Daniel B. Rowe

441 citations
15 papers · 302 · h-index 8

Impact in

Papers in

Daniel B. Rowe

15 papers receiving 280 citations

Peers

Daniel B. Rowe
Comparison fields: 5 of 56
  • Renewable Energy, Sustainability and the Environment 56
  • Artificial Intelligence 89
  • Signal Processing 25
  • Computer Vision and Pattern Recognition 41
  • Cognitive Neuroscience 38
Replace Miguel Pinzolas with:
Miguel Pinzolas Spain
ShahRukh Athar United States
Jiawei Wang China
Jan Pidanič Czechia
Xuegui Zhu China
J.F. Frenzel United States
Domenico De Carlo Italy
Runze Hu China
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Citations per field
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Citations per year

Countries citing papers authored by Daniel B. Rowe

Since Specialization
Citations

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

Fields of papers citing papers by Daniel B. Rowe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel B. Rowe. 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 Daniel B. Rowe. The network helps show where Daniel B. Rowe may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel B. Rowe, 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 Daniel B. Rowe Line = papers co-authored together Daniel B. Rowe links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 201484
2 200541
3 202138
4 201530
5 201729
6 200223
7 200321
8 200817
9 20046
10 20024
11 20023
12 20223
13
MSX1 and high cognitive ability
20021
14 20101
15 20141

About Daniel B. Rowe

Daniel B. Rowe is a scholar working on Electrical and Electronic Engineering, Building and Construction, Computer Vision and Pattern Recognition, Cognitive Neuroscience and Artificial Intelligence, having authored 15 papers that have together received 302 indexed citations. Recurring topics across this work include Radio Frequency Integrated Circuit Design (3 papers), Building Energy and Comfort Optimization (3 papers), Statistical Methods and Bayesian Inference (2 papers), Advancements in PLL and VCO Technologies (2 papers), Statistical Methods and Inference (2 papers), Advancements in Semiconductor Devices and Circuit Design (2 papers), Functional Brain Connectivity Studies (2 papers) and Adsorption and Cooling Systems (2 papers). The work is most often cited by research in Renewable Energy, Sustainability and the Environment (56 citations), Artificial Intelligence (89 citations), Signal Processing (25 citations), Computer Vision and Pattern Recognition (41 citations) and Cognitive Neuroscience (38 citations). Daniel B. Rowe has collaborated with scholars based in United States, Australia and Saudi Arabia. Frequent co-authors include Samuel R. West, Saad Sayeef, Adam Berry, Sarah Jane Hamilton, Andreas Hauptmann, Stephen D. White, Subbu Sethuvenkatraman, Bryan Palmintier, Tim Moore and John Ward. Their work appears in journals such as IEEE Transactions on Computational Imaging, Image and Vision Computing, NeuroImage, Solar Energy and Biometrics.

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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