Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject’s body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

Over the previous two decades, a notable array of space exploration missions have been initiated with the primary aim of facilitating the return of both humans and robots from Earth to the moon. The significance of these endeavors cannot be emphasized enough as numerous entities, both public and private, from across the globe have invested substantial resources into this pursuit. Researchers have committed their efforts to addressing the challenges linked to lunar communication. Even with all of these efforts, only a few of the many suggested designs for communication and antennas on the moon have been evaluated and compared. These designs have also not been shared with the scientific community. To bridge this gap in the existing body of knowledge, this paper conducts a thorough review of lunar surface communication and the diverse antenna designs employed in lunar communication systems. This paper provides a summary of the findings presented in lunar surface communication research while also outlining the assorted challenges that impact lunar communication. Apart from various antenna designs reported in this field, based on their intended usage, two additional classifications are introduced: (a) mission-based antennas—utilized in actual lunar missions—and (b) research-based antennas—employed solely for research purposes. Given the critical need to comprehend and predict lunar conditions and antenna behaviors within those conditions, this review holds immense significance. Its relevance is particularly pronounced in light of the numerous upcoming lunar missions that have been announced.

This paper presents an in-depth exploration of machine learning (ML) and deep learning (DL) for the optimization and design of dual-band antennas in Internet of Things (IoT) applications. Dual-band antennas, which are essential for the functionality of current and forthcoming flexible wireless communication systems, face increasing complexity and design challenges as demands and requirements for IoT-connected devices become more challenging. The study demonstrates how AI can streamline the antenna design process, enabling customization for specific frequency ranges or performance characteristics without exhaustive manual tuning. By utilizing ML and DL tools, this research not only enhances the efficiency of the design process but also achieves optimal antenna performance with significant time savings. The integration of AI in antenna design marks a notable advancement over traditional methods, offering a systematic approach to achieving dual-band functionality tailored to modern communication needs. We approached the antenna design as a regression problem, using the reflection coefficient, operating frequency, bandwidth, and voltage standing wave ratio as input parameters. The ML and DL models then are used to predict the corresponding design parameters for the antenna by using 1,000 samples, from which 700 are allocated for training and 300 for testing. This effectiveness of this approach is demonstrated through the successful application of various ML techniques, including Fine Gaussian Support Vector Machines (SVM), as well as Regressor and Residual Neural Networks (ResNet) with different activation functions, to optimize the design of a dual-band T-shaped monopole antenna, thereby substantiating AI's transformative potential in antenna design.

Early fault detection in photovoltaic grid-connected systems is crucial for optimizing energy output and ensuring system reliability. However, challenges such as noise interference, limited computational resources, and the need for high diagnostic accuracy make fault detection complex and resource-intensive. This study introduces a novel fault diagnosis approach aimed at enhancing diagnostic accuracy and efficiency, even in noisy environments, while being efficient enough for real-time deployment on resource-limited devices. Raw one-dimensional time series data are transformed into 2D pixel-wise images to meet the requirements of a lightweight MobileNet architecture, specifically chosen for its ability to efficiently extract discriminative features in this context. Alongside, an ensemble stacked classifier incorporating Support Vector Machines, Random Forest, and Decision Trees is employed, enabling the use of MobilNet features for improving the classification. This combination provides insights into the relative strengths of each model type, showcasing the benefits of contrasting traditional machine learning with more complex deep learning methods for fault detection in PV systems. The model demonstrates high test accuracy on both noiseless (97.36%) and noisy datasets (91.67%), with precision, recall, and F1 scores above 91% across all conditions, while maintaining low processing times. This approach not only advances fault diagnosis in grid-connected PV systems but also offers a practical framework for selecting optimal models tailored to specific application requirements.

In the emerging world of near-field Internet of Things (NF-IoT), small footprint and energy efficient wireless sensor nodes are essential components. This brief presents a V-band transceiver for NF-IoT without the conventional low noise amplifier, mixers, power amplifier, and phase-locked loop building blocks. Also, there are no external components except a battery or energy scavenger. The choice of V-band enables a small footprint and is conducive to low-power and low energy/bit. The receiver has a low-power, on-off keying detection circuit, while the transmitter is an amplitude-redistribution cross-coupled oscillator with on-chip resonator, quenched by the transmit data. The transceiver has an integrated antenna shared by the transmitter and the receiver. The design, realized in the GlobalFoundries 65-nm low-power enhanced CMOS process, occupies 2.75 mm ² of chip area and consumes 52 μW in receive mode and 1 mW (10 μW @ 1% duty cycle) in transmit mode with a data rate of 100 kb/s. For a transmitter power of -10 dBm, the receiver sensitivity is -42 dBm for a BER of 10 ^-3 .

This paper tackles significant challenges in the design of millimeter-wave MIMO (Multi-input Multi-output) antennas for the Ka-band (26-30 GHz) as utilized in 5G applications. Current antenna designs suffer from limitations such as constrained bandwidth, inadequate gain, and restricted beam scanning capabilities, adversely affecting performance, particularly in high-density urban environments. A novel MIMO antenna featuring a 12-port dielectric lens has been proposed to address these issues, introducing several advancements to improve key performance metrics. The antenna demonstrates a peak realized gain of approximately 14 dBi and achieves a bandwidth exceeding 4 GHz. Importantly, it features full 360∘ beam scanning capabilities and maintains an exceptionally low envelope correlation coefficient (ECC) of less than 0.0014. The design also shows impressive radiation efficiency consistently exceeding 95%, complemented by excellent isolation characteristics with mutual coupling maintained below -30 dB. This research provides a scalable solution that could significantly enhance the performance of next-generation wireless networks, addressing the high demands of urban 5G deployments.