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Vindiola M.M.,DRC High Performance Technologies Group | Vindiola M.M.,U.S. Army | Vettel J.M.,U.S. Army | Gordon S.M.,DCS Corporation | And 2 more authors.
Journal of Neuroscience Methods | Year: 2014

Background: Recent neuroimaging analyses aim to understand how information is integrated across brain regions that have traditionally been studied in isolation; however, detecting functional connectivity networks in experimental EEG recordings is a non-trivial task. New method: We use neural mass models to simulate 10-s trials with coupling between 1-3 and 5-8. s and compare how well three phase-based connectivity measures recover this connectivity pattern across a set of experimentally relevant conditions: variable oscillation frequency and power spectrum, feed forward connections with or without feedback, and simulated signals with and without volume conduction. Results: Overall, the results highlight successful detection of the onset and offset of significant synchronizations for a majority of the 28 simulated configurations; however, the tested phase measures sometimes differ in their sensitivity and specificity to the underlying connectivity. Comparison with existing methods: Prior work has shown that these phase measures perform well on signals generated by a computational model of coupled oscillators. In this work we extend previous studies by exploring the performance of these measures on a different class of computational models, and we compare the methods on 28 variations that capture a set of experimentally relevant conditions. Conclusions: Our results underscore that no single phase synchronization measure is substantially better than all others, and experimental investigations will likely benefit from combining a set of measures together that are chosen based on both the experimental question of interest, the signal to noise ratio in the EEG data, and the approach used for statistical significance. © 2014. Source

BACKGROUND: U.S. military personnel face challenging situations including frequent deployments, family separations, and exposure to war. Identifying coping strategies used by the most resilient service members and veterans could positively influence military resiliency training programs. OBJECTIVE: The purposes of this paper are to investigate the relationship between coping and resilience among U.S. military active service members and veterans, to identify the coping strategies used by those considered most resilient, and to discuss coping and resilience as they relate to the workplace. METHODS: U.S. military active service members and veterans (N=191) completed a demographic survey and two self-report questionnaires: The 14-Item Resilience Scale [1] and the Brief COPE [2]. RESULTS: Active duty service members had higher resilience scores than veterans (p<0.05), but both fell into the moderate range. Coping strategies were not significantly different between the two groups (p>0.05). Active service members' resilience was predicted by their use of positive reframing and less use of self-blame as coping strategies, accounting for 52.3 of the variance (R2=0.523, F(2, 60)=32.92, p=0.000). Veterans' resilience was predicted by longer time-in-service, greater use of humor, and less use of self-blame as coping strategies, explaining 44.8 of the variance (R2=0.448, F(3, 116)=31.408, p=0.000). CONCLUSIONS: This research identifies the positive coping strategies, and least-used negative coping strategies, of the U.S. service members and veterans in our study population with higher resilience scores. Incorporating this information into military- or veteran-based resilience training is likely to increase training effectiveness. © 2016 - IOS Press and the authors. Source

Cerkez P.S.,DCS Corporation
Proceedings - Applied Imagery Pattern Recognition Workshop | Year: 2013

Semagrams are a subset of steganography. When a message is transmitted in a non-textual format, (i.e., in the visual content of an image), it is referred to as a semagram. While semagrams are relatively easy to create (as shown in published papers covering hiding techniques), detecting a hidden message in or embedded as an image-based semagram is a greater magnitude of difficultly than typical digital steganography. US Patents issued based on semagram technology show that this feature has been exploited in the copyright/watermarking world to increase protection. In a semagram, the image is the message and they work well for simple messages and dead drops. Attacks on semagrams are primarily visual examinations of artifacts. In the counter-espionage world, the rule of the thumb is that there is always a message hidden in an image or graphic, it is simply up to the steganalyst to find it. In short, detecting semagrams is a matter of recognizing patterns of patterns that represent a hidden message within an image. This presentation provides a brief summary of the technology underlying semagrams, present a short non-technical discussion of the technology used in the attack on semagrams, followed by a discussion on current work and planned future implementations of the proven semagram detection ANN. It will focus on extending the ANN to other domains (e.g., non-visual spectrums, multi/cross spectrum correlation, scene identification, image classification) and efforts to improve the processing speed and throughput via parallel/distributed methods. © 2013 IEEE. Source

Rice V.,U.S. Army | Liu B.,DCS Corporation
Work | Year: 2016

BACKGROUND: Interest in resilience has increased in recent years. The U.S. military focus is on personal health and adaptation following exposure to battle, while the civilian interest centers on adjustments subsequent to disastrous events. Coping skills are also relevant, yet the relationships between coping and resilience are unclear. OBJECTIVE: This brief review examines personal resilience and individual coping strategies, exploring definitions of each, along with their potential relationships to one another. Their potential contributions within a work setting are described. METHODS: A literature review was conducted using search terms of resilience, resiliency, personal resilience, coping and resilient coping. RESULTS: Coping refers to one's using purposeful actions to handle life situations. Coping techniques can be functional or dysfunctional and the situations one copes with may be acute or long term, severe or minor. Resilience refers to positive and functional handling of oneself and ones' life, referring to the ability to recover, recuperate, and regenerate following tragic events. CONCLUSIONS: While coping and resilience are related to one another, they are distinct concepts. Positive coping techniques may contribute to resilience. However, which coping techniques improve resilience, and in what circumstances, are questions for future research. © 2016 - IOS Press and the authors. Source

Brabson S.,DCS Corporation | Anderson T.,U.S. Navy
IEEE Aerospace and Electronic Systems Magazine | Year: 2015

The US Navy's collision avoidance systems (CAS) software product consists of five different system capabilities: ground proximity warning system (GPWS), terrain awareness warning system (TAWS), obstacle avoidance system (OAS), mid-air collision avoidance system (MCAS), and auto-recovery CAS (AutoCAS). GPWS provides directive controlled fight into terrain (CFIT) protection against fight into level or descending terrain. TAWS provides directive CFIT protection against level, descending, and rising terrain. OAS provides directive protection and situational awareness, through a display, of man-made obstacles. MCAS provides passive protection and awareness of other airborne aircraft. AutoCAS provides active protection by interacting with the avionics system to take control of the aircraft to automatically avoid impact with terrain, obstacles, and/or aircraft. For each capability, the software product provides advisories or directive aural warnings and visual indications to the aircrew. The goal is to maximize protection and minimize nuisance warnings to ensure aircrew trust the system in a dynamic tactical environment [1]. Balancing protection with nuisance warnings is done by embedding an aircraft-specific performance model into the CAS software product. © 1986-2012 IEEE. Source

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