Ilaria De Munari

99 papers receiving 1.7k citations

Hit Papers

IoT Wearable Sensor and Deep Learning: An Integrated Appr...20192026202120232019100200300

Peers

Ilaria De Munari
Comparison fields: 5 of 126
  • Electrical and Electronic Engineering 809
  • Biomedical Engineering 405
  • Computer Vision and Pattern Recognition 396
  • Computer Networks and Communications 297
  • Electronic, Optical and Magnetic Materials 172
Replace Ran Liu with:
Ran Liu China
Wilhelm Stork Germany
Faisal Bashir Pakistan
Ching‐Hsing Luo Taiwan
Joo‐Ho Lee Japan
Diego P. Morales Spain
Jun Zhou China
Salina Abdul Samad Malaysia
Hanjun Jiang China
Jin-Hyung Kim South Korea
Ilaria De Munari relative to Ran Liu China Ran Liu's profile →
Citations per field
00.5×4.5×
Ran Liu · 1×
Citations per year

Countries citing papers authored by Ilaria De Munari

Since Specialization
Citations

This map shows the geographic impact of Ilaria De Munari's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ilaria De Munari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ilaria De Munari more than expected).

Fields of papers citing papers by Ilaria De Munari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ilaria De Munari. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ilaria De Munari. The network helps show where Ilaria De Munari may publish in the future.

Co-authorship network of co-authors of Ilaria De Munari

This figure shows the co-authorship network connecting the top 25 collaborators of Ilaria De Munari. A scholar is included among the top collaborators of Ilaria De Munari based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ilaria De Munari. Ilaria De Munari is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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MuSA: A multisensor wearable device for AAL
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Home smart home: brain-computer interface control for real smart home environments
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A Biased Resistor Network Model for Electromigration Phenomena in Metallic Lines
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About Ilaria De Munari

Ilaria De Munari is a scholar working on Electronic, Optical and Magnetic Materials, Electrical and Electronic Engineering and Hardware and Architecture, having authored 106 papers that have together received 1.8k indexed citations. Recurring topics across this work include Copper Interconnects and Reliability (26 papers), Electronic Packaging and Soldering Technologies (19 papers) and Context-Aware Activity Recognition Systems (16 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (396 citations), Electrical and Electronic Engineering (809 citations) and Computer Networks and Communications (297 citations). Ilaria De Munari has collaborated with scholars based in Italy, Greece and Belgium. Frequent co-authors include Valentina Bianchi, Paolo Ciampolini, Monica Mordonini, Marco Bassoli, A. Scorzoni, F. Fantini, Gianfranco Lombardo, Paolo Fornacciari, Guido Matrella and Stefano Cagnoni. Their work appears in journals such as Journal of Applied Physics, Physical Review B and Carbohydrate Polymers.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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