Published by: American Institute of Aeronautics and Astronautics
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mechanical and Aerospace Engineering | 302 | 15 | 36 | 8 |
| Engineering and Technology | 1023 | 15 | 33 | 7 |
The foci of Journal of Aerospace Information Systems are Real-time computing, Artificial intelligence, Systems engineering, Simulation and Control theory. The research on Real-time computing featured in Journal of Aerospace Information Systems combines topics in other fields like Automatic dependent surveillance-broadcast and Global Positioning System. Markov decision process and Computer vision are some topics wherein Artificial intelligence research discussed in Journal of Aerospace Information Systems have an impact.
Computer vision research is the primary subject tackled in it with a focus on Inertial measurement unit. Systems engineering study tackled is connected to the field of Aerospace. Journal of Aerospace Information Systems focuses on Simulation as well as the interrelated topic of Trajectory.
Systems engineering, Simulation, Anomaly detection, Data mining and Computer security are the main subjects of interest in the published articles. While Simulation is the focus of the most cited papers, it also provides insights into the studies of Fault injection, Cruise and Trajectory, Trajectory optimization. While work presented in the journal publications provide substantial information on Computer security, it also covers topics in Inertial navigation system, Model checking, Inertial measurement unit, Global Positioning System and Cyber-physical system.
Journal of Aerospace Information Systems facilitates discussions on Artificial intelligence, Real-time computing, Reinforcement learning, Convolutional neural network and Aerospace engineering. The overlapping concepts between Computer vision and Stochastic gradient descent are the key highlights of Artificial intelligence study. In the journal, Model predictive control, Artificial neural network, Tracking (particle physics), Real-time operating system and Telemetry are investigated in conjunction with one another to address concerns in Real-time computing research.
The study of Tracking (particle physics) encompasses disciplines such as Extended Kalman filter, as well as fields such as Inertial measurement unit, all of which overlap with one another. The concepts on Reinforcement learning presented in the journal can also apply to other research fields, including Control engineering, Particle swarm optimization, Markov decision process and Motion planning. Research on Flight simulator presented in it concerns the broader topic of Simulation.
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 Journal of Aerospace Information Systems (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 Journal of Aerospace Information Systems (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, 7.06% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 39.24% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.13% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.72% of all publications and 32.91% 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.
Charles A. Mader;Gaetan K. W. Kenway;Anil Yildirim;Joaquim R. R. A. Martins
(2020)Patrick W. Kenneally;Scott Piggott;Hanspeter Schaub
(2020)Meir Pachter;Alexander Von Moll;Eloy Garcia;David Casbeer
(2020)Tejas G. Puranik;Dimitri N. Mavris
(2020)Jueming Hu;Heinz Erzberger;Kai Goebel;Kai Goebel;Yongming Liu
(2020)Junghyun Kim;Cedric Justin;Dimitri Mavris;Simon Briceno
(2021)Adam P. Herrmann;Hanspeter Schaub
(2021)Beyond studying Computer Science in the USA, numerous online degree options open doors to complementary and interdisciplinary career paths. For students interested in engineering principles, exploring a mechanical engineering degree online cost can reveal affordable programs blending practical and theoretical knowledge essential for innovation.
If your interests lean towards the fundamental sciences, online physics degrees offer rigorous curricula that deepen understanding of the natural world while enhancing analytical skills highly applicable to tech fields.
Alternatively, for those fascinated by big data and analytics, finding the cheapest data science degree can provide a cost-effective entry into a rapidly growing field that overlaps extensively with computer science.
Lastly, aspiring engineers focused on electrical systems should consider accredited online electrical engineering programs. These ensure quality education and open pathways to careers in multiple tech-driven industries.
Exploring these related online degrees allows students to tailor their education to evolving interests and market demands, strengthening future career prospects across technology and science sectors.