Search Results (936)

In this study, four types of fall detection systems for seniors living alone using x-y scatter and Doppler range images measured from frequency-modulated continuous wave (FMCW) millimeter-wave (mmWave) sensors were introduced. Despite advancements in fall detection, existing long short-term memory (LSTM)-based approaches often struggle with effectively distinguishing falls from similar activities of daily living (ADLs) due to their uniform treatment of all time steps, potentially overlooking critical motion cues. To address this limitation, an attention mechanism has been integrated. Data was collected from seven participants, resulting in a dataset of 669 samples, including 285 falls and 384 ADLs with walking, lying, inactivity, and sitting. Four LSTM-based architectures for fall detection were proposed and evaluated: Raw-LSTM, Raw-LSTM-Attention, HOG-LSTM, and HOG-LSTM-Attention. The histogram of oriented gradient (HOG) method was used for feature extraction, while LSTM networks captured temporal dependencies. The attention mechanism further enhanced model performance by focusing on relevant input features. The Raw-LSTM model processed raw mmWave radar images through LSTM layers and dense layers for classification. The Raw-LSTM-Attention model extended Raw-LSTM with an added self-attention mechanism within the traditional attention framework. The HOG-LSTM model included an additional preprocessing step upon the RAW-LSTM model where HOG features were extracted and classified using an SVM. The HOG-LSTM-Attention model built upon the HOG-LSTM model by incorporating a self-attention mechanism to enhance the model’s ability to accurately classify activities. Evaluation metrics such as Sensitivity, Precision, Accuracy, and F1-Score were used to compare four architectural models. The results showed that the HOG-LSTM-Attention model achieved the highest performance, with an Accuracy of 95.3% and an F1-Score of 95.5%. Optimal self-attention configuration was found at a 2:64 ratio of number of attention heads to channels for keys and queries. Full article

This paper proposes a novel Global Navigation Satellite System (GNSS)-based remote sensing method for simulating Radar Doppler Map (RDM) features through joint electromagnetic scattering modeling and signal processing, enabling characteristic parameter extraction for both point and ship targets in multi-satellite scenarios. Simulations demonstrate that the B3I signal achieves a significantly enhanced range resolution (tens of meters) compared to the B1I signal (hundreds of meters), attributable to its wider bandwidth. Furthermore, we introduce an Unscented Particle Filter (UPF) algorithm for dynamic target tracking and state estimation. Experimental results show that four-satellite configurations outperform three-satellite setups, achieving <10 m position error for uniform motion and <18 m for maneuvering targets, with velocity errors within ±2 m/s using four satellites. The joint detection framework for multi-satellite, multi-target scenarios demonstrates an improved detection accuracy and robust localization performance. Full article

Ubiquitous digital array radar (UDAR) extends the integration time of moving targets by deploying a wide transmitting beam and multiple narrow receiving beams to cover the entire observed airspace. By exchanging time for energy, it effectively improves the detection ability for weak targets. Nevertheless, target motion introduces severe across-range unit (ARU), across-Doppler unit (ADU), and across-beam unit (ABU) effects, dispersing target energy across the range–Doppler-beam space. This paper proposes a beam domain angle rotation compensation and keystone-matched filtering (BARC-KTMF) algorithm to address the “three-crossing” challenge. This algorithm first corrects ABU by rotating beam–domain coordinates to align scattered energy into the final beam unit, reshaping the signal distribution pattern. Then, the KTMF method is utilized to focus target energy in the time-frequency domain. Furthermore, a special spatial windowing technique is developed to improve computational efficiency through parallel block processing. Simulation results show that the proposed approach achieves an excellent signal-to-noise ratio (SNR) gain over the typical single-beam and multi-beam long-time coherent integration (LTCI) methods under low SNR conditions. Additionally, the presented algorithm also has the capability of coarse estimation for the target incident angle. This work extends the LTCI technique to the beam domain, offering a robust framework for high-speed weak target detection. Full article

The high resolution of the Synthetic Aperture Radar (SAR) imagery, in addition to its capability to see through clouds and rain, makes it a crucial remote sensing technique. However, SAR images are very sensitive to radar parameters, the observation geometry and the scene’s characteristics. Moreover, for a complex scene of interest with targets located on a rough soil, a composite scattering between the target and the surface occurs and creates distortions on the SAR image. These characteristics can make the SAR images difficult to analyse and process. To better understand the complex EM phenomena and their signature in the SAR image, we propose a methodology to generate raw SAR signals and SAR images for scenes of interest with a target located on a rough surface. With this prospect, the entire radar acquisition chain is considered: the sensor parameters, the atmospheric attenuation, the interactions between the incident EM field and the scene, and the SAR image formation. Simulation results are presented for a rough dielectric soil and a canonical target considered as a Perfect Electric Conductor (PEC). These results highlight the importance of the composite scattering signature between the target and the soil. Its power is 21 dB higher that that of the target for the target–soil configuration considered. Finally, these simulations allow for the retrieval of characteristics present in actual SAR images and show the potential of the presented model in investigating EM phenomena and their signatures in SAR images. Full article

The WIVERN mission promises to deliver the first global observations of the three-dimensional wind field and the associated cloud and precipitation structure in a wide range of atmospheric phenomena, including isolated thunderstorms, tropical cyclones, mid-latitude frontal systems, and polar lows. A critical element in the development of the mission’s wind products is the differentiation between stratiform and convective regions. Convective regions are defined as those where vertical wind velocities exceed 1 m/s. This work introduces CONSTRAINN, a family of U-Net-based neural network models that utilise all of WIVERN observables—including vertical profiles of reflectivity and Doppler velocity, as well as brightness temperatures—to reconstruct convective wind activity within the Earth’s atmosphere. Results show that the retrieved convective/stratiform masks are well reconstructed, with an equitable threat score exceeding 0.6. Ablation experiments further reveal that Doppler velocity signals are the most informative for the reconstruction task. Full article

With the rapid development of drone technology, target detection and estimation of radar parameters for maneuvering targets have become crucial. Drones, with their small radar cross-sections and high maneuverability, cause range migration (RM) and Doppler frequency migration (DFM), which complicate the use of traditional radar methods and reduce detection accuracy. Furthermore, the detection of multiple targets exacerbates the issue, as target interference complicates detection and impedes parameter estimation. To address this issue, this paper presents a method for high-resolution multi-drone target detection and parameter estimation based on the adjacent cross-correlation function (ACCF), fractional Fourier transform (FrFT), and deep learning techniques. The ACCF operation is first utilized to eliminate RM and reduce the higher-order components of DFM. Subsequently, the FrFT is applied to achieve coherent integration and enhance energy concentration. Additionally, a convolutional neural network (CNN) is employed to address issues of spectral overlap in multi-target FrFT processing, further improving resolution and detection performance. Experimental results demonstrate that the proposed method significantly outperforms existing approaches in probability of detection and accuracy of parameter estimation for multiple maneuvering targets, underscoring its strong potential for practical applications. Full article

In high-frequency surface wave radar (HFSWR) systems, the retrieval of the directional wave spectrum has remained challenging, especially in the case of echoes from long ranges with a low signal-to-noise ratio (SNR). Therefore, a quadratic programming algorithm based on the regularization technique is proposed with an empirical criterion for estimating the optimal regularization parameter, which minimizes the effect of noise to obtain more accurate inversion results. The reliability of the inversion method is preliminarily verified using simulated Doppler spectra under different wind speeds, wind directions, and SNRs. The directional wave spectra inverted from a radar network with two multiple-input multiple-output (MIMO) systems are basically consistent with those from the ERA5 data, while there is a limitation for the very concentrated directional distribution due to the truncated second order in the Fourier series. Further, in the field experiment during a storm that lasted three days, the wave parameters are calculated from the inverted directional spectra and compared with the ERA5 data. The results are shown to be in reasonable agreement at four typical locations in the core detection area. In addition, reasonable performance is also obtained under the condition of low SNRs, which further verifies the effectiveness of the proposed inversion algorithm. Full article

Integrated sensing and communication (ISAC) has become a key enabler in 5G-Advanced (5G-A) and future 6G systems, with Orthogonal Frequency Division Multiplexing (OFDM) widely adopted as the underlying waveform. However, due to the inherent structure of OFDM signals, traditional sensing algorithms often suffer from a limited sensing range in practical applications. To address this issue, we propose a delay compensation algorithm that mitigates the impact of delay and ensures the gain of range-Doppler processing. Furthermore, we analyze the issue of ambiguous targets in CP-OFDM systems, considering both single-target and multi-target scenarios. To improve the detection probability and suppress the accumulated echo energy corresponding to ambiguous targets, we propose a sequence design criterion, in which part of the original signal is replaced with a designed sequence. Simulation results demonstrate that the proposed algorithm effectively improves detection range and ensures unambiguous target identification, while achieving effective suppression of ambiguous target energy. Compared with a conventional algorithm, it achieves a processing gain of up to 20 dB. Moreover, the results show that different redundancy ratios can be selected in varying scenarios to balance communication and sensing performance in ISAC systems. Full article

Background: Iron deficiency (ID) is a prevalent comorbidity of heart failure (HF), affecting up to 59% of patients, regardless of the presence of anaemia. Although its negative impact on left ventricular (LV) function is well documented, its effect on right ventricular (RV) function remains unclear. This study assessed the effects of ID on RV global longitudinal strain (RV-GLS) in patients diagnosed with acute decompensated HF (ADHF). Methods: This study included data from 100 patients hospitalised with ADHF irrespective of LV ejection fraction (LVEF) value. ID was defined according to the European Society of Cardiology HF guidelines as serum ferritin <100 ng/mL or ferritin 100–299 ng/mL, with transferrin saturation <20%. Anaemia was defined according to World Health Organization criteria as haemoglobin level <12 g/dL in women and <13 g/dL in men. RV systolic function was assessed using parameters including RV ejection fraction (RVEF), tricuspid annular plane systolic excursion (TAPSE), RV fractional area change (FAC), peak systolic tissue Doppler velocity of the RV annulus (RV TDI S′), acceleration time of the RV outflow tract, and RV free wall GLS. Results: The mean (±SD) age of the study population (64% male) was 70 ± 10 years. The median LVEF was 35%, with 66% of patients classified with HF with reduced ejection fraction, 6% with HF with mid-range ejection fraction, and 28% with HF with preserved ejection fraction. Fifty-eight percent of patients had ID. There were no significant differences between patients with and without ID regarding demographics, LVEF, RV FAC, RV TDI S′, or systolic pulmonary artery pressure. However, TAPSE (15.6 versus [vs.] 17.2 mm; p = 0.05) and RV free wall GLS (−14.7% vs. −18.2%; p = 0.005) were significantly lower in patients with ID, indicating subclinical RV systolic dysfunction. Conclusions: ID was associated with subclinical impairment of RV systolic function in patients diagnosed with ADHF, as evidenced by reductions in TAPSE and RV-GLS, despite the preservation of conventional RV systolic function parameters. Further research validating these findings and exploring the underlying mechanisms is warranted. Full article

Background and Objectives: Visceral arterial pseudoaneurysms (VAPAs) are rare vascular lesions characterized by the disruption of partial disruption of the arterial wall, most commonly involving the intima and media. They have an estimated incidence of 0.1–0.2%, with the splenic artery most commonly affected. Their management poses unique challenges due to the high risk of rupture. Timely recognition is crucial, as unmanaged pseudoaneurysms have a mortality rate of 90%. This narrative review aims to synthesize current knowledge regarding the epidemiology, etiology, clinical presentation, diagnostic methods, and management strategies for VAPAs. Materials and Methods: A literature search was performed across Pubmed for articles reporting on VAPAs, including case reports, review articles, and cohort studies, with inclusion of manuscripts that were up to (date). VAPAs are grouped by embryological origin—foregut, midgut, and hindgut. Results: Chronic pancreatitis is a primary cause of VAPAs, with the splenic artery being involved in 60–65% of cases. Other causes include acute pancreatitis, as well as iatrogenic trauma from surgeries, trauma, infections, drug use, and vascular diseases. VAPAs often present as abdominal pain upon rupture, with symptoms like nausea, vomiting, and gastrointestinal hemorrhage. Unruptured pseudoaneurysms may manifest as pulsatile masses or bruits but are frequently asymptomatic and discovered incidentally. Diagnosis relies on both non-invasive imaging techniques, such as CT angiography and Doppler ultrasound, and invasive methods like digital subtraction angiography, which remains the gold standard for detailed evaluation and treatment. A range of management options exists that are tailored to individual cases based on the aneurysm’s characteristics and patient-specific factors. This encompasses both surgical and endovascular approaches, with a growing preference for minimally invasive techniques due to lower associated morbidity. Conclusions: VAPAs are a critical condition requiring prompt early recognition and intervention. This review highlights the need for ongoing research to improve diagnostic accuracy and refine treatment protocols, enhancing patient outcomes in this challenging domain of vascular surgery. Full article

Background: Immune checkpoint inhibitors (ICIs) transformed cancer treatment, producing significant survival benefits. However, ICIs can trigger toxicities called immune-related adverse events (irAEs), including inflammatory arthritis (IA) and polymyalgia rheumatica (PMR)-like syndromes. Our study aimed to systematically further characterize musculoskeletal ultrasound (MSKUS) findings in patients with ICI-IA and ICI-PMR, collectively referred to as “MSK-irAEs”, and explore the role of US in guiding treatment. Methods: The authors conducted a comprehensive chart review for patients receiving ICIs undergoing MSKUS at our center’s rheumatology clinics. US examinations were performed and reviewed by two MSKUS-certified rheumatologists. Descriptive statistics were performed to summarize demographic, clinical, and treatment-related variables. US findings were categorized with a novel scoring system: 0—no signs of inflammatory arthropathy or tendinopathy, 1—potential signs of inflammation (grayscale ≥ 2, effusion without power Doppler, synovial hypertrophy in the joint), and 2—active inflammation in joints and/or tendons (characterized by power Doppler) and signs of inflammation. Results: Twenty-three patients were included. The median age was 63 years, 52% were male, and 87% were White. Melanoma was the most common cancer (48%). MSK-irAEs were diagnosed in nineteen (83%), with MSKUS showing inflammation in seventeen (74%). Sixteen (70%) received escalation in MSK-irAE treatment after MSKUS. Four (17%) had erosive disease due to MSK-irAEs, while one had erosive osteoarthritis. Individuals with inflammatory erosive changes experienced prolonged intervals between symptom onset and MSKUS, ranging from 17 to 82 months, suggesting that erosions may reflect chronic, under-recognized inflammation. On MSK-irAE therapy, nine (47%) experienced symptomatic improvement, five (26%) achieved resolution, and in four (21%) cases, it was too early to assess the response. MSKUS detected other causes of MSK symptoms besides MSK-irAEs in several patients, allowing ICI resumption in one. Conclusions: Our study highlights the clinical utility of MSKUS not only as a diagnostic tool but also to guide therapeutic decision-making. Full article

A comprehensive framework for ultrasound imaging simulations is presented. Solutions to an inhomogeneous wave equation are provided, yielding a linear model for characterizing ultrasound propagation and scattering in soft tissue. This simulation approach, which is based upon the fast nearfield method, is implemented in the Fast Object-oriented C++ Ultrasound Simulator (FOCUS) and is extended to a range of imaging modalities, including synthetic aperture, B-mode, plane wave, and color Doppler imaging. The generation of radiofrequency (RF) data and the receive beamforming techniques employed for each imaging modality, along with background on color Doppler imaging, are described. Simulation results demonstrate rapid convergence and lower error rates compared to conventional spatial impulse response methods and Field II, resulting in substantial reductions in computation time. Notably, the framework effectively simulates hundreds of thousands of scatterers without the need for a full three-dimensional (3D) grid, and the inherent randomness in the scatterer distributions produces realistic speckle patterns. A plane wave imaging example, for instance, achieves high fidelity using 100,000 scatterers with five steering angles, and the simulation is completed on a personal computer in a few minutes. Furthermore, by modeling scatterers as moving particles, the simulation framework captures dynamic flow conditions in vascular phantoms for color Doppler imaging. These advances establish FOCUS as a robust, versatile tool for the rapid prototyping, validation, and optimization of both established and novel ultrasound imaging techniques. Full article

With the availability of more ground-based remote-sensing meteorological equipment at Hong Kong International Airport, many more interesting features of terrain-disrupted airflow have been observed, such as the applications of short-range Doppler LIDAR. This paper documents a number of new features observed at the airport area, such as the hydraulic jump-like feature, vortex, and extensive mountain wake/reverse flow. The technical feasibility of using a numerical resolution weather prediction model to simulate such features is also explored. It is found that the presently available input data and numerical model may not be able to capture the fine features of the atmospheric boundary layer, and thus they are not very successful in reproducing many small-scale terrain-disrupted airflow features downstream of an isolated hill. On the other hand, more larger-scale terrain-disrupted flow features may be better captured, but there are still limitations with the available turbulence parameterization schemes. This paper aims at documenting the newly observed flow features at the Hong Kong International Airport, enhancing the understanding of low-level windshear, and evaluating the outputs of numerical resolution simulations for reproducing such observed features and its technical feasibility on short-term forecasting. Full article

Because of terrain disruption of the airflow and interface between different airmasses, wave motions may be observed in the vicinity of the Hong Kong International Airport using plan position indicator scans and range height indicator scans with Doppler light detection and ranging systems. This paper documents three cases of wave motion that are not commonly observed near this airport and have never been described before in the literature for Hong Kong, including one mountain wave case and two cases of interfaces between airmasses. These waves may have impacts on aviation safety by leading to the occurrence of low-level windshear and turbulence. They are studied further using a high-resolution numerical weather prediction model. It is found that the model is capable of capturing some features of the waves, such as their occurrence mechanisms. However, some details of the waves are not successfully reproduced, such as the changes in the number of wave crests/troughs with time. Further study should also be conducted to reproduce the wavelengths of these waves. Full article

A reliable ocean surface current (OSC) estimate is difficult to retrieve from synthetic aperture radar (SAR) data due to the challenge of accurately partitioning the Doppler shifts induced by wind waves and OSC. Recent research on SAR-based OSC retrieval is typically based on the assumption that the SAR Doppler shifts caused by wind waves and OSC are linearly superimposed. However, this assumption may lead to large errors in regions where nonlinear wave–current interactions are significant. To address this issue, we developed a novel deep learning model, OSCNet, for OSC retrieval. The model leverages Sentinel-1 Interferometric Wide (IW) Level 2 Ocean products collected from July 2023 to September 2024, combined with wave data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and geostrophic currents from newly available SWOT Level 3 products. The OSCNet model is optimized by refining input ocean surface physical parameters and introducing a ResNet structure. Moreover, the Normalized Radar Cross-Section (NRCS) is incorporated to account for wave breaking and backscatter effects on Doppler shift estimates. The retrieval performance of the OSCNet model is evaluated using SWOT data. The mean absolute error (MAE) and root mean square error (RMSE) are found to be 0.15 m/s and 0.19 m/s, respectively. This result demonstrates that the OSCNet model enhances the retrieval of OSC from SAR data. Furthermore, a mesoscale eddy detected in the OSC map retrieved by OSCNet is consistent with the collocated sea surface chlorophyll-a observation, demonstrating the capability of the proposed method in capturing the variability of mesoscale eddies. Full article