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Optical Computing Network


OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy, Microelectronics, Integrated Network System of Systems The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Design and build a programable optical network equivalent to an electrical network to solve Markovian graphs with cycles. Forney-style factor graphs can be solved while avoiding the creation of trees. DESCRIPTION: The use of digital image processing to enable target detection, classification, recognition and identification, as well as targe state estimation for fire control solutions is computationally intensive. It requires significant processing power, which in turn requires significant electrical power. A programable optical network can be used to perform these computations at reduced Size weight and power and at faster speeds. Factor graphs have been used to describe Bayesian networks (Pearl, 1988) and were applied to SLAM (Simultaneous Location and Mapping) by Dellaert (2017). These problems tend to be decomposed into trees for solution. The most general graph, and the one that is most difficult to solve, is the undirected graph with cycles. This is related to quantum computing and such difficult logistical problems such as the travel salesman conundrum. It is desirable to try to develop a room temperature solution, based on optical networks, that can at least reliably solve all convex Kalman filter problems. PHASE I: Design and develop programable optical circuit elements that map the nodes in a factor graph to those of an optical network much as Vontobel did for electrical components. PHASE II: Develop and demonstrate a prototype system consisting of the optical elements to create a network that can solve a problem. PHASE III DUAL USE APPLICATIONS: Build an integrated optic that can be deployed that can implement a Kalman filter with real world application. REFERENCES: 1. Dellaert, F. (2017). Factor Graphs for Robot Perception; Foundations and Trends in Robotics, Vol 6. No. 1-2 (2017) 1-139; DOI: 10.1561/2300000043 2. Pearl, J. (1988), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference; Morgan Kaufmann Publishers Inc. San Francisco Calf; ISBN 1-55860-479-0 3. Vontobel, P.O.; Factor Graphs, Electrical Networks, and Entropy; 4. Vontobel, P.O.; Kalman Filtering, Factor Graphs and Electrical Networks. 5. Wang,S. (2020); A Factor Graph-Based Distributed Consensus Kalman Filter; IEEE Signal Processing Letters, Vol 27, 2020. KEYWORDS: Graphs, networks, Kalman Filter, trees, cycles.
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