Image formation in microwave holography

Microwave holograms are made without offset reference beam, but it has been found that Van der Lugt filter can be used to produce image offset. Also, filter permits "decoding" of holograms in contrast with usual practice of reconstructing visible-light analogs of original micro-wave wave fronts

Microwave holography for nondestructive testing

Holographic methods permit use of very large effective apertures so that weak signals can be collected over wide area and integrated to form image. Technique, modification of side-looking radar principle, can be used at very short ranges needed for nondestructive inspection of test specimens

Effect of Reynolds number on stability characteristics of a cruciform wing-body

An experimental investigation was conducted to determine the effect of Reynolds number on the stability characteristics of a body with cruciform wings at large angles of attack. Pressure distributions and force and moment data (axial force not measured) are presented for Mach 1.60 and 2.70, Reynolds numbers based on body diameter from approximately 130,000 to 2,800,000, and angles of attack from 0 deg to 50 deg. In general, the data show only small effects of Reynolds number throughout the range of test condition. Also discussed are force balance and pressure data that suggest a direct relationship between wind choking and the onset of a nonlinear stability variaton with angle of attack

Development of microwave NDT inspection techniques for large solid propellant rocket motors Final report

Microwave nondestructive testing techniques for large solid propellant rocket engine

Discovery of Pulsed X-ray Emission from the SMC Transient RX J0117.6-7330

We report on the detection of pulsed, broad-band, X-ray emission from the transient source RX J0117.6-7330. The pulse period of 22 seconds is detected by the ROSAT/PSPC instrument in a 1992 Sep 30 - Oct 2 observation and by the CGRO/BATSE instrument during the same epoch. Hard X-ray pulsations are detectable by BATSE for approximately 100 days surrounding the ROSAT observation (1992 Aug 28 - Dec 8). The total directly measured X-ray luminosity during the ROSAT observation is 1.0E38 (d/60 kpc)^2 ergs s-1. The pulse frequency increases rapidly during the outburst, with a peak spin-up rate of 1.2E-10 Hz s-1 and a total frequency change 1.8%. The pulsed percentage is 11.3% from 0.1-2.5 keV, increasing to at least 78% in the 20-70 keV band. These results establish RX J0117.6-7330 as a transient Be binary system.Comment: 17 pages, Latex, aasms, accepted for publication in ApJ Letter

A Sample of OB Stars That Formed in the Field

We present a sample of 14 OB stars in the Small Magellanic Cloud that meet strong criteria for having formed under extremely sparse star-forming conditions in the field. These stars are a minimum of 28 pc in projection from other OB stars, and they are centered within symmetric, round HII regions. They show no evidence of bow shocks, implying that the targets are not transverse runaway stars. Their radial velocities relative to local HI also indicate that they are not line-of-sight runaway stars. A friends-of-friends analysis shows that 9 of the objects present a few low-mass companion stars, with typical mass ratios for the two highest-mass stars of around 0.1. This further substantiates that these OB stars formed in place, and that they can and do form in extremely sparse conditions. This poses strong constraints on theories of star formation and challenges proposed relations between cluster mass and maximum stellar mass.Comment: Accepted to ApJ, 12 page

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure