Tadanobu Inoue

588 total citations
9 papers, 325 citations indexed

About

Tadanobu Inoue is a scholar working on Signal Processing, Electrical and Electronic Engineering and Biomedical Engineering. According to data from OpenAlex, Tadanobu Inoue has authored 9 papers receiving a total of 325 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Signal Processing, 3 papers in Electrical and Electronic Engineering and 3 papers in Biomedical Engineering. Recurrent topics in Tadanobu Inoue's work include Advancements in Photolithography Techniques (3 papers), Speech and Audio Processing (3 papers) and Music and Audio Processing (3 papers). Tadanobu Inoue is often cited by papers focused on Advancements in Photolithography Techniques (3 papers), Speech and Audio Processing (3 papers) and Music and Audio Processing (3 papers). Tadanobu Inoue collaborates with scholars based in Japan, United States and France. Tadanobu Inoue's co-authors include Ryuki Tachibana, Asim Munawar, Giovanni De Magistris, Akashi Satoh, Kafai Lai, Jaione Tirapu-Azpiroz, Alan E. Rosenbluth, Masaharu Sakamoto, Alexander Tritchkov and David Melville and has published in prestigious journals such as Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena, Integration and Journal of the Robotics Society of Japan.

In The Last Decade

Tadanobu Inoue

8 papers receiving 312 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tadanobu Inoue Japan	8	157	107	97	73	57	9	325
Shivashankar B. Nair India	9	49 0.3×	125 1.2×	42 0.4×	29 0.4×	36 0.6×	49	398
Yingyi Sun China	9	88 0.6×	65 0.6×	59 0.6×	20 0.3×	22 0.4×	18	302
H. Işıl Bozma Türkiye	11	76 0.5×	56 0.5×	30 0.3×	69 0.9×	33 0.6×	55	410
Shih‐An Li Taiwan	11	120 0.8×	91 0.9×	18 0.2×	22 0.3×	54 0.9×	30	304
Gang Peng China	11	112 0.7×	49 0.5×	35 0.4×	32 0.4×	111 1.9×	72	385
Alberto Izaguirre Spain	12	67 0.4×	48 0.4×	26 0.3×	21 0.3×	41 0.7×	26	331
Michael Hofbaur Austria	11	200 1.3×	127 1.2×	43 0.4×	30 0.4×	24 0.4×	53	417
Chandranath Adak India	10	38 0.2×	211 2.0×	49 0.5×	114 1.6×	49 0.9×	36	452
F. Choong Malaysia	9	78 0.5×	42 0.4×	39 0.4×	17 0.2×	229 4.0×	29	329
Marco A. Contreras-Cruz Mexico	7	110 0.7×	95 0.9×	31 0.3×	33 0.5×	18 0.3×	13	330

Countries citing papers authored by Tadanobu Inoue

Since Specialization

Citations

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

Fields of papers citing papers by Tadanobu Inoue

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tadanobu Inoue

This figure shows the co-authorship network connecting the top 25 collaborators of Tadanobu Inoue. A scholar is included among the top collaborators of Tadanobu Inoue 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 Tadanobu Inoue. Tadanobu Inoue is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Inoue, Tadanobu, et al.. (2021). Data-Efficient Framework for Real-World Multiple Sound Source 2d Localization. 3425–3429. 11 indexed citations

2.

Inoue, Tadanobu, et al.. (2019). Learning Multiple Sound Source 2D Localization. 1–6. 8 indexed citations

3.

Inoue, Tadanobu, et al.. (2019). Shuffling and Mixing Data Augmentation for Environmental Sound Classification. Faculty Digital Archive (New York University Florence). 109–113. 13 indexed citations

4.

Inoue, Tadanobu, et al.. (2017). Deep reinforcement learning for high precision assembly tasks. 819–825. 203 indexed citations

5.

Tritchkov, Alexander, Jaione Tirapu-Azpiroz, Alan E. Rosenbluth, et al.. (2011). Applicability of global source mask optimization to 22/20nm node and beyond. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7973. 79730C–79730C. 7 indexed citations

6.

Melville, David, Alan E. Rosenbluth, Jaione Tirapu-Azpiroz, et al.. (2011). Computational lithography: Exhausting the resolution limits of 193-nm projection lithography systems. Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena. 29(6). 06FH04–06FH04. 14 indexed citations

7.

Rosenbluth, Alan E., David Melville, Saeed Bagheri, et al.. (2009). Intensive optimization of masks and sources for 22nm lithography. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7274. 727409–727409. 25 indexed citations

8.

Satoh, Akashi & Tadanobu Inoue. (2006). ASIC-hardware-focused comparison for hash functions MD5, RIPEMD-160, and SHS. Integration. 40(1). 3–10. 43 indexed citations

9.

Inoue, Tadanobu, et al.. (2002). . Journal of the Robotics Society of Japan. 20(8). 791–795. 1 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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