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This paper discusses the option of hardware-software integration, according to the assessment of human cognitive potential. The presented complex is based on an assessment of the integrative activity of the brain that allows one to get an idea of the current state of relations between the different areas of the brain structures (i.e., allows numerically estimate the number of structures involved in the activity). Evaluation of the integrative activity performed by a coherent analysis of the patient's EEG. The analysis is performed in real time using module coherent analysis developed in OpenVibe software.

In paper different standards for intelligent medical systems and multimodal architecture interfaces are under review. Application of Fast Healthcare Interoperability Resource (FHIR) standard for data model of intelligent multimodal interface are proposed. Intelligent multimodal interface for EEG and accelerometer bio-signals based on FHIR are shown. Use case diagrams, resource diagrams and class diagrams for production and research mode of this interface are described.

Accurate simulation of a power grid requires use of detailed power equipment models in order to reflect maximum of complex dynamics occurs in the grid. Conventional approaches are not always sufficient to fulfill necessity of meticulous description of processes in power devices. Existence of physical difference even between devices of exactly the same type pulls the accuracy of the whole grid simulation using one model for each type of equipment down. A completely new approach of power equipment modeling – modeling based on Complex-Valued Neural Networks (CVNN) – gives an opportunity to build a high-quality models which are able to track dynamics of grid devices. The nature of the approach makes it relatively easy to build models of all electric network devices even individually taking into account the uniqueness of each one. Power transformer, being quite common and, generally, complicated nonlinear element of power grid, has been chosen for demonstration of CVNN method. Results obtained from this work show that application of CVNN in power engineering modeling appears as quite promising method.