State Estimation Filtering using Recent Finite Measurements and Inputs for Active Suspension System with Temporary Uncertainties

In this paper, the finite memory structure(FMS) filter using most recent finite measured outputs and control inputs is applied for the state estimation filtering of automotive suspension systems to verify intrinsic robustness of FMS filter. Firstly, the single-corner model for the automotive suspension system and its state-space model are described. Secondly, FMS as well as infinite memory structure(IMS) filters are briefly introduced and represented by the summation form. Thirdly, a couple of temporary uncertainties, model uncertainty and unknown input, are discussed. Finally, extensive computer simulations are performed for both nominal system and temporarily uncertain system. It is shown that the FMS filter can be better than the IMS filter for both temporary uncertainties. In addition, the FMS filter can be shown to be comparable to the IMS filter after the effects of a couple of temporary uncertainties have completely disappeared

Listening to Distances and Hearing Shapes:Inverse Problems in Room Acoustics and Beyond

A central theme of this thesis is using echoes to achieve useful, interesting, and sometimes surprising results. One should have no doubts about the echoes' constructive potential; it is, after all, demonstrated masterfully by Nature. Just think about the bat's intriguing ability to navigate in unknown spaces and hunt for insects by listening to echoes of its calls, or about similar (albeit less well-known) abilities of toothed whales, some birds, shrews, and ultimately people. We show that, perhaps contrary to conventional wisdom, multipath propagation resulting from echoes is our friend. When we think about it the right way, it reveals essential geometric information about the sources--channel--receivers system. The key idea is to think of echoes as being more than just delayed and attenuated peaks in 1D impulse responses; they are actually additional sources with their corresponding 3D locations. This transformation allows us to forget about the abstract \emph{room}, and to replace it by more familiar \emph{point sets}. We can then engage the powerful machinery of Euclidean distance geometry. A problem that always arises is that we do not know \emph{a priori} the matching between the peaks and the points in space, and solving the inverse problem is achieved by \emph{echo sorting}---a tool we developed for learning correct labelings of echoes. This has applications beyond acoustics, whenever one deals with waves and reflections, or more generally, time-of-flight measurements. Equipped with this perspective, we first address the ``Can one hear the shape of a room?'' question, and we answer it with a qualified ``yes''. Even a single impulse response uniquely describes a convex polyhedral room, whereas a more practical algorithm to reconstruct the room's geometry uses only first-order echoes and a few microphones. Next, we show how different problems of localization benefit from echoes. The first one is multiple indoor sound source localization. Assuming the room is known, we show that discretizing the Helmholtz equation yields a system of sparse reconstruction problems linked by the common sparsity pattern. By exploiting the full bandwidth of the sources, we show that it is possible to localize multiple unknown sound sources using only a single microphone. We then look at indoor localization with known pulses from the geometric echo perspective introduced previously. Echo sorting enables localization in non-convex rooms without a line-of-sight path, and localization with a single omni-directional sensor, which is impossible without echoes. A closely related problem is microphone position calibration; we show that echoes can help even without assuming that the room is known. Using echoes, we can localize arbitrary numbers of microphones at unknown locations in an unknown room using only one source at an unknown location---for example a finger snap---and get the room's geometry as a byproduct. Our study of source localization outgrew the initial form factor when we looked at source localization with spherical microphone arrays. Spherical signals appear well beyond spherical microphone arrays; for example, any signal defined on Earth's surface lives on a sphere. This resulted in the first slight departure from the main theme: We develop the theory and algorithms for sampling sparse signals on the sphere using finite rate-of-innovation principles and apply it to various signal processing problems on the sphere

Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods

The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS). Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements

The Doubly Adaptive LASSO Methods for Time Series Analysis

In this thesis, we propose a systematic approach called the doubly adaptive LASSO tailored to time series analysis, which includes four specific methods for four time series models, respectively: The PAC-weighted adaptive LASSO for univariate autoregressive (AR) models. Although the LASSO methodology has been applied to AR models, the existing methods in the literature ignore the temporal dependence information embedded in AR time series data. Consequently, the methods may not reflect the characteristics of underlying AR processes, especially, the lag order of AR models. The PAC-weighted adaptive LASSO incorporates the partial autocorrelation (PAC) into the adaptive LASSO weights. The PAC-weighted adaptive LASSO estimator has asymptotic oracle properties and a Monte Carlo study shows promising results. The PAC-weighted adaptive positive LASSO for autoregressive conditional heteroscedastic (ARCH) models. We have not found any results in the literature that apply the LASSO methodology to ARCH models. The PAC-weighted adaptive positive LASSO incorporates the PAC information embedded in squared ARCH process into adaptive LASSO weights. The word positive reflects the fact that the parameters in ARCH models are non-negative. We introduce a new concept named the surrogate of the second-order approximate likelihood, and propose a modified shooting algorithm to implement the PAC-weighted adaptive positive LASSO computationally. The PAC-weighted adaptive positive LASSO estimator has asymptotic oracle properties and a Monte Carlo study shows promising results. The PLAC-weighted adaptive LASSO for vector autoregressive (VAR) models. Although the LASSO methodology has been applied to building VAR time series models, the existing methods in the literature ignore the temporal dependence information embedded in VAR time series data. Consequently, the methods may not reflect the characteristics of VAR time series data, especially, the lag order of VAR models. The PLAC-weighted adaptive LASSO incorporates the partial lag autocorrelation (PLAC) into the adaptive LASSO weights. The PLAC-weighted adaptive LASSO estimator has oracle properties and Monte Carlo studies show promising results. The PLAC-weighted adaptive LASSO for BEKK vector ARCH (VARCH) models. We have not found any results in the literature that apply the LASSO methodology to VARCH processes. We focus on the BEKK VARCH models. The PLAC-weighted adaptive LASSO incorporates the PLAC information embedded in the squared BEKK VARCH process into the adaptive LASSO weights. We extend the concept of the surrogate of the second-order approximate likelihood, and propose a modified shooting algorithm to implement the PLAC-weighted adaptive LASSO computationally. We conduct a Monte Carlo study and have preliminary results from the study. Keywords: Time series, financial time series, data mining, oracle property, LASSO, adaptive LASSO, doubly adaptive LASSO, positive LASSO, PAC-weighted adaptive LASSO, PAC-weighted adaptive positive LASSO, PLAC-weighted adaptive LASSO, autoregressive, AR(P), autoregressive conditional heteroscedastic, ARCH(q), vector autoregressive, multivariate autoregressive, VAR(p), vector ARCH, multivariate ARCH, VARCH(q), analytical score, analytical Hessian, quadratic approximation, surrogate to approximate likelihood, S\&P 500, Nikkei

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Sequential nonlinear learning

Cataloged from PDF version of article.We study sequential nonlinear learning in an individual sequence manner, where we provide results that are guaranteed to hold without any statistical assumptions. We address the convergence and undertraining issues of conventional nonlinear regression methods and introduce algorithms that elegantly mitigate these issues using nested tree structures. To this end, in the second chapter, we introduce algorithms that adapt not only their regression functions but also the complete tree structure while achieving the performance of the best linear mixture of a doubly exponential number of partitions, with a computational complexity only polynomial in the number of nodes of the tree. In the third chapter, we propose an incremental decision tree structure and using this model, we introduce an online regression algorithm that partitions the regressor space in a data driven manner. We prove that the proposed algorithm sequentially and asymptotically achieves the performance of the optimal twice differentiable regression function for any data sequence with an unknown and arbitrary length. The computational complexity of the introduced algorithm is only logarithmic in the data length under certain regularity conditions. In the fourth chapter, we construct an online finite state (FS) predictor over hierarchical structures, whose computational complexity is only linear in the hierarchy level. We prove that the introduced algorithm asymptotically achieves the performance of the best linear combination of all FS predictors defined over the hierarchical model in a deterministic manner and and in a mean square error sense in the steady-state for certain nonstationary models. In the fifth chapter, we introduce a distributed subgradient based extreme learning machine algorithm to train single hidden layer feedforward neural networks (SLFNs). We show that using the proposed algorithm, each of the individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN in a strong deterministic sense.Vanlı, Nuri DenizcanM.S

Principal component based system identification and its application to the study of cardiovascular regulation

Includes bibliographical references (p. 197-212).Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2004.(cont.) Our methods analyze the coupling between instantaneous lung volume and heart rate and, subsequently, derive representative indices of parasympathetic and sympathetic control based on physiological and experimental findings. The validity of each method is evaluated via experimental data collected following interventions with known effect on the parasympathetic or sympathetic control. With the above techniques, this thesis explores an important topic in the field of space medicine: effects of simulated microgravity on cardiac autonomic control and orthostatic intolerance (OI). Experimental data from a prolonged bed rest study (simulation of microgravity condition) are analyzed and the conclusions are: 1) prolonged bed rest may impair autonomic control of heart rate; 2) orthostatic intolerance after bed rest is associated with impaired sympathetic responsiveness; 3) there may be a pre-bed rest predisposition to the development of OI after bed rest. These findings may have significance for studying Earth-bound orthostatic hypotension as well as for designing effective countermeasures to post-flight OI. In addition, they also indicate the efficacy of our proposed methods for autonomic function quantification.System identification is an effective approach for the quantitative study of physiologic systems. It deals with the problem of building mathematical models based on observed data and enables a dynamical characterization of the underlying physiologic mechanisms specific to the individual being studied. In this thesis, we develop and validate a new linear time-invariant system identification approach which is based on a weighted-principal component regression (WPCR) method. An important feature of this approach is its asymptotic frequency-selective property in solving time-domain parametric system identification problems. Owing to this property, data-specific candidate models can be built by considering the dominant frequency components inherent in the input (and output) signals, which is advantageous when the signals are colored, as are most physiologic signals. The efficacy of this method in modeling open-loop and closed-loop systems is demonstrated with respect to simulated and experimental data. In conjunction with the WPCR-based system identification approach, we propose new methods to noninvasively quantify cardiac autonomic control. Such quantification is important in understanding basic pathophysiological mechanisms or in patient monitoring, treatment design and follow-up.by Xinshu Xiao.Ph.D

A principal components analysis of the UK term structure and the influence of fiscal policy

This study examines the use of principal component techniques in analysing term structures of interest rates. It employs original methods of estimating B-Spline models with endogenous knot positions and applies the method of Dierckx (1981) to generate new data sets for the study. The variability of the knots suggests that natural market boundaries do not exist in the UK gilts market. Few, if any, of the previous studies of term structures using principal components have subjected the components to statistical testing. This is remedied in this thesis. The results suggest that only two components, a level and a slope component, are required to describe most of the variability in the term structures irrespective of the data used, but these components are not stable over time. The thesis extends the method to include partial common principal components, and using this method demonstrated the difference in the major components of selected data sets. The thesis found that changes in the principal component scores could not be accounted for by regularly published economic news, including news about the PSBR. A macromodel was estimated. This showed that the term structures in the sample were altered by changes in government spending but the movement in interest rates would depend upon how this was funded and what maturity of interest rates was studied. The model also showed that significant changes would take a long time to manifest themselves and that there was evidence that some forms of funding had unstable effects. These results provide an explanation of why news effects are difficult to discern and why there is no consensus on whether or not fiscal variables affect the term structure

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically