The notion of a proling sensor was rst realized by a near-IR, retro-re
ective prototype consisting of a vertical
column of sparse detectors. Alternative arrangements of detectors have been implemented in which a subset of the
detectors have been oset from the vertical column and placed at arbitrary locations along the anticipated path
of the objects of interest. All prior work with the near-IR, retro-re
ective proling sensors has consisted of wired
detectors. This paper advances this prior work by designing and implementing a wireless prototype version of a
near-IR, retro-re
ective proling sensor in which each detector is a wireless sensor node. In this novel architecture,
a base station is responsible for collecting all data from the detector sensor nodes and coordinating all pre-
processing of data collected from the sensor nodes, including data re-alignment, before subsequent classication
algorithms are executed. Such a wireless detector conguration advances deployment options for near-IR, retro-
re
ective proling sensors.
KEYWORDS: Sensors, Human subjects, Surveillance, Visualization, Video surveillance, Environmental sensing, Infrared sensors, Defense and security, Data modeling, Information security
Remote detection of harmful intent is necessary for eective and appropriate countermeasures and will reduce
risks to life and property. Trained human observers and sensor systems typically use facial expressions, gaits,
gestures, perspiration, and a number of other observable characteristics as possible indicators of harmful intent
with mixed results. It is proposed that responses of human subjects to external stimuli can be used as additional
indicators of harmful intent in surveillance contexts. A variety of alerting stimuli, possible responses to the
stimuli, features to be sensed by sensors, and the utility of these sensed features as indicators of harmful intent
are discussed in this paper. An ontology-based data-to-decision framework for assessing human intent, which
would leverage the formal representations of the alerting stimuli, as well as the variety of possible responses, is
proposed in the context of Semantic Web infrastructure.
A profiling sensor has been realized using a vertical column of sparse detectors with the sensor's optical axis configured
perpendicular to the plane of the vertical column of detectors. Traditionally, detectors of the profiling sensor are placed
in a sparse vertical column configuration. A subset of the detectors may be removed from the vertical column and placed
at arbitrary locations along the anticipated path of the objects of interest, forming a custom detector array configuration.
Objects passing through the profiling sensor's field of view have traditionally been classified via algorithms processed
off-line. However, reconstruction of the object profile is impossible unless the detectors are placed at a known location
relative to each other. Measuring these detector locations relative to each other can be particularly time consuming,
making this process impractical for custom detector configuration in the field. This paper describes a method that can be
used to determine a detector's relative location to other detectors by passing a known profile through the sensor's field of
view as part of the configuration process. Real-time classification results produced by the embedded controller for a
variety of objects of interest are also described in the paper.
This paper provides a feasibility analysis and details of implementing a classification algorithm on an embedded
controller for use with a profiling sensor. Such a profiling sensor has been shown to be a feasible approach to a low-cost
persistent surveillance sensor for classifying moving objects such as humans, animals, or vehicles. The sensor produces
data that can be used to generate object profiles as crude images or silhouettes, and/or the data can be subsequently
automatically classified. This paper provides a feasibility analysis of a classification algorithm implemented on an
embedded controller, which is packaged with a prototype version of a profiling sensor. Implementation of the embedded
controller is a necessary extension of previous work for fielded profiling sensors and their appropriate applications.
Field data is used to confirm accurate automated classification.
The Common IED Exploitation Target Set (CIEDETS) ontology provides a comprehensive semantic data model for
capturing knowledge about sensors, platforms, missions, environments, and other aspects of systems under test. The
ontology also includes representative IEDs; modeled as explosives, camouflage, concealment objects, and other
background objects, which comprise an overall threat scene. The ontology is represented using the Web Ontology
Language and the SPARQL Protocol and RDF Query Language, which ensures portability of the acquired knowledge
base across applications. The resulting knowledge base is a component of the CIEDETS application, which is intended
to support the end user sensor test and evaluation community. CIEDETS associates a system under test to a subset of
cataloged threats based on the probability that the system will detect the threat. The associations between systems under
test, threats, and the detection probabilities are established based on a hybrid reasoning strategy, which applies a
combination of heuristics and simplified modeling techniques. Besides supporting the CIEDETS application, which is
focused on efficient and consistent system testing, the ontology can be leveraged in a myriad of other applications,
including serving as a knowledge source for mission planning tools.
This paper presents initial object profile classification results using range and elevation independent features from a
simulated infrared profiling sensor. The passive infrared profiling sensor was simulated using a LWIR camera. A field
data collection effort to yield profiles of humans and animals is reported. Range and elevation independent features
based on height and width of the objects were extracted from profiles. The profile features were then used to train and
test four classification algorithms to classify objects as humans or animals. The performance of Naïve Bayesian (NB),
Naïve Bayesian with Linear Discriminant Analysis (LDA+NB), K-Nearest Neighbors (K-NN), and Support Vector
Machines (SVM) are compared based on their classification accuracy. Results indicate that for our data set SVM and
(LDA+NB) are capable of providing classification rates as high as 98.5%. For perimeter security applications where
misclassification of humans as animals (true negatives) needs to be avoided, SVM and NB provide true negative rates of
0% while maintaining overall classification rates of over 95%.
This paper presents a simple prototype sparse detector imaging sensor built using sixteen off-the-shelf, retro-reflective,
infrared-sensing elements placed at five-inch intervals in a vertical configuration. Profiling experiments for broad-scale
classification of objects, such as humans, humans wearing large backpacks, and humans wearing small backpacks were
conducted from data acquired from the sensor. Empirical analysis on models developed using fusion of various
classifiers based on a diversity measure shows over ninety percent (90.07%) accuracy (using 10-fold cross validation) in
categorizing sensed data into specific classes of interest, such as, humans wearing a large backpack. The results
demonstrate that shadow images of sufficient resolution can be captured by the sensor such that broad-scale
classification of objects is feasible. The sensor appears to be a low-cost alternative to traditional, high-resolution imaging
sensors, and, after industrial packaging, it may be a good candidate for deployment in vast geographic regions in which
low-cost, unattended ground sensors with highly accurate classification algorithms are needed.
Identification of significantly differentially expressed genes (marker genes) among sample groups is a central issue in microarray analysis. This identification is important to understand the molecular pathway of diseases. Many statistical
methods have been proposed to locate marker genes. These methods depend on a cutoff value for selection. A tightfisted
cutoff may omit some of the important marker genes, whereas a generous threshold increases the number of false
positives. Although robust models for identifying marker genes more accurately is an area of intense research, effective
tools for the evaluation of results is often ignored in the literature. Despite the robustness of many of these methods,
there is always some probability of false positives. In this paper, we propose a novel approach that exploits parallel
coordinates to visualize the gene expression patterns so that one can compare the expression level changes of the marker
genes between sample groups and determine whether the selected marker genes are valid. Such visualization is useful to
measure the validity of the marker gene selection process as well as to fine tune the parameters of a particular method.
A prediction method based on the selected marker genes is used to measure the reliability of our process.
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