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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.
Manzanilla Salazar, O. G., Mellah, H., & Sanso, B. (2023). Recognizing Emergencies and Multi-User Behavior Patterns Using Imperfect Data From Distributed Access Points. A Non-Intrusive Proof of Concept. IEEE Access, 11, 91234-91246. Available
Firouzabadi, A. D., Mellah, H., Manzanilla Salazar, O. G., Khalvandi Ilezoole, R., Therrien, V., Boutin, V., & Sanso, B. (2023). PIoT: A Performance IoT Simulation System for a Large-Scale City-Wide Assessment. IEEE Access, 11, 56273-56286. External link
Manzanilla Salazar, O. G., Mellah, H., Malandra, F., & Sanso, B. (2021, September). An algorithm for threading assignment in large-scale wireless network mobile simulations [Paper]. 25th IEEE/ACM International Symposium on Distributed Simulation and Real Time Application (DS-RT 2021), Valencia, Spain (10 pages). External link
Manzanilla Salazar, O. G., Boutin, V., Mellah, H., Wetté, C., & Sanso, B. (2021). Framework for Vertical Performance Assessment in Very Large-Scale Cellular Networks. IEEE Internet of Things Journal, 9(14), 12272-12284. External link
Manzanilla Salazar, O. G., Malandra, F., Mellah, H., Wetté, C., & Sanso, B. (2020). A machine learning framework for sleeping cell detection in a smart-city IoT telecommunications infrastructure. IEEE Access, 8, 61213-61225. Available
Manzanilla Salazar, O. G., Malandra, F., Mellah, H., Wetté, C., & Sanso, B. (2020). A machine learning framework for sleeping cell detection in a smart-city IoT telecommunications infrastructure. (Technical Report n° G-2019-70). External link
Manzanilla Salazar, O. G., Malandra, F., & Sanso, B. (2019, March). eNodeB Failure Detection from Aggregated Performance KPIs in Smart-city LTE Infrastructures [Paper]. 15th International Conference on the Design of Reliable Communication Networks (DRCN 2019), Coimbra, Portugal. External link
Manzanilla Salazar, O. G., Malandra, F., & Sanso, B. (2018). eNodeB failure detection from aggregated performance KPIs in smart-city LTE infrastructures. (Technical Report n° G-2018-79). External link
