In this paper, direct model predictive control (DMPC) of the noninverting buck-boost DC-DC converter with magnetic coupling between input and output is proposed. Unlike most of the other converters, the subject converter has the advantage of exhibiting minimum phase behavior in the boost mode. However, a major issue that arises in the classical control of the converter is the dead zone near the transition of the buck and boost mode. The reason for the dead zone is practically unrealizable duty cycles, which are close to zero or unity, of pulse width modulation (PWM) near the transition region. To overcome this issue, we propose to use DMPC. In DMPC, the switches are manipulated directly by the controller without the need of PWM. Thereby, avoiding the dead zone altogether. DMPC also offers several other advantages over classical techniques that include optimality and explicit current constraints. Simulations of the proposed DMPC technique on the converter show that the dead zone has been successfully avoided. Moreover, simulations show that the DMPC technique results in a significantly improved performance as compared to the classical control techniques in terms of response time, reference tracking, and overshoot.

In this paper, we explore the ever-changing field of Digital Twins (DT) in the Industrial Internet of Things (IIoT) context, emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0. The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT, focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration. It discusses the emergence of DT, contextualizing its evolution within the framework of IIoT. The study categorizes the different types of DT, including prototypes and instances, and provides an in-depth analysis of the enabling technologies such as IoT, Artificial Intelligence (AI), Extended Reality (XR), cloud computing, and the Application Programming Interface (API). The paper demonstrates the DT advantages through the practical integration of real-world case studies, which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the IIoT. This paper offers a focused, application-driven perspective on DTs in IIoT, specifically highlighting their role in key production phases such as designing, intelligent manufacturing, maintenance, resource management, automation, security, and safety. By emphasizing their potential to support human-centric, sustainable advancements in Industry 5.0, this study distinguishes itself from existing literature. It provides valuable insights that connect theoretical advancements with practical implementation, making it a crucial resource for researchers, practitioners, and industry professionals.

Specific emitter identification (SEI) is a technique to distinguish among different emitters of the same type using weak individual characteristics instead of conventional modulation parameters. The biggest challenge in SEI is to not only distinguish the different emitters with close modulation parameters but also to identify a specific emitter when its modulation parameters change significantly. For this paper, individual differences in pulse envelopes were investigated and four types of pulse envelopes were modeled. To exploit the individual features along with the pulse envelope for the identification of a specific emitter, an intrinsic mode function distinct native attribute (IMF-DNA) feature extraction algorithm and a joint feature selection (JFS) algorithm were proposed, which together constitute the final proposed SEI technique. Compared with four other feature selection methods, the proposed feature selection algorithm performed better for finding the most useful features for classification, which greatly helps in the reduction of feature dimension. Compared with radio frequency DNA (RF-DNA), IMF-DNA had a far superior correct emitter identification rate under similar conditions. A real data verification method was developed to verify the performance of IMF-DNA for specific emitter identification. The method achieved a correct identification rate of 85.3% at a sampling rate of 200 MHz and had an estimated signal-to-noise ratio (SNR) of approximately 10 dB.

Non-contact gesture recognition is a new type of human-computer interaction with broad prospects in many applications. Motivated by the need for more precise micro-motion gesture recognition using mm-wave radar in recent years, a novel micro-motion gesture recognition network based on the Convolutional Block Attention Module (CBAM) is proposed here. The MMWCAS radar from TI is used to collect gesture echoes. During data pre-processing, the Range-time Map, Doppler-time Map, Azimuth-time Map and Elevation-time Map of the gestures are extracted and employed to characterize the motion features. A DenseNet and CBAM-based gesture recognition network is designed to identify the 12 types of micro-motion gestures using the mixed Feature-time Map as input. According to the experimental results, the accuracy rate reaches 99.03%, achieving high-accuracy gesture recognition. It has been discovered that the network focuses on the first half of the gesture movement, which improves recognition accuracy.