Vladimir Dyo

880 total citations
31 papers, 519 citations indexed

About

Vladimir Dyo is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Vladimir Dyo has authored 31 papers receiving a total of 519 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Electrical and Electronic Engineering, 8 papers in Computer Networks and Communications and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Vladimir Dyo's work include Indoor and Outdoor Localization Technologies (6 papers), Energy Efficient Wireless Sensor Networks (5 papers) and Energy Harvesting in Wireless Networks (4 papers). Vladimir Dyo is often cited by papers focused on Indoor and Outdoor Localization Technologies (6 papers), Energy Efficient Wireless Sensor Networks (5 papers) and Energy Harvesting in Wireless Networks (4 papers). Vladimir Dyo collaborates with scholars based in United Kingdom, Australia and Italy. Vladimir Dyo's co-authors include Vladan Velisavljević, Cecilia Mascolo, Bence Pásztor, Stephen A. Ellwood, Andrew Markham, David W. Macdonald, Niki Trigoni, Salvatore Scellato, Ben Allen and Tahmina Ajmal and has published in prestigious journals such as IEEE Access, Sensors and Methods in Ecology and Evolution.

In The Last Decade

Vladimir Dyo

30 papers receiving 496 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vladimir Dyo United Kingdom 13 231 181 78 72 67 31 519
Noelia Hernández Spain 16 224 1.0× 64 0.4× 61 0.8× 26 0.4× 20 0.3× 47 576
Timo Sukuvaara Finland 11 266 1.2× 179 1.0× 30 0.4× 19 0.3× 61 0.9× 44 567
Vaibhav Verma India 12 339 1.5× 24 0.1× 44 0.6× 36 0.5× 124 1.9× 47 682
Mohamad Hazwan Mohd Ghazali Malaysia 8 84 0.4× 36 0.2× 54 0.7× 86 1.2× 105 1.6× 21 346
Shinichiro Okazaki Japan 14 250 1.1× 76 0.4× 41 0.5× 30 0.4× 17 0.3× 67 625
Kamarulzaman Kamarudin Malaysia 12 259 1.1× 68 0.4× 121 1.6× 27 0.4× 45 0.7× 66 550
Tommaso Polonelli Switzerland 16 419 1.8× 231 1.3× 161 2.1× 63 0.9× 50 0.7× 52 746
Stanley Baek United States 11 120 0.5× 42 0.2× 251 3.2× 43 0.6× 80 1.2× 23 490
Salviano Soares Portugal 12 174 0.8× 108 0.6× 12 0.2× 86 1.2× 30 0.4× 74 717
Yunlu Wang China 16 1.6k 6.9× 172 1.0× 157 2.0× 47 0.7× 116 1.7× 39 1.9k

Countries citing papers authored by Vladimir Dyo

Since Specialization
Citations

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

Fields of papers citing papers by Vladimir Dyo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vladimir Dyo

This figure shows the co-authorship network connecting the top 25 collaborators of Vladimir Dyo. A scholar is included among the top collaborators of Vladimir Dyo 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 Vladimir Dyo. Vladimir Dyo 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.
Velisavljević, Vladan, et al.. (2024). Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning. Sensors. 24(8). 2442–2442. 9 indexed citations
2.
Dyo, Vladimir, et al.. (2024). Revolutionizing Higher Education: Unleashing the Potential of Large Language Models for Strategic Transformation. IEEE Access. 12. 67738–67757. 16 indexed citations
3.
Haxha, Shyqyri, et al.. (2024). Mobile Calibration for Bus-Based Urban Sensing. IEEE Sensors Journal. 25(3). 5576–5583.
4.
Haxha, Shyqyri, et al.. (2024). Effect of Skin Pigmentation and Finger Choice on Accuracy of Oxygen Saturation Measurement in an IoT-Based Pulse Oximeter. Sensors. 24(11). 3301–3301. 6 indexed citations
5.
Dyo, Vladimir, et al.. (2022). Review of Machine Learning Based Fault Detection for Centrifugal Pump Induction Motors. IEEE Access. 10. 71344–71355. 65 indexed citations
6.
Velisavljević, Vladan, et al.. (2022). Visual SLAM algorithms and their application for AR, mapping, localization and wayfinding. Array. 15. 100222–100222. 25 indexed citations
7.
Velisavljević, Vladan, et al.. (2022). Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal. Sensors. 22(19). 7553–7553. 14 indexed citations
8.
Dyo, Vladimir, et al.. (2021). Tracking Human Motion Direction With Commodity Wireless Networks. IEEE Sensors Journal. 21(20). 23344–23351. 3 indexed citations
9.
Dyo, Vladimir, et al.. (2020). Battery-assisted Electric Vehicle Charging: Data Driven Performance Analysis. arXiv (Cornell University). 429–433. 1 indexed citations
10.
Dyo, Vladimir, et al.. (2020). Counting calories without wearables: Device-free Human Energy Expenditure Estimation. University of Bedfordshire Repository (University of Bedfordshire). 1–6. 3 indexed citations
11.
Zhang, Sijing, et al.. (2018). Timely and Efficient Multihop Broadcast Scheme for Reliable Inter-Vehicular Communication. University of Bedfordshire Repository (University of Bedfordshire). 1–7. 1 indexed citations
12.
Dyo, Vladimir, et al.. (2018). On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance. Sensors. 18(11). 3597–3597. 36 indexed citations
13.
Ellwood, Stephen A., Chris Newman, Robert A. Montgomery, et al.. (2017). An active‐radio‐frequency‐identification system capable of identifying co‐locations and social‐structure: Validation with a wild free‐ranging animal. Methods in Ecology and Evolution. 8(12). 1822–1831. 23 indexed citations
14.
Velisavljević, Vladan, et al.. (2016). Wireless Magnetic Sensor Network for Road Traffic Monitoring and Vehicle Classification. Transport and Telecommunication Journal. 17(4). 274–288. 19 indexed citations
15.
Ajmal, Tahmina, et al.. (2014). Design and optimisation of compact RF energy harvesting device for smart applications. Electronics Letters. 50(2). 111–113. 19 indexed citations
16.
Allen, Ben, et al.. (2012). Harvesting energy from ambient radio signals: A load of hot air?. University of Bedfordshire Repository (University of Bedfordshire). 1–4. 9 indexed citations
17.
Dyo, Vladimir, Stephen A. Ellwood, David W. Macdonald, et al.. (2010). Evolution and sustainability of a wildlife monitoring sensor network. Oxford University Research Archive (ORA) (University of Oxford). 127–140. 107 indexed citations
18.
Dyo, Vladimir, Stephen A. Ellwood, David W. Macdonald, et al.. (2009). Wildlife and environmental monitoring using RFID and WSN technology. University of Bedfordshire Repository (University of Bedfordshire). 371–372. 12 indexed citations
19.
Dyo, Vladimir & Cecilia Mascolo. (2006). Adaptive Distributed Indexing for Spatial Queries in Sensor Networks. University of Bedfordshire Repository (University of Bedfordshire). 1103–1107. 6 indexed citations
20.
Dyo, Vladimir. (2005). Middleware design for integration of sensor network and mobile devices. University of Bedfordshire Repository (University of Bedfordshire). 1–5. 10 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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