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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Abou-Khalil, M., Schreurs, D., Matsui, T., & Wu, K. (1997, December). Calculation of the HF characteristics in high electron mobility transistors by considering capture and escape phenomena in the Monte Carlo technique [Paper]. Asia-Pacific Microwave Conference (APMC 1997), Hong Kong. External link
Barmuta, P., Ribeiro, D., Wang, K., Avolio, G., Rajabi, M., Lewandowski, A., Gibiino, G. P., Szatkowski, J., Schreurs, D., Hale, P., Remley, K., & Williams, D. (2016). Comparing LSNA Calibrations: Large-Signal Network Analyzer Round Robin. IEEE Microwave Magazine, 17(2), 59-64. External link
Mertens, M., Pasquali, M., & Schreurs, D. (2025, April). Experiment Setup Design for In-Vivo Microwave Spectroscopy of Galleria Mellonella Larvae [Paper]. IEEE MTT-S International Microwave Biomedical Conference (IMBioC 2025), Kaohsiung, Taiwan. External link
Martinic, M., Mertens, M., Schreurs, D., Nauwelaers, B., Vandenbosch, G. A. E., & Marković, T. (2025). Highly Sensitive Impedance-matched Microwave Dielectric Sensor for Glucose Concentration Measurements. IEEE Sensors Journal, 11 pages. External link
Mertens, M., Trouillon, R., Markovic, T., Wu, K., & Schreurs, D. (2025). Principal Component Regression for Broadband Microwave-Microfluidic Chemometrics on Small Sample Counts. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 1-9. External link
Mertens, M., Martinic, M., Marković, T., Trouillon, R., Wu, K., & Schreurs, D. (2024, June). Principal Component Regression for Small-Sample Microwave-Microfluidic Chemometrics Without De-Embedding [Paper]. 2024 IEEE MTT-S International Microwave Biomedical Conference (IMBioC 2024), Montreal, QC, Canada. External link
Mertens, M., Marković, T., Trouillon, R., Wu, K., & Schreurs, D. (2023, September). Design of an Interconnect Box for Dielectric Spectroscopy Using Disposable Samples [Paper]. IEEE MTT-S International Microwave Biomedical Conference (IMBioC 2023), Leuven, Belgium. External link
Mertens, M., Chavoshi, M., Peytral-Rieu, O., Grenier, K., & Schreurs, D. (2023). Dielectric Spectroscopy: Revealing the True Colors of Biological Matter. IEEE Microwave Magazine, 24(4), 49-62. External link
Mai, J.-Y., Tissir, H., Zhu, H.-T., Trouillon, R., Schreurs, D., & Wu, K. (2023, September). Free-Space THz Characterization of PC12 Cells [Paper]. IEEE MTT-S International Microwave Biomedical Conference (IMBioC 2023), Leuven, Belgium. External link
Mertens, M., Trouillon, R., Wu, K., Nauwelaers, B., Markovic, T., & Schreurs, D. (2023, September). Tapered CPW Transmission Line for Enlarging Dynamic Range of Transmission Spectroscopy [Paper]. 53rd European Microwave Conference (EuMC 2023), Berlin, Germany. External link
Palazzi, V., Correia, R., Gu, X., Hemour, S., Wu, K., Costanzo, A., Masotti, D., Fazzini, E., Georgiadis, A., Kazemi, H., Pereira, R., Shinohara, N., Schreurs, D., Chiao, J.-C., Takacs, A., Dragomirescu, D., & Carvalho, N. B. (2023). Radiative Wireless Power Transfer: Where We Are and Where We Want to Go. IEEE Microwave Magazine, 24(2), 57-79. External link
Seyyedmasoumian, S., Chavoshi, M., Mertens, M., & Schreurs, D. (2025). The Integration of Machine Learning in Microwave Dielectric Sensing: From Design to Postprocessing. IEEE Microwave Magazine, 26(3), 60-77. External link
