Automated Knowledge Generation with Persistent Surveillance Video

The Air Force has increasingly invested in persistent surveillance platforms gathering a large amount of surveillance video. Ordinarily, intelligence analysts watch the video to determine if suspicious activities are occurring. This approach to video analysis can be a very time and manpower intensive process. Instead, this thesis proposes that by using tracks generated from persistent video, we can build a model to detect events for an intelligence analyst. The event that we chose to detect was a suspicious surveillance activity known as a casing event. To test our model we used Global Positioning System (GPS) tracks generated from vehicles driving in an urban area. The results show that over 400 vehicles can be monitored simultaneously in real-time and casing events are detected with high probability (43 of 43 events detected with only 4 false positives). Casing event detections are augmented by determining which buildings are being targeted. In addition, persistent surveillance video is used to construct a social network from vehicle tracks based on the interactions of those tracks. Social networks that are constructed give us further information about the suspicious actors flagged by the casing event detector by telling us who the suspicious actor has interacted with and what buildings they have visited. The end result is a process that automatically generates information from persistent surveillance video providing additional knowledge and understanding to intelligence analysts about terrorist activities

Position and Volume Estimation of Atmospheric Nuclear Detonations from Video Reconstruction

Recent work in digitizing films of foundational atmospheric nuclear detonations from the 1950s provides an opportunity to perform deeper analysis on these historical tests. This work leverages multi-view geometry and computer vision techniques to provide an automated means to perform three-dimensional analysis of the blasts for several points in time. The accomplishment of this requires careful alignment of the films in time, detection of features in the images, matching of features, and multi-view reconstruction. Sub-explosion features can be detected with a 67% hit rate and 22% false alarm rate. Hotspot features can be detected with a 71.95% hit rate, 86.03% precision and a 0.015% false positive rate. Detected hotspots are matched across 57-109o viewpoints with 76.63% average correct matching by defining their location relative to the center of the explosion, rotating them to the alternative viewpoint, and matching them collectively. When 3D reconstruction is applied to the hotspot matching it completes an automated process that has been used to create 168 3D point clouds with 31.6 points per reconstruction with each point having an accuracy of 0.62 meters with 0.35, 0.24, and 0.34 meters of accuracy in the x-, y- and z-direction respectively. As a demonstration of using the point clouds for analysis, volumes are estimated and shown to be consistent with radius-based models and in some cases improve on the level of uncertainty in the yield calculation

Timing Mark Detection on Nuclear Detonation Video

During the 1950s and 1960s the United States conducted and filmed over 200 atmospheric nuclear tests establishing the foundations of atmospheric nuclear detonation behavior. Each explosion was documented with about 20 videos from three or four points of view. Synthesizing the videos into a 3D video will improve yield estimates and reduce error factors. The videos were captured at a nominal 2500 frames per second, but range from 2300-3100 frames per second during operation. In order to combine them into one 3D video, individual video frames need to be correlated in time with each other. When the videos were captured a timing system was used that shined light in a video every 5 milliseconds creating a small circle exposed in the frame. This paper investigates several method of extracting the timing from images in the cases when the timing marks are occluded and washed out, as well as when the films are exposed as expected. Results show an improvement over past techniques. For normal videos, occluded videos, and washed out videos, timing is detected with 99.3%, 77.3%, and 88.6% probability with a 2.6%, 11.3%, 5.9% false alarm rate, respectively

Fractal scale-invariant and nonlinear properties of cardiac dynamics remain stable with advanced age: A new mechanistic picture of cardiac control in healthy elderly

We analyze heartbeat interval recordings from two independent databases: (a) 19 healthy young (avg. age 25.7 years) and 16 healthy elderly subjects (avg. age 73.8 years) during 2h under resting conditions from the Fantasia database; and (b) 29 healthy elderly subjects (avg. age 75.9 years) during $\approx{}8$h of sleep from the SHHS database, and the same subjects recorded 5 years later. We quantify: (1) The average heart rate ; (2) the SD $\sigma_{RR}$ and $\sigma_{\Delta{}RR}$ of the heartbeat intervals RR and their increments $\Delta{}RR$; (3) the long-range correlations in RR as measured by the scaling exponent $\alpha_{RR}$ using the Detrended Fluctuation Analysis; (4) fractal linear and nonlinear properties as represented by the scaling exponents $\alpha^{sign}$ and $\alpha^{mag}$ for the time series of the sign and magnitude of $\Delta{}RR$; (5) the nonlinear fractal dimension $D(k)$ of $RR$ using the Fractal Dimension Analysis. We find: (1) No significant difference in \left (P>0.05); (2) a significant difference in $\sigma_{RR}$ and $\sigma_{\Delta{}RR}$ for the Fantasia groups (P<10^{-4}) but no significant change with age between the elderly SHHS groups (P>0.5); (3) no significant change in the fractal measures $\alpha_{RR}$ (P>0.15), $\alpha^{sign}$ (P>0.2), $\alpha^{mag}$ (P>0.3), and D(k) with age. Our findings do not support the hypothesis that fractal linear and nonlinear characteristics of heartbeat dynamics break down with advanced age in healthy subjects. While our results indeed show a reduced SD of heartbeat fluctuations with advanced age, the inherent temporal fractal and nonlinear organization of these fluctuations remains stable.Comment: 19 pages, 14 figure

Finestructure and microstructure in the North Atlantic Current

The relationship between intrusive finestructure and optical microstructure was studied by simultaneous CTD Tow-yos and deployments of the shadowgraph profiler SCIMP. Strong thermohaline intrusions, 5 to 50 m thick, were tracked laterally for 5 to 10 km in the front associated with the North Atlantic Current...

Machine Learning Nuclear Detonation Features

Nuclear explosion yield estimation equations based on a 3D model of the explosion volume will have a lower uncertainty than radius based estimation. To accurately collect data for a volume model of atmospheric explosions requires building a 3D representation from 2D images. The majority of 3D reconstruction algorithms use the SIFT (scale-invariant feature transform) feature detection algorithm which works best on feature-rich objects with continuous angular collections. These assumptions are different from the archive of nuclear explosions that have only 3 points of view. This paper reduces 300 dimensions derived from an image based on Fourier analysis and five edge detection algorithms to a manageable number to detect hotspots that may be used to correlate videos of different viewpoints for 3D reconstruction. Furthermore, experiments test whether histogram equalization improves detection of these features using four kernel sizes passed over these features. Dimension reduction using principal components analysis (PCA), forward subset selection, ReliefF, and FCBF (Fast Correlation-Based Filter) are combined with a Mahalanobis distance classifiers to find the best combination of dimensions, kernel size, and filtering to detect the hotspots. Results indicate that hotspots can be detected with hit rates of 90% and false alarms ¡ 1%

The Difficulty of Making Reparations Affects the Intensity of Collective Guilt

We examined how the difficulty of making reparations for the harm done to another group affects the intensity of collective guilt. Men were confronted with information documenting male privilege and were told that they would have a chance to help women and reduce patriarchy by collecting signatures on a petition. We manipulated the difficulty of making reparations by asking participants to collect 5, 50, or 100 signatures. As predicted by Brehm's (1999) theory of emotional intensity, collective guilt was a non-monotonic function of the difficulty of making reparations. Men in the moderate difficulty (50 signatures) condition expressed greater collective guilt than participants in the low (5) or high (100) difficulty conditions. Results are discussed in terms of the implications for the theory of emotional intensity, collective guilt, and collective emotions more generally

Hot Groups : Ein Leitfaden zur Gestaltung von Innovationsprozessen in Teams

Hot Groups sind gemischt zusammengesetzte Arbeitsgruppen, die für einen bestimmten Zeitraum an einer konkreten Zielstellung arbeiten. Derartige Arbeitsgruppen können für Unternehmen einen entscheidenden Erfolgsfaktor darstellen, vor allem auch in Zeiten des demografischen Wandels. Der Leitfaden informiert über wirkungsvolle Teamarbeit mit Hot Groups und gibt Anregungen, wie diese in Unternehmen etabliert werden können. Hierfür werden wissenschaftliche Erkenntnisse zusammengetragen und in praxisnahe Handlungsempfehlungen überführt

Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals

We investigate how various coarse-graining methods affect the scaling properties of long-range power-law correlated and anti-correlated signals, quantified by the detrended fluctuation analysis. Specifically, for coarse-graining in the magnitude of a signal, we consider (i) the Floor, (ii) the Symmetry and (iii) the Centro-Symmetry coarse-graining methods. We find, that for anti-correlated signals coarse-graining in the magnitude leads to a crossover to random behavior at large scales, and that with increasing the width of the coarse-graining partition interval $\Delta$ this crossover moves to intermediate and small scales. In contrast, the scaling of positively correlated signals is less affected by the coarse-graining, with no observable changes when $\Delta1$ a crossover appears at small scales and moves to intermediate and large scales with increasing $\Delta$. For very rough coarse-graining ($\Delta>3$) based on the Floor and Symmetry methods, the position of the crossover stabilizes, in contrast to the Centro-Symmetry method where the crossover continuously moves across scales and leads to a random behavior at all scales, thus indicating a much stronger effect of the Centro-Symmetry compared to the Floor and the Symmetry methods. For coarse-graining in time, where data points are averaged in non-overlapping time windows, we find that the scaling for both anti-correlated and positively correlated signals is practically preserved. The results of our simulations are useful for the correct interpretation of the correlation and scaling properties of symbolic sequences.Comment: 19 pages, 13 figure