Search Results (224)

Recent studies reported that circulating microRNAs (miRNAs) can target different metalloproteases (MMPs) involved in matrix remodeling and plaque vulnerability. Consequently, they might have a role in the diagnosis and prognosis of coronary artery disease. To quantify circulating miRNAs (miRNA126, miRNA146, and miRNA21) suggested to have possible cardiovascular implications, as well as levels of MMP-1 and MMP-9, and to determine their association with left ventricular (LV) function and with arterial function, in patients with either ST-segment elevation acute myocardial infarction (STEMI) or stable ischemic heart disease (SIHD). A total of 90 patients with coronary artery disease (61% men, 58 ± 12 years), including 60 patients with STEMI and 30 patients with SIHD, were assessed within 24 h of admission, by measuring serum microRNAs, and serum MMP-1 and MMP-9. LV function was assessed by measuring ejection fraction (EF) by 2D and 3D echocardiography, and global longitudinal strain (GLS) by speckle tracking. Arterial function was assessed by echo tracking, CAVI, and peripheral Doppler. Circulating levels of miRNA146, miRNA21, and MMP1 were significantly increased in patients with STEMI vs. SIHD (p = 0.0001, p = 0.0001, p = 0.04, respectively). MiRNA126 negatively correlated with LVEF (r = −0.33, p = 0.01) and LV deformation parameters (r = −0.31, p = 0.03) in patients with STEMI and negatively correlated with ABI parameters (r = −0.39, p = 0.03, r = −0.40, p = 0.03, respectively) in patients with SIHD. MiRNA146 did not have any significant correlations, while higher values of miRNA21 were associated with lower values of GLS in STEMI patients and with higher values of GLS in SIHD patients. Both MMP1 and MMP9 correlated negatively with LVEF (r = −0.27, p = 0.04, r = −0.40, p = 0.001, respectively) and GLS in patients with STEMI, and positively with arterial stiffness in patients with SIHD (r = 0.40 and r = 0.32, respectively; both p < 0.05). MiRNA126, miRNA21, and both MMP1 and MMP9 are associated with LV and arterial function parameters in patients with acute coronary syndrome. Meanwhile, they inversely correlate with arterial function in patients with chronic atherosclerotic disease. However, further studies are needed to establish whether these novel biomarkers have diagnosis and prognosis significance. Full article

Unmanned Aerial Vehicles (UAVs) represent a rapidly increasing technology with profound implications for various domains, including surveillance, security, and commercial applications. Among the number of detection and classification methodologies, radar technology stands as a cornerstone due to its versatility and reliability. This paper presents a comprehensive primer written specifically for researchers starting on investigations into UAV detection and classification, with a distinct emphasis on the integration of full-wave electromagnetic computer-aided design (EM CAD) tools. Commencing with an elucidation of radar’s pivotal role within the UAV detection paradigm, this primer systematically navigates through fundamental Frequency-Modulated Continuous-Wave (FMCW) radar principles, elucidating their intricate interplay with UAV characteristics and signatures. Methodologies pertaining to signal processing, detection, and tracking are examined, with particular emphasis placed on the pivotal role of full-wave EM CAD tools in system design and optimization. Through an exposition of relevant case studies and applications, this paper underscores successful implementations of radar-based UAV detection and classification systems while elucidating encountered challenges and insights obtained. Anticipating future trajectories, the paper contemplates emerging trends and potential research directions, accentuating the indispensable nature of full-wave EM CAD tools in propelling radar techniques forward. In essence, this primer serves as an indispensable roadmap, empowering researchers to navigate the complex terrain of radar-based UAV detection and classification, thereby fostering advancements in aerial surveillance and security systems. Full article

For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with ‘low, slow, and small’ characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance but also to effectively complete the recognition of small UAV targets. In this paper, a multi-dimensional feature fusion framework for small UAV target recognition utilizing a small-sized and low-cost high-resolution radar is proposed, which can fully extract and combine the geometric structure features and the micro-motion features of small UAV targets. For the performance analysis, the echo data of different small UAV targets was measured and collected with a millimeter-wave radar, and the dataset consists of high-resolution range profiles (HRRP) and micro-Doppler time–frequency spectrograms was constructed for training and testing. The effectiveness of the proposed method was demonstrated by a series of comparison experiments, and the overall accuracy of the proposed method can reach 98.5%, which demonstrates that the proposed multi-dimensional feature fusion method can achieve better recognition performance than that of classical algorithms and higher robustness than that of single features for small UAV targets. Full article

Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human action recognition using Frequency Modulated Continuous Wave (FMCW) radar based on an asymmetric convolutional residual network. First, the radar echo data are analyzed and processed to extract the micro-Doppler time domain spectrograms of different actions. Second, a strategy combining asymmetric convolution and the Mish activation function is adopted in the residual block of the ResNet18 network to address the limitations of linear and nonlinear transformations in the residual block for micro-Doppler spectrum recognition. This approach aims to enhance the network’s ability to learn features effectively. Finally, the Improved Convolutional Block Attention Module (ICBAM) is integrated into the residual block to enhance the model’s attention and comprehension of input data. The experimental results demonstrate that the proposed method achieves a high accuracy of 98.28% in action recognition and classification within complex scenes, surpassing classic deep learning approaches. Moreover, this method significantly improves the recognition accuracy for actions with similar micro-Doppler features and demonstrates excellent anti-noise recognition performance. Full article

In this paper, we present a pedestrian detection and avoidance scheme utilizing multi-sensor data collection and machine learning for intelligent transportation systems (ITSs). The system integrates a video camera, an infrared (IR) camera, and a micro-Doppler radar for data acquisition and training. A deep convolutional neural network (DCNN) is employed to process RGB and IR images. The RGB dataset comprises 1200 images (600 with pedestrians and 600 without), while the IR dataset includes 1000 images (500 with pedestrians and 500 without), 85% of which were captured at night. Two distinct DCNNs were trained using these datasets, achieving a validation accuracy of 99.6% with the RGB camera and 97.3% with the IR camera. The radar sensor determines the pedestrian’s range and direction of travel. Experimental evaluations conducted in a vehicle demonstrated that the multi-sensor detection scheme effectively triggers a warning signal to a vibrating motor on the steering wheel and displays a warning message on the passenger’s touchscreen computer when a pedestrian is detected in potential danger. This system operates efficiently both during the day and at night. Full article

The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually lies on both sides. Equipping vehicles, especially autonomous vehicles, with frequency-modulated continuous-wave (FMCW) radar and dedicated algorithms for analyzing signals in the time–frequency domain as well as algorithms for recognizing objects in radar imaging through deep neural networks can positively affect safety. This paper presents a method for recognizing and distinguishing a group of objects based on radar signatures of objects and a special convolutional neural network structure. The proposed approach is based on a database of radar signatures generated on pedestrian, cyclist, and car models in a Matlab environment. The obtained results of simulations and positive tests provide a basis for the application of the system in many sectors and areas of the economy. Innovative aspects of the work include the method of discriminating between multiple objects on a single radar signature, the dedicated architecture of the convolutional neural network, and the use of a method of generating a custom input database. Full article

Background: perioperative stroke is one of the major complications after surgery. Patent foramen ovale (PFO) increases the risk of stroke in non-cardiac surgery by right-to-left shunt related to intraoperative hemodynamic alterations, leading to paradoxical embolism. Transesophageal echocardiography is the best tool for obtaining anatomical confirmation of PFO and essential details such as the PFO measure and the degree and direction of the shunt. Despite this, preoperative PFO screening is not routinely performed. Methods and results: we described the features of ten consecutive patients undergoing major abdominal surgery at the Abdominal Organ Transplant Intensive Care Unit, IRCCS Sant’Orsola, Bologna, Italy, who were screened for PFO using a PFO diagnostic and monitoring standardized intraoperative protocol by transesophageal echocardiography and transcranial color Doppler ultrasound. Finally, we highlighted the neurological and respiratory outcomes, the course and the management of three patients with intracardiac and extracardiac shunts. Conclusions: identifying an unknown PFO by a TCCD-TEE approach allowed the intraoperative monitoring of the shunt direction. It prevents the risk of complications secondary to paradoxical embolism in non-cardiac high-embolic-risk surgery. Full article

In this study, we present a novel approach for the real-time detection of high-speed moving objects with rapidly changing velocities using a high-resolution millimeter-wave (MMW) radar operating at 94 GHz in the W-band. Our detection methodology leverages continuous wave transmission and heterodyning of the reflected signal from the moving target, enabling the extraction of motion-related attributes such as velocity, position, and physical characteristics of the object. The use of a 94 GHz carrier frequency allows for high-resolution velocity detection with a velocity resolution of 6.38 m/s, achieved using a short integration time of 0.25 ms. This high-frequency operation also results in minimal atmospheric absorption, further enhancing the efficiency and effectiveness of the detection process. The proposed system utilizes cost-effective and less complex equipment, including compact antennas, made possible by the low sampling rate required for processing the intermediate frequency signal. The experimental results demonstrate the successful detection and characterization of high-speed moving objects with high acceleration rates, highlighting the potential of this approach for various scientific, industrial, and safety applications, particularly those involving targets with rapidly changing velocities. The detailed analysis of the micro-Doppler signatures associated with these objects provides valuable insights into their unique motion dynamics, paving the way for improved tracking and classification algorithms in fields such as aerospace research, meteorology, and collision avoidance systems. Full article

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%. Full article

Human pose estimation (HPE) is an integral component of numerous applications ranging from healthcare monitoring to human-computer interaction, traditionally relying on vision-based systems. These systems, however, face challenges such as privacy concerns and dependency on lighting conditions. As an alternative, short-range radar technology offers a non-invasive, lighting-insensitive solution that preserves user privacy. This paper presents a novel radar-based framework for HPE, SCRP-Radar (space-aware coordinate representation for human pose estimation using single-input single-output (SISO) ultra-wideband (UWB) radar). The methodology begins with clutter suppression and denoising techniques to enhance the quality of radar echo signals, followed by the construction of a micro-Doppler (MD) matrix from these refined signals. This matrix is segmented into bins to extract distinctive features that are critical for pose estimation. The SCRP-Radar leverages the Hrnet and LiteHrnet networks, incorporating space-aware coordinate representation to reconstruct 2D human poses with high precision. Our method redefines HPE as dual classification tasks for vertical and horizontal coordinates, which is a significant departure from existing methods such as RF-Pose, RF-Pose 3D, UWB-Pose, and RadarFormer. Extensive experimental evaluations demonstrate that SCRP-Radar significantly surpasses these methods in accuracy and robustness, consistently exhibiting lower average error rates, achieving less than 40 mm across 17 skeletal key-points. This innovative approach not only enhances the precision of radar-based HPE but also sets a new benchmark for future research and application, particularly in sectors that benefit from accurate and privacy-preserving monitoring technologies. Full article

Multi-rotor aircraft have the advantages of a simple structure, low cost, and flexible operation in the unmanned aerial vehicle (UAV) family, and have developed rapidly in recent years. Radar surveillance and classification of the growing number of multi-rotor aircraft has become a challenging problem due to their low-slow-small (LSS) characteristics. Estimation of the blade number is an important step in distinguishing LSS targets. However, most of the current research on micro-motion parameters estimation has focused on the analysis of rotational frequency, length, and the initial phase of blades with a prior of blade number, affecting its ability to identify LSS targets. In this article, a micro-motion parameters estimation method for multi-rotor targets without a prior is proposed. On the basis of estimating the flashing frequency of the blades, a validation function is constructed through spectral analysis to judge the number of blades, and then the rotational frequency is estimated. The blade length is calculated by estimating the maximum Doppler shift. Moreover, the variational mode decomposition (VMD)-based atomic scaling orthogonal matching pursuit (AS-OMP) method is jointly applied to estimate the blade length when suffering from the low PRF and insufficient SNR conditions. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms robust micro-motion parameter estimation capability in low PRF and insufficient SNR conditions compared to the traditional time-frequency analysis methods. Full article

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user’s quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model’s potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework. Full article

The problem of separating multicomponent micro-Doppler (m-D) signals is common in the field of radar signal processing. In some implementations, it is necessary to separate the multicomponent m-D signal that contains missing samples. To address this issue, an optimization model has been developed to recover and decompose multicomponent m-D signals with missing samples. To solve the underlying optimization problem, a two-algorithm-based alternate iteration framework is proposed. This method uses three techniques—the null space property, ridge regression method, and matching pursuit principle—to estimate the individual component, complex-valued differential operator, and regularization parameter. Finally, as shown by both simulation and measured data processing results, the proposed method can accurately separate the multicomponent m-D signal from incomplete data. Full article

The large quantity of resident space objects orbiting Earth poses a threat to safety and efficient operations in space. Radar sensors are well suited to detecting objects in space including decommissioned satellites and debris, whereas the more commonly used optical sensors are limited by daylight and weather conditions. Observations of three non-operational satellites using a VHF radar system are presented in this paper in the form of micro Doppler signatures associated with rotational motion. Micro Doppler signatures are particularly useful for characterising resident space objects at VHF given the limited bandwidth resulting in poor range resolution. Electromagnetic simulations of the micro Doppler signatures of the defunct satellites are also presented using simple computer-aided design (CAD) models to assist with interpretation of the radar observations. The simulated micro Doppler results are verified using the VHF radar data and provide insight into the attitude and spin axis of the three resident space objects. As future work, this approach will be extended to a larger number of resident space objects which requires a automated processing. Full article

In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task traditionally hindered by low spatial resolution and complex inverse problems. The difficulty is further exacerbated by the ambiguity in 3D pose reconstruction, as multiple 3D poses may correspond to similar 2D projections. Our solution, termed the Radar PoseLifter network, leverages the micro-Doppler signatures inherent in 1D radar echoes to effectively convert 2D pose information into 3D structures. The network is specifically designed to handle the long-range dependencies present in sequences of 2D poses. It employs a fully convolutional architecture, enhanced with a dilated temporal convolutions network, for efficient data processing. We rigorously evaluated the Radar PoseLifter network using the HPSUR dataset, which includes a diverse range of human movements. This dataset comprises data from five individuals with varying physical characteristics, performing a variety of actions. Our experimental results demonstrate the method’s robustness and accuracy in estimating complex human poses, highlighting its effectiveness. This research contributes significantly to the advancement of human motion capture using radar technology. It presents a viable solution for applications where precision and reliability in motion capture are paramount. The study not only enhances the understanding of 3D pose estimation from radar data but also opens new avenues for practical applications in various fields. Full article