Please check back often for the latest award announcements, events, and corporate happenings at IAI...
October 2013: Visit IAI at ATCA 2013 – Booth 829.
IAI will exhibit at the Air Traffic Control Association (ATCA) National Conference October 21-23, 2013 at the Gaylord Hotel in National Harbor, Maryland. We are booth #829. Please stop by and view demonstrations of our air traffic tools, including visualizations, demonstrations of NextGen concept applications in Atlanta Hartsfield and other airports, and learn about our other innovative products and cutting-edge tools and demonstrations.
August 2013: NOAA awards IAI a new contract to develop Climate Impact Visualization Tools for Community based Planning and Outreach.
Small and medium-sized towns and cities need help in studying and communicating local climate change impacts, and in using this information in planning and decision-making. Several free GIS mappers developed by NOAA and other agencies allow visualization of storm surge impact, but these are not integrated with local planning tools. To address this, IAI and its collaborator, Towson University have been awarded a contract entitled, “Climate Impact Visualization Tools using Virtual 3D City for Community based Planning and Outreach.” IAI will develop an innovative visualization tool that transforms online GIS mappers into a climate impact assessment and planning tool, allowing planners to visualize the impact of storm surges with sea level rise and coastal erosion using a 3D virtual city. The tool will assist planners and emergency services to adapt to climate change at three tiers: Tier 3 (Long-term Planning & Near-term Mitigation) to facilitate city planning, zoning and building code decisions, and mitigation plans such as levees and barriers for existing structures; Tier 2 (Readiness) to facilitate developing mitigation action to minimize material damage from a forecasted weather event within 120 hours of landfall; and Tier 1 (Response) to establish a community-based tiered communication mechanism (built on Commercial Mobile Alert System (CMAS)/Wireless Emergency Alerts (WEA) systems) to plan for efficient use of limited emergency service resources within 48 hours of storm landfall. Key features include the Unity 3D Game Engine based visualization and what-if planning analysis, hydrodynamic model based damage assessment and visualization, integration with SLOSH generated storm surge data, coastal inundation model, and a community based outreach tool. This interactive tool does not require substantial training or infrastructure, and can be easily adopted by urban planners.
September 2013: IAI to Showcase RFnestTM Products at MOBICOM 2013.
Intelligent Automation, Inc. (IAI) will showcase its Radio Frequency Network channel Emulation and Simulation Tool (RFnestTM) products on the demo day of MOBICOM 2013, in Miami, Florida on 1 October, 2013. IAI’s family of RFnestTM products allows a full mesh of wireless nodes to experience realistic wireless channels and is designed to address the specific needs of each customer. This family includes the RFnestTM Analog and the RFnestTM Digital series, both powered by RFsimTM software. The RFnestTM Analog series is an affordable network channel emulation tool with the capability to emulate full mesh frequency flat fading channels. The RFnestTM Digital series offers an advanced solution for the emulation of frequency selective channels in a highly dynamic environment, with a modular and expandable architecture that supports testing from a handful of SISO nodes up to 96 MIMO nodes in full mesh setting.
Please visit our MOBICOM booth, or for complete product details, please visit www.i-a-i.com/rfnest.
July 2013: Air Force awards IAI a new contract to develop a Secure Cloud-Centric IR Data Management Platform.
The Intelligence Surveillance and Reconnaissance (ISR) data captured by space-based Infrared (IR) sensors is typically massive and needs to be delivered to warfighters in the field via a very narrow (56k) communication channel. The current point-to-point solution of IR data compression implementation at the data downlink server reduces the system’s resiliency. To address this issue, IAI will develop a distributed and secure, cloud-centric IR data management platform, to provide resilient IR data storage, processing and management services with a customizable web-based user interface for IR surveillance data. A key innovation is including a Google Maps-like user interface for the presentation of Geospatial IR data, and an application programming interface (API) to provide a feedback loop to warfighters for specific spatiotemporal data requests. This will be created using IAI’s in-house Java based solution, QuickMaps, which allows customized creation and display of maps. The API would enable users to access powerful cloud functionalities, and provide a tool to develop customized applications using the API platform offered by the cloud services to meet their unique requirements. IR data processing modules will use the computing services offered by the cloud platform to provide IR data specific compression, feature extraction and other functionalities, which will be optimized to minimize data transfer over the narrow communication channel. IAI will leverage its expertise in cloud computing, distributed information networks, security, airborne networks to develop this system, and use available IR datasets and its in-house cloud computing cluster to evaluate its feasibility.
July 2013: Department of Transportation awards IAI a new contract to develop an Advance Radar Based Low Ground Clearance Vehicle Detection and Warning System.
Low ground clearance vehicles (LGCV) getting stuck at tracks of highway-rail grade crossings (HRGC) poses a severe safety risk. Vehicles with long wheelbases and low ground clearance are more likely to encounter this on high-profile HRGCs. There are currently no commercial automated systems to detect and compute minimum clearance, and suspended or protruding rigid parts of LGCVs, and provide advance warning before reaching HRGCs. To address this, IAI and its collaborator, Sabra, Wang & Associates, have been awarded a contract entitled, “Advance Radar Based Low Ground Clearance Vehicle Detection and Warning System. An innovative, inexpensive, stand-alone, lane specific and vehicle specific LGCV detection and warning system will be built. First, vehicles with longer wheelbases will be detected and prescreened using a pair of inductive loops or magnetometers with a high resolution interface card. Next, a millimeter wave imaging radar downstream will be triggered to capture a high resolution profile of the vehicle and to calculate the lowest clearance to the pavement. IAI’s proprietary Scan Synthetic Aperture Radar (ScanSAR) algorithm will be used to perform 3D imaging of vehicle’s underbody profiles. The real-time ScanSAR algorithm will be implemented on IAI’s Multi-Channel Digital Signal Processing (MCDSP) system. This system supports multi-mode warning, with the ability to support both “infrastructure” and futuristic “onboard” warning systems. The system will work for all high-profile HRGCs and vehicles of various wheelbases and underbody clearance, and supports multiple lane operations at highway speeds. The proposed system has very fast processing and response time, and can function in all weather, visibility and road conditions.
June 2013: Air Force awards IAI a new contract for Multidisciplinary Design Optimization of Advanced Propulsion Systems (MDO-APS).
The US Air Force’s goal to reduce fuel consumption is driving the need for advanced aircraft and propulsion system requirements. For subsonic aircraft, increasing turbine bypass ratio lowers thrust-specific fuel consumption. The limiting case for high-bypass ratio is a turboprop, and for higher performance applications, it is the open-rotor propulsion system. Methods of propeller or open-rotor analysis are either very low-order or proprietary, and research engineers cannot make rational systems decisions without access to accurate, flexible and multi-disciplinary design methods. To address this, IAI will develop a “Multidisciplinary Design Optimization of Advanced Propulsion Systems (MDO-APS),” a novel framework for integrated conceptual and preliminary design of advanced propulsion concepts. The novel multi-point multidisciplinary design architecture is an enhancement of the Collaborative Optimization technique, enabling a degree of ‘loose coupling,’ while providing relative autonomy to disciplinary analyses. The architecture readily handles multi-design-point optimization, which is important in the multidisciplinary study of advanced propulsion systems, which focuses on aerodynamics, structure, acoustics and propulsion systems. MDO-APS uses Genetic Algorithms for system level optimization, allowing discrete design choices to be made at the system level. It leverages Variable Complexity Optimization within each discipline’s subspace to perform high-fidelity designs at moderate costs. MDO-APS can study propulsion system design in context of the airplane, along with the challenge of airframe integration. The modular architecture leverages IAI’s expertise in modeling, simulation, and agent-based systems, and allows any analysis code to be used for the different disciplines. This tool will be demonstrated for open-rotor design for military transports like the C-130 and C-17.The benefits of using open-rotor technology versus state of the art engines will also be studied, both in isolation as well as in context of the aircraft.
May 2013: Navy awards IAI a new contract to develop a tool for Rapid and Accurate Radar Signature Prediction.
Modeling of radar signature of sea targets in dynamic sea states is a critically important problem in developing methods of detection and identification of potentially threatening ships. As most maritime radars operate at X-band, this electromagnetic (EM) problem has an extremely large electric-size and it is further complicated by the sea wave phenomena. Simulation tools exist for high-frequency EM simulation. However, existing tools are incapable of modeling the fine features on the topside of ships that often have significant scattering contributions due to their comparable size to X-band wavelength. They are also incapable of capturing the interaction between ships and complex sea states and are not suitable for state-of-the-art Graphic Processing Unit (GPU) or GPU-cluster acceleration. To address this, IAI and its collaborators from Auburn University have been awarded a new contract entitled, “RARSP: Rapid and Accurate Radar Signature Prediction.” A hybrid method will be developed based on the novel Bidirectional Analytic Ray Tracing (BART) algorithm and the 3D fast Method of Moments (MoM) algorithm. In addition to the fine features of ships, the proposed tool can also take into account the scattering of rough sea surfaces. Both BART and MoM can be accelerated by inexpensive GPUs. This technology will provide significant benefits to a variety of commercial and military radar sensing applications of mixed-scale targets, including aviation, boats and ships, spacecraft, ground vehicles, and fixed installations.
May 2013: OSD/Army awards IAI a new contract to develop an Integrated Threat feed Aggregation, Analysis, and Visualization (TAAV) Tool for Cyber Situational Awareness.
Cyber security analysts are inundated with heterogeneous threat feeds created by different cyber security monitoring tools. Streamlining threat analysis can help operators focus on early identification and comprehension of security threats. However, this information must be extracted from several types of unstructured and seemingly unrelated threat feeds. Tools to amalgamate threat elements and identify critical cyber security threats that span multiple types of threat feeds, and to aggregate disparate multi-structured threat feeds in a meaningful way are important. To address this critical need, IAI will create an integrated tool for heterogeneous and multi-structured Threat feed Aggregation, Analysis, and Visualization (TAAV) for security analysts. TAAV is a web-based tool for automatic threat feed aggregation, categorization, and intuitive visualization of imminent cyber threats. The proposed framework will execute automated expert and data driven multi-dimensional and time correlated threat feed analysis that will categorize threat feeds into “threat baskets.” The identified relevant threat baskets will be prioritized according to their perceived scale of vulnerability by performing latent threat association detection and alert correlation over ongoing as well as historical attack information. The result of the automated analysis will be presented in an easily understandable display with accompanying maps and charts. An empirical study will be performed on the developed prototype using a scalable test bed. TAAV will be developed end-to-end in Java using open source tools, and leveraging the unique expertise of the IAI team in the fields of network analysis, cyber situational awareness, visualization, data mining, and web technologies and standards.
May 2013: OSD/Army awards IAI a new contract to develop a comprehensive and dynamic framework for Realtime Network Traffic Resiliency.
Firewalls and intrusion detection systems (IDSs) are helpful against sophisticated attacks over the Internet, but they are a static defense mechanism, requiring human intervention and providing no historical analysis. A real time network traffic monitoring, detection and filtering system that can adaptively, dynamically and intelligently redirect malicious traffic to safe locations for further analysis will be useful. To address this, IAI has been awarded a new contract entitled “RADAR: A Comprehensive and Dynamic Framework towards Realtime Network Traffic Resiliency.” The proposed approach for Realtime, Adaptive and Dynamic trAffic Resiliency, called RADAR, uses existing algorithms and technologies like commercial traffic monitoring and filtering tools, threat detection algorithms, and commercial router configurations to develop a complete traffic resilience enabling system. It dynamically monitors, detects and intelligently redirects suspicious traffic to isolated locations in the network in a real time manner. The complete RADAR system includes RADAR-enabled routers with network monitoring and traffic filtering/diversion capabilities, threat handler units with thread detection, rule set generation and BGP FLOWSPEC rule configuration components and threat analyzers for advance threat analysis, packet manipulation and covert response. The proposed framework will establish modular and flexible architecture to redirect and manipulate suspicious traffic throughout the network. RADAR’s feasibility will be studied on IAI’s experimental network tested, over a network of hybrid sets of routers. Software routers, routers that support open source firmware, such as OpenWrt or DD-WRT, and other widely used commercial routers such as CISCO, Juniper and Linksys will be integrated into the implementation. RADAR would be beneficial to the military as well as any commercial ISP or security service provider responsible for computer network defense operations.
May 2013: NASA awards IAI a new contract to develop a Cloud-Based Analytics, Store and Query System for Data-Intensive Scientific Processing.
The size of NASA’s observational data sets is growing dramatically as new missions come on line. Data is collected at unprecedented rates due to a wide range of high-resolution, high-throughput sensors, and new scientific models also produce huge data sets. However, algorithms and tools to satisfactorily analyze this Big Data are not available. Cloud computing offers a promising compute-and-store environment, that is complementary to supercomputing, in order to handle the massive data created by NASA missions. IAI and its collaborators from Indiana University have been awarded a contract entitled “Cloud-Based Analytics, Store and Query System (CASQUE) for Data-Intensive Scientific Processing.” A Cloud-based open architecture Analytics, Store and QUEry (CASQUE) system will be developed for data-intensive scientific computing for NASA missions. The architecture and workflow will be tailored for specific NASA missions and will be thoroughly tested in large-scale relevant cloud environments. Methods, tools and a cloud-based architecture will seamlessly submit and incorporate diverse data and metadata, allow open-access to scientifically collected data, support a cost-efficient working environment for scientific computation and open development, along with preferably open-source software and tools for efficient data management, processing, analysis and visualization. Though various data pre-processing and analysis tools are independently available for the research and science community, a seamless compute-and-analyze collaboration environment, which can lead to crowdsourcing for researchers and scientists, is still lacking. A cloud computing workflow and architecture, tailored for the needs of the scientific and research community, would be a cost-efficient asset for data analytics for different levels of processing.
May 2013: NASA awards IAI a new contract to develop An Uninhabited Aerial System Safety Analysis Model.
There is an increasing need to fly unmanned aircraft systems (UAS) in the U.S. National Airspace System (NAS) to perform missions of vital importance to national security and defense, emergency management, science, and to enable commercial applications. The increased air traffic and the complex array of commercial and general aviation aircraft, UAS, reusable launch vehicles, rotorcraft, airports, air traffic control, weather services, and maintenance operations, requires the application of systematic safety risk analysis methods to understand, reduce, and eliminate or mitigate risk factors. IAI and its collaborators, Luxhoj Consulting and Research LLC (LCR) and Coherent Technical Services Incorporated (CTSI) have been awarded a contract entitled “An Uninhabited Aerial System Safety Analysis Model (USAM).” USAM incorporates UAS scenarios and encounter geometries to populate existing safety analysis models, thereby producing credible future UAS safety metrics. The synergistic combination of existing models makes this proposed effort unique and valuable to NASA. This work leverages IAI’s experience in developing future UAS demand and performance models, LCR’s work in developing UAS safety analysis methods for the NASA UAS program, and CTSI’s expertise in developing UAS collision probability models for the United States Navy for their Unmanned Combat Aerial System (UCAS). The combination of the IAI-provided encounter geometries along with the existing safety analysis framework and collision probability models will allow USAM to provide an in-depth look at the safety issues surrounding the introduction of UAS aircraft in the National Airspace System. Combining these three capabilities into an integrated risk picture in USAM represents an unprecedented capability that can assign realistic probabilities for various risk scenarios with regards to UAS aircraft.
May 2013: NASA awards IAI a new contract to develop a Metroplex-Wide Flight Planning and Optimization System.
Futuristic tools to increase the efficiency of the air traffic control system must deal with the increasing complexity of the aviation system due to a mixture of different aircraft types and business models, increased traffic volume, and varying aircraft performance. A comprehensive Metroplex planning system will help coordinate flights between the enroute and Metroplex domains, and manage surface traffic, using Next Generation Air Transportation System (NEXTGEN) concepts. To address this, IAI has been awarded a new contract entitled “METROSIM: Metroplex-Wide Flight Planning and Optimization.” MetroSim is a Metroplex-based arrival, departure, and surface optimization system that seamlessly links with NASA’s Traffic Management Advisor (TMA) and System Oriented Runway Management (SORM) tools. The MetroSim architecture contains a collection of different tools, including simulations, physics-based computations, and mathematical optimization calculations. These tools interoperate in a distributed computational environment to provide real-time airport planning and optimization at the Metroplex level for arrival, departure, and surface movement operations. MetroSim’s suite of highly interconnected and interacting tools can execute quickly on a variety of processors, and can exploit parallel processing through multicore computer architectures. Its distributed architecture is expandable, and is ideal for modern computer systems with multiple cores and Graphics Processor Units (GPUs). MetroSim builds on IAI’s experience in developing computer software for aviation systems, while including NextGen concepts like combined air/ground separation assurance, interval management, and dynamic wake vortex spacing, to help safely increase traffic density while reducing controller workload. MetroSim helps airport planners, traffic flow management experts, airline dispatchers, air traffic controllers and pilots to reduce the uncertainty in operations planning, recover quickly from disruptive events, maintain high throughput even in adverse weather conditions, and handle the uncertainties associated with weather forec.
May 2013: DOT awards IAI a supplement to the follow-on contract for developing a Novel Multi-Sensor Wireless Network for Bridge Structural Health Monitoring.
Bridges represent significant investments in the U.S. highway transportation network, and a reliable, low cost bridge monitoring and inspection system is a critical need. To address this, IAI and its collaborators from Penn State University, Z-DAC, and Hardesty & Hanover, have been awarded a supplement to a follow-on contract entitled, “A Novel Multi-Sensor Wireless Network for Bridge Structural Health Monitoring.” A wireless multi-sensor network for Structural Health Monitoring (SHM) of steel bridges and concrete pavements is being currently developed, which includes acoustic emission sensors, ultrasonic guided wave sensors and strain gage sensors, and provides early warnings of critical structural problems through cooperative network sensor data processing and diagnosis. The sensor data are collected, processed, and then transferred to a remote maintenance office via a commercial Global System for Mobile Communications (GSM) cellular network. Field operation validation test of the developed prototype will be conducted on real bridge structures from Florida and Maryland. Tasks will include field instrumentation and testing of the prototype sensor system, conducting a live load test for data collection, collecting long-term sensor data for the bridges, and developing a software tool for data collection, management and processing. The end product will be weather proof, self-sufficient, and easy to install and maintain. It will include a comprehensive, automated data collection and management software tool that can collect sensor data remotely and analyze the data for damage diagnosis. The wireless multi-sensor network will also be adapted for embedded concrete quality control and condition monitoring. The current efforts will help to mature and commercialize the prototype bridge health monitoring system, which can be readily marketed to state and local agencies, and bridge owners.
May 2013: OSD/Army awards IAI a new contract to develop Advanced Network Security Metrics for Cyber Resilience and Asset Criticality Measurement in Mission Success.
Cyber-dependent systems are increasingly complex, susceptible to adversary attacks, and difficult to reliably defend. Cyber assets usually support military missions with different priorities, making it necessary to measure the defense and resilience effectiveness of individual and collective cyber assets. To address this, IAI has been awarded a contract entitled “CREACT: Advanced Network Security Metrics for Cyber REsilience and Asset CriTicality Measurement in Mission Success.” A set of advanced network security metrics will be developed for cyber resilience and asset criticality measurement, evaluation, and measurement in a large-scale and dynamic network environment. Advanced value-based goal models and efficient mission-to-asset mapping, resource allocation, vulnerability assessment, threat analysis and impact mitigation techniques, along with network monitoring and protocol analysis tools will be developed in this framework. This integrated system can evaluate and measure cyber security and resilience of networks in three stages: network regular and scheduled maintenance phase, mission planning phase and mission execution phase. The developed technologies will be implemented into an integrated software toolkit for comprehensive cyber security analysis and network defense to achieve mission success. Key innovations of CREACT are providing real-time cyber situational awareness and the use of value-based goal models for cyber resilience analysis, which allow cyber resilience and mission assurance to be quantitatively evaluated and measured. Further, CREACT is scalable and has a powerful cyber impact analysis capability. Lastly, the proposed functionalities are formulated and implemented in a distributed, modular and open control manner, improving the reliability, scalability, and robustness of CREACT. Emerging techniques can be integrated into this framework, which can be installed as a stand-alone software application, or as an add-on component to existing security systems.
May 2013: DARPA awards IAI a new contract to develop Spectrum Efficient Communications and Advanced Networking Technology.
Scarcity of available spectrum resources imposes limits on wireless system performance, as spectrum has to be shared by multiple users across time, space, and frequency dimensions. Increasing spectrum efficiency at all layers and developing a common framework with significant efficiency gains, will be very useful. IAI proposes to develop Spectrum Efficient Communications and Advanced Networking Technology (SECANT), a novel multiuser communication system that integrates advanced physical (PHY) layer techniques for dynamic multiple access and interference management, and supports spectrum efficiency gains through a clean slate design of medium access control (MAC). SECANT combines dynamic interference management, including interference avoidance/cancellation and interference alignment (IA) with spectrum-aware multiuser detection (MUD) at PHY layer to increase spectrum efficiency in a distributed wireless network. A new adaptive MAC scheme is proposed to enable PHY layer gains by allowing concurrent transmissions in a collision domain and dynamically controlling interference and channel access with local spectrum information. SECANT is supported by reliable spectrum sensing and adapts to spectrum dynamics by optimizing transmission and sensing times. Multi-layer design is realistic in terms of cost-effectiveness and network performance, and allows practical SDR deployment for prototyping and testing under real radio hardware performance. Theoretical analysis, protocol design, software-defined-radio (SDR) implementation with Universal Software Radio Peripherals (USRPs), and experiments based on high-fidelity wireless network emulation will be undertaken on IAI’s SDR test bed platform, CREATE, with full network protocol stack implemented for fast prototyping. Spectrum efficiency gains will be compared with traditional SISO QPSK TDMA schemes under realistic channel conditions and real hardware performance. This design allows wireless nodes to adaptively utilize local information and optimally exploit PHY layer resources to achieve increased spectrum efficiency.
May 2013: DARPA awards IAI a new contract to develop a light and secure satellite hypervisor.
The defense and intelligence community’s payload development for on-board processing traditionally has been hardware based. Hardware payloads require increased algorithm development effort to benefit from the available processing speed. Virtualized payloads can increase the beneficial mission utility and life of space platforms, while decreasing acquisition time and cost. Virtualization of satellite payload can increase the capability and flexibility of the payload developer. To address this issue, IAI has been awarded a new contract to develop SafeHype, a tiny, light and secure hypervisor that virtualizes satellite payload. SafeHype can securely isolate the virtual machines running concurrently on the same hardware resources such as CPU, memory and I/O devices. Hardware support will be utilized to reduce virtualization overhead. SafeHype adopts both resources pre-allocation and para-virtualized I/O techniques to simplify its functions and support real time applications. The code size of SafeHype is small and can be verified. SafeHype also reduces surface attacks by bringing the guest virtual machine in greater direct contact with the underlying hardware resources. A workable SafeHype prototype will be developed to show its feasibility. This work will increase the capability and flexibility of spacecraft, allowing them to adapt their mission and processing to meet the constantly evolving needs of the defense and intelligence community. In addition to military communications or intelligence, surveillance and reconnaissance (ISR) missions, commercial civilian applications for a space-qualified hypervisor could include space-based satellite communications.
May 2013: DHS has awarded IAI a new contract to develop Scalable and Reliable RF sensing for Long-Duration Personnel Tracking.
Recently, the U.S. Government Accountability Office reported that less than 1 percent of the 4,000-mile border between the U.S. and Canada is under the operational control of U.S. Border Patrol. Solutions that address this must detect, localize and track people in wooded terrain using mesh networks comprised of low power, covert, RF sensors based on standard, inexpensive commercial off the shelf (COTS) products used widely in wireless data networks. To address this, IAI has been awarded a contract entitled “ARGUS: Scalable and Reliable RF sensing for Long-Duration Personnel Tracking.” A novel mesh networked RF sensor will be developed based on IAI’s current and proven RF perimeter monitoring ARGUS system. ARGUS was developed by IAI and is manufactured under license by HARRIS Corp. ARGUS uses Zigbee-based COTS radios to establish radio links of length of about 50-100 m between nodes. Intruders near nodes are detected by changes in the RF channel due to motion and resulting changes in received signal strength. ARGUS scales to tens (tested) or hundreds (by analysis) of nodes, and all radio links can be used to detect intruders. ARGUS is a mature, well-tested technology, and will be adapted to meet the specific challenges in the DHS border application. First, sophisticated algorithms will be developed to reduce the Probability of False Alarm (PFA) in densely wooded environments. The ability of ARGUS measurements on different RF channels, bi-directional measurements and Time Of Flight measurements to provide independent information to reduce PFA will also be investigated. Secondly, use of node sleeping, low power detection algorithms, and efficient components will increase battery life. Lastly, economies of scale and eliminating unnecessary features will be used to decrease system cost.
April 2013: Navy awards IAI a new contract to develop Scalable Autonomic Fault Detection and Root-Cause Analysis in a Heterogeneous Network.
A suitable network analysis tool is essential for accurately and efficiently detecting, diagnosing, and predicting faults, and ensuring reliable and secure communications in future heterogeneous tactical military networks with mobile and wireless networking technologies. To address this, IAI will develop a SCalable AutoNomic Fault detection and root-cause analysis (SCAN-Fault) scheme to analyze monitored networks and assist network operators in maintaining, optimizing, and securing the managed networks. SCAN-Fault will improve reliable and secure access to battlefield networks, and reduce costs and risks for network management. It can monitor network performance in real-time or near-real-time, automatically collect network data, status and configuration, perform cross-layer multi-domain fault detection and analysis to pinpoint the root cause, and assist users in determining efficient actions to fix issues. SCAN-Fault has a Fault Detection and Analysis package with three components for automatic fault detection and diagnosis. The Network Monitoring and Conceptualization component identifies faults and failures in its domain(s) through network monitoring and abstraction, and lists them along with observed symptoms. The Fault Diagnosis component pinpoints the root causes of observed network issues and constructs a comprehensive multi-layer topology-based global root-cause graph model online, to assist in troubleshooting network issues. The Action and Evaluation component determines the most efficient action based on the collected data from network monitoring tools, the available Events Database, Interdependency Graph, and Recovery Rule Database. The action is then validated before it is applied in the network, and further refined with user interactions if necessary. SCAN-Fault can take advantage of existing commercial off-the-shelf (COTS) and open-source network monitoring tools by taking their collected data as input for fault detection and analysis components through adaptors.
April 2013: OSD/Navy awards IAI a new contract to develop Data-centric Sensor Planning in a Cloud Environment.
DoD’s Data-to-Decisions (D2D) program aims to develop an open-source architecture system that enables rapid integration of existing and future data exploitation tools, creating a new paradigm in data management and analysis. Automated sensor planning and management based on shared situation awareness (SA) would be useful. Designing an overall systematic framework for cloud-based sensor planning that can ingest real-time SA data, compute the application and mission-dependent quality of data and metadata, and make near-real time decisions for the sensors, is challenging. To address this need, IAI has been awarded a contract entitled, “Data-Centric Sensor Planning in a Cloud Environment.” A systematic approach based on cloud computing will be used to provide scalable data mining and analysis algorithms, and tools and platforms for ingesting real-time sensor data for shared SA and predictive processing, and to drive the sensor planning loop. A scalable workflow will be implemented with various components of Hadoop ecosystem to mature an information-theoretic sensor management framework, and integrate it with DoD data and metadata to achieve structural and semantic interoperability. A user interface layer will be implemented using Ozone Widgets. Based on a selected application scenario and mission, actions of sensors will be determined and sensor outputs will be emulated based on these decisions. One key innovation is implementing and integrating sensor planning and management loop in the cloud. Further, a mission and application-driven framework systematically computes the information quality as a fusion of both a set of relevant information characteristics and entropy. A prototype of the system will be tested on IAI’s private cloud DRACO, using IAI’s Hadoop distribution Elastic LocoMotive, which already has many Hadoop services running in a Virtual Machine (VM) and will be configured for this project.
March 2013: OSD/Navy awards IAI a new contract to develop a Self-adaptive System Monitoring Architecture for Adaptive Computing System.
The ability for software systems to self-diagnose and self-adjust is highly desirable in military and civilian applications in the cyber domain. The capability of automatic adaptive reasoning and monitoring is the first step towards this objective. To address this, IAI has been awarded a new contract entitled “SAM: A Self Adaptive System Monitoring Architecture Multi-Abstractions System Reasoning Infrastructure toward Achieving Adaptive Computing System.” IAI’s in-house machine learning framework, ABMiner, and ontological knowledge representation workbench, ALARM, will be leveraged to develop a self-adaptive monitoring architecture, called SAM, as the system reasoning infrastructure for adaptive computing system. The conceptual architecture of SAM has a data mining component, a multi-feature monitoring model, an ontological knowledge representation, and a human expert and data acquisition controller. The human expert supervises the execution of the monitoring system, and defines the features used by data mining to set up the multi-feature monitoring model for the application program. This model is used during runtime to detect any abnormal behavior of the application based on the multi-abstracted features. ABMiner allows the use of various machine learning techniques, including ensembles, to select the most significant semantic features to characterize the monitored application. IAI’s work on ontological knowledge representation techniques will be leveraged to represent the execution of an application program in an understandable way to enable human experts to adjust the automatic reasoning system iteratively. Finally, the proposed techniques will be integrated in a workable self-adaptive monitoring prototype that checks the execution of dynamically evolving applications. SAM has many uses in application protection, software characterization, and self-adaptive and self-healing computing systems.
April 2013: OSD/Navy award IAI a new contract to develop Situational Awareness for Cyber-security Evaluation and Training.
DOD and owners of large, commercial enterprise networks need appropriate tools and well-trained personnel to counter cyber-attacks and enforce cyber-security. Cyber situational awareness requires understanding the context of network vulnerabilities, their interrelations, and how they may be exploited. Human cyber analysts face information overload and a concomitant lack of comprehensive cyber situation awareness. To address this, IAI and its collaborator, University of Utah, will develop Situational Awareness for Cyber-secURity Evaluation and training (SACURE) by utilizing current visualization principles and cognitive task analyses (CTA) of analysts’ workflow. IAI and its collaborator, the University of Utah, will use expertise in cyber-security, interface design, and visualizations of large, heterogeneous data, to develop a next-generation cyber-security tool to improve analysts’ and managers’ performance. This approach leverages prior Cognitive Task Analyses of cyber-security personnel to design interfaces that support personnel workflow, and to provide visualizations of the networks that enable users to develop Situational Awareness of the security of their networks. This approach combines existing CTA of cyber security personnel, a state-of-the-art cyber-security tool that will be refined to enhance the capabilities of its users, research-based design and visualization capabilities, innovative user analysis methods, and embedded assessment and training systems to help users practice their capabilities before real attacks occur. SACURE is flexible to new threats and responses to those threats, and provides different views of enterprise networks depending on the role and current needs of the user. SACURE will be integrated with IAI’s Network Intrusion Risk and Vulnerability Analysis (NIRVANA), to illustrate how SACURE can assist users of cyber-security tools. Together, they provide a fully functioning, next-generation cyber-security tool with an interface designed based on users’ needs for situational awareness.
April 2013: Navy awards IAI a follow-on contract to develop a Cognitive Ultra-Low Power Sensor System (CUPSS).
The Navy’s role in the Global War on Terrorism (GWOT) requires reliable long-term intelligence, reconnaissance, and surveillance. For long time frame scenarios, the main challenge faced by methods of automated surveillance and threat identification is the significant power resources required by sensor systems. Developing power efficient, low cost, long term, smart sensing technologies will be very useful to address this challenge. IAI, in collaboration with University of Michigan, is developing a novel sensing system called Cognitive Ultra-low Power Sensor System (CUPSS). CUPPS is a multimodal sensing node with wireless fencing capability supported by ultra low power (pW-nW), and a compact Phoenix hardware platform that leverages a comprehensive sleep strategy using a unique power gating approach, a CPU with compact instruction set, a custom low leakage memory cell, adaptive leakage management in the data memory, and data memory compression. In addition, CUPSS has a cognitive sensor management framework for heterogeneous, sensor modalities; sensed events; and signal propagation that allows for smart sensor positioning and power management. It features hierarchical sensor placement and fusion architecture to efficiently reduce model complexity and uncertainty and to identify friendly encounters from threat intrusions. Finally, it has a sensor middleware supporting hardware and algorithm abstraction and software wizards to rapidly integrate new sensors, and easily update the sensor sleep/trigger hierarchy and rules by a non-technical human operator. A CUPSS architecture will be built and demonstrated for a wide variety of sensing modalities, including radio and vision-based sensors, seismic and acoustic sensors suitable for advanced laboratory and supervised field-testing.
April 2013: Navy awards IAI a new contract to develop a Highly Scalable and Autonomous NetOps Analytics System for Navy Tactical Networks.
The current naval networking environment is primarily composed of several enterprise computing and communications environments, which can be characterized as large scale heterogeneous network environments. Due to the inherited complex nature of the network, it is quite challenging to provide a synchronized view of data, and perform autonomous analytics from the large volume of geographically dispersed data. IAI has been awarded a new contract entitled “Highly Scalable and Autonomous NetOps Analytics (SANA) System for Navy Tactical Networks.” While there are currently many network situational awareness tools available, the objective of this program is to develop superior and secure situational awareness and user efficiency that would benefit any large network operating in a cloud environment. IAI proposes to develop a highly scalable and autonomous NetOps Analytics (SANA) system, which defines and captures abnormalities in information and network behaviors to generate alerts if any abnormalities are detected. In addition, the proposed system maps missions to required resources and guarantees synchronized information sharing to increase level of information value, health and trust. SANA can be extended to a broad range of large commercial network operations to deal with issues of network performance, operational availability, and security.
March 2013: Army awards IAI a follow-on contract to develop a Radio Frequency Network Emulation Tool for evaluating Wireless Mesh Networks.
Current wireless communication hardware is optimized to receive signals from a single user. IAI has developed new technology to allow simultaneous reception and decoding of signals from multiple sources, thus increasing spectral efficiency in analog and digital signal transmission. Further development of this technology requires rigorous testing in a networked environment, including performance evaluation of protocols at different layers and the behavior of physical layer hardware. Evaluating protocols or solutions for wireless networks is difficult, as simulation is inadequate, and wireless field tests are costly, slow, and not easily scalable, controllable, or repeatable. IAI has been awarded a contract entitled, “Preservation of Information from Non-Collaborative Sources (PINSs),” to develop a network emulation tool that can test and evaluate its new technology. A next generation radio frequency network channel emulator simulator tool called RFnest-48 will be developed. It can evaluate a fully connected wireless mesh network consisting of up to 48 nodes, called ‘real’ nodes, represented using physical devices; and 200 nodes called ‘virtual’ nodes, simulated in software. Virtual and real nodes interact seamlessly through devices called ‘surrogate’ nodes that represent the interference on virtual nodes due to real nodes, and vice-versa. RFnest-48 supports devices with bandwidths up to 150 MHz, and models a multipath fading propagation channel with a flexible number of resolvable components. Additionally, RFnest-48 has multiple-antenna capabilities to support at least 12 RF devices with up to 4 antennas for each node. RFnest-48 combines direct digital conversion and an RF front-end design to support incoming frequencies between DC and 3 GHz. RFnest-48 will improve the design, development and evaluation of dynamic wireless networks. Applications include realistic protocol evaluation, large-scale evaluation, replaying field tests, and model validation.
March 2013: Air Force awards IAI a new contract to develop a Robust Decision Tool for Persistent Space Self Defense Systems.
The increasing demands of space superiority require a space self defense system with space situation awareness (SSA), or understanding of dynamic events with space assets. Such a system requires sensors to determine the position and velocity of space objects, predict orbits, detect adversary behavior and prevent attacks. IAI and its collaborator, University of New Orleans, propose to apply an advanced game theoretic approach to design a robust, decision-making tool for space self defense systems. A realistic cyber-physical system model will be developed. Then, a dynamic and distributed sensor management algorithm will be developed via a game theoretic approach for maneuver detection and persistent multi-object tracking. Advanced estimation and tracking techniques will be designed by applying nonlinear filters. Finally, a pursuit-evasion game will be designed among all observers and targets to represent their interactions in terms of orbit maneuver behavior and the resulting collision alert for decision support. This approach uses innovative game models to track space objects, analyze their orbits, and provide decision tools for space surveillance systems with self-defense capabilities. It incorporates threat modeling and analysis based on the pursuit-evasion game based active learning of deceptive behavior, nonlinear filters, cooperative sensing for persistent space object tracking by using comprehensive and realistic models of space platforms and service oriented architectures. The proposed framework will be tested via IAI’s wireless network emulation platform RFnestTM with respect to heterogeneous tracking objectives including accuracy, delay, and communication overhead. Supported by the repeatable and scalable experimentation capability of RFnestTM, this approach will facilitate advanced prototyping of effective decision making tools for persistent space self defense systems.
March 2013: OSD/Air Force awards IAI a new contract to develop LASER: A Linguistic enriched And Scalable Event attribute extRaction System.
The fast growth of web data and HUMINT reports has resulted in intelligence analysts having to rapidly monitor and analyze event information from massive amounts of unstructured textual data, in order to achieve and maintain persistent Situational Awareness (SA). A tool to automatically extract accurate events while distinguishing between real and hypothetical events, true and false events, frequently occurring and specific events, and past, ongoing and future events, will be useful. IAI proposes to develop an innovative Linguistic enriched And Scalable Event attribute extRaction System: LASER, to address this issue. LASER’s first innovation is that it integrates a rich set of specialized linguistic features exploited from lexical, syntactic and semantic levels into each of the four classification models for the event attributes of modality, polarity, genericity and tense. Secondly, it adopts robust classification models that can handle the unbalanced class problem in event attribute extraction. Finally, it incorporates three post-correction approaches to bring more performance gains to event attribute extraction. LASER also leverages a state-of-the-art event tagger that ensures that the four event attributes can be extracted on top of accurately extracted events. Furthermore, LASER uses powerful cloud computing architecture for information management and algorithmic computation. LASER leverages IAI’s data mining framework, ABMiner, to enable distributed processing of the computational intensive algorithms involved in event extraction and event attribute extraction. ABMiner provides more than 400 algorithms for both supervised and unsupervised learning aggregated from IAI’s machine learning projects and open source libraries such as Weka and RapidMiner. LASER has a wide range of potential applications in the military, as well as in commercial law enforcement, Homeland Security, financial and medical markets.
February 2013: OSD/Navy awards IAI a follow-on contract to develop a Dynamic Warehousing and Mining System for Human Social Cultural Behavioral Data.
Non-kinetic, asymmetric and sustainability operations all require good data for modeling human social cultural behavior (HSCB). Text communication provides good HSCB data, but it is vast, becomes obsolete quickly, and is not warehoused or mined in ways relevant to HSCB analysis. A system that can dynamically collect unfiltered textual communication data for use in HSCB modeling and analysis would be useful. The Dynamic Warehousing and Mining (DWM) framework being developed by IAI handles in-situ HSCB data collection and analysis. It is a large-scale, dynamic approach for collecting, organizing and analyzing massive data to assess HSCB dimensions for a given group, and to predict current belief states and likely intended actions. DWM has three layers: the data collection layer, the analysis layer, and the application layer. The data collection layer includes HSCB data sources, and an agent-based data collection component that automatically extracts, transforms, and loads various types of data from large-scale data sources. The analysis layer includes a HSCB feature analysis component, a data cube engine, and a data mining and modeling engine. The application layer includes a set of integrated data/model query, and visualization services. DWM utilizes software agent technology to achieve automated, scalable collection and real-time processing of HSCB data. Multiple agents work in parallel to scale up the execution of complex analysis tasks including linguistic feature analysis, data cubing, mining and modeling. DWM leverages IAI’s Agent-Based Data Miner (ABMiner) data mining platform for HSCB modeling. ABMiner integrates hundreds of data mining algorithms from IAI’s machine learning projects and open sources libraries such as Weka and RapidMiner. DWM has standardized search and query capability and can support many HSCB applications and products.
February 2013: OSD/Air Force awards IAI a new contract to develop a Toolkit for Automated Cyber Security Assessment and Evaluation Tests.
Large datasets are created by the high-fidelity security assessments of military and commercial software systems. Managing this captured information is hard, especially when cyber tests are performed in a distributed networking environment. An automated tool to manage, process and analyze these datasets will simplify the manual work in current cyber evaluations, and increase the efficiency of the tests. To address these issues, IAI has been awarded a contract entitled, “Hermes: A Visualized and Automated Cyber Security Assessment Toolkit.” The Hermes framework and toolkit for cyber security assessment and evaluation tests facilitates managing, processing, and analyzing large logging datasets from distributed and service-centric computing test bed. Further, it gains an aggregate system view before, during, and after assessment exercises, and delivers analytical results on various performance metrics qualitatively, quantitatively, and visually. Hermes also gives predictive analysis based on network topology, attack progression, and host or application status. It provides a holistic DVR-like visualization that captures the hardware and software resources in the test bed, and the progress of attack and defense, and allows testers to record and repeatedly replay assessment exercises. The key innovations of Hermes are the distributed logging report and centralized analysis for complex cyber security assessments, the customizable real-time and post-exercise visualization of evaluation progress with aggregated insight, and the automated cyber test execution through iterative and progressive generation of test scripts. The Hermes framework will give cyber testers insights into attack and defense mechanisms and Systems Under Test (SUT). Hermes leverages IAI’s expertise in Cyber Security, Computer Networking, and Situation Awareness and will utilize IAI’s available technologies and open source software to create a powerful, flexible, and robust framework for Cyber Security assessment tests.
February 2013: Missile Defense Agency awards IAI a new contract to develop an innovative Human-In-Control Model Design for BMDS simulations.
The performance of human operators significantly shapes the capabilities of the entire BMDS. Simulations of warfighter performance can benefit from incorporating real-world variations in operator proficiency, timeliness, creativity, fatigue or morale. Modeling Human-in-Control actions will increase the credibility of such simulations, which can be used in Performance Assessment, Futuristic Concept Analyses, Ground Tests, and Training, Exercises and Wargames. To address this, IAI and its collaborator, University of Michigan, have been awarded a contract entitled “Human-In-Control (HIC) Modeling and Integration Framework for BMDS Simulations.” An innovative HIC software modeling and integration framework will be built around objectively quantified cognitive models of human operators. These include the Executive-Process/Interactive Control (EPIC) and the GOMS (Goal Operator Method and Selection rules) Language Evaluation and Analysis (GLEAN). The framework provides the ability to model existing or futuristic operator workstations, which is used as declarative knowledge to develop high & low-fidelity cognitive models. It supports operator modeling of varying proficiency by allowing users to define task-specific timeline distributions, while the decisions are driven by fixed perceptual, cognitive, motor processor parameters and constraints. The cognitive model can interact with external decision models to determine the quantitative value of the outcome of certain motor tasks, so that meaningful simulation events can be generated for other BMDS simulation elements and components. A proposed HLA framework to interface with perceptual signal and motor actions declared in the HIC model provides an integration layer and a middleware to manage simulation time, and seeds to repeatable stochastic behavior external to the HIC model. Finally, a proposed enterprise service with scenario planning capability, to configure a simulation event from a repository of operator and workstation models, will support training and BMDS Performance Assessment.
March 2013: IAI releases the MOXIEfit™ App for Android mobile devices.
MOXIEfit™, developed by Intelligent Automation, Inc. (IAI), provides a virtual personal trainer on a smartphone, to motivate and support a step-by-step achievable weight loss program. MOXIEfit’s virtual personal trainer, Mandy, provides personalized coaching during workouts to help users stay motivated and stick with their fitness program. Mandy helps the user create a weight loss program that includes a weekly workout schedule and weigh-ins. Mandy reminds the user when it is time to exercise and provides motivational feedback throughout the workout. Mandy praises the user for keeping on track with their weight loss goal and automatically adjusts the plan if the user gets off track – all without counting calories! MOXIEfit is the first smartphone application to provide a virtual personal trainer that cares about helping users get fit.
Another key feature of MOXIEfit is MOXIEmatch™, an adaptive music feedback system that matches songs from the user’s music library to the workout tempo. If there are no songs that perfectly match the tempo, MOXIEmatch increases or decreases the music speed to match the workout pace. This feature, coupled with Mandy’s personalized coaching, ensures users get the most benefits from their exercise. MOXIEfit can sync user data online using CarePass, Aetna’s personal health cloud that automatically syncs data between the user’s favorite health and wellness apps. As part of the CarePass ecosystem, MOXIEfit syncs fitness data such calories burned, distance, and steps as well as body measurements from weekly weigh-ins that can then be viewed online alongside health and wellness data such as nutrition from other apps.
“MOXIEfit takes a simple approach to losing weight,” according to Timothy Judkins, IAI’s mHealth Program Manager. “While MOXIEfit has all the capabilities you would expect in today’s fitness apps, the addition of the virtual personal trainer and features like MOXIEmatch will significantly help users adopt and follow a weight loss program.”
Beyond helping users lose weight, MOXIEfit was originally developed as part of an Army SBIR program to support physical rehabilitation. “Adherence to a rehabilitation program is critical for a successful recovery from injury for our wounded warriors. We see real potential for an application that could provide motivation during the recovery period," said Ashley Fisher, Health and Wellness Domain Coordinator, U.S. Army Telemedicine and Advanced Technology Research Center (TATRC).
MOXIEfit is available for Android mobile devices as a 30-day free trial and can be downloaded from the Google Play store to any Android mobile device. MOXIEfit also offers users a price discount if the application is purchased within 14 days. You can download the MOXIEfit App at: https://play.google.com/store/apps/details?id=com.iai.moxie.fit
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Intelligent Automation, Inc. (IAI) is a multifaceted private company that focuses on technology research and development. For over 25 years, IAI has developed cutting edge solutions to complex problems in areas that include mobile health, information technology, education and training, sensors and communication, signal processing, distributed systems, and robotics.
This work is supported, in part, by the US Army Medical Research and Materiel Command under Contract No.W81XWH-09-C-0134. The views, opinions and/or findings contained in this press release are those of the author(s) and should not be construed as an official Department of the Army position, policy or decision unless so designated by other documentation.
February 2013: Air Force awards IAI a new contract for Standoff Coherent Optical Detection of Acoustic Signals.
Detection of clandestine tunnels and underground facilities is of continuing interest to DoD and Customs and Border Protection. Ongoing research to find functional and reliable sensing methods include seismic methods, since dynamic activity in tunnels emits mechanical energy that propagates in seismic waves. Resulting ground vibrations can be measured at offset distances and these signals can be used in sensing algorithms for detection, location, and discrimination of the activity. Current detection methods rely largely on emplaced sensors, such as geophones. Space-based or airborne surveillance can drastically improve standoff and surveillance coverage. To address this critical need, IAI and its collaborator, University of Maryland (UMD), have been awarded a new contract entitled, “Standoff Coherent Optical Detection of Acoustic Signals (SCODAS).” A vibrometry technique will be developed for long-range detection of very small vibrations induced in passive objects exposed to acoustic disturbances. SCODAS will leverage the vibrometry experience of UMD, and signal processing and hardware expertise of IAI. A powerful, eye-safe, infrared laser is aimed at the ground or an airborne target from an airborne platform or satellite. The possible vibrations of an object causes scattered or reflected light from the laser to return to the transmitter with a superimposed phase modulation. SCODAS measures the amplitude and phase change of the probe beam simultaneously, and the measured phase will directly translate into displacement of the sensed area or object. The distinctive frequency spectrum and other signal features will be exploited for both interference due to turbulence, and the target signatures of vehicles and underground activities. The standoff distance will be extended by trading resolution for SNR, and the effects of atmosphere induced phase fluctuations will be corrected and compensated for using a reference beam.
February 2013: Army awards IAI a new contract to develop a Virtual Clinical Assistant for PTSD professionals.
The significant number of veterans and active military personnel with potential Posttraumatic Stress Disorder (PTSD) makes it important to improve PTSD diagnosis and treatment. A powerful PTSD Clinical Decision Support System (CDSS) based on evidence-based treatment strategies will be useful. To address this critical need, IAI has been awarded a new contract entitled, “A Virtual PTSD Clinical Assistant with Cloud Computing and Mobile Interface: VPCA.” The key innovation of VPCA is using the Hadoop Ecosystem with scalable data storage and processing capability to enable the evidence based clinical decision support empowered by Watson-like predictive analytics across different domains for PTSD patients. VPCA consists of a set of indispensable components grouped in four sub-systems, and supported by a Hadoop storage system, a map reduce engine and standard vocabularies and terminologies of clinical PTSD. The first sub-system is for knowledge and rule authorization, and it transforms the PTSD decision guidelines used by practicing PTSD professionals, into business rules. The second sub-system is for data integration and warehousing in a distributed fashion, including as many clinical data as possible for Watson-like predictive analytics. The third sub-system is for knowledge and rule discovery that can automatically and continuously improve the reasoning potential of the proposed virtual assistant. The data mining algorithms in IAI’s ABMiner and its meta-optimization for model selection will be migrated into the Hadoop Ecosystem. The fourth sub-system is for online clinical decision support. VPCA uses cutting-edge natural language processing technology for clinical data, an open-source clinical decision support platform called OpenCDS, and scalable architecture based on cloud and distributed computation to handle big data. VPCA’s online decision support for diagnostics and treatment will help experienced PTSD clinicians, general practitioners and residents.
February 2013: Navy awards IAI a new contract to develop a Data Storage and Transition System for Mobile Ad Hoc Networks.
Implementing distributed data storage and sharing faces unique challenges in the military wireless domain. Military tactical networks have mobile nodes with different, specialized mission functions. They operate in a noisy, often-obstructed, and adversarial environment, where nodes can be easily lost. The network is often partitioned into smaller sub-networks, and the data is highly dynamic and time sensitive. Solutions must address performance, reliability, security, and resilience, and compatibility with future tactical platforms and components. To address these issues, IAI has been awarded a contract entitled, “DataGuard: A Secure, Reliable and Efficient Data Storage and Transition System for Mobile Ad Hoc Network.” DataGuard is a distributed system that integrates various coding techniques, data migration protocol and service evaluation model, to enable secure data storage and efficient data access in wireless ad hoc networks. Various coding techniques will be adopted, including erasure coding scheme, threshold crypto algorithm, and symmetric crypto primitives to protect data at rest and in transit against eavesdropping and Byzantine attacks. A data migration protocol will allow the data storage system to self-adapt to network dynamics. Additionally, the service evaluation model will allow data users to evaluate the data delivery service provided by the data storage nodes. The combination of coding schemes, data migration protocol, and service evaluation model will significantly improve data availability with distributed implementation in wireless ad hoc networks. Finally, the feasibility of the coding techniques, data migration protocol and service evaluation model will be demonstrated in a workable data storage and transmission prototype for wireless ad hoc networks.
February 2013: OSD/Army awards IAI a new contract to develop a Human Machine Interface for Control and Communication in Power Networks.
Power system operators in military and industrial networks have access to large amounts of data due to the transition from hardwired analog meters to large computerized panels. Limitations include the mentally intensive nature of translating data into actionable intelligence, and the reliance on operator experience to interpret and balance options. To address this, IAI and its collaborators from Florida State University have been awarded a new contract entitled “Human Machine Interface (HMI) for Efficient, Lithe, and Intuitive Operator-communication and Sense-making in Power Networks (HELIOS-PN).” IAI’s state-of-the-art agent-based software will be combined with expertise in advanced human factors engineering and power systems to create an innovative, adaptive agent-based Human Machine Interface (HMI) system. The modular, flexible, and scalable system will have an agent-based automation and decision support engine, visualization libraries, and front-end adaptive screens to integrate state-of-the-art automation and visualization techniques. The system assists in the complex task of providing the right representation of the right data at the right time, relieving the operator of lower levels of data aggregation and contingency analysis. Tools, visualizations and methods will be developed for improving the operators’ situational awareness, and enabling efficient decision-making, facilitating reliable and intuitive operator interaction. HELIOS-PN is based on the tenets of information visualization, and techniques that leverage the cognitive and perceptual capability of the human operator. The components of the system will be integrated using a distributed and scalable agent framework based on IAI’s Cybele Distributed Agent based platform. The interface system provides effective remote control and monitoring, both for stationary and mobile operators.
January 2013: IAI receives a new Army contract to develop Bio-Inspired Visual Navigation for Unmanned Ground Vehicles.
Unmanned Ground Vehicles (UGVs) are currently tele-operated, requiring continuous human assistance. Video streaming between the UGV and the Operator Control Unit (OCU) is challenging under degraded communications, and low-texture indoor environments. Controlling a UGV needs detection and tracking of viewpoint-invariant landmarks, a navigation system for semi-autonomous operation, and a simple user interface for operator command. In a low-texture indoor environment, landmark-based navigation is difficult due to lack of distinctive micro-features such as Shape Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF), which are commonly used image features in computer vision. To address these issues, IAI and its collaborator, University of Pennsylvania have been awarded a contract entitled “Bio-Inspired Visual Navigation: From Landmarks via Bearing to Controls.” The Bio-Inspired Visual Navigation System requires only a standard EO camera payload. A segmentation-based method extracts landmarks as closed contours around visually salient objects in the image, which can be detected even in the low-texture environment. View-invariant shape and topological features are used to recognize these landmarks from different viewpoints. The locations of these landmarks are transformed into visual guides for bearing-only navigation. Finally, an intuitive OCU user interface enables the operator to specify waypoints and destination on an image. Using this interface, the UGV can semi-autonomously navigate its surroundings to the required destination with minimal input from the operator even in a degraded communication environment. A robust and efficient fixation-based landmark tracking algorithm extracts the object boundary, and tracks landmarks smoothly and efficiently even with changes in scale and viewpoints during camera motion. This technology is useful in military surveillance and reconnaissance applications, in civilian search and rescue operations, and to control robots via the Internet or cell phone.
February 2013: IAI Distinguished Lecture Series: Presents a seminar by Professor Norman Wereley of the University of Maryland.
Dr. Wereley is the Minta Martin Professor of Aerospace Engineering and Department Chair at the University of Maryland. He serves as the Director, Smart Structures Laboratory and Composites Research Laboratory. His research interests are in dynamics and control of smart structures, with emphasis on active and passive vibration damping control applied to rotorcraft aerospace and automotive systems. The Department of Aerospace Engineering at The University of Maryland (UMD), ranked 8th among all U.S. Aerospace Graduate Programs by U.S. News and World Report, has a strong research program spanning rotorcraft - aerodynamics, aeroelasticity, CFD, flight mechanics and handing qualities,; hypersonic vehicles - aerodynamics and propulsion; space systems including space robotics, human factors, and small satellites; space propulsion; dynamics and control – collective systems (swarms), insect navigation and control, optimal control, and smart structures and materials applied to adaptive energy absorption systems for occupant protection systems in air and ground vehicles, robotics, and helicopter rotor control; and composite structures. First, an overview of collaborative research opportunities will be provided. There are many opportunities to collaborate with UMD faculty and students on a range of topics and levels of effort including grants, contracts, fellowships, lectures delivered by company personnel on campus, and support of the curriculum via design reviews, topics for research projects, and internships. The seminar discusses of how businesses, both large and small, can leverage existing capabilities by teaming with university research laboratories, and also realize large gains in productivity effectively and efficiently. The importance of developing a fruitful intellectual property strategy in this context is presented.
Date February 14th at 10:00am in the Main Conference Room.
January 2013: IAI sponsors Blair High School Robotics Team in FIRST Robotics Competition.
IAI is proudly sponsoring the Montgomery Blair High School robotics team again as they compete in the 2013 FIRST Robotics Competition. "The Blair Robot Project" (Team 449) will be competing in the Chesapeake Regional and Pittsburgh Regional events in an attempt to win a spot in the international FRC Championship in St. Louis, Missouri in April. For more information, see the team's website at http://robot.mbhs.edu/ or the FIRST website at http://www.usfirst.org.