On Whitespace Identification Using Randomly Deployed Sensors

This work considers the identification of the available whitespace, i.e., the regions that do not contain any existing transmitter within a given geographical area. To this end, n sensors are deployed at random locations within the area. These sensors detect for the presence of a transmitter within their radio range r(s) using a binary sensing model, and their individual decisions are combined to estimate the available whitespace. The limiting behavior of the recovered whitespace as a function of n and r(s) is analyzed. It is shown that both the fraction of the available whitespace that the nodes fail to recover as well as their radio range optimally scale as log(n)/n as n gets large. The problem of minimizing the sum absolute error in transmitter localization is also analyzed, and the corresponding optimal scaling of the radio range and the necessary minimum transmitter separation is determined.

Bayesian parameter identification in dynamic state space models using modified measurement equations

When Markov chain Monte Carlo (MCMC) samplers are used in problems of system parameter identification, one would face computational difficulties in dealing with large amount of measurement data and (or) low levels of measurement noise. Such exigencies are likely to occur in problems of parameter identification in dynamical systems when amount of vibratory measurement data and number of parameters to be identified could be large. In such cases, the posterior probability density function of the system parameters tends to have regions of narrow supports and a finite length MCMC chain is unlikely to cover pertinent regions. The present study proposes strategies based on modification of measurement equations and subsequent corrections, to alleviate this difficulty. This involves artificial enhancement of measurement noise, assimilation of transformed packets of measurements, and a global iteration strategy to improve the choice of prior models. Illustrative examples cover laboratory studies on a time variant dynamical system and a bending-torsion coupled, geometrically non-linear building frame under earthquake support motions. (C) 2015 Elsevier Ltd. All rights reserved.

Potential source identification for aerosol concentrations over a site in Northwestern India

The collocated measurements of aerosols size distribution (ASD) and aerosol optical thickness (AOT) are analyzed simultaneously using Grimm aerosol spectrometer and MICROTOP II Sunphotometer over Jaipur, capital of Rajasthan in India. The contrast temperature characteristics during winter and summer seasons of year 2011 are investigated in the present study. The total aerosol number concentration (TANC, 0.3-20 mu m) during winter season was observed higher than in summer time and it was dominated by fine aerosol number concentration (FANC < 2 mu m). Particles smaller than 0.8 mu m (at aerodynamic size) constitute similar to 99% of all particles in winter and similar to 90% of particles in summer season. However, particles greater than 2 mu m contribute similar to 3% and similar to 0.2% in summer and winter seasons respectively. The aerosols optical thickness shows nearly similar AOT values during summer and winter but corresponding low Angstrom Exponent (AE) values during summer than winter, respectively. In this work, Potential Source Contribution Function (PSCF) analysis is applied to identify locations of sources that influenced concentrations of aerosols over study area in two different seasons. PSCF analysis shows that the dust particles from That Desert contribute significantly to the coarse aerosol number concentration (CANC). Higher values of the PSCF in north from Jaipur showed the industrial areas in northern India to be the likely sources of fine particles. The variation in size distribution of aerosols during two seasons is clearly reflected in the log normal size distribution curves. The log normal size distribution curves reveals that the particle size less than 0.8 pm is the key contributor in winter for higher ANC. (C) 2015 Elsevier B.V. All rights reserved.

Dissecting pi-helices: sequence, structure and function

A new procedure for the identification of regular secondary structures using a C-alpha trace has identified 659 pi-helices in 3582 protein chains, solved at high resolution. Taking advantage of this significantly expanded database of pi-helices, we have analysed the functional and structural roles of helices and determined the position-wise amino acid propensity within and around them. These helices range from 5 to 18 residues in length with the average twist and rise being 85.2 +/- 7.2 and 1.28 +/- 0.31 angstrom, respectively. A total of 546 (similar to 83%) out of 659 pi-helices occur in conjunction with alpha-helices, with 101 pi-helices being interspersed between two alpha-helices. The majority of interspersed pi-helices were found to be conserved across a large number of structures within a protein family and produce a significant bend in the overall helical segment as well as local distortions in the neighbouring a-helices. The presence of a pi-helical fragment leads to appropriate orientation of the constituent residues, so as to facilitate favourable interactions and also help in proper folding of the protein chain. In addition to intra helical 6 -> 1 N H center dot center dot center dot O hydrogen bonds, pi-helices are also stabilized by several other non-bonded interactions. pi-Helices show distinct positional residue preferences, which are different from those of a-helices.

Archeal lectins: An identification through a genomic search

Forty-six lectin domains which have homologues among well established eukaryotic and bacterial lectins of known three-dimensional structure, have been identified through a search of 165 archeal genomes using a multipronged approach involving domain recognition, sequence search and analysis of binding sites. Twenty-one of them have the 7-bladed -propeller lectin fold while 16 have the -trefoil fold and 7 the legume lectin fold. The remainder assumes the C-type lectin, the -prism I and the tachylectin folds. Acceptable models of almost all of them could be generated using the appropriate lectins of known three-dimensional structure as templates, with binding sites at one or more expected locations. The work represents the first comprehensive bioinformatic study of archeal lectins. The presence of lectins with the same fold in all domains of life indicates their ancient origin well before the divergence of the three branches. Further work is necessary to identify archeal lectins which have no homologues among eukaryotic and bacterial species. Proteins 2016; 84:21-30. (c) 2015 Wiley Periodicals, Inc.

Regulation of Energy Metabolism by the Extracytoplasmic Function (ECF) σ Factors of Arcobacter butzleri

11 p.

Identification and characterization of the egg-laying hormone from the neurosecretary bag cells of Aplysia

This investigation has resulted in the chemical identification and isolation of the egg-laying hormone from Aplysia californica, Aplysia vaccaria, and Aplysia dactylomela. The hormone, which was originally identified as the Bag Cell-Specific protein (BCS protein) on polyacrylamide gels, is a polypeptide of molecular weight ≈ 6000, which is localized in the neurosecretory bag cells of the parietovisceral ganglion and the surrounding connective tissue sheath which contains the bag cell axons. All three species produce a hormone of similar molecular weight, but varying electrophoretic mobility as determined on polyacrylamide gels. As tested, the hormone is completely cross-reactive among the three species.

Although the bag cells of sexually immature animals contain the active hormone, sexual maturation of the animal results in a 10-fold increase in the BCS protein content of these neurons.

A seasonal variation in the BCS protein content was also observed, with 150 times more hormone contained in the bag cells of Aplysia californica in August than in January. This correlates well with the variation in the animals' ability to lay eggs throughout the year (Strumwasser et al., 1969). There are some indications that the receptivity of the animal to the available hormone also fluctuates during the year, being lower in winter than in swmner. The seasonal rhythm of the other species, Aplysia vaccaria and Aplysia dactylomela, has not been investigated.

A polyacrylamide gel electrophoresis analysis of water-soluble proteins in Aplysia californica revealed several other nerve-specific proteins. One of these is also located in the bag cell somas and stains turquoise with Amido Schwarz. The function of this protein has not been investigated.

Distributed Optimal Control of Cyber-Physical Systems: Controller Synthesis, Architecture Design and System Identification

The centralized paradigm of a single controller and a single plant upon which modern control theory is built is no longer applicable to modern cyber-physical systems of interest, such as the power-grid, software defined networks or automated highways systems, as these are all large-scale and spatially distributed. Both the scale and the distributed nature of these systems has motivated the decentralization of control schemes into local sub-controllers that measure, exchange and act on locally available subsets of the globally available system information. This decentralization of control logic leads to different decision makers acting on asymmetric information sets, introduces the need for coordination between them, and perhaps not surprisingly makes the resulting optimal control problem much harder to solve. In fact, shortly after such questions were posed, it was realized that seemingly simple decentralized optimal control problems are computationally intractable to solve, with the Wistenhausen counterexample being a famous instance of this phenomenon. Spurred on by this perhaps discouraging result, a concerted 40 year effort to identify tractable classes of distributed optimal control problems culminated in the notion of quadratic invariance, which loosely states that if sub-controllers can exchange information with each other at least as quickly as the effect of their control actions propagates through the plant, then the resulting distributed optimal control problem admits a convex formulation.

The identification of quadratic invariance as an appropriate means of "convexifying" distributed optimal control problems led to a renewed enthusiasm in the controller synthesis community, resulting in a rich set of results over the past decade. The contributions of this thesis can be seen as being a part of this broader family of results, with a particular focus on closing the gap between theory and practice by relaxing or removing assumptions made in the traditional distributed optimal control framework. Our contributions are to the foundational theory of distributed optimal control, and fall under three broad categories, namely controller synthesis, architecture design and system identification.

We begin by providing two novel controller synthesis algorithms. The first is a solution to the distributed H-infinity optimal control problem subject to delay constraints, and provides the only known exact characterization of delay-constrained distributed controllers satisfying an H-infinity norm bound. The second is an explicit dynamic programming solution to a two player LQR state-feedback problem with varying delays. Accommodating varying delays represents an important first step in combining distributed optimal control theory with the area of Networked Control Systems that considers lossy channels in the feedback loop. Our next set of results are concerned with controller architecture design. When designing controllers for large-scale systems, the architectural aspects of the controller such as the placement of actuators, sensors, and the communication links between them can no longer be taken as given -- indeed the task of designing this architecture is now as important as the design of the control laws themselves. To address this task, we formulate the Regularization for Design (RFD) framework, which is a unifying computationally tractable approach, based on the model matching framework and atomic norm regularization, for the simultaneous co-design of a structured optimal controller and the architecture needed to implement it. Our final result is a contribution to distributed system identification. Traditional system identification techniques such as subspace identification are not computationally scalable, and destroy rather than leverage any a priori information about the system's interconnection structure. We argue that in the context of system identification, an essential building block of any scalable algorithm is the ability to estimate local dynamics within a large interconnected system. To that end we propose a promising heuristic for identifying the dynamics of a subsystem that is still connected to a large system. We exploit the fact that the transfer function of the local dynamics is low-order, but full-rank, while the transfer function of the global dynamics is high-order, but low-rank, to formulate this separation task as a nuclear norm minimization problem. Finally, we conclude with a brief discussion of future research directions, with a particular emphasis on how to incorporate the results of this thesis, and those of optimal control theory in general, into a broader theory of dynamics, control and optimization in layered architectures.

Stability analysis of RF amplifiers based on MIMO pole-zero identification

Spurious oscillations are one of the principal issues faced by microwave and RF circuit designers. The rigorous detection of instabilities or the characterization of measured spurious oscillations is still an ongoing challenge. This project aims to create a new stability analysis CAD program that tackles this chal- lenge. Multiple Input Multiple Output (MIMO) pole-zero identification analysis is introduced on the program as a way to create new methods to automate the stability analysis process and to help designers comprehend the obtained results and prevent incorrect interpretations. The MIMO nature of the analysis contributes to eliminate possible controllability and observability losses and helps differentiate mathematical and physical quasi-cancellations, products of overmodeling. The created program reads Single Input Single Output (SISO) or MIMO frequency response data, and determines the corresponding continuous transfer functions with Vector Fitting. Once the transfer function is calculated, the corresponding pole/zero diagram is mapped enabling the designers to analyze the stability of an amplifier. Three data processing methods are introduced, two of which consist of pole/zero elimina- tions and the latter one on determining the critical nodes of an amplifier. The first pole/zero elimination method is based on eliminating non resonant poles, whilst the second method eliminates the poles with small residue by assuming that their effect on the dynamics of a system is small or non-existent. The critical node detection is also based on the residues; the node at which the effect of a pole on the dynamics is highest is defined as the critical node. In order to evaluate and check the efficiency of the created program, it is compared via examples with another existing commercial stability analysis tool (STAN tool). In this report, the newly created tool is proved to be as rigorous as STAN for detecting instabilities. Additionally, it is determined that the MIMO analysis is a very profitable addition to stability analysis, since it helps to eliminate possible problems of loss of controllability, observability and overmodeling.

Identification of damping: Part 4, error analysis

In previous papers (S. Adhikari and J. Woodhouse 2001 Journal of Sound and Vibration 243, 43-61; 63-88; S. Adhikari and J. Woodhouse 2002 Journal of Sound and Vibration 251, 477-490) methods were proposed to obtain the coefficient matrix for a viscous damping model or a non-viscous damping model with an exponential relaxation function, from measured complex natural frequencies and modes. In all these works, it has been assumed that exact complex natural frequencies and complex modes are known. In reality, this will not be the case. The purpose of this paper is to analyze the sensitivity of the identified damping matrices to measurement errors. By using numerical and analytical studies it is shown that the proposed methods can indeed be expected to give useful results from moderately noisy data provided a correct damping model is selected for fitting. Indications are also given of what level of noise in the measured modal properties is needed to mask the true physical behaviour.

Probabilistic fault identification using vibration data and neural networks

Bayesian formulated neural networks are implemented using hybrid Monte Carlo method for probabilistic fault identification in cylindrical shells. Each of the 20 nominally identical cylindrical shells is divided into three substructures. Holes of (12±2) mm in diameter are introduced in each of the substructures and vibration data are measured. Modal properties and the Coordinate Modal Assurance Criterion (COMAC) are utilized to train the two modal-property-neural-networks. These COMAC are calculated by taking the natural-frequency-vector to be an additional mode. Modal energies are calculated by determining the integrals of the real and imaginary components of the frequency response functions over bandwidths of 12% of the natural frequencies. The modal energies and the Coordinate Modal Energy Assurance Criterion (COMEAC) are used to train the two frequency-response-function-neural-networks. The averages of the two sets of trained-networks (COMAC and COMEAC as well as modal properties and modal energies) form two committees of networks. The COMEAC and the COMAC are found to be better identification data than using modal properties and modal energies directly. The committee approach is observed to give lower standard deviations than the individual methods. The main advantage of the Bayesian formulation is that it gives identities of damage and their respective confidence intervals.

Towards identification of a general model of damping

Characterization of damping forces in a vibrating structure has long been an active area of research in structural dynamics. In spite of a large amount of research, understanding of damping mechanisms is not well developed. A major reason for this is that unlike inertia and stiffness forces it is not in general clear what are the state variables that govern the damping forces. The most common approach is to use `viscous damping' where the instantaneous generalized velocities are the only relevant state variables. However, viscous damping by no means the only damping model within the scope of linear analysis. Any model which makes the energy dissipation functional non-negative is a possible candidate for a valid damping model. This paper is devoted to develop methodologies for identification of such general damping models responsible for energy dissipation in a vibrating structure. The method uses experimentally identified complex modes and complex natural frequencies and does not a-priori assume any fixed damping model (eg., viscous damping) but seeks to determine parameters of a general damping model described by the so called `relaxation function'. The proposed method and several related issues are discussed by considering a numerical example of a linear array of damped spring-mass oscillators.

Identification of triosephosphate isomerase (TIM) genes from Microcystis (Cyanobacteria : Chroococcales) and theoretical analyses of the potential of cyanobacterial TIM as a target for designing specific inhibitors

The genes encoding triosephosphate isomerase (TIM) in three species of Microcystis (M. aeruginosa, M. viridis and M. wesenbergii) were investigated. Reverse transcriptase-polymerase chain reaction indicated that they were transcribed in the cells. Analyses showed that their DNA and deduced amino acid sequences were highly conserved between all the three species, only a single nonsynonymous substitution was seen at position 31, from an Asp in M. aeruginosa and M. viridis to Glu in M. wesenbergii. Sequence alignment of these with 12 other known cyanobacterial TIM sequences showed that all the cyanobacterial TIMs had a very high level of amino acid identity (over 50% between each two). Comparison of the cyanobacterial TIMs with other reported TIMs (from diverse lineages of the three Domains) showed that they possessed common active-site residues and sequence motifs. All cyanobacterial TIMs have two common cysteine residues (Cys127 and Cys176), and the Cys176 is almost cyanobacteria-specific with only one exception in Streptomyces coelicolor. Both secondary structure alignment and comparative modelling of Synechocystis sp. TIM showed that Cys176 was located at the hinge region of the flexible loop-6 and might therefore be critical to the movement of TIM's loop-6, which is important to the function of the enzyme. Thus, the cyanobacterial TIM-specific Cys176 may be a potential site for the discovery of suitable drugs against cyanobacteria, and such drugs may have utility in controlling water blooms due to cyanobacteria.

Bayesian parameter identification for vibro-acoustic models with nonparametric uncertainty

Vibration and acoustic analysis at higher frequencies faces two challenges: computing the response without using an excessive number of degrees of freedom, and quantifying its uncertainty due to small spatial variations in geometry, material properties and boundary conditions. Efficient models make use of the observation that when the response of a decoupled vibro-acoustic subsystem is sufficiently sensitive to uncertainty in such spatial variations, the local statistics of its natural frequencies and mode shapes saturate to universal probability distributions. This holds irrespective of the causes that underly these spatial variations and thus leads to a nonparametric description of uncertainty. This work deals with the identification of uncertain parameters in such models by using experimental data. One of the difficulties is that both experimental errors and modeling errors, due to the nonparametric uncertainty that is inherent to the model type, are present. This is tackled by employing a Bayesian inference strategy. The prior probability distribution of the uncertain parameters is constructed using the maximum entropy principle. The likelihood function that is subsequently computed takes the experimental information, the experimental errors and the modeling errors into account. The posterior probability distribution, which is computed with the Markov Chain Monte Carlo method, provides a full uncertainty quantification of the identified parameters, and indicates how well their uncertainty is reduced, with respect to the prior information, by the experimental data. © 2013 Taylor & Francis Group, London.

Identification of differential transcript profiles between mutual crossbred embryos of zebrafish (Danio rerio) and Chinese rare minnow (Gobiocypris rarus) by cDNA-AFLP

The crosstalk between naive nucleus and maternal factors deposited in egg cytoplasm before zygotic genome activation is crucial for early development. In this study, we utilized two laboratory fishes, zebrafish (Danio rerio) and Chinese rare minnow and Chinese rare minnow (Gobiocypris rarus), to obtain mutual crossbred embroys and examine the interaction between nucleus and egg cytoplasm from different species. Although these two types of crossbred embryos originated from common nuclei, various developmental capacities were gained due to different origins of the egg cytoplasm. Using cDNA amplified fragment length polymorphism (cDNA-AFLP), We Compared transcript profiles between the mutual crossbred embryos at two developmental stages (50%- and 90%-epiholy). Three thousand cDNA fragments were generated in four cDNA pools with 64 primer combinations. All differently displayed transcript-derived fragments (TDFs) were screened by (lot blot hybridization, and the selected sequences were further analyzed by semi-quantitative RT-PCR and quantitative real-time RT-PCR. Compared with ZR embryos, 12 genes were up-regulated and 12 were down-regulated in RZ embryos. The gene fragments were sequenced and subjected to BLASTN analysis. The sequences encoded various proteins which functioned at various levels of proliferation, growth, and development. One gene (ZR6), dramatically down-regulated in RZ embryos, was chosen for loss-of-function study; the knockdown of ZR6 gave rise to the phenotype resembling that of RZ embryos. (c) 2008 Elsevier Inc. All rights reserved.