Being an open access on-line journal, PIER gives great prominence to special issues that draw together significant and emerging works to promote key advances on specific topics. The special issues are devoted to timely, relevant, and cutting-edge research and aim to provide a unique platform for researchers interested in selected topics.We are now calling for papers for the following PIER Spe
The 43rd PIERS in Hangzhou, CHINA
21 - 25, November 2021
(from Sunday to Thursday)
--- Where microwave and lightwave communities meet
Hybrid PIERS: Onsite + Web Access
To organize a Special Session, please fill out this
PIERS Survey Form.
Join Us in this Harvest Season - Onsite + Web Access
Night West Lake - PIERS 2021 Hangzhou, CHINA
PIERS 2021, Hangzhou, CHINA
Late autumn - PIERS 2021 Hangzhou, CHINA
West Lake - Hangzhou, CHINA
PIER Journals are a family of journals supported by the PhotonIcs and Electromagnetics Research Symposium (PIERS), which has become a major symposium in the area related to photonics and electromagnetics. The scope includes all aspects of electromagnetic theory plus its wide-ranging applications. Hence, it includes topics motivated by mathematics, sciences as well as topics inspired by advanced technologies. The spectrum ranges from very low frequencies to ultra-violet frequencies. The length scale spans from nanometer length scale to kilometer length scale. The physics covers the classical regime as well as the quantum regime.
Despite recent advances, fast and reliable Human Activity Recognition in confined space is still an open problem related to many real-world applications, especially in health and biomedical monitoring. With the ubiquitous presence of Wi-Fi networks, the activity recognition and classification problems can be solved by leveraging some characteristics of the Channel State Information of the 802.11 standard. Given the well-documented advantages of Deep Learning algorithms in solving complex pattern recognition problems, many solutions in the Human Activity Recognition domain are taking advantage of those models. To improve the time and precision of activity classification of time-series data stemming from Channel State Information, we propose herein a fast deep neural model encompassing concepts not only from state-of-the-art recurrent neural networks, but also using convolutional operators with added randomization. Results from real data in an experimental environment show promising results.....