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. Many approaches for recovering the missing signal parts have been developed, depending on the signal nature. Here, several approaches and their applications are presented for medical signals and images. The possibility to analyze results using different statistical parameters is provided, with the aim to choose the most suitable approach for a specific application. The instrument provides manifold possibilities such as fitting different parameters for the considered signal and testing the efficiency under different percentages of missing data. The reconstruction accuracy is measured by the mean square error (MSE) between original and reconstructed signal. Computational time is important from the aspect of power requirements, thus enabling the selection of a suitable algorithm. The instrument contains its own signal database, but there is also the possibility to load any external data for analysis.

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.

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. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time-frequency domain. Thirdly, compressive sensing based time-frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field.

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. Their combination is applied for video signals. Particularly, we focus on the 2-D case: space/spatial-frequency based image watermarking procedure that will be subsequently extended to video signal. A method that selects coefficients for watermarking by estimating the local frequency content is proposed. In order to provide watermark imperceptibility, the nonstationary filtering is used to model the watermark which corresponds to the host signal components. Furthermore, the watermark detection within the multidimensional time-frequency domain is proposed. The efficiency and robustness of the procedure in the presence of various attacks is proven experimentally.