Stretching single DNA-molecules with temperature-stabilized optical tweezers

This thesis consists of two parts; in the first part we performed a single-molecule force extension measurement with 10kb long DNA-molecules from phage-λ to validate the calibration and single-molecule capability of our optical tweezers instrument. Fitting the worm-like chain interpolation formula to the data revealed that ca. 71% of the DNA tethers featured a contour length within ±15% of the expected value (3.38 µm). Only 25% of the found DNA had a persistence length between 30 and 60 nm. The correct value should be within 40 to 60 nm. In the second part we designed and built a precise temperature controller to remove thermal fluctuations that cause drifting of the optical trap. The controller uses feed-forward and PID (proportional-integral-derivative) feedback to achieve 1.58 mK precision and 0.3 K absolute accuracy. During a 5 min test run it reduced drifting of the trap from 1.4 nm/min in open-loop to 0.6 nm/min in closed-loop.

Functional imaging of peripheral vision and dorsal stream function in the human cerebral cortex

Visual information processing in brain proceeds in both serial and parallel fashion throughout various functionally distinct hierarchically organised cortical areas. Feedforward signals from retina and hierarchically lower cortical levels are the major activators of visual neurons, but top-down and feedback signals from higher level cortical areas have a modulating effect on neural processing. My work concentrates on visual encoding in hierarchically low level cortical visual areas in human brain and examines neural processing especially in cortical representation of visual field periphery. I use magnetoencephalography and functional magnetic resonance imaging to measure neuromagnetic and hemodynamic responses during visual stimulation and oculomotor and cognitive tasks from healthy volunteers. My thesis comprises six publications. Visual cortex forms a great challenge for modeling of neuromagnetic sources. My work shows that a priori information of source locations are needed for modeling of neuromagnetic sources in visual cortex. In addition, my work examines other potential confounding factors in vision studies such as light scatter inside the eye which may result in erroneous responses in cortex outside the representation of stimulated region, and eye movements and attention. I mapped cortical representations of peripheral visual field and identified a putative human homologue of functional area V6 of the macaque in the posterior bank of parieto-occipital sulcus. My work shows that human V6 activates during eye-movements and that it responds to visual motion at short latencies. These findings suggest that human V6, like its monkey homologue, is related to fast processing of visual stimuli and visually guided movements. I demonstrate that peripheral vision is functionally related to eye-movements and connected to rapid stream of functional areas that process visual motion. In addition, my work shows two different forms of top-down modulation of neural processing in the hierachically lowest cortical levels; one that is related to dorsal stream activation and may reflect motor processing or resetting signals that prepare visual cortex for change in the environment and another local signal enhancement at the attended region that reflects local feed-back signal and may perceptionally increase the stimulus saliency.

Robust multivariable controllers for a tublar ammonia reactor

The specific objective of this paper is to develop multivariable controllers that would achieve asymptotic regulation in the presence of parameter variations and disturbance inputs for a tubular reactor used in ammonia synthesis. A ninth order state space model with three control inputs and two disturbance inputs is generated from the nonlinear distributed model using linearization and lumping approximations. Using this model, an approach for control system design is developed keeping in view the imperfections of the model and the measurability of the state variables. Specifically, the design of feedforward and robust integral controllers using state and output feedback is considered. Also, the design of robust multiloop proportional integral controllers is presented. Finally the performance of these controllers is evaluated through simulation.

Tonically Active Kainate Receptors (tKARs) : A Novel Mechanism Regulating Neuronal Function in the Brain

Fast excitatory transmission between neurons in the central nervous system is mainly mediated by L-glutamate acting on ligand gated (ionotropic) receptors. These are further categorized according to their pharmacological properties to AMPA (2-amino-3-(5-methyl-3-oxo-1,2- oxazol-4-yl)propanoic acid), NMDA (N-Methyl-D-aspartic acid) and kainate (KAR) subclasses. In the rat and the mouse hippocampus, development of glutamatergic transmission is most dynamic during the first postnatal weeks. This coincides with the declining developmental expression of the GluK1 subunit-containing KARs. However, the function of KARs during early development of the brain is poorly understood. The present study reveals novel types of tonically active KARs (hereafter referred to as tKARs) which play a central role in functional development of the hippocampal CA3-CA1 network. The study shows for the first time how concomitant pre- and postsynaptic KAR function contributes to development of CA3-CA1 circuitry by regulating transmitter release and interneuron excitability. Moreover, the tKAR-dependent regulation of transmitter release provides a novel mechanism for silencing and unsilencing early synapses and thus shaping the early synaptic connectivity. The role of GluK1-containing KARs was studied in area CA3 of the neonatal hippocampus. The data demonstrate that presynaptic KARs in excitatory synapses to both pyramidal cells and interneurons are tonically activated by ambient glutamate and that they regulate glutamate release differentially, depending on target cell type. At synapses to pyramidal cells these tKARs inhibit glutamate release in a G-protein dependent manner but in contrast, at synapses to interneurons, tKARs facilitate glutamate release. On the network level these mechanisms act together upregulating activity of GABAergic microcircuits and promoting endogenous hippocampal network oscillations. By virtue of this, tKARs are likely to have an instrumental role in the functional development of the hippocampal circuitry. The next step was to investigate the role of GluK1 -containing receptors in the regulation of interneuron excitability. The spontaneous firing of interneurons in the CA3 stratum lucidum is markedly decreased during development. The shift involves tKARs that inhibit medium-duration afterhyperpolarization (mAHP) in these neurons during the first postnatal week. This promotes burst spiking of interneurons and thereby increases GABAergic activity in the network synergistically with the tKAR-mediated facilitation of their excitatory drive. During development the amplitude of evoked medium afterhyperpolarizing current (ImAHP) is dramatically increased due to decoupling tKAR activation and ImAHP modulation. These changes take place at the same time when the endogeneous network oscillations disappear. These tKAR-driven mechanisms in the CA3 area regulate both GABAergic and glutamatergic transmission and thus gate the feedforward excitatory drive to the area CA1. Here presynaptic tKARs to CA1 pyramidal cells suppress glutamate release and enable strong facilitation in response to high-frequency input. Therefore, CA1 synapses are finely tuned to high-frequency transmission; an activity pattern that is common in neonatal CA3-CA1 circuitry both in vivo and in vitro. The tKAR-regulated release probability acts as a novel presynaptic silencing mechanism that can be unsilenced in response to Hebbian activity. The present results shed new light on the mechanisms modulating the early network activity that paves the way for oscillations lying behind cognitive tasks such as learning and memory. Kainate receptor antagonists are already being developed for therapeutic use for instance against pain and migraine. Because of these modulatory actions, tKARs also represent an attractive candidate for therapeutic treatment of developmentally related complications such as learning disabilities.

Modeling and Field Oriented Control of Salient Pole Wound Field Synchronous Machine in Stator Flux Coordinates

This paper describes the field oriented control of a salient pole wound field synchronous machine in stator flux coordinates. The procedure for derivation of flux linkage equations along any general rotating axes including stator flux axes is given. The stator flux equations are used to identify the cross-coupling occurring between the axes due to saliency in the machine. The coupling terms are canceled as feedforward terms in the generation of references for current controllers to achieve good decoupling during transients. The design of current controller for stator-flux-oriented control is presented. This paper proposes the method of extending rotor flux closed loop observer for sensorless control of wound field synchronous machine. This paper also proposes a new sensorless control by using stator flux closed loop observer and estimation of torque angle using stator current components in stator flux coordinates. Detailed experimental results from a sensorless 15.8 hp salient pole wound field synchronous machine drive are presented to demonstrate the performance of the proposed control strategy from a low speed of 0.8 Hz to 50 Hz.

Machine learning modelling for predicting soil liquefaction susceptibility

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Learning the global maximum with parameterized learning automata

A feedforward network composed of units of teams of parameterized learning automata is considered as a model of a reinforcement teaming system. The internal state vector of each learning automaton is updated using an algorithm consisting of a gradient following term and a random perturbation term. It is shown that the algorithm weakly converges to a solution of the Langevin equation implying that the algorithm globally maximizes an appropriate function. The algorithm is decentralized, and the units do not have any information exchange during updating. Simulation results on common payoff games and pattern recognition problems show that reasonable rates of convergence can be obtained.

A Unified Approach to Encoding and Classification Using Bimodal Projection-Based Features

In general the objective of accurately encoding the input data and the objective of extracting good features to facilitate classification are not consistent with each other. As a result, good encoding methods may not be effective mechanisms for classification. In this paper, an earlier proposed unsupervised feature extraction mechanism for pattern classification has been extended to obtain an invertible map. The method of bimodal projection-based features was inspired by the general class of methods called projection pursuit. The principle of projection pursuit concentrates on projections that discriminate between clusters and not faithful representations. The basic feature map obtained by the method of bimodal projections has been extended to overcome this. The extended feature map is an embedding of the input space in the feature space. As a result, the inverse map exists and hence the representation of the input space in the feature space is exact. This map can be naturally expressed as a feedforward neural network.

Performance evaluation of neural network algorithms for multisensor data fusion in an airborne track while scan radar

This paper deals with the solution to the problem of multisensor data fusion for a single target scenario as detected by an airborne track-while-scan radar. The details of a neural network implementation, various training algorithms based on standard backpropagation, and the results of training and testing the neural network are presented. The promising capabilities of RPROP algorithm for multisensor data fusion for various parameters are shown in comparison to other adaptive techniques

Control of a Duct Flow Network for Wind Display in a Virtual Environment

Control of flow in duct networks has a myriad of applications ranging from heating, ventilation, and air-conditioning to blood flow networks. The system considered here provides vent velocity inputs to a novel 3-D wind display device called the TreadPort Active Wind Tunnel. An error-based robust decentralized sliding-mode control method with nominal feedforward terms is developed for individual ducts while considering cross coupling between ducts and model uncertainty as external disturbances in the output. This approach is important due to limited measurements, geometric complexities, and turbulent flow conditions. Methods for resolving challenges such as turbulence, electrical noise, valve actuator design, and sensor placement are presented. The efficacy of the controller and the importance of feedforward terms are demonstrated with simulations based upon an experimentally validated lumped parameter model and experiments on the physical system. Results show significant improvement over traditional control methods and validate prior assertions regarding the importance of decentralized control in practice.

Synconset Waves and Chains: Spiking Onsets in Synchronous Populations Predict and Are Predicted by Network Structure

Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define `synconset wave' as a cascade of first spikes within a synchronisation event. Synconset waves would occur in `synconset chains', which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it's utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it's utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony.

Parameters Inversion of Fluid-Saturated Porous Media Based on Neural Networks

The multi-layers feedforward neural network is used for inversion of material constants of fluid-saturated porous media. The direct analysis of fluid-saturated porous media is carried out with the boundary element method. The dynamic displacement responses obtained from direct analysis for prescribed material parameters constitute the sample sets training neural network. By virtue of the effective L-M training algorithm and the Tikhonov regularization method as well as the GCV method for an appropriate selection of regularization parameter, the inverse mapping from dynamic displacement responses to material constants is performed. Numerical examples demonstrate the validity of the neural network method.

On Bounded Strictly Positive Operators of Closed Range and Some Applications to Asymptotic Hyperstability of Dynamic Systems

The problem discussed is the stability of two input-output feedforward and feedback relations, under an integral-type constraint defining an admissible class of feedback controllers. Sufficiency-type conditions are given for the positive, bounded and of closed range feed-forward operator to be strictly positive and then boundedly invertible, with its existing inverse being also a strictly positive operator. The general formalism is first established and the linked to properties of some typical contractive and pseudocontractive mappings while some real-world applications and links of the above formalism to asymptotic hyperstability of dynamic systems are discussed later on.

Contribution of the temporoammonic pathway to hippocampal processing

The temporoammonic (TA) pathway is the direct, monosynaptic projection from layer III of entorhinal cortex to the distal dendritic region of area CA1 of the hippo­ campus. Although this pathway has been implicated in various functions, such as memory encoding and retrieval, spatial navigation, generation of oscillatory activity, and control of hippocampal excitability, the details of its physiology are not well understood. In this thesis, I examine the contribution of the TA pathway to hippocampal processing. I find that, as has been previously reported, the TA pathway includes both excitatory, glutamatergic components and inhibitory, GABAergic components. Several new discoveries are reported in this thesis. I show that the TA pathway is subject to forms of short-term activity-dependent regulation, including paired-pulse and frequency­ dependent plasticity, similar to other hippocampal pathways such as the Schaffer collateral (SC) input from CA3 to CA1. The TA pathway provides a strongly excitatory input to stratum radiatum giant cells of CA1. The excitatory component of the TA pathway undergoes a long-lasting decrease in synaptic strength following low-frequency stimulation in a manner partially dependent on the activation of NMDA receptors. High­ frequency activation of the TA pathway recruits a feedforward inhibition that can prevent CA1 pyramidal cells from spiking in response to SC input; this spike-blocking effect shows that the TA pathway can act to regulate information flow through the hippocampal trisynaptic pathway.

Lock Acquisition and Sensitivity Analysis of Advanced LIGO Interferometers

Laser interferometer gravitational wave observatory (LIGO) consists of two complex large-scale laser interferometers designed for direct detection of gravitational waves from distant astrophysical sources in the frequency range 10Hz - 5kHz. Direct detection of space-time ripples will support Einstein's general theory of relativity and provide invaluable information and new insight into physics of the Universe.

Initial phase of LIGO started in 2002, and since then data was collected during six science runs. Instrument sensitivity was improving from run to run due to the effort of commissioning team. Initial LIGO has reached designed sensitivity during the last science run, which ended in October 2010.

In parallel with commissioning and data analysis with the initial detector, LIGO group worked on research and development of the next generation detectors. Major instrument upgrade from initial to advanced LIGO started in 2010 and lasted till 2014.

This thesis describes results of commissioning work done at LIGO Livingston site from 2013 until 2015 in parallel with and after the installation of the instrument. This thesis also discusses new techniques and tools developed at the 40m prototype including adaptive filtering, estimation of quantization noise in digital filters and design of isolation kits for ground seismometers.

The first part of this thesis is devoted to the description of methods for bringing interferometer to the linear regime when collection of data becomes possible. States of longitudinal and angular controls of interferometer degrees of freedom during lock acquisition process and in low noise configuration are discussed in details.

Once interferometer is locked and transitioned to low noise regime, instrument produces astrophysics data that should be calibrated to units of meters or strain. The second part of this thesis describes online calibration technique set up in both observatories to monitor the quality of the collected data in real time. Sensitivity analysis was done to understand and eliminate noise sources of the instrument.

Coupling of noise sources to gravitational wave channel can be reduced if robust feedforward and optimal feedback control loops are implemented. The last part of this thesis describes static and adaptive feedforward noise cancellation techniques applied to Advanced LIGO interferometers and tested at the 40m prototype. Applications of optimal time domain feedback control techniques and estimators to aLIGO control loops are also discussed.

Commissioning work is still ongoing at the sites. First science run of advanced LIGO is planned for September 2015 and will last for 3-4 months. This run will be followed by a set of small instrument upgrades that will be installed on a time scale of few months. Second science run will start in spring 2016 and last for about 6 months. Since current sensitivity of advanced LIGO is already more than factor of 3 higher compared to initial detectors and keeps improving on a monthly basis, upcoming science runs have a good chance for the first direct detection of gravitational waves.