Advancing profiling sensors with a wireless approach.

The notion of a profiling sensor was first realized by a Near-Infrared (N-IR) retro-reflective prototype consisting of a vertical column of wired sparse detectors. This paper extends that prior work and presents a wireless version of a profiling sensor as a collection of sensor nodes. The sensor incorporates wireless sensing elements, a distributed data collection and aggregation scheme, and an enhanced classification technique. In this novel approach, a base station pre-processes the data collected from the sensor nodes and performs data re-alignment. A back-propagation neural network was also developed for the wireless version of the N-IR profiling sensor that classifies objects into the broad categories of human, animal or vehicle with an accuracy of approximately 94%. These enhancements improve deployment options as compared with the first generation of wired profiling sensors, possibly increasing the application scenarios for such sensors, including intelligent fence applications.

Ontological problem-solving framework for dynamically configuring sensor systems and algorithms.

The deployment of ubiquitous sensor systems and algorithms has led to many challenges, such as matching sensor systems to compatible algorithms which are capable of satisfying a task. Compounding the challenges is the lack of the requisite knowledge models needed to discover sensors and algorithms and to subsequently integrate their capabilities to satisfy a specific task. A novel ontological problem-solving framework has been designed to match sensors to compatible algorithms to form synthesized systems, which are capable of satisfying a task and then assigning the synthesized systems to high-level missions. The approach designed for the ontological problem-solving framework has been instantiated in the context of a persistence surveillance prototype environment, which includes profiling sensor systems and algorithms to demonstrate proof-of-concept principles. Even though the problem-solving approach was instantiated with profiling sensor systems and algorithms, the ontological framework may be useful with other heterogeneous sensing-system environments.

Ontological problem-solving framework for assigning sensor systems and algorithms to high-level missions.

The lack of knowledge models to represent sensor systems, algorithms, and missions makes opportunistically discovering a synthesis of systems and algorithms that can satisfy high-level mission specifications impractical. A novel ontological problem-solving framework has been designed that leverages knowledge models describing sensors, algorithms, and high-level missions to facilitate automated inference of assigning systems to subtasks that may satisfy a given mission specification. To demonstrate the efficacy of the ontological problem-solving architecture, a family of persistence surveillance sensor systems and algorithms has been instantiated in a prototype environment to demonstrate the assignment of systems to subtasks of high-level missions.

A Geographic Information System framework for the management of sensor deployments.

A prototype Geographic Information System (GIS) framework has been developed to map, manage, and monitor sensors with respect to other geographic features, including land base and in-plant features. The GIS framework supports geographic placement and subsequent discovery, query, and tasking of sensors in a network-centric environment using Web services. The framework couples the GIS feature placement logic of sensors with an extensible ontology which captures the capabilities, properties, protocols, integrity constraints, and other parameters of interest for a large variety of sensor types. The approach is significant in that custom, GIS-based interfaces can be rapidly developed via the integration of sensors and sensor networks into applications without having detailed knowledge of the sensors' underlying device drivers by leveraging service-oriented computing infrastructure within the GIS framework.

Sparse Detector Imaging Sensor with Two-Class Silhouette Classification.

This paper presents the design and test of a simple active near-infrared sparse detector imaging sensor. The prototype of the sensor is novel in that it can capture remarkable silhouettes or profiles of a wide-variety of moving objects, including humans, animals, and vehicles using a sparse detector array comprised of only sixteen sensing elements deployed in a vertical configuration. The prototype sensor was built to collect silhouettes for a variety of objects and to evaluate several algorithms for classifying the data obtained from the sensor into two classes: human versus non-human. Initial tests show that the classification of individually sensed objects into two classes can be achieved with accuracy greater than ninety-nine percent (99%) with a subset of the sixteen detectors using a representative dataset consisting of 512 signatures. The prototype also includes a Webservice interface such that the sensor can be tasked in a network-centric environment. The sensor appears to be a low-cost alternative to traditional, high-resolution focal plane array imaging sensors for some applications. After a power optimization study, appropriate packaging, and testing with more extensive datasets, the sensor may be a good candidate for deployment in vast geographic regions for a myriad of intelligent electronic fence and persistent surveillance applications, including perimeter security scenarios.

A Prolog-based centroid algorithm for isovolume extraction from finite element torso simulations.

Computer modeling and simulation of the human torso provides a rapid and non-invasive means to observe the effects of implanted defibrillators. The objective of this study was to improve a method of extracting data from an implanted defibrillator simulation for subsequent visualization. Electrical quantities, such as the potential and gradient fields, are computed at points throughout various regions of a three-dimensional (3-D) torso model via a finite element solution. Software is then implemented in the Prolog language to extract and visualize a subset of the data, from within any subregion of the model, satisfying a given declarative constraint. In past work, membership in these subsets had been determined solely by the electrical quantities at the vertices of the tetrahedral elements within the model along with an arbitrary choice made by the user. However, this study expands upon previous work to utilize an alternative means of classification, calculating the centroid of each tetrahedron and assigning electrical properties to these centroids based on the distances of each centroid to the four corners of the tetrahedron. After the modifications, it is expected that the extracted subsets of the model will represent the data in a more realistic and conservative manner and provide more insight into the process of defibrillation than previous methods of data extraction and visualization.

Evaluation of supervised and unsupervised 3D star visualisation algorithms.

The 3D Star Coordinate Projection (3DSCP) visualisation algorithm has been developed to address the following key issues: choosing the projection configuration autonomously. preserving the data topology after projection. enhancing resolution. A supervised version of 3DSCP (S3DSCP) is also introduced to improve the computational efficiency of 3DSCP. Comparison with other linear, non-linear and axis-based techniques is performed to illustrate the efficacy of the 3DSCP and S3DSCP methods. Empirical analyses indicate that the 3DSCP and S3DSCP algorithms find hidden patterns in data while overcoming limitations of other techniques.