L. Yang

5.2k citations
89 papers · 1.1k indexed · h-index 17

L. Yang

78 papers receiving 1.1k citations

Peers

L. Yang
Comparison fields: 5 of 117
  • Human-Computer Interaction 91
  • Computer Vision and Pattern Recognition 324
  • Radiation 113
  • Media Technology 101
  • General Dentistry 16
Replace Klaus Engel with:
Klaus Engel Germany
Zbisław Tabor Poland
Κ. H. Höhne Germany
Takeyoshi Dohi Japan
Xiaodong Yang China
Peng He China
Hans Roehrig United States
Philip Edwards United Kingdom
Orçun Göksel Switzerland
Shu Liao Taiwan
L. Yang relative to Klaus Engel Germany Klaus Engel's profile →
Citations per field
00.5×4.2×
Klaus Engel · 1×
Citations per year

Countries citing papers authored by L. Yang

Since Specialization
Citations

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

Fields of papers citing papers by L. Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20251
2 20251
3 20250
4 202322
5 20230
6 20231
7 202315
8 202026
9 202014
10 201870
11
Detect-Focus-Track-Servo (DFTS): A vision-based workflow algorithm for robotic image-guided micromanipulation
20171
12 201511
13 201541
14 201424
15 201484
16 2014164
17 20136
18
Towards Precision Measurement of the Neutron Lifetime using Magnetically Trapped Neutrons
20062
19 200510
20 20055

About L. Yang

L. Yang is a scholar working on Media Technology, Biophysics, Biomedical Engineering, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 89 papers that have together received 1.1k indexed citations. Recurring topics across this work include Soft Robotics and Applications (22 papers), Atomic and Subatomic Physics Research (17 papers), Augmented Reality Applications (13 papers), Surgical Simulation and Training (13 papers), Quantum, superfluid, helium dynamics (12 papers), Robotics and Sensor-Based Localization (11 papers), Image Processing Techniques and Applications (10 papers) and Robot Manipulation and Learning (9 papers). The work is most often cited by research in Human-Computer Interaction (91 citations), Computer Vision and Pattern Recognition (324 citations), Radiation (113 citations), Media Technology (101 citations) and General Dentistry (16 citations). L. Yang has collaborated with scholars based in United States, China and Singapore. Frequent co-authors include Etsuko Kobayashi, Ichiro Sakuma, Junchen Wang, Hideyuki Suenaga, U-Xuan Tan, Hongen Liao, Kazuto Hoshi, Kamal Youcef‐Toumi, Chee‐Kong Chui and P.R. Huffman. Their work appears in journals such as Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, Sensors, IEEE Robotics and Automation Letters, IEEE Transactions on Automation Science and Engineering and Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms.

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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