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Scope
The aim of Sensor Fusion and Machine Perception is to publish original research which addresses the scientific and engineering challenges of deriving meaningful information from: multiple sources of electronic inputs with differing modality, current knowledge and its interpretation, and trends and patterns learnt from historical observations; and converting the derived information into actionable intelligence to transform the behaviors of physical systems.
Information fusion, structured at data, feature or decision level, is important for synthesis of autonomous systems that are capable of robust, accurate, and enhanced perception of a physical process, than what would be possible from traditional single-source, single-sensor methods. We encourage submissions that advance machine intelligence beyond its current limitations via cross-sensory fusion to address complex societal problems including contextual interpretation of derived information, spatio-temporal situation awareness, and network learning.
Machine learning papers will also be considered provided they present advancements in the state-of-the-art methods for feature extraction, transfer learning, neural computing and classifier design, and/or their novel applications which lead to development of smart systems and societies. Perceptual user interfaces that add human understandable rendering of complex data sources and facilitate human-machine interactions are also of interest.
We welcome papers concerned with harnessing the potential of a data-rich world through advanced data-to-decision approaches with diverse applications including but not limited to:
- object classification
- target tracking
- distributed sensor networks
- surveillance and monitoring
- autonomous vehicles
- cyber-physical systems
- single/multi-sensor robot collaboration
- human-robot collaboration
- sensor-based path planning
- smart buildings and smart grids
- smart manufacturing systems
- smart networked infrastructures
- structural health monitoring
- fault diagnosis and prognosis
- geo-spatial analyses
- social networking, economy and humanities
- smart healthcare
Our ultimate goal is to accelerate and promote the progress in machine perception and cognition of sensed information.
Indexed in: Scopus, Google Scholar, DOAJ, CrossRef, dblp, Ulrich's Periodicals Directory, ESCI, Emerging Sources Citation Index, CLOCKSS
PMCID: NA
Sensor Fusion and Machine Perception welcomes submissions of the following article types: Brief Research Report, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review and Technology and Code.
All manuscripts must be submitted directly to the section Sensor Fusion and Machine Perception, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Sensor Fusion and Machine Perception will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Robotics and AI.
Frontiers Editorial Office
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Fax +41 (0)21 510 17 01
Frontiers Support
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support@frontiersin.org
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Contact Editorial Office
Avenue du Tribunal Fédéral 34
CH – 1005 Lausanne
Switzerland
Tel +41(0)21 510 17 40
Fax +41 (0)21 510 17 01
For all queries regarding manuscripts in Review and potential conflicts of interest, please contact roboticsandai.editorial.office@frontiersin.org
For queries regarding Research Topics, Editorial Board applications, and journal development, please contact roboticsandai@frontiersin.org
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Fax +41 (0)21 510 17 01
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