Promising Research Challenges Research Articles

A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

The key objective of this paper is to explore the recent state-of-the-art artificial intelligence (AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented beamforming studies are thoroughly investigated in order to correctly comprehend and profitably interpret the AI contribution in the beamforming performance. Starting from a brief overview of beamforming, including adaptive beamforming algorithms and direction of arrival (DOA) estimation methods, our analysis probes further into the main machine learning (ML) classes, the basic neural network (NN) topologies, and the most efficient deep learning (DL) schemes. Subsequently, and based on the prior aspects, the paper explores several concepts regarding the optimal use of ML and NNs either as standalone beamforming and DOA estimation techniques or in combination with other implementations, such as ultrasound imaging, massive multiple-input multiple-output structures, and intelligent reflecting surfaces. Finally, particular attention is drawn on the realization of beamforming or DOA estimation setups via DL topologies. The survey closes with various important conclusions along with an interesting discussion on potential future aspects and promising research challenges.

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

The possibility of sensing and predicting the movements of crowds in modern cities is of fundamental importance for improving urban planning, urban mobility, urban safety, and tourism activities. However, it also introduces several challenges at the level of sensing technologies and data analysis. The objective of this survey is to overview: (i) the many potential application areas of crowd sensing and prediction; (ii) the technologies that can be exploited to sense crowd along with their potentials and limitations; (iii) the data analysis techniques that can be effectively used to forecast crowd distribution. Finally, the article tries to identify open and promising research challenges.

Business growth models: review of past 60 years

This study reviews growth configurations research published over the past 60 years. Numerous configuration models seek to clarify the management priorities in the early growth of companies. However, an extensive review is missing from the literature and the variability of attributes among the models leads to confusion. The study identifies 90 configuration studies, describes the common patterns and trends, and identifies well-covered areas and promising research challenges. This meta-analysis reveals increasing consensus on growth indicators, sharpening focus, increasing context and process specificity, and diversification. Future approaches are encouraged to provide context-specific empirical studies, and to open new viewpoints.