F. Palmieri

3.7k citations
149 papers · 2.5k · h-index 25

Impact in

Papers in

F. Palmieri

133 papers receiving 2.3k citations

Peers

F. Palmieri
Comparison fields: 5 of 120
  • Signal Processing 300
  • Artificial Intelligence 711
  • Materials Chemistry 849
  • Computer Vision and Pattern Recognition 358
  • Electrical and Electronic Engineering 901
Replace Paul D. Groves with:
Paul D. Groves United Kingdom
Xiang Cui China
Lin Zhao China
Jürgen Becker Germany
He Wen China
Bo Yang China
Sang-Ho Oh South Korea
Raed A. Abd‐Alhameed United Kingdom
Zhi Chen China
Jun Fang China
F. Palmieri relative to Paul D. Groves United Kingdom Paul D. Groves's profile →
Citations per field
00.5×3.2×
Paul D. Groves · 1×
Citations per year

Countries citing papers authored by F. Palmieri

Since Specialization
Citations

This map shows the geographic impact of F. Palmieri'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 F. Palmieri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites F. Palmieri more than expected).

Fields of papers citing papers by F. Palmieri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by F. Palmieri. 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 F. Palmieri. The network helps show where F. Palmieri may publish in the future.

Co-authors

The 25 scholars most cited alongside F. Palmieri, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with F. Palmieri Line = papers co-authored together F. Palmieri links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 149 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004185
2 2001171
3 2006141
4 1993117
5 1992117
6 199497
7 198979
8 200376
9 200174
10 201572
11 200867
12 201863
13 202157
14 200356
15 199450
16 199246
17 199046
18 199642
19 200641
20 199940

About F. Palmieri

F. Palmieri is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Computer Networks and Communications, Signal Processing and Computer Vision and Pattern Recognition, having authored 149 papers that have together received 2.5k indexed citations. Recurring topics across this work include High voltage insulation and dielectric phenomena (24 papers), Neural Networks and Applications (21 papers), Blind Source Separation Techniques (16 papers), Wireless Communication Networks Research (14 papers), Electrostatic Discharge in Electronics (13 papers), Bayesian Modeling and Causal Inference (13 papers), Power Transformer Diagnostics and Insulation (13 papers) and Target Tracking and Data Fusion in Sensor Networks (12 papers). The work is most often cited by research in Signal Processing (300 citations), Artificial Intelligence (711 citations), Materials Chemistry (849 citations), Computer Vision and Pattern Recognition (358 citations) and Electrical and Electronic Engineering (901 citations). F. Palmieri has collaborated with scholars based in Italy, United States and France. Frequent co-authors include Gian Carlo Montanari, Giovanni Mazzanti, Xiaofeng Qi, Pierluigi Salvo Rossi, Charles Boncelet, G. Teyssèdre, Francesco Castaldo, M.D. Fox, Domenico Ciuonzo and G.C. Montanari. Their work appears in journals such as IEEE Transactions on Aerospace and Electronic Systems, IEEE Signal Processing Letters, IEEE Transactions on Signal Processing, Journal of the Optical Society of America A and Journal of Physics D Applied Physics.

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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