Electromagnetic (EM) structures play a significant role in wireless communication, radar detection, medical imaging, and so on. Machine learning (ML) has been increasingly applied to facilitate the design and analysis of EM structures. Data acquisition is a major bottleneck. Conventional methods blindly sweep geometric parameters on a uniform grid and acquire corresponding responses via simulation. Acquired data have unstable quality due to inconsistent informativeness of responses, leading to a low ratio of model performance to data amount. This article proposes a high-quality data acquisition method to increase the ratio of model performance to data amount. It anticipates and generates high-quality data by analyzing the distribution of existing data iteratively. Comparative analysis of four implementations proves that the proposed method reduces the required data amount by around 40% for the same model performance and hence saves around 40% simulation and computing resources. The proposed method benefits ML applications of metasurfaces, antennas, and many other microwave structures.

Continuous ergonomic risk assessment of the human body is critical to avoid various musculoskeletal disorders (MSDs) for people involved in physical jobs. This paper presents a digital upper limb assessment (DULA) system that automatically performs rapid upper limb assessment (RULA) in real-time for the timely intervention and prevention of MSDs. While existing approaches require human resources for computing the RULA score, which is highly subjective and untimely, the proposed DULA achieves automatic and objective assessment of musculoskeletal risks using a wireless sensor band embedded with multi-modal sensors. The system continuously tracks and records upper limb movements and muscle activation levels and automatically generates musculoskeletal risk levels. Moreover, it stores the data in a cloud database for in-depth analysis by a healthcare expert. Limb movements and muscle fatigue levels can also be visually seen using any tablet/computer in real-time. In the paper, algorithms of robust limb motion detection are developed, and an explanation of the system is provided along with the presentation of preliminary results, which validate the effectiveness of the new technology.

Modulation recognition (MR) plays a pivotal role due to its application in the spectrum sensing of 5G industrial cognitive communications and radio interference detection at the physical layer of the Internet of Things (IoT). Previous works have mainly focused on simulated fourth-generation (4G) multicarrier systems and ideal radio frequency (RF) scenarios. To bridge the gap between practice and theory, we propose a viable MR algorithm on all-physical testbeds, with nonlinear impairments of 28 GHz active phased arrays (APA). Specifically, our testbed is built on the Rohde&Schwarz (R&S) vector signal generation R&S-SMBV100B and spectrum analyzer R&S-FSW 67 GHz. To extract salient modulation patterns, we develop a physical-informed scattering transform (SCT) MR network (SCTMR-Net). With SCT modules, SCTMR-Net produces the translation-invariant and deformation-stable representations of 5-G signals by wavelet convolution, nonlinear modulus and low-pass filters. Extensive experiments on real-world measurement verify the viability of SCTMR-Net for high robustness to APA impairments.

Blind modulation recognition (BMR) involves identifying the modulation scheme of intercepted signals, an essential component of terrestrial radio management. While deep learning (DL) has advanced BMR research in terrestrial contexts, 6 G multi-beam mobile satellite (MMS) systems present many challenges. One pivotal hurdle is the reliance on perfect channel-state information (CSI) for signal equalization in terrestrial MIMO BMR literature. In MMS systems, the vast satellite-ground distance can lead to outdated CSI, making traditional equalization techniques less effective. Addressing this, we introduce a viable BMR algorithm that leverages blind and semi- blind channel equalization to overcome challenges like inter-beam interference (IBI), limited CSI, and Shadowed-Rician (SR) fading. By transforming the MMS system into an equivalent MIMO satellite system, we enable enhanced MIMO channel equalization to counteract IBI and SR attenuations. Our proposed locality-globality convolutional-transformer deep neural network (LG-CTDNet) merges the strengths of CNNs in local feature extraction with Transformers in global feature discernment. A decision fusion strategy is then incorporated to utilize cooperative diversity across multiple eavesdroppers. Numerical tests validate the effectiveness of our blind and semi- blind equalization techniques, emphasizing their superiority over existing DL-based methods in satellite modulation signal identification even in conditions with limited or absent CSI.

Force myography (FMG) can detect changes in the muscle volume which can be interpreted to recognize human intention. FMG data, however, is highly dependent on the placement of sensors over muscles, and interpretation becomes challenging if the sensors get displaced. This paper presents a robust sensor over muscle independence (SOMI) preprocessing algorithm combined with lightweight deep neural network (DNN) which shows high classification accuracy of the FMG data. SOMI organizes the irregular sensory data into regular patterns. Proposed algorithm makes the DNN insensitive to not only position and rotation shift but also to the flip of the sensors arrangement. A custom designed FMG band is used for payload recognition to experimentally validate the proposed method with five payload statuses and eight subjects. A five-fold cross validation comparative study demonstrated that the proposed method is 19.8% and 7.1% more accurate than support vector machine (SVM) and DNN without SOMI, respectively, and showed superior performance against k-nearest neighbors (KNN) and decision tree (DT). SOMI empowered a lightweight DNN to maintain the accuracy over 98% for different arbitrary wearing schemes of the FMG band over both left and right upper arms.