Good news! Three (3) of our papers made it to the list of the most popular ones for April 2021 of the corresponding journals. In more details, our paper entitled “Machine learning in nano-scale biomedical engineering,” which was published in IEEE Transactions on Molecular, Biological and Multi-Scale Communications, is the second most popular paper for the month of April 2021. The abstract of this paper is the following:
Abstract: Machine learning (ML) empowers biomedical systems with the capability to optimize their performance through modeling the available data extremely well, without using strong assumptions about the modeled system. Especially in nano-scale biosystems, where the generated data sets are too vast and complex to mentally parse without computational assist, ML is instrumental in analyzing and extracting new insights, accelerating material and structure discoveries, and designing experience as well as supporting nano-scale communications and networks. However, despite these efforts, the use of ML in nano-scale biomedical engineering remains still under-explored in certain areas and research challenges are still open in fields such as structure and material design and simulations, communications and signal processing, and bio-medicine applications. In this article, we review the existing research regarding the use of ML in nano-scale biomedical engineering. In more detail, we first identify and discuss the main challenges that can be formulated as ML problems. These challenges are classified into three main categories: structure and material design and simulation, communications and signal processing, and biomedicine applications. Next, we discuss the state-of-the-art ML methodologies that are used to countermeasure the aforementioned challenges. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations. Finally, we conclude the article with insightful discussions, that reveal research gaps and highlight possible future research directions.
Moreover, our paper entitled “Coverage Analysis of Reconfigurable Intelligent Surface Assisted THz Wireless Systems,” which was published in IEEE Open Journal of Vehicular Technology, received more than 120 reads in April 2021. Its abstract is the following:
Abstract: This paper presents a connectivity analysis of reconfigurable intelligent surface (RIS) assisted terahertz (THz) wireless systems. Specifically, a system model that accommodates the particularities of THz band links, as well as the characteristics of the RIS, is reported, accompanied by a novel general end-to-end (e2e) channel attenuation formula. Based on this formula, we derive a closed-form expression that returns the optimal phase shifting of each reflection unit (RU) of the RIS. Moreover, we provide a tractable e2e channel coefficient approximation that is suitable for analyzing the RIS-assisted THz wireless system performance. Building upon the aforementioned approximation as well as the assumption that the user equipments are located in random positions within a circular cluster, we present the theoretical framework that quantifies the coverage performance of the system under investigation. In more detail, we deliver a novel closed-form expression for the coverage probability that reveals that there exists a minimum transmission power that guarantees 100% coverage probability. Both the derived channel model as well as the coverage probability are validated through extensive simulations and reveal the importance of taking into account both the THz channel particularities and the RIS characteristics, when assessing the system’s performance and designing RIS-assisted THz wireless systems.
Finally, our work entitled “Reconfigurable Intelligent Surface Optimal Placement in Millimeter-Wave Networks,” which was published in the IEEE Open Journal of the Communications Society, received more than 100 reads in April 2021. Its abstract is the following:
Abstract: This work discusses the optimal reconfigurable intelligent surface placement in highly directional millimeter-wave links. In particular, we present a novel system model that takes into account the relationship between the transmission beam footprint at the RIS plane and the RIS size. Subsequently, based on the model we derive the end-to-end expression of the received signal power and, furthermore, provide approximate closed-form expressions in the case that the RIS size is either much smaller or at least equal to the transmission beam footprint. Moreover, building upon the expressions, we derive the optimal RIS placement that maximizes the end-to-end signal-to-noise ratio. Finally, we substantiate the analytical findings by means of simulations, which reveal important trends regarding the optimal RIS placement according to the system parameters.
You can read the aforementioned contributions in #IEEEXplore in the following links:
1. Machine Learning in Nano-Scale Biomedical Engineering: https://ieeexplore.ieee.org/document/9247172
2. Coverage Analysis of Reconfigurable Intelligent Surface Assisted THz Wireless Systems: https://ieeexplore.ieee.org/document/9320587
3. Reconfigurable Intelligent Surface Optimal Placement in Millimeter-Wave Networkshttps://ieeexplore.ieee.org/document/9386246
Meanwhile, according to #ResearchGate, our early-submitted preprint entitled “Autonomous Reconfigurable Intelligent Surfaces Through Wireless Energy Harvesting” has attracted considerable attention, since it has more than 110 reads with 4 or 5 days and 2 recommendations. If you are interested in this contribution, you can read its full version here.
I would like to take this opportunity to thank all my co-authors for the great collaboration and discussions that we have! 🙂