PAPERS LISTED AS MOST POPULAR FOR January 2021!

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Good news! Four (4) of our paper made it to the list of the most popular ones for the month of January for the corresponding #IEEE #journals. In particular, the papers’ titles are the following:

1. “How Much do Hardware Imperfections Affect the Performance of Reconfigurable Intelligent Surface-Assisted Systems?”

2. “Coverage analysis of reconfigurable intelligent surface assisted THz wireless systems”

3. “All-Optical Cochlear Implants”

4. “Machine Learning in Nano-Scale Biomedical Engineering”

The first one was published in #IEEE #Open #Journal of the #Communications #Society and it has been included in this list from Aug. 2020 till Jan. 2021. This paper reveals the importance of taking into account the impact of #transceiver#hardware#imperfections when assessing the performance of reconfigurable intelligent surface (RIS) assisted wireless systems.

The second one is still in early access mode in #IEEE #Open#Journal of #Vehicular #Technology since it was accepted for publication in early Jan. 2021. This paper reports a new #pathloss model for RIS-assisted wireless THz networks and provides the theoretical framework to quantify their coverage capabilities.

The third one was published in #IEEE #Transactions on #Molecular, #Biological and #Multi-Scale #Communications in July 2020. This is a very interesting paper that introduces a novel #cochlear#implant (CI) architecture, namely all-optical CI (AOCI), which directly converts acoustic to optical signals capable of stimulating the cochlear neurons. In my opinion, the main advantage of this architecture compared to conventional ones is that neither battery nor processing units is required by the in-body device. As a result, it guarantees compactness, while relieving the need for wireless power transfer.

Last but not least, our fourth contribution was published in #IEEE#Transactions on #Molecular, #Biological and #Multi-Scale #Communications. Although it is still in early access mode, it is the third time that is included in this list. In this work, we review the existing research regarding the use of machine learning in nano-scale biomedical engineering and we provide a comprehensive tutorial concerning the principles, applications, and limitations of each one of them.

The interesting readers can find the aforementioned publications in #ieeexplore in the following links:

1. https://ieeexplore.ieee.org/document/9159653

2. https://ieeexplore.ieee.org/document/9320587

3. https://ieeexplore.ieee.org/document/9098913

4. https://ieeexplore.ieee.org/document/9247172

or in #ResearchGate

1. https://www.researchgate.net/…/343295287_How_Much_do…

2. https://www.researchgate.net/…/348364715_Coverage…

3. https://www.researchgate.net/…/341348537_All-Optical…

4. https://www.researchgate.net/…/344764320_Machine…

Seizing this opportunity, once again, I would like to thank all my co-authors for their contributions.

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