F. Sargeni

1.2k total citations
59 papers, 261 citations indexed

About

F. Sargeni is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, F. Sargeni has authored 59 papers receiving a total of 261 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Computer Networks and Communications, 32 papers in Electrical and Electronic Engineering and 29 papers in Artificial Intelligence. Recurrent topics in F. Sargeni's work include Neural Networks Stability and Synchronization (33 papers), Neural Networks and Applications (26 papers) and Advanced Memory and Neural Computing (24 papers). F. Sargeni is often cited by papers focused on Neural Networks Stability and Synchronization (33 papers), Neural Networks and Applications (26 papers) and Advanced Memory and Neural Computing (24 papers). F. Sargeni collaborates with scholars based in Italy, Switzerland and Germany. F. Sargeni's co-authors include Vincenzo Bonaiuto, Mário Sérgio Salerno, G.C. Cardarilli, Marco Bonifazi, G. Paoluzzi, Giovanni Miano, S. Quondam Antonio, Alessandro Salvini, C. Visone and Á. Salamon and has published in prestigious journals such as Electronics Letters, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment and Materials science forum.

In The Last Decade

F. Sargeni

47 papers receiving 252 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
F. Sargeni Italy 10 177 170 132 25 24 59 261
P. Christie United States 11 60 0.3× 387 2.3× 24 0.2× 47 1.9× 7 0.3× 39 458
Xiaojian Tian China 10 61 0.3× 154 0.9× 74 0.6× 27 1.1× 30 1.3× 43 302
Navid Anjum Aadit United States 10 56 0.3× 179 1.1× 221 1.7× 13 0.5× 13 0.5× 12 354
Vikram K. Narayana United States 10 135 0.8× 138 0.8× 78 0.6× 33 1.3× 3 0.1× 34 330
John A. Flanagan Finland 9 90 0.5× 157 0.9× 80 0.6× 36 1.4× 2 0.1× 23 297
Luc Le Magoarou France 11 37 0.2× 143 0.8× 108 0.8× 12 0.5× 29 1.2× 30 297
Andrea Grimaldi Italy 9 50 0.3× 156 0.9× 194 1.5× 11 0.4× 13 0.5× 13 303
Shinsaku Mori Japan 9 106 0.6× 150 0.9× 33 0.3× 26 1.0× 102 4.3× 70 299
Giovanni Cancellieri Italy 11 205 1.2× 337 2.0× 72 0.5× 12 0.5× 17 0.7× 80 443
K. Torki France 10 67 0.4× 133 0.8× 74 0.6× 30 1.2× 4 0.2× 40 270

Countries citing papers authored by F. Sargeni

Since Specialization
Citations

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

Fields of papers citing papers by F. Sargeni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of F. Sargeni

This figure shows the co-authorship network connecting the top 25 collaborators of F. Sargeni. A scholar is included among the top collaborators of F. Sargeni 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 F. Sargeni. F. Sargeni 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
1.
Licciardi, Silvia, Guido Ala, Elisa Francomano, et al.. (2024). Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons. Mathematics. 12(21). 3363–3363. 1 indexed citations
2.
Antonio, S. Quondam, Vincenzo Bonaiuto, F. Sargeni, & Alessandro Salvini. (2022). Neural Network Modeling of Arbitrary Hysteresis Processes: Application to GO Ferromagnetic Steel. Magnetochemistry. 8(2). 18–18. 4 indexed citations
3.
Antonio, S. Quondam, Francesco Riganti Fulginei, Gabriele Maria Lozito, et al.. (2022). Computing Frequency-Dependent Hysteresis Loops and Dynamic Energy Losses in Soft Magnetic Alloys via Artificial Neural Networks. Mathematics. 10(13). 2346–2346. 7 indexed citations
4.
Badoni, D., R. Gunnella, Á. Salamon, et al.. (2020). Design and test of silicon photonic Mach-Zehnder interferometers for data transmission applications. Cineca Institutional Research Information System (Tor Vergata University). 1–3. 3 indexed citations
5.
Bonaiuto, Vincenzo, et al.. (2016). Microretina: An “Open Source” Tool for Retinal Images Analysis. Materials science forum. 879. 244–249.
6.
Bifaretti, Stefano, et al.. (2016). A fuzzy control system for energy management in a domestic environment. 379–386. 1 indexed citations
7.
Ammendola, Roberto, M. Barbanera, M. Bizzarri, et al.. (2016). The Level-0 calorimetric trigger of the NA62 experiment. Journal of Instrumentation. 11(2). C02084–C02084.
8.
Badoni, D., M. Bizzarri, Vincenzo Bonaiuto, et al.. (2014). Use of FPGA embedded processors for fast cluster reconstruction in the NA62 liquid krypton electromagnetic calorimeter. Journal of Instrumentation. 9(1). C01010–C01010. 3 indexed citations
9.
Sargeni, F., et al.. (2014). The K+→π+νν¯ decay in the NA62 experiment at CERN. 1 indexed citations
10.
Sargeni, F., et al.. (2014). Prospects for K+→π+νν¯ at CERN in NA62.
11.
Bonaiuto, Vincenzo, A. Fucci, G. Paoluzzi, et al.. (2011). The NA62 Liquid Krypton Electromagnetic Calorimeter Level 0 Trigger. Cineca Institutional Research Information System (Tor Vergata University). a574. 901–903. 4 indexed citations
12.
Sargeni, F. & Vincenzo Bonaiuto. (2010). Multi-chip Integrate and Fire neural network architecture. Cineca Institutional Research Information System (Tor Vergata University). 18. 630–634. 1 indexed citations
13.
Sargeni, F. & Vincenzo Bonaiuto. (2009). Programmable non-linearity for STAR cellular neural networks. Cineca Institutional Research Information System (Tor Vergata University). 547–550. 1 indexed citations
14.
Sargeni, F. & Vincenzo Bonaiuto. (2009). An interconnection architecture for integrate and fire neuromorphic multi-chip networks. Cineca Institutional Research Information System (Tor Vergata University). 877–880. 4 indexed citations
15.
Cardarilli, G.C., R. Lojacono, Mário Sérgio Salerno, & F. Sargeni. (2002). VLSI implementation of a Cellular Neural Network with programmable control operator. 14. 1089–1092. 3 indexed citations
16.
Salerno, Mário Sérgio, F. Sargeni, & Vincenzo Bonaiuto. (2002). 9×9 DPCNN board: A multichip approach to CNN implementation. Cineca Institutional Research Information System (Tor Vergata University). 1. 513–516.
17.
Salerno, Mário Sérgio, F. Sargeni, & Vincenzo Bonaiuto. (2002). High performance interconnection architecture for large cellular neural networks. Cineca Institutional Research Information System (Tor Vergata University). 1. 195–198. 2 indexed citations
18.
Lojacono, R., Mário Sérgio Salerno, & F. Sargeni. (2002). A novel architecture of digitally programmable continuous-time OTA-C filters. Cineca Institutional Research Information System (Tor Vergata University). 2. 1103–1106. 1 indexed citations
19.
Sargeni, F. & Vincenzo Bonaiuto. (2001). CNN cell for computing disparity map. Electronics Letters. 37(11). 682–683. 1 indexed citations
20.
Salerno, Mário Sérgio, F. Sargeni, & Vincenzo Bonaiuto. (1999). A Dedicated Multi-Chip Programmable System for Cellular Neural Networks. Analog Integrated Circuits and Signal Processing. 18(2-3). 277–288. 12 indexed citations

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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