This PDF file contains the front matter associated with SPIE Proceedings Volume 7707, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Protonex is a leading provider of advanced fuel cell power systems for portable, remote and mobile applications. Protonex has been working on optimizing longer endurance of PEM fuel cell systems for applications such as small UAVs and UGVs. These applications require power systems with high specific energy (Wh/kg). Within the 100-1500 watt power range, we have shown that fuel cell systems can outperform battery systems by a factor of 2-8x. Integration of fuel cell power systems into these platforms can provide significant additional mission capabilities for military and civilian applications. The performance benefit over incumbent power technologies is leading to significant near term product opportunities. Protonex has developed advanced power sources based on fuel cell-battery hybrid systems that outperform existing battery technology by several factors. Our advanced power systems have been demonstrated in a variety of existing and experimental platforms including AeroVironment's Puma UAV, Foster-Miller's Talon UGV, NRL's Ion Tiger, and XFC UAVs.

When considering energy requirements and optimization for robots, it is important to consider mission requirements, and the type of robot performing the mission. For example, the small robots used in today's reconnaissance and explosive ordnance disposal applications have weak manipulators, and do not perform significant physical work on their surrounding environment. In this paper, we focus on robots that will be required to do much more physically demanding tasks, such as manipulating large heavy objects in unstructured environments, and carrying such objects over challenging terrain. Energy considerations for such systems must include models of physical work performed for basic manipulation, pose transition, and locomotion maneuvers. Given the scarcity of robots that can perform useful work in unstructured environments, it is useful to begin the investigation of energy optimization for such robots by considering typical tasks they might perform. This paper makes three contributions in this direction. First, we develop a set of standard tasks that would be useful in unstructured environments. The tasks are expressed in terms of the objects being manipulated, and the work being done, so they are independent of robot morphology. Second, we develop energy metrics and analytical results for theoretical energy requirements for these tasks. These requirements assume no losses due to friction, so they give a best-case estimate of what is achievable. Such metrics are useful in subsequent evaluation of real systems that are not as efficient. Third, we perform preliminary comparisons between different actuation technologies in performing these tasks. These actuation technologies will include electro-mechanical and hydraulic systems. We compare these technologies in terms of power density, and evaluate expected energy efficiency when performing the metric tasks.

In this paper, we demonstrate an adaptive multicarrier multi-function waveform generator for cognitive radio via software defined radio. Using a USRP (universal software radio peripheral) software defined radio boards and GNU radio software, we implement a multi-carrier waveform generator which can generate multi-function waveforms such as OFDM, NC-OFDM, MC-CDMA, NC-MC-CDMA, CI/MC-CDMA, NCCI/ MC-CDMA, TDCS for cognitive radio. Additionally, we demonstrate a portable overlay cognitive radio using this multicarrier multi-function waveform generator. This cognitive radio is capable of detecting primary users in real time and adaptively adjusting its transmission parameters to avoid interference to primary users. More importantly, this cognitive radio can take advantage of multiple spectrum holes by employing non-contiguous multi-carrier transmission technologies. Additionally, we demonstrate that when the primary user transmission changes, the cognitive radio dynamically adjusts its transmission accordingly. We also demonstrate seamless real time video transmission between two cognitive radio nodes, while avoiding interference from primary users and interference to primary users operating in the same spectrum.

Compressive sensing (CS) relies on the fact that CS sampled signals are much closer to their information rate rather than the signal bandwidth. This attribute helps to provide the much needed benefits of reduced storage or transmission bandwidth for the next-generation broadband wireless communications and to overcome the hardware limitations for wideband spectrum sensing in dynamic spectrum access. In order to opportunistically reuse holes in the spectrum, it is essential to have a spectral detection and estimation technique that is capable of sensing and identifying available frequency bands. Conventional methods of detection are saddled with the high sampling rate requirement of Nyquist rate, however timing requirements limits the number of samples that can be taken from the signals. In a situation whereby the signal spectrum in open-access networks is sparse in nature, this work develops a detection mechanism for identifying spectrum holes using compressive sensing based algorithm technique. Different compressive sensing reconstruction algorithms are investigated and FFT spectrogram with an edge detection algorithm is used to identify the holes in the spectrum. A quick wideband spectrum sensing can be achieved using the compressive sensing technique and a more refined sensing can be used by any of the other available methods such as energy detection. The proposed model is evaluated in different fading propagation environments, taking into account of both additive and multiplicative noise.

The need to efficiently and effectively monitor the frequency spectrum for identification of unoccupied bands is essential in communication systems such as Cognitive Radio (CR), battlefield communications, etc. The Nyquist Folding Analog-to-Information Receiver (NYFR) which is based on the theory of Compressed Sensing has been proposed recently to address this problem in a sparse environment. Although, typical CS techniques, involve random projections followed by a computationally intensive signal reconstruction process, the methods used in NYFR does not requires the laborious l1 minimization algorithm. The NYFR performs analog compression via a non-uniform sampling process that induces a chirp-like modulation on each received signal. Signal parameters can simply be determined by using timefrequency analysis techniques without full signal reconstruction. This paper revisits the detection problem of using NYFR for information recovery for appropriate frequency detection when the original signal in the presence of both the additive white Gaussian noise and Rice multipath fading. An automatic detection algorithm was also developed to determine the detected frequency parameters without looking at the FFT spectrogram plot.

Dynamic spectrum access is a way of gaining access to individual frequencies on a temporary basis. This makes use of the frequency assigned to a specific user (primary user) by using specific devices and/or spectrum management techniques. The spectrum management techniques can be done by allocating the spectrum (a) through auctions (market based), (b) using management techniques (c) spectrum sharing (detects and utilizes the unutilized part of the spectrum) (d) command and control, and (e) through opportunistic spectrum access. In opportunistic spectrum access, the secondary or unlicensed user transfers the data with high speed and at short distances with tolerable interference (without disturbing) to the primary signal. Efficient spectrum allocation techniques were discussed using stochastic models, economic models, genetic algorithms, and optimization techniques. The existing models need to be tuned for better performance with optimum utilization of the power. In this paper, we proposed a model that provides access with tolerable interference from secondary users to the primary users while maximizing the spectrum utilization. Furthermore, we designed a congestion game model for efficient utilization of the spectrum by secondary users with minimum interference to primary users. The simulation results show that the congestion game model helps to utilize the spectrum efficiently.

Methods for selection and optimizing the selection of end-to-end network paths is crucial to offer any degree of quality of service to end users. In many wireless networks, route selection is based in a single route metric. When considering multi-hop cognitive radio networks (CRNs), in which frequent link outages are expected due to primary user interference, we must consider more metrics to ensure not only that routes are of sufficient quality, but have an reasonable expectation of availability. In this paper, we propose the use of fuzzy-logic modeling to optimize route selection based on a set of collected cross-layer information.

Various methods for discovering the location of radio frequency (RF) emitters using unmanned aerial vehicles (UAVs) have been the focus of research over the past several years. Our work is aimed at determining the effectiveness of lowaccuracy direction finding (DF) technology to locate RF emissions using multiple UAVs. Small, commercial-off-theshelf (COTS) antenna systems can provide a rough estimate of an emitter's location within a 90 degree or 45 degree sector. Using these DF systems, a team of inexpensive UAVs can be deployed to collect low-accuracy data from multiple positions. A ground station would combine the information. In contrast to typical angle-of-arrival (AOA) methods, this unique technique does not require precise antenna arrays, complex hardware, or significant processing time to locate RF emissions. We present simulation results that show that accurate geolocation of emitters is possible with DF systems using only low accuracy (90-20 degrees) Angle of Arrival (AOA) information.

In this paper, we present a brief overview on the recent development of the dynamical spectrum access (DSA) techniques, focusing on the following areas: characteristics of the available bandwidth of the primary users, sensing technologies and sensing intervals to detect white spaces, spectrum allocation and management, reliability of the secondary user, tradeoff between spectrum/performance gain versus additional overhead used in implementing DSA, and cross layer optimization in spectrum sharing. Some of tradeoff studies are presented in terms of price of anarchy, which is defined as the price that a decentralized system should pay for not being coordinated. We will also discuss some of the open issues in deploying DSA.

Applications resident on tactical wireless networks are levying increasing offered loads. Tradeoffs can be made between range and throughput, but the wireless network is destined to be considered a limitation in information transfer. If managed correctly, the network can be an intelligent aid in ensuring the right information gets to the right place at the right time. Over the last 5 years, SAIC has worked with Natick Soldier Center (NSRDEC) to provide reliable communication with guaranteed service quality for the dismounted soldier. The effort utilizes a series of tools to mark, shape, condense, fragment and persist information for congestion and corruption control. The critical aspect of the congestion control solution is accomplished by adaptively throttling lower priority information at the sending node before it gets pushed to the wireless realm. Of note is that the solution adapts through passive processes without control messages. The solution also implements compression of messages and images, along with fragmentation techniques to alleviate congestion. Information corruption is purely a radio phenomenon and cannot be overcome through cognitive solutions. However, the solution mitigates corruption through information persistence and reliable retransmission. The implemented solution, unlike Transport Control Protocol, is optimized for wireless networks and demonstrates reduction of added signaling traffic. Combined congestion and corruption techniques have demonstrated how soldiers can get the right information at the right time during high traffic loads or network segmentation.

Existing distributed approaches to topology control are poor at exploiting the large configuration space of cognitive radios and use extensive inter-node synchronization to aim at optimality. We have created a framework to design and study distributed topology control algorithms that combine network-formation games with machine learning. In our approach, carefully designed incentive mechanisms drive distributed autonomous agents towards a pre-determined system-wide optimum. The algorithms rely on game players to pursue selfish actions through low-complexity greedy algorithms with low or no signaling overhead. Convergence and stability are ensured through proper mechanism design that eliminates infinite adaptation process. The framework also includes game-theoretic extensions to influence behavior such as fragment merging and preferring links to weakly connected neighbors. Learning allows adaptations that prevent node starvation, reduce link flapping, and minimize routing disruptions by incorporating network layer feedback in cost/utility tradeoffs. The algorithms are implemented in Telcordia Wireless IP Scalable Network Emulator. Using greedy utility maximization as a benchmark, we show improvements of 13-40% for metrics such as the numbers of disconnected fragments and weakly connected nodes, topology stability, and disruption to user flows. The proposed framework is particularly suitable to cognitive radio networks because it can be extended to handle heterogeneous users with different utility functions and conflicting objectives. Desired outcome is then achieved by application of standard cooperation techniques such as utility transfer (payments). Additional cross-layer optimizations are possible by playing games at multiple layers in a highly scalable manner.

The Mine Warfare (MIW) Community of Interest (COI) was established to develop data strategies in support of a future information-based architecture for naval MIW. As these strategies are developed and deployed, the ability for these datafocused efforts to enable technology insertion is becoming increasingly evident. This paper explores and provides concrete examples as to the ways in which these data strategies are supporting the technology insertion process for software-based systems and ultimately contribute to the establishment of an Open Business Model virtual environment. It is through the creation of such a collaborative research platform that a truly transformation MIW capability can be realized.

Locative Viewing is a method for visualizing geographically-referenced 3-D objects in the local coordinate system of a geographically-referenced observer. A computer-graphics rendering of nearby geo-objects is superimposed over the visual surroundings of the observer as seen by a camera. This rendering changes as the observer moves. Locative viewing can be accomplished with a mobile device that 1) is able to determine its geographic location, and orientation, 2) contains a camera and image display, and 3) can project and overlay objects within the field of view of the camera with the camera image. A preliminary implementation of a locative viewer using Apple's iPhone is described and results presented.

Since 2003, the US Army's RDECOM CERDEC Night Vision Electronic Sensor Directorate (NVESD) has been developing a distributed Sensor Management System (SMS) that utilizes a framework which demonstrates application layer, net-centric sensor management. The core principles of the design support distributed and dynamic discovery of sensing devices and processes through a multi-layered implementation. This results in a sensor management layer that acts as a System with defined interfaces for which the characteristics, parameters, and behaviors can be described. Within the framework, the definition of a protocol is required to establish the rules for how distributed sensors should operate. The protocol defines the behaviors, capabilities, and message structures needed to operate within the functional design boundaries. The protocol definition addresses the requirements for a device (sensors or processes) to dynamically join or leave a sensor network, dynamically describe device control and data capabilities, and allow dynamic addressing of publish and subscribe functionality. The message structure is a multi-tiered definition that identifies standard, extended, and payload representations that are specifically designed to accommodate the need for standard representations of common functions, while supporting the need for feature-based functions that are typically vendor specific. The dynamic qualities of the protocol enable a User GUI application the flexibility of mapping widget-level controls to each device based on reported capabilities in real-time. The SMS approach is designed to accommodate scalability and flexibility within a defined architecture. The distributed sensor management framework and its application to a tactical sensor network will be described in this paper.

We address supporting unanticipated users and uses of limited information resources (sensors, databases, weapons - any resource intrinsically tied to digital information) in a timely and efficient fashion. Platform-centric systems often preclude users and uses not identified when the system was developed and deployed. Net-centric approaches, however, can address these problems by allowing services and information to be discovered and accessed at run-time. We have developed a resource brokering service that uses net-centric principles and semantic metadata to enable multi-domain information and resource sharing and support for unanticipated users and uses. The resource brokering service uses federated brokering agents and a modular software component framework for dynamically composing and tasking heterogeneous resources including sensors, data feeds, processors, archived data, networks, and even analysts into resilient, mission-oriented workflows. The resource brokering service is applicable to multiple sense-decide-act military domains including missile defense, space situation awareness, ISR, border protection, and cyber defense. In this paper we present a concept and architecture for resource brokering and describe current applications. Our architecture is aligned with the U.S. DoD's NCES (Net-Centric Enterprise Services).

This paper analyzes secure data sharing outside its security domain with services, agencies, coalition partners and state/local authorities. There is a high demand for multiple levels of secure data at the tactical edge; however the threat level at that point is elevated compared to the enterprise environment. This paper investigates the requirements, technologies and risk mitigation techniques for securely sharing information with the tactical warfighter while protecting the data and the information systems from intruders and malware. The new CD Systems need to eliminate the stovepipe architectures and open the doors to share information across traditional and non-traditional domain boundaries.

An enterprise data strategy outlines an organization's vision and objectives for improved collection and use of data. We propose generic metrics and quantifiable measures for each of the DoD Net-Centric Data Strategy (NCDS) data goals. Data strategy metrics can be adapted to the business processes of an enterprise and the needs of stakeholders in leveraging the organization's data assets to provide for more effective decision making. Generic metrics are applied to a specific application where logistics supply and transportation data is integrated across multiple functional groups. A dashboard presents a multidimensional view of the current progress to a state where logistics data shared in a timely and seamless manner among users, applications, and systems.

Securing the DoD information network is a tremendous task due to its size, access locations and the amount of network intrusion attempts on a daily basis. This analysis investigates methods/architecture options to deliver capabilities for secure information sharing environment. Crypto-binding and intelligent access controls are basic requirements for secure information sharing in a net-centric environment. We introduce many of the new technology components to secure the enterprise. The cooperative mission requirements lead to developing automatic data discovery and data stewards granting access to Cross Domain (CD) data repositories or live streaming data. Multiple architecture models are investigated to determine best-of-breed approaches including SOA and Private/Public Clouds.

To improve detecting rates and reduce false detection of distributed network intrusion detection system, and to improve parallel processing ability of distributed intrusion detection system,co-evaluation computation-based distributed intrusion detection system is proposed. Optimized immune detecting method is used to reduce redundancy of detector. Multiagents evaluation computation is used to enhance self-learning ability and self-adaptation ability of network intrusion detection. Co-evaluation technology is used to speed co-evaluating of multi-agents in network intrusion detection system and then improve evaluating ability of distributed system. Experiments verify the validity of the method.