Adaptive modeling, adaptive data assimilation and adaptive sampling

For efficient progress, model properties and measurement needs can adapt to oceanic events and interactions as they occur. The combination of models and data via data assimilation can also be adaptive. These adaptive concepts are discussed and exemplified within the context of comprehensive real-time ocean observing and prediction systems. Novel adaptive modeling approaches based on simplified maximum likelihood principles are developed and applied to physical and physical–biogeochemical dynamics. In the regional examples shown, they allow the joint calibration of parameter values and model structures. Adaptable components of the Error Subspace Statistical Estimation (ESSE) system are reviewed and illustrated. Results indicate that error estimates, ensemble sizes, error subspace ranks, covariance tapering parameters and stochastic error models can be calibrated by such quantitative adaptation. New adaptive sampling approaches and schemes are outlined. Illustrations suggest that these adaptive schemes can be used in real time with the potential for most efficient sampling.     

Optimally balancing data and pilot power for adaptive MIMO-OFDM systems

While pilot symbols facilitate channel estimation, they reduce the transmit energy for data symbols per OFDM symbol under a fixed total transmit power constraint. In this paper, we investigate the effect of the pilot-to-data power ratio (PDPR) on multilevel quadrature amplitude modulation (M-QAM) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with adaptive modulation in order to provide a basic framework for finding the optimal PDPR in current and emerging standards using MIMO-OFDM. In particular, we derive the optimal PDPR in terms of average symbol error rate (SER) and spectral efficiency according to different receiver types such as zero-forcing (ZF) and minimum mean square error (MMSE). Employing the optimal PDPR results in higher spectral efficiency and lower SER without using any additional resource.     

Heterogeneity,quality, and reputation in an adaptive recommendation model

Recommender systems help people cope with the problem of information overload. A recently proposed adaptive news recommender model [M. Medo, Y.-C. Zhang, T. Zhou, Europhys. Lett. 88, 38005 (2009)] is based on epidemic-like spreading of news in a social network. By means of agent-based simulations we study a “good get richer” feature of the model and determine which attributes are necessary for a user to play a leading role in the network. We further investigate the filtering efficiency of the model as well as its robustness against malicious and spamming behaviour. We show that incorporating user reputation in the recommendation process can substantially improve the outcome.     

Gwyddion: an open-source software for SPM data analysis

In this article, we review special features of Gwyddion—a modular, multiplatform, open-source software for scanning probe microscopy data processing, which is available at . We describe its architecture with emphasis on modularity and easy integration of the provided algorithms into other software. Special functionalities, such as data processing from non-rectangular areas, grain and particle analysis, and metrology support are discussed as well. It is shown that on the basis of open-source software development, a fully functional software package can be created that covers the needs of a large part of the scanning probe microscopy user community.     

Classifier ensembles for fMRI data analysis: an experiment

Functional magnetic resonance imaging (fMRI) is becoming a forefront brain–computer interface tool. To decipher brain patterns, fast, accurate and reliable classifier methods are needed. The support vector machine (SVM) classifier has been traditionally used. Here we argue that state-of-the-art methods from pattern recognition and machine learning, such as classifier ensembles, offer more accurate classification. This study compares 18 classification methods on a publicly available real data set due to Haxby et al. [Science 293 (2001) 2425–2430]. The data comes from a single-subject experiment, organized in 10 runs where eight classes of stimuli were presented in each run. The comparisons were carried out on voxel subsets of different sizes, selected through seven popular voxel selection methods. We found that, while SVM was robust, accurate and scalable, some classifier ensemble methods demonstrated significantly better performance. The best classifiers were found to be the random subspace ensemble of SVM classifiers, rotation forest and ensembles with random linear and random spherical oracle.     

CIVA: an expertise platform for simulation and processing NDT data

Ultrasonic modeling and simulation are more and more widely used by the different actors of industrial NDT. The applications are numerous and show a great variety: help for diagnosis, data reconstruction, performance demonstration, probe design and inspection parameters settling, virtual testing etc. The CEA (the French Atomic Energy Commission) is strongly involved in this evolution with the development of the CIVA expertise platform which gathers in the same software advanced processing and modeling tools. In the aim of fulfilling requirements of an intensive use the choice has been made to mainly adopt semi-analytical approximated methods. The wave propagation modeling is based on an integral formulation of the radiated field and applies the so-called pencil method. The modeling of beam-defect interaction and echoes formation mechanisms apply approximated theories such as Kirchhoff approximation or GTD. Over the years and with successive versions of the software, this approach is enriched by adaptations and improvements of the existing models or by new models, in order to extend the field of applicability of the simulation.     

An automatic step-size adjustment algorithm for LMS adaptive filters,and an application to channel estimation

We propose a least-mean-square adaptive filter with automatic step-size adjustment (ASSA). At each time instant when a new observation of the input signal arrives, a new step-size parameter is chosen such that the sum of the squares of the measured estimation errors up to that current time instant is minimized. This step size, after being normalized by the power of the current tapped filter input, is used to update the filter weights for the next time instant. The filter weights are thus updated automatically without the aid of any preset control parameters. When applied to channel estimation, simulation results show the performance advantage of the ASSA algorithm over the existing step-size adjustment algorithms under different wireless channel environments.     

Scanning transmitted and reflected light microscopy: a novel microscopy for visualizing biomaterials at interfaces

Two new types of light microscopy, scanning transmitted light and scanning reflected light microscopy (STLM and SRLM, respectively) were developed. STLM and SRLM are based on optical density recognition (ODR) of the scanned transmitted or reflected light, respectively, from the object to be visualized. The obtained image is a result of enhanced interference between the scanning and transmitted/reflected beams from the object. The new microscopy, in its initial phase, is ideally suited for monitoring macroscopic and sub-millimeter size self-assembly and for elucidating the connection between the macroscopic and nanoscopic worlds if combined with atomic force microscopy (AFM) or electron microscopy (EM). The method is demonstrated by monitoring the growth of 3D crystals from their original liquid phase. Some preliminary measurements carried out using the prototype of the new microscopy are presented and its current and future possible applications are described.     

Correlative microscopy: a powerful tool for exploring neurological cells and tissues

Imaging tools for exploring the neurological samples have seen a rapid transformation over the last decade. Approaches that allow clear and specific delineation of targeted tissues, individual neurons, and their cell–cell connections as well as subcellular constituents have been especially valuable. Considering the significant complexity and extent to which the nervous system interacts with every organ system in the body, one non-trivial challenge has been how to identify and target specific structures and pathologies by microscopy. To this end, correlative methods enable one to view the same exact structure of interest utilizing the capabilities of typically separate, but powerful, microscopy platforms. As such, correlative microscopy is well-positioned to address the three critical problems of identification, scale, and resolution inherent to neurological systems. Furthermore, the application of multiple imaging platforms to the study of singular biological events enables more detailed investigations of structure–function relationships to be conducted, greatly facilitating our understanding of relevant phenomenon. This comprehensive review provides an overview of methods for correlative microscopy, including histochemistry, transgenic markers, immunocytochemistry, photo-oxidation as well as various probes and tracers. An emphasis is placed on correlative light and electron microscopic strategies used to facilitate relocation of neurological structures. Correlative microscopy is an invaluable tool for neurological research, and we fully anticipate developments in automation of the process, and the increasing availability of genomic and transgenic tools will facilitate the adoption of correlative microscopy as the method of choice for many imaging experiments.     

Digital holographic microscopy for automated 3D cell identification: an overview (Invited Paper)

Digital holographic （DH） microscopy is a promising technique for quantitative phase contrast imaging. It provides complex amplitude of the object wavefront, which in turn yields the thickness distribution of the object. An added advantage of the technique is its ability for numerical focusing, which provides the thickness distribution of the object at different axial planes. In this invited paper, we present an overview of our reported work on two beam DH microscopyto acquire different cell parameters for cell imaging and automated cell identification. Applications to automated cells and automated identification of malaria infected red monitoring of stem cells without destroying the blood ceils are discussed.     

A semi-automatic method for measuring interferometry fringes: prototype design and first results

A device for automatically obtaining the information contained in holographic interferometry fringes is proposed. The method may be applied to any kind of interferometric fringes. Accuracy has been demonstrated by contrasting the results obtained with our prototype system and those with the single-beam speckle interferometry technique for the measurement of displacements in a plane. The results show good correlation between the two series of measurements.     

Sustainable synthesis and automated deposition: an accessible discovery screening library of fragment-like purines

A sub-library of 88 information-rich lead-like purine derivatives were prepared and deposited in an open access academic screening facility. The rationale for the synthesis of these rigid low complexity structures was the privileged character of the purine heterocycle associated with its inherent probability of interactions with multiple adenine-related targets. Although generally expected to be weak binders in many assays, such fragment-like compounds are estimated to match diverse binding sites. It is suggested that heterocycles with many anchor points for hydrogen bonds can be anticipated to undergo very specific interactions to produce more negative enthalpies and thus provide superior starting points for lead optimization than compounds that owe their activity to entropic effects. The in vitro cytotoxicity of the small compounds on a panel of human cancer cell lines has been investigated and some of them showed marked unselective or selective toxicity. This data may be useful if these fragments are to be incorporated into drug-like structures via metabolically cleavable connections. The sub-library will be implemented as part of the ChemBioNet ( www.chembionet.info ) library, and it is open to screening campaigns of academic research groups striving for a fragment-based approach in their biological assays.     

Smart microscope: an adaptive optics learning system for aberration correction in multiphoton confocal microscopy

Off-axis aberrations in a beam-scanning multiphoton confocal microscope are corrected with a deformable mirror. The optimal mirror shape for each pixel is determined by a genetic learning algorithm, in which the second-harmonic or two-photon fluorescence signal from a reference sample is maximized. The speed of the convergence is improved by use of a Zernike polynomial basis for the deformable mirror shape. This adaptive optical correction scheme is implemented in an all-reflective system by use of extremely short (10-fs) optical pulses, and it is shown that the scanning area of an f:1 off-axis parabola can be increased by nine times with this technique.     

Material damage diagnosis and characterization for turbine rotors using three-dimensional adaptive ultrasonic NDE data reconstruction techniques

Damage diagnosis for turbine rotors plays an essential role in power plant management. Ultrasonic non-destructive examinations (NDEs) have increasingly been utilized as an effective tool to provide comprehensive information for damage diagnosis. This study presents a general methodology of damage diagnosis for turbine rotors using three-dimensional adaptive ultrasonic NDE data reconstruction techniques. Volume reconstruction algorithms and data fusion schemes are proposed to map raw ultrasonic NDE data back to the structural model of the object being examined. The reconstructed volume is used for automatic damage identification and quantification using region-growing algorithms and the method of distance-gain-size. Key reconstruction parameters are discussed and suggested based on industrial experiences. A software tool called AutoNDE Rotor is developed to automate the overall analysis workflow. Effectiveness of the proposed methods and AutoNDE Rotor are explored using realistic ultrasonic NDE data.     

Angular- and spatial-dependent intensity behaviour for a prototype laterally inhomogeneous sample studied with an ES200B spectrometer

An investigation into the surface area sampled in an ESCA experiment for a laterally inhomogeneous sample is described. The experiments use a special sample with gold “islands” in a polymeric matrix machined to the normal dimensions as encountered when using an ES200B spectrometer. The spatial distribution of the X-rays is not symmetrical about the anode and the effective area of acceptance into the analyzer changes with the electron take-off angle.     

Structure of some liquid alcohols, diols and triols: an analysis of dielectric data

This work is a continuation of our studies of the liquid structure of polyhydric alcohols and their solutions. Dielectric parameters (relaxation time, average cluster dipole moment μ¯_c, etc.) have been calculated for 1-hexanol in a wide temperature range in the framework of the cluster model proposed by Dissado and Hill; the values of apparent activation enthalpy ΔH_DHexp^≠ have been evaluated. These data were compared with the parameters previously determined by us for 2,5-hexanediol and 1,2,6-hexanetriol; the ratio μ¯_c/μ¯_v was used for an estimation of the number of molecules in clusters. It was found that at all temperatures in the range 303–393 K, the μ¯_c/μ¯_v values for 1-hexanol are significantly larger than the values of average degree of association p¯ calculated from equilibrium dielectric data; the ΔH_DHexp^≠ values for 1-hexanol are considerably lower than those for 2,5-hexanediol and 1,2,6-hexanetriol.