Luca Maggiani

530 total citations
25 papers, 316 citations indexed

About

Luca Maggiani is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Luca Maggiani has authored 25 papers receiving a total of 316 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Electrical and Electronic Engineering, 11 papers in Artificial Intelligence and 10 papers in Computer Networks and Communications. Recurrent topics in Luca Maggiani's work include Neural Networks and Reservoir Computing (7 papers), Optical Network Technologies (7 papers) and Photonic and Optical Devices (5 papers). Luca Maggiani is often cited by papers focused on Neural Networks and Reservoir Computing (7 papers), Optical Network Technologies (7 papers) and Photonic and Optical Devices (5 papers). Luca Maggiani collaborates with scholars based in Italy, France and United States. Luca Maggiani's co-authors include Matteo Petracca, Federico Civerchia, Claudio Salvadori, Stefano Bocchino, Luca Valcarenghi, Nicola Andriolli, François Berry, Lorenzo De Marinis, Jocelyn Sérot and Marco Cococcioni and has published in prestigious journals such as IEEE Access, Neural Networks and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

In The Last Decade

Luca Maggiani

20 papers receiving 298 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Luca Maggiani Italy 8 97 91 82 55 53 25 316
Claudio Salvadori Italy 8 120 1.2× 90 1.0× 80 1.0× 32 0.6× 60 1.1× 16 318
Stefano Bocchino Italy 7 126 1.3× 82 0.9× 84 1.0× 22 0.4× 56 1.1× 15 295
Federico Civerchia Italy 7 167 1.7× 104 1.1× 81 1.0× 41 0.7× 56 1.1× 12 341
Stamatis Voliotis Greece 5 65 0.7× 67 0.7× 109 1.3× 51 0.9× 81 1.5× 11 315
Rafał Cupek Poland 10 63 0.6× 54 0.6× 155 1.9× 38 0.7× 70 1.3× 47 382
Chunhai Gao China 12 56 0.6× 58 0.6× 137 1.7× 25 0.5× 60 1.1× 35 317
Adrian Korodi Romania 13 98 1.0× 54 0.6× 121 1.5× 36 0.7× 76 1.4× 43 336
Veronika Lesch Germany 8 68 0.7× 39 0.4× 49 0.6× 54 1.0× 85 1.6× 16 260
Yasmin Fathy United Kingdom 10 134 1.4× 74 0.8× 72 0.9× 80 1.5× 18 0.3× 20 371
Attila Frankó Hungary 8 135 1.4× 114 1.3× 127 1.5× 35 0.6× 38 0.7× 15 357

Countries citing papers authored by Luca Maggiani

Since Specialization
Citations

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

Fields of papers citing papers by Luca Maggiani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luca Maggiani

This figure shows the co-authorship network connecting the top 25 collaborators of Luca Maggiani. A scholar is included among the top collaborators of Luca Maggiani 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 Luca Maggiani. Luca Maggiani 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.
Valcarenghi, Luca, et al.. (2024). Enabling Lightweight Federated Learning in NextG Wireless Networks. CINECA IRIS Institutial research information system (University of Pisa). 304–306. 1 indexed citations
2.
Valcarenghi, Luca, et al.. (2024). Real-Time Network Packet Classification Exploiting Computer Vision Architectures. IEEE Open Journal of the Communications Society. 5. 1155–1166. 9 indexed citations
3.
Maggiani, Luca, et al.. (2024). Convolutional Neural Networks and Transformers-Based Techniques for Underwater Marine Debris Classification: A Comparative Study. IEEE Journal of Oceanic Engineering. 50(2). 594–607. 1 indexed citations
4.
Valcarenghi, Luca, et al.. (2024). Efficient Distributed Learning Over Lossy Wireless Networks. 1–7.
5.
6.
Marinis, Lorenzo De, et al.. (2024). Activation Stretching for Tackling Noise in Photonic Aware Neural Networks. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). Th2A.13–Th2A.13.
7.
Marinis, Lorenzo De, et al.. (2023). CHARLES: A C++ fixed-point library for Photonic-Aware Neural Networks. Neural Networks. 162. 531–540. 1 indexed citations
8.
Valcarenghi, Luca, et al.. (2023). Protecting NextG Military Networks with Convolutional Neural Networks. CINECA IRIS Institutial research information system (University of Pisa). 5. 209–213. 1 indexed citations
9.
Valcarenghi, Luca, et al.. (2023). Real-Time Clustering Based on Deep Embeddings for Threat Detection in 6G Networks. IEEE Access. 11. 115827–115835. 15 indexed citations
10.
Maggiani, Luca, et al.. (2023). Comparative analysis of machine learning algorithms for the classification of underwater marine debris. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 116–120. 2 indexed citations
11.
Civerchia, Federico, et al.. (2022). Photonic-aware Neural Networks for Packet Classification in Beyond 5G Networks. CINECA IRIS Institutial research information system (University of Pisa). 1–5. 1 indexed citations
12.
Marinis, Lorenzo De, et al.. (2022). Accelerating Pooling Layers in Photonic Convolutional Neural Networks. CINECA IRIS Institutial research information system (University of Pisa). 1–3. 1 indexed citations
13.
Marinis, Lorenzo De, et al.. (2022). Photonic-aware neural networks. Neural Computing and Applications. 34(18). 15589–15601. 24 indexed citations
14.
Marinis, Lorenzo De, et al.. (2022). Photonic-Aware Neural Network: a fixed-point emulation of photonic hardware. CINECA IRIS Institutial research information system (University of Pisa). 1–3. 1 indexed citations
15.
Lemaire, Jean‐Jacques, et al.. (2021). Risk-Taking Behaviors of Adult Bedridden Patients in Neurosurgery: What Could/Should We Do?. Frontiers in Medicine. 8. 676538–676538. 2 indexed citations
16.
Civerchia, Federico, Maxime Pelcat, Luca Maggiani, et al.. (2020). Is OpenCL Driven Reconfigurable Hardware Suitable for Virtualising 5G Infrastructure?. IEEE Transactions on Network and Service Management. 17(2). 849–863. 12 indexed citations
17.
Maggiani, Luca, et al.. (2017). Towards softwarization in the IoT: Integration and evaluation of t-res in the oneM2M architecture. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 1–5. 9 indexed citations
18.
Maggiani, Luca, et al.. (2016). Dataflow object detection system for FPGA‐based smart camera. IET Circuits Devices & Systems. 10(4). 280–291. 6 indexed citations
19.
Civerchia, Federico, et al.. (2016). Lightweight error correction technique in industrial IEEE802.15.4 networks. CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies). 6044–6048. 3 indexed citations
20.
Maggiani, Luca, et al.. (2016). Bio-inspired heterogeneous architecture for real-time pedestrian detection applications. Journal of Real-Time Image Processing. 14(3). 535–548. 16 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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