| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Electronics and Electrical Engineering | 112 | 196 | 287 | 28 |
IEEE Signal Processing Letters investigates areas of study like Algorithm, Artificial intelligence, Pattern recognition, Computer vision and Mathematical optimization. While the journal focused on Algorithm, it was also able to explore topics like Control theory, Speech recognition, Estimator and Signal, Signal processing. Control theory research discussed connects with the study of MIMO.
In IEEE Signal Processing Letters, Speech enhancement and Noise are investigated in conjunction with one another to address concerns in Speech recognition research. Studies on Artificial intelligence discussed in it link to the field of Machine learning. The Pattern recognition study tackled is a key component of adjacent topics in the area of Robustness (computer science).
The study on Mathematical optimization presented is investigated in conjunction with research in Applied mathematics.
The most cited publications mainly deal with areas of study such as Artificial intelligence, Algorithm, Pattern recognition, Computer vision and Mathematical optimization. The most cited articles deal with Artificial intelligence in conjunction with Speech recognition and similar fields in Noise and Speech enhancement. The published papers tackle studies in Control theory and the interrelated subject of MIMO to gain insights into Algorithm.
IEEE Signal Processing Letters mainly tackles studies in Artificial intelligence, Algorithm, Feature extraction, Pattern recognition and Computer vision. The presentations discussing Artificial intelligence offer insights in topics such as Deep learning, Feature (computer vision), Convolutional neural network, Artificial neural network and Visualization. Issues in Algorithm were discussed, taking into consideration concepts from other disciplines like Noise measurement, Signal, Filter (signal processing) and Estimator.
The work on Feature extraction tackled in it brings together disciplines like Segmentation, Task analysis, Focus (optics), Semantics and Convolution. The journal investigates Convolution research which frequently intersects with Kernel (image processing). Some problems in Pattern recognition that were presented in IEEE Signal Processing Letters overlapped with concepts under Image (mathematics), Graph (abstract data type) and Benchmark (computing).
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in IEEE Signal Processing Letters (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in IEEE Signal Processing Letters (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 9.13% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 19.35% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.80% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.83% of all publications and 53.02% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Jun Qi;Jun Du;Sabato Marco Siniscalchi;Xiaoli Ma
(2020)Peilan Wang;Jun Fang;Huiping Duan;Hongbin Li
(2020)Hui-Ming Wang;Jiale Bai;Limeng Dong
(2020)Khaled Ardah;Sepideh Gherekhloo;Andre L. F. de Almeida;Martin Haardt
(2021)Zhi Zheng;Yixiao Huang;Wen-Qin Wang;Hing Cheung So
(2020)Huy Phan;Ian V. McLoughlin;Lam Pham;Oliver Y. Chen
(2020)Unknown
(2022)Hang Zheng;Zhiguo Shi;Chengwei Zhou;Martin Haardt
(2021)Stefano Buzzi;Emanuele Grossi;Marco Lops;Luca Venturino
(2021)Armed Tusha;Seda Dogan;Huseyin Arslan
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