient implementation of identification methodologies to realize “intelligent” sensors that elaborate local models and exchange data, information and models on a local network managed by a control/storage unit usually accessible also in a remote way. The purpose of this paper is to describe the results obtained from a MEMS-based sensing system starting from accelerations measured from a laboratory prototype and the preliminary analysis of the dynamic behavior of the tower of the Faculty of Engineering in Bologna. The results are shown comparing the MEMS based sensing system with a traditional system.

Most aircraft can be considered as rigid bodies whose motion is determined by a set of forces due to aerodynamic effects, propulsion and gravity. Their dynamics can thus be described by computing the position and velocity of the center of gravity as well as the orientation and angular velocity of body-fixed axes with respect to a set of earth reference axes. The dynamic equations of motion are well–known so that aircraft modeling leads to very accurate results provided that an equally accurate knowledge of the aircraft parameters and of the acting forces is available. This information is, however, seldom available particularly for light or ultralight aircraft. The alternative to modeling is identification; the practical application of identification techniques is however conditioned by the choice of the class of models (often linear), the design of suitable data–gathering experiments and by the final validation of the obtained model i.e. by the evaluation of the degree of approximation of its description of the real process. This paper describes the identification of the longitudinal and lateral dynamics of an ultralight aircraft and shows that the consistency of the obtained descriptions can heavily depend on the considered class of models. In particular, it is shown that traditional equation-error approaches relying on ARX or those relying on Output Error models can prove unreliable while the less known ARX+noise models can give very consistent results.

AR modeling is used in a wide range of applications from speech processing to Rayleigh fading channel simulation. When the observations are disturbed by an additive white noise, the standard Least Squares estimation of the AR parameters is biased. Some authors of this paper recently reformulated this problem as an errors-in-variables (EIV) issue and proposed an off-line solution, which outperforms other existing methods. Nevertheless, its computational cost may be high. In this paper, we present a blind recursive EIV method that can be implemented for real-time applications. It has the advantage of converging faster than the noise-compensated LMS based solutions. In addition, unlike EKF or Sigma Point Kalman filter, it does not require a priori knowledge such as the variances of the driving process and the additive noise. The approach is first tested with synthetic data; then, its relevance is illustrated in the field of radar sea clutter rejection.

Dynamical systems can be described by several classes of models and it is also possible to define, inside most of these classes, equivalence relations that have no influence on the input–output behaviour of the system. For most applications it is thus possible to use any model inside an equivalence class and, in fact, this possibility is widely used in the solution of analysis and synthesis problems (e.g. changes of basis in the state space for state space models). When the models are used for identification purposes, however, the use of non minimally parameterized models leads to a larger dispersion of the parameter estimates with obvious negative effects on fault detection and localization procedures based on the values of the parameters of identified models. This paper defines the minimal number of parameters that can describe a dynamical system on the basis of the invariance properties of equivalence relations defined inside the considered class of models and shows, by means of Monte Carlo simulations, that also modest overparameterizations can lead to remarkable increases in the dispersion of the parameter estimates.

This paper proposes an efficient algorithm for identifying FIR models when also the input is assumed as affected by additive noise. This procedure is more accurate than instrumental variables approaches and, differently from total least squares, does not require the a priori knowledge of the ratio between the input and output noise variances. The accuracy of the whole procedure has been tested by means of Monte Carlo simulations and compared with that of compensated and total least squares ones.

This paper describes a fault detection procedure based on the analysis of the properties of the residuals of an errors-in-variables model. This procedure has been applied to the detection of flaps servo actuator faults on a light sport aircraft. The results obtained on real data collected in actual flight conditions have shown that this kind of fault and even its magnitude can be reliably detected by means of the proposed procedure.

This paper considers the problem of determining linear relations from data affected by additive noise in the context of the Frisch scheme. The loci of solutions of the Frisch scheme and their properties are first described in the algebraic case. In this context two main problems are analyzed: the evaluation of the maximal number of linear relations compatible with data affected by errors and the determination of the linear relation actually linking the noiseless data. Subsequently the extension of the Frisch scheme to the identification of dynamical systems is considered for both SISO and MIMO cases and the problem of its application to real processes is investigated.

This paper describes the application of identification techniques in the design and optimization of a vertical navigation (VNAV) autopilot for a light sport aviation (LSA) high performance aircraft(Flight Design CT 2K). The whole design has been based, to reduce global costs, weight and complexity, on the control of the stabilator trim instead than, as is more common, on the direct control of the stabilator by means of a dedicated servo actuator. This solution, despite the abovementioned advantages, is characterized by some critical aspects due to the introduction of additional delays in the control chain and also to potential safety problems thatmust be carefully considered. The first design step has seen the construction of an accurate model concerning the aircraft response to the stabilator trim. This model has been obtained by means of identification techniques applied to data sequences collected in specific flights and has been validated by means of simulations performed on data sets concerning different flights. The model has then been used to design and optimize a PID controller whose performance has been tested first in simulation contexts and subsequently, after its implementation into the autopilot, in flight conditions. This design approach has allowed, on the one hand, a sensible reduction of inflight tests and of trial and error procedures and, on the other hand, to obtain a good final autopilot behavior confirmed by all inflight validation tests.

Autoregressive (AR) models are used in a wide variety of applications concerning the recovery of signals from noise-corrupted observations. In all real contexts of this kind also an additive broadband observation noise is present and the filtering of the observations is usually performed by means of standard Kalman filtering that requires a state space realization of the AR model to describe the observed process and the solution, at every step, of the Riccati equation. This paper proposes a faster filtering algorithm suitable for stationary processes and based on the decomposition of Toeplitz matrices described in [J. Rissanen, Algorithms for triangular decomposition of block Hankel and Toeplitz matrices with application to factoring positive matrix polynomials, Math. Comput. 27 (January 1973) 147-154] that operates directly on AR models. The computational complexity of the proposed algorithm increases only linearly with the order of the process.

ARX (AutoRegressive models with eXogenous variables) are the simplest models within the equation error family but are endowed with many practical advantages concerning both their estimation and their predictive use. On the other hand the (implicit) assumption of noise-free inputs and of outputs affected by an additive colored noise whose spectrum is defined only by the model poles can be considered as non realistic when all measures are affected by additive errors. This paper considers the family of ARX + noise models that describe ARX processes whose measures are affected by additive white noise. The identification of these models is then mapped into the problem of identifying errors-in-variables models in the context of the Frisch scheme and a specific identification algorithm is described. A Monte Carlo simulation confirms the good results that can be obtained with the whole procedure.

Parametric approaches based on a priori models of the speech are often used in the framework of speech enhancement using a single microphone. When the speech is modeled by means of a stationary autoregressive (AR) process, a frame-by-frame approach is usually considered. However, it requires the unbiased estimations of the autoregressive parameters and of the noise variances for the subsequent implementation of a filter (Kalman, H-infinity, etc.). The purpose of this paper is twofold. Firstly, we propose to view the AR parameter estimation as an errors-in-variables issue. Secondly, we implement an optimal smoothing procedure based on a constrained minimum variance estimation of the signal. Then, we test the procedure based on both steps in the field of speech enhancement.

The problem considered in this paper is the design of a low cost MEMS based attitude estimation unit to be used in ultralight, experimental and sport pilot aircrafts as auxiliary safety tool in VFR flight conditions. The proposed approach relies on a new data fusion scheme based on a dual Kalman filter design and on acceleration-based switch criteria. Attitude information is extracted from the states of the filters under the restriction of non aerobatic uses that allows the introduction of a priori limits in roll and pitch angles.

Deriving linear relations from data affected by additive noise is a problem of high practical relevance in many fields. All available procedures are based on sets of assumptions concerning the noise process; the results obtained from their use depend, consequently, from the distance between the actual process and the specific assumptions. The Frisch scheme introduces less a priori assumptions than other commonly used methods like, for instance, Least Squares, but leads to a whole family of models compatible with a set of noisy data so that its use is impractical in most cases. This paper introduces, on the basis of definitions and properties previously described, a robust and consistent procedure to extract a single model from two independent sets of noisy data in the context of the Frisch scheme. This allows the use of this approach also in all practical cases requiring a single model to describe the process behind the data.

A new method for identifying linear dynamic errors-in-variables (EIV) models, whose input and output are affected by additive white noise, is proposed. This approach takes advantage of the properties of both the dynamic Frisch scheme and Yule-Walker equations and allows to identify the system parameters and the noise variances in a congruent way since the estimates assure the positive definiteness of the autocorrelation matrix of the EIV process. The effectiveness of the method has been tested by means of Monte Carlo simulations and compared with those of other EIV identification methods. The proposed procedure is characterized by a good compromise between estimation accuracy and computational efficiency.

This paper deals with the problem of identifying autoregressive models in presence of additive measurement noise. A new approach, based on some theoretical results concerning the so-called dynamic Frisch scheme, is proposed. This method takes advantage of both low and high order Yule-Walker equations and allows to identify the AR parameters and the driving and output noise variances in a congruent way since the estimates assure the positive definiteness of the autocorrelation matrix of the AR process. Simulation results are reported to show the effectiveness of the proposed procedure and compare its performance with those of other identification methods.

The geographical position of Comacchio Valleys and the low quality of their associated interconnection rivers contribute to the delicate ecological equilibrium of this vulnerable area. This fragility and unpredictability heavily conditions the area economy that takes advantage of the peculiar environment characteristics to exploit the potentialities of the fish harvest. Previous studies have analyzed the hypertrophic state of water and the instability of the lagoon ecosystem, confirming the difficult exchange with the sea and the high times of the inside waters turn over. Moreover they have underlined that these modest exchanges should, in any case, be considered only as theoretical, because large lagoon areas, in absence of particularly favorable meteorological conditions, are completely excluded from significant water exchanges. The improvement of the structure of the valleys is a problem so important, delicate and of such dimension to require the aid of an effective computational tool allowing the simulation of the evolution of the hydrodynamic, physical and chemical parameters (superficial water speed, dissolved oxygen in the column of water and in the interstitial water inside the sediments, production in sediments of hydrogen sulfide and its dispersion in the impending zones), consequent to different environmental conditions or to morphological changes due to the presence of man.

In the framework of speech enhancement, many approaches have been developed when the speech signal is only corrupted by additive noise. However, in an auditorium, when echoes appear, spatial transformations between the sources and the microphones must be considered. For this reason, we propose to deal with speech contaminated by both convolutive and additive coloured noise. The two-microphone based noise canceller we present operates as follows: firstly, a prewhitening step is carried out. Secondly, the blind deconvolution method we use makes it possible to estimate the finite impulse responses (FIR), their orders and the variances of the additive noise, which is a great advantage. Then, the filtered versions of speech, estimated by means of a subspace method, are used to retrieve the original speech.

In many practical situations the process data are affected by noise on both inputs and outputs. In these contexts, errors–in–variables (EIV) models can be the best choice for identification purposes and several approaches based on these representation are present in the literature. This work refers to one of these methods, the so–called dynamic Frisch scheme. In particular, two different Frisch scheme–based algorithms are analyzed and compared by means of Monte Carlo simulations.

This paper introduces a novel approach, noise signature, in fault detection and isolation, based on the use of errors–in–variables(EIV) models. Differently from more common stochastic environments, in these models all variables (inputs and outputs) are assumed as affected by additive and uncorrelated noise. The identification procedures developed for EIV models allow to estimate the covariance matrix of the noise that constitutes, in absence of faults, a signature for the system. In fact the variations in the estimated noise variances in presence of faults lead to effective ways to detect and isolate faults on both sensors and actuators.

In addition to measurement noise, echoes and reverberations may disturb speech recorded by a microphone. To describe the spatial transformation between the sources and the microphones, FIR filters are considered when modelling the system. Therefore, speech is contaminated by both convolutive and additive noises. In this paper, we propose to retrieve the speech signal from the noisy observations by using two microphones. This speech enhancement method operates in two steps. Firstly, the blind estimations of the FIRs are based on the non negative definiteness property of the autocorrelation matrix of the reverberated versions of speech. The estimation of the original speech is then viewed as an errors-in-variables interpolation issue. It should be noted that this method first deals with a white background noise and has the advantage of not using a voice activity detector (VAD), which is usually required to estimate the noise variances.

Structural health monitoring is a field that relies on different technologies to realize systems that can characterize the dynamic properties of physical structures and identify possible deteriorations of their behavior. These systems include usually a data acquisition subsystem capable of recording the structural response to ambient or external loads and algorithms suitable for the analysis of the recorded response and for determining the dynamic properties of the monitored structures. This paper describes the results obtained in the preliminary analysis of the dynamic behavior of a structure constituted by a truncated middle-age tower presently included in an ancient palace of the XVI century located in the central part of Bologna.

Time Varying Autoregressive (TVAR) models play a key role in various applications such as radar processing, aeronautics and speech processing. Nevertheless, tracking TVAR parameters may be difficult, especially when the process is disturbed by an additive white noise. In this paper, we suggest the use of a recursive Errors-In-Variables method to estimate the variances of the driving process and the additive noise and to track TVAR parameters. This method is based on a Newton-Raphson algorithm. A comparative study with EKF, UKF and CDKF is also proposed.

The Errors-in-Variables (EIV) stochastic environment constitutes a superset of most common stochastic environments considered, for instance, in Kalman filtering or in equation-error identification here the process input is assumed as noise-free. Errors-in-variables models assume, on the contrary, the presence of unknown additive noise also on the inputs; the associated filtering procedures concern thus the optimal (minimal variance) estimation not only of the system state and output but also of the input. Optimal EIV filtering has been formulated and solved only recently making reference to SISO models; this paper extends the efficient algorithm proposed recently by the authors, based on the Cholesky factorization, to the more general multivariable case.

This paper considers the problem of determining linear relations from data affected by additive noise in the context of the Frisch scheme. The loci of solutions of the Frisch scheme and their properties are first described in the algebraic case. In this context two main problems are analyzed: the evaluation of the maximal number of linear relations compatible with data affected by errors and the determination of the linear relation actually linking the noiseless data. Subsequently the extension of the Frisch scheme to the identification of dynamical systems is considered for both SISO and MIMO cases and the problem of its application to real processes is investigated. For this purpose suitable identification criteria and model parametrizations are described. Finally two classical identification problems are mapped into the Frisch scheme, the blind identification of FIR channels and the identification of AR + noise models. This allows some theoretical and practical extensions.

This paper describes the design philosophy and the realization of a basic course on dynamic system identification that relies on constructivist methodologies and on the use of e-learning environments. It reports also some of the opinions formulated by the users on the effectiveness of the available tools and on their role in acquiring proficiency in the application of identification techniques in modelling real processes.