In this paper we present an adaptive incremental learning system for underwater mine detection and classification that
utilizes statistical models of seabed texture and an adaptive nearest-neighbor classifier to identify varied underwater
targets in many different environments. The first stage of processing uses our Background Adaptive ANomaly detector
(BAAN), which identifies statistically likely target regions using Gabor filter responses over the image. Using this
information, BAAN classifies the background type and updates its detection using background-specific parameters. To
perform classification, a Fully Adaptive Nearest Neighbor (FAAN) determines the best label for each detection. FAAN
uses an extremely fast version of Nearest Neighbor to find the most likely label for the target. The classifier perpetually
assimilates new and relevant information into its existing knowledge database in an incremental fashion, allowing
improved classification accuracy and capturing concept drift in the target classes. Experiments show that the system
achieves >90% classification accuracy on underwater mine detection tasks performed on synthesized datasets provided
by the Office of Naval Research. We have also demonstrated that the system can incrementally improve its detection
accuracy by constantly learning from new samples.
Wireless Sensor Networks (WSNs) are systems that may contain hundreds to thousands of low-power and low-cost
sensor nodes. The potential applicability of such systems is enormous; security surveillance and intrusion detection
systems for smart buildings and military bases, monitoring chemical plants for safety, wireless body area networks for
first responders, and monitoring habitats and natural environments for scientific and other purposes, among others. As
sensor network technology matures, we expect to witness an increasing number of such systems deployed in the real
world. This renders sensor networks more accessible to a wide variety of possible attacks and functional faults, as they
are deployed in remote, un-trusted, hostile environments. While different basic cryptographic building blocks and
hardened hardware architectures for most sensor network platforms are currently available and allow for protection on a
single sensor node basis, such building blocks are not effective in preventing wider scale attacks once a node has been
compromised. To this end, UtopiaCompression is proposing Proactive Trust Management System (PTMS) for WSNs.
Our solution is based on an easily extensible framework, tailored to deal with the resource constrained WSNs, and uses a
combination of novel outlier detection mechanisms and trust management algorithms to effectively cope with common
sensor faults and network attack models. Moreover, our solution is based on distributed in-network processing, which
significantly improves scalability and extends life time of the system. This paper also discusses the implementation and
evaluation of our solution on Sun SPOT sensors.
The effectiveness of autonomous munitions systems can be enhanced by transmitting target images to a man-in-the-loop
(MITL) as the system deploys. Based on the transmitted images, the MITL could change target priorities or conduct
damage assessment in real-time. One impediment to this enhancement realization is the limited bandwidth of the system
data-link. In this paper, an innovative pattern-based image compression technology is presented for enabling efficient
image transmission over the ultra-low bandwidth system data link, while preserving sufficient details in the
decompressed images for the MITL to perform the required assessments. Based on a pattern-driven image model, our
technology exploits the structural discontinuities in the image by extracting and prioritizing edge segments with their
geometric and intensity profiles. Contingent on the bit budget, only the most salient segments are encoded and
transmitted, therefore achieving scalable bit-streams. Simulation results corroborate the technology efficiency and
establish its subjective quality superiority over JPEG/JPEG2000 as well as feasibility for real-time implementation.
Successful technology demonstrations were conducted using images from surrogate seekers in an aircraft and from a
captive-carry test-bed system. The developed technology has potential applications in a broad range of network-enabled
weapon systems.
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