Montenegrin Digital Academic Innovation Hub: Experience in Supporting Digital Health Innovations.

Montenegrin Digital Academic Innovation Hub established within Erasmus+ project DigNEST is essential institutional support for developing innovations in the field of health in academic-business cooperation and partnership. Experience of 18 months in running Hub service provides preliminary results in analysis received innovation ideas, provided support and potentials/capacities in medical informatics advancements at national, regional and global level.

Montenegrin Digital Academic Innovation Hub: Supporting Digital Education, Business and Innovations in Medical Informatics.

This poster presents a Montenegrin Digital Academic Innovation Hub aimed to support education, innovations, and academia-business cooperation in medical informatics (as one of four priority areas) at national level in Montenegro. The Hub topology and its organisation in the form of two main nodes, with services established within key pillars: Digital Education; Digital Business Support; Innovations and cooperation with industry; and Employment support.

Digital Entrepreneurial Nest: Supporting Digitization of Healthcare at National Level in Montenegro.

Health and health systems are not excluded from the influence of digitalization. In Montenegro, regarding the digitization process, when compared to other sectors, the health sector is lagging. In this poster presentation, we present an ambitious Erasmus+ DigN€ST project aimed on modernization of digitalization of healthcare system in Montenegro, as one of priority fields at national level.

Sparse Analyzer Tool for Biomedical Signals.

The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals' recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applicable for different types of biomedical signals and different applications with under-sampled data. Incomplete sampling/sensing can be considered as a sort of signal damage, where missing data can occur as a result of noise or the incomplete signal acquisition procedure.

A software companion for compressively sensed time-frequency processing of sparse nonstationary signals.

Compressive sensing is a computational framework for acquisition and processing of sparse signals at sampling rates below the rates mandated by the Nyquist sampling theorem. In this paper, we present seven MATLAB functions for compressive sensing based time-frequency processing of sparse nonstationary signals. These functions are developed to reproduce figures in our companion review paper.

Compressive sensing meets time-frequency: An overview of recent advances in time-frequency processing of sparse signals.

Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time-frequency representations. Hence, we overview recent advances dealing with time-frequency processing of sparse signals acquired using compressive sensing approaches.

An application of multidimensional time-frequency analysis as a base for the unified watermarking approach.

A watermarking approach based on multidimensional time-frequency analysis is proposed. It represents a unified concept that can be used for different types of data such as audio, speech signals, images or video. Time-frequency analysis is employed for speech signals, while space/spatial-frequency analysis is used for images.