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This PDF file contains the front matter associated with SPIE Proceedings Volume 8059, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.
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Observing nature has been a cornerstone of engineering design. Today, engineers look not only at finished products, but
imitate the evolutionary process by which highly optimized artifacts have appeared in nature. Evolutionary computation
began by capturing only the simplest ideas of evolution, but today, researchers study natural evolution and incorporate an
increasing number of concepts in order to evolve solutions to complex engineering problems. At the new BEACON
Center for the Study of Evolution in Action, studies in the lab and field and in silico are laying the groundwork for new
tools for evolutionary engineering design. This paper, which accompanies a keynote address, describes various steps in
development and application of evolutionary computation, particularly as regards sensor design, and sets the stage for
future advances.
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This paper provides a summary of preliminary RF direction finding results generated within an AFOSR funded testbed
facility recently developed at Louisiana Tech University. This facility, denoted as the Louisiana Tech University Micro-
Aerial Vehicle/Wireless Sensor Network (MAVSeN) Laboratory, has recently acquired a number of state-of-the-art
MAV platforms that enable us to analyze, design, and test some of our recent results in the area of multiplatform
position-adaptive direction finding (PADF) [1] [2] for localization of RF emitters in challenging embedded multipath
environments. Discussions within the segmented sections of this paper include a description of the MAVSeN Laboratory
and the preliminary results from the implementation of mobile platforms with the PADF algorithm. This novel approach
to multi-platform RF direction finding is based on the investigation of iterative path-loss based (i.e. path loss exponent)
metrics estimates that are measured across multiple platforms in order to develop a control law that
robotically/intelligently positionally adapt (i.e. self-adjust) the location of each distributed/cooperative platform. The
body of this paper provides a summary of our recent results on PADF and includes a discussion on state-of-the-art
Sensor Mote Technologies as applied towards the development of sensor-integrated caged-MAV platform for PADF
applications. Also, a discussion of recent experimental results that incorporate sample approaches to real-time singleplatform
data pruning is included as part of a discussion on potential approaches to refining a basic PADF technique in
order to integrate and perform distributed self-sensitivity and self-consistency analysis as part of a PADF technique with
distributed robotic/intelligent features. These techniques are extracted in analytical form from a parallel study denoted as
"PADF RF Localization Criteria for Multi-Model Scattering Environments". The focus here is on developing and
reporting specific approaches to self-sensitivity and self-consistency within this experimental PADF framework via the
exploitation of specific single-agent caged-MAV trajectories that are unique to this experiment set.
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This paper introduces a novel geometric constraint to boresight calibration for aerial multi-head camera systems. Using
the precise EOPs (exterior orientation parameters) estimated for each physical camera and the surface information of the
area of interest, multi head camera provides a synthetic image at each time epoch. The camera EOPs can be computed
directly from the navigation solution provided by an onboard GPS/INS system and camera platform geometric
calibration parameters, which represent the geometric relationship between the camera heads. For direct acquisition of
EOPs from the navigation system, the camera frame and the INS frame should be precisely aligned. Boresight can be
defined as mounting angles between the INS frame and camera frame. Since small but unknown misalignment angles
could cause large errors on the ground, which suggests that they should be precisely estimated. In this paper, unknown
boresight angles are estimated by using camera platform geometric calibration parameters as constraints. Since each
physical camera of the multi-head camera system is tightly affixed to the platform, the geometry between camera frames
remains constant. Simulation results show that the constrained method provides better estimation in terms of both
accuracy and precision compared to traditional approach which does not use the constraint.
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Evolutionary computation (EC) techniques in design optimization such as genetic algorithms (GA) or efficient global
optimization (EGO) require an initial set of data samples (design points) to start the algorithm. They are obtained by
evaluating the cost function at selected sites in the input space. A two-dimensional input space can be sampled using a
Latin square, a statistical sampling technique which samples a square grid such that there is a single sample in any given
row and column. The Latin hypercube is a generalization to any number of dimensions. However, a standard random
Latin hypercube can result in initial data sets which may be highly correlated and may not have good space-filling
properties. There are techniques which address these issues. We describe and use one technique in this paper.
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We present an improvement to the fundamental Group Method of Data Handling (GMDH) Data Modeling algorithm
that overcomes the parameter sensitivity to novel cases presented to derived networks. We achieve this result by
regularization of the output and using a genetic weighting that selects intermediate models that do not exhibit
divergence. The result is the derivation of multi-nested polynomial networks following the Kolmogorov-Gabor
polynomial that are robust to mean estimators as well as novel exemplars for input. The full details of the algorithm are
presented. We also introduce a new method for approximating GMDH in a single regression model using F, H, and G
terms that automatically exports the answers as ordinary differential equations. The MathCAD 15 source code for all
algorithms and results are provided.
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The layered sensing framework, in application, provides a useful, but complex integration of information sources,
e.g. multiple sensing modalities and operating conditions. It is the implied trade-off between sensor fidelity
and system complexity that we address here. Abstractly, each sensor/source of information in a layered sensing
application can be viewed as a node in the network of constituent sensors. Regardless of the sensing modality,
location, scope, etc., each sensor collects information locally to be utilized by the system as a whole for further
exploitation. Consequently, the information may be distributed throughout the network and not necessarily
coalesced in a central node/location. We present, initially, an analysis of polarimetric infrared data, with two
novel features, as one of the input modalities to such a system. We then proceed with statistical and geometric
analyses of an example network, thus quantifying the advantages and drawbacks of a specific application of the
layered sensing framework.
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We propose a mathematical formulation for a layered sensing architecture based on the theory of categories
that will allow us to abstractly define agents and their interactions in such a way that we can treat human and
machine (or systems of these) agents homogeneously. One particular advantage is that this general formulation
will allow the development of multi-resolution analyses of a given situation that is independent of the particular
models used to represent a given agent or system of agents. In this paper, we define the model and prove basic
facts that will be fundamental in future work. Central to our approach is the integration of uncertainty into our
model. Such a framework is necessitated by our desire to define (among other things) measures of alignment
and efficacy for systems of heterogeneous agents operating in a diverse and complex environment.
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Uncertainty plays decisive role in the confidence of the decisions made about events. For example, in situation awareness,
decision-making is faced with two types of uncertainties; information uncertainty and data uncertainty. Data uncertainty
exists due to noise in sensor measurements and is classified as randomness. Information uncertainty is due to ambiguity of
using (words) to describe events. This uncertainty is known as fuzziness. Typically, these two types of uncertainties are
handled separately using two different theories. Randomness is modeled by probability theory, while fuzzy-logic is used to
address fuzziness. In this paper we used the Cloud computation theory to treat data randomness and information fuzziness in
one single model. First, we described the Cloud theory then used the theory to generate one and two-dimensional Cloud
models. Second, we used the Cloud models to capture and process data randomness and fuzziness in information relative to
decision-making in situation awareness. Finally, we applied the models to generate security decisions for security monitoring
of sensitive area. Testing results are reported at the end of the paper.
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In this paper, we present results of adversarial activity recognition using data collected in the Empire
Challenge (EC 09) exercise. The EC09 experiment provided an opportunity to evaluate our probabilistic
spatiotemporal mission recognition algorithms using the data from live air-born and ground sensors. Using
ambiguous and noisy data about locations of entities and motion events on the ground, the algorithms inferred
the types and locations of OPFOR activities, including reconnaissance, cache runs, IED emplacements,
logistics, and planning meetings. In this paper, we present detailed summary of the validation study and
recognition accuracy results. Our algorithms were able to detect locations and types of over 75% of hostile
activities in EC09 while producing 25% false alarms.
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Cancer stem cells (CSC) represent a very small percentage of the total tumor population however they pose a
big challenge in treating cancer. Glycans play a key role in cancer therapeutics since overexpression of them
depending on the glycan type can lead either to cell death or more invasive metastasis. Two major components,
fetal bovine serum (FBS) and STAT3, are known to up- or down-regulate certain glycolipid or phospholipid compositions
found in glioblastoma CSCs. The analysis and the understanding of the global interactional behavior of
lipidomic networks remains a challenging task and can not be accomplished solely based on intuitive reasoning.
The present contribution aims at applying graph clustering networks to analyze the functional aspects of certain
activators or inhibitors at the molecular level in glioblastoma stem cells (GSCs). This research enhances our
understanding of the differences in phenotype changes and determining the responses of glycans to certain treatments
for the aggressive GSCs, and represents together with a quantitative phosphoproteomic study1 the most detailed systems biology study of GSCs differentiation known so far. Thus, these new paradigms are providing unique understanding of the mechanisms involved in GSC maintenance and tumorigenicity and are thus opening a new window to biomedical frontiers.
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Motion-based artifacts lead in breast MRI to diagnostic misinterpretation and therefore represents an important
prerequisite to automatic lesion detection and diagnosis. In the present paper, we evaluate the performance of a
computer-aided diagnosis (CAD) system consisting of motion correction, lesion segmentation, feature extraction
and classification. Many novel feature extraction techniques are proposed and tested in conjunction with motion
correction and classification. Our simulation results have shown that motion compensation combined with
Minkowsi functionals and Bayesian classifier can improve the lesion detection and classification.
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The 9/7 wavelet is used for a wide variety of image compression tasks. Recent research, however, has established a
methodology for using evolutionary computation to evolve wavelet and scaling numbers describing transforms that
outperform the 9/7 under lossy conditions, such as those brought about by quantization or thresholding. This paper
describes an investigation into which of three possible approaches to transform evolution produces the most effective
transforms. The first approach uses an evolved forward transform for compression, but performs reconstruction using the
9/7 inverse transform; the second uses the 9/7 forward transform for compression, but performs reconstruction using an
evolved inverse transform; the third uses simultaneously evolved forward and inverse transforms for compression and
reconstruction. Three image sets are independently used for training: digital photographs, fingerprints, and satellite
images. Results strongly suggest that it is impossible for evolved transforms to substantially improve upon the
performance of the 9/7 without evolving the inverse transform.
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Wavelets provide an attractive method for efficient image compression. For transmission across noisy or bandwidth
limited channels, a signal may be subjected to quantization in which the signal is transcribed onto a
reduced alphabet in order to save bandwidth. Unfortunately, the performance of the discrete wavelet transform
(DWT) degrades at increasing levels of quantization. In recent years, evolutionary algorithms (EAs) have been
employed to optimize wavelet-inspired transform filters to improve compression performance in the presence of
quantization. Wavelet filters consist of a pair of real-valued coefficient sets; one set represents the compression
filter while the other set defines the image reconstruction filter. The reconstruction filter is defined as the
biorthogonal inverse of the compression filter. Previous research focused upon two approaches to filter optimization.
In one approach, the original wavelet filter is used for image compression while the reconstruction
filter is evolved by an EA. In the second approach, both the compression and reconstruction filters are evolved.
In both cases, the filters are not biorthogonally related to one another. We propose a novel approach to filter
evolution. The EA optimizes a compression filter. Rather than using a wavelet filter or evolving a second filter
for reconstruction, the reconstruction filter is computed as the biorthogonal inverse of the evolved compression
filter. The resulting filter pair retains some of the mathematical properties of wavelets. This paper compares
this new approach to existing filter optimization approaches to determine its suitability for the optimization of
image filters appropriate for defense applications of image processing.
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The development of novel image processing algorithms requires a diverse and relevant set of training images
to ensure the general applicability of such algorithms for their required tasks. Images must be appropriately
chosen for the algorithm's intended applications. Image processing algorithms often employ the discrete wavelet
transform (DWT) algorithm to provide efficient compression and near-perfect reconstruction of image data.
Defense applications often require the transmission of images and video across noisy or low-bandwidth channels.
Unfortunately, the DWT algorithm's performance deteriorates in the presence of noise. Evolutionary algorithms
are often able to train image filters that outperform DWT filters in noisy environments. Here, we present and
evaluate two image sets suitable for the training of such filters for satellite and unmanned aerial vehicle imagery
applications. We demonstrate the use of the first image set as a training platform for evolutionary algorithms that
optimize discrete wavelet transform (DWT)-based image transform filters for satellite image compression. We
evaluate the suitability of each image as a training image during optimization. Each image is ranked according
to its suitability as a training image and its difficulty as a test image. The second image set provides a test-bed
for holdout validation of trained image filters. These images are used to independently verify that trained filters
will provide strong performance on unseen satellite images. Collectively, these image sets are suitable for the
development of image processing algorithms for satellite and reconnaissance imagery applications.
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In this paper we explore the use of histogram features extracted from 3D point clouds of human subjects for gender
classification. Experiments are conducted using point clouds drawn from the CAESAR anthropometric database
provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International. This
database contains approximately 4400 high resolution LIDAR whole body scans of carefully posed human subjects.
Features are extracted from each point cloud by embedding the cloud in series of cylindrical shapes and computing a
point count for each cylinder that characterizes a region of the subject. These measurements define rotationally invariant
histogram features that are processed by a classifier to label the gender of each subject. Preliminary results using
cylinder sizes defined by human experts demonstrate that gender can be predicted with 98% accuracy for the type of
high density point cloud found in the CAESAR database. When point cloud densities are reduced to levels that might be
obtained using stand-off sensors; gender classification accuracy degrades. We introduce an evolutionary algorithm to
optimize the number and size of the cylinders used to define histogram features. The objective of this optimization
process is to identify a set of cylindrical features that reduces the error rate when predicting gender from low density
point clouds. A wrapper approach is used to interleave feature selection with classifier evaluation to train the
evolutionary algorithm. Results of classification accuracy achieved using the evolved features are compared to the
baseline feature set defined by human experts.
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Malicious code (malware) that spreads through the Internet-such as viruses, worms and trojans-is a major threat
to information security nowadays and a profitable business for criminals. There are several approaches to analyze
malware by monitoring its actions while it is running in a controlled environment, which helps to identify malicious
behaviors. In this article we propose a tool to analyze malware behavior in a non-intrusive and effective way,
extending the analysis possibilities to cover malware samples that bypass current approaches and also fixes some
issues with these approaches.
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Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical
applications due to the flexibility and extensibility of the technology. While these networks possess numerous
advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same
types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth
available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the
technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an
approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the
network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the
autonomic processes of biological systems. Each component of the network recognizes activity in its local environment
and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm
intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network.
The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to
identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established
interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted
collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network
attacks.
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Published studies have focused on the application of one bio-inspired or evolutionary computational method to the
functions of a single protocol layer in a wireless ad hoc sensor network (WSN). For example, swarm intelligence in the
form of ant colony optimization (ACO), has been repeatedly considered for the routing of data/information among nodes,
a network-layer function, while genetic algorithms (GAs) have been used to select transmission frequencies and power
levels, physical-layer functions. Similarly, artificial immune systems (AISs) as well as trust models of quantized data
reputation have been invoked for detection of network intrusions that cause anomalies in data and information; these act
on the application and presentation layers. Most recently, a self-organizing scheduling scheme inspired by frog-calling
behavior for reliable data transmission in wireless sensor networks, termed anti-phase synchronization, has been applied
to realize collision-free transmissions between neighboring nodes, a function of the MAC layer. In a novel departure
from previous work, the cross-layer approach to WSN protocol design suggests applying more than one evolutionary
computational method to the functions of the appropriate layers to improve the QoS performance of the cross-layer
design beyond that of one method applied to a single layer's functions. A baseline WSN protocol design, embedding
GAs, anti-phase synchronization, ACO, and a trust model based on quantized data reputation at the physical, MAC,
network, and application layers, respectively, is constructed. Simulation results demonstrate the synergies among the bioinspired/
evolutionary methods of the proposed baseline design improve the overall QoS performance of networks over
that of a single computational method.
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