DoD 2019.C STTR Solicitation

TECHNOLOGY AREA(S): Sensors, Electronics 

OBJECTIVE: To develop high-dynamic-range, high-power-handling, and high-survivability high frequency (HF) radio receivers with significant enhancement in intrinsic interference and jamming resistance. 

DESCRIPTION: High frequency (HF) band covering frequency range between 3 to 30 MHz provides over-the-horizon communications with ranges over thousands of kilometers through skywave propagation. It offers an alternative to satellite communications (SATCOM) for many commercial and military applications that requires non-line-of-sight communications. Although HF radio technology is generally considered quite mature, recent new advancements in RF/MW (radio-frequency/microwave) technology has resulted in renewed interest and new opportunities for HF communication. HF skywave propagation through ionosphere diffraction is a challenging communication channel that exhibits both low and short terms temporal fluctuations. It is also easily susceptible to unintentional interference because of spectral congestion, and intentional and adversarial jamming. To avoid interference, a frequency agile approach can be used to search for usable frequencies. However, this would not be effective if the HF communication link is under broadband barrage jamming attack. Alternatively, recent advances in RF front-end and software defined radio technologies have suggested that HF radios can be intrinsically hardened so that they can withstanding and survive interference and jamming attacks without causing service interruption and even suffering electrical damages to the circuitries. Examples of these technologies include, gallium nitride (GaN) low-noise amplifiers (LNA) offering front-end high-linearity and high-power-handling capability; precise high-resolution and high-linearity analog-to-digital convertors offering high dynamic range and high waveform fidelity; digital-signal-processing (DSP) and mixed signal techniques employing machine learning and artificial intelligence for signal filtering with extremely low signal-to-noise ratio. The goal of the topic is to develop HF radio receivers based on these recent technical developments to achieve uninterrupted operation under high power interference and jamming attacks. 

PHASE I: During the Phase I effort, a prototype HF radio receiver architecture incorporating interference and jamming hardening techniques will be investigated. Different hardening techniques should be investigated and analyzed to achieve optimum performance. A system level block model should be developed and analyzed. Performance metrics such as maximum power handling, lowest receivable signal-to-noise ratio, etc., should be defined and calculated for the selected system, and compared with existing HF radio receivers. Detailed circuit implementation of the system level block model should be designed and its performance analyzed. 

PHASE II: The prototype HF radio receiver designed in Phase I will be built, assembled, and tested. Performance metrics established in Phase I should be measured and compared with simulation results. Comparative field demonstration together with an existing HF radio should be performed to demonstrate enhancement in interference and jamming resistance. Additional interference and jamming hardening techniques should be investigated and existing circuit designs improved. 

PHASE III: Phase III effort will explore opportunities for developing HF radio transceivers incorporating interference and jamming hardened HF radio receiver designs developed in Phase II and potential transition into a Program of Record. 

REFERENCES: 

1: E.E. Johnson et al,. "Advanced High-Frequency Radio Communications," Artech House, 1997.

2:  R.A. Poisel, "Modern Communications Jamming: Principles and techniques," Artech House, 2011.

3:  R. Quay, "Gallium Nitride Electronics," Springer, 2008.

4:  B. Murmann, "ADC Performance Survey 1997-2018," [Online]. Available: http://web.stanford.edu/~murmann/adcsurvey.html.

KEYWORDS: HF Communication; Communication Jamming; Gallium Nitride; Analog-to-Digital Converter; Software-Defined Radio; Machine Learning 

TECHNOLOGY AREA(S): Sensors, Electronics 

OBJECTIVE: Enable high throughput, low latency and robust waveform networking technologies at the maneuver company and below. 

DESCRIPTION: The proliferation of 5G networking at the millimeter wavelength spectra opens new opportunities in the modernization of the Army’s communication systems. Commercial 5G systems are not expected to fully satisfy Army’s requirements on resiliency to jamming, enhanced Low Probability of Interception/Low Probability of Detection (LPI/LPD), privacy and authentication. Furthermore, the Army does not assume the availability of cellular infrastructure in theaters of operation. The forthcoming commercial 5G systems will deliver many of the components and technologies that can be used in millimeter wave communication systems designed for the Army. The goal of this project is to enhance 5G communication systems in terms of jamming resistance, LPI/LPD and all aspects of communications security. This may be accomplished by taking an industry standard, or plausible 5G design and provide enhancements that push the limits on jam resistance, LPI/LPD and security requirements. More radical approaches that involve a complete re-design of millimeter wave networks that makes use of selective components of 5G cellular systems can also be within the scope of this research. The final design should be implementable in a hand held device that is as close as possible to commercially available cellular devices in terms of its Space Weight and Power (SWAP). 

PHASE I: During Phase I effort, a complete design of millimeter wave handheld radio with enhanced jam resistance, LPI/LPD and security will be delivered. The design will be validated using simulations. Devices should be able to communicate with each other in ad hoc mode, without having to use cellular infrastructure. Its performance in terms of latency, throughput, reach, and spectrum use should be comparable to commercially available units and their jam resistance, LPI/LPD and security characteristics should push the limits of what is feasible. 

PHASE II: Two or more prototype hand held devices will be designed based on the numerical model and design methodology developed in Phase I. The prototype devices will be built, assembled, and tested, and demonstrated. The implementation of physical layer designs should make use of Field Programmable Gate Array (FPGA) devices and over the counter components. Technical risks will be identified and plans for minimizing these risks will be devised. 

PHASE III: Phase III effort will explore opportunities for integrating the technology into the Army’s use cases and production of devices ready for use. 

REFERENCES: 

1: T. Bai et al., "Coverage and Rate Analysis for Millimeter-Wave Cellular Networks," IEEE Transactions on Wireless Communications (Volume:14, Issue:2, Feb. 2015)

2:  Z Pi, F Khan, "An introduction to millimeter-wave mobile broadband systems", IEEE communications magazine, 2011.

3:  S. Hur et al., "Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks," IEEE Transactions on Communications Volume:61, Issue:10, October 2013

KEYWORDS: Millimeter Wave Networks, Beamforming, LPI/LPD, Jam Resistant Networks, 5G Cellular Systems 

TECHNOLOGY AREA(S): Sensors, Electronics 

OBJECTIVE: Provide precise location and time synchronization data in the absence of satellite based GPS systems. 

DESCRIPTION: Some of the Army’s operations require precise location and time synchronization data. Satellite based GPS systems is a universal source of such information, but they may not be available either due to natural obstacles, or man-made hindrances such as jamming. Furthermore, adversaries may spoof GPS signals to mislead its users. Other sources of PNT information whose precision is comparable to GPS signals are needed to ensure safety and continuity of operations. There are potential technology areas such as, but not limited to, fiber-optic gyros, magnetometry, and chip-scale atomic clocks that could enable the creation of the required PNT data. The goal of this project is to provide precise localized position data in near-real time, using multiple integrated systems to maximize accuracy and to identify and mitigate its drift over time. The maximum navigation and timing error of a stand-alone device should not exceed 20 m and 1 microsecond at 1 hour. This requirement could be accomplished by possibly using multiple technologies integrated into networked system of devices. Integral to solution will be a networking protocol that will transmit the PNT data to the nodes in the local network. The protocol may use existing wireless data services on the devices. The networking protocol will be able to synchronize all nodes by taking into account signal propagation delays and maintain synchronization within the error bounds for all devices in the system. This synchronization should hold within 150% of the above error bounds even when up to half of the PNT source nodes become non-available for periods up to 96 hours. 

PHASE I: During Phase I, the best technologies for PNT generation will be selected using testing and simulations. A networking protocol will be designed and validated using simulations. The protocol will be resistant to man-in-the-middle and spoofing attacks. 

PHASE II: Two or more prototype PNT data source nodes will be designed based on the numerical model and design methodology developed in Phase I, possibly using multiple technologies. The prototype devices will be built, assembled and tested, along with the supporting network protocol. The weight of the PNT source devices should make no discernable burden to the user. The accuracy of the PNT data will be no less than normally available GPS services. Technical risks will be identified and plans for minimizing these risks will be devised. 

PHASE III: Phase III effort will explore opportunities for integrating PNT generation and supporting network technologies into various weapon and communications systems used by the Army. 

REFERENCES: 

1: S. Pudlewski, "RF Based Time Synchronization and Ranging for Communications in a GPS-Contested Environment," in IEEE MILCOM, Tampa, 2015.

2:  L. Tippenhauer et al., "On the requirements for successful GPS spoofing attacks", Proceedings of the 18th ACM conference on Computer and communications security Pages 75-86, 2011

3:  R. He, "Planning in information space for a quadrotor helicopter in a GPS-denied environment," 2008 IEEE International Conference on Robotics and Automation.

KEYWORDS: GPS, PNT, Synchronization, Navigation, Spoofing 

TECHNOLOGY AREA(S): Sensors 

OBJECTIVE: To enable efficient sensor edge processing in the Internet Battlefield of Things (IBoT) 

DESCRIPTION: Army’s tactical operations will be assisted by data collected from a variety of sensors which are part of the IoBT devices. Data from these devices should be processed and interpreted locally over hardware systems in extremely small form factors in terms of size, memory and power. Due to locality of data and constraints on the hardware, distributed computations over interconnected systems may be needed in processing sensor data. Given the congested and contested nature of battlefield environment, connectivity among the devices may be low rate and unreliable. Supervised and unsupervised adaptation and learning methods which are amenable to distributed computing over small devices using local data are needed. Outcomes of local computations will be integrated and fused at centralized locations, which may have more powerful computational capabilities. Envisioned applications of this system include, but are not limited to, autonomous cyber recognition, target recognition and electromagnetic spectrum awareness. 

PHASE I: During Phase I effort, a complete design of distributed and hierarchical adaptation and learning system is required, where data is local and power is limited. The design will be validated using a combination of simulations and emulations, where the number of networked computation devices is 100, each controlling 10 sensors. 

PHASE II: A prototype of adaptation and learning modules will be designed based on the model and design methodology developed in Phase I. The prototype system will be trained using simulated data, and its performance and convergence properties will be demonstrated. Technical risks will be identified and plans for minimizing these risks will be devised. 

PHASE III: Explore opportunities for integrating the technology into the Army’s tactical edge operations. 

REFERENCES: 

1: J. Dean et al., "Large Scale Distributed Deep Networks," Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, Pages 1223-1231, 2012.

2:  L. Panait and S. Luke, "Cooperative Multi-Agent Learning: The State of the Art," Autonomous Agents and Multi-Agent Systems, 11, 387–434, 2005.

3:  M. Abadi et al., "TensorFlow: A System for Large-Scale Machine Learning," Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation Pages 265-283, 2016

KEYWORDS: Machine Learning, Distributed Computation, Tactical Edge, Hierarchical Machine Learning, Information Fusion 

TECHNOLOGY AREA(S): Sensors, Electronics 

OBJECTIVE: To enable optimal operation regimes for tactical radios in contested spectrum conditions 

DESCRIPTION: Army’s tactical operations often take place in congested and contested spectrum conditions, which may impair the ability to transmit information over wireless networks. Identifying the source of impairment, that is, whether it is due to malicious jamming, or due to ordinary congestion in the spectrum is an important step in addressing the issue. Then, one can take a proper action to improve the performance of communications, such as modifying the modulation scheme, switch to a spread spectrum mode, routing around the problem area, or change the location of the transmitting device. To this end, a spectrum sensing system is needed. The system should use information such as terrain data, data from sensors, and network management records to identify the occurrence and the principal cause of network impairments using machine learning techniques. Then, using dynamic adaptation techniques for making optimal decisions, such as reinforcement learning, the system should identify the optimal course of action for the user. The optimal course of action may include automatically adjusting the configuration parameters of radios to maximize useful throughput, or informing the user to move to a more favorable location. 

PHASE I: During Phase I effort, a complete design of sensing and optimal action selection system is required. The design will be validated using simulations. Of primary importance is the computational complexity and the convergence of the proposed learning algorithms designed for this setting. Mathematical analysis and simulations should indicate that the proposed methodology will converge in a short period of time using standard computational resources. 

PHASE II: A prototype of sensing and learning modules will be designed based on the model and design methodology developed in Phase I. The prototype system will be trained using simulated data, and its performance and convergence properties will be demonstrated. Technical risks will be identified and plans for minimizing these risks will be devised. 

PHASE III: Explore opportunities for integrating the technology into the Army’s tactical communication systems. 

REFERENCES: 

1: Richard S. Sutton and Andrew G. Barto, Reinforcement Learning, MIT Press, 2018

2:  O. Punal et al., "Machine learning-based jamming detection for IEEE 802.11: Design and experimental evaluation", Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, 2014

3:  M. Lichtman et al., "A communications jamming taxonomy," IEEE Security & Privacy ( Volume: 14 , Issue: 1 , Jan.-Feb. 2016 )

KEYWORDS: Reinforcement Learning, Jamming, Machine Learning, Contested Spectrum, Network Management 

TECHNOLOGY AREA(S): Sensors, Electronics 

OBJECTIVE: To develop low-cost phased-array antennas for advanced extremely high frequency (AEHF) satellite communications (SATCOM) based on integrated circuit technologies. 

DESCRIPTION: SATCOM On-The-Move (SOTM) offers a means for Beyond-Line-Of-Sight communications. Ubiquitous SOTM requires mobile-based tactical ground terminals with reduced cost, size, weight and power (CSWaP). Phased-array beamforming antenna technology enables fast electronic beam steering without moving antenna apertures and is a key technology enabler for SOTM. However, phased arrays have yet to be widely deployed mainly because of high implementation cost. The last two decades have seen significant reduction in the costs associated with front-end monolithic microwave integrated circuit (MMIC) technologies. This is enabled not only by continuous improvement in widely used III-V semiconductor technologies (GaAs, GaN, and InP), but also new development of low cost, high performance RFICs based on Si CMOS and SiGe BiCMOS technologies. In particular, the use of Silicon technologies could result in a ten-fold reduction in the cost for phased array antennas and enable large-scale phased arrays with a very large number of radiating elements. On-Chip mixed signal processing capabilities such as built-in self-test and calibration, amplifier linearization, etc., can also be easily incorporated, even at individual element level, because of the high level of integration and mixed signal capabilities offered by CMOS. In the past several years, Si based phased array demonstrators have been built for emerging applications such as SATCOM and upcoming 5G systems, covering frequencies ranging from microwave (3-30 GHz) to EHF (30-300 GHz). In spite of these encouraging developments, many technical challenges, such as packaging, thermal management, reliability, noise performance, and linearity, etc., still need to be addressed before IC based phased array systems can be widely deployed. The goal of this topic is to develop deployable low cost phased array antennas based on IC technologies for AEHF SATCOM applications by addressing these technical challenges. 

PHASE I: During the Phase I effort, a prototype phased-array antenna system based on integrated circuit technologies will be designed and analyzed. The design could be based on Silicon, III-V semiconductor, or heterogeneous integration of multiple technologies. Suitable TX/RX frequencies and an output power level for an existing SATCOM system at the EHF frequency range should be selected. The overall design should include radiating antenna elements and circuits for front-end RF, frequency-down-conversion to baseband, analog-to-digital conversion for digital interface. A beam-steering scheme should be included for the phased array design. Both analog and digital implementations can be explored. Electromagnetic performance for an individual antenna element and the overall phased array should be characterized and analyzed. 

PHASE II: The prototype phased array antenna will be fabricated, assembled, and tested. The phased array IC designed in Phase I will be fabricated through a suitable IC foundry. The antenna radiating structure designed in phase I will be fabricated. The overall radiating structure should include packaging to incorporate the phased array IC and feeding path from the IC to the radiating elements as well as thermal management and mechanical support. The overall antenna radiation pattern and beam-steering should be characterized, analyzed, and compared with electromagnetic simulation. A data link demonstration incorporating one or more prototype phased array antennas should be performed. 

PHASE III: Phase III effort will explore opportunities for incorporating the phased array antenna developed in Phase II with exiting SATCOM ground terminals with suitable frequencies and output power levels. 

REFERENCES: 

1: G.M. Rebeiz and L.M. Pailsen, "Advances in SATCOM phased arrays using silicon technologies," 2017 IEEE International Microwave Symposium.

2:  S. Zihir et al, "60-GHz 64- and 256-Elements Wafer-Scale Phased-Array Transmitters Using Full-Reticle and Subreticle Stitching Techniques," IEEE Transcations on Microwave Theory and Techniques, Vol. 64, No. 12, pp. 4701-4719, 2016.

KEYWORDS: SATCOM; EHF; MMIC; RFIC; Phased Array; CMOS; SiGe; InP; GaN; GaAs 

TECHNOLOGY AREA(S): Weapons 

OBJECTIVE: Develop a directed energy system capable of disrupting, disabling or destroying the electronics on a remote target within milliseconds of detection. 

DESCRIPTION: Directed energy (DE) for the destruction of military targets, whether vehicles or communication systems, typically requires enormous amounts of power and long dwell times on target. Advanced tracking and aiming may be necessary to maintain DE on a precise location on a target long enough to eliminate the threat. The size and power requirements of such systems greatly limits the platforms from which such systems could be utilized. This topic seeks to alleviate both limitations by considering alternative solutions that eliminate threats through the disruption of a target's electronic control systems rather than destroying the target by directing enormous amounts of power onto it as quickly as possible. For example, the coupling between electromagnetic radiation and electrons in solids suggests that short, high-intensity laser pulses rather than high-energy continuous wave lasers or microwaves may provide this alternative solution. Even without reaching the electronics directly, the interaction between a laser pulse and a material can generate broadband radiation that may disrupt nearby electronics. Other potential solutions involving directed energy will also be considered. Any DE system able to remotely disrupt naturally packaged electronics in a realistic target in less than a few milliseconds is of interest, especially if the solution may be scaled to neutralize targets more than 1 km away. 

PHASE I: The offeror will experimentally demonstrate the feasibility of remotely disrupting or disabling electronics representative of those found in military targets. By "disruption", it is meant that the device electronics are temporarily disrupted or permanently disabled so that the target cannot perform its mission. Theoretical studies and/or modeling to explain the phenomenon employed and/or engineer system design characteristics as appropriate to the proposed solution should also be included. The deliverable for Phase I will be the design of the system that will be constructed and tested in Phase II. 

PHASE II: The offeror will construct and deliver a system that can apply short-pulse directed energy on a remote target such that the target's electronics are disrupted or permanently disabled. No specific target is necessary for a proposal to be of interest but it must be demonstrated that the technique can be applied to targets of military relevance. A system with a capability of disrupting or disabling military targets beyond 1 km is desired. The offeror should also demonstrate - through theory, modeling or experiment – the extent to which the system may disrupt or disable targets greater than 1 km away. The system should draw less than 1 kW average power when successfully disrupting or disabling electronics from up to 1 km. Beam divergence must be specified so that the trade space of targeting accuracy versus range can be evaluated as part of the deliverable. 

PHASE III: The DE system must be outfitted with the ability to acquire and track targets. It must be ruggedized and made mobile. Field tests are to be conducted with various kinds of realistic targets representative of those found in military targets. 

REFERENCES: 

1: Couairon, A., A. Mysyrowicz. "Femtosecond Filamentation in Transparent Media." Physics Report, vol. 441, no. 2–4, p. 47, 07.

2:  Bergé, L., S. Skupin, R. Nuter, J. Kasparian, and J.-P. Wolf. "Ultrashort Filaments of Light in Weakly Ionized, Optically Transparent Media." Reports on the Progress of Physics, vol. 70, No. 10, p. 1633, 2007.

3:  Kandidov, V. P., S. A. Shlenov, and O. G. Kosareva. "Filamentation of High-Power Femtosecond Laser Radiation." Quantum Electronics, vol. 39, no. 3, p. 205, 2009.

4:  Durand, M., A. Houard, B. Prade, A. Mysyrowicz, A. Durécu, B. Moreau, D. Fleury, O. Vasseur, H. Borchert, K. Diener, R. Schmitt, F. Théberge, M. Chateauneuf, J.-F. Daigle, and J. Dubois. "Kilometer Range Filamentation." Optics Express, vol. 21, no. 22, 26836, 2013.

5:  Kasparian, J., R. Sauerbrey, D. Mondelain, S. Niedermeier, J. Yu, J.-P. Wolf, Y.-B. André, M. Franco, B. Prade, S. Tzortzakis, A. Mysyrowicz, M. Rodriguez, H. Wille, and L. Wöste. "Infrared Extension of the Supercontinuum Generated by Femtose

KEYWORDS: Directed Energy, Lasers, Electromagnetic Pulse, Standoff Electronic Denial 

TECHNOLOGY AREA(S): Bio Medical 

OBJECTIVE: Develop a three dimensional brain model using human myelinated neurons, astrocytes, oligodendrocytes and microglia to study neuroinflammation and neural plasticity following exposure to key stressors found in the operational environment. 

DESCRIPTION: Warfighters are constantly exposed to mission and non-mission degradation activities and extreme stress situations during combat. These stress responses have the potential to decrease cognitive function along with human performance, ultimately putting the warfighter at risk during missions. Therefore, understanding the effects of these stressors at the cellular level will allow for creating knowledge on how resilient specific cell pathways are, define which molecular systems are sensitive to change, and determine the key components that could be varied to defuse stressor induced cognitive degradation. In order for these questions to be addressed at the molecular level, it is imperative to have an in vitro model that physiologically represents the human brain. This model needs to incorporate myelinated neurons, astrocytes, oligodendrocytes, and microglia. Microglia are neuro-immune cells that respond to stress and injury in the brain. However, they also play a critical role in learning. In the absence of injury or stress, microglia have been shown to be responsible for secreting mediators for synaptic plasticity, memory, and neurogenesis. Following activation from injury or stress, their role switches to produce inflammatory cytokines, which cause neurons to reduce secretions of chemicals that keep microglia in an inactivated state allowing them to assist with neural plasticity (1). With the lack of human correlation observed within animal testing along with the push to find alternatives to animal use, the biggest challenge facing in vitro biology today is choosing/developing models that realistically represent a target organ. Given that greater than 95% of therapeutic drugs for neurologic disorders appear promising in rodent studies but fail in humans due to intrinsic brain differences between the species, a reproducible model for neuroscience testing needs to be generated. Development of in vitro neural models has advanced over the last few years. Neuronal cell lines such as the rat PC-12 (2) or the human SH-SY5Y (3) have benefits such as easy growth and maintenance, however, they also bring issues such as difficult extrapolation for not being of human origin (PC-12) or for being a cancer cell line with an unstable genome (SH-SY5Y). Primary cell cultures such as the rat midbrain (4) provided a brief boon to the field but are fading due to issues of interspecies differences and low biological yield. Furthermore, available in vitro models traditionally lack the multicellular complexity that allows for enhanced structural components such as myelination along with the three dimensional architecture that are found in vivo. Some researchers have been able to develop three dimensional microtissues using induced pluripotent stem cells, but microglia are not represented in this model (5). Currently, there is not an optimal human in vitro model available to address operational environmental stressors on molecular pathways critical for cognition. A novel in vitro human brain model incorporating all the cell types that are critical in learning, memory, neural plasticity and neurogenesis is imperative for advancing cognitive science investigations to benefit warfighter performance. No government furnished materials, equipment, or facilities will be provided. 

PHASE I: Develop a 3D human brain model incorporating myelinated neurons, microglia, astrocytes, & oligodendrocytes. Demonstrate proper cell ratios, distribution, and functionality of each cell type. Identify neuroinflammatory responses using viruses, bacteria, or other key stressors. Identify promising technology development pathways that will allow improvements beyond the scope of the STTR effort. 

PHASE II: Multiple regulatory and research agencies at the government level are looking to identify benchmark in vitro cell systems to be used for toxicology and health related research. Demonstrate feasibility of testing stress induced responses using this system. Collaborate with government personnel to evaluate neural plasticity and neuroinflammation following exposure to stressors found in operational environments. Based on the results of Phase I, develop prototypes for evaluation. 

PHASE III: Develop a commercial brain model that can be used for biomedical applications, drug screening, as well as general R&D. Establish baseline genetic and epigenetic profiles for this system and each sub-cell type within the system. Non-government customers: academia/pharmacology industry. 

REFERENCES: 

1. Yirmiya, R. & Goshen, I. Immune modulation of learning, memory, neural plasticity and neurogenesis Brain, Behavior, and Immunity 2011;25:181–213.; 2. Bercury KK, and Macklin WB. Dynamics and mechanisms of CNS myelination. Developmental cell. 2015;32(4):447-58.; 3. Greene LA, and Tischler AS. Establishment of a noradrenergic clonal line of rat adrenal pheochromocytoma cells which respond to nerve growth factor. Proceedings of the National Academy of Sciences of the United States of America. 1976;73(7):2424-8.; 4. Constantinescu R CA, Reichmann H, Janetzky DB. . Neuropsychiatric disorders an integrative approach. . Vienna: Springer; 2007.

KEYWORDS: In Vitro, 3D, Inflammation, Brain, Microglia, Biomedical 

TECHNOLOGY AREA(S): Space Platforms 

OBJECTIVE: Self-Correcting Multiple Source Classification and Fusion 

DESCRIPTION: Recent progress in machine learning techniques allows training of high accuracy classifiers for different sensor modalities. This creates opportunities for autonomous exploitation of vast amounts of sensor data. Unfortunately, classifier accuracy decreases when the statistical characteristics of the data change due to variations in operating conditions, such as weather, sensor state, geographical location, etc. The decrease in accuracy results in higher false alarm rates and consequently decreases trust in the classifier system. This is a well-known problem in the scientific community and is currently an area of active research. In the case of multiple streams of sensor data being fused, changes in one of the streams may adversely affect the entire fusion system. To avoid this, all components of the fusion system must be able to correct for changes in the incoming data and simultaneously produce a measure of confidence (reliability) in reported results. The objective of this topic is to transfer existing state of the art methodologies and develop novel machine learning technologies that, in addition to classification and fusion, are capable of monitoring the incoming streams of data and adapt existing classifiers to changes in the input distribution. The system must adapt the classifiers without retraining them and at the same time estimate and report the level of confidence in classifier results, again based on the deviation of the incoming data from what the classifier was trained on. The ability of individual classifiers to produce a measure of confidence must positively affect the performance of an entire fusion system. Performance will be evaluated on multiple labeled datasets collected under different operating conditions. In the first phase evaluation will be done using Wide Area data collected using different Electro-Optical, Infrared, and Radar sensors. This technology has both military and commercial applications. In the commercial area, quick adaptation to changes in operating conditions is crucial to data analysis in all domains, from stock market to self-driving vehicles. In order to create commercial value, the core of this technology will be sensor-independent. However, the initial thrust of this work will focus on wide area sensor data that is of interest to the DoD. Such data includes multiple data streams originated from different sensor modalities. Access to such data will require the ability to work on SIPRNet. 

PHASE I: Propose novel machine learning techniques that are capable of quick adaptation to changes in domain distributions without retraining the classifiers. Conduct preliminary testing using unclassified sensor data. The evaluation dataset can be chosen at the performer’s discretion. Demonstrate domain adaptation for individual classifiers and its effect on the overall quality of fusion. 

PHASE II: Develop a prototype multi-source domain adaptation system incorporating the technology in phase 1 and apply it to multi-source heterogeneous forensic datasets of interest to the DoD. Demonstrate the detection and false alarm performance under various operating conditions. 

PHASE III: Integrate and deploy the prototype within ISR/IC communities. Search for commercial applications. 

REFERENCES: 

1. Sugiyama, M., Yamada, M., & du Plessis, M. C . Learning under non-stationarity: Covariate shift and class-balance change. WIREs Computational Statistics, 2013.; 2. K. Saenko, B. Kulis, M. Fritz and T. Darrell, "Adapting Visual Category Models to New Domains" In Proc. ECCV, September 2010, Heraklion, Greece; 3. Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., & Sugiyama, M. (2013). Relative density-ratio estimation for robust distribution comparison. Neural computation, 25(5), 1324-1370.; 4. Zheng, Yu. "Methodologies for cross-domain data fusion: An overview." IEEE transactions on big data 1.1 (2015): 16-34.

KEYWORDS: Sensor Data Processing; Object Recognition; Classifiers; Domain Adaptation; Information Fusion; 

TECHNOLOGY AREA(S): Air Platform 

OBJECTIVE: Develop adaptable cyber defense capabilities to support autonomous Air Force weapon system operations 

DESCRIPTION: An air platform operating in a cyber-contested environment requires the ability to rapidly adapt to unforeseen and changing circumstances during mission operations [1]. Immutable software/hardware defenses that require offline patches and upgrades to mitigate cyber threats encountered in-flight are insufficient to meet mission assurance requirements. Cyber defense mechanisms for autonomous air vehicles must be resilient and have the ability to quickly analyze large amounts of data to predict attacks, subsequently self-repair and/or self-protect targeted software and hardware, learn from on-going attacks, and adapt both the software and underlying hardware to prevent the attacks from becoming successful. Two critical limiting factors to develop an adaptable cyber defense are that most software languages used operationally are not designed to be easily changed or mutated at the binary level, and the underlying hardware on which the software executes is generally fixed or only partially reconfigurable at runtime. The implications of these limitations is that the software and hardware remain vulnerable to attack during the mission and for a period of time until which the software and hardware can be repaired or replaced. The goal of this topic is to develop adaptable cyber defense capabilities that are inherently resilient to cyber-attacks and/or can rapidly detect, respond, and adapt to malware and other threats targeting the air platform during mission operations. The threat can be a result of a remote attack or malware introduced in the software, firmware or hardware supply chain of the air platform and triggered during flight. The ultimate goal of the project is to develop an architecture whose constitutive defensive software, firmware and/or hardware components can be self-assembled or rapidly evolved based upon new and unforeseen circumstances to repair or protect critical susceptibilities in real-time, and to learn from on-going attacks to rapidly counter similar threats in the future. The primary goal of this research is to develop and/or leverage game-changing adaptable and evolvable software and hardware architectures that have the promise to deliver one or more of the above capabilities [2-3]. An incremental approach with prototypes demonstrating increasing levels of capability should be used to minimize risk. A secondary goal is to investigate emergent behavior resulting from the adaptable and evolvable nature of the hardware that may lead to unconventional hardware design approaches that have only briefly been previously explored [4]. 

PHASE I: Develop a concept, architecture and limited-scope prototype or simulation that demonstrates the ability to provide an adaptable cyber defense capability 

PHASE II: Expand the concept into a working prototype and develop increasing levels of adaptable cyber defense capabilities, such as software and hardware self-repair. Design considerations to support legacy software should be addressed. 

PHASE III: The final product will have both commercial and military system applications. Military applications include air platforms and satellite systems. Commercial applications include self-driving cars, mobile devices, SCADA systems, and artificial intelligence applications. 

REFERENCES: 

1. Office of the Air Force Chief Scientist, “Technology Horizons: A Vision for Air Force Science and Technology 2010-2030,” Sept. 2011, https://apps.dtic.mil/dtic/tr/fulltext/u2/a525912.pdf; 2. C. Ofria, C. Adami, T. C. Collier, “Design of Evolvable Computer Languages”, IEEE Transactions on Evolutionary Computation 6(4):420 - 424 · September 2002.; 3. Adrian Thompson, “An evolved circuit, intrinsic in silicon, entwined with physics,” Proc. 1st Conf. on Evolvable Systems (ICES96), Springer LNCS, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.9691&rep=rep1&type=pdf

KEYWORDS: Adaptable Computing, Evolvable Hardware, Malware Response, Embedded System Security, Avionics Cyber Security 

TECHNOLOGY AREA(S): Sensors 

OBJECTIVE: Develop militarily relevant machine learning, to include deep learning, algorithms to transfer knowledge obtained from one labeled dataset (source) to an unlabeled (target) dataset of a possibly different domain (e.g., EO <> SAR, Satellite <> airborne <> 

DESCRIPTION: This research will enable us to build Aided Target Recognition (AiTR) and other algorithms for environments and targets where we currently lack data or lack labeled data. The concept is to ‘transfer’ learned classifiers from one domain or set of targets to classify different targets or the same targets but in different domains. For example learning how to classify vehicles in Radar data by leveraging what we know about classifying vehicles in EO data. Statistical learning theory has produced powerful methods for learning feature mappings that maximize classification accuracy while minimizing divergence between the source and target distributions. In transfer learning we utilize the terms ‘source’ and ‘target’ data since there are really two classification problems. One classification problem uses the source data, the data that is labeled, well-understood, and has well-understood classification performance. The other classification problem is the ‘target’ classification problem where there is a small amount of data and/or a small amount of labeled data and the idea is to learn and leverage as much as possible from the source to classify the objects in the target domain. A recent example is learning how to classify positive and negative lung cancer samples by leveraging the classification knowledge of how to classify positive and negative breast cancer samples. One approach to such a problem, is to regularize standard discriminant analysis and other manifold embedding techniques with a divergence penalty. Doing so allows us to transfer the knowledge from the source domain to the target and achieve improved classification in the new domain. One class of methods finds feature embeddings or mappings that preserve manifold structure while separating the different targets and minimizing the divergence between the target and source data. This assumes an isomorphism between the source and target classes. Other approaches try to find representations that are robust across the source and target data. Our goal is to extend those ideas to a Deep learning framework. Current approaches in deep learning do not leverage the approach described above and instead use a simpler method called fine-tuning, which does not posess a firm theoretical underpinning. Instead of employing general features from a large general dataset (like ImageNet) with fine-tuning, we plan to explicitly consider the divergence between source and target distributions when learning a classifier within a deep learning framework. Specifically, we focus on the context of applying classification knowledge learned in one source setting (labeled dataset in one modality or one set of object classes) to a new target setting (unlabeled data in new modality or new object classes). This particular transfer problem, called transductive transfer learning, applies to several relevant scenarios such as i) transferring knowledge from simulated to measured data, ii) transferring from one domain such as EO to SAR, or iii) transferring knowledge to new imaging conditions or measurement devices, to name a few examples. The machine learning and statistical learning fields have made significant progress in this research area (see Pan & Yang, 2010 for a comprehensive review). Meanwhile, the area of Deep Learning has been advancing rapidly with relatively few methods dedicated to transfer (Ganin, et al. 2016). The goal of this SBIR is to extend some of the theory of transfer learning to a deep learning framework, in ways which go beyond the typical deep learning transfer approaches which use robust features from a large general dataset, then fine tune for new datasets. Instead, the methods should develop approaches based on statistical learning theory for transfer to deep learning. 

PHASE I: Design and develop a proof-of-concept deep transfer learning framework. This phase should focus on theoretical development with experiments to verify the theory and performance on synthetic and measured datasets. Benchmark against existing approaches in Deep Learning and transfer learning. The research should be documented in a final report and implemented in a proof-of-concept software deliverable. Government materials, equipment, data, or facilities will not be provided in Phase I. 

PHASE II: Mature the algorithm for use in the real world where training data may be sparse, noisy, or imbalanced. Characterize the algorithm performance, training time and testing time according to data quality and availability. Develop benchmarks for transfer across a variety of domains and datasets. The research should be documented in a final report and implemented in a proof-of-concept software deliverable. Government data may be provided in Phase II if necessary. 

PHASE III: Transition the algorithm to one or more AF weapon systems. This will include a strategy for supporting the requisite knowledge representation approach for both source and target data in an operational setting and will specifically include addressing the dynamic nature of source/target data evolution over time. The research should be documented in a final report and implemented in a proof-of-concept software deliverable. 

REFERENCES: 

1. S. J. Pan and Q. Yang. "A Survey on Transfer Learning." IEEE Transactions on Knowledge and Data Engineering, 22.10 (2010): 1345-1359.; 2. Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky. "Domain-Adversarial Training of Neural Networks." Journal of Machine Learning Research, 17 (2016): 1-35.; 3. Si, Si, Dacheng Tao, and Bo Geng. "Bregman divergence-based regularization for transfer subspace learning." IEEE Transactions on Knowledge and Data Engineering, 22.7 (2010): 929-942.; 4. Mendoza-Schrock, Olga, Mateen M. Rizki, and Vincent J. Velten. "Manifold Transfer Subspace Learning (MTSL) for Applications in Aided Target Recognition." International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 5.3 (2017): 15-3

KEYWORDS: Statistical Learning Theory, Transfer Learning, Deep Learning, Transductive Transfer Learning, Source-Target Divergence, Discriminant Analysis, Manifold Embedding, Classification, Identification 

TECHNOLOGY AREA(S): Sensors 

OBJECTIVE: Design, fabricate, and test passive ultrafast photography systems with effective frame rates approaching 5 trillion frames per second for wavelengths between 200 nm – 1700 nm with at least 512x512 pixels 

DESCRIPTION: Current high speed cameras are commonly used for particle tracking, crack propagation, laser imaging, and in other applications. They exist in two types: continuous image sensors and burst image sensors. Continuous image sensors for high speed photography typically employ coded aperture compression and are widely available. In this mode, the camera acquires images at is normal frame rate of 5 – 30 fps. A spatial filter with a pseudo-random area of optically blocking and transmissive regions is placed between the collection optics and camera. The spatial filter is moved at a high rate using piezo stages, for example. When a fast moving object crosses the image plane of the camera, its optical image is blurred. The oscillating spatial filter, along with image processing algorithms such as TwIST, de-blur the image. The spatial filter essentially sub-divides each frame, yielding effective frame rates up to about 1000 fps. Burst image sensors feature significantly higher effective frame rates from 10,000 fps to 20 million fps, but at the expense of field of view and acquisition time. This area has a mix of commercial and experimental systems. The best experimental burst mode imagers feature a complimentary metal-oxide semiconductor (CMOS) chips. Contrary to charge coupled devices (CCDs), each pixel in a CMOS chip has its own 10 x 8 bit memory. Combined with windowing processes and parallel readout, extremely high frame rates can be acquired, but only for about 10 ms at a time before the camera memory must be purged. Burst mode obviously presents challenges for rapidly evolving events. In windowing mode, only a portion of the sensor array is read out; this increases the frame rate, but at the expense of field of view. Windowing often reduces the sensor field of view by more than 50%. Recently, the concept of compressed ultrafast photography was introduced. It is a continuous image sensor that also uses a coded spatial filter, but the sensor is a commercially available streak camera. It utilizes the entire sensor field of view. When the camera’s entrance slit is fully opened, full images can be acquired at frame rates up to 100 billion fps. Significant image blurring results, which is minimized using a spatial filter as described above. With this technique, picosecond laser pulses could be imaged while reflecting off of mirrors and “raced” through dielectric media. Additionally, objects obscured by a trubid atmosphere could be easily discerned. This technique is currently the fastest high speed camera in the experimental literature, and it used all commercial of the shelf components. No Government furnished data, facilities or equipment will be offered. 

PHASE I: An analysis of alternatives, preliminary system design, and a test plan for the conceptualized system in a laboratory environment. The AoA will discuss the choice of camera, wavelength range, image de-blurring, and image processing approaches. A preliminary system design will include the layout of specific optical and hardware components and an outline of the image processing approach. No Government furnished data, facilities or equipment will be offered. 

PHASE II: A breadboard system prototype at technology readiness level (TRL) four and a set of images demonstrating its effectiveness at clearly imaging events faster than 20 ps. A test plan for the Phase III deliverable and a conceptualized system meeting the Phase III requirements will also be included. The system will weigh no more than 75 lbs, have a 304 mm x 381 mm x 304 mm footprint, and operate off of standard 120V or 220 V, 10 A, 60 Hz circuits. No Government furnished data, facilities or equipment will be offered. 

PHASE III: A ruggedized and flight tested system; its ability to clearly image objects in outdoor, highly turbid environments will be demonstrated. A plan for military commercialization will also be included. No Government furnished data, facilities or equipment will be offered. 

REFERENCES: 

1. J. Liang, L. Gao, P. Hai, C. Li and L. V. Wang, Encrypted Three-Dimensional Dynamic Imaging Using Snapshot Time-of-Flight Compressed Ultrafast Photography, Scientific Reports 5, 15504 (2015).; 2. L. Gao, J. Liang, C. Li and L. V. Wang, Single-Shot Compressed Ultrafast Photography at One Hundred Billion Frames Per Second, Nature 516, 74 (2014).; 3. L. Zhu, Y. Chen, J. Liang, Q. Xu, L. Gao, C. Ma and L. V. Wang, Space- and Intensity-Constrained Reconstruction for Compressed Ultrafast Photography, Optica 3, 694 (2016).

KEYWORDS: Ultrafast Imaging, Focal Plane Array, Streak Camera, Image Processing, Spatial Filtering, Compressed Photography 

TECHNOLOGY AREA(S): Sensors 

OBJECTIVE: Design, develop, demonstrate, and produce a prototype 4 x 4 avalanche photodiode focal plane array with dynamic biasing and low multiplication noise. 

DESCRIPTION: Low photon flux detectors are a major focus of military electro-optical systems for sensing, communications, and quantum computing. Historically, these detectors have been implemented on active systems (e.g. LADAR) in the form of linear or Geiger mode avalanche photodiode (APD) focal plane arrays (FPAs). The two APD FPA forms can be differentiated by the timing circuitry of the read-out, and the dynamic range of the signal. A Geiger mode FPA will momentarily bias the APD beyond breakdown, creating a strong response to the presence of any electron present in the multiplication region. These electrons can be generated optically (signal) or thermally (noise), but the output level is the same. Meanwhile, a linear mode FPA will maintain a time invariant gain and can produce varying output levels for any number of photons, but cannot detect single photons as easily as Geiger mode APDs. The limiting factors for linear mode detectors are the multiplication noise and dark current. Given these constraints, electro-optical system designers are forced to trade signal dynamic range and overall receiver sensitivity, even when they have knowledge of their photon flux and pulse timing. An opportunity exists to create APD arrays that have higher sensitivity for known pulse trains, which would enhance signal, reduce noise, and maintain dynamic range across 100s of signal photons. Dynamically biasing an APD below its breakdown field is a potential method to increase low-noise gain and improve detectivity. The goal of this program is (a) to explore dynamic bias APD FPA designs in Phase I, (b) to produce a photodiode array with dynamic bias in Phase II, and (c) to demonstrate an imaging array with enhanced detectivity in Phase III. The basic requirements for meeting these goals are: the detector should operate at or above 200 K; the readout should be capable of adapting to changes in the pulse train to maximize SNR at frequencies greater than 10 MHz; and the APD spectral cutoff should be 1.6 microns or greater. Preference will be given to designs that offer better noise equivalent photon values and higher signal dynamic range. No government materials, equipment, data, or facilities will be provided. 

PHASE I: Develop a generic model for dynamic biasing of a given APD design. Optimize performance concurrently in the APD and readout for several different pulse train examples. Demonstrate lower excess noise using dynamic bias. Provide simulation code for testing/verification. 

PHASE II: Demonstrate statically biased APD operation on single element devices (Gain x EQE > 100%). Demonstrate spectral cut-off wavelengths of 1.6 microns or greater. Demonstrate APD operation with dynamic bias and various pulse trains. Model and design a dynamically biased readout FPA (50 micron pitch or smaller). A proof of concept FPA is desirable, but not required. 

PHASE III: Demonstrate a 4 x 4 APD FPA, or larger, using dynamic biasing and adapting to the pulse train for maximum SNR. 

REFERENCES: 

1. Hayat, M. M. and Ramirez, D. A., Multiplication theory for dynamically biased avalanche photodiodes: new limits for gain bandwidth product. Optics Express, 2012. 20(7): p. 8024.; 2. Hayat, M. M., et al, Breaking the buildup-time limit of sensitivity in avalanche photodiodes by dynamic biasing. Optics Express, 2015. 23(18): p. 24035.; 3. Namekata, N., 1.5 GHz single-photon detection at telecommunication wavelengths using sinusoidally gated InGaAs/InP avalanche photodiode. Optics Express, 2009. 17(8): p. 6275.; 4. US Patent US9354113 B1. “Impact ionization devices under dynamic electric fields” The United States has certain rights, including a license to have practiced on behalf of the United States the invention. Please see USPTO Reel/Frame number 034697/0153

KEYWORDS: APD, Avalanche Photodiode, Infrared Detector, SWIR, III/V, Compound Semiconductor, ROIC, Readout Integrated Circuit, Dynamic Bias, Gain Modulation, LADAR, LIDAR 

TECHNOLOGY AREA(S): Human Systems 

OBJECTIVE: To research and develop a human behavior analytics tool to algorithmically process data sets, flagging potential needs or risks that indicate wellness issues, advance agency goals, and improve performance. 

DESCRIPTION: Recent studies by organizations such as Facebook, Cogito, and the National Center for Veterans Studies have applied machine learning technology to predict behavioral patterns. Additionally, Florida State University has trained algorithms on data from 2 million health records and achieved suicide prediction capabilities of 80-90%. All of this represents current efforts to harness machine learning technology for the detection and prediction of at risk behaviors. This would allow potential intervention in ways that enhance protective factors and advance healthy goals. Leave use patterns have been identified as a potential indicator for wellness intervention. The application of machine learning to the expanded data fields can highlight additional correlations currently unknown. Anecdotal evidence suggests not just a correlation between leave use and wellness, but also burnout and overtime trends, retention and sick leave usage, and regular aged comp-time and credit hour usage. For Phase 2, this project intends to use personnel systems data from selected sites to provide a study set of approximately 30,000 cases. The scope of the study will extend back 10 years and will explore more than 1200 data points or features for correlations and contrasts. Personally Identifiable Information (PII) protection, DoD computer system requirements, and security constraints will be appropriately addressed throughout the project. The required disciplines for this work include but are not limited to: academic research specialties in quantitative psychology, suicidology, advanced mathematics related to deep neural network modeling, data science related to Artificial Intelligence (AI), computer programing related to AI and DoD computer systems, and cultural experts. The project will require a balanced approach in which deep neural networks are one of many applicable models under consideration. A theory driven approach shall be applied throughout the project as it relates to wellness, agency goals, and performance in order to distill from the architecture and trained weights/biases of the neural network what factors it views as highly predictive. A systems viewpoint in data reduction shall demonstrate relevant correlations between the selected dataset and the aforementioned factors. The data needs to be adequately cleaned addressing corrupt, inaccurate, sparse and rogue elements for proper analysis. In support of this effort, all applicable Configuration Management policies shall be followed including source code management - the source code shall be provided with documentation. DoD, and Air Force architectures, policies, and standards shall be followed. Throughout this project all data shall be protected and secured according to applicable laws/DoD and AF directives/policies. Remote and onsite customer support shall be provided as requested/required. The final outcome of this project shall be the development and demonstration of an algorithmic tool that processes personnel systems data bi-weekly and performs predictive correlations to identify potential needs/risk patterns. This could include issues relating to wellness, performance, or other behaviors that affect agency goals (such as, but not limited to suicidality, use of overtime, comp time, and credit hours, burnout, and retention). 

PHASE I: R&D solution(s) that approximates the above requirements from a publicly available dataset. A selection of personnel action markers shall be identified for algorithmic training and testing to identify wellness needs and risk patterns. Proof-of-concept prototype(s) shall be developed and demonstrated using the data selected. 

PHASE II: Apply a balanced approach in which deep neural networks are one of multiple models under consideration for redacted data provided from personnel systems. Personnel action markers may be provided for algorithmic training and testing. Prototype(s) shall be refined to installation-ready package and shall undergo testing to verify and validate all requirements. This process may require multiple iterations before a final design is selected. 

PHASE III: If developed technology/tool passes verification, validation, and qualification testing, then it shall proceed to transitioning and implementation. 

REFERENCES: 

1. K. Krysinska and G. Martin. “The Struggle to Prevent and Evaluate: Application of Population Attributable Risk and Preventive Fraction to Suicide Prevention Research,” Suicide and Life-Threatening Behavior, 39 (5): 548-557, 2009; 2. Suicide and Suicidal Attempts in the United States: Costs and Policy Implications” by Donald S. Shepard PhD, Deborah Gurewich PhD, Aung K. Lwin MBBS, MS, Gerald A. Reed Jr PhD, MSW, Morton M. Silverman MD in the Journal of the American Association of; 3. Megan Molteni, “Artificial Intelligence is Learning To Predict and Prevent Suicide” Science (March 17, 2017).; 4. K. Szanto, S. Kalmar, H. Hendin, Z. Rihmer, and J.J. Mann. “A Suicide Prevention Program in a Region with a Very High Suicide Rate,” Archives of General Psychiatry, 64 (8): 914-920, 2007

KEYWORDS: AI, Machine Learning, Behavioral Patterns, Trained Algorithms, Suicide, Wellness 

TECHNOLOGY AREA(S): Materials 

OBJECTIVE: Troubleshoot machine operation issues through ‘Monitoring and Diagnosis via Machinery Vibration Auditing 

DESCRIPTION: Machine faults can be diagnosed by the changes of the system parameters or modal parameters, such as the natural frequency, damping, stiffness, etc. Since most manufacturing process generates vibrations, vibration analysis plays a major role in detecting machinery degradation before the equipment fails and potentially damages other related equipment for the ultimate purpose of avoiding unwanted breakdowns and downtime. Vibration analysis can help increase the lifetime of equipment when degradation is detected and then dealt with at an early stage. Vibration analysis of a rotating table top model has shown that some faults might exist even though they are not visible to the naked eye. The statistical features of the vibration signals in time, frequency and time–frequency domains have different representation capabilities for fault patterns. Singularity point detection, fault feature extraction, weak signal extraction, and system identification can be implemented based on vibration signals. A sophisticated vibration-fault relation model can be developed based on the vibration feature analysis. Industry has performed extensive work on smart seismic networks and data analytics through collaboration with geophysicists from NASA, USGS, energy exploration industry and academic. Characteristics of machinery vibration signals (including amplitude, frequency, phase) can be efficiently extracted using signal decomposition methods. Industry has also developed innovative intrinsic oscillation mode analysis or signal feature extraction methods, which can directly apply to machine health monitoring and diagnosis. Advanced signal processing and machine learning methods could be explored to enhance the sensitivity, robustness, reconstruction accuracy, classification specificity, and efficiency. 

PHASE I: Develop statistical feature extraction methods from vibration sensor signals. Time domain measurement and the corresponding frequency domain spectrum are capable of separately describing machinery vibration in terms of time and frequency. For jointly representing vibration features, it would be required to extract time-frequency domain features for signal processing and analysis. 

PHASE II: Develop monitoring and diagnosis software via vibration auditing. Based on the features extracted from Phase I, focus would be channeled toward optimizing statistical features in different domains from different types of faults in different diagnostic applications. Once a relationship between vibration features and faults is built, root cause diagnosis can be discerned based upon vibration signals. Then, fault diagnosis experiments on real devices could be conducted. Thereafter, based on the vibration-fault model, typical machinery systems could be constructed to validate the proposed approach. 

PHASE III: Monitoring and diagnosis via vibration auditing would have many commercial applications. A successful system could be marketed to commercial manufacturing, aerospace industry, as well as other defense customers. Additional markets might include “oil and gas” and homeland security. 

REFERENCES: 

1. Peng, Z. K., and F. L. Chu. "Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography." Mechanical systems and signal processing 18, no. 2 (2004): 199-221.; 2. Yan, Ruqiang, Robert X. Gao, and Xuefeng Chen. "Wavelets for fault diagnosis of rotary machines: A review with applications." Signal processing 96 (2014): 1-15.; 3. Li, Chuan, René-Vinicio Sánchez, Grover Zurita, Mariela Cerrada, and Diego Cabrera. "Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning." Sensors 16, no. 6 (2016): 895.

KEYWORDS: Manufacturing System, Monitoring And Diagnosis, Vibration 

TECHNOLOGY AREA(S): Materials 

OBJECTIVE: Address issues pertaining to machine health through Monitoring and Diagnosis via Electrical Waveform Auditing 

DESCRIPTION: Some types of machine health threats (fault or attack) may be subtle and not necessarily change the power consumption of machines, but cause distorted electrical waveforms (e.g., increased harmonics) in power networks which may affect the precision and functions of electrical machines. The attacks may be direct or indirect. In direct attacks, a malicious device may be plugged into a power outlet and inject harmonics to the power network of machines. The indirect methods may involve hacking and controlling an electrical machine in order to generate distorted electrical waveforms to affect other machines in the power network. For instance, if denial of service (DOS) occurs, the system might become unstable, resulting in unusual harmonics and torque ripples, which later affect product quality. In this task, it is proposed to analyze electrical waveform data from strategically-placed sensors in manufacturing systems for health monitoring and diagnosis. No record in industry suggests this approach has not been attempted before. To achieve this goal, a high dimensional analysis method and information incorporation mode would need to be developed. Traditional time series analysis or machine learning methods ignore some unique characteristics of the multi-stream measurement data; in particular, the coexistence of strong temporal correlation and inter-stream relatedness is not accounted for. The machine learning formulation proposed in this task for multiple time series is intuitively nonparametric regression in statistical learning theory, which uses multiple coevolving time series data to capture both the temporal dependence and inter-series relatedness. 

PHASE I: Build the relationship model between system statistics (e.g. “normal state, controller attack, attack, DOS attack, short circuit fault,” etc.) and electrical waveform data (e.g, total harmonic distortion, current ripples, voltage/current unbalance, etc.). Firstly, validation that electrical waveform of manufacturing systems can be used to detect cyber and physical attacks would need to take place. Then, a disaggregation model to map the relationship to assist root cause diagnosis could be developed. 

PHASE II: Develop monitoring and diagnosis software to classify the observed data into “trend” functions and anomalies based on the “normal” behavior data and simulated “faulty” data. Once the trend and fault libraries are built, the monitoring system detects anomalies when the fitting error is larger than the threshold. Then a classification model can be learned to classify the threat source to the most possible location. 

PHASE III: Monitoring and diagnosis via electrical waveform have many commercial applications. A successful system could be marketed to commercial manufacturing, aerospace industry as well as other defense customers. Additional markets might include the smart home, construction, and power industries. 

REFERENCES: 

1. F. Li, B. Yang, J. Ye, and W. Song, “Generator fault diagnosis based on sparsely placed sensors in power networks,” Sensors, 2019, submitted.; 2. B. Yang, F. Li, J. Ye, and W. Song, Condition Monitoring and Fault Diagnosis of Generators in Power Networks Conference IEEE Power & Energy Society General Meeting, 2019.; 3. J. Guo, J. Ye, and A. Emadi, “DC-Link current and voltage ripple analysis considering anti-parallel diode reverse recovery in voltage source inverters,” IEEE Transactions on Power Electronics, vol. 33, no. 6, pp. 5171-5180, June 2018.; 4. F. Peng, J. Ye, A. Emadi, and Y. Huang, “Position sensorless control of switched reluctance motor drives based on numerical method,” IEEE Transactions on Industry Applications, vol. 53, no.3, pp. 2159-2168, May-June 2017.

KEYWORDS: Manufacturing System, Monitoring And Diagnosis, Electrical Waveform 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: This is an AF Special Topic partnership, please see the above AF Special Topic instructions for further details. A Phase I award will be completed over 3 months with a maximum award of $25K and a Phase II may be awarded for a maximum period of 12 months 

DESCRIPTION: Academia is producing disruptive science and technology innovations at an increasingly rapid pace. Hence, rather than utilizing a pre-defined requirements approach, this topic is intended to be an open call for ideas and technologies that may not be currently listed (i.e. the unknown-unknown) under STTR topics, but nonetheless still fit within broad interest areas of the Air Force basic research level. These broad areas (Engineering and Complex Systems, Information and Networks, Physical Sciences, and Chemistry/Biological Sciences) are covered in greater detail at https://www.wpafb.af.mil/Welcome/Fact-Sheets/Display/Article/842026/. To be eligible, offeror(s) must be teams that have formed companies and partnered with a university (e.g. university entrepreneurship centers, university technology transfer offices). The offeror should demonstrate their technical capability by demonstrating a credible and high-potential minimum viable product (MVP) along with a credible plan for developing the prototype to a commercially available solution. This topic is not looking for fully formed products, and it is acceptable if the solutions are earlier stage. If the offeror has a later stage solution that already has paying customers, it may make more sense to apply to the SBIR ‘Open Innovation Topic’ AF19.2-001. The offeror should demonstrate their ability to perform the Phase I research by showing that they have an understanding of which Air Force stakeholders could make use of their solution. In general, it will be beneficial to be more specific about the stakeholder, (i.e. listing a person’s name and their exact position and organization is better than just saying ‘pilots could use this’). For early-stage (e.g. student) teams who have never learned about the Air Force and are unsure of where to start, we recommend reaching out to AFWERX (https://www.afwerx.af.mil). The offeror should demonstrate their commercialization capability by demonstrating the results of the commercialization efforts of their partner university or non-profit partner (i.e. a university entrepreneurship center, tech transition office, non-profit entrepreneurship center) and showing a credible plan for turning the prototype or MVP into a sustainable business. It will also be important to show the potential for commercialization in the non-defense market (i.e. Dual-Use technologies). FOCUS AREAS: While This topic is open to all research areas and business ideas that meet the above criteria, there are some areas that are of particular interest to the Air Force right now, these are listed below. If your solution may meet one of these focus areas, please list the focus area number in your proposal FA-001 Quantum Computing: Due to its rapidly emerging nature and increasing impact to all science and technology, this topic also includes a special focus area of consideration for quantum science. Submission topics could include quantum sensing, quantum communications and quantum computing. Possible applications include quantum navigation sensors, quantum clocks for more precise and robust communications and quantum computational algorithmic solutions to tasks such as aircraft radar cross-section, computational aerodynamics and software verification and validation FA-002 Artificial Intelligence(AI) : Due to the increased importance of AI in many areas that the Air Force works in, this is a focus area for this topic. More information on the Air Force’s interest in AI can be found below in the attachment to this topic titled: Summary of the 2018 Department Of Defense Artificial Intelligence Strategy. If you believe your solution can help address one of the ‘Focus Areas’, please note this on the first slide of your application slide deck AND please include the Focus Area ID # in your ‘Keywords’ in the online SBIR application (Example: FA-001). The alignment between a proposal and a Focus Area can strengthen an application. This also does not preclude companies who are looking to solve other problems that are not listed in the Focus Areas to submit to this topic, it is simply intended to give indications of areas of special focus for the Air Force at this particular point in time. 

PHASE I: Validate the product-market fit between the proposed solution and a potential US Air Force stakeholder and define a clear and immediately actionable plan for running a trial with the proposed solution and the proposed US Air Force customer. The period of performance for Phase I is targeted at under an academic semester (ideally 3 months or less) with monetary awards in Phase I not to exceed $25k. This feasibility study should directly address: 1. Offeror(s) must focus on who the prime potential US Air Force end user(s) is and articulate how they would use your solution(s) (i.e., the one who is most likely to be an early adopter, first user, and initial transition partner). 2. Deeply explore the problem or benefit area(s) which are to be addressed by the solution(s) - specifically focusing on how this solution will impact the end user of the solution. 3. Define clear objectives and measurable key results for a potential trial of the proposed solution with the identified Air Force end user(s). 4. Identify any additional specific stakeholders beyond the end user(s) who will be critical to the success of any potential trial. This includes, but is not limited to, program offices, contracting offices, finance offices, information security offices and environmental protection offices. 5. Describe if and how the demonstration can be used by other DoD or governmental customers. 6. Development of plans for MVPs, prototypes, manufacturing, distribution and scaling of the idea into an actual solution for DoD customers. 7. Development of the business, including interest from non-governmental customers, potential sources of private funding, and formation of the team (to include new employees, partners, advisors and investors). The funds obligated on the resulting Phase I STTR contracts are to be used for the sole purpose of conducting a thorough feasibility study using scientific experiments, laboratory studies, commercial research and interviews. MVPs or Prototypes may be developed with STTR funds during Phase I studies to better address the risks and potential payoffs in innovative technologies. Phase I will conclude with a short report and video outbrief and/or telecon with select members of the Air Force Office of Scientific Research. 

PHASE II: Develop, install, integrate and demonstrate a prototype system determined to be the most feasible solution during the Phase I feasibility study. If selected, Phase II awards will be granted up to $200k and are targeted for periods of performance less than one year in duration. This demonstration should focus specifically on: 1. Evaluating the proposed solution against the objectives and measurable key results as defined in the Phase I feasibility study. 2. A clear transition path for the proposed solution that takes into account input from all affected stakeholders including but not limited to: end users, engineering, sustainment, contracting, finance, legal, and cyber security. 3. Specific details about how the solution can integrate with other current and potential future solutions. 4. How the solution can be sustainable (i.e. supportability) 5. Clearly identify other specific DoD or governmental customers who want to use the solution 6. Clearly identify other non-governmental customers who want to use the solution. 

PHASE III: The student-led team small business will pursue commercialization of the various technologies developed in Phase II for transitioning expanded mission capability to a broad range of potential government and civilian users and alternate mission applications. Direct access with end users and government customers will be provided with opportunities to receive Phase III awards for providing the government additional research & development, or direct procurement of products and services developed in coordination with the program. NOTES: a. This SBIR is NOT awarding grants, it is awarding contracts, when registering in SAM.gov, be sure to select ‘YES’ to the question ‘Do you wish to bid on contracts?’ in order to be able to compete for this SBIR topic. If you are only registered to compete for grants, you will be ineligible for award under this topic. For more information please visit https://www.afwerx.af.mil/sbir.html b. We are working to move fast, please register in SAMs and if already registered please double check your CAGE codes, company name, address information, DUNS numbers, ect. , If they are not correct at time of submission, you will be ineligible for this topic. In order to ensure this, please include, in your 15-slide deck, a screenshot from SAM.gov as validation of your correct CAGE code, DUNS number and current business address along with the verification that you are registered to compete for All Contracts. It is the responsibility of the contractor to ensure that the data in the proposal and the data in SAM.gov are aligned. For more information please visit https://www.afwerx.af.mil/sbir.htmlc. Please note that each company may only have one active ‘Open Topic’ award at a time. If a company submits multiple technically acceptable proposals, only the proposal with the highest evaluation will be awarded. If multiple proposals are evaluated to be equal, the government will decide which proposal to award based upon the needs of the Air Force.The ‘DoD SBIR/STTR Programs Funding Agreement Certification’ form must be completed and signed at the time of *Proposal Submission* and can be found at https://www.afsbirsttr.af.mil/Programs/Phase-I-and-II/*****Proposals submitted under this topic may relate to technologies restricted under the International Traffic in Arms Regulation (ITAR) which controls defense-related materials/services import/export, or the Export Administration Regulation (EAR) which controls dual use items. Foreign National is defined in 22 CFR 120.16 as a natural person who is neither a lawful permanent resident (8 U.S.C. § 1101(a)(20)), nor a protected individual (8 U.S.C. § 1324b(a)(3)). It also includes foreign corporations, business associations, partnerships, trusts, societies, other entities/groups not incorporated/organized to do business in the United States, international organizations, foreign governments, and their agencies/subdivisions.Offerors must identify Foreign National team members, countries of origin, visa/work permits possessed, and Work Plan tasks assigned. Additional information may be required during negotiations to verify eligibility. Even if eligible, participation may be restricted due to U.S. Export Control Laws.NOTE: Export control compliance statements are not all-inclusive and do not remove submitters’ liability to 1) comply with applicable ITAR/EAR export control restrictions or 2) inform the Government of potential export restrictions as efforts proceed.***** 

REFERENCES: 

1. FitzGerald, B., Sander, A., & Parziale, J. (2016). Future Foundry: A New Strategic Approach to Military-Technical Advantage. Retrieved June 12, 2018, from https://www.cnas.org/publications/reports/future-foundry; 2. Blank, S. (2016). The Mission Model Canvas – An Adapted Business Model Canvas for Mission-Driven Organizations. Retrieved June 12, 2018, from https://steveblank.com/2016/02/23/the-mission-model-canvas-an-adapted-business-model-canvas-for-mission-drive; 3. US Department of Defense. (2018). 2018 National Defense Strategy of the United States Summary, 11. Retrieved from https://www.defense.gov/Portals/1/Documents/pubs/2018-National-Defense-Strategy-Summary.pdf; 4. Torrance, W. E. (2013). Entrepreneurial campuses: Action, impact, and lessons learned from the Kauffman campuses initiative. Retrieved from https://www.kauffman.org/-/media/kauffman_org/research-reports-and-covers/2013/08/eshipedcomesofage_report.pdf

KEYWORDS: Open, Other, Disruptive, Innovation, Defense Related Technologies, Quantum Computing 

TECHNOLOGY AREA(S): Bio Medical 

OBJECTIVE: Obtain an in-ear materiel solution that provides desired attenuation of ambient noise without undesirable occlusion effects related to discomfort and/or the increased awareness of body-generated sounds/vibrations (e.g., heavy breathing, chewing, foot falls, vocalization, etc). The solution may provide inherent attenuation of ambient sounds or integrate with existing hearing protection devices to provide the required attenuation. Additionally, the solution must allow for use of level dependent filters and/or electronic pass-through communications and meet the insertion, wear, and mass-use requirements of Service members. 

DESCRIPTION: Operational noise requires Service members to use hearing protection during the performance of their duties. Use of traditional (passive) hearing protection often results in an unacceptable loss of tactical/auditory situation awareness when ambient sounds are attenuated or masked. The use of level-dependent acoustic filters and electronic pass-through headsets have been used to overcome unwanted attenuation and retain tactical/auditory situation awareness with some success (Dancer and Hamery 1998 and Dancer et.al. 1999). However, masking of ambient sounds from the occlusion effect still remains a problem for in-ear devices and small volume ear-muffs. This is especially true when Service members are physically exerting themselves and body generated sounds/vibrations become much louder, masking critical ambient sounds or radio communications (Casali et. al. 2009). This loss of tactical/auditory situation awareness is unacceptable during many military operations/duties and results in poor compliance with hearing protection use. Undesired occlusion effects are also common for hearing aid users. Hearing aid users attempt to overcome the occlusion effect with deep insertion hearing aids or ear canal venting (Kuk et.al. 2005 and Kiessling et.al. 2005). These same principles have been applied to hearing protectors with some success (McKinley et.al. 2005). The use of venting in a hearing protection device may unavoidably reduce desired attenuation to unacceptable levels. Deep fit earmolds, while effective, are often difficult to insert and may cause discomfort for the user (Huttunen, K. H. 2011 and Davis R.R. 2008). Difficulty with insertion and discomfort have prevented these devices from being widely accepted. Furthermore, the need for a custom fit, requiring impressions of the ear canal for each user, will prevent wide use within the military. A materiel solution is needed that will meet the desired attenuation (inherently or through integration with existing hearing protection) and reduce or eliminate the occlusion effects while being easy to insert, comfortable for long term wear, and cost effective. 

PHASE I: Phase I will focus on a design concept for a technology solution that will eliminate or greatly reduce the occlusion effect, while still allowing or providing protection from hazardous ambient noise. The candidate solution should provide desired attenuation levels or integrate with existing in-ear hearing protection products without degrading their attenuation performance. An example of hearing protection currently used in the military can be found at the following link. <https://hearing.health.mil/-/media/Images/HCE/Materials/Posters/Passive-Device-Poster.ashx> Barriers to clear in Phase I. Concept or design must have a reasonable expectation of: -Reducing undesirable occlusion effects -Providing adequate attenuation for continuous and impulse noise or integrating with existing hearing protection devices without degrading their attenuation performance -Fitting in the ear without requiring custom molds or ear impressions -Comfortable long term wear -Being simple and easy to insert/apply by the end-user RESEARCH INVOLVING ANIMAL OR HUMAN SUBJECTS: The SBIR/STTR Programs discourage offerors from proposing to conduct Human or Animal Subject Research during Phase 1 due to the significant lead time required to prepare the documentation and obtain approval, which will delay the Phase 1 award. All research involving human subjects (to include use of human biological specimens and human data) and animals, shall comply with the applicable federal and state laws and agency policy/guidelines for human subject and animal protection. Research involving the use of human subjects may not begin until the U.S. Army Medical Research and Materiel Command's Office of Research Protections, Human Research Protections Office (HRPO) approves the protocol. Written approval to begin research or subcontract for the use of human subjects under the applicable protocol proposed for an award will be issued from the U.S. Army Medical Research and Materiel Command, HRPO, under separate letter to the Contractor. Non-compliance with any provision may result in withholding of funds and or the termination of the award. 

PHASE II: Phase II will develop a prototype and conduct feasibility testing of a technology solution that will meet attenuation and occlusion effect requirements stated below. The Prototype should also work with level dependent acoustic filters and external communication systems. Attenuation Candidate solutions with inherent hearing protection characteristics should meet minimum attenuation values for both steady-state noise and impulse noise. Candidate solutions that integrate with existing hearing protection must not degrade performance of the hearing protector below the criteria stated below. Steady-state noise attenuation will be tested according to method A of ANSI S12.6 (Methods For Measuring The Real-Ear Attenuation Of Hearing Protectors) (preferred) or ANSI S12.42 (Methods For The Measurement Of Insertion Loss Of Hearing Protection Devices In Continuous Or Impulsive Noise Using Microphone-In-Real-Ear Or Acoustic Test Fixture Procedures). Performance will be calculated by subtracting one standard deviation from the mean attenuation. Minimum steady-state attenuation values are listed in Table 1. Table 1. Minimum attenuation values for single hearing protection (mean minus one sigma). Octave band (Hz) 125 250 500 1000 2000 4000 8000 Attenuation (dB) 13.6 14.7 16.4 18.3 26.3 32.6 31.3 Impulse attenuation will be tested according to ANSI S12.42 (Methods For The Measurement Of Insertion Loss Of Hearing Protection Devices In Continuous Or Impulsive Noise Using Microphone-In-Real-Ear Or Acoustic Test Fixture Procedures). Candidate products should be capable of reducing peak pressure levels to 140 dB Peak or below. Occlusion Effect The occlusion effect will be measured using both subjective and objective means. Objective measures of the occlusion effect must be within 5 dB of un-occluded measures in the frequency range of 200-1000 Hz. Acoustic Filters and External Communication Headsets Demonstrate ability for candidate solution to be used with level dependent hearing protection filters (such as those used in the Moldex BattlePlugs®, 3M Combat Arms earplug, or developers novel solution) and/or in-ear tactical communication and protection systems (TCAPS) (such as the Invisio® X5 headset or the 3M Peltor Tactical Earplug TEP-100). Implementation Acceptance The candidate solution should be able to fit a wide range of Service members and be comfortably worn on a daily basis (up to 12 hours daily). The costs of the solution should be comparable to current hearing protection devices used in the military. 

PHASE III: Phase III will result in a commercial product that meets all previous requirements and will function in various industries and environments. The candidate solution should allow for proper maintenance and function in extreme environments (e.g., heat, cold, humid, and dry climates) and in the presence of possible contaminates (e.g., grease, fuel, dirt, sand, etc.). Integration with existing protective, safety, and field equipment systems (e.g., weapon systems, gas masks, helmets, etc.) is also critical to implementation success. Commercial industries that require auditory situation awareness, verbal communication, or conduct hearing critical tasks in the presence of hazardous noise will benefit from this solution. Successful implementation of this solution is expected to improve user performance in hearing critical tasks, safety, and user acceptance.  

REFERENCES: 

1: Casali, J. G., Ahroon, W. A., and Lancaster, J. A. (2009). "A field investigation of hearing protection and hearing enhancement in one device: For soldiers whose ears and lives depend upon it," Noise & Health, 11(42), 69-90.

2:  Dancer, A. and Hamery, P., (1998). "A new non-linear earplug for use in high-level impulse noise environment", J. Acoust. Soc. Am., 103, 5, pp 2878.

3:  Dancer A, Buck K, Hamery P, Parmentier G., (1999). "Hearing protection in the military environment," Noise Health, 2:1-15.

4:  Kiessling, J., Brenner, B., Jespersen, C.T., Groth, J., & Jensen, O.D. (2005). Occlusion effect of earmolds with different venting systems. Journal of the American Academy of Audiology, 16, 237–249.

5:  Kuk, F., Keenan, D., & Lau. C.C. (2005). Vent configurations on subjective and objective occlusion effect. Journal of the American Academy of Audiololgy, 16(9), 747–762.

6:  McKinley, R. L., Bjorn, V. S., and Hall, J. A., (2005). Paper 13 - Improved Hearing Protection for Aviation Personnel. NATO RTO HFM-1

KEYWORDS: Occlusion Effect, Hearing Protection, Earplug, Attenuation, Ear, Auditory, Tactical Communication And Protective System (TCAPS) 

TECHNOLOGY AREA(S): Info Systems, Sensors, Battlespace 

OBJECTIVE: Develop advanced sensor data association algorithms to correctly associate new object detections with existing tracks to maintain track purity. 

DESCRIPTION: This topic seeks innovative sensor data association algorithms capable of performing correct data association in multi-target tracking environments with one or more sensors. Current air and missile threats have the ability to fly non-ballistic, highly maneuvering hypersonic trajectories, and the ability to maintain closely spaced trajectories. Improved detection/track association is an enabling technology for enhancing tracking and object identification. Data association is a critical precursor to the track filtering process. If data association is not performed correctly, it becomes problematic to create an accurate track on the correct target of interest. This could potentially lead to track swaps, track drops, or the creation of dual tracks. The data association process is highly coupled to the tracking process. However, there are separate and unique algorithms that perform the data association function, as many track filters are designed assuming perfect data association (which is not a valid assumption for a real-world application). The objectives for this topic should include utilization of advancements in technology, the application to the full air and missile defense threat set (focused on advanced threats), potential use in any portion of the kill chain, and its potential applicability to many current and future systems. Algorithms developed under this topic may take advantage of recent technological advances in the areas of computer processing, parallel processing, deep neural networks, artificial intelligence, and distribution independent association techniques (techniques that do not require assumptions about the distribution of the true measurement uncertainty space). Successful proposals should focus strictly on performing correct data association against advanced/emerging threats (including threats across the air and missile defense spectrum: air, ballistic and cruise missile threats). Performance capability should be assessed parametrically against challenging threats and maneuver levels using a variety of different sensors and track filters. 

PHASE I: Develop proof-of-concept data association algorithm(s) and show how these algorithm(s) are applicable to meeting the emerging threat environment. Compare the advanced algorithms performance to a baseline algorithm (current technique) using a generic data set. 

PHASE II: Translate the proof-of-concept approaches into prototypical code that can be evaluated in an “apples-to-apples” comparison against current state of the art approaches within a lab environment. Integrate these algorithms with a current suite of track filters to evaluate performance improvements against realistic threat environments. 

PHASE III: Integrate algorithms into real sensor and command and control systems to evaluate performance improvements. Evaluate applicability of algorithms to areas such as unmanned ground and aerial vehicles and video based tracking systems that have uses outside of the Department of Defense. 

REFERENCES: 

1: Kwangjin Yoon, Du Yong Kim, Young-Chul Yoon and Moongu Jeon, "Data Association for Multi-Object Tracking via Deep Neural Networks" Sensors 2019, 19, 559.

2:  Samuel Blackman, Robert Popoli. Design and Analysis of Modern Tracking Systems, 1999 Artech House.

3:  Bar-Shalom, Yaakov, et al. "Tracking and Data Fusion." YBS Publishing, Storrs, CT, 2011.

4:  Klein, Lawrence A. "Sensor and Data Fusion: A Tool for Information Assessment and Decision Making", 2012, SPIE – The International Society for Optical Engineering

5:  Yi-Nung Chung, "Applying Image Processing and Neural Network Techniques To Data Association Algorithm", International Journal of Innovative Computing, Information and Control, Volume 7, Number 5(A), May 2011, pp. 2427-2439.

KEYWORDS: Multi-target Tracking, Data Association, Trackers, Multidimensional Assignment, Machine Learning, Track Filter, Filtering, Association 

TECHNOLOGY AREA(S): Info Systems, Materials, Weapons 

OBJECTIVE: Develop a capability to analyze the effects of high temperature environments on the fracture characteristics of high temperature materials in a hypervelocity impact. 

DESCRIPTION: This topic seeks models, concepts, and studies on the effects of the hypersonic flight regime on the mechanical fracture properties of materials. The aerodynamic heating (2,000° F) produced by extreme velocities in the atmosphere can affect the strength of materials. The goal of this topic is to further the state-of-the-art of modeling methods within fracture models. The materials under consideration could include, but are not limited to, silicon carbide composites, carbon-carbon composites, aluminum, and titanium. The model should characterize the fracture of these materials under high strain rates and high heat loadings simultaneously over short time periods. Previous topics have sought to characterize the fracture of materials after fatigue loading cycles. This topic does not seek that type of characterization; it is concerned with hypervelocity, high-temperature impacts. 

PHASE I: Develop research and development model(s) for the fracture and mechanics of the aforementioned materials under the impact conditions and the high temperatures resulting from travel at hypersonic velocities (i.e. above Mach 5), incorporating existing test data from the technical literature. Provide a methodology for modeling fracture that is proved out for feasibility via analytical studies that shows results against existing data. Develop a test plan to benchmark the model with empirical data. 

PHASE II: Implement the model(s) and test plan developed in Phase I for testing against relevant materials in a high-temperature environment. Include comparison against model runs as part of the Phase II effort. Improve the fidelity of the models based on the information gathered in testing. 

PHASE III: Transition the high temperature fracture modeling capability to an appropriate hydro-code and execute model runs for design and analysis cases of interest to the government. 

REFERENCES: 

1: V.C.D. Dawson, R. Piacesi, and R.H. Waser, "Temperature Yield Strength Correlation of the Crater Size Produced in Aluminum by the Hypervelocity Impact of Aluminum Spheres," 1st AIAA Annual Meeting 1964.

2:  H.B. Probst and H.T. McHenry, "A Study of the Impact Behavior of High-Temperature Materials," NACA Technical Note 3894.

3:  J.H. Underwood, P.J. Cote, and G.N. Vigilante, "Themomechanical and Fracture Analysis of Silicon Carbide in Cannon Bore Applications," Technical Report ARCCD-TR-03009.

4:  O. Heuze, J.C. Goutelle, and G. Baudin, "A New Temperature-Dependent Equation of State for Inert, Reactive, and Composite Materials," AIP Conference Proceedings, 620, 169 (2002).

5:  R. Krueger, "Virtual crack closure technique: History, approach, and applications," Appl Mech Rev Vol 57, no 2, p. 109, March 2004.

6:  R. Vignjevic, J.C. Campbell, et al, "Modelling Shock Waves in Composite Materials," AIP Conference Proceedings 955, 287 (2007).

7:  R.A. Cunningham and H.L. McManus, "effects o

KEYWORDS: Hypersonic, Fracture Mechanics, Aerothermodynamics, High-rate Loading, High-temperature Materials, High-temperature Mechanical Properties 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: Develop a Natural Language (NL) processing application that can probe big data sets and answer questions posed by a user. 

DESCRIPTION: This topic seeks an innovative NL solution for answering questions that data analysts ask when analyzing data sets to increase efficiency and consistency. The government is working on an all-digital missile defense simulation architecture that will deliver massive amounts of simulation data. Data analysts will soon be inundated with more data than they can possibly analyze manually. A critical part of the analysis will be identifying key trends and relationships within and across data sets; and identifying them quickly and efficiently. Candidate solutions should identify intuitive approaches of NL (textual or verbal) to probe massive data sets (potentially disparate) to enable quick access for the data analysts to identify the deepest and most interesting parts of data trends and relationships. The expectation is that the final product will eliminate the need for any user to possess knowledge about querying languages, allowing analysts and casual users equivalent access to big data. A final product should be capable of quickly returning relevant answers to any question asked of the data in an easy to interpret manner, enabling analysts and casual users to heuristically answer the questions they pose. Flexibility of design will be imperative as the digital and data management architectures have yet to be finalized. The ability to translate NL into many querying languages and to have an application with a flexible back end will be crucial to the effectiveness of a final product. 

PHASE I: Develop a proof-of-concept to demonstrate that the NL processing application can effectively probe big data sets and answer questions posed by a user. Provide evidence that the application is capable of interfacing with multiple databases and querying languages. 

PHASE II: Update and develop the approach to provide repeatable and quick results. Incorporate any additional features/tools that increase the utility of the application. Ensure that application is capable of interfacing with numerous databases and querying languages. Demonstrate that application outputs desired accurate results from big data sets and is intuitive to users (requires no special training or subject matter knowledge). 

PHASE III: Integrate application into data management systems and data analysis labs and demonstrate upgraded functionality for all users. Identify additional government entities that can integrate application. 

REFERENCES: 

1: V. Gudivada, D. Rao, and V. Raghavan. "Big Data Driven Natural Language Processing Research and Applications". In: Big Data Analytics. Ed. by V. Govindaraju, V. Raghavan, and C. R. Rao. Vol. 33. Handbook of Statistics. New York, NY: Elsevier, July 2015, pp. 203-238.

2:  V. N. Gudivada, D. L. Rao, and A. R. Gudivada, "Information Retrieval: Concepts, Models, and Systems" in Handbook of Statistics, Vol. 38, Elsevier B.V., pp. 331–401. *received directly from author, have PDF copy

3: * A. Vertsel and M. Rumiantsau, "Pragmatic approach to structured data querying via natural language interface," tech., Jul. 2018. https://arxiv.org/ftp/arxiv/papers/1807/1807.00791.pdf

4:  Zhong, V., Xiong, C., & Socher, R. (n.d.). Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning (Tech.). Retrieved March 20, 2019, from Salesforce Research website: https://einstein.ai/static/images/pages/research/seq2sql/seq2sql.pdf

5:  Yin, P., Lu, Z., Li, H., & Kao, B. (n.d.). Neural Enquirer: Learning to

KEYWORDS: Natural Language, Question Answering, Heuristic, Big Data, Natural Language To Database, Natural Language Querying, NLP 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: Develop an agile, dedicated, cyber test range for secure cyber resiliency validation of missile defense systems. 

DESCRIPTION: This topic seeks to develop a Secure Virtual Environment (SVE) that enables the validation of cyber defenses using virtual representations of tactical hardware. In order to ensure the safe operation of missile defense systems, the government is actively working on cyber resiliency and defensive measures. Currently, the validation of these measures require secure and isolated systems that draw assets away from other intended uses. Therefore, a dedicated cyber test range is desired. This dedicated cyber range should be hardened to cyber effects such that the systems they operate on will not be vulnerable to cyber-attack/effects. It should be reconfigurable to be able to emulate the desired configurations of missile defense systems, and have scalability for future missile defense advancements. The proposed solution should also have the ability to safely inject actual cyber-attacks without putting the host or supporting systems at risk. Lastly, the proposed solution should follow all Department of Defense cybersecurity policy and guidance. 

PHASE I: Develop a proof-of-concept architecture for a real time virtual emulation of a missile defense system. Develop the architecture for a virtual host platform/environment that can have actual cyber threats safely implanted within without putting the host or supporting systems at risk. This virtual environment should run the virtual emulation at real time. 

PHASE II: Working with a missile defense system prime contractor, build the proposed virtual emulation of the tactical element and verify its performance. Build the SVE and run the embedded tactical element during nominal operations. Implant an actual cyber-attack into the secure virtual platform and verify it attacks the virtual emulation in the same manner as it would attack a physical system. Validate the host system remains unaffected. 

PHASE III: Working with a missile defense system prime, use the verified SVE to evaluate the performance during candidate threat scenarios with and without cyber-attacks present. 

REFERENCES: 

1: Yoginath, S. B., Perumalla, K. S., & Henz, B. J. (2015). Virtual machine-based simulation platform for mobile ad-hoc network-based cyber infrastructure. The Journal of Defense Modeling and Simulation, 12(4), 439–456

2:  DoD Cybersecurity policy handbook https://dodcio.defense.gov/Portals/0/Documents/Cyber/DoD%20CIO%20CS%20Reference%20and%20Resource%20Guide%202018_v9.1_Final_2018.pdf?ver=2018-08-23-103824-243

3:  DoD cybersecurity Policy chart https://www.csiac.org/wp-content/uploads/2019/03/2019-02-28-the-dod-cs-policy-chart.pdf

KEYWORDS: Tactical Hardware Virtualization, Cyber Resiliency 

TECHNOLOGY AREA(S): Nuclear 

OBJECTIVE: Develop innovative designs and enabling technologies for a table top Free Electron Laser (FEL) capable of generating radiation (X-ray to UV) suitable for radiation testing and material characterization applications. 

DESCRIPTION: This topic seeks advanced table top FEL concepts to accelerate radiation and material characterization testing timelines through the maturation of laser systems, sub-systems, and/or component technology. The government currently relies primarily on Department of Energy designated National Facilities to generate X-ray spectra to investigate effects on materials (i.e. coatings on selected mirror substrates) in order to anchor Modeling & Simulation results. The process is effective and serves as the gold standard for evaluation, but requires long lead times, involves significant planning and preparation, and is subject to availability of unique facilities. FELs are able to produce coherent electromagnetic radiation over a broad range of wavelengths (or frequencies). FELs generate light through coherent emission of synchrotron radiation from unbound “free” relativistic electrons moving through a periodic array of magnets with alternating (north and south) poles called an undulator (or wiggler). As electrons pass through the undulator, a small fraction of each electrons' energy is converted into an emitted photon as the electron is deflected by the magnetic field. After leaving the wiggler, the electrons are returned to the accelerator and energy lost through photon emission is replaced as the beam is recirculated. The wavelength of emitted radiation depends on the electron beam energy, magnetic field strength, and wiggler period. The wavelength of radiation is tunable over the entire electromagnetic spectrum from X-ray to millimeter wave by varying one or more of the aforementioned parameters. The photons can be linearly, circularly, or elliptically polarized by appropriate design of magnets in the periodic array. It is this ability to tailor the properties of the emitted radiation to meet the needs of a specific investigation that make the FEL an invaluable research tool. Unfortunately, FELs are traditionally very large and expensive to build, maintain, and operate. This topic seeks proposals that leverage recent advances in material science (e.g. magnetic materials), manufacturing technology (e.g. additive manufacturing), modeling and simulation software, and computational capabilities to reduce FEL system size, weight, power, and cost while improving performance to make the technology more readily available for scientific use. FEL components include: electron injector; accelerator to generate a bunched relativistic electron beam; undulator magnets; optical resonator and/or amplifier components; as well as sensors, components, and software for controlling electromagnetic radiation beam propagation. Other important subsystems include: undulator alignment systems; electron beam diagnostics; vibration free cryogenic support for superconducting magnets; and vacuum system components. Proposed efforts may seek to develop a robust compact FEL design and/or any of the above components at an appropriate scope for a contract award. 

PHASE I: Conduct a feasibility study of the technical proof-of-concept approach. This study should address key program risk areas including: 1) Cost and schedule, 2) availability of appropriate test facilities, 3) anticipated technology performance, and 4) estimated Technology Readiness Level (TRL) at the conclusion of Phase II. Specifically, the Phase I effort should identify whether or not the proposed component technology has a realistic technology roadmap to achieve applicability for Missile Defense Agency use and that a sufficient industry base exists to warrant further investment. 

PHASE II: Build on the program plan developed under Phase I and incorporate additional technical details as the program plan matures. Identify the state of the current technology, where the technology needs to be for Agency purposes, and what level of follow-on funding would be required to close that capability gap. Identify an appropriate test facility to demonstrate the designated component technology prototype has achieved the predicted TRL identified in the Phase I technology roadmap. 

PHASE III: Refine, update, and integrate the demonstration prototype developed under Phase II into a sensor package capable of participating in Agency relevant testing to demonstrate the applicability and TRL of the prototype technology. The proposer should also pursue commercialization opportunities of the prototype technology and/or transition of the technology to industry, University, or FFRDC partners. 

REFERENCES: 

1: Add G. Dattoli, et.al, Introduction to the Physics of Free Electron Laser and Comparison with Conventional Laser Sources, IntechOpen, March 14th 2012. https://www.intechopen.com/books/free-electron-lasers/free-electron-laser-devices-a-comparison-with-ordinary-laser-sources

2:  Add Re S. Seckel, When going small is big news: ASU professor shrinking electron-laser technology, ASU News, October 14, 2015. https://biodesign.asu.edu/news/when-going-small-big-news-asu-professor-shrinking-electron-laser-technology

3:  Table-Top Free-Electron Laser, U. Nebraska-Lincoln. https://unlcms.unl.edu/physics-astronomy/fuchs-group/table-top-free-electron-laser

4:  F. Gruner, et.al, Design considerations for table-top, laser-based VUV and X-ray free electron lasers, Appl. Phys. B 86, 431–435 (2007).

5:  John M. J. Madey, Stimulated Emission of Bremsstrahlung in a Periodic Magnetic Field, Journal of Applied Physics 42, 1906 (1971).

6:  J.M.J. Madey, "Stimulated Emission of Radiation in Periodically Deflected Electron Beam," United

KEYWORDS: Free Electron Laser, Radiation Hardening, High Energy Physics, X-Ray 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: Demonstrate a Dynamic Emulated System (Software + Firmware + Hardware) In Loop (DESIL) capability through missile defense hardware emulation with missile defense software co-simulation. 

DESCRIPTION: This topic seeks a hardware emulation architecture capable of emulating embedded tactical processing architectures of mixed computing elements (e.g., microprocessors, digital signal processors, system on a chip (SOC), Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Chips (ASICs), etc.) to provide real-time dynamic code execution. Hardware emulation is a developing field with broad application to hardware/software simulation and integration. Emulation builds upon traditional hardware simulation techniques by compiling designs into system realistic hardware allowing for several orders of magnitude increases in simulation speed. Obstacles for current missile defense testing include the availability of tactical hardware and tactical processors and the costs associated with acquiring and maintaining system hardware. Emulation can provide missile defense systems with the capability to test hardware and firmware designs near real-time during system development before actual hardware is available, thus providing in-phase tactical hardware and tactical firmware verification and validation analysis capabilities (Hardware Independent Verification and Validation (IV&V), Firmware IV&V) as well as system integration capabilities before a Computer-In-the-Loop (CIL) or Hardware-In-the-Loop (HWIL) simulator is available. Hardware emulation also enables the execution of tactical software on realistic processors allowing for early testing of software near real-time. These combined capabilities should provide improved hardware, firmware, and software reliability, quality and maturity earlier in the schedule than would otherwise be possible. 

PHASE I: Develop and conduct proof-of-principle studies and/or demonstrations of a hardware emulation architecture capable of emulating embedded tactical processing architectures of mixed computing elements to provide real-time dynamic code execution. 

PHASE II: Update/develop architecture based on Phase I results and develop a prototype hardware emulation architecture capable of representing multiple tactical architectures consisting of mixed computing elements. Demonstrate the technology utilizing unclassified tactical software. 

PHASE III: Revise the hardware emulation architecture to support dynamic software testing on multiple missile defense product/systems utilizing embedded processing elements. Pursue partnerships with other government software test organizations. 

REFERENCES: 

1: Lauro Rizzatti, (May 2017), Embedded Computing Design Web Article: http:/ /www.embedded-computing.com/embedded-computing-design/hardware-emulation-tool-of-choice-for-verification-and-validation

2:  Lawrence Vivolo (June 2017), Electronic Design Web Article: https:/ /www.electronicdesign.com/eda/transaction-based-verification-and-emulation-combine-multi-megahertz-verification-performance

KEYWORDS: Hardware Emulation, Dynamic Software Analysis, Real-time Code Execution, FPGAs, Hardware/Firmware Code Coverage, Timing Analysis, Instruction Set Level Execution 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: Develop a re-configurable virtual processing hardware environment to allow earlier and more thorough dynamic code analysis for missile defense ground systems and missile product software. 

DESCRIPTION: This topic seeks a virtual hardware processing architecture capable of emulating embedded tactical processing architectures to provide realistic dynamic code execution. The government performs Software Independent Verification and Validation (SW IV&V) for multiple configurations and versions for kill vehicles, boosters, guidance and fire control, communication systems, and launch management systems. The most realistic and valuable code analysis involves dynamically executing the tactical software on the tactical processors in real-time (i.e. Dynamic Code Analysis (DCA)). However, several obstacles limit the government’s ability to conduct DCA over the program lifecycle. Late availability of tactical hardware prevents the government from early software testing where findings would be less costly to correct. Limited availability of expensive tactical hardware also limits the amount of testing that can be conducted prior to key events (i.e. flight tests and fielding decisions). These problems can be minimized by providing a re-configurable virtual hardware processing environment where a generic high-speed processing architecture can be programed to closely execute the same instruction set as the tactical processors (i.e. execute the same flight binary codes). This virtual hardware architecture can be used to perform earlier and more thorough dynamic software analysis for less cost than can be accomplished with multiple copies of tactical hardware. 

PHASE I: Develop and conduct proof-of-principle studies and/or demonstrations of a virtual hardware processing architecture capable of emulating embedded tactical processing architectures to provide realistic dynamic code execution. 

PHASE II: Update/develop architecture based on Phase I results and develop a prototype virtual hardware architecture capable of representing multiple tactical architectures. Demonstrate the technology utilizing unclassified tactical software. 

PHASE III: Revise the virtual architecture to support dynamic software testing on multiple missile defense product systems utilizing embedded processors. Pursue partnerships with other government agencies and software test organizations. 

REFERENCES: 

1: Justin Morris (2018), NASA Web Site for JSTARS program use of Hardware Emulation. https://www.nasa.gov/centers/ivv/jstar/jstar_simulation.html

2:  Michael Asbury (2018), NASA Operational Simulator for Small Satellites (NOS3) build and test of flight software with simulated hardware models. https://www.nasa.gov/centers/ivv/jstar/nos3.html

3:  Scott D. Lowe (2013), Web article on hardware emulation vs. virtualization. http://techgenix.com/what-difference-between-emulation-vs-virtualization/

KEYWORDS: Virtual Hardware, Embedded Processors, Dynamic Software Testing, Realistic Software Execution 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: Develop efficient and scalable Monte Carlo (MC) modeling techniques to model missile defense operator execution performance of weapon system Tactics, Techniques, and Procedures (TTPs). 

DESCRIPTION: This topic seeks innovative modeling approaches for modeling operator/warfighter execution of TTPs in a MC simulation environment to support analysis of weapon system performance. The ability to statistically characterize system behavior that comprehends normal user interactions, execution, and influence is vital to modeling the system at a level of resolution that confidently captures system performance. Specifically, application of MC simulation methods for weapon system TTPs is important to help understand effects on missile defense engagement solutions. Furthermore, using MC methods facilitates the ability to exercise stressing scenarios to accurately bound weapon system performance within the context of warfighter TTPs. The goal of this topic is to develop concepts for using game play algorithms, artificial intelligence solutions, Human performance modeling or other innovative technologies to model operator execution of TTPs in a MC simulation environment, and evaluate concept feasibility with respect to the desired characteristics. Successful proposals should address the following characteristics: 1. Execute simultaneous MC repetitions on single/multiple platforms. 2. Execute multiple simultaneous weapon system engagements during a MC repetition. 3. Consider TTP execution as an MC variable (i.e., TTP execution can be varied over the MC run). 4. Integrate the MC solution into existing high fidelity weapons system simulations. 

PHASE I: Design and demonstrate a MC software/simulation proof-of-concept architecture capable of providing the desired topic characteristics. Support execution on Linux and Windows Operating Systems (Cross-platform). 

PHASE II: Develop a fully executable and modular software program capable of modeling operator/warfighter execution of TTPs in a Monte Carlo simulation environment. The software should provide a method for varying operator execution of TTPs over a Monte Carlo run set, and should be able to execute simultaneously with other instances of the program to demonstrate parallel execution of Monte Carlo runs. The program should provide a user-selectable reporting capability of modeled TTPs inputs and execution outputs and should support execution on Linux and Windows Operating Systems (Cross-platform). 

PHASE III: Deliver a software program capable of interfacing with existing missile defense high fidelity simulations for use in system performance analysis. Develop a plan/roadmap for development of enhanced modeling capabilities, including the expansion of this technology to TTP development, assessment, and validation. 

REFERENCES: 

1: Agent Based Modeling and Simulation, AI Magazine, September 2012 Reference

2:  The Complexities of Agent-based Modeling Output Analysis, Journal of Artificial Societies and Social Simulation, October 2015

3:  Agent Based Modeling for Security Risk Assessment, Lecture Note in Computer Science, June 2017

4:  Human-in-the-Loop Simulations: Methods and Practice, Springer, 2011

KEYWORDS: Human In Control, Artificial Intelligence, Modeling And Simulation 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: Develop a simulation to model the variability of real-time schedulers in multi-processor/multi-threaded system architectures. 

DESCRIPTION: This topic seeks to develop a deterministic (repeatable) model of a real-time scheduler to support Monte Carlo (MC) simulation of a multi-processor/multi-threaded weapon fire control system. The real-time scheduler is a module used to schedule the events/tasks within the tactical fire control and it inherently exhibits variability characteristics driven by real-time constraints. The objective of this topic is to model the variability of the tactical scheduler in a simulated environment. The capability to capture real-time multi-threaded software/hardware systems behavior for use in a digital simulation environment provides the unique capability to validate models used to represent the tactical weapon system performance. The MC model should be capable of being integrated into other simulations, be configurable to reflect existing scheduler designs, and should include user-defined random variables to support MC analysis. 

PHASE I: Develop design concepts for simulating real time scheduler variability in a MC environment and evaluate design concept feasibility with respect to meeting the desired model characteristics. Completion of Phase I should result in the selection and demonstration of a software/simulation architecture and preliminary design capable of providing the desired characteristics. 

PHASE II: Develop a fully executable software program capable of modeling real-time scheduler variability in a MC simulation environment. The software should provide a method for varying scheduler performance over a Monte Carlo run set, and should be able to execute simultaneously with other instances of the program to demonstrate parallel execution of MC runs. The program should provide user-selectable reporting capability of modeled scheduler inputs and execution outputs. 

PHASE III: Deliver a software program capable of interfacing with existing missile defense high fidelity simulations for use in missile defense system performance analysis. Develop a plan/roadmap for development of enhanced modeling capabilities, including the expansion of this technology to real time scheduler development, assessment, and validation. 

REFERENCES: 

1: Modeling Real-Time Schedulers for Use in Simulations Through a Graphical Interface, Spring Sim-ANSS 2017, April 23-26

2:  A Survey on Scheduling Approaches for Hard Real Time Systems, International Journal of Computer Applications, December 2015

3:  Urunuela, Richard & Déplanche, Anne-Marie & Trinquet, Yvon. (2010). Simulation for multiprocessor real-time scheduling evaluation.

4:  Audsley, N.

5:  Burns, A. (1990). Real-Time System Scheduling (PDF) (Technical report). University of York, UK

KEYWORDS: Real Time Scheduler, Modeling And Simulation, Multi-threaded / Multi-Processor Architectures