###### Abstract  
The sheer exposure to vast amounts of signals created in our modern society and an ever increasing need to make sense of such data calls for continuing advances in signal processing as an enabling technology for a huge number of applications, ranging from wireless communication and medicine, through to bioengineering, the economy and entertainment. Despite enormous diversity in physical and physiological signal generating mechanisms in such applications, the mathematical background at the core of the majority of signal detection, estimation, classification or fusion algorithms is common to most practical applications, both current and future. It is important to note that we consider the notion of signals in a general sense, to include not only signals processed and manipulated by computers or smart devices but also by means of biological systems such as our brains. Such biological systems process information from sensors in an intelligent way to perform even the most complex of tasks such as recognizing objects, sounds, and taste or smooth navigation in complex and cluttered environments. The goal of this journal is therefore to answer the need to communicate the large volume of exciting research activity in this area, by providing a new platform for academicians, scientists and engineers to share, promote, and discuss various new issues and developments in the areas concerned with signals, images and videos. The aim of the MDPI International Journal _SIGNALS_ is to promote and expedite the dissemination, not only of new research results, but also of tutorial and review-type articles focusing on current trends and the state-of-the-art on all subjects relevant to analysis and/or processing of signals in either discrete or continuous time and their applications, especially intelligent/smart signal/data processing. The signals of interest can include sound and audio, time-varying measurements and sensor data, biological data such as electroencephalogram (EEG), neuroimages, including functional magnetic resonance images (fMRI) and positron emission tomography (PET), control system signals, telecommunication transmission signals, and more abstract signals like multidimensional time series or tensors. There are a variety of scientific journals devoted to signal processing, and the _SIGNALS_ journal is developing its own profile with a special focus on intelligent signal processing, blind signal processing, blind source separation, and smart biomedical/bioengineering applications. In recent years, artificial intelligence (AI) and machine learning (ML) have become well-established research areas and attract a large number of researchers across the fields of science and engineering, and include deep learning, big data, IoT, and smart things. Recent dramatic improvements in artificial intelligence and machine learning have only reinforced the importance of signal and image processing research, especially in biomedical signal processing. Our goal in this Journal is therefore to enhance and promote these trends towards Intelligent Signal/Data Processing. The editorial policy of _SIGNALS_ is to publish high quality research papers which contain substantial mathematical contributions and also validations/evaluation of performance by extensive computer simulations. Mathematically and machine learning oriented papers on topics of exceptional interest to the signal processing community will also be considered. Priority will be given to contributions concerned with a discussion of the background of practical problems, establishment of appropriate models, determination of a solution, approximate or exact, analytical or numerical, and a discussion of the relevance of the results when applied to the real-life problems.