Since the attempted shoot down of an Israeli airliner departing from Mombasa, Kenya in November of 2002, there has been heightened concern that Al Qaeda, or other terrorist factions, will use shoulder-fired heat seeking missiles as part of their tactics. These weapons, known more formally as man-portable air defense systems, or MANPADS, have been widely proliferated, are easy to conceal and deploy, and can be purchased on the black market for as little as $10,000. Recognizing that MANPADS pose a potential threat to commercial airplanes throughout the world, the Department of Homeland Security (DHS) is executing a system design and development (SDD) program to evaluate the viability of missile countermeasures that would be installed on commercial airplanes. This paper provides an overview of the MANPADS threat, a discussion of associated countermeasure requirements for systems installed on commercial airplanes, and a description of a laser countermeasure system that is being prototyped and demonstrated as part of the DHS Counter-MANPADS program.

Great strides have been made in reducing optical losses of chalcogenide glass fibers using improved chemical purification and fiberization techniques. The losses are low enough for practical applications which include laser power delivery for infrared missile protection systems. Fiber cables have been fabricated and successfully used in field demonstrations for missile defense

Since the terrible events of 11 Sep 2001 the response to security vulnerabilities has been to throw “Guns, Gates and Guards” at the problem. Three years later and it is clear that, although this may have had a short-term effect, it is unsustainable and unaffordable in the long term. The war on terrorism is going to be fought for a very long time. Defending against terrorism and enhancing the resilience and robustness of society and its processes now requires constant vigilance. Only technology can provide that vigilance at an efficiency that can provide certainty of detection and fast response. A technology led approach, integrating with people and their processes calls for innovation and a new generation of technology that fuses the physical world with the logical world. This approach is measurable in terms of capability and investment, in the way that the previous Newtonian security approach of cause and effect is not. This paper will address this new security environment and the different approach that R&D has to take to ensure that life and Democracy thrive and terrorism is defeated.

The terahertz (1 THz = 10¹² Hz, 3 mm or 33 cm^-1) region of the electromagnetic spectrum is typically defined in the frequency range 100 GHz to 10 THz, corresponding to a wavelength range of 3 mm to 30 microns. Following the development of coherent sources and detectors in the early eighties, there has been growing interest in the role of terahertz technology in global homeland security. The terahertz region offers a huge expanse of unused bandwidth. The ability of terahertz radiation to probe intermolecular interactions, large amplitude vibrations and rotational modes, in addition to showing polarization sensitivity makes terahertz radiation a unique and diverse region of the electromagnetic spectrum. Terahertz radiation is also able to 'see through' common materials, such as clothing, thick smoke and dust, which are often considered as opaque in other regions of the electromagnetic spectrum. This paper reviews the role of terahertz technology in homeland security and associated limitations of this field.

The eleven universities of the State University System of Florida (SUS-FL) have established a consortium to address the full range of homeland security and domestic preparedness requirements for both Florida and the U.S. The Consortium has established the Florida Homeland Security Institute to provide an effective and efficient mechanism to coordinate, mobilize, combine, and form into teams the diverse, cross-disciplinary expertise, facilities, and established large base of technology development activities within the SUS-FL institutions and their established associates at industrial companies, governmental labs, and other universities. The Florida Consortium and Institute may provide a model for other state university systems for how to combine established resources effectively to address specific homeland security and domestic preparedness needs. This paper describes the Consortium and Institute goals, structure, and operations, with examples of how it has functioned in its brief existence as an effective mechanism for integrating the wide range of university, industry, and government capabilities within the state for addressing homeland security requirements.

Drinking water is one of the nation's key infrastructure assets that have been deemed vulnerable to deliberate terrorist attacks. While the threat to reservoir systems and water sources is deemed to be minimal, the vulnerability of the drinking water distribution systems to accidental or deliberate contamination due to a backflow event is becoming a well-recognized possibility. The myriad possible points of incursion into a distribution system and the ease of mounting a backflow event, combined with the fact that little or no quality monitoring occurs after the water has left the treatment plant, makes the danger of such an attack acute. This was clearly stated in a General Accounting Office (GAO) report to Congress that listed the vulnerability of the distribution system to attack as the largest security risk to water supplies. Prior to this there has not been a system capable of detecting such an event and alerting the system's managers so that effects of an attack or accident can be contained. The general scientific consensus is that no practical, available, or cost-effective real-time technology exists to detect and mitigate intentional attacks or accidental incursions in drinking water distribution systems. The rapid detection and identification of breaches of security in the water distribution system is crucial in initiating appropriate corrective action. The ability of a technology system to detect incursion on a real time basis and give indications as to the cause could dramatically reduce the impact of any such scenario. As the vulnerability of the distribution system becomes more widely recognized, the development of a system such as the one described will be an invaluable tool in maintaining the integrity of the nations drinking water supply.

As the federal lead for water infrastructure security, EPA draws upon its long history of environmental protection to develop new tools and technologies that address potential attacks on drinking water and wastewater systems. The critical research described is improving awareness, preparedness, prevention, response, and recovery from threats or attacks against water systems.

Planning for high impact very low probability events is very difficult. This is particularly true when dealing with the analysis arising from potable water emergency pollution incidents. The main issues are: how to rapidly detect when significant contamination has occurred; to identify the cause or convincingly prove a negative in the absence of contamination and finally maintain an efficient and effective 24h/365d response system on a long-term basis for very low frequency events. This paper considers the handling of these issues for a water laboratory responsible for the regulatory analysis of drinking water for a population of over 8 million. Other key issue are how to assess the emergency response performance on a regular basis and the need to minimise operational costs. Chemical, radiological and ecotoxicological screening protocols are discussed. Microbiological emergency incidents are not covered. The numerous benefits of setting up a mutual aid laboratory response scheme are outlined.

Distribution system monitoring has typically included a minimal set of water quality parameters, acquired at low frequency. The parameter set, and frequency of data acquisition are insufficient for the surveillance of typical distribution systems' water quality in the event of agent introduction. An improved methodology is discussed. The method includes a more complete set of water quality parameters acquired at higher frequency, mathematical processing to alarm on deviations from operational baseline, pattern recognition of deviations, statistical analysis of recurring events, and a learning function which allows recurring events to be recognized and categorized as normal operation or unknown. Examples of events from distribution systems are presented and discussed.

Recently, there has been a significant interest in employing Terahertz (THz) technology, spectroscopy and imaging for standoff detection applications. There are three prime motivations for this interest: (a) THz radiation can detect concealed weapons since many non-metallic, non-polar materials are transparent to THz radiation, (b) target compounds such as explosives, and bio/chemical weapons have characteristic THz spectra that can be used to identify these compounds and (c) THz radiation poses no health risk for scanning of people. This paper will provide an overview of THz standoff detection of explosives and weapons including discussions of effective range, spatial resolution, and other limitations. The THz approach will be compared to alternative detection modalities such as x-ray and millimeter wave imaging.

This paper describes a passive millimeter-wave image scanner that leverages technologies previously developed for a video-rate passive millimeter-wave camera (PMC) [1, 2]. The imager has a prime focus elliptical frequency scanned antenna operating in the 75-93 GHz millimeter-wave band, a low noise receiver and a vertical beam former that allows the instantaneous capture of 128 pixel (vertical) column images in 1/30th of a second, with 2-3 K sensitivity. Two dimensional images are created by mechanically rotating the antenna, which produces a 128x60 raster image in 2 seconds. By integrating (averaging) images over a longer time period, we have demonstrated a sub-degree temperature resolution. This sensor has proven itself as a low cost tool for studying the potential of W-band passive imaging for various applications.

We present an overview of signal and image processing techniques developed for the concealed weapon detection (CWD) application. The signal/image processing chain is described and the tasks include image denoising and enhancement, image registration and fusion, object segmentation, shape description, and weapon recognition. Finally, a complete CWD example is presented for illustration.

The National Geospatial-Intelligence Agency (NGA) is faced with the difficult task of extracting geospatial intelligence information on complex, time-sensitive targets from a growing volume of images. Neuroscience promises to provide basic research findings that could translate into tools, training, and procedures capable of enhancing the current analysts' performance and productivity, as well as leading toward tools for automated image analysis. The Neuroscience-Enabled Geospatial Intelligence (NEGI) advanced research program has been developed to identify useful neuroscience research results and guide translational neuroscience research to provide a new generation of geospatial analysis tools. In addition, the capability of current models of the human vision system to perform basic geospatial analysis tasks has been assessed, with encouraging results.

The challenge of effectively focusing a large array of vision system in time-constrained and uncertain environments is a daunting challenge. The biology provides us some clues as higher-level organisms routinely accomplish complex tasks through iterative learning and invariance processing. In this talk, we discuss the present state-of-the-art in biological vision and recent computing models for recognition. We investigate invariance processing and reinforcement learning as some of the key ingredients for such computing. We present few implementations of these novel computing concepts.

The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the "wake-sleep" algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensory feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.

This paper discusses two novel artificial neural network architectures applied to multi-class classification problems of remote-sensing data. These approaches are 1) a spiking-neural-network model for the partitioning of data into clusters, and 2) a neuron model based on complex-valued weights (CVN). In the former model, the learning process is based on the Spike Timing-Dependent Plasticity rule under the Hebbian Learning framework. With temporally encoded inputs, the synaptic efficiencies of the delays between the pre- and post-synaptic spikes can store the information of different data clusters. With the encoding method using Gaussian receptive fields, the model was applied to the remote-sensing data. The result showed that it could provide more useful information than using traditional clustering method such as K-means. The CVN model has proved to be more powerful than traditional neuron models in solving the XOR problem and image processing problems. This paper discusses an implementation of the complex-valued neuron in NRBF neural networks to improve the NRBF structure. The complex-valued weights are used in the supervised learning part of an NRBF neural network. This classifier was tested with satellite multi-spectral image data and results show that this neural network model is more accurate and powerful than the conventional NRBF model.

Unmanned aerial vehicles with high quality video cameras are able to provide videos from 50,000 feet up that show a surprising amount of detail on the ground. These videos are difficult to analyze, because the airplane moves, the camera zooms in and out and vibrates, and the moving objects of interest can be in the scene, out of the scene, or partly occluded. Recognizing both the moving and static objects is important in order to find events of interest to human analysts. In this paper, we describe our approach to object and event recognition using multiple stages of classification.

The Image Content Engine (ICE) is being developed to provide cueing assistance to human image analysts faced with increasingly large and intractable amounts of image data. The ICE architecture includes user configurable feature extraction pipelines which produce intermediate feature vector and match surface files which can then be accessed by interactive relational queries. Application of the feature extraction algorithms to large collections of images may be extremely time consuming and is launched as a batch job on a Linux cluster. The query interface accesses only the intermediate files and returns candidate hits nearly instantaneously. Queries may be posed for individual objects or collections. The query interface prompts the user for feedback, and applies relevance feedback algorithms to revise the feature vector weighting and focus on relevant search results. Examples of feature extraction and both model-based and search-by-example queries are presented.

The problem of Tailgating/Piggybacking through secure portals is a long standing potential breach in virtually all controlled access portals. Absent a human guard, the access control system is totally dependant on honesty by the authorized users to prevent unauthorized passage when the portal is open for authorized passages. Newton Security has developed a system using stereo machine vision technology to defeat such unauthorized passages. While systems with very high rates of detection coupled with low false alarms have been developed and are currently deployed, further research is necessary to bring the cost of deployment down to facilitate the use of this technology in all doors protected with access control, not just the areas of high sensitivity where the product is currently utilized.