The IEEE CRFID Training Course on Additively Manufactured Electromagnetic Devices for IoT, organized by the IEEE CRFID Technical Committee on Additively Manufactured Electronic Systems (TC-AMES), provides insights into the advancements and potential applications of 3D printing in the field of electromagnetics. The course brings together renowned experts in the field to discuss various aspects of additive manufacturing technology for IoT devices. The training course offers a comprehensive overview of the possibilities and challenges associated with additively manufactured electromagnetic devices for IoT. Participants will gain valuable insights from leading experts in the field and acquire a deeper understanding of the potential applications and advancements in this emerging area of research and technology.

The workshop aims to provide participants with an introduction to the exciting field of deep learning, which forms the basis of modern artificial intelligence systems. Through hands-on exercises and interactive sessions, attendees will gain practical knowledge and skills to apply deep learning techniques in their projects and research. The workshop will cover fundamental concepts while also exploring advanced topics such as convolutional neural networks (CNNs). Participants will have the opportunity to work with a popular deep learning framework Pytorch, and they will learn how to leverage cloud-based platforms, such as Google Colab, for efficient model development and training. By the end of the workshop, attendees will be equipped with the necessary tools and knowledge for further independent study and research in the field.

The proposed IoT learning system is being recognized as a through-hole module integration board, aimed to enable learners with practical HW assembly experience before firmware development. Therefore, learners familiarize themselves with each component, its purpose, and functionality, that is, in a logical sequence, they build a system that will finally be unified by firmware development. The main functionalities learners can develop during the practical training are as follows:
- To measure atmospheric pressure, dry bulb temperature, and relative humidity,
- To measure conduction-based body temperature,
- To measure heart rate and the saturation of oxygen in the blood,
- To initiate/terminate measurement via a touch sensor,
- To establish RGB LED indication signaling,
- To correlate measurements and time units,
- To establish a data acquisition and storage system of time series data locally,
- To print the desired data on display,
- To transfer data via Wi-Fi network, and
- To link the developed system with the Blynk platform.
Having this in mind, learners could develop an application of IoT in medical science and related practices and provide monitoring and diagnostics related to the abovementioned parameters.