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- W3215210411 abstract "This paper advances dynamic-probabilistic analysis in data-driven information processing applications, investigates information governance, researches heterogeneous Markov formalism, and, examines Turing processing structures. The objective is to empower high-fidelity characterization of cyber-physical processes, develop new algorithms, and, research design schemes. Evaluation of dynamic-probabilistic information processes and data governance is verified using experimental data. As a case study, we consider processing in MEMS-technology multi-degree-of-freedom inertial measurement units. Data fusion and management are studied in multi-level data acquisition system which consists of a system-in-package triaxial accelerometer and gyroscope, digital motion processor, as well as supervisory microprocessor. The matrix factorization of datasets are found studying high-definition images in order to obtain quantitative measures and probabilistic features." @default.
- W3215210411 created "2021-12-06" @default.
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- W3215210411 date "2021-10-14" @default.
- W3215210411 modified "2023-09-25" @default.
- W3215210411 title "Probabilistic Models of Information Management in Cyber-Physical Systems" @default.
- W3215210411 doi "https://doi.org/10.1109/itms52826.2021.9615274" @default.
- W3215210411 hasPublicationYear "2021" @default.
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