IEEE天线与传播汇刊宣布将于2021年11月推出人工智能特刊，聚焦实时逆散射与电磁成像新进展。该特刊旨在汇集人工智能，特别是深度学习技术在解决高复杂度电磁逆散射和成像问题上的最新研究成果。深度学习凭借其强大的数据处理和计算能力，已在诸多领域展现出巨大潜力，特别是在电磁学领域，它能够实现高精度、实时性的图像重建，为生物医学成像、文物检测、工业无损检测等应用带来突破。特刊鼓励研究人员提交相关理论与应用成果，共同推动该跨学科领域的发展。

✨ **AI在电磁成像领域崭露头角**：人工智能，尤其是深度学习（DL）技术，正成为解决复杂电磁逆散射（IS）和成像问题的强大工具，以前所未有的计算效率和高精度实现了实时估计，这在生物医学成像、艺术品及考古检测、工业无损检测、穿墙成像及地下成像等领域具有重要应用价值。

🚀 **深度学习的技术优势**：通过大数据技术、大规模并行计算和快速优化算法，深度学习技术极大地提升了在语音、图像、电力传输网络及生物电磁学等多个应用领域的性能。在天线与传播领域，DL能够让机器从大量物理数据中学习，并在特定边界条件下掌握物理规律，有望实现更高级、更鲁棒且计算效率更高的逆散射和电磁成像技术。

🗓️ **特刊信息与提交日期**：IEEE天线与传播汇刊将于2021年11月出版以“人工智能：实时逆散射与电磁成像的新前沿”为主题的特刊。特刊由Manuel ARREBOLA、Maokun LI和Marco SALUCCI担任客座编辑。论文提交截止日期为2021年3月31日，最终决定日期为2021年8月31日，出版日期为2021年11月30日。

💡 **多学科融合的未来展望**：文章展望了将基本物理原理与大数据“知识”相结合的混合方法，能够开启许多因数据信息和计算能力限制而无法实现的工程应用。这种跨学科的融合有望推动电磁成像技术在精度、鲁棒性和计算效率上实现飞跃，从而为科学研究和工程实践带来更多可能性。

The IEEE Transactions on Antennas and Propagation has just announced an upcoming Special issue to appear in November 2021 on Artificial Intelligence: New Frontiers in Real‐Time Inverse Scattering and Electromagnetic Imaging which will be guest edited by Manuel ARREBOLA, Maokun LI, Marco SALUCCI. Please find below additional information and important dates and visit the dedicated web page for submission procedures.

Guest Editors

Manuel ARREBOLA, Universidad de Oviedo, Spain (arrebola@uniovi.es)
Maokun LI, Tsinghua University (maokunli@tsinghua.edu.cn)
Marco SALUCCI, University of Trento (marco.salucci@unitn.it)

Outline

Understanding and solving complex problems in the physical world has been an intelligent endeavor of humankind. Moreover, the study of artificial intelligence embodies the dream of designing machines like humans. Research in deep learning (DL) techniques has attracted much attention in many application areas. With the help of big data technology, massive parallel computing, and fast optimization algorithms, DL has greatly improved the performance of many problems in the speech and image research, power transportation networks or bio‐electromagnetics, among others. Nowadays, DL is rapidly emerging in the antennas and propagation community as an extremely powerful paradigm for solving high‐complexity electromagnetic inverse scattering (IS) and imaging problems with unprecedented computational efficiency without reducing the accuracy and therefore reliability. As a matter of fact, DL is a promising solution to achieve accurate pixel‐wise reconstructions with real‐time estimation performance, a desirable feature in many applications such as biomedical imaging, works of art and archaeological inspection, industrial non destructive testing and evaluation, trough‐the‐wall imaging, and subsurface imaging. With the spreading of DL techniques, improvement in learning capacity may allow machines to “learn” from a large amount of physical data and “master” the physical laws in certain controlled boundary conditions. In the long run, a hybridization of fundamental physical principles with “knowledge” from big data could unleash numerous engineering applications that used to be impossible due to the limit of data information and ability of computation. As a result, more advanced IS and electromagnetic imaging techniques can be developed with improved accuracy, robustness, and computational efficiency. The objective of this Special Issue is to report recent advancements in theory and applications of artificial intelligence and DL to solve electromagnetic IS and imaging problems within the research scope of Antennas and Propagation with extremely fast but reliable techniques. With this Special Issue, we hope to bring more attention and research efforts in our society to this emerging multi‐disciplinary field, resulting in an evolution of the state of the art.

Important Dates

Submission: March 31, 2021Final decision: August 31, 2021Publication: November 30, 2021