Visual recognition systems in a car passenger compartment with the focus on facial driver identification

Magdeburg, Univ., Fak. für Informatik, Diss., 2014

Statistical model identification : dynamical processes and large-scale networks in systems biology

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Statistical model identification : dynamical processes and large-scale networks in systems biology

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Comparative study and identification of potent eukaryotic transcriptional repressors in gene switch systems.

In mammalian cells, proper gene regulation is achieved by the complex interplay of transcription factors that activate or repress gene expression by binding to the regulatory regions of target promoters. While transcriptional activators have been extensively characterised and classified into functional groups, relatively little is known about the comparative strength and cell type-specificity of transcriptional repressors. Here, we have compared the ability of a series of eukaryotic repression domains to silence basal and activated transcription. A series of the most potent repression domains was further tested in the context of a gene therapy gene-switch system in various cell types. The results indicate that the analysed repression domains exert varying silencing activities in different promoter contexts. Furthermore, their potential for gene silencing varies also depending on the cellular context. When multimerised within one chimeric repressor protein, particular combinations of repressor domains were found to display synergistic repressing effects and efficient repression in a panel of cell lines. This approach thus allowed the identification of transcriptional repressors that are both potent and versatile in terms of cellular specificity as a basis for gene switch systems.

Automated systems in the identification and determination of methicillin resistance among coagulase negative staphylococci

Coagulase-negative staphylococci (CoNS) are an important cause of nosocomial bacteremia, specially in patients with indwelling devices or those submitted to invasive medical procedures. The identification of species and the accurate and rapid detection of methicillin resistance are directly dependent on the quality of the identification and susceptibility tests used, either manual or automated. The objective of this study was to evaluate the accuracy of two automated systems MicroScan and Vitek - in the identification of CoNS species and determination of susceptibility to methicillin, considering as gold standard the biochemical tests and the characterization of the mecA gene by polymerase chain reaction, respectively. MicroScan presented better results in the identification of CoNS species (accuracy of 96.8 vs 78.8%, respectively); isolates from the following species had no precise identification: Staphylococcus haemolyticus, S. simulans, and S. capitis. Both systems were similar in the characterization of methicillin resistance. The higher discrepancies for gene mec detection were observed among species other than S. epidermidis (S. hominis, S. saprophyticus, S. sciuri, S. haemolyticus, S. warneri, S. cohnii), and those with borderline MICs.

Evidence evaluation in fingerprint comparison and automated fingerprint identification systems: modeling between finger variability

In the context of the investigation of the use of automated fingerprint identification systems (AFIS) for the evaluation of fingerprint evidence, the current study presents investigations into the variability of scores from an AFIS system when fingermarks from a known donor are compared to fingerprints that are not from the same source. The ultimate goal is to propose a model, based on likelihood ratios, which allows the evaluation of mark-to-print comparisons. In particular, this model, through its use of AFIS technology, benefits from the possibility of using a large amount of data, as well as from an already built-in proximity measure, the AFIS score. More precisely, the numerator of the LR is obtained from scores issued from comparisons between impressions from the same source and showing the same minutia configuration. The denominator of the LR is obtained by extracting scores from comparisons of the questioned mark with a database of non-matching sources. This paper focuses solely on the assignment of the denominator of the LR. We refer to it by the generic term of between-finger variability. The issues addressed in this paper in relation to between-finger variability are the required sample size, the influence of the finger number and general pattern, as well as that of the number of minutiae included and their configuration on a given finger. Results show that reliable estimation of between-finger variability is feasible with 10,000 scores. These scores should come from the appropriate finger number/general pattern combination as defined by the mark. Furthermore, strategies of obtaining between-finger variability when these elements cannot be conclusively seen on the mark (and its position with respect to other marks for finger number) have been presented. These results immediately allow case-by-case estimation of the between-finger variability in an operational setting.

Identification of C(4)-dicarboxylate transport systems in Pseudomonas aeruginosa PAO1.

Pseudomonas aeruginosa utilizes preferentially C(4)-dicarboxylates such as malate, fumarate, and succinate as carbon and energy sources. We have identified and characterized two C(4)-dicarboxylate transport (Dct) systems in P. aeruginosa PAO1. Inactivation of the dctA(PA1183) gene caused a growth defect of the strain in minimal media supplemented with succinate, fumarate or malate, indicating that DctA has a major role in Dct. However, residual growth of the dctA mutant in these media suggested the presence of additional C(4)-dicarboxylate transporter(s). Tn5 insertion mutagenesis of the ΔdctA mutant led to the identification of a second Dct system, i.e., the DctPQM transporter belonging to the tripartite ATP-independent periplasmic (TRAP) family of carriers. The ΔdctA ΔdctPQM double mutant showed no growth on malate and fumarate and residual growth on succinate, suggesting that DctA and DctPQM are the only malate and fumarate transporters, whereas additional transporters for succinate are present. Using lacZ reporter fusions, we showed that the expression of the dctA gene and the dctPQM operon was enhanced in early exponential growth phase and induced by C(4)-dicarboxylates. Competition experiments demonstrated that the DctPQM carrier was more efficient than the DctA carrier for the utilization of succinate at micromolar concentrations, whereas DctA was the major transporter at millimolar concentrations. To conclude, this is the first time that the high- and low-affinity uptake systems for succinate DctA and DctPQM have been reported to function coordinately to transport C(4)-dicarboxylates and that the alternative sigma factor RpoN and a DctB/DctD two-component system regulates simultaneously the dctA gene and the dctPQM operon.

Patterns of calcium-binding proteins in human inferior colliculus: identification of subdivisions and evidence for putative parallel systems.

The subdivisions of human inferior colliculus are currently based on Golgi and Nissl-stained preparations. We have investigated the distribution of calcium-binding protein immunoreactivity in the human inferior colliculus and found complementary or mutually exclusive localisations of parvalbumin versus calbindin D-28k and calretinin staining. The central nucleus of the inferior colliculus but not the surrounding regions contained parvalbumin-positive neuronal somata and fibres. Calbindin-positive neurons and fibres were concentrated in the dorsal aspect of the central nucleus and in structures surrounding it: the dorsal cortex, the lateral lemniscus, the ventrolateral nucleus, and the intercollicular region. In the dorsal cortex, labelling of calbindin and calretinin revealed four distinct layers.Thus, calcium-binding protein reactivity reveals in the human inferior colliculus distinct neuronal populations that are anatomically segregated. The different calcium-binding protein-defined subdivisions may belong to parallel auditory pathways that were previously demonstrated in non-human primates, and they may constitute a first indication of parallel processing in human subcortical auditory structures.

On Modelling, System Identification and Control of Servo-Systems with a Flexible Load

The present study was done with two different servo-systems. In the first system, a servo-hydraulic system was identified and then controlled by a fuzzy gainscheduling controller. The second servo-system, an electro-magnetic linear motor in suppressing the mechanical vibration and position tracking of a reference model are studied by using a neural network and an adaptive backstepping controller respectively. Followings are some descriptions of research methods. Electro Hydraulic Servo Systems (EHSS) are commonly used in industry. These kinds of systems are nonlinearin nature and their dynamic equations have several unknown parameters.System identification is a prerequisite to analysis of a dynamic system. One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) for solving global optimization problems. In the study, the DE algorithm is proposed for handling nonlinear constraint functionswith boundary limits of variables to find the best parameters of a servo-hydraulic system with flexible load. The DE guarantees fast speed convergence and accurate solutions regardless the initial conditions of parameters. The control of hydraulic servo-systems has been the focus ofintense research over the past decades. These kinds of systems are nonlinear in nature and generally difficult to control. Since changing system parameters using the same gains will cause overshoot or even loss of system stability. The highly non-linear behaviour of these devices makes them ideal subjects for applying different types of sophisticated controllers. The study is concerned with a second order model reference to positioning control of a flexible load servo-hydraulic system using fuzzy gainscheduling. In the present research, to compensate the lack of dampingin a hydraulic system, an acceleration feedback was used. To compare the results, a pcontroller with feed-forward acceleration and different gains in extension and retraction is used. The design procedure for the controller and experimental results are discussed. The results suggest that using the fuzzy gain-scheduling controller decrease the error of position reference tracking. The second part of research was done on a PermanentMagnet Linear Synchronous Motor (PMLSM). In this study, a recurrent neural network compensator for suppressing mechanical vibration in PMLSM with a flexible load is studied. The linear motor is controlled by a conventional PI velocity controller, and the vibration of the flexible mechanism is suppressed by using a hybrid recurrent neural network. The differential evolution strategy and Kalman filter method are used to avoid the local minimum problem, and estimate the states of system respectively. The proposed control method is firstly designed by using non-linear simulation model built in Matlab Simulink and then implemented in practical test rig. The proposed method works satisfactorily and suppresses the vibration successfully. In the last part of research, a nonlinear load control method is developed and implemented for a PMLSM with a flexible load. The purpose of the controller is to track a flexible load to the desired position reference as fast as possible and without awkward oscillation. The control method is based on an adaptive backstepping algorithm whose stability is ensured by the Lyapunov stability theorem. The states of the system needed in the controller are estimated by using the Kalman filter. The proposed controller is implemented and tested in a linear motor test drive and responses are presented.

Broadband excitation in the system identification of active magnetic bearing rotor systems

One of the targets of the climate and energy package of the European Union is to increase the energy efficiency in order to achieve a 20 percent reduction in primary energy use compared with the projected level by 2020. The energy efficiency can be improved for example by increasing the rotational speed of large electrical drives, because this enables the elimination of gearboxes leading to a compact design with lower losses. The rotational speeds of traditional bearings, such as roller bearings, are limited by mechanical friction. Active magnetic bearings (AMBs), on the other hand, allow very high rotational speeds. Consequently, their use in large medium- and high-speed machines has rapidly increased. An active magnetic bearing rotor system is an inherently unstable, nonlinear multiple-input, multiple-output system. Model-based controller design of AMBs requires an accurate system model. Finite element modeling (FEM) together with the experimental modal analysis provides a very accurate model for the rotor, and a linearized model of the magneticactuators has proven to work well in normal conditions. However, the overall system may suffer from unmodeled dynamics, such as dynamics of foundation or shrink fits. This dynamics can be modeled by system identification. System identification can also be used for on-line diagnostics. In this study, broadband excitation signals are adopted to the identification of an active magnetic bearing rotor system. The broadband excitation enables faster frequency response function measurements when compared with the widely used stepped sine and swept sine excitations. Different broadband excitations are reviewed, and the random phase multisine excitation is chosen for further study. The measurement times using the multisine excitation and the stepped sine excitation are compared. An excitation signal design with an analysis of the harmonics produced by the nonlinear system is presented. The suitability of different frequency response function estimators for an AMB rotor system are also compared. Additionally, analytical modeling of an AMB rotor system, obtaining a parametric model from the nonparametric frequency response functions, and model updating are discussed in brief, as they are key elements in the modeling for a control design. Theoretical methods are tested with a laboratory test rig. The results conclude that an appropriately designed random phase multisine excitation is suitable for the identification of AMB rotor systems.

Accuracy of modelling and identification of malfunctions in rotor systems: experimental results

The accuracy of modelling of rotor systems composed of rotors, oil film bearings and a flexible foundation, is evaluated and discussed in this paper. The model validation of different models has been done by comparing experimental results with numerical results by means. The experimental data have been obtained with a fully instrumented four oil film bearing, two shafts test rig. The fault models are then used in the frame of a model based malfunction identification procedure, based on a least square fitting approach applied in the frequency domain. The capability of distinguishing different malfunctions has been investigated, even if they can create similar effects (such as unbalance, rotor bow, coupling misalignment and others) from shaft vibrations measured in correspondence of the bearings.

Identification of novel retinoid receptors and their roles in vertebrate and invertebrate nervous systems

In vertebrates, signaling by retinoic acid (RA) is known to play an important role in embryonic development, as well as organ homeostasis in the adult. In organisms such as adult axolotls and newts, RA is also important for regeneration of the CNS, limb, tail, and many other organ systems. RA mediates many of its effects in development and regeneration through nuclear receptors, known as retinoic acid receptors (RARs) and retinoid X receptors (RXRs). This study provides evidence for an important role of the RA receptor, RAR~2, in ,( '. regeneration ofthe spinal cord and tail of the adult newt. It has previously been proposed that the ability of the nervous system to regenerate might depend on the presence or absence of this RAR~2 isoform. Here, I show for the very first time, that the regenerating spinal cord of the adult newt expresses this ~2 receptor isoform, and inhibition of retinoid signaling through this specific receptor with a selective antagonist inhibits tail and spinal cord regeneration. This provides the first evidence for a role of this receptor in this process. Another species capable of CNS ~~generation in the adult is the invertebrate, " Lymnaea stagnalis. Although RA has been detected in a small number of invertebrates (including Lymnaea), the existence and functional roles of the retinoid receptors in most invertebrate non-chordates, have not been previously studied. It has been widely believed, however, that invertebrate non-chordates only possess the RXR class of retinoid receptors, but not the RARs. In this study, a full-length RXR cDNA has been cloned, which was the first retinoid receptor to be discovered in Lymnaea. I then went on to clone the very first full-length RAR eDNA from any non-chordate, invertebrate species. The functional role of these receptors was examined, and it was shown that normal molluscan development was altered, to varying degrees, by the presence of various RXR and RAR agonists or antagonists. The resulting disruptions in embryogenesis ranged from eye and shell defects, to complete lysis of the early embryo. These studies strongly suggest an important role for both the RXR and RAR in non-chordate development. The molluscan RXR and RAR were also shown to be expressed in the adult, nonregenerating eNS, as well as in individual motor neurons regenerating in culture. More specifically, their expression displayed a non-nuclear distfibution, suggesting a possible non-genomic role for these 'nuclear' receptors. It was shown that immunoreactivity for the RXR was present in almost all regenerating growth cones, and (together with N. Farrar) it was shown that this RXR played a novel, non-genomic role in mediating growth cone turning toward retinoic acid. Immunoreactivity for the novel invertebrate RAR was also found in the regenerating growth cones, but future work will be required to determine its functional role in nerve cell regeneration. Taken together, these data provide evidence for the importance of these novel '. retinoid receptors in development and regeneration, particularly in the adult nervous system, and the conservation of their effects in mediating RA signaling from invertebrates to vertebrates.

Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.

Identification of nonlinear systems using generalized kernel models

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.

Neural networks for identification of nonlinear systems under random piecewise polynomial disturbances

The problem of identification of a nonlinear dynamic system is considered. A two-layer neural network is used for the solution of the problem. Systems disturbed with unmeasurable noise are considered, although it is known that the disturbance is a random piecewise polynomial process. Absorption polynomials and nonquadratic loss functions are used to reduce the effect of this disturbance on the estimates of the optimal memory of the neural-network model.