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Probabilistic Models of Information Management in Cyber-Physical Systems

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.