This PDF file contains the front matter associated with SPIE Proceedings Volume 6943, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and the Conference Committee listing.

Homeland security requires technologies capable of positive and reliable identification of humans for law enforcement, government, and commercial applications. As artificially intelligent agents improve in their abilities and become a part of our everyday life, the possibility of using such programs for undermining homeland security increases. Virtual assistants, shopping bots, and game playing programs are used daily by millions of people. We propose applying statistical behavior modeling techniques developed by us for recognition of humans to the identification and verification of intelligent and potentially malicious software agents. Our experimental results demonstrate feasibility of such methods for both artificial agent verification and even for recognition purposes.

To successfully predict the actions of an adversary and develop effective counteractions, knowledge of the enemy's mission and organization are needed. In this paper, we present new models and algorithms to identify behaviors of adversaries based on probabilistic inference of two main signatures of behavior: plans (what the enemy wants to do) and organizations (how the enemy is organized and who is responsible for what). The technology allows extraction, classification, and temporal tracking of behavior signatures using multi-source data, as well as prescribes intelligence collection plans to reduce the ambiguity in current predictions.

Miniature robots have many advantages over their larger counterparts, such as low cost, low power, and easy to build a large scale team for complex tasks. Heterogeneous multi miniature robots could provide powerful situation awareness capability due to different locomotion capabilities and sensor information. However, it would be expensive and time consuming to develop specific embedded system for different type of robots. In this paper, we propose a generic modular embedded system architecture called SMARbot (Stevens Modular Autonomous Robot), which consists of a set of hardware and software modules that can be configured to construct various types of robot systems. These modules include a high performance microprocessor, a reconfigurable hardware component, wireless communication, and diverse sensor and actuator interfaces. The design of all the modules in electrical subsystem, the selection criteria for module components, and the real-time operating system are described. Some proofs of concept experimental results are also presented.

Improvised explosive devices (IEDs) are an important concern to coalition forces during the conflicts in the Middle East. These devices are responsible for many casualties to American armed forces in the Middle East. These explosives are particularly dangerous because they are improvised with materials readily available to the designer, and there is no systematic way of explosive ordinance disposal. IEDs can be made from things such as standard military ammunition and can be detonated with common electronic devices such as cell phones and garage door openers. There is a great need for a low cost solution to neutralize these IEDs. At the Applied Physics Institute we are building a single function disrupter robot whose sole purpose is to neutralize these IEDs. We are modifying a toy remote control car to control it either wirelessly using WI-FI (IEEE 802.11) or wired by tethering the vehicle with an Ethernet cable (IEEE 802.3). The robot will be equipped with a high velocity fuze disrupter to neutralize the IED as well as a video camera for inspection and aiming purposes. This robot utilizes commercial-off-the-shelf (COTS) components which keeps the cost relatively low. Currently, similar robot systems have been deployed in Iraq and elsewhere but their method of operation is such that it is impractical to use in non-combat situations. We will discuss our design and possible deployment scenarios.

The number of missions in which unmanned vehicles are required to work collaboratively is increasing. In these applications, maintaining continuous communication among the vehicles is crucial. Wireless Mobile Ad Hoc Networks are being used in swarming platforms of unmanned vehicles given the increased range of coverage and the extra reliability that they provide. However, autonomous navigation includes the possibility of vehicles going out of communication range, producing network partitions and hindering the mission's success. In this paper, a new algorithm is proposed that uses the Received Signal Strength (RSSI) to determine when the vehicle has to modify its mobility pattern to remain in contact with the rest of the group. The algorithm, implemented in a platform of unmanned ground vehicles, was tested in indoor and outdoor environments. The results show that the proposed algorithm can effectively filter out unexpected propagation effects and provide a smooth estimate of the signal strength that the vehicles can use to control their mobility and maintain their connectivity at all times. In addition, the algorithm is simple to implement and has low computational requirements.

Multi-modal sensor suite mounted on a mobile platform such as a robot has several advantages. The robot can be sent into a cave or a cleared building to observe and determine the presence of unwanted people prior to entering those facilities. The robotic platform poses several challenges, for example, it can be noisy while it is in motion. Its electrical activity might interfere with the magnetic and electric field sensors. Its vibrations may induce noise into seismic sensors. We study the performance of acoustic, seismic, passive infrared (PIR), magnetic, electrostatic and video sensors for detection of personnel mounted on a robotic platform such as a Packbot. The study focuses on the quality of sensor data collected. In turn, the study would determine whether additional processing of data is required to mitigate the platform induced noise for detection of personnel. In particular, the study focuses on the following: Whether different sensors interfere with one another operating in close proximity, for example, the effect on magnetic and electrostatic sensors. Comparison of personnel detection algorithms developed for mobile platform and stationary sensor suite in terms of probability of detection, false alarms, and effects of fusion.

We investigate effects of bending stress on piezoelectric properties of polyvinylidene fluoride (PVDF) as a polymer sensor. The sensor was designed and fabricated into a special size and shape so that it can be attached to small insects, such as the American cockroach (Periplaneta Americana) to measure the insects' locomotion. The performance of the sensor is studied using a controlled linear stage to buckle the sensor mimicking the bending of the sensor due to the leg movements of cockroaches. For comparison, a roach robot was used for multi-leg study. Results indicate that buckling motion of the sensor produce an output that is different from regular stretching effect. The sensor-generated charge depends on the localized stress distribution and dipole alignment. This paper discusses the methods of characterization of piezoelectricity useful for insect applications.

CBRN Crime Scene Modeler (C2SM) is a prototype 3D modeling system for first responders investigating environments contaminated with Chemical, Biological, Radiological and Nuclear agents. The prototype operates on board a small robotic platform or a hand-held device. The sensor suite includes stereo and high resolution cameras, a long wave infra red camera, chemical detector, and two gamma detectors (directional and non-directional). C2SM has been recently tested in field trials where it was teleoperated within an indoor environment with gamma radiation sources present. The system has successfully created multi-modal 3D models (geometry, colour, IR and gamma radiation), correctly identified location of radiation sources and provided high resolution images of these sources.

Integrating a sensor suite with ability to discriminate potential Chemical/Biological (CB) events from high-explosive (HE) events employing an acoustic sensor array with a Time Difference of Arrival (TDOA) algorithm. Developing a cueing mechanism for more power intensive and range limited sensing CB techniques. Enabling the event detection algorithm to locate to a blast event using TDOA further information is provided of the event as either Launch/Impact and further as either CB/HE. The point of interest information is gathered to give a viewing window to a range limited chemical sensing system that exploits spectroscopy to determine the contents of the chemical event. The sensor suite is the system that will provide this information on the move while the chemical sensor will have adequate time to determine the contents of the event from a safe stand-off distance. The system exploits acoustic sensors to provide early detection and identification of CB attacks at ranges exceeding 2500m. The integration of these algorithms with the TDOA algorithm provides a complex suite of algorithms that can give early warning detection and highly reliable look direction from a great stand-off distance for a moving vehicle to determine if a candidate blast event is of potential CB type.

The detection, classification and tracking of chemical agents (explosives) being surreptitiously smuggled into public areas, such as airports, for destructive purposes is difficult to solve by unobtrusive means. We propose the use of a novel Alkaline Energy Cell (AEC) with gas/vapor sniffing capability as a potential solution. Variants of such devices are routinely used by police to detect alcohol emanating from the breath of suspected impaired vehicle drivers. We reported previously at the SPIE Symposium in 2007 the details of our technology and results. We have continued to advanced this capability with the development of an AEC which is capable of detecting gaseous emissions ultimately in the parts per billion range. Our work is described in terms of detecting TATP (acetone peroxide). Other explosive materials have also been investigated and will be reported on.

We study current-voltages and low-frequency noise characteristics of the metal--porous silicon--silicon single crystal--metal structure with 50% and 73% porosity of porous silicon. The study is performed in dry air and in a mix of dry air with carbon monoxide of different concentrations. The Hooge noise parameter α_H and the parameter γ in the frequency dependence of the noise voltage spectral density S_U(ƒ) were determined from experimental data. High sensitivity of spectral dependence of noise to gas concentration allows offering powerful method for determination of gas concentration in the air or environment.

Hydrogen nanosensor working near room temperature made of porous silicon covered by the TiO_2-x or ZnO thin film was realized. Porous silicon layer was formed by electrochemical anodization on a p- and n-type silicon surface. Thereafter, n-type TiO_2-x and ZnO thin films were deposited onto the porous silicon surface by electron-beam evaporation and magnetron sputtering, respectively. Platinum catalytic layer and gold electric contacts were for further measurements deposited onto obtained structures by ion-beam sputtering. The sensitivity of manufactured structures to 1000-5000 ppm of hydrogen was studied. Results of measurements showed that it is possible to realize a hydrogen nanosensor which has relatively high sensitivity and selectivity to hydrogen, durability, and short recovery and response times. Such a sensor can also be a part of silicon integral circuit and work near room temperatures.

A compact remote Raman sensor system was developed at NASA Langley Research Center. This sensor is an improvement over the previously reported system, which consisted of a 532 nm pulsed laser, a 4-inch telescope, a spectrograph, and an intensified CCD camera. One of the attractive features of the previous system was its portability, thereby making it suitable for applications such as planetary surface explorations, homeland security and defense applications where a compact portable instrument is important. The new system was made more compact by replacing bulky components with smaller and lighter components. The new compact system uses a smaller spectrograph measuring 9 x 4 x 4 in. and a smaller intensified CCD camera measuring 5 in. long and 2 in. in diameter. The previous system was used to obtain the Raman spectra of several materials that are important to defense and security applications. Furthermore, the new compact Raman sensor system is used to obtain the Raman spectra of a diverse set of materials to demonstrate the sensor system's potential use in the identification of unknown materials.

We present the results of investigations of double-layer thin-film hydrogen sensors that show high sensitivity at low operating temperatures and improved reliability. These hydrogen sensors are manufactured using the both ion-plasma assisted sputtering and sol-gel technique. It was established that the highest sensitivity of the sensors occurred at 100-130°C. The hydrogen sensitivity depends on hydrogen concentration linearly starting at 50 ppm, and reaches 10⁴ at 5000 ppm. The response time was 1-2 s and the recovery times were less than 10 s. We show that compared to constant power supply, pulse heating of the sensor improves the stability of the sensor, reduces the sensitivity to humidity, and reduces performance drift. Various possibilities of reducing CO gas cross sensitivity are also presented.

The brain is first and foremost a control system that is capable of building an internal representation of the external world, and using this representation to make decisions, set goals and priorities, formulate plans, and control behavior with intent to achieve its goals. The computational model proposed here assumes that this internal representation resides in arrays of cortical columns. More specifically, it models each cortical hypercolumn together with its underlying thalamic nuclei as a Fundamental Computational Unit (FCU) consisting of a frame-like data structure (containing attributes and pointers) plus the computational processes and mechanisms required to maintain it. In sensory-processing areas of the brain, FCUs enable segmentation, grouping, and classification. Pointers stored in FCU frames link pixels and signals to objects and events in situations and episodes that are overlaid with meaning and emotional values. In behavior-generating areas of the brain, FCUs make decisions, set goals and priorities, generate plans, and control behavior. Pointers are used to define rules, grammars, procedures, plans, and behaviors. It is suggested that it may be possible to reverse engineer the human brain at the FCU level of fidelity using nextgeneration massively parallel computer hardware and software. Key Words: computational modeling, human cortex, brain modeling, reverse engineering the brain, image processing, perception, segmentation, knowledge representation

Information enabled by technology is used to support good decision-making in the public safety arena. In the information age, the current implementation includes the installation of laptop computers and other mobile computing technology into mobile environments including ambulances, fire vehicles and police cruisers. Typically, these computing devices are equipped with various multi-purpose and custom software applications to replicate the public safety officers' headquarters work environment. In our analysis, we have found that there are large amounts of publiclyavailable datasets that are not available to the public safety officer in the field, but if made available in a real-time environment, could greatly assist the decision-making tasks of the officer and increase the safety of the public entrusted to the officer's watch. Information delivery and integration in the public safety environment are key elements required to maximize data support of public safety. Utilizing aspects of requirements engineering, we propose and develop a variety of applications that allow public safety officials to synthesize and interact with real-time data in the field. Information integration used to locate a topographic area map, satellite image, or immediate alert of the prevailing weather conditions assist in the timely decision-making process, especially in areas where such data is not available in the field. Public and private data sources are delivered according to agency protocols. Our environment takes advantage of the existing Project54 application and hardware, developed at the University of New Hampshire, as well as existing datacasting technology and other wireless mobile communications technologies.

The response, rescue and recovery teams that are engaged in disaster management operations require a continuous and comprehensive information flow of the disaster environment and a situational awareness in order to undertake fast and coordinated actions. Because of highly dynamic and often unpredictable disaster situations the teams need to adjust their goals, resources and actions both on an individual member level (agent) and on an entire team level (multi-agent system). This paper investigates a new approach to an agent's adaptability based on cognitive feedback introduced into the framework of inter-agent collaboration. The paper is a continuation of our work on situation-aware multi-agent systems. We discuss how agent adaptation and cognitive feedback is applied in the architecture of multi-agent systems for disaster situation management.

Bayesian performance metrics, based on such parameters, as: prior probability, probability of detection (or, accuracy), false alarm rate, and positive predictive value, characterizes the performance of binary sensors; i.e., sensors that have only binary response: true target/false target. Such binary sensors, very common in Homeland Security, produce an alarm that can be true, or false. They include: X-ray airport inspection, IED inspections, product quality control, cancer medical diagnosis, part of ATR, and many others. In this paper, we analyze direct and inverse conditional probabilities in the context of Bayesian inference and binary sensors, using X-ray luggage inspection statistical results as a guideline.

Over the last 15 years, the Pacific Northwest National Laboratory has performed significant research and development activities to enhance the state of the art of holographic radar imaging systems to be used at security checkpoints for screening people for concealed threats hidden under their garments. These enhancement activities included improvements to privacy techniques to remove human features and providing automatic detection of body-worn concealed threats. The enhanced privacy and detection methods used both physical and software imaging techniques. The physical imaging techniques included polarization-diversity illumination and reception, dual-frequency implementation, and high-frequency imaging at 60 GHz. Software imaging techniques to enhance the privacy of the person under surveillance included extracting concealed threat artifacts from the imagery to automatically detect the threat. This paper will focus on physical privacy techniques using dual-frequency implementation.

The history of perimeter protection is based on building fences. That basic concept evolved into detecting activity along fences using a variety of sensors. Today a wide variety of fiber and wire-based sensors are available to mount on a fence, and many different types of IR, radar, optical, seismic and acoustic sensors to place along the fence line. Generally some camera support is provided, with the cameras programmed to point to pre-set locations along the fence. A more robust perimeter protection would consist of wide area sensors with the capability to look out beyond the fence to detect potential intrusion and track intruders. In looking beyond the perimeter, wide area sensors can provide precious time to plan and initiate the appropriate response. In addition, because they sweep a 360-degree circle, the sensors can provide continued tracking of the intrusion, greatly enhancing the effectiveness and safety of the response team. The new wide-area concept consists of using modern radar technology for wide area detection of objects which are moving, and then using the precise location information from the radar to point a camera for assessment. Without having to continually stare at a bank of video monitors, the operator is presented with the location, direction of travel and identification and number of potential intruders, all in a matter of seconds. This paper presents the features of this new wide area system, followed by an overview of radar technology. It closes with a discussion on the benefits of the FMCW topology over Pulse Doppler in security and surveillance applications.

Human motion can be characterized as a periodic, temporal process of a mechanical system and can be detected by active and passive ultrasonic methods. The active method utilizes Doppler ultrasound to characterize the motion of individual body parts (torso, legs, arms, etc.). The friction forces of a footstep produce broadband sound signals that can be measured by passive ultrasonic sensors. Comparison of Doppler motion and the footstep signals reveals a strong correlation of features between the footstep friction and the maximum Doppler shift. This article presents test results from measurements of human motion and evaluates the detection range for the passive ultrasonic method.

Target detection and classification are considered the primary tasks in through-the-wall radar imaging. Indoor targets can be stationary or in motion. In this paper, we apply the matched illumination concept to the scattered electromagnetic field of two stationary targets that are commonly found in an indoor environment, namely, a wooden chair and a wooden table. The optimal waveform was obtained by choosing the eigenvector corresponding to the largest eigenvalue of the target's autocorrelation matrix. The scattered field over the frequency band of 1-3 GHz was obtained by full wave numerical simulations using a commercially available Finite-Difference Time Domain solver (XFDTD from REMCOM). The detection performance of the optimum waveform against the commonly used linear frequency modulated (LFM) signal of the same bandwidth was compared.

Using radar in a through-the-wall imaging application is an expanding field of research both for civilian and military uses. Thus far, most of the attention has been directed toward building radar imaging systems to detect objects within a room or building. The resulting images are full of ambiguity and difficult to interpret what the image is displaying. Presented here is a novel approach that addresses the interpretation of the images produced by the aforementioned imaging systems. We propose a classification scheme that provides an interpretation of an urban environment imaged in 3D. This approach builds probabilistic object models from feature vectors extracted from a volumetric radar image. A minimum-distance classifier is used to label radar image data and provide a 3D visualization of an urban scene. Results using real radar backscatter data validate the effectiveness of our method.

Sensor networks are finding application as monitoring systems and as tools in the study of complex natural systems. In either situation, the primary goal is computation of some inference from the observations and available models. From this basic problem flows a broad set of practical and theoretical issues, among them assurance of data integrity, sufficiency of data to support the inferences made concerning models/hypotheses, deployment density, and what tools and hardware are required not just to take observations but enable a community of non-engineers to participate in and adapt a sequence of experiments as new observations are obtained. The resulting constraints for designing systems for such purposes are quite different from those commonly assumed in the infancy of wireless sensor network research, and even now in much ongoing systems research. We describe these constraints in light of experience in deploying sensor networks in support of scientific study at the Center for Embedded Networked Sensors (CENS).

The Congressional and Army sponsored WaterWATCH^TM Program has developed and demonstrated a fully integrated shallow water port and facility monitoring system. It provides fully automated monitoring of domains above and below the surface of the water using primarily off-the-shelf sensors and software. The system is modular, open architecture and IP based, and elements can be mixed and matched to adapt to specific applications. The sensors integrated into the WaterWATCH^TM system include cameras, radar, passive and active sonar, and various motion detectors. The sensors were chosen based on extensive requirements analyses and tradeoffs. Descriptions of the system and individual sensors are provided, along with data from modular and system level testing. Camera test results address capabilities and limitations associated with using "smart" image analysis software with stressing environmental issues such as bugs, darkness, rain and snow. Radar issues addressed include achieving range and resolution requirements. The passive sonar capability to provide near 100% true positives with zero false positives is demonstrated. Testing results are also presented to show that inexpensive active sonar can be effective against divers with or without SCUBA gear and that false alarms due to fish can be minimized. A simple operator interface has also been demonstrated.

The US Navy sumbmarine Imaging and Electronic Warfare Program Office, NAVSEA PMS435, is constantly pursuing new technologies and enhanced capabilities in order to allow the submarine fleet to meet quick reaction mission requirements, anticipated future threats and to provide for improvements in overall situational awareness. NAVSEA PMS 435 is actively pursuing the development of applicable technologies and capabilities in the following areas: Periscope Headwindow Watershedding, Mid-Wave Infrared, Low Cost Expendable Imaging Sensors (LCES), Auto Detection and Tracking (ADAT), Auto Target Recognition (ATR), 360 Degree Imaging Systems, and Image Stitching Algorithms. This presentation provides a status of where NAVSEA PMS 435 is in regard to the development of these technologies and provides an opportunity to share ideas as to how they might be more effectively developed by leveraging information and other resources available in other government agencies, commercial partners and academia.

In harbour environments operators should perform tasks as detection and classification. Present-day threats of small objects, as jet skis etc, should be detected, classified and recognized. Furthermore threat intention should be analysed. As harbour environments contain several hiding spaces, due to fixed and floating neutral objects, correct assessment of the threats is complicated when detection tracks are intermittently known. For this purpose we have analysed the capability of our image enhancement and detection technology to assess the performance of the algorithms in a harbour environment. Data were recorded in a warm harbour location. During these trials several small surfaces targets were used, that were equipped with ground truth equipment. In these environments short-range detection is mandatory, followed by immediate classification. Results of image enhancement and detection are shown. An analysis was made into the performance assessment of the detection algorithms.

For several years ARL has studied acoustics to track vehicles, helicopters, Unmanned Aerial Vehicles (UAV) and others targets of interest. More recently these same acoustic sensors were placed on a "simulated" buoy in an attempt to detect and track aircraft over a large body of water. This report will investigate the advantages of using acoustic arrays to track air and water craft from a fixed floating platform as well as potential concerns associated with this technology. Continuous monitoring of aircraft overflight will increase situational awareness while persistent monitoring of commercial and military flight paths increases overall homeland security.

This paper reports on recent advances made in real-time intruder detection for an intrusion system utilizing a phasesensitive optical time-domain reflectometer developed at Texas A&M University. The system uses light pulses from a highly coherent laser to interrogate an optical fiber. The Rayleigh backscattered light is detected, and real-time processing of the received signal is implemented using an FPGA-based system. Signatures from a single human on foot and automobile have been obtained, and are comparable to results obtained with previous signal processing techniques. Individual footsteps are clearly identified for the single human intruder. With the introduction of real-time signal processing, the system can be run continuously, only triggering intrusions when they are detected. These recent advancements allow us to process intruder signatures more effectively. With these advancements, this technology is a prime candidate for low-cost perimeter monitoring of high-value and high-security targets such as nuclear power plants, military bases, and national borders.

This paper describes a design for an innovative command and control system for an intelligent coastal maritime security system. The architecture for this intelligent coastal maritime security system is derived from the forth generation real-time control (RCS) system architecture1 developed by the National Institute of Science and Technology (NIST) over the past twenty years. This command and control system is a decision support system for real-time monitoring, response and training for security scenarios that can be hosted at various locations along the coast of the United States where homeland security surveillance and response activities are required. Additionally, this paper describes the design for a derivative real-time simulation based environment that can be used as a state-of-art test bed for developing new hardware and software components to be integrated into previous versions of deployed real-time control systems.

The spare detectors from NASA Compton Gamma-Ray Observatory COMPTEL instrument have been reconfigured to demonstrate the capability at ground level to remotely locate and identify sources of g radiation or the movement of material that might shield γ-ray sources. The Gamma-Ray Experimental Telescope Assembly (GRETA) employs two 28 cm diameter scintillation detectors separated by 81 cm: one 8.5 cm thick liquid scintillator detector and one 7.5 cm thick NaI(Tl) detector. The assembly electronics and real-time data acquisition system measures the energy deposits and time-of- flight for each coincident detection and compiles histograms of total energy and incident angle as computed using the kinematics of Compton scattering. The GRETA field of view is a cone with full angle approximately 120°. The sensitive energy range is 0.3 to 2.6 MeV. Energy resolution is ~10% FWHM. The angular resolution, ~19° in the simplified configuration tested, will improve to better than 5° with well-defined enhancements to the data acquisition hardware and data analysis routines. When operated in the mode that was used in space, the instrument is capable of measuring and imaging up to 30 MeV with an angular resolution of 1.5°. The response of the instrument was mapped in the laboratory with 14 Ci ²²Na source 3 m from the instrument. Later, we conducted demonstrations under two measurement scenarios. In one, the remotely located instrument observed an increase of background radiation counts at 1.4 MeV when a large amount of lead was removed from a building and a corresponding decrease when the lead was replaced. In the other scenario, the location and isotope-identifying energy spectrum of a 500 μCi¹³⁷Cs source 3-5 m from the instrument with two intervening walls was determined in less than one minute. We report details of the instrument design and these measurements.

In this paper, we have presented two approaches addressing visual target tracking and localization in complex urban environment. The two techniques presented in this paper are: fusion-based multi-target visual tracking, and multi-target localization via camera calibration. For multi-target tracking, the data fusion concepts of hypothesis generation/evaluation/selection, target-to-target registration, and association are employed. An association matrix is implemented using RGB histograms for associated tracking of multi-targets of interests. Motion segmentation of targets of interest (TOI) from the background was achieved by a Gaussian Mixture Model. Foreground segmentation, on other hand, was achieved by the Connected Components Analysis (CCA) technique. The tracking of individual targets was estimated by fusing two sources of information, the centroid with the spatial gating, and the RGB histogram association matrix. The localization problem is addressed through an effective camera calibration technique using edge modeling for grid mapping (EMGM). A two-stage image pixel to world coordinates mapping technique is introduced that performs coarse and fine location estimation of moving TOIs. In coarse estimation, an approximate neighborhood of the target position is estimated based on nearest 4-neighbor method, and in fine estimation, we use Euclidean interpolation to localize the position within the estimated four neighbors. Both techniques were tested and shown reliable results for tracking and localization of Targets of interests in complex urban environment.

McQ has developed an advanced sensor system tailored for border monitoring that has been delivered as part of the SBInet program for the Department of Homeland Security (DHS). Technology developments that enhance a broad range of features are presented in this paper, which address the overall goal of the system to improving unattended ground sensor system capabilities for border monitoring applications. Specifically, this paper addresses a system definition, communications architecture, advanced signal processing to classify targets, and distributed sensor fusion processing.

The Department of Homeland Security and the Department of Health and Human Services have targeted bulk food contamination as a focus for attention. The contamination of bulk food poses a high consequence threat to our society. Milk transport falls into three of the 17 targeted NIPP (National Infrastructure Protection Plan) sectors including agriculture-food, public health, and commercial facilities. Minimal security safeguards have been developed for bulk milk transport. The current manual methods of securing milk are paper intensive and prone to errors. The bulk milk transportation sector requires a security enhancement that will both reduce recording errors and enable normal transport activities to occur while providing security against unauthorized access. Milk transportation companies currently use voluntary seal programs that utilize plastic, numbered seals on milk transport tank openings. Our group has developed a Milk Transport Security System which is an electromechanical access control and communication system that assures the secure transport of milk, milk samples, milk data, and security data between locations and specifically between dairy farms, transfer stations, receiving stations, and milk plants. It includes a security monitoring system installed on the milk transport tank, a hand held device, optional printers, data server, and security evaluation software. The system operates automatically and requires minimal or no attention by the bulk milk hauler/sampler. The system is compatible with existing milk transport infrastructure, and has the support of the milk producers, milk transportation companies, milk marketing agencies, and dairy processors. The security protocol developed is applicable for transport of other bulk foods both nationally and internationally. This system adds significantly to the national security infrastructure for bulk food transport. We are currently demonstrating the system in central Kentucky and will report on the results of the demonstration.

In 2006 SEPA has carried on the installation and tests of a demonstrator for an integrated subway protection system at a new subway station in the Naples (Italy) metropolitan area. Protection of a subway system is a difficult task given the amount of passengers transported every day. The demonstrator has been limited to non-intrusive detection techniques not to impair the passenger flow into the station. The demonstrator integrates several technologies and products that have been developed by SEPA or are already available on the market (MKS Instruments,...). The main purpose is to provide detection capabilities for attempts to introduce radioactive substances in the subway station, in order to foil possible attempts to place a dirty bomb, and threat detection and identification following release of chemical agents. The system integrates additional sensors such as video surveillance cameras and air flow sensing to complement the basic sensors suite. The need to protect sensitive installations such as subway stations has been highlighted by the series of terroristics actions carried out in recent years in the subway in London. However, given the number of passengers of a metro system, it is impossible to propose security techniques operating in ways similar to the screening of passengers in airports. Passengers screening and threat detection and identification must be quick, non-intrusive and capable of screening a large number of passengers to be applicable to mass transit systems. In 2005 SEPA, a small company operating in the field of trains video-surveillance systems and radiation detectors, started developing an integrated system to provide a comprehensive protection to subway stations, based on ready available or off-the-shelf components in order to quickly develop a reliable system with available technology. We ruled out at the beginning any new development in order to speed up the fielding of the system in less than one year. The system was developed with commercial sensors and deployed in a new station of the Naples metropolitan transit system in Mugnano. The station was particularly suitable for the demonstration since it is a new station that includes air venting control, water barriers (for fire and smoke containment) and a complete SCADA system to integrate technical and video surveillance operations. In order to protect the subway, we tackled four basic technologies, all readily available in-house or on the market: - radiation detection, to detect the introduction in the station of radionuclides, that may be dispersed by conventional explosive (a "dirty" bomb); - chemical agents detection and identification (after release), complemented with air speed and velocity sensors to estimate, track and predict the contamination plume; - video surveillance, integrated with the SCADA system and already available in the station.

General Sensing Systems (GSS) has achieved outstanding and verifiable results in the design and development of various seismic detection and identification systems. These results include, in particular, new seismic miniature sensor design and seismic signal recording and research for many traditional and nontraditional targets - walking, running and jumping persons, heavy and light vehicles, helicopters and aircraft, ships, trains, etc. These results also include the hardware design for up-to-date unattended seismic detection and identification systems. The main outcome of our effort is detection and identification algorithms and corresponding software for personnel and vehicle detection and identification which were tested in real environment conditions. These algorithms provide a zero false alarm rate with no target missing and can be used for many real and important military and homeland security applications. We also report on future seismic detection and identification systems for various military and civil applications.

A gunshot location system can be implemented in complex urban environments using a distributed array of acoustic sensors. A primary difficulty in computing the source location is that unknown path obstructions in the environment interfere with the reception of the sound at the sensor, by blocking the sound entirely, by refracting the path, or by creating echoes. Other complications are created by the similarity between gunshot sounds and other less interesting urban noises, frequency-dependent absorption of sound, and possible computational difficulty when multiple gunshots generate large data sets that stress real-time analysis routines. The ShotSpotter Gunshot Location System®1, deployed in over two dozen cities in the United States, detects and locates gunfire using a network of acoustic sensors placed on rooftops and utility poles, on moving vehicles, or on personnel. This sensor network, combined with a software system to collate and compute location results from the array of sensors, accurately locates gunshot sounds in complex urban environments. A classifier discards solutions incorporating non-gunshot audio pulses produced by the complex environment. Examples of difficult detection problems, including gunshots from a moving source, show that the detection and classification algorithms described are effective at recovering useful results from signals found in real-world urban scenarios.

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

An acoustic array, integrated with an algorithm to discriminate potential Launch (LA) or Impact (IM) events, was augmented by employing the Launch Impact Discrimination (LID) algorithm for mortar events. We develop an added situational awareness capability to determine whether the localized event is a mortar launch or mortar impact at safe standoff distances. The algorithm utilizes a discrete wavelet transform to exploit higher harmonic components of various sub bands of the acoustic signature. Additional features are extracted via the frequency domain exploiting harmonic components generated by the nature of event, i.e. supersonic shrapnel components at impact. The further extrapolations of these features are employed with a neural network to provide a high level of confidence for discrimination and classification. The ability to discriminate between these events is of great interest on the battlefield. Providing more information and developing a common picture of situational awareness. Algorithms exploit the acoustic sensor array to provide detection and identification of IM/LA events at extended ranges. The integration of this algorithm with the acoustic sensor array for mortar detection provides an early warning detection system giving greater battlefield information for field commanders. This paper will describe the integration of the algorithm with a candidate sensor and resulting field tests.

JPEG 2000 image compression allows many formatting alternatives, but users frequently have insufficient knowledge or experience to direct the choice. At compression time many of these options may seem approximately equal, but during exploitation the file structure differences can have a huge impact on access speed. This is particularly true for very large images such as those regularly used in remote sensing and many defense systems. This paper examines the impacts of JPEG 2000 options such as tiling, tile-parts, precincts, and packet ordering on large single band images, particularly in relationship to random access speed.

The author's recent participation in the Small Business Innovative Research (SBIR) program has resulted in the development of a patent pending technology that enables the construction of very large and fast artificial neural networks. Through the use of UNICON's CogniMax^TM pattern recognition technology we believe that systems can be constructed that exploit the power of "exhaustive learning" for the benefit of certain types of challenging pattern recognition problems. The Viacom lawsuit against YouTube^TM in early 2007 brought to light the magnitude of the video piracy problem and caused us to examine the associated technical challenges to determine whether our technology might enable an effective solution. This paper presents a theoretical study that describes how a massive-scale anti-piracy video pattern recognition system might be constructed using a large/fast Radial Basis Function (RBF) artificial Neural Network (NN) to enable a solution. Several daunting technical challenges exist. First, the amount of copyrighted video content that has been generated over time and now must be protected is enormous. Second, the activity level that is generally present on a large file-sharing site such as YouTube presents any pattern recognition system with a torrent of video content to inspect. Third, the concept of "fair-use" implies that an anti-piracy policy is not simply based on identifying a few copyrighted video frames. To determine system feasibility, this paper derives a set of example requirements for such a system, lays out a hypothetical anti-piracy data processing architecture, and evaluates the performance of the example system configuration.

Phase diversity imaging methods work well in removing atmospheric turbulence and some system effects from predominantly near-field imaging systems. However, phase diversity approaches can be computationally intensive and slow. We present a recently adapted, high-speed phase diversity method using a conventional, software-based neural network paradigm. This phase-diversity method has the advantage of eliminating many time consuming, computationally heavy calculations and directly estimates the optical transfer function from the entrance pupil phases or phase differences. Additionally, this method is more accurate than conventional Zernike-based, phase diversity approaches and lends itself to implementation on parallel software or hardware architectures. We use computer simulation to demonstrate how this high-speed, phase diverse imaging method can be implemented on a parallel, highspeed, neural network-based architecture-specifically the Cellular Neural Network (CNN). The CNN architecture was chosen as a representative, neural network-based processing environment because 1) the CNN can be implemented in 2-D or 3-D processing schemes, 2) it can be implemented in hardware or software, 3) recent 2-D implementations of CNN technology have shown a 3 orders of magnitude superiority in speed, area, or power over equivalent digital representations, and 4) a complete development environment exists. We also provide a short discussion on processing speed.

In an effort to enhance situation awareness, DHS is sponsoring the development of hardware and software systems to aid visualization of structures in which urban search and rescue (USAR) crews will be operating. Given positional data generated by virtual badges worn by first responders, the Hierarchical Grid Referenced Normalized Display (HiGRND) system dynamically creates visualization of the structure in which the responders are operating. In this paper, we discuss some of the recent work in progress on using virtual badge tracks to create visualizations of orthonormal structures. The method described here is used to seed a powerful mapping tool which is used by human operators to enhance an incident commander's situation awareness.

The author's recent participation in the Small Business Innovative Research (SBIR) program has resulted in the development of a patent pending technology that enables the construction of very large and fast artificial neural networks. Through the use of UNICON's CogniMax™ pattern recognition technology we believe that systems can be constructed that exploit the power of "exhaustive learning" for the benefit of certain types of complex and slow computational problems. This paper presents a theoretical study that describes one potentially beneficial application of exhaustive learning. It describes how a very large and fast Radial Basis Function (RBF) artificial Neural Network (NN) can be used to implement a useful computational system. Viewed another way, it presents an unusual method of transforming a complex, always-precise, and slow computational problem into a fuzzy pattern recognition problem where other methods are available to effectively improve computational performance. The method described recognizes that the need for computational precision in a problem domain sometimes varies throughout the domain's Feature Space (FS) and high precision may only be needed in limited areas. These observations can then be exploited to the benefit of overall computational performance. Addressing computational reliability, we describe how existing always-precise computational methods can be used to reliably train the NN to perform the computational interpolation function. The author recognizes that the method described is not applicable to every situation, but over the last 8 months we have been surprised at how often this method can be applied to enable interesting and effective solutions.