Multi-Modal Fusion: Present and Future
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (10 July 2022) | Viewed by 293
Special Issue Editors
Interests: estimation; signal processing; statistical signal and image analysis; machine learning
Interests: image & signal processing; computer vision; machine learning; biometrics
Special Issue Information
Dear Colleagues,
Sensor–information fusion, with its long and lasting interest, is re-emerging with critical importance across various disciplines and applications, largely due to the ubiquity of low-cost sensors and computational power commensurate with new fusion demands. The increasingly sophisticated applications invariably call on multi-modal sensing, thereby raising the theoretical, algorithmic, and computational challenges. Innovative and creative techniques have gradually become the norm. Therefore, this Special Issue not only aims to update the state of the art in multi-modal fusion and associated theories and methodologies but at also to pave the path forward for future research.
Machine Learning has, for example, shed some light on the science of analyzing and exploiting data, with new theoretical understanding incrementally emerging to further enrich principles relating to multi-modal fusion.
This Special Issue is expected to broadly address multi-modal fusion, including the heterogeneity of the data, the sensor deployment topology and site, and the science of analysis of the data and its exploitation. Innovation and creative solutions may hence lie at all levels.
Topics of interest in this Special Issue include (but are not limited to):
- Statistical foundations of multi-modal fusion;
- Theory of machine learning for multi-modal fusion;
- Novel architectures and methodologies for deep learning-based fusion;
- Guaranteed and reliable multi-modal fusion frameworks;
- Multi-modal sensor network design and optimization;
- Exploration of new data types that are typically overlooked in inference;
- Fusion of human data and data from physical sensors;
- Emerging applications of multi-modal fusion, including camera/radar/lidar data fusion for autonomous driving, data-efficient recommendation systems, human behavior understanding and human–computer interaction, and modern audiovisual learning platforms.
Prof. Dr. Hamid Krim
Dr. Benjamin Riggan
Dr. Mojtaba Soltanalian
Guest Editors
If you have any questions or need further information, please free to contact Special Issue Editor Larissa Zhang <[email protected]>.
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