In this paper, we propose a novel system prototype for human activity recognition using a low-cost, low-power millimeter-wave (mmWave) frequency-modulated continuous wave (FMCW) radar. Our approach applies the Fast Fourier Transform on the slow time axis and employs a Capon filter to generate range-Doppler, range-azimuth, and range-elevation maps, respectively. It can also effectively mitigate noise and multipath effects. We then use principal component analysis for feature reduction, reducing the dimensionality of the feature vectors extracted from these maps, which can be used to train conventional machine learning classifiers. This approach aims to achieve a balance between computational complexity, accuracy, and overall system performance. Our proposed system demon-strates promising recognition rates and robustness across varying levels of activity granularity, achieving recognition rates from 90.28% for four activities up to 70.97% for seven fine-grained ac-tivities. These findings highlight the potential of millimeter wave radar and suggested range maps combined with conventional machine learning classifiers for noninvasive, privacy-preserving activity recognition, with significant implications for healthcare, elderly care, and ambient assisted living.

As 5G networks proliferate globally, the need for accurate, reliable, and scalable positioning solutions has become increasingly critical across industries, such as Internet of Things (IoT), healthcare, and autonomous systems. This article comprehensively reviews current and emerging positioning techniques within 5G, exploring the advancements enabled by sidelink communication, reconfigurable intelligent surfaces (RISs), machine learning, and massive multiple-input–multiple-output. We examine the evolution of 5G positioning as defined by key 3GPP releases, and provide a comparative analysis of the techniques in terms of accuracy, cost, and robustness. The review also highlights key challenges, including non-line-of-sight (NLOS) environments, real-time data processing, and security concerns, which must be addressed for widespread adoption. Finally, we discuss future directions for 5G-Advanced and 6G positioning technologies, offering insights into potential improvements and the ongoing evolution of the field.

In low-altitude airspace surveillance, distinguishing between birds and drones is crucial due to their overlapping radar signatures. Radar, the preferred technology for long-range surveillance, struggles with this differentiation. To address this, our study introduces an unsupervised deep learning method utilizing real radar data from birds and unmanned aerial vehicles (UAVs). This approach starts with data cleaning and upsampling using the synthetic minority oversampling technique (SMOTE) to manage dataset imbalance. We integrate principal component analysis (PCA) with deep learning to reduce the feature set efficiently. This integration minimizes computational demands while retaining essential information for precise clustering, enhancing real-world applicability. A deep clustering network (DCN) exploits the reduced-dimensional space created by PCA to identify distinct signal clusters for birds and drones, optimized for radar surveillance without relying on predefined labels. A deep neural network (DNN) maps data into a cluster-friendly hidden space, designed for radar signal analysis. The model’s effectiveness, with an average normalized mutual information (NMI) score of 0.878 through K-fold cross-validation, underscores the innovative potential of combining PCA with unsupervised learning. This method overcomes traditional radar techniques’ limitations, offering a scalable and efficient solution for surveillance scenarios.

In this work, we present a cloud-based system for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. To train deep learning models, we utilize range-Doppler maps generated from a dataset of real-life in-home activities. The performance of several deep learning models is evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. The overall accuracy of the GRU model for classifying in-home physical activities of trained subjects is 93%, with 86% accuracy for a new subject. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices.

A growing demand has been established over the recent years for quick and inexpensive oil adulteration detection testing to convoy the automated and computerized processes in the industry. This research study presents a practical application of a simple low-resource microwave sensor of small size and high sensitivity to rapidly identify oil types, and monitor its quality and authenticity without having to open any bottles off-shelf. The sensor utilizes the nonreciprocal whispering-gallery-modes (WGMs) traveling on a ferrite ring resonator (FRR) when coupled to a microstrip line (MTL). The magnetic anisotropy of the ferrite is exploited to acquire four sensitive WGM resonances of nonreciprocal nature in the 22–32-GHz spectrum. A fabricated prototype is practically tested for identifying oil samples of different ingredients and brands, when loaded onto the FRR at consistent volume inside glass bottles of identical geometry. The measured scattering responses have shown a high detection sensitivity for the small contrast between the edible oils as demonstrated by the explicit frequency shifts in magnitude and phase of both S21and S12. The article also discusses a generalized concept for the complementing system layers in an Internet of Things (IoT) architecture for potential implementation in the industry.

Abstract A robust method for ultra‐wideband (UWB) imaging of buried shallow objects based on time gating, Wiener filtering, as well as constant false alarm rate (CFAR) is proposed. Moreover, it is demonstrated that Wiener filtering can be used as a clutter removal tool in UWB signal applications. Basically, the problem with time gating method is that the length of the timing window for unknown targets cannot be determined accurately in advance. In fact, it is a blind methodology and some targets can be missed due to a lack of pre‐knowledge about their depth. Imprecise window length selection leads to missing some parts of the target signals along with the clutter, which in turn increases the missed detection rate. Herein, an algorithm to tackle this problem is proposed by using a Wiener filter along with CFAR as a primary detector of the target positions employing average similarity function imaging. The time gating method is then built on top of the information achieved for the window length selection from the primary detection. The combination of the two steps provides better detection of shallowly buried objects with less missed detection of targets, besides having fewer artefacts in comparison to other methods.

In this paper, we present a novel solution for the remote breathing and sleep position monitoring by using a multi-input-multi-output (MIMO) radar. Our proposed system is able to monitor a number of people simultaneously, and therein we use a high-resolution direction of arrival (DOA) detection for finding closely separated targets. So, it effectively increases the number of target detection and reduces the cost by reducing the number of sensors. Furthermore, our proposed system is capable of identifying the sleep position of each monitored person by selecting appropriate target features and using a support vector machine (SVM) classifier. The breathing analysis involves designing an optimum filter for estimating both the breathing rate and the noiseless breathing waveform. In addition, we use the radar in a bedroom environment above a bed where two subjects sleep next to each other. The accuracy of the breathing monitoring subsystem is more than 97% for human subjects in the bedroom compared with a reference sensor. Also, the correct rate for sleep position detection is more than 83%.

This study reports the in-vitro characterization of blood samples based on the dielectric property measurements across the microwave spectrum 2 - 67 GHz using a newly developed commercial coaxial probe kit. This is done for different ABO-Rh types of synthetic blood samples as well as authentic specimens drawn from healthy adult donors. Measured results have indicated an appreciably dielectric contrast between blood samples of various types at certain frequency regions. This phenomenon is also demonstrated by the resonant measurements on a novel developed CSRR-based biosensor in the centimeter-band using two different setups of Vector Network Analyzer and 2.45 GHz radar board.

Monitoring glycemia levels in people with diabetes has developed rapidly over the last decade. A broad range of easy-to-use systems of reliable accuracies are now deployed in the market following the introduction of the invasive self-monitoring blood glucose meters (i.e. glucometers) that utilize the capillary blood samples from the fingertips of diabetic patients. However, the limitations and discomforts associated with these painful finger pricking devices have established a new demand for non-invasive pain-free blood glucose monitors to encourage more frequent glucose checks and thereby contribute more generously to diabetes care and prevention. In this study, a novel microwave biosensor is developed in a wearable format to enable non-invasive real-time monitoring of blood glucose level. The design comprises three cells of circular complementary split ring resonators (CSRRs) incorporated in the ground plane of an FR4 dielectric substrate. The passive sensing elements (CSRRs) are excited remotely via a coupled antenna to enable the wearable sensing in a reader/tag configuration. The CSSR-sensor is numerically modeled and analyzed for sensing the glucose concentrations relevant to diabetes condition (60-500mg/dL) by tracking the resonant amplitude variations in the frequency range 1-4GHz. The sensitivity performance of the TP-CSRR tag is practically demonstrated through in-lab measurements using a VNA setup.