This PDF file contains the front matter associated with SPIE Proceedings Volume 8403, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Semiconductor optical amplifiers are important for wide range of applications in optical networks, optical tomography and optical logic systems. For many of these applications particularly for optical networks and optical logic, high speed performance of the SOA is important. All optical Boolean operations such as XOR, OR, AND and NOR has been demonstrated using SOA based Mach-Zhender interferometers (SOA-MZI). A rate equation model for SOA-MZI has been developed. The model has been used to analyze the Set-Reset (S-R) latch, the gated S-R latch and the D-Flip-Flop devices. The modeling results suggest that the Flip-Flop circuits should work at high speeds. An optical pseudo-random bit stream (PRBS) generator is important for all-optical encryption circuits. A model of a PRBS generator using SOAMZI based devices has been developed. We show that a PRBS generator can work @ 80 Gb/s using regular SOAs and @ ~ 250 Gb/s or at higher speeds using two-photon absorption based processes in SOAs.

The increasing use of infrared sensors requires development of advanced infrared training and simulation tools to meet current Warfighter needs. In order to prepare the force, a challenge exists for training and simulation images to be both realistic and consistent with each other to be effective and avoid negative training. The US Army Night Vision and Electronic Sensors Directorate has corrected this deficiency by developing and implementing infrared image collection methods that meet the needs of both real image trainers and real-time simulations. The author presents innovative methods for collection of high-fidelity digital infrared images and the associated equipment and environmental standards. The collected images are the foundation for US Army, and USMC Recognition of Combat Vehicles (ROC-V) real image combat ID training and also support simulations including the Night Vision Image Generator and Synthetic Environment Core. The characteristics, consistency, and quality of these images have contributed to the success of these and other programs. To date, this method has been employed to generate signature sets for over 350 vehicles. The needs of future physics-based simulations will also be met by this data. NVESD's ROC-V image database will support the development of training and simulation capabilities as Warfighter needs evolve.

The development and testing of thermal signature tracking algorithms burdens the developer with a method o f testing the algorith m's fidelity. Collected video is normally used for testing tracking algorithms to evaluate performance in a variety of configurations. Acquiring suitable volumes of collected video data in multiple configurations can be prohibitive. As an alternative to collected video, the development of accurate synthetic thermal infrared vehicle models are incorporated into background infrared scenes generated using the Digital Image and Remote Sensing Image Generat ion (DIRSIG) software package. Additional software models for thermally emissive targets and motion are being implemented. The goals are to accurately incorporate thermal signatures of moving targets into realistic radiomet rically calibrated scenes. This aids in evaluating tracking algorithms using both visible and thermal infrared signatures for improved day and night detection capability. The software packages are integrated together to produce synthetic video.

Effective use of passive and active sensors for surveillance, security, and intelligence must consider terrain and atmospheric effects on the sensor performance. Several years ago, U.S. Army ERDC undertook development of software for modeling environmental effects on target signatures, signal propagation, and battlefield sensors for many signal modalities (e.g., optical, acoustic, seismic, magnetic, radio-frequency, chemical, biological, and nuclear). Since its inception, the software, called Environmental Awareness for Sensor and Emitter Employment (EASEE), has matured and evolved significantly for simulating a broad spectrum of signal-transmission and sensing scenarios. The underlying software design involves a flexible, object-oriented approach to the various stages of signal modeling from emission through processing into inferences. A sensor placement algorithm has also been built in for optimizing sensor selections and placements based on specification of sensor supply limitations, coverage priorities, and wireless sensor communication requirements. Some recent and ongoing enhancements are described, including modeling of active sensing scenarios and signal reflections, directivity of signal emissions and sensors, improved handling of signal feature dependencies, extensions to realistically model additional signal modalities such as infrared and RF, and XML-based communication with other calculation and display engines.

We present the study of two important trade-offs in heterogeneous systems (i.e., between performance versus portability and between performance and accuracy) for a relevant linear algebra problem, matrix multiplication modulo primes. Integer matrix linear algebra methods rely heavily on matrix multiplication modulo primes. Double precision is necessary for exact representation of sufficiently many primes. We examine the performance losses due to the use of OpenCL versus CUDA and the use of double versus single precision. Our results indicate that performance losses from the former are minimal with the benefit of cross-platform portability and from the latter are acceptable when double precision is required.

ArrayFire is a GPU matrix library for the rapid development of general purpose GPU (GPGPU) computing applications within C, C++, Fortran, and Python. ArrayFire contains a simple API and provides full GPU compute capability on CUDA and OpenCL capable devices. ArrayFire provides thousands of GPU-tuned functions including linear algebra, convolutions, reductions, and FFTs as well as signal, image, statistics, and graphics libraries. We will further describe how ArrayFire enables development of GPU computing applications and highlight some of its key functionality using examples of how it works in real code.

The acquisition of systems to locate and interdict Special Nuclear Material (SNM) is significantly enhanced when trade space analysis of and CONOPS development for various proposed sensor systems is performed using realistic operational scenarios in a synthetic simulation environment. To this end, the U. S. Defense Threat Reduction Agency (DTRA) has developed a collaborative constructive simulation environment hosted at the DTRA Center at Ft. Belvoir, VA. The simulation environment includes a suite of modeling and simulation (M&S) tools, scenario vignette representations, geographic information databases, and authoritative sensor system representations. Currently focused on modeling the detection and interdiction of in-transit SNM, the M&S tools include the Monte Carlo N-Particle (MCNP) simulation for detailed nuclear transport calculations and the JHU/APL enhanced Joint Semi-Automated Forces (JSAF) synthetic simulation environment and several associated High-Level Architecture (HLA) federate simulations for engagement-level vignette executions. This presentation will focus on the JHU/APL enhancements to JSAF which have enabled the execution of SNM detection vignettes. These enhancements include the addition of a user-configurable Radioactive Material (RM) module for representation of SNM objects, a user-configurable RM Detection Module to represent operational and notional gamma and neutron detectors, a Radiation Attenuation Module to calculate net emissions at the detector face in the dynamic JSAF environment, and an RM Stimulation Module to represent notional proton and photon beam systems in active interrogation scenarios.

Data Modeling is a process that can convert non-real-time algorithms into functional approximations that can be executed in near real-time as platform independent mathematical equations or information transfer functions. These functional approximations are converted into a form amenable for streaming real-time execution by being converted into pre-calculated look-up table (LUT) form. We present the technique and relevant theory and demonstrate how this method can be applied to high level interactions, system level modeling and component modeling using a common framework. An important benefit of our technique is the ability to predict anomalous parameters from our models.

This paper presents a method that integrates Modeling and Simulation (M&S) Verification and Validation (V&V) as part of the M&S design. Experience indicates that in the past, very few models were developed with V&V as part of the design process. Formal V&V was usually done after the model had been released to the user community and was being used by major programs to support major decisions. This has changed in recent years as declining resources have resulted in a growing reliance on M&S. As awareness of the issues and the risks involved has increased, Department of Defense (DoD) policies have been written that require V&V to be implemented as part of the M&S design, development, and acquisition process. Many things can go wrong when a model is not carefully verified and validated. Not only does lack of V&V make a model difficult, if not impossible, to use, but the model may fail to support its intended use. V&V reduces the risks of developing an M&S that does not meet requirements or of using an inappropriate simulation to support a decision. While risks can never be eliminated entirely, they can be quantified in a way that they provide the decision maker with an indication of how high the cost of using an erroneous M&S result can be. This approach therefore allows optimal decisions to be made. This paper gives a description of a methodology for implementing V&V processes and documentation into the M&S design process.

The problem of defending a specific airspace is among the main issues a military commander to solve. Proper protection of own airspace is crucial for mission success at the battlefield. The military doctrines of most world armed forces involve two main options of defending the airspace. One of them is utilizing formations of fighter aircraft, which is a flexible choice. The second option is deploying modern SAM (Surface to Air Missile) systems, which is more expansive. On the other hand the decision makers are to cope with miscellaneous restrictions such as the budgeting problems. This study defines air defense concept according to modern air warfare doctrine. It considers an air defense scenario over an arbitrary airspace and compares the performance and cost-effectiveness of employing fighter aircraft and SAM systems. It also presents SWOT (Strenghts - Weakness - Opportunities - Threats) analyses of air defense by fighter aircraft and by modern SAMs and tries to point out whichever option is better. We conclude that deploying SAMs has important advantages over using fighter aircraft by means of interception capacity within a given time period and is cost-effective.

Traditionally, military simulation has been problem domain specific. Executing an exercise currently requires multiple simulation software providers to specialize, deploy, and configure their respective implementations, integrate the collection of software to achieve a specific system behavior, and then execute for the purpose at hand. This approach leads to rigid system integrations which require simulation expertise for each deployment due to changes in location, hardware, and software. Our alternative is Software as a Service (SaaS) predicated on the virtualization of Night Vision Electronic Sensors (NVESD) sensor simulations as an exemplary case. Management middleware elements layer self provisioning, configuration, and integration services onto the virtualized sensors to present a system of services at run time. Given an Infrastructure as a Service (IaaS) environment, enabled and managed system of simulations yields a durable SaaS delivery without requiring user simulation expertise. Persistent SaaS simulations would provide on demand availability to connected users, decrease integration costs and timelines, and benefit the domain community from immediate deployment of lessons learned.

The development of an Integrated Base Defense (IBD) is a significant challenge for the Army with many analytical gaps. The IBD problem space is complex, with evolving requirements and a large stakeholder base. In order to evaluate and analyze IBD decisions, the Training & Doctrine Command (TRADOC) Maneuver Support Center of Excellence (MSCoE) led and continues to lead a series of IBD focused experiments and wargames. Modeling and Simulation (M&S) significantly contributes to this effort. To improve IBD M&S capabilities, a collaborative demonstration with the Research, Development and Engineering Command's (RDECOM's) M&S Decision Support Environment (MSDSE) was held in September 2011. The results of this demonstration provided key input to MSCoE IBD related concepts and technologies. Moreover, it established an initial M&S toolset that will significantly improve force protection in combat zones and Army installations worldwide by providing leaders a capability to conduct analysis of defense and mission rehearsals. The demonstration was executed with a "human in the loop" Battle Captain, who was aided by mission command assets such as Base Expeditionary Targeting and Surveillance Sensors-Combined (BETSS-C). The Common Operating Picture was populated and stimulated using Science & Technology (S&T) M&S, allowing for a realistic representation of physical phenomena without the need for real systems. Novel methods were used for simulation orchestration, and for initializing the simulations and Opposing Force (OPFOR) activities. Ultimately, this demonstration showed that the MSDSE is suitable to support TRADOC IBD analyses and that S&T M&S is ready to be used in a demanding simulation environment. This paper will highlight the event's outcomes and lessons identified.

The amount of data processed annually over the Internet has crossed the zetabyte boundary, yet this Big Data cannot be efficiently processed or stored using today's mobile devices. Parallel to this explosive growth in data, a substantial increase in mobile compute-capability and the advances in cloud computing have brought the state-of-the- art in mobile-cloud computing to an inflection point, where the right architecture may allow mobile devices to run applications utilizing Big Data and intensive computing. In this paper, we propose the MObile Cloud-based Hybrid Architecture (MOCHA), which formulates a solution to permit mobile-cloud computing applications such as object recognition in the battlefield by introducing a mid-stage compute- and storage-layer, called the cloudlet. MOCHA is built on the key observation that many mobile-cloud applications have the following characteristics: 1) they are compute-intensive, requiring the compute-power of a supercomputer, and 2) they use Big Data, requiring a communications link to cloud-based database sources in near-real-time. In this paper, we describe the operation of MOCHA in battlefield applications, by formulating the aforementioned mobile and cloudlet to be housed within a soldier's vest and inside a military vehicle, respectively, and enabling access to the cloud through high latency satellite links. We provide simulations using the traditional mobile-cloud approach as well as utilizing MOCHA with a mid-stage cloudlet to quantify the utility of this architecture. We show that the MOCHA platform for mobile-cloud computing promises a future for critical battlefield applications that access Big Data, which is currently not possible using existing technology.