The output distribution of important LULU-operators

Two procedures to compute the output distribution phi_S of certain stack filters S (so called erosion-dilation cascades) are given. One rests on the disjunctive normal form of S and also yields the rank selection probabilities. The other is based on inclusion-exclusion and e.g. yields phi_S for some important LULU-operators S. Properties of phi_S can be used to characterize smoothing properties of S. One of the methods discussed also allows for the calculation of the reliability polynomial of any positive Boolean function (e.g. one derived from a connected graph).Comment: 20 pages, up to trivial differences this is the final version to be published in Quaestiones Mathematicae 201

The Ensemble Kalman Filter: A Signal Processing Perspective

The ensemble Kalman filter (EnKF) is a Monte Carlo based implementation of the Kalman filter (KF) for extremely high-dimensional, possibly nonlinear and non-Gaussian state estimation problems. Its ability to handle state dimensions in the order of millions has made the EnKF a popular algorithm in different geoscientific disciplines. Despite a similarly vital need for scalable algorithms in signal processing, e.g., to make sense of the ever increasing amount of sensor data, the EnKF is hardly discussed in our field. This self-contained review paper is aimed at signal processing researchers and provides all the knowledge to get started with the EnKF. The algorithm is derived in a KF framework, without the often encountered geoscientific terminology. Algorithmic challenges and required extensions of the EnKF are provided, as well as relations to sigma-point KF and particle filters. The relevant EnKF literature is summarized in an extensive survey and unique simulation examples, including popular benchmark problems, complement the theory with practical insights. The signal processing perspective highlights new directions of research and facilitates the exchange of potentially beneficial ideas, both for the EnKF and high-dimensional nonlinear and non-Gaussian filtering in general

Kajian motivasi ekstrinsik di antara Pelajar Lepasan Sijil dan Diploma Politeknik Jabatan Kejuruteraan Awam KUiTTHO

Kajian ini dijalankan untuk menyelidiki pengaruh dorongan keluarga, cara pengajaran pensyarah, pengaruh rakan sebaya dan kemudahan infrastruktur terhadap motivasi ekstrinsik bagi pelajar tahun tiga dan tahun empat lepasan sijil dan diploma politeknik Jabatan Kejuruteraan Awain Kolej Universiti Teknologi Tun Hussein Onn. Sampel kajian ini beijumlah 87 orang bagi pelajar lepasan sijil politeknik dan 38 orang bagi lepasan diploma politeknik. Data kajian telah diperolehi melalui borang soal selidik dan telah dianalisis menggunakan perisian SPSS (Statical Package For Sciences). Hasil kajian telah dipersembahkan dalam bentuk jadual dan histohgrapi. Analisis kajian mendapati bahawa kedua-dua kumpulan setuju bahawa faktor-faktor di atas memberi kesan kepada motivasi ekstrinsik mereka. Dengan kata lain faktpr-faktor tersebut penting dalam membentuk pelajar mencapai kecemerlangan akademik

Calibration Uncertainty for Advanced LIGO's First and Second Observing Runs

Calibration of the Advanced LIGO detectors is the quantification of the detectors' response to gravitational waves. Gravitational waves incident on the detectors cause phase shifts in the interferometer laser light which are read out as intensity fluctuations at the detector output. Understanding this detector response to gravitational waves is crucial to producing accurate and precise gravitational wave strain data. Estimates of binary black hole and neutron star parameters and tests of general relativity require well-calibrated data, as miscalibrations will lead to biased results. We describe the method of producing calibration uncertainty estimates for both LIGO detectors in the first and second observing runs.Comment: 15 pages, 21 figures, LIGO DCC P160013

Detailed comparison of Milky Way models based on stellar population synthesis and SDSS star counts at the north Galactic pole

We test the ability of the TRILEGAL and Besancon models to reproduce the CMD of SDSS data at the north Galactic pole (NGP). We show that a Hess diagram analysis of colour-magnitude diagrams is much more powerful than luminosity functions (LFs) in determining the Milky Way structure. We derive a best-fitting TRILEGAL model to simulate the NGP field in the (g-r, g) CMD of SDSS filters via Hess diagrams. For the Besancon model, we simulate the LFs and Hess diagrams in all SDSS filters. We use a chi2 analysis and determine the median of the relative deviations in the Hess diagrams to quantify the quality of the fits by the TRILEGAL models and the Besancon model in comparison and compare this with the Just-Jahreiss model. The input isochrones in the colour-absolute magnitude diagrams of the thick disc and halo are tested via the observed fiducial isochrones of globular clusters (GCs). We find that the default parameter set lacking a thick disc component gives the best representation of the LF in TRILEGAL. The Hess diagram reveals that a metal-poor thick disc is needed. In the Hess diagram, the median relative deviation of the TRILEGAL model and the SDSS data amounts to 25 percent, whereas for the Just-Jahreiss model the deviation is only 5.6 percent. The isochrone analysis shows that the representation of the MS of (at least metal-poor) stellar populations in the SDSS system is reliable. In contrast, the RGBs fail to match the observed fiducial sequences of GCs. The Besancon model shows a similar median relative deviation of 26 percent in (g-r, g). In the u band, the deviations are larger. There are significant offsets between the isochrone set used in the Besancon model and the observed fiducial isochrones. In contrast to Hess diagrams, LFs are insensitive to the detailed structure of the Milky Way components due to the extended spatial distribution along the line of sight.Comment: 21 pages, 17 figures and 5 tables. Accepted by publication of A&

Distributed classifier based on genetically engineered bacterial cell cultures

We describe a conceptual design of a distributed classifier formed by a population of genetically engineered microbial cells. The central idea is to create a complex classifier from a population of weak or simple classifiers. We create a master population of cells with randomized synthetic biosensor circuits that have a broad range of sensitivities towards chemical signals of interest that form the input vectors subject to classification. The randomized sensitivities are achieved by constructing a library of synthetic gene circuits with randomized control sequences (e.g. ribosome-binding sites) in the front element. The training procedure consists in re-shaping of the master population in such a way that it collectively responds to the "positive" patterns of input signals by producing above-threshold output (e.g. fluorescent signal), and below-threshold output in case of the "negative" patterns. The population re-shaping is achieved by presenting sequential examples and pruning the population using either graded selection/counterselection or by fluorescence-activated cell sorting (FACS). We demonstrate the feasibility of experimental implementation of such system computationally using a realistic model of the synthetic sensing gene circuits.Comment: 31 pages, 9 figure