The 3XMM/SDSS Stripe 82 Galaxy Cluster Survey: Cluster catalogue and discovery of two merging cluster candidates

We present a galaxy cluster survey based on XMM-Newton observations that are located in Stripe 82 of the Sloan Digital Sky Survey (SDSS). The survey covers an area of 11.25 deg$^2$. The X-ray cluster candidates were selected as serendipitously extended detected sources from the third XMM-Newton serendipitous source catalogue (3XMM-DR5). A cross-correlation of the candidate list that comprises 94 objects with recently published X-ray and optically selected cluster catalogues provided optical confirmations and redshift estimates for about half of the candidate sample. We present a catalogue of X-ray cluster candidates previously known in X-ray and/or optical bands from the matched catalogues or NED. The catalogue consists of 54 systems with redshift measurements in the range of 0.05-1.19 with a median of 0.36. Of these, 45 clusters have spectroscopic confirmations as stated in the matched catalogues. We spectroscopically confirmed another 6 clusters from the available spectroscopic redshifts in the SDSS-DR12. The cluster catalogue includes 17 newly X-ray discovered clusters, while the remainder were detected in previous XMM-Newton and/or ROSAT cluster surveys. Based on the available redshifts and fluxes given in the 3XMM-DR5 catalogue, we estimated the X-ray luminosities and masses for the cluster sample. We also present the list of the remaining X-ray cluster candidates (40 objects) that have no redshift information yet in the literature. Of these candidates, 25 sources are considered as distant cluster candidates beyond a redshift of 0.6. We also searched for galaxy cluster mergers in our cluster sample and found two strong candidates for newly discovered cluster mergers at redshifts of 0.11 and 0.26. The X-ray and optical properties of these systems are presented.Comment: 17 pages, 12 figures, accepted for publication in A&A, revised version after language editin

A smart ultrasonic actuator with multidegree of freedom for autonomous vehicle guidance industrial applications

A piezoelectric ultrasonic actuator with multidegree of freedom for autonomous vehicle guidance industrial applications is presented in this paper. The actuator is aiming to increase the visual spotlight angle of digital visual data capture transducer. It consists of three main parts, the stator, rotor and housing unit. The stator is a piezoelectric ring made from S42 piezoelectric ceramics material, bonded to three electrodes made from a material that has a close Characteristics to the S42. The rotor is a ball made from stainless steel materials. The actuator working principles is based on creating micro elliptical motions of surface points, generated by superposition of longitudinal and bending vibration modes, of oscillating structures. Transferring this motion from flexible ring transducer through the three electrodes, to the attached rotor, create 3D motions. The actuator Design, structures, working principles and finite element analysis are discussed in this paper. A prototype of the actuator was fabricated and its characteristics measured. Experimental tests showed the ability of the developed prototype to provide multidegree of freedom with typical speed of movement equal to 35 rpm, a resolution of less than 5μm and maximum load of 3.5 Newton. These characteristics illustrated the potential of the developed smart actuator, to gear the spotlight angle of digital visual data capture transducers and possible improvement that such micro-actuator technology could bring to the autonomous vehicle guidance and machine vision industrial applications. Furthermore research are still undertaken to develop a universal control prototype, integrate the actuator with an infrared sensor, visual data capture digital transducers and obtain the trajectory of motion control algorithm

Cutting edges at random in large recursive trees

We comment on old and new results related to the destruction of a random recursive tree (RRT), in which its edges are cut one after the other in a uniform random order. In particular, we study the number of steps needed to isolate or disconnect certain distinguished vertices when the size of the tree tends to infinity. New probabilistic explanations are given in terms of the so-called cut-tree and the tree of component sizes, which both encode different aspects of the destruction process. Finally, we establish the connection to Bernoulli bond percolation on large RRT's and present recent results on the cluster sizes in the supercritical regime.Comment: 29 pages, 3 figure

Activity Recognition and Prediction in Real Homes

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201

Superconducting cavity-electromechanics on silicon-on-insulator

Fabrication processes involving anhydrous hydrofluoric vapor etching are developed to create high-Q aluminum superconducting microwave resonators on free-standing silicon membranes formed from a silicon-on-insulator wafer. Using this fabrication process, a high-impedance 8.9-GHz coil resonator is coupled capacitively with a large participation ratio to a 9.7-MHz micromechanical resonator. Two-tone microwave spectroscopy and radiation pressure backaction are used to characterize the coupled system in a dilution refrigerator down to temperatures of T_f=11  mK, yielding a measured electromechanical vacuum coupling rate of g_0/2π = 24.6  Hz and a mechanical resonator Q factor of Q_m = 1.7 × 10^7. Microwave backaction cooling of the mechanical resonator is also studied, with a minimum phonon occupancy of n_m ≈ 16 phonons being realized at an elevated fridge temperature of T_f = 211  mK

Data provenance and management in radio astronomy: a stream computing approach

New approaches for data provenance and data management (DPDM) are required for mega science projects like the Square Kilometer Array, characterized by extremely large data volume and intense data rates, therefore demanding innovative and highly efficient computational paradigms. In this context, we explore a stream-computing approach with the emphasis on the use of accelerators. In particular, we make use of a new generation of high performance stream-based parallelization middleware known as InfoSphere Streams. Its viability for managing and ensuring interoperability and integrity of signal processing data pipelines is demonstrated in radio astronomy. IBM InfoSphere Streams embraces the stream-computing paradigm. It is a shift from conventional data mining techniques (involving analysis of existing data from databases) towards real-time analytic processing. We discuss using InfoSphere Streams for effective DPDM in radio astronomy and propose a way in which InfoSphere Streams can be utilized for large antennae arrays. We present a case-study: the InfoSphere Streams implementation of an autocorrelating spectrometer, and using this example we discuss the advantages of the stream-computing approach and the utilization of hardware accelerators