This PDF file contains the front matter associated with SPIE Proceedings Volume 7666, including the Title Page, Copyright information, Table of Contents, Conference Committee listing, Chair Introduction, and a series of slides from a Keynote Presentation.

IEEE 802.11 wireless Local Area Network (WLAN) becomes very prevalent nowadays. Either as a simple range extender for a home wired Ethernet interface, or as a wireless deployment throughout an enterprise, WLAN provides mobility, convenience, and low cost. However, an IEEE 802.11b/g wireless network uses the frequency of unlicensed 2.4GHz, which makes the network unsafe and more vulnerable than traditional Ethernet networks. As a result, anyone who is familiar with wireless network may initiate a Denial of Service (DoS) attack to influence the common communication of the network or even make it crash. In this paper, we present our studies on the DoS attacks and mitigation strategies for IEEE 802.11b/g WLANs and describe some initial implementations using IEEE 802.11b/g wireless devices.

This work concerns cyber attack and defense in the context of game theory—specifically hypergame theory. Hypergame theory extends classical game theory with the ability to deal with differences in players' expertise, differences in their understanding of game rules, misperceptions, and so forth. Each of these different sub-scenarios, or subgames, is associated with a probability—representing the likelihood that the given subgame is truly "in play" at a given moment. In order to form an optimal attack or defense policy, these probabilities must be learned if they're not known a-priori. We present hidden Markov model and maximum entropy approaches for accurately learning these probabilities through multiple iterations of both normal and modified game play. We also give a widely-applicable approach for the analysis of cases where an opponent is aware that he is being studied, and intentionally plays to spoil the process of learning and thereby obfuscate his attributes. These are considered in the context of a generic, abstract cyber attack example. We demonstrate that machine learning efficacy can be heavily dependent on the goals and styles of participant behavior. To this end detailed simulation results under various combinations of attacker and defender behaviors are presented and analyzed.

This work addresses new approaches to behavioral analysis of networks and hosts for the purposes of security monitoring and anomaly detection. Most commonly used approaches simply implement anomaly detectors for one, or a few, simple metrics and those metrics can exhibit unacceptable false alarm rates. For instance, the anomaly score of network communication is defined as the reciprocal of the likelihood that a given host uses a particular protocol (or destination);this definition may result in an unrealistically high threshold for alerting to avoid being flooded by false positives. We demonstrate that selecting and adapting the metrics and thresholds, on a host-by-host or protocol-by-protocol basis can be done by established multivariate analyses such as PCA. We will show how to determine one or more metrics, for each network host, that records the highest available amount of information regarding the baseline behavior, and shows relevant deviances reliably. We describe the methodology used to pick from a large selection of available metrics, and illustrate a method for comparing the resulting classifiers. Using our approach we are able to reduce the resources required to properly identify misbehaving hosts, protocols, or networks, by dedicating system resources to only those metrics that actually matter in detecting network deviations.

In many security environments, the textual content of communications may be unavailable. In these instances, it is often desirable to infer the status of the network and its component entities from patterns of communication flow. Conversational dynamics among entities in the network may provide insight into important aspects of the underlying social network such as the formational dynamics of group structures, the active state of these groups, individuals' roles within groups, and the likelihood of individual participation in conversations. To gain insight into the use of conversational dynamics to facilitate Dynamic Social Network Analysis, we explore the use of interevent timings to associate entities in the Twitter social networking and micro-blogging environment. Specifically, we use message timings to establish inter-nodal relationships among participants. In addition, we demonstrate a new visualization technique for tracking levels of coordination or synchronization within the community via measures of socio-temporal coherence of the participants.

This work presents a collection of methods that is used to effectively identify users of computers systems based on their particular usage of the software and the network. Not only are we able to identify individual computer users by their behavioral patterns, we are also able to detect significant deviations in their typical computer usage over time, or compared to a group of their peers. For instance, most people have a small, and relatively unique selection of regularly visited websites, certain email services, daily work hours, and typical preferred applications for mandated tasks. We argue that these habitual patterns are sufficiently specific to identify fully anonymized network users. We demonstrate that with only a modest data collection capability, profiles of individual computer users can be constructed so as to uniquely identify a profiled user from among their peers. As time progresses and habits or circumstances change, the methods presented update each profile so that changes in user behavior can be reliably detected over both abrupt and gradual time frames, without losing the ability to identify the profiled user. The primary benefit of our methodology allows one to efficiently detect deviant behaviors, such as subverted user accounts, or organizational policy violations. Thanks to the relative robustness, these techniques can be used in scenarios with very diverse data collection capabilities, and data privacy requirements. In addition to behavioral change detection, the generated profiles can also be compared against pre-defined examples of known adversarial patterns.

Spatial reuse of optical frequencies in optical fibers is possible through a novel Spatial Domain Multiplexing (SDM) technique that enables simultaneous propagation of two or more spatially multiplexed channels of exactly the same wavelength by confining them to unique spatial locations inside the fiber. Spatial filtering techniques are employed at the output end to separate the individual optical channels. The SDM channels follow helical path inside the carrier fiber and do not interfere with each other. This paper presents electromagnetic wave based model to analyze two such co-propagating SDM channels and then compares the model predictions to experimental data. The comparison of attenuation and dispersion show a close match to prove that SDM technique can be used to enhance the bandwidth of optical fiber systems.

Free-space optics (FSO) communication links for relaying video from cameras are investigated in relation to atmospheric attenuation. Through MODTRAN-based modeling of transmission bands across the NIR to MWIR (1.5-4.2 μm) portion of the infrared spectrum in atmospheric conditions including clear maritime, desert extinction, and various levels of rain and fog, we seek to identify which wavelength ranges are the most practical for minimizing transmission losses in both ideal and unfavorable conditions. Atmospheric, free-space, and scintillation losses are investigated for various FSO configurations and atmospheric conditions to determine incident beam power required for successful data transmission in view of a 2 km FSO link at various path elevation angles from the horizon. In addition, FSO transmitter and receiver circuits were designed to optically relay an analog video signal at IR wavelengths. Using advanced tunable laser sources to provide illumination across wavelength ranges from visible to mid-wave infrared, it should be possible to overcome transmission limitations associated with adverse weather and atmospheric conditions for communication networks to benefit border protection.

Most developments in data transfer techniques are incremental by nature and the goal of increasing total capacity in optical communications and networking requires new concepts for basic transmission media. The transmission data rates can only be enhanced by introducing new modulation and multiplexing techniques. In this paper different single mode tapered fiber waveguides are used to design a Spatial Multiplexer Unit (SMU) for a novel optical fiber multiplexing technique called the Spatial Domain Multiplexing (SDM) that allows co-propagation of two or more channels of exactly same wavelength without interfering with each other. This paper also presents a CAD model for the SMU and then compares the output beam profiles from different single mode tapered fibers to determine the optimum geometry for the SMU. Finally experimental and simulated beam profiles for the SMU are presented.

McQ developed for the U.S. Army Armament Research, Development and Engineering Center (ARDEC) an acoustic and infrared measurement, node localization, and building characterization prototype system. The system is designed for both manned and unmanned use to develop greater situational awareness through the exploration of unknown structures and relay of mapping data through ARDEC's Firestorm network. This research covers ultrasonic and infrared ranging sensor performance, GPS-denied positioning solutions, sensor data fusion, and mapping algorithms. Applications of McQ's Structure Mapping system also include first responder mapping and positioning. McQ will present development methodology and performance.

Situational awareness of CBRN robot operators is quite limited, as they rely on images and measurements from on-board detectors. This paper describes a novel framework that enables a uniform and intuitive access to live and recent data via 2D and 3D representations of visited sites. These representations are created automatically and augmented with images, models and CBRNE measurements. This framework has been developed for CBRNE Crime Scene Modeler (C2SM), a mobile CBRNE mapping system. The system creates representations (2D floor plans and 3D photorealistic models) of the visited sites, which are then automatically augmented with CBRNE detector measurements. The data stored in a database is accessed using a variety of user interfaces providing different perspectives and increasing operators' situational awareness.

In this paper, we propose a unified framework of multistage parametric inference with wide applications. Within the new framework, we have developed specific multistage parametric estimation and hypothesis testing procedures which are rigorous and unprecedentedly efficient as compared to existing methods. Our multistage parametric inferential techniques have immediate applications to performance evaluation of information and dynamic control systems.

In order for First Responder Command and Control personnel to visualize incidents at urban building locations, DHS sponsored a small business research program to develop a tool to visualize 3D building interiors and movement of First Responders on site. 21st Century Systems, Inc. (21CSI), has developed a toolkit called Hierarchical Grid Referenced Normalized Display (HiGRND). HiGRND utilizes three components to provide a full spectrum of visualization tools to the First Responder. First, HiGRND visualizes the structure in 3D. Utilities in the 3D environment allow the user to switch between views (2D floor plans, 3D spatial, evacuation routes, etc.) and manually edit fast changing environments. HiGRND accepts CAD drawings and 3D digital objects and renders these in the 3D space. Second, HiGRND has a First Responder tracker that uses the transponder signals from First Responders to locate them in the virtual space. We use the movements of the First Responder to map the interior of structures. Finally, HiGRND can turn 2D blueprints into 3D objects. The 3D extruder extracts walls, symbols, and text from scanned blueprints to create the 3D mesh of the building. HiGRND increases the situational awareness of First Responders and allows them to make better, faster decisions in critical urban situations.

Natural disasters take hundreds of thousands of lives each year. They generate billions of dollars in financial losses annually. Some of these losses are inevitable due to the high population densities in areas at risk for earthquakes, tornados, hurricanes and other powerful forces in nature. However with improvements in weather forecasting, the emergence of better modeling and simulation tools, and the application of these tools to disaster preparation and recovery planning these losses in human life and infrastructure can be greatly mitigated. ADEPT is an application developed by Rite-Solutions that joins storm surge modeling with evacuation route planning. It can be used to train citizens and first responders in best disaster evacuation practices.

In this paper, Bayesian inference is applied to performance metrics definition of the important class of recent Homeland Security and defense systems called binary sensors, including both (internal) system performance and (external) CONOPS. The medical analogy is used to define the PPV (Positive Predictive Value), the basic Bayesian metrics parameter of the binary sensors. Also, Small System Integration (SSI) is discussed in the context of recent Homeland Security and defense applications, emphasizing a highly multi-technological approach, within the broad range of clusters ("nexus") of electronics, optics, X-ray physics, γ-ray physics, and other disciplines.

Recent events highlight the need for efficient tools for anticipating the threat posed by terrorists, whether individual or groups. Antiterrorism includes fostering awareness of potential threats, deterring aggressors, developing security measures, planning for future events, halting an event in process, and ultimately mitigating and managing the consequences of an event. To analyze such components, one must understand various aspects of threat elements like physical assets and their economic and social impacts. To this aim, we developed a three-layer Bayesian belief network (BBN) model that takes into consideration the relative threat of an attack against a particular asset (physical layer) as well as the individual psychology and motivations that would induce a person to either act alone or join a terrorist group and commit terrorist acts (social and economic layers). After researching the many possible motivations to become a terrorist, the main factors are compiled and sorted into categories such as initial and personal indicators, exclusion factors, and predictive behaviors. Assessing such threats requires combining information from disparate data sources most of which involve uncertainties. BBN combines these data in a coherent, analytically defensible, and understandable manner. The developed BBN model takes into consideration the likelihood and consequence of a threat in order to draw inferences about the risk of a terrorist attack so that mitigation efforts can be optimally deployed. The model is constructed using a network engineering process that treats the probability distributions of all the BBN nodes within the broader context of the system development process.

Draper Laboratory and MRAC have recently completed a comprehensive study to quantitatively evaluate deception detection performance under different interviewing styles. The interviews were performed while multiple physiological waveforms were collected from participants to determine how well automated algorithms can detect deception based upon changes in physiology. We report the results of a multi-factorial experiment with 77 human participants who were deceptive on specific topics during interviews conducted with one of two styles: a forcing style which relies on more coercive or confrontational techniques, or a fostering approach, which relies on open-ended interviewing and elements of a cognitive interview. The interviews were performed in a state-of-the-art facility where multiple sensors simultaneously collect synchronized physiological measurements, including electrodermal response, relative blood pressure, respiration, pupil diameter, and ECG. Features extracted from these waveforms during honest and deceptive intervals were then submitted to a hypothesis test to evaluate their statistical significance. A univariate statistical detection algorithm then assessed the ability to detect deception for different interview configurations. Our paper will explain the protocol and experimental design for this study. Our results will be in terms of statistical significances, effect sizes, and ROC curves and will identify how promising features performed in different interview scenarios.

Designers and consumers of various security, intelligence, surveillance and reconnaissance (ISR) systems as well as various unattended ground sensors pay most attention to their commonly used performance characteristics such as probability of a target detection and probability of a false alarm. These characteristics are used for systems comparison and evaluation. However, it is not enough for end-users of these systems as well as for their total/final effectiveness assessment. This article presents and discusses a system approach to an efficiency estimation of the security and ISR systems. Presented approach aims at final result of the system's function and use. It allows setting up reasonable technical and structural requirements for the security and ISR systems, to make trustworthy comparison and practical application planning of such systems. It also allows finding forward-looking, perspective ways of systems development. Presented results can be guidance to both designers and consumers.

For through-the-air optical communication applications, we present a high speed detector module with high bandwidth and large active area. The detector has achieved a rise time of 220 pS with a full-width-half-max of 420 pS. Data rates are expected to approach 2 GHz. The active area of the input window is 12 mm, giving a large collection surface for through-the-air applications. The detector module includes an integrated power supply having low power consumption. In comparison with other detector technologies, this new detector exceeds the speed of conventional photomultiplier designs by 3 to 5 times. In comparison with microchannel plate detectors, the speed is comparable, but the throughput of the new detector is much higher - tens of microamperes of signal current can be obtained indefinitely. Optical communication applications can be served by two different designs. In the first case, the module utilizes gain based on ordinary secondary emission materials to achieve current gains of 1500. This design is suitable for applications at the limit of the detector's bandwidth where light power is relatively high. In another design, the secondary emission material was changed to diamond film which allows five times higher gain. While the current design uses an ordinary, blue sensitive input light conversion material, higher efficiency materials are in development for signals at longer wavelength.

Military personnel are often asked to accomplish rigorous missions in extremes of climate, terrain, and terrestrial altitude. Personal protective clothing and individual equipment such as body armor or chemical biological suits and excessive equipment loads, exacerbate the physiological strain. Health, over even short mission durations, can easily be compromised. Measuring and acting upon health information can provide a means to dynamically manage both health and mission goals. However, the measurement of health state in austere military environments is challenging; (1) body worn sensors must be of minimal weight and size, consume little power, and be comfortable and unobtrusive enough for prolonged wear; (2) health states are not directly measureable and must be estimated; (3) sensor measurements are prone to noise, artifact, and failure. Given these constraints we examine current successful ambulatory physiological status monitoring technologies, review maturing sensors that may provide key health state insights in the future, and discuss unconventional analytical techniques that optimize health, mission goals, and doctrine from the perspective of thermal work strain assessment and management.

Public Health organizations throughout the world are called upon to be at the forefront of responding to emerging infectious disease events or natural catastrophes such as the Haitian and Chilean earthquakes. One of the key components to effective public health engagement is situational awareness. Situational awareness means understanding what is going on around you. Whilst that may seem to be a simple statement it is not. True situational awareness means acquiring all relevant information about the event and translating that information into actionable knowledge.

The peptide hormone hepcidin, has emerged as the master regulator of iron homeostasis. Dysregulation of hepcidin is a principal or contributing factor in most genetic and acquired systemic iron disorders, including anemia of inflammation (anemia of chronic disease). Hepcidin maintains healthy blood iron levels by regulating dietary iron absorption and transport from body iron stores to plasma. High serum hepcidin levels observed in chronic and acute inflammatory conditions can cause anemia by limiting plasma iron available for erythropoiesis. Chronically low serum hepcidin levels cause iron-overload and ultimately, accumulation of iron in liver and heart. We recently validated the first immunoassay for serum hepcidin and established the normal ranges in adults. Hepcidin has excellent potential as a biomarker and has a known mechanism of action, good stability, and rapid response to iron stores, inflammatory stimuli, and bacterial infections. Hepcidin can be measured in blood, urine, and saliva, and is generally not measurable in iron deficient/anemic patients and highly elevated in inflammatory diseases and infections. Intrinsic LifeSciences (ILS) is developing second generation hepcidin immunoassays and lateral-flow POC devices for hepcidin, a well characterized multi-purpose biomarker with applications in global health security.

The number of technologies and consumer products that incorporate engineered nanomaterials (ENMs) has grown rapidly. Indeed, ENMs such as carbon nanotubes and nano-silver, are revolutionizing many commercial technologies and have already been incorporated into more than 800 commercial products, including polymer composites, cell phone batteries, sporting equipment and cosmetics. The global market for ENMs has grown steadily from $7.5 billion in 2003 to $12.7 billion in 2008. Over the next five years, their market value is expected to exceed $27 billion. This surge in demand has been responsible for a corresponding increase in the annual production rates of ENMs. For example, Bayer anticipates that single and multi-walled carbon nanotubes (SWNT and MWNT) production rates will reach 3,000 tons/yr by 2012. Inevitably, some of these synthetic materials will enter the environment either from incidental release during manufacture and transport, or following use and disposal. Consequently, intense scientific research is now being directed towards understanding the environmental, health and safety (EHS) risks posed by ENMs. I will highlight some of the key research challenges and needs in this area, include (i) developing structure-property relationships that will enable physicochemical properties of ENMs to be correlated with environmentally relevant behavior (e.g. colloidal properties, toxicity), (ii) determining the behavior of nanoproducts, and (iii) developing analytical techniques capable of detecting and quantifying the concentration of ENMs in the environment.

Luminescent semiconductor quantum dots (QDs) possess several unique optical and spectroscopic properties including high quantum yields, broad absorption spectra coupled to narrow symmetric, size-tunable emissions allowing large achievable Stokes shifts, and exceptional resistance to photo- and chemical degradation. These properties make QDs unique enabling materials for the development of the next generation of highly efficient biosensors for health security applications, particularly within the context of living and fixed cells. Paramount in this developmental process is addressing the biocompatibility of the QD materials. We are developing robust and facile delivery schemes for the selective intracellular delivery of QD-based nanoassemblies. These schemes are based upon the self-assembly and subsequent cellular uptake of QD-peptide and QD-polymer bioconjugates. Cellular delivery experiments utilizing both delivery schemes will be presented. The advantages and disadvantages of each approach will be discussed, including the intracellular fate and stability of the QD-nanoassemblies.

The Homeland Security News Wire has been reporting on new ways to fight epidemics using digital tools such as iPhone, social networks, Wikipedia, and other Internet sites. Instant two-way communication now gives consumers the ability to complement official reports on emerging infectious diseases from health authorities. However, there is increasing concern that these communications networks could open the door to mass panic from unreliable or false reports. There is thus an urgent need to ensure that epidemiological monitoring for emerging infectious diseases gives health authorities the capability to identify, analyze, and report disease outbreaks in as timely and efficient a manner as possible. One of the dilemmas in the global dissemination of information on infectious diseases is the possibility that information overload will create inefficiencies as the volume of Internet-based surveillance information increases. What is needed is a filtering mechanism that will retrieve relevant information for further analysis by epidemiologists, laboratories, and other health organizations so they are not overwhelmed with irrelevant information and will be able to respond quickly. This paper introduces a self-organizing ontology that could be used as a filtering mechanism to increase relevance and allow rapid analysis of disease outbreaks as they evolve in real time.

High-density resequencing microarrays support simultaneous detection and identification of multiple viral and bacterial pathogens. Because detection and identification using RPM is based upon multiple specimen-specific target pathogen gene sequences generated in the individual test, the test results enable both a differential diagnostic analysis and epidemiological tracking of detected pathogen strains and variants from one specimen to the next. The RPM assay enables detection and identification of pathogen sequences that share as little as 80% sequence similarity to prototype target gene sequences represented as detector tiles on the array. This capability enables the RPM to detect and identify previously unknown strains and variants of a detected pathogen, as in sentinel cases associated with an infectious disease outbreak. We illustrate this capability using assay results from testing influenza A virus vaccines configured with strains that were first defined years after the design of the RPM microarray. Results are also presented from RPM-Flu testing of three specimens independently confirmed to the positive for the 2009 Novel H1N1 outbreak strain of influenza virus.

A previously developed resequencing microarray, "Tropical and Emerging Infections (RPM-TEI v.1.0 chip)", designed to identify and discriminate between tropical diseases and other potential biothreat agents, their near-neighbor species, and/or potential confounders, was used to characterize the microbes present in the silt/clay fraction of surface soils and airborne dust collected from the Middle East. Local populations and U.S. military personnel deployed to the Middle East are regularly subjected to high levels of airborne desert dust containing a significant fraction of inhalable particles and some portion require clinical aid. Not all of the clinical symptoms can be directly attributed to the physical action of material in the human respiratory tract. To better understand the potential health effects of the airborne dust, the composition of the microbial communities associated with surface soil and/or airborne dust (air filter) samples from 19 different sites in Iraq and Kuwait was identified using RPM-TEI v.1.0. Results indicated that several microorganisms including a class of rapidly growing Mycobacterium, Bacillus, Brucella, Clostridium and Coxiella burnetti, were present in the samples. The presence of these organisms in the surface soils and the inhalable fraction of airborne dust analyzed may pose a human health risk and warrants further investigation. Better understanding of the factors influencing the composition of these microbial communities is important to address questions related to human health and is critical to achieving Force Health Protection for the Warfighter operating in the Middle East, Afghanistan, North Africa and other arid regions.

The World Health Organization (WHO) is rapidly expanding antiretroviral treatment (ART) in sub-Saharan countries. However, virological failure of ART is rarely monitored due to the lack of affordable and sustainable viral load assays suitable for resource-limited settings. Here, we report a prototype of a rapid virus detection method based on microfluidic technologies. In this method, HIV-1 particles from 10 μL whole blood were captured by anti-gp120 antibody coated on the microchannel surface and detected by dual fluorescence signals under microscopy. Next, captured HIV-1 particles were counted using the free software, ImageJ (http://rsbweb.nih.gov/ij/). This rapid HIV-1 detection method has potential to be further developed for viral load monitoring at resource-limited settings.

To improve the reliability of fluorescence data in the life and material sciences and to enable accreditation of fluorescence techniques, standardization concepts are required that guarantee and improve the comparability of fluorescence measurements. At the core of such concepts are simple and evaluated fluorescence standards for the consideration of instrument-specific spectral and intensity distortions of measured signals and for instrument performance validation (IPV). Similarly in need are fluorescence intensity standards for the quantification from measured intensities and for signal referencing, thereby accounting for excitation light-induced intensity fluctuations. These standards should be preferably certified, especially for use in regulated areas like medical diagnostics. This encouraged us to develop liquid and solid standards for different fluorescence parameters and techniques for use under routine measurement conditions in different formates. Special emphasis was dedicated to the determination and control of the spectral responsivity of detection systems, wavelength accuracy, homogeneity of illumination, and intensity referencing for e.g. spectrofluorometers, fluorescence sensors and confocal laser scanning fluorescence microscopes. Here, we will present design concepts and examples for mono- and multifunctional fluorescence standards that provide traceability to radiometric units and present a first step towards a toolbox of standards.

We have developed a laser-induced fluorescence (LIF) system to detect and continuously observe in real time the levels of colored dissolved organic matter (CDOM) or Dissolved Organic Compounds (DOCs) in water from various sources, such as tap water and reverse osmosis processed water. At the same time, we have studied deep-UV light emitting diodes (LEDs) as alternative light sources for our system, which would make the apparatus cheaper and more compact. Our portable LIF system had two interchangeable microchip Nd:YAG lasers, operating at 266 nm and 355 nm, as UV sources, and fluorescence was measured over the range of 260-680 nm. The fluorescence was collected at 90º to the laser beam. We have also studied deep-UV LEDs emitting between 265 nm and 355 nm as alternative sources of fluorescence excitation. The average laser power was approximately 30 times that of the LED. Fluorescence spectra from sea water, tap water, and reverse osmosis water for both excitation sources were also measured, and similar spectra were observed. Differences in the signal intensity due to the difference in the laser and LED excitation intensity were consistent with theory.

Techniques for dynamic behavioral analysis and modeling have recently become an increasingly researched topic. In essence, they aim to understand the mechanics of a set of variables over time, allowing for prediction of future data, anomaly or change detection, or estimation of a latent variable. Much of this research has focused on the sequential analysis of individual tracks of data - for example, in multi-target tracking (MTT). In recent years, massive amounts of behavioral and usage data have become available due to the proliferation of online services and their large users bases. The data from these applications can not be said to be monolithically generated - there are many processes and activities occurring simultaneously. However, it also cannot be said that this data consists of a set of independently running processes, as there are often strong correlations among subsets of the variables. Therefore we have a potentially large set of loosely coupled entities that can be modeled neither as a single, large process, or a large set of individual processes. "Static" applications, e.g. rating predictors for recommender systems, have greatly exploited entity to entity correlations through processes such as collaborative filtering. In this paper, we present a probabilistic model for loosely coupled and correlated dynamic data sets and techniques for making inference about the model. Experimental results are presented using data gathered from instrumented wireless access points around a college campus.

Developing an integrated person-borne improvised explosive device (IED) countermeasure to protect unstructured crowds at large public venues is the goal of the Standoff Technology Integration and Demonstration Program (STIDP), sponsored in part by the U.S. Department of Homeland Security (DHS). The architecture being developed includes countermeasure technologies deployed as a layered defense and enabling technologies for operating the countermeasures as an integrated system. In the architecture, early recognition of potentially higher-risk individuals is crucial. Sensors must be able to detect, with high accuracy, explosives' threat signatures in varying environmental conditions, from a variety of approaches and with dense crowds and limited dwell time. Command-and-control technologies are needed to automate sensor operation, reduce staffing requirements, improve situational awareness, and automate/facilitate operator decisions. STIDP is developing technical and operational requirements for standoff and remotely operated sensors and is working with federal agencies and foreign governments to implement these requirements into their research and development programs. STIDP also is developing requirements for a software platform to rapidly integrate and control various sensors; acquire, analyze, and record their data; and present the data in an operationally relevant manner. Requirements also are being developed for spatial analysis, tracking and assessing threats with available screening resources, and data fusion for operator decision-making.

A fusion system that accommodates both text-based extracted information along with more conventional sensor-derived input has been developed and demonstrated in a terrorist attack scenario as part of the Empire Challenge (EC) 09 Exercise. Although the fusion system was developed to support Army military analysts, the system, based on a set of foundational fusion principles, has direct applicability to department of homeland security (DHS) & defense, law enforcement, and other applications. Several novel fusion technologies and applications were demonstrated in EC09. One such technology is location normalization that accommodates both fuzzy semantic expressions such as behind Library A, across the street from the market place, as well as traditional spatial representations. Additionally, the fusion system provides a range of fusion products not supported by traditional fusion algorithms. Many of these additional capabilities have direct applicability to DHS. A formal test of the fusion system was performed during the EC09 exercise. The system demonstrated that it was able to (1) automatically form tracks, (2) help analysts visualize behavior of individuals over time, (3) link key individuals based on both explicit message-based information as well as discovered (fusion-derived) implicit relationships, and (4) suggest possible individuals of interest based on their association with High Value Individuals (HVI) and user-defined key locations.

Currently, ships and crews in port interact with local authorities, and other ships, through standard routine and emergency radio and phone connections. Not withstanding the US Coast Guard National Response Center and its online incident reporting service, there is currently no standardized two-way digital communications between ships and local authorities for the purpose of local real-time information sharing and coordination. In the context of maritime port operations we are exploring the PIERS concept, a digital in port information sharing service to enable a ship's crew to effectively operate with the port authority and, should the need arise, with emergency response teams.

The defense, intelligence, and homeland security communities are driving a need for software dominant, real-time or near-real time atmospheric turbulence compensated imagery. The development of parallel processing capabilities are finding application in diverse areas including image processing, target tracking, pattern recognition, and image fusion to name a few. A novel approach to the computationally intensive case of software dominant optical and near infrared imaging through atmospheric turbulence is addressed in this paper. Previously, the somewhat conventional wavelength diversity method has been used to compensate for atmospheric turbulence with great success. We apply a new correlation based approach to the wavelength diversity methodology using a parallel processing architecture enabling high speed atmospheric turbulence compensation. Methods for optical imaging through distributed turbulence are discussed, simulation results are presented, and computational and performance assessments are provided.

We describe a design concept for a flat (or conformal) thin-plate laser phased-array aperture. The aperture consists of a substrate supporting a grid of single-mode optical waveguides fabricated from a linear electro-optic material. The waveguides are coupled to a single laser source or detector. An arrangement of electrodes provides for two-dimensional beam steering by controlling the phase of the light entering the grid. The electrodes can also be modulated to simultaneously provide atmospheric turbulence modulation for long-range free-space optical communication. An approach for fabrication is also outlined.

Due to increased security concerns, the commitment to monitor and maintain security in the maritime environment is increasingly a priority. A country's coast is the most vulnerable area for the incursion of illegal immigrants, terrorists and contraband. This work illustrates the ability of a low-cost, light-weight, multi-spectral, multi-channel imaging system to handle the environment and see under difficult marine conditions. The system and its implemented detecting and tracking technologies should be organic to the maritime homeland security community for search and rescue, fisheries, defense, and law enforcement. It is tailored for airborne and ship based platforms to detect, track and monitor suspected objects (such as semi-submerged targets like marine mammals, vessels in distress, and drug smugglers). In this system, automated detection and tracking technology is used to detect, classify and localize potential threats or objects of interest within the imagery provided by the multi-spectral system. These algorithms process the sensor data in real time, thereby providing immediate feedback when features of interest have been detected. A supervised detection system based on Haar features and Cascade Classifiers is presented and results are provided on real data. The system is shown to be extendable and reusable for a variety of different applications.

Border Defense remains an urgent priority in the coming years. In order to meet DHS goals for 24/7 border surveillance capability, long range state of the art infrared imagers are needed to cover large area's economically. The long range gives both greater detection range, and therefore more response time, as well as a reduced number of units to cover the same area thus reducing infrastructure cost. L-3 Communications Cincinnati Electronics is a premier supplier of longrange infrared imagers and has a portfolio of solutions with unmatched detection ranges. The sensitivity and resolution of these instruments offer a wide range of applications spaces including both Northern and Southern borders. As part of an integrated systems level solution, potentially including technology to control the environment such as atmospheric turbulence corrections, these imagers can enable high confidence border security.

InGaN alloy fluctuations have been exploited in many nitride optoelectronic devices. This work reports on the application of InGaN alloy fluctuations in a packaged vacuum electronic device utilizing an InGaN photocathode as the detector element. The resulting image intensifier is the first ever InGaN imaging detector. Exploitation of the particular InGaN properties of alloy fluctuations has several positive consequences for photocathodes. One, it is advantages because of the possibility of extending the spectral response to the longer wavelengths with lower average indium concentrations. Two, in achieving a longer wavelength response, this lessens the strain at the sapphire-AlN-InGaN interface because a lower average In percentage can be used. Thirdly, the larger bandgap InGaN matrix material will have a lower amount of thermionic emission coupled with this longer wavelength photoresponse. Finally, an InGaN alloy with visible response holds the promise in that it can be grown directly on a sapphire window as opposed to the compression bonding of GaAs as originally reported by Antypas and Edgecumbe.

A secure border necessitates the development of new technology for remote sensing and surveillance. We investigate and develop wireless sensor network systems consisting of spatially distributed sensor nodes that can monitor various environmental parameters including temperature, humidity, motion, and vibration, etc. The sensors, nodes and transceivers have low-power consumption and are powered by solar energy so that the systems can work over long time periods with minimal human intervention and maintenance. This paper presents the technology development, wireless sensor integration, power management, and communication architecture, as well as a demonstration of environmental monitoring.

We have reported previously on the use of a novel Electric Potential Sensor, developed and patented at the University of Sussex, for remote monitoring of life signs and through-wall sensing of movement and proximity. In this paper we present the data obtained using a sparse (4-element) array of sensors to image a volume of space for target movements. This is achieved by passive monitoring of the disturbances which result from the movement of a dielectric object through the ambient electric field. Numerical computation is used to simulate the expected sensor responses for a given pattern of movement and comparison with these simulations allows the trajectory to be followed. With this 4-element array, it is possible to track the movement of a single subject, for example an intruder, or the lone occupant of a room. However, with the addition of just a few extra sensors, it is possible to resolve the ambiguities caused by multiple targets. The advantage of this approach over competing technologies such as radar, for through-wall surveillance and tracking, is that the method is passive. It requires no excitation field or probe signal and relies instead on the ambient static electric field which exists between the ionosphere and the surface of the Earth. It therefore only works well if the array is not obstructed by earthed conducting materials, in common with the other technologies. On the other hand, the passive nature of the technique provides a low power system which is potentially undetectable.

An effective public safety sensor system for heavily-populated applications requires sophisticated and geographically-distributed infrastructures, centralized supervision, and deployment of large-scale security and surveillance networks. Artificial intelligence in sensor systems is a critical design to raise awareness levels, improve the performance of the system and adapt to a changing scenario and environment. In this paper, a highly-distributed, fault-tolerant, and energy-efficient Smart Sensing Surveillance System (S4) is presented to efficiently provide a 24/7 and all weather security operation in crowded environments or restricted areas. Technically, the S4 consists of a number of distributed sensor nodes integrated with specific passive sensors to rapidly collect, process, and disseminate heterogeneous sensor data from near omni-directions. These distributed sensor nodes can cooperatively work to send immediate security information when new objects appear. When the new objects are detected, the S4 will smartly select the available node with a Pan- Tilt- Zoom- (PTZ) Electro-Optics EO/IR camera to track the objects and capture associated imagery. The S4 provides applicable advanced on-board digital image processing capabilities to detect and track the specific objects. The imaging detection operations include unattended object detection, human feature and behavior detection, and configurable alert triggers, etc. Other imaging processes can be updated to meet specific requirements and operations. In the S4, all the sensor nodes are connected with a robust, reconfigurable, LPI/LPD (Low Probability of Intercept/ Low Probability of Detect) wireless mesh network using Ultra-wide band (UWB) RF technology. This UWB RF technology can provide an ad-hoc, secure mesh network and capability to relay network information, communicate and pass situational awareness and messages. The Service Oriented Architecture of S4 enables remote applications to interact with the S4 network and use the specific presentation methods. In addition, the S4 is compliant with Open Geospatial Consortium - Sensor Web Enablement (OGC-SWE) standards to efficiently discover, access, use, and control heterogeneous sensors and their metadata. These S4 capabilities and technologies have great potential for both military and civilian applications, enabling highly effective security support tools for improving surveillance activities in densely crowded environments. The S4 system is directly applicable to solutions for emergency response personnel, law enforcement, and other homeland security missions, as well as in applications requiring the interoperation of sensor networks with handheld or body-worn interface devices.

Wireless networks of seismic sensors have proven to be a valuable tool for providing security forces with intrusion alerts even in densely forested areas. The cost of replenishing the power source is one of the primary obstacles preventing the widespread use of wireless sensors for passive barrier protection. This paper focuses on making use of energy from multiple sources to power these sensors. A system comprising of Texas Micropower's (TMP's) energy harvesting device and Crane Wireless Monitoring Solutions' sensor nodes is described. The energy harvesters are suitable for integration and for low cost, high volume production. The harvesters are used for powering sensors in Crane's wireless hub and spoke type sensor network. TMP's energy harvesting methodology is based on adaptive power management circuits that allow harvesting from multiple sources making them suitable for underground sensing/monitoring applications. The combined self-powered energy harvesting solutions are expected to be suitable for broad range of defense and industry applications. Preliminary results have indicated good feasibility to use a single power management solution that allows multi-source energy harvesting making such systems practical in remote sensing applications.

Traditional site security systems are susceptible to high individual sensor nuisance alarm rates that reduce the overall system effectiveness. Visual assessment of intrusions can be intensive and manually difficult as cameras are slewed by the system to non intrusion areas or as operators respond to nuisance alarms. Very little system intrusion performance data are available other than discrete sensor alarm indications that provide no real value. This paper discusses the system architecture, integration and display of a multi-sensor data fused system for wide area surveillance, local site intrusion detection and intrusion classification. The incorporation of a novel seismic array of smart sensors using FK Beamforming processing that greatly enhances the overall system detection and classification performance of the system is discussed. Recent test data demonstrates the performance of the seismic array within several different installations and its ability to classify and track moving targets at significant standoff distances with exceptional immunity to background clutter and noise. Multi-sensor data fusion is applied across a suite of complimentary sensors eliminating almost all nuisance alarms while integrating within a geographical information system to feed a visual-fusion display of the area being secured. Real-time sensor detection and intrusion classification data is presented within a visual-fusion display providing greatly enhanced situational awareness, system performance information and real-time assessment of intrusions and situations of interest with limited security operator involvement. This approach scales from a small local perimeter to very large geographical area and can be used across multiple sites controlled at a single command and control station.

In this paper, a novel concept of energy harvesting, applicable to both wired and wireless self-powered low-power electronic devices, is discussed. Types of energy harvesting include solar/optical, thermal, IR, and mechanical. Overall power budgets and control circuitry are discussed, including maximizing Mean Time between Battery Replacement/Recharge values. It is shown that in the case of low-power wireless electronics, surprisingly low amounts of daily direct solar exposure are sufficient to satisfy overall system power consumption.

Localization of acoustic sources using miniature microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound propagation. With sub-wavelength distances between the microphones, resolving acute localization cues become difficult due to precision artifacts. In this work, we present the design of a miniature, microphone array sensor based on a patented Multiple-input Multiple-output (MIMO) analog-to-feature converter (AFC) chip-sets which overcomes the limitations due to precision artifacts. Measured results from fabricated prototypes demonstrate a bearing range of 0 degrees to 90 degrees with a resolution less than 2 degrees. The power dissipation of the MIMO-ADC chip-set for this task was measured to be less than 75 microwatts making it ideal for portable, battery powered sniper and gunshot detection applications.

Various passive as well as active surveillance and security systems try to detect and identify very low frequency signals. Diversity of such real time working systems is very broad. Those are seismic, acoustic, hydroacoustic, IR, ultrasound, etc. systems. Detected target spectrum is also broad: from human motion on the ground surface and under water to lownoise submarines. In real application corresponding signals have poor signal-to-noise ratio, unstable shape and amplitude, short duration, and even missing parts of the signal. This paper describes test records of some raw seismic, acoustic, acoustic-seismic, hydroacoustic, and IR signals with proper characteristics. We investigate those signals specifics and possible approach to target oriented reliable signal processing that allows drastically increasing detection range and reducing false alarm rate. We also report on the preliminary field-testing that was implemented with active ultrasonic detector.

Cross-border smuggling tunnels enable unmonitored movement of people, drugs and weapons and pose a very serious threat to homeland security. Recently, Klar and Linker (2009) [SPIE paper No. 731603] presented an analytical study of the feasibility of a Brillouin Optical Time Domain Reflectometry (BOTDR) based system for the detection of small sized smuggling tunnels. The current study extends this work by validating the analytical models against real strain measurements in soil obtained from small scale experiments in a geotechnical centrifuge. The soil strains were obtained using an image analysis method that tracked the displacement of discrete patches of soil through a sequence of digital images of the soil around the tunnel during the centrifuge test. The results of the present study are in agreement with those of a previous study which was based on synthetic signals generated using empirical and analytical models from the literature.

Gunshot recordings have the potential for both tactical detection and forensic evaluation particularly to ascertain information about the type of firearm and ammunition used. Perhaps the most significant challenge to such an analysis is the effect of recording conditions on the audio signature of recorded data. In this paper we present a first study of using an exemplar embedding approach to automatically detect and classify firearm type across different recording conditions. We demonstrate that a small number of exemplars can span the space of gunshot audio signatures and that this optimal set can be obtained using a wrapper function. By projecting a given gunshot to the subspace spanned by the exemplar set a distance measure/feature vector is obtained that enables comparisons across recording conditions. We also investigate the use of a hierarchy of gunshot classifications that assists in improving finer level classification by pruning out gunshot labeling that is inconsistent with its higher level type. The embedding based approach can thus be used both by itself and as a pruning stage for other search techniques. Our dataset includes 20 different gun types captured in a number of different conditions. This data acts as our original exemplar set. The dataset also includes 12 gun types each with multiple shots recorded in the same conditions as the exemplar set. This second set provides our training and testing sets. We show that we can reduce our exemplar space from 20 to only 4 uniquely different gunshots without significantly limiting the ability of our embedding approach to discriminate different gunshots in the training and testing sets. The basic hypothesis in the embedding approach is that the relationship between the set of exemplars and space of gunshots including the testing/training set would be robust to a change in recording conditions or the environment. That is to say the embedding distance between a particular gunshot and the exemplars would tend to remain the same in changing environments. The implication of this are two-fold; first, unlike other dimensionality reduction approaches we have access to particular instances/examples of entities (the exemplars), which act as bridges to connect different recording conditions. Second, the embedding distances are invariant across recording conditions, the embedded vector can be used as a feature of similarity between gunshots recorded in different conditions. Unlike other dimensionality reduction approaches , our approach generates descriptions that are always in terms of the same exemplars. In other approaches such as PCA, the data driven nature makes it difficult if not impossible to make correspondence in the dimensions in one space to another. We have shown that gunshot classification across different recording conditions can be performed at a reasonable degree of certainty (60-72%) at a finer level (gunshot to weapon model) and at a high degree of certainty (95-100%) at a higher degree of abstraction (gunshot to `handgun' or `rifle'). We also investigate the use of simulated recording conditions and artificial noise to quantitatively evaluate the performance of our approach.

This paper describes experimental results from recent live-fire data collects that demonstrate the capability of a prototype system for projectile detection and tracking. This system, which is being developed at Applied Research Associates, Inc., under the FightSight program, consists of a high-speed thermal camera and sophisticated image processing algorithms to detect and track projectiles. The FightSight operational vision is automated situational intelligence to detect, track, and graphically map large-scale firefights and individual shooting events onto command and control (C2) systems in real time (shot location and direction, weapon ID, movements and trends). Gaining information on enemy-fire trajectories allows educated inferences on the enemy's intent, disposition, and strength. Our prototype projectile detection and tracking system has been tested at the Joint Readiness Training Center (Ft Polk, LA) during live-fire convoy and mortar registration exercises, in the summer of 2009. It was also tested during staged military-operations- on-urban-terrain (MOUT) firefight events at Aberdeen Test Center (Aberdeen, MD) under the Hostile Fire Defeat Army Technology Objective midterm experiment, also in the summer of 2009, where we introduced fusion with acoustic and EO sensors to provide 3D localization and near-real time display of firing events. Results are presented in this paper that demonstrate effective and accurate detection and localization of weapon fire (5.56mm, 7.62mm, .50cal, 81/120mm mortars, 40mm) in diverse and challenging environments (dust, heat, day and night, rain, arid open terrain, urban clutter). FightSight's operational capabilities demonstrated under these live-fire data collects can support closecombat scenarios. As development continues, FightSight will be able to feed C2 systems with a symbolic map of enemy actions.

In this paper an algorithm for sniper localization using disparate single microphone sensors that uses only the time difference of arrival (TDOA) between muzzle blast and shock wave is presented. Just as in any algorithm that looks for optimal solution this algorithm also faces the local minima (possible sniper locations) problem. In order to find the global or near global solution one has to perform search over a large area. In order to reduce the computational burden, the search space needs to be small. In this paper, an upper and lower bound on the range for the search space are estimated using the sensor configuration. Based on this, the area around the bullets path is searched with the bounds on range to determine the exact or near global solution for the sniper location. The results of sniper localization algorithm applied to real data collected in a field test will be presented.

The paper discusses technical possibilities to build an effective system for sniper detection using infrared cameras. Descriptions of phenomena which make it possible to detect sniper activities in infrared spectra as well as analysis of physical limitations were performed. Cooled and uncooled detectors were considered. Three phases of sniper activities were taken into consideration: before, during and after the shot. On the basis of experimental data the parameters defining the target were determined which are essential in assessing the capability of infrared camera to detect sniper activity. A sniper body and muzzle flash were analyzed as targets. The simulation of detection ranges was done for the assumed scenario of sniper detection task. The infrared sniper detection system was discussed, capable of fulfilling the requirements. The discussion of the results of analysis and simulations was finally presented.

Hull and harbor infrastructure inspections are frequently performed manually and involve quite a bit of risk and human and monetary resources. In any kind of threat and resource constrained environment, this involves unacceptable levels of risk and cost. Modern Remotely Operated Vehicles are highly refined machines that provide features and capabilities previously unavailable. Operations once carried out by divers can now be carried out more quickly, efficiently and safely by smart enabled ROVs. ROVs are rapidly deployable and capable of continuous, reliable operations in adverse conditions. They also provide a stable platform on which multiple sensors may be mounted and utilized to meet the harbor inspection problem. Automated Control software provides ROV's and their pilots with the capability to inspect complex, constrained environments such as those found in a harbor region. This application and the user interface allow the ROV to automatically conduct complex maneuvers relative to the area being inspected and relieves the training requirements and work load for the pilot, allowing he or she to focus on the primary task of survey, inspection and looking for possible threats (such as IEDs, Limpet Mines, signs of sabotage, etc). Real-time sensor processing tools can be integrated into the smart ROV solution to assist the operator. Automatic Target Recognition (ATR) algorithms are used to search through the sensor data collected by the ROV in real time. These algorithms provide immediate feedback on possible threats and notify the operator of regions that may require manual verification. Sensor data (sonar or video) is also mosaiced, providing the operator with real-time situational awareness and a coverage map of the hull or seafloor. Detected objects may also be placed in the context of the large scale characteristics of the hull (or bottom or pilings) and localized. Within the complex areas such as the harbor pier pilings and the running gear of the ship, real-time 3D reconstruction techniques may be used to process profiling sonar data for similar applications. An observation class ROV equipped with sensors, running an operator in the loop, Automated Surface-Computer (ASC) system can inspect an entire harbor region. These systems can autonomously provide coverage information, identify possible threats and provide the level of control required to operate in confined environments. The system may be controlled autonomously or by the operator. Previous inspection results may also be used for change detection applications. This paper presents the SeeByte Smart ROV and sensor processing technology relevant to the harbor inspection problem. These technologies have been tested extensively in real world applications and trials and are demonstrated using real data and examples.

We describe a real-time wide area surveillance system (WA-ACTV) for the automatic tracking of vessels using a network of PTZ cameras. The system is capable of optimally managing hundreds of PTZ cameras to simultaneously track a large numbers of vessels. The tracked vessels are fingerprinted using a scale-invariant part-based representation and subsequently used for reacquiring the tracks when the vessel comes back into view on a different sensor thus allowing the system to extend the tracking range over the wide area of surveillance. We have realized a small-scale version of the system and demonstrated it in an inland waterway as well as a small section of a sea port. The system operates in real-time at a frame rate of 15 Hz and is easily scalable to hundreds of PTZ cameras. The fingerprint-based reacquisition of targets has been evaluated to have a accuracy of 91%.

Present-day naval operations take place in coastal environments as well as narrow straits all over the world. Coastal environments around the world are exhibiting a number of threats to naval forces. In particular, a large number of asymmetric threats can be present in environments with cluttered backgrounds as well as rapidly varying atmospheric conditions. The automatic detection of small targets by electro-optical systems may be hampered by small surface structure variations at the surface and near the horizon. In current electro-optical sensor systems processing of imagery is seldom task-specific. Using task-specific settings of sensors, processing and fusion, can improve the performance of electro-optical systems dramatically. This paper discusses the effect of dynamic sensor settings as function of specific tasks and environmental parameters and how these can play a role in the management of sensors in a naval application. In addition, a series of experiments with different targets are presented to demonstrate the benefit of sensor management. Some sensor management approaches for application in infrared systems are discussed.

Government funding for the security at shipping facilities and ports is limited so there is a need for low cost scalable security systems. With over 20 million sea, truck, and rail containers entering the United States every year, these facilities pose a large risk to security. Securing these facilities and monitoring the variety of traffic that enter and leave is a major task. To accomplish this, the authors have developed and fielded a low cost fully distributed building block approach to port security at the inland Port of Catoosa in Oklahoma. Based on prior work accomplished in the design and fielding of an intelligent transportation system in the United States, functional building blocks, (e.g. Network, Camera, Sensor, Display, and Operator Console blocks) can be assembled, mixed and matched, and scaled to provide a comprehensive security system. The following functions are demonstrated and scaled through analysis and demonstration: Barge tracking, credential checking, container inventory, vehicle tracking, and situational awareness. The concept behind this research is "any operator on any console can control any device at any time."

Improvised Explosive Devices (IEDs) have presented a major threat in the wars in Afghanistan and Iraq. These devices are powerful homemade land mines that can be small and easily hidden near roadsides. They are then remotely detonated when Coalition Forces pass by either singly or in convoys. Their rapid detection, classification and destruction is key to the safety of troops in the area. These land based bombs will have an analogue in the underwater theater especially in ports, lakes, rivers and streams. These devices may be used against Americans on American soil as an element of the global war on terrorism (GWOT) Rapid detection and classification of underwater improvised explosive devices (UIED) is critical to protecting innocent lives and maintaining the day to day flow of commerce. This paper will discuss a strategy and tool set to deal with this potential threat.

Wireless marine sensor networks support an assortment of services in industries ranging from national security and defense to communications and environmental stewardship. Expansion of marine sensor networks has been inhibited by the limited availability and high cost of long-term power sources. Benthic Microbial Fuel Cells (BMFCs) are a novel form of energy harvesting for marine environments. Through research conducted in-lab and by academic collaborators, Trophos Energy has developed a series of novel BMFC architectures to improve power generation capability and overall system robustness. When integrated with Trophos' power management electronics, BMFCs offer a robust, long-term power solution for a variety of remote marine applications. The discussions provided in this paper outline the architectural evolution of BMFC technology to date, recent experimental results that will govern future BMFC designs, and the present and future applicability of BMFC systems as power sources for wireless marine sensor networks.

Maritime domain operators/analysts have a mandate to be aware of all that is happening within their areas of responsibility. This mandate derives from the needs to defend sovereignty, protect infrastructures, counter terrorism, detect illegal activities, etc., and it has become more challenging in the past decade, as commercial shipping turned into a potential threat. In particular, a huge portion of the data and information made available to the operators/analysts is mundane, from maritime platforms going about normal, legitimate activities, and it is very challenging for them to detect and identify the non-mundane. To achieve such anomaly detection, they must establish numerous relevant situational facts from a variety of sensor data streams. Unfortunately, many of the facts of interest just cannot be observed; the operators/analysts thus use their knowledge of the maritime domain and their reasoning faculties to infer these facts. As they are often overwhelmed by the large amount of data and information, automated reasoning tools could be used to support them by inferring the necessary facts, ultimately providing indications and warning on a small number of anomalous events worthy of their attention. Along this line of thought, this paper describes a proof-of-concept prototype of a rule-based expert system implementing automated rule-based reasoning in support of maritime anomaly detection.

In this paper, we conduct performance evaluation study for an aviation security cargo inspection queuing system for material flow and accountability. The queuing model employed in our study is based on discrete-event simulation and processes various types of cargo simultaneously. Onsite measurements are collected in an airport facility to validate the queuing model. The overall performance of the aviation security cargo inspection system is computed, analyzed, and optimized for the different system dynamics. Various performance measures are considered such as system capacity, residual capacity, throughput, capacity utilization, subscribed capacity utilization, resources capacity utilization, subscribed resources capacity utilization, and number of cargo pieces (or pallets) in the different queues. These metrics are performance indicators of the system's ability to service current needs and response capacity to additional requests. We studied and analyzed different scenarios by changing various model parameters such as number of pieces per pallet, number of TSA inspectors and ATS personnel, number of forklifts, number of explosives trace detection (ETD) and explosives detection system (EDS) inspection machines, inspection modality distribution, alarm rate, and cargo closeout time. The increased physical understanding resulting from execution of the queuing model utilizing these vetted performance measures should reduce the overall cost and shipping delays associated with new inspection requirements.

We address an image registration and segmentation method to detect concealed objects captured by passive millimeter wave (MMW) imaging. Passive MMW imaging can create interpretable imagery on the objects concealed under clothing. Due to the penetrating property of the MMW imaging, the MMW imaging system is often employed for the security and defense system. In this paper, we utilize a multi-channel PMMW imaging system operating at the 8 mm regime with linear polarization. Image registration and segmentation are performed to detect concealed objects under clothing. The registration is preceded to align different channel images by means of geometric feature extraction and a matching process. The Linde-Buzo-Gray (LBG) vector quantization with multi-channel information is adopted to segment the concealed object from the body area. In the experiment, the automated image registration and segmentation are performed with various concealed objects including a metal axe and a liquid container.

A tagged neutron inspection system has been used for the detection of explosive and illicite drugs. Simulant of the RDX explosive was measured in different environments and its gamma ray spectra were compared with the gamma ray spectra of benign materials like paper, sugar and rise. "Fingerprint" of the RDX simulant was found by detecting the nitrogen as well as by making the triangle plot which coordinates show the carbon and oxygen content and density. Density was obtained by measuring the intensity of the transmited tagged neutrons. Hence, the presence of the simulant can be confirmed by using two different methods. The possibility of using the triangle plot for detection of illicit drugs like heroin, cocain and marihuana is also discused.

This paper presents a distributed sensor system for the detection of toxic and hazardous gases like carbon monooxide (CO) and hydrogen (H₂), respectively. The system is based on AlGaN/GaN heterostructures with floating gates as the catalytic absorption surfaces for the target gases. Due to the presence of 2-dimensional electron gas (2-DEG) at the heterointerface, AlGaN/GaN based High Electron Mobility Transistor (HEMT) and Metal- Oxide-Semiconductor HEMT (MOS-HEMT) is highly sensitive to any changes in the surface charge conditions. The individual sensor is made selective towards a specific target by the design of different gate structures. From the simulation results, it has been found that this type of high mobility field-effect devices can be applied to detect very low concentrations of gases with widely linear sensor characteristics. The modeled AlGaN/GaN HEMT with the Pt floating gate can detect hydrogen gas with the least concentrations at the sub-ppb level and with the linear current variations for ~ ppb to 100 ppm level of hydrogen. For the MOSHEMT structure, we simulated sub-ppm detection of CO with the linearity of responses near 1000 ppm.