Marco Lapegna

627 total citations
39 papers, 280 citations indexed

About

Marco Lapegna is a scholar working on Computer Networks and Communications, Hardware and Architecture and Information Systems. According to data from OpenAlex, Marco Lapegna has authored 39 papers receiving a total of 280 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Networks and Communications, 14 papers in Hardware and Architecture and 10 papers in Information Systems. Recurrent topics in Marco Lapegna's work include Parallel Computing and Optimization Techniques (12 papers), Distributed and Parallel Computing Systems (8 papers) and Cloud Computing and Resource Management (7 papers). Marco Lapegna is often cited by papers focused on Parallel Computing and Optimization Techniques (12 papers), Distributed and Parallel Computing Systems (8 papers) and Cloud Computing and Resource Management (7 papers). Marco Lapegna collaborates with scholars based in Italy, Denmark and Spain. Marco Lapegna's co-authors include Diego Romano, Giuliano Laccetti, Raffaele Montella, Walter Balzano, Sokol Kosta, Diana Di Luccio, Norbert Meyer, A. Murli, Giulio Giunta and Łukasz Szustak and has published in prestigious journals such as IEEE Access, Sensors and Future Generation Computer Systems.

In The Last Decade

Marco Lapegna

33 papers receiving 270 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco Lapegna Italy 10 122 68 54 37 34 39 280
Diego Romano Italy 9 53 0.4× 68 1.0× 16 0.3× 37 1.0× 50 1.5× 30 218
R. C. Steinke United States 10 213 1.7× 28 0.4× 56 1.0× 38 1.0× 15 0.4× 20 366
S. Rangarajan United States 14 336 2.8× 57 0.8× 77 1.4× 9 0.2× 21 0.6× 55 604
Haihang You China 11 180 1.5× 78 1.1× 167 3.1× 121 3.3× 129 3.8× 55 530
Jon M. Hjelmervik Norway 6 86 0.7× 38 0.6× 86 1.6× 41 1.1× 30 0.9× 13 251
Eduardo R. B. Marques Portugal 9 162 1.3× 21 0.3× 45 0.8× 84 2.3× 63 1.9× 31 438
Xinning Wang China 12 92 0.8× 44 0.6× 111 2.1× 25 0.7× 46 1.4× 33 317
Ahmed Sallam China 9 98 0.8× 62 0.9× 10 0.2× 17 0.5× 126 3.7× 17 277
Mallika De India 9 52 0.4× 19 0.3× 29 0.5× 62 1.7× 45 1.3× 32 259

Countries citing papers authored by Marco Lapegna

Since Specialization
Citations

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

Fields of papers citing papers by Marco Lapegna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Lapegna

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Lapegna. A scholar is included among the top collaborators of Marco Lapegna 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 Marco Lapegna. Marco Lapegna 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.
Romano, Diego, et al.. (2025). A deep learning-based method for efficient floating garbage debris recognition on high-performance edge computing platform. Future Generation Computer Systems. 174. 108000–108000.
2.
Acampora, Giovanni, et al.. (2025). Generalizing Reinforcement Learning-Based Quantum Circuit Synthesis across Multiple Topologies. 1–7. 1 indexed citations
3.
4.
Lapegna, Marco, Giuliano Laccetti, Raffaele Montella, & Diego Romano. (2024). Striking Trade-off Between High Performance and Energy Efficiency in an Edge Computing Application for Detecting Floating Plastic Debris. CINECA IRIS Institutial research information system (Parthenope University of Naples). 51–58. 2 indexed citations
5.
Lapegna, Marco, et al.. (2023). Unlocking the potential of edge computing for hyperspectral image classification: An efficient low-energy strategy. Future Generation Computer Systems. 147. 207–218. 4 indexed citations
6.
Coviello, Giuseppe, et al.. (2023). Citizen Science for the Sea with Information Technologies: An Open Platform for Gathering Marine Data and Marine Litter Detection from Leisure Boat Instruments. CINECA IRIS Institutial research information system (Parthenope University of Naples). 28. 1–9. 3 indexed citations
7.
Aldinucci, Marco, Elena Baralis, Valeria Cardellini, et al.. (2023). A Systematic Mapping Study of Italian Research on Workflows. CINECA IRIS Institutial research information system (University of Pisa). 2065–2076. 1 indexed citations
8.
Lapegna, Marco, et al.. (2023). Clustering Algorithms for Enhanced Trustworthiness on High-Performance Edge-Computing Devices. Electronics. 12(7). 1689–1689.
9.
Luccio, Diana Di, et al.. (2023). Parallel and hierarchically-distributed Shoreline Alert Model (SAM). CINECA IRIS Institutial research information system (Parthenope University of Naples). 109–113. 4 indexed citations
10.
Balzano, Walter, et al.. (2022). Competitive-blockchain-based parking system with fairness constraints. Soft Computing. 26(9). 4151–4162. 7 indexed citations
11.
Lapegna, Marco, et al.. (2022). Towards explainable AI for hyperspectral image classification in Edge Computing environments. Computers & Electrical Engineering. 103. 108381–108381. 22 indexed citations
12.
Laccetti, Giuliano, Marco Lapegna, & Raffaele Montella. (2022). Toward a high-performance clustering algorithm for securing edge computing environments. CINECA IRIS Institutial research information system (Parthenope University of Naples). 145. 820–825. 2 indexed citations
13.
Romano, Diego & Marco Lapegna. (2021). A GPU-Parallel Image Coregistration Algorithm for InSar Processing at the Edge. Sensors. 21(17). 5916–5916. 6 indexed citations
14.
Lapegna, Marco, Walter Balzano, Norbert Meyer, & Diego Romano. (2021). Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments. Sensors. 21(16). 5395–5395. 23 indexed citations
15.
Laccetti, Giuliano, et al.. (2020). Performance enhancement of a dynamic K-means algorithm through a parallel adaptive strategy on multicore CPUs. Journal of Parallel and Distributed Computing. 145. 34–41. 20 indexed citations
16.
Romano, Diego, et al.. (2020). Designing a GPU-parallel algorithm for raw SAR data compression: A focus on parallel performance estimation. Future Generation Computer Systems. 112. 695–708. 15 indexed citations
17.
Barone, Giovanni, et al.. (2016). An Approach to Forecast Queue Time in Adaptive Scheduling: How to Mediate System Efficiency and Users Satisfaction. International Journal of Parallel Programming. 45(5). 1164–1193. 10 indexed citations
18.
Montella, Raffaele, Giulio Giunta, Giuliano Laccetti, et al.. (2016). On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework. International Journal of Parallel Programming. 45(5). 1142–1163. 26 indexed citations
19.
D’Ambra, Pasqua, Marco Danelutto, Daniela di Serafino, & Marco Lapegna. (2003). Integrating MPI-based numerical software into an advanced parallel computing environment. CINECA IRIS Institutial research information system (University of Pisa). 10. 283–291. 4 indexed citations
20.
D’Ambra, Pasqua, Marco Danelutto, Daniela di Serafino, & Marco Lapegna. (2002). Advanced environments for parallel and distributed applications: a view of current status. Parallel Computing. 28(12). 1637–1662. 9 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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