Current Army logistical systems and databases contain massive amounts of data that need an effective method to extract
actionable information. The databases do not contain root cause and case-based analysis needed to diagnose or predict
breakdowns. A system is needed to find data from as many sources as possible, process it in an integrated fashion, and
disseminate information products on the readiness of the fleet vehicles. 21st Century Systems, Inc. introduces the Agent-
Enabled Logistics Enterprise Intelligence System (AELEIS) tool, designed to assist logistics analysts with assessing the
availability and prognostics of assets in the logistics pipeline. AELEIS extracts data from multiple, heterogeneous data
sets. This data is then aggregated and mined for data trends. Finally, data reasoning tools and prognostics tools evaluate
the data for relevance and potential issues. Multiple types of data mining tools may be employed to extract the data and
an information reasoning capability determines what tools are needed to apply them to extract information. This can be
visualized as a push-pull system where data trends fire a reasoning engine to search for corroborating evidence and then
integrate the data into actionable information. The architecture decides on what reasoning engine to use (i.e., it may start
with a rule-based method, but, if needed, go to condition based reasoning, and even a model-based reasoning engine for
certain types of equipment). Initial results show that AELEIS is able to indicate to the user of potential fault conditions
and root-cause information mined from a database.
Unmanned aerial vehicles (UAVs) capture real-time video data of military targets while keeping the warfighter at a safe
distance. This keeps soldiers out of harm's way while they perform intelligence, surveillance and reconnaissance (ISR)
and close-air support troops in contact (CAS-TIC) situations. The military also wants to use UAV video to achieve force
multiplication. One method of achieving effective force multiplication involves fielding numerous UAVs with cameras
and having multiple videos processed simultaneously by a single operator. However, monitoring multiple video streams
is difficult for operators when the videos are of low quality. To address this challenge, we researched several promising
video enhancement algorithms that focus on improving video quality. In this paper, we discuss our video enhancement
suite and provide examples of video enhancement capabilities, focusing on stabilization, dehazing, and denoising. We
provide results that show the effects of our enhancement algorithms on target detection and tracking algorithms. These
results indicate that there is potential to assist the operator in identifying and tracking relevant targets with aided target
recognition even on difficult video, increasing the force multiplier effect of UAVs. This work also forms the basis for
human factors research into the effects of enhancement algorithms on ISR missions.
In order for First Responder Command and Control personnel to visualize incidents at urban building locations, DHS
sponsored a small business research program to develop a tool to visualize 3D building interiors and movement of First
Responders on site. 21st Century Systems, Inc. (21CSI), has developed a toolkit called Hierarchical Grid Referenced
Normalized Display (HiGRND). HiGRND utilizes three components to provide a full spectrum of visualization tools to
the First Responder. First, HiGRND visualizes the structure in 3D. Utilities in the 3D environment allow the user to
switch between views (2D floor plans, 3D spatial, evacuation routes, etc.) and manually edit fast changing environments.
HiGRND accepts CAD drawings and 3D digital objects and renders these in the 3D space. Second, HiGRND has a First
Responder tracker that uses the transponder signals from First Responders to locate them in the virtual space. We use the
movements of the First Responder to map the interior of structures. Finally, HiGRND can turn 2D blueprints into 3D
objects. The 3D extruder extracts walls, symbols, and text from scanned blueprints to create the 3D mesh of the building.
HiGRND increases the situational awareness of First Responders and allows them to make better, faster decisions in
critical urban situations.
In an effort to enhance situation awareness, DHS is sponsoring the development of hardware and software systems to aid
visualization of structures in which urban search and rescue (USAR) crews will be operating. Given positional data
generated by virtual badges worn by first responders, the Hierarchical Grid Referenced Normalized Display (HiGRND)
system dynamically creates visualization of the structure in which the responders are operating. In this paper, we
discuss some of the recent work in progress on using virtual badge tracks to create visualizations of orthonormal
structures. The method described here is used to seed a powerful mapping tool which is used by human operators to
enhance an incident commander's situation awareness.
While there are many good ways to map sensual reality to two dimensional displays, mapping non-physical and
possibilistic information can be challenging. The advent of faster-than-real-time systems allow the predictive and
possibilistic exploration of important factors that can affect the decision maker. Visualizing a compressed picture of the
past and possible factors can assist the decision maker summarizing information in a cognitive based model thereby
reducing clutter and perhaps related decision times. Our proposed semantic bifurcated importance field visualization uses
saccadic eye motion models to partition the display into a possibilistic and sensed data vertically and spatial and
semantic data horizontally. Saccadic eye movement precedes and prepares decision makers before nearly every directed
action. Cognitive models for saccadic eye movement show that people prefer lateral to vertical saccadic movement.
Studies have suggested that saccades may be coupled to momentary problem solving strategies. Also, the central 1.5
degrees of the visual field represents 100 times greater resolution that then peripheral field so concentrating factors can
reduce unnecessary saccades. By packing information according to saccadic models, we can relate important decision
factors reduce factor dimensionality and present the dense summary dimensions of semantic and importance. Inter and
intra ballistics of the SBIFV provide important clues on how semantic packing assists in decision making. Future
directions of SBIFV are to make the visualization reactive and conformal to saccades specializing targets to ballistics,
such as dynamically filtering and highlighting verbal targets for left saccades and spatial targets for right saccades.
This paper presents a reasoning system that pools the judgments from a set of inference agents with information
from heterogeneous sources to generate a consensus opinion that reduces uncertainty and improves knowledge
quality. The system, called Collective Agents Interpolation Integral (CAII), addresses a high level data fusion
problem by combining, in a mathematically sound manner, multi-models of inference in knowledge intensive
multi agent architecture. Two major issues are addressed in CAII. One is the ability of the inference mechanisms
to deal with hybrid data inputs from multiple information sources and map the diverse data sets to a uniform
representation in an objective space of reasoning and integration. The other is the ability of the system
architecture to allow the continuous and discrete outputs of a diverse set of inference agents to interact, cooperate,
It is theoretically shown that using saturable absorbers in unstable resonators can lead to large-volume super-Gaussian beams generation under certain conditions. Practical aspects for Q-switched super-Gaussian beam generation are discussed. The method is experimentally improved.