T.J. Moir

880 citations
90 papers · 626 indexed · h-index 14

T.J. Moir

83 papers receiving 588 citations

Peers

T.J. Moir
Comparison fields: 5 of 84
  • Signal Processing 325
  • Developmental Biology 30
  • Computational Mechanics 167
  • Control and Systems Engineering 123
  • Artificial Intelligence 166
Replace T.V. Sreenivas with:
T.V. Sreenivas India
Yuma Koizumi Japan
José A. Apolinário Brazil
Ben Milner United Kingdom
Makoto Kumon Japan
M.R. Portnoff United States
Shuo-Yiin Chang United States
Athanasios Mouchtaris Greece
Marco Crocco Italy
T.J. Moir relative to T.V. Sreenivas India T.V. Sreenivas's profile →
Citations per field
00.5×
T.V. Sreenivas · 1×
Citations per year

Countries citing papers authored by T.J. Moir

Since Specialization
Citations

This map shows the geographic impact of T.J. Moir'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 T.J. Moir with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T.J. Moir more than expected).

Fields of papers citing papers by T.J. Moir

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by T.J. Moir. 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 T.J. Moir. The network helps show where T.J. Moir may publish in the future.

Co-authorship network

The 13 scholars most cited alongside T.J. Moir, 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 T.J. Moir Line = papers co-authored together T.J. Moir links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20211
2 20214
3 201848
4 20171
5 201521
6 201410
7 20140
8 20130
9 20124
10
Improved Language Modeling for English-Persian Statistical Machine Translation
20102
11 20093
12 20082
13
Tests on a real-time acoustic beamformer as a virtual instrument
20061
14 19935
15 19917
16 19911
17 19891
18
Real-time implementation of a self-tuning filter and smoother using a TMS32010 microprocessor
19874
19 198627
20 198448

About T.J. Moir

T.J. Moir is a scholar working on Signal Processing, Computational Mechanics, Control and Systems Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 90 papers that have together received 626 indexed citations. Recurring topics across this work include Speech and Audio Processing (48 papers), Advanced Adaptive Filtering Techniques (44 papers), Blind Source Separation Techniques (27 papers), Music and Audio Processing (16 papers), Control Systems and Identification (11 papers), Acoustic Wave Phenomena Research (7 papers), Advancements in PLL and VCO Technologies (5 papers) and Target Tracking and Data Fusion in Sensor Networks (5 papers). The work is most often cited by research in Signal Processing (325 citations), Developmental Biology (30 citations), Computational Mechanics (167 citations), Control and Systems Engineering (123 citations) and Artificial Intelligence (166 citations). T.J. Moir has collaborated with scholars based in New Zealand, United Kingdom and United States. Frequent co-authors include Roneel V. Sharan, M.J. Grimble, J. F. Barrett, Guido Lemos de Souza Filho, Gwo-Jia Jong, Te‐Jen Su, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh, Jaehoon Jeong and Andrew J. Anderson. Their work appears in journals such as Electronics Letters, International Journal of Control, International Journal of Adaptive Control and Signal Processing, Neurocomputing and IEEE Transactions on Industrial Electronics.

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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