Accreting Millisecond X-Ray Pulsars

Accreting Millisecond X-Ray Pulsars (AMXPs) are astrophysical laboratories without parallel in the study of extreme physics. In this chapter we review the past fifteen years of discoveries in the field. We summarize the observations of the fifteen known AMXPs, with a particular emphasis on the multi-wavelength observations that have been carried out since the discovery of the first AMXP in 1998. We review accretion torque theory, the pulse formation process, and how AMXP observations have changed our view on the interaction of plasma and magnetic fields in strong gravity. We also explain how the AMXPs have deepened our understanding of the thermonuclear burst process, in particular the phenomenon of burst oscillations. We conclude with a discussion of the open problems that remain to be addressed in the future.Comment: Review to appear in "Timing neutron stars: pulsations, oscillations and explosions", T. Belloni, M. Mendez, C.M. Zhang Eds., ASSL, Springer; [revision with literature updated, several typos removed, 1 new AMXP added

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Relativistic Binaries in Globular Clusters

Galactic globular clusters are old, dense star systems typically containing 10\super{4}--10\super{7} stars. As an old population of stars, globular clusters contain many collapsed and degenerate objects. As a dense population of stars, globular clusters are the scene of many interesting close dynamical interactions between stars. These dynamical interactions can alter the evolution of individual stars and can produce tight binary systems containing one or two compact objects. In this review, we discuss theoretical models of globular cluster evolution and binary evolution, techniques for simulating this evolution that leads to relativistic binaries, and current and possible future observational evidence for this population. Our discussion of globular cluster evolution will focus on the processes that boost the production of hard binary systems and the subsequent interaction of these binaries that can alter the properties of both bodies and can lead to exotic objects. Direct {\it N}-body integrations and Fokker--Planck simulations of the evolution of globular clusters that incorporate tidal interactions and lead to predictions of relativistic binary populations are also discussed. We discuss the current observational evidence for cataclysmic variables, millisecond pulsars, and low-mass X-ray binaries as well as possible future detection of relativistic binaries with gravitational radiation.Comment: 88 pages, 13 figures. Submitted update of Living Reviews articl

Ensemble empirical mode decomposition of photoplethysmogram signals in biometric recognition

This research focuses on using photoplethysmogram (PPG) signals for biometric recognition. Specifically, the biometric traits studied are the ensemble empirical mode decomposition (EEMD) and power spectral density (PSD) of the PPG signals. The classifiers used for testing the performance of the algorithm were K-nearest neighbors algorithm (KNN), support vector machine (SVM), and random forest (RF). Training, testing, and k-fold cross validation were done using data from public database. PPG was found to be suitable for biometric recognition, although with weakness that may be addressed through gathering and training of larger sets of data. © 2019 IEEE

Ensemble empirical mode decomposition of photoplethysmogram signals for assessment of ventricular fibrillation

Ventricular fibrillation is a type of cardiac arrhythmia which is responsible for several cases of sudden cardiac arrests. As many cases of arrhythmia result to fatality, it is the goal of this research to develop a method to analyze this condition through the use of ensemble empirical mode decomposition (EEMD). EEMD is a variant of empirical mode decomposition (EMD) which solves its weakness in terms of mode mixing. EEMD results to the decomposition of a signal into its intrinsic mode functions(IMFs). The IMFs, together with their power spectral densities (PSDs) of photoplethysmogram (PPG) signals are analyzed for cases with and without ventricular fibrillation. Also, IMFs and PSDs are used as the features for classifying the presence of this condition. Principal component analysis (PCA) is used to reduce the large dimension of the features. In classifying, k-NN classifier was used. It was found that the IMFs of a signal with and without ventricular fibrillation resampled at 250 Hz and at window length of 1000 has most of its signal energy at the 5thto 8th siftings. The highest overall classification accuracy of 83.75%was achieved with noise width of 0.1. Thus, the ensemble empirical mode decomposition of PPG signals was successfully used for assessment of ventricular fibrillation and further modifications with the parameters and pre-processing techniques may be done to improve classification accuracy based on this feature. © 2018 IEEE

A machine learning approach for coconut sugar quality assessment and prediction

This study presents a machine learning approach to accurately assess the quality of coconut sugar using RGB values. Python and scikit-learn were used to run the following machine learning algorithms: artificial neural network (ANN), stochastic gradient descent (SGD), k-nearest neighbors (k-NN) algorithm, support vector machine (SVM), decision tree (DT) and random forest (RF). Comparisons were made between the aforementioned machine learning algorithms by evaluating the accuracy and the average running time of each training model. Results of the study show that the SGD is superior in terms of accuracy but falls short to k-NN and SVC in terms of running time. In this fashion, a plot between the accuracy and the running time was made and it was observed that algorithms with higher accuracies correspondingly have also higher running times. By this very nature, experimental results show that the SGD holds merit in accurately assessing the coconut sugar quality, despite its expense in running time. © 2018 IEEE