Progress in the study of CdZnTe strip detectors

We report new performance measurements and computer simulations of a sub-millimeter pitch CdZnTe strip detector under study as a prototype imaging spectrometer for astronomical x-ray and gamma-ray observations. The prototype is 1.5 mm thick with 375 micron strip pitch in both the x and y dimensions. Previously reported work included demonstrations of half-pitch spatial resolution (approximately 190 microns) and good energy resolution and spectral uniformity. Strip detector efficiency measurements have also been presented. A model that includes the photon interaction, carrier transport and the electronics was developed that qualitatively reproduced the measurements. The new studies include measurements of the CdZnTe transport properties for this prototype in an effort to resolve quantitative discrepancies between the measurements and the simulations. Measurements of charge signals produced by laser pulses and (alpha) -rays are used to determine these transport properties. These are then used in the model to predict gamma-ray efficiencies that are compared with the data. The imaging performance of the detector is studied by scanned laser and gamma beam spot measurements. The results support the model\u27s prediction of nearly linear sharing of the charge for interactions occurring in the region between electrodes. The potential for strip detectors with spatial resolution much finer than the strip pitch is demonstrated. A new design scheme for strip detectors is shortly discussed

Region-Referenced Spectral Power Dynamics of EEG Signals: A Hierarchical Modeling Approach

Functional brain imaging through electroencephalography (EEG) relies upon the analysis and interpretation of high-dimensional, spatially organized time series. We propose to represent time-localized frequency domain characterizations of EEG data as region-referenced functional data. This representation is coupled with a hierarchical modeling approach to multivariate functional observations. Within this familiar setting, we discuss how several prior models relate to structural assumptions about multivariate covariance operators. An overarching modeling framework, based on infinite factorial decompositions, is finally proposed to balance flexibility and efficiency in estimation. The motivating application stems from a study of implicit auditory learning, in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. Using the proposed model, we examine differential band power dynamics as brain function is interrogated throughout the duration of a computer-controlled experiment. Our work offers a novel look at previous findings in psychiatry, and provides further insights into the understanding of ASD. Our approach to inference is fully Bayesian and implemented in a highly optimized Rcpp package

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Big Data and Reliability Applications: The Complexity Dimension

Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges in big data analytics. Meeker and Hong (2014, Quality Engineering, pp. 102-116) provided an extensive discussion of the opportunities and challenges in big data and reliability, and described engineering systems that can generate big data that can be used in reliability analysis. Meeker and Hong (2014) focused on large scale system operating and environment data (i.e., high-frequency multivariate time series data), and provided examples on how to link such data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. This paper intends to extend that discussion by focusing on how to use data with complicated structures to do reliability analysis. Such data types include high-dimensional sensor data, functional curve data, and image streams. We first provide a review of recent development in those directions, and then we provide a discussion on how analytical methods can be developed to tackle the challenging aspects that arise from the complexity feature of big data in reliability applications. The use of modern statistical methods such as variable selection, functional data analysis, scalar-on-image regression, spatio-temporal data models, and machine learning techniques will also be discussed.Comment: 28 pages, 7 figure