Comparison of subspace and ARX models of a waveguide's terahertz transient response after optimal wavelet filtering

A quasi-optical deembedding technique for characterizing waveguides is demonstrated using wide-band time-resolved terahertz spectroscopy. A transfer function representation is adopted for the description of the signal in the input and output port of the waveguides. The time-domain responses were discretized and the waveguide transfer function was obtained through a parametric approach in the z-domain after describing the system with an AutoRegressive with eXogenous input (ARX), as well as with a state-space model. Prior to the identification procedure, filtering was performed in the wavelet domain to minimize both signal distortion, as well as the noise propagating in the ARX and subspace models. The optimal filtering procedure used in the wavelet domain for the recorded time-domain signatures is described in detail. The effect of filtering prior to the identification procedures is elucidated with the aid of pole-zero diagrams. Models derived from measurements of terahertz transients in a precision WR-8 waveguide adjustable short are presented.

On the Filtering Problem for Continuous-Time Markov Jump Linear Systems with no Observation of the Markov Chain

We consider in this paper the optimal stationary dynamic linear filtering problem for continuous-time linear systems subject to Markovian jumps in the parameters (LSMJP) and additive noise (Wiener process). It is assumed that only an output of the system is available and therefore the values of the jump parameter are not accessible. It is a well known fact that in this setting the optimal nonlinear filter is infinite dimensional, which makes the linear filtering a natural numerically, treatable choice. The goal is to design a dynamic linear filter such that the closed loop system is mean square stable and minimizes the stationary expected value of the mean square estimation error. It is shown that an explicit analytical solution to this optimal filtering problem is obtained from the stationary solution associated to a certain Riccati equation. It is also shown that the problem can be formulated using a linear matrix inequalities (LMI) approach, which can be extended to consider convex polytopic uncertainties on the parameters of the possible modes of operation of the system and on the transition rate matrix of the Markov process. As far as the authors are aware of this is the first time that this stationary filtering problem (exact and robust versions) for LSMJP with no knowledge of the Markov jump parameters is considered in the literature. Finally, we illustrate the results with an example.

A Separation Principle for the Continuous-Time LQ-Problem With Markovian Jump Parameters

In this paper, we devise a separation principle for the finite horizon quadratic optimal control problem of continuous-time Markovian jump linear systems driven by a Wiener process and with partial observations. We assume that the output variable and the jump parameters are available to the controller. It is desired to design a dynamic Markovian jump controller such that the closed loop system minimizes the quadratic functional cost of the system over a finite horizon period of time. As in the case with no jumps, we show that an optimal controller can be obtained from two coupled Riccati differential equations, one associated to the optimal control problem when the state variable is available, and the other one associated to the optimal filtering problem. This is a separation principle for the finite horizon quadratic optimal control problem for continuous-time Markovian jump linear systems. For the case in which the matrices are all time-invariant we analyze the asymptotic behavior of the solution of the derived interconnected Riccati differential equations to the solution of the associated set of coupled algebraic Riccati equations as well as the mean square stabilizing property of this limiting solution. When there is only one mode of operation our results coincide with the traditional ones for the LQG control of continuous-time linear systems.

The use of tuned filters as an attenuator device of harmonics generated by multipulse converters

The purpose of this paper is to present a computer model that enables the operation analysis of a tuned filter as an attenuator device of harmonic generated 12 and 18-pulses converters with Y-generalized differential connection. Are presented in this study physical considerations, mathematical modeling and digital simulations in the frequency domain using the software Orcad-Pspice®, which allows a spectral analysis of the harmonic components and supports the search for an optimal filtering process. It is unequivocally demonstrated the feasibility of the application as an alternative to optimize the use of multipulse converters, and enable the operation of this device within the established regulatory standards. The validation of the proposed model is based on results obtained in the time domain using Matlab/Simulink®. © 2011 IEEE.

Water isotopic ratios from a continuously melted ice core sample

A new technique for on-line high resolution isotopic analysis of liquid water, tailored for ice core studies is presented. We built an interface between a Wavelength Scanned Cavity Ring Down Spectrometer (WS-CRDS) purchased from Picarro Inc. and a Continuous Flow Analysis (CFA) system. The system offers the possibility to perform simultaneuous water isotopic analysis of δ18O and δD on a continuous stream of liquid water as generated from a continuously melted ice rod. Injection of sub μl amounts of liquid water is achieved by pumping sample through a fused silica capillary and instantaneously vaporizing it with 100% efficiency in a~home made oven at a temperature of 170 °C. A calibration procedure allows for proper reporting of the data on the VSMOW–SLAP scale. We apply the necessary corrections based on the assessed performance of the system regarding instrumental drifts and dependance on the water concentration in the optical cavity. The melt rates are monitored in order to assign a depth scale to the measured isotopic profiles. Application of spectral methods yields the combined uncertainty of the system at below 0.1‰ and 0.5‰ for δ18O and δD, respectively. This performance is comparable to that achieved with mass spectrometry. Dispersion of the sample in the transfer lines limits the temporal resolution of the technique. In this work we investigate and assess these dispersion effects. By using an optimal filtering method we show how the measured profiles can be corrected for the smoothing effects resulting from the sample dispersion. Considering the significant advantages the technique offers, i.e. simultaneuous measurement of δ18O and δD, potentially in combination with chemical components that are traditionally measured on CFA systems, notable reduction on analysis time and power consumption, we consider it as an alternative to traditional isotope ratio mass spectrometry with the possibility to be deployed for field ice core studies. We present data acquired in the field during the 2010 season as part of the NEEM deep ice core drilling project in North Greenland.

A generalized adaptive mathematical morphological filter for LIDAR data

Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in processing LIDAR data to effectively and efficiently identify ground measurements for the complex terrains in a large LIDAR data set. The GAPM filter is highly automatic and requires little human input. Therefore, it can significantly reduce the effort of manually processing voluminous LIDAR measurements.

Costruzione di un filtro ottimale per la ricerca di ammassi di galassie in survey fotometriche

Gli ammassi di galassie sono le strutture più grandi che possiamo osservare nell’Universo. La loro formazione deriva direttamente dalla crescita delle perturbazioni primordiali di densità e dal loro conseguente collasso gravitazionale indotto appunto dalla gravità. Gli ammassi di galassie sono molto importanti in Astrofisica in quanto possono essere considerati come dei laboratori per lo studio di molti aspetti fisici legati al gas, all’ICM e all’evoluzione delle galassie. Lo studio degli ammassi di galassie è molto importante anche per la Cosmologia in quanto è possibile effettuare delle stime sui parametri cosmologici ed ottenere dei vincoli sulla geometria dell’Universo andando a valutare la loro massa e la loro distribuzione nell’Universo. Diventa quindi fondamentale l’utilizzo di algoritmi che ci permettano di utilizzare i dati ottenuti dalle osservazioni per cercare ed individuare gli ammassi di galassie in modo tale da definire meglio la loro distribuzione nell’Universo. Le più recenti survey di galassie ci forniscono molteplici informazioni a riguardo delle galassie, come ad esempio la loro magnitudine in varie bande osservative, il loro colore, la loro velocità ecc. In questo lavoro abbiamo voluto testare la performance di un algoritmo Optimal Filtering nella ricerca degli ammassi di galassie utilizzando prima solo l’informazione della magnitudine delle galassie e successivamente anche l’informazione sul loro colore. Quello che abbiamo voluto fare, quindi, è stato valutare se l’utilizzo combinato della magnitudine delle galassie e del loro colore permette all’algoritmo di individuare più facilmente, e in numero maggiore, gli ammassi di galassie.

Optimal duty cycle and asymmetric VSB filtering in a wavelength allocated DWDM CS-RZ transmission

Impact of duty cycle on the optimisation of ultra-narrow VSB filtering in wavelength allocated CS-RZ Nx40Gbit/s DWDM transmission is investigated. A feasibility has been confirmed of over 600 km with 0.64 bit/s/Hz spectral efficiency.

The effect of high-pass filtering on TEOAE in 2-month-old infants

The aim of the present study was to investigate the effect of high-pass filtering on TEOAE obtained from 2-month-old infants as a function of filter cut-off frequency, activity states and pass/fail status of infants. Two experiments were performed. In Experiment 1, 100 2-month-old infants (200 ears) in five activity states (asleep, awake but peaceful, sucking a pacifier, feeding, restless) were tested by use of TEOAE technology. Five different filter conditions were applied to the TEOAE responses post hoc. The filter conditions were set at 781 Hz (default setting), 1.0, 1.2, 1.4 and 1.6 kHz. Results from this experiment showed that TEOAE parameters, such as whole-wave reproducibility (WR) and signal-to-noise ratio (SNR) at 0.8 kHz and 1.6 kHz, changed as a function of the cut-off frequency. The findings suggest that the 1.6 kHz and 1.2 kHz filter conditions are optimal for WR and SNR pass/fail criteria, respectively. Although all infant recordings appeared to benefit from the filtering, infants in the noisy states seemed to benefit the most. In Experiment 2, the high-pass filtering technique was applied to 23 infants (35 ears) who apparently failed the TEOAE tests on initial screening but were subsequently awarded a pass status based on the results from a follow-up auditory brainstem response (ABR) assessment. The findings showed a significant decrease in noise contamination of the TEOAE with a corresponding significant increase in WR. With high-pass filtering at 1.6 kHz, 21/35 ears could be reclassified into the pass category.

Gene filtering with optimal threshold selection

Gene filtering is a useful preprocessing technique often applied to microarray datasets. However, it is no common practice because clear guidelines are lacking and it bears the risk of excluding some potentially relevant genes. In this work, we propose to model microarray data as a mixture of two Gaussian distributions that will allow us to obtain an optimal filter threshold in terms of the gene expression level.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Histogram filtering as a tool in variational Monte Carlo optimization

Optimization of wave functions in quantum Monte Carlo is a difficult task because the statistical uncertainty inherent to the technique makes the absolute determination of the global minimum difficult. To optimize these wave functions we generate a large number of possible minima using many independently generated Monte Carlo ensembles and perform a conjugate gradient optimization. Then we construct histograms of the resulting nominally optimal parameter sets and "filter" them to identify which parameter sets "go together" to generate a local minimum. We follow with correlated-sampling verification runs to find the global minimum. We illustrate this technique for variance and variational energy optimization for a variety of wave functions for small systellls. For such optimized wave functions we calculate the variational energy and variance as well as various non-differential properties. The optimizations are either on par with or superior to determinations in the literature. Furthermore, we show that this technique is sufficiently robust that for molecules one may determine the optimal geometry at tIle same time as one optimizes the variational energy.

Filtering techniques in self-tuning

In the last few years a state-space formulation has been introduced into self-tuning control. This has not only allowed for a wider choice of possible control actions, but has also provided an insight into the theory underlying—and hidden by—that used in the polynomial description. This paper considers many of the self-tuning algorithms, both state-space and polynomial, presently in use, and by starting from first principles develops the observers which are, effectively, used in each case. At any specific time instant the state estimator can be regarded as taking one of two forms. In the first case the most recently available output measurement is excluded, and here an optimal and conditionally stable observer is obtained. In the second case the present output signal is included, and here it is shown that although the observer is once again conditionally stable, it is no longer optimal. This result is of significance, as many of the popular self-tuning controllers lie in the second, rather than first, category.

Neurofuzzy control using Kalman filtering state feedback with coloured noise for unknown non-linear processes

This paper presents a controller design scheme for a priori unknown non-linear dynamical processes that are identified via an operating point neurofuzzy system from process data. Based on a neurofuzzy design and model construction algorithm (NeuDec) for a non-linear dynamical process, a neurofuzzy state-space model of controllable form is initially constructed. The control scheme based on closed-loop pole assignment is then utilized to ensure the time invariance and linearization of the state equations so that the system stability can be guaranteed under some mild assumptions, even in the presence of modelling error. The proposed approach requires a known state vector for the application of pole assignment state feedback. For this purpose, a generalized Kalman filtering algorithm with coloured noise is developed on the basis of the neurofuzzy state-space model to obtain an optimal state vector estimation. The derived controller is applied in typical output tracking problems by minimizing the tracking error. Simulation examples are included to demonstrate the operation and effectiveness of the new approach.

Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy

In this letter, a new approach for crop phenology estimation with remote sensing is presented. The proposed methodology is aimed to exploit tools from a dynamical system context. From a temporal sequence of images, a geometrical model is derived, which allows us to translate this temporal domain into the estimation problem. The evolution model in state space is obtained through dimensional reduction by a principal component analysis, defining the state variables, of the observations. Then, estimation is achieved by combining the generated model with actual samples in an optimal way using a Kalman filter. As a proof of concept, an example with results obtained with this approach over rice fields by exploiting stacks of TerraSAR-X dual polarization images is shown.