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Rockville, MD, United States

Agency: Department of Defense | Branch: Air Force | Program: STTR | Phase: Phase I | Award Amount: 100.00K | Year: 2011

ABSTRACT: The test and evaluation (T & E) mission in complex test facilities results in large amounts of data. Moreover, the data may have different characteristics, including images from particle image velocimetry data, air flow data, etc. Finally, some of the data may come from a network of similar sensors. We propose a novel, flexible, and comprehensive system for mission prioritized lossless data compression. First, we propose to apply our latest compressed sensing (CS) algorithm known as singular value decomposition-QR (SVD-QR) to jointly compress sensor data in a sensor network. We have successfully applied SVD-QR to actual radar sensor network data with 30 sensors from the Air Force and achieved a compression ratio of 192 without loss of information. Second, we propose a high performance CS algorithm that can efficiently compress sensor data that can be modeled as auto-regressive hidden Markov model (AR-HMM). Acoustic and radio frequency (RF) signals can be characterized by AR-HMM. Third, for isolated sensors such as particle image velocimetry and other pressure and force sensors, we propose to apply a new algorithm called CS-SVD (compressed sensing - singular value decomposition) to perform the compression. All of our algorithms have parameters that allow users to choose for different mission priorities. BENEFIT: The proposed technology will be useful for large data compression in test facilities such as military bases and NASA. Other applications include data compression for sensor networks, image compression for surveillance and reconnaissance operations, and also compression for commercial camcorder and digital cameras. We envision the market for the system developed will be 50 million dollars over the next decade.

Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 1.50M | Year: 2016

Navy wants us to focus on contingency planning for MQ-4C. A software system is required that can perform the following: 1) Automatic generation of contingency plans; 2) Complete plan generation in less than 5 minutes; 3) Publish plan in a website; 4) Interface with JMPS.Our goal in Phase II.5 is to develop a high performance software to achieve the above requirements. This software will be an intelligent proxy for commanders. It is a decision aid that can operate under high pressure and short time constraint situations. It can effectively generate contingency plans when pop-up threats occur during the mission, assess the goodness of the flight plan, and dynamically optimize the Air Tasking Order (ATO) for the mission.The Advanced Automatic Mission Planning System (AAMPS) to be developed in Phase II.5 will further improve the Phase II prototype by adding functionalities that can deal with some practical issues such as replanning during a mission, low altitude approach, and windy conditions. Moreover, contingency plan generation for UCLASS will also be carried out in Phase II.5.

Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 750.00K | Year: 2012

In Phase 2, our Phase 1 version of the Automated Mission Planning System (AMPS) will be modified and expanded to generate contingency plans to handle unexpected problems such as popup threats or electrical and mechanical failures in UAV/UCAVs. The new software system must be adaptive and can react quickly to changes detected in the situation context. Conventional contingency plans are generated by human operators. The process is tedious, time consuming, and does not respond to unexpected situations quickly. In addition, plans generated by inexperienced operators under high pressure environments are not optimal. The new Advanced AMPS (called AAMPS) to be developed in Phase II will eventually need to interface with the Navy Joint Mission Planning System (JMPS) to obtain the required inputs for its reasoning algorithms, and its contingency plans will be published through Web Services to the concerned users (e.g., BAMS and Global Hawk).

Agency: National Aeronautics and Space Administration | Branch: | Program: STTR | Phase: Phase I | Award Amount: 100.00K | Year: 2011

We propose novel and real-time smart software tools to process spectroscopy data. Material abundance or compositional maps will be generated for rover guidance, sample selection, and other scientific missions. First, we propose a novel anomaly detector called clustered kernel Reed-Xiaoli (CKRX) algorithm. This tool was developed by us, is fast, and can achieve very high anomaly detection rate in hyperspectral images from the Air Force. This is important in planetary missions because we may need to look for some anomalous regions in a scene. Second, if target material signatures are available, then we propose a fast matched signature identification algorithm called Adaptive Subspace Detector (ASD). We compared ASD with several other tools and found that ASD outperformed other methods. Third, if target material signatures are not available, then we propose a new technique called minimum volume constrained non-negative matrix factorization (MVCNMF) to perform unsupervised material identification. In a recent comparative study by using hyperspectral images from the Air Force, the MVCNMF performed better than some conventional unsupervised methods. Fourth, the above tools can be implemented in a parallel processing architecture, in which the computations are distributed to multiple cores. We have applied it to speech processing and genomic processing recently. Real-time performance is achievable.

Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 750.00K | Year: 2011

We propose a novel, high performance, and standalone system for improving close air support (CAS) effectiveness. We have several important elements. First, we propose a standalone noise cancellation device (NCD) that can be inserted between the handset and the transceiver. That is, the handset output is connected to the NCD and the NCD output is connected to the transceiver. As a result, there is no change to the existing communication system. The NCD is self-powered from its own battery and equipped with one or two microphones and a digital signal processing (DSP) chip to process the signals from the handset mic and the mics in the NCD. Second, we have extensively evaluated numerous algorithms in Phase 1 for battlefield noise suppression. The best algorithms will be implemented in DSP in Phase 2. A hardware prototype with small size and low weight (

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