Search

Summary Multiple‐input multiple‐output (MIMO) radar, employing multiple transmitters and receivers, enhances radar capabilities. It detects and tracks objects like aircraft and ships using radio waves. Compared with traditional phased‐array radar, MIMO systems offer greater flexibility, improving angular resolution and target detection. Researchers focus on direction of arrival (DoA) evaluation for closely spaced targets. Effective beamforming and accurate DoA estimation are crucial for MIMO radar performance. This study explores two methods: Capon beamforming with Dolph–Chebyshev windowing and the MUSIC algorithm with Eigenvalue thresholding. Tested under low signal‐to‐noise ratio (SNR) and fewer snapshots, these techniques notably reduce side lobes and enhance angular resolution, validated by experiments. Additionally, the suppression of side lobes significantly improves the clarity and accuracy of target detection, minimizing potential interference and false targets. This enhancement in side lobe suppression facilitates a more precise spatial differentiation between multiple targets, thus contributing to the overall effectiveness and reliability of MIMO radar systems. [ABSTRACT FROM AUTHOR]

In recent years, the safety of cables of cable‐stayed bridges has attracted extensive attention, and the rise of radar technology has provided new possibilities for the performance testing of cable‐stayed bridges. In this regard, this paper introduces the principle of microwave radar measurement technology and uses the microwave cable force measuring instrument to perform noncontact microwave radar cable force measurement on a total of 72 stay cables in Shifeng Bridge and San‐Jiadian Bridge compared with the measurement results of the cloud smart collector. The results show that the fundamental frequency deviation rate of 71 cables is within 3%, and the cable force deviation rate of 67 cables is within 5%. This shows that the microwave radar cable force measurement method has a high degree of reliability and accuracy; meanwhile, it is also found that the forces on the left and right sides of the two bridges are in a symmetrical state, and the structure is in good condition; the signal characteristics are obvious, and the simultaneous measurement of multiple cables can be realized, and it is not limited by weather, site, and the length of the stay cable and has strong practical value. [ABSTRACT FROM AUTHOR]

Currently, hydraulic fracturing technology is widely implemented for controlling the surrounding rock and enhancing permeability in low-permeability coal seams. Evaluating the effectiveness of hydraulic fracturing is a critical component of hydraulic fracturing operations. This study addresses the challenges in assessing the location, extension angle, and initial width of fracturing fractures within the current framework of hydraulic fracturing effectiveness evaluation in coal mines. We propose utilizing single-hole reflection borehole radar to evaluate the hydraulic fracturing effect, and through numerical simulation, we analyze the response characteristics of borehole radar when detecting various hydraulic fracturing-induced cracks. Initially, five models representing hydraulic fracturing cracks and two models for non-hydraulic fracturing cracks were established. Subsequently, the responses of borehole radar with central frequencies of 100, 200, and 400 MHz to cracks of identical shapes were analyzed. Additionally, the response characteristics of borehole radar with a 200 MHz central frequency to cracks of varying lengths (1, 2, 3 m), widths (4, 8, 40 cm), and angles (90°, 45°, 15°) were examined. Finally, a comparative analysis was conducted between hydraulic and non-hydraulic fracturing cracks. A branch hole was employed to simulate a hydraulic fracturing crack, allowing for an analysis of the borehole radar's response characteristics in practical scenarios. The findings indicate that borehole radar is a viable tool for assessing hydraulic fracturing effects, providing a theoretical foundation for identifying the position of cracks, evaluating their effectiveness, and determining the regional effectiveness of the hydraulic fracturing crack system. [ABSTRACT FROM AUTHOR]

Convective snow (CS) presents a significant hazard to motorists and is one of the leading causes of weather-related fatalities on Pennsylvania roadways. Thus, understanding environmental factors promoting CS formation and organization is critical for providing relevant and accurate information to those impacted. Prior research has been limited, mainly focusing on frontal CS bands often called "snow squalls"; thus, these studies do not account for the diversity of CS organizational modes that is frequently observed, highlighting a need for a robust climatology of broader CS events. To identify such events, a novel, radar-based CS detection algorithm was developed and applied to WSR-88D radar data from 10 cold seasons in central Pennsylvania, during which 159 cases were identified. Distinct convective organization modes were identified: linear (frontal) snow squalls, single cells, multicells, and streamer bands. Each algorithm-flagged radar scan containing CS was manually classified as one of these modes. Interestingly, the most-studied frontal mode only occurred <5% of the time, whereas multicellular modes dominated CS occurrence. Using the times associated with each CS mode, synoptic and local environmental information from model analyses was investigated. Key characteristics of CS environments compared to null cases include a 500-hPa trough in the vicinity, lower-tropospheric conditional instability, and sufficient moisture. Environments favorable for the different CS modes featured statistically significant differences in the 500-hPa trough axis position, surface-based CAPE, and the unstable layer depth, among others. These results provide insights into forecasting CS mode, explicitly presented in a forecasting decision tree. Significance Statement: Convective snow events such as snow squalls are a leading cause of weather-related deaths on Pennsylvania roads. Research into these events is limited, thus negatively impacting forecast skill. To better understand convective snow event frequency of occurrence, inter- and intra-annual variability, and their supporting environments, we performed a 10-yr radar-based climatology of these events. We report the results of this climatology and on the statistically significant differences in their supporting environments. The latter are used to propose a forecasting framework for convective snow, which may improve the predictability of convective snow in an operational setting. [ABSTRACT FROM AUTHOR]

To ensure the ship navigation safety, it is necessary to detect the targets in the background of sea clutter around the ship. The most commonly used target detection equipments for ship navigation are marine radar and automatic identification system (AIS) device. However, marine radar echoes are often mixed with multiple clutter, and weak targets are not always equipped with AIS device, neither provides AIS information with low accuracy, leading to difficulty with target detection. Moreover, existing marine monitoring systems are accustomed to using the traditional information fusion methods for target detection, and the accuracy of identifying the weak targets is relatively low. To make full use of radar echo and AIS information and improve the accuracy of target detection, a Marine radar monitoring Internet of Things system is proposed, in which marine radars work in both scanning and staring modes. By adopting image segmentation and deep learning methods, the proposed design can enable accurate perception of weak target detection based on plan‐position indicator (PPI) images. A case study based on the selected X‐band radar datasets shows that the proposed design can achieve high target identification accuracy. [ABSTRACT FROM AUTHOR]

Radar technology is critical for subsurface sensing applications since it enables the extraction of surface information remotely without being affected by environmental factors such as illumination and weather conditions. Hence, their usage on drones has become extremely essential as these vehicles need to accumulate data while flying over different and possibly difficult-to-access terrains regardless of visibility conditions. A frequency-modulated continuous-wave (FMCW) single input multiple output (SIMO) radar system is developed to provide near real-time radar images onboard. The system is comprised of a transceiver module, specifically designed for synthetic aperture radar (SAR) applications, to produce images in real-time. GPU-based computation is utilized to optimize processing power, while enabling range profiles to be generated and SIMO SAR images to be created during flight. The system can be easily mounted on UAV or multirotor systems. The detection and ranging performance of the system are examined by performing several experiments at the campus. Objects up to 100 metres are successfully detected by the radar system. The system demonstrates excellent ability to generate 2D SIMO SAR images, effectively identifying building and guard-box exteriors that are consistent with images from Google Earth. [ABSTRACT FROM AUTHOR]

Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring. [ABSTRACT FROM AUTHOR]

A 77 GHz frequency-modulated continuous wave (FMCW) radar was utilized to extract biomechanical parameters for gait analysis in indoor scenarios. By preprocessing the collected raw radar data and eliminating environmental noise, a range–velocity–time (RVT) data cube encompassing the subjects' information was derived. The strongest signals from the torso in the velocity and range dimensions and the enveloped signal from the toe in the velocity dimension were individually separated for the gait parameters extraction. Then, six gait parameters, including step time, stride time, step length, stride length, torso velocity, and toe velocity, were measured. In addition, the Qualisys system was concurrently utilized to measure the gait parameters of the subjects as the ground truth. The reliability of the parameters extracted by the radar was validated through the application of the Wilcoxon test, the intraclass correlation coefficient (ICC) value, and Bland–Altman plots. The average errors of the gait parameters in the time, range, and velocity dimensions were less than 0.004 s, 0.002 m, and 0.045 m/s, respectively. This non-contact radar modality promises to be employable for gait monitoring and analysis of the elderly at home. [ABSTRACT FROM AUTHOR]

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna. [ABSTRACT FROM AUTHOR]

In this letter, a novel algorithm is proposed to design symbol‐level waveform for dual‐functional radar‐communication (DFRC) systems with low range sidelobe. Different from current schemes design waveform at the block level, a phase response constraint is introduced at the symbol level to provide an additional degree of freedom to decrease the range sidelobe in the pulse compression procedure, which is highly desired in radar systems. In particular, the phase response at the target direction is constrained to be similar to the given reference phase, and the matching error between the designed beampattern and the desired one is minimized subject to the constant modulus constraint. Furthermore, to guarantee the quality of service for communication, constructive interference is exploited at the symbol level for each communication user. An alternating direction method of multipliers algorithm is also proposed to solve the resulting nonconvex optimization problem with tractable subproblems solved sufficiently by the manifold optimization and standard quadratic problem. Numerical simulation results demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The rapid identification of structural damage in pavements remains a challenging issue. This paper proposes an automatic identification method that combines nondestructive testing, the ground penetrating radar (GPR), and object detection (OD) models for pavement structural assessment. Radar wave image data were collected from a 40 km single-lane track road and processed to create the training data set of 2,000 labeled images with two types of structural distress. Four OD models, including Faster R-CNN, YOLOv5, YOLOv8, and DETR, were adapted and trained on the data set to recognize structural damage in the images, and their prediction performances were compared. The results demonstrated that YOLOv8 was the state-of-the-art (SOTA) model, which achieved precise identification of the location and type of pavement structural damage with a mean average precision (mAP) of 98.5%. This study contributes to the improvement of latest OD models in the non-destructive inspection, facilitating the development of real-time automated pavement assessment and maintenance decision systems. Practical Applications: This research addresses a common problem in civil engineering: how to quickly detect all hidden damage in pavement structures over long stretches of highway projects. Although there has been previous research in this area, finding a more reliable solution has proven to be a challenging goal. This study presents a method that utilizes the geological radar, a nondestructive testing technology, meaning it will not harm the road, along with artificial intelligence (AI) computer models to identify damage under the pavement surface. Think of it as teaching a computer to recognize cracks or looseness in the radar images of the road. Four different computer models were trained using 2,000 road images with known damage to teach them how to identify different damage features. The results were impressive—the computer could accurately identify the location and type of damage with a high level of confidence. In fact, the best model achieved a 98.5% score measured by mean average precision, which indicates its effectiveness. This research has significant practical applications as it streamlines the process of inspecting roads for structural problems, making it faster and more efficient. It can help with real-time evaluations and decisions about road maintenance. [ABSTRACT FROM AUTHOR]

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications. [ABSTRACT FROM AUTHOR]

In recent years, the high-frequency hybrid sky-surface wave radar (HSSWR) has been increasingly used for target detection applications. Nevertheless, the specific bistatic system layout and the phase path disturbances induced by the ionospheric propagation channel may severely spread the sea clutter spectrum, thereby deteriorating the detection ability of the HSSWR for slow-moving targets. In this work, a novel subspace method based on the hybrid use of the amplitude and phase estimator (APES) and the orthogonal projection (OP) in the spatial–temporal domain, denoted as the APES-OP method, is proposed to suppress the spreading first-order sea clutter of the HSSWR. The distribution characteristics of targets and first-order sea clutter in the spatial–temporal domain were investigated, and a time-domain subspace signal model was adopted to describe targets perturbed by ionospheric phase path modulation. An APES filter was adopted to filter out the potential targets with a traversal approach to avoid attenuating desired signals while suppressing sea clutter. After that, sampling data from multi-channels and slow-time domains at the cell under test were employed to construct a spatial–temporal matrix, which was then utilized to obtain the sea clutter subspace by singular value decomposition. Simulation results indicate that the proposed algorithm can suppress sea clutter while retaining the target, even if the target is buried by sea clutter. The processing results of measured data further demonstrate the efficiency of the proposed algorithm. After sea clutter suppression, the target obscured by clutter can be revealed, and the signal-to-clutter ratio of the target is greatly improved. [ABSTRACT FROM AUTHOR]

High-frequency radars (HFRs) provide remote information on ocean surface velocity in extended coastal areas at high resolutions in space (O (km)) and time (O (h)). They directly produce radial velocities (in the radar antenna's direction) combined to provide total vector velocities in areas covered by at least two radars. HFRs are a key element in ocean observing systems, with several important environmental applications. Here, we provide an assessment of the HFR-TirLig network in the NW Mediterranean Sea, including results from the gap-filling open-boundary modal analysis (OMA) using in situ velocity data from drifters. While the network consists of three radars, only two were active during the assessment experiment, so the test also includes an area where the radial velocities from only one radar system were available. The results, including several metrics, both Eulerian and Lagrangian, and configurations, show that the network performance is very satisfactory and compares well with the previous results in the literature in terms of both the radial and total combined vector velocities where the coverage is adequate, i.e., in the area sampled by two radars. Regarding the OMA results, not only do they perform equally well in the area sampled by the two radars but they also provide results in the area covered by one radar only. Even though obviously deteriorated with respect to the case of adequate coverage, the OMA results can still provide information regarding the velocity structure and speed as well as virtual trajectories, which can be of some use in practical applications. A general discussion on the implications of the results for the potential of remote sensing velocity estimation in terms of HFR network configurations and complementing gap-filling analysis is provided. [ABSTRACT FROM AUTHOR]

In the increasingly complex electromagnetic environment, main lobe jamming significantly degrades the performance of a wideband radar system. To mitigate this issue, this paper developed a wideband main lobe jamming suppression algorithm based on a distributed array radar. Firstly, this algorithm utilizes eigen-projection matrix processing in the main array to cancel out the main lobe jamming for main lobe maintenance, and then suppresses side lobe jamming through null constraint beamforming. Subsequently, the large aperture of the full array is leveraged to form a narrow beam directed toward the main lobe interference. Finally, joint beamforming using the minimum mean square error criterion is employed. In scenarios where both main lobe jamming and side lobe jamming exist, this algorithm can adaptively cancel main lobe wideband jamming, suppress side lobe wideband jamming, and effectively control the significant loss of the desired wideband signals caused by main lobe jamming within a smaller angular range. Simulation results validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, to achieve effective interference, the integrated waveform must overlap with the adversary radar signal within the frequency band. Consequently, the detection echoes are susceptible to the strong co-frequency direct wave generated by the adversary signals. This paper proposes a co-frequency direct wave interference suppression algorithm based on 2D generalized smoothed- l 0 norm sparse recovery. The algorithm exploits a joint dictionary comprising our integrated signals and adversary signals, along with the sparsity of 2D range-Doppler maps. The direct solution of the sparse decomposition optimization problem, formulated for the entire echo matrix, enhances the target detection performance for integrated signals even in the presence of robust co-frequency direct wave interference. Furthermore, the proposed method achieves robustness to interference of varying intensities through the adaptive updating and adjustment of relevant parameters. The effectiveness of the proposed method is validated through simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper presents a novel, cost-effective technique for estimating the Doppler effect in the time domain using a single pulse and subsequently leveraging the precise Doppler value to recover the radar waveform. The proposed system offers several key advantages over existing techniques, including the ability to calculate the target speed without any frequency ambiguity and the ability to detect a wide range of target speeds. These two features are not available in any existing techniques, including the conventional moving target detection (MTD) processor. To ensure improved accuracy and robust estimation, the system employs ensemble and iterative techniques by recursively and efficiently reducing the Doppler residues from the signal. Furthermore, the proposed system demonstrates effective signal recovery of a well-known bi-phase code shape at low signal-to-noise ratios in just a few iterations. The performance evaluation of the new algorithm demonstrates its practicability and its superiority over traditional radar systems. Implementation on software-defined radio (SDR) reveals that the proposed system excels in Doppler estimation and signal recovery at low SNRs, demonstrating promising results. [ABSTRACT FROM AUTHOR]

Hand gesture recognition is pivotal in facilitating human–machine interaction within the Internet of Things. Nevertheless, it encounters challenges, including labeling expenses and robustness. To tackle these issues, we propose a semi-supervised learning framework guided by pseudo-label consistency. This framework utilizes a dual-branch structure with a mean-teacher network. Within this setup, a global and locally guided self-supervised learning encoder acts as a feature extractor in a teacher–student network to efficiently extract features, maximizing data utilization to enhance feature representation. Additionally, we introduce a pseudo-label Consistency-Guided Mean-Teacher model, where simulated noise is incorporated to generate newly unlabeled samples for the teacher model before advancing to the subsequent stage. By enforcing consistency constraints between the outputs of the teacher and student models, we alleviate accuracy degradation resulting from individual differences and interference from other body parts, thereby bolstering the network's robustness. Ultimately, the teacher model undergoes refinement through exponential moving averages to achieve stable weights. We evaluate our semi-supervised method on two publicly available hand gesture datasets and compare it with several state-of-the-art fully-supervised algorithms. The results demonstrate the robustness of our method, achieving an accuracy rate exceeding 99% across both datasets. [ABSTRACT FROM AUTHOR]

Over several decades, high-frequency (HF) radars have been employed for remotely measuring various ocean surface parameters, encompassing surface currents, waves, and winds. Wind direction and speed are usually estimated from both first-order and second-order Bragg-resonant scatter from two or more HF radars monitoring the same area of the ocean surface. This limits the observational domain to the common area where second-order scatter is available from at least two radars. Here, we propose to estimate wind direction and speed from the first-order scatter of a single HF radar, yielding the same spatial coverage as for surface radial currents. Wind direction is estimated using the ratio of the positive and negative first-order Bragg peaks intensity, with a new simple algorithm to remove the left/right directional ambiguity from a single HF radar. Wind speed is estimated from wind direction and de-tided surface radial currents using an artificial neural network which has been trained with in situ wind speed observations. Radar-derived wind estimations are compared with in situ observations in the Lower Saint-Lawrence Estuary (Quebec, Canada). The correlation coefficients between radar-estimated and in situ wind directions range from 0.84 to 0.95 for Wellen Radars (WERAs) and from 0.79 to 0.97 for Coastal Ocean Dynamics Applications Radars (CODARs), while the root mean square differences range from 8° to 12° for WERAs and from 10° to 19° for CODARs. Correlation coefficients between the radar-estimated and the in situ wind speeds range from 0.89 to 0.93 for WERAs and from 0.81 to 0.93 for CODARs, while the root mean square differences range from 1.3 m.s−1 to 2.3 m.s−1 for WERAs and from 1.6 m.s−1 to 3.9 m.s−1 for CODARs. [ABSTRACT FROM AUTHOR]

An ensemble three‐dimensional ensemble‐variational (3DEnVar) data assimilation (En3DA) approach that directly assimilates radar reflectivity was developed based on the Weather Research and Forecasting model data assimilation system. This system adopts radar reflectivity as the control variable to avoid the need for a tangent linear and adjoint of the observation operator. Flow‐dependent covariance was introduced via ensemble forecasts updated by a group of 3DEnVar. The performance of the En3DA system was examined for two selected cases of high‐impact severe tornadic supercells over China. Results for both cases indicated that the structure of the storms in terms of intensity, coverage, and associated low‐level mesocyclones were analysed more accurately when using the En3DA approach than when adopting the 3DVar method. Hydrometeor analysis showed that En3DA provided a more physically reasonable increment of hydrometeors compared to 3DVar, especially for the graupel mixing ratio. Furthermore, the En3DA forecast was better than the 3DVar forecast throughout the forecast period for both studied cases. En3DA produced smaller errors in terms of intensity and location for supercell forecasts with respect to reflectivity and reflectivity swaths. Furthermore, the quantitative forecast skill of radar reflectivity was improved using En3DA. Errors in the wind, temperature, and water vapor forecast fields produced by En3DA were also reduced compared to those of 3DVar. Diagnostics revealed that En3DA predicted an enhanced low‐level cold pool and stronger outflows in the forward‐flank downdraft and the rear‐flank downdraft regions, which are important for tornadogenesis. [ABSTRACT FROM AUTHOR]

Adding high-dose-rate brachytherapy (BT) boost to external beam radiation therapy (EBRT) improves biochemical control but may affect patient-reported quality of life (QOL). We sought to determine long-term QOL outcomes for EBRT+BT versus EBRT alone. This was a post hoc analysis of the Trans-Tasman Radiation Oncology Group 03.04 Randomized Androgen Deprivation and Radiotherapy (TROG 03.04 RADAR) trial. Only patients who received 74 Gy conventionally fractionated EBRT (n = 260) or 46 Gy conventionally fractionated EBRT plus 19.5 Gy in 3 fractions high-dose-rate BT boost (n = 237) were included in this analysis. The primary endpoint was patient-reported QOL measured using the European Organisation for Research and Treatment of Cancer QOL (EORTC QLQ-C30) and prostate-specific QOL module (EORTC QLQ-PR25) questionnaires. We evaluated temporal changes in QOL scores, rates of symptom resolution, and the proportion of men who had decrements from baseline of >2 × the threshold for minimal clinically important change (2 × MCIC) for each domain. At 5, 17, and 29 months after radiation therapy, the EBRT+BT group had 2.5 times (95% confidence interval [CI], 1.4-4.2; P <.001), 2.9 times (95% CI, 1.7-4.9; P <.001), and 2.6 times (95% CI, 1.4-4.6; P =.002) greater odds of reporting 2 × MCIC in urinary QOL score compared with EBRT. There were no differences beyond 29 months. EBRT+BT led to a slower rate of urinary QOL symptom score resolution up to 17 months after radiation therapy compared with EBRT (P <.001) but not at later intervals. In contrast, at the end of the radiation therapy period and at 53 months after radiation therapy, the EBRT+BT group had 0.65 times (95% CI, 0.44-0.96; P =.03) and 0.51 times (95% CI, 0.32-0.79; P =.003) the odds of reporting 2 × MCIC in bowel QOL symptom scores compared with EBRT. There were no significant differences in the rate of bowel QOL score resolution. There were no significant differences in global health status or sexual activity scores between the 2 groups. There were no persistent differences in patient-reported QOL measures between EBRT alone and EBRT+BT. BT boost does not appear to negatively affect long-term, patient-reported QOL. [ABSTRACT FROM AUTHOR]

To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains for the period of 2021–2023. The experiment objective was to improve understanding of the sensitivity of Sentinel-1 C-band backscatter radar signals to snow. The data were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The data show that scattering is present throughout the snow volume, although it is limited for low snow densities. Contrasting layer interfaces, ice features and metamorphic snow can have considerable impact on the backscatter signal. During snowmelt periods, wet snow absorbs the signal, and the soil backscatter becomes negligible. A comparison of the vertically integrated tower radar data with Sentinel-1 data shows that both systems have similar temporal behavior, and both feature an increase in backscatter during the dry-snow period in 2021–2022, even during weeks of nearly constant snow depth, likely due to morphological changes in the snowpack. The results demonstrate that C-band radar is sensitive to the dominant seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy, melt–freeze cycles and snow wetness may complicate satellite-based snow depth retrievals. [ABSTRACT FROM AUTHOR]

Climate change is altering the timing of seasonal events for many taxa.There is limited understanding of how northward/southward songbird migration follows or is limited by the latitudinal progression of seasonal transitions. Consistent environmental conditions that migrating birds encounter across latitudes likely represent or correlate with important resources or limiting factors for migration.We tested whether migratory passage—observed via radar—consistently tracked land surface variables and phenophases across latitudes in the US Central Flyway in both spring and fall.The daily temperatures, precipitation and vegetation greenness occurring on 10%, 50% and 90% cumulative passage dates changed substantially with latitude, indicating that most migrants experienced rapidly changing conditions as they headed north or south. Temperature did not limit the progression of migration in either season.Peak spring migration in the southern US occurred nearly 40 days after the spring green wave, the northward progression of vegetation growth, but nearly caught up to green‐up at 48° N. Spring migration phenology may have evolved to prioritize earlier arrival for breeding.Across all latitudes, peak fall migration coincided with the same land surface phenophase, an interval of 26 days prior to dormancy onset. Migrants may rely on phenological events in vegetation during fall stopovers.Considering that (a) migratory passage tracked fall land surface phenology across latitudes at a continental scale, (b) previous studies at local scales have demonstrated the importance of fruit during fall migratory stopover and (c) fruiting phenology in North America is occurring later over time while fall migration is advancing, the potential for mismatch between fall fruiting and bird migration phenology urgently needs further investigation. [ABSTRACT FROM AUTHOR]

To enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios. [ABSTRACT FROM AUTHOR]

The application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non‐linear filter problems, when prediction equations are continuous‐time and the update equations are discrete‐time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete‐time non‐linear filter problems, when prediction equations and update equations are discrete‐time. In order to assess the performance of the filters, the authors consider the non‐linear dynamics for a re‐entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re‐entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re‐entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins‐Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter. [ABSTRACT FROM AUTHOR]

Modern radar systems often face various interference signals in complex and rapidly changing electronic environments. The task of suppressing this interference in the radar echo signal to extract vital information is challenging. A radar interference suppression method is proposed based on a generative adversarial network (GAN). This method effectively recovers the target signal from the echo signal, which contains interference and noise, by leveraging the powerful fitting ability of GAN. Specifically, this method was tested using coherent suppression interference, smart noise interference, and noise frequency modulation suppression interference. We compared the proposed GAN method with recurrent neural network, short‐time Fourier transform time‐varying filtering, short‐time fractional Fourier transform time‐varying filtering algorithms and RNN approach. The results show that the interference suppression algorithm based on GAN is superior to the other three algorithms. [ABSTRACT FROM AUTHOR]

Beamforming is an effective way of resolving target direction and anti‐jamming in short wave (SW) radar systems. In conventional beamforming (CBF) at a certain frequency, to get high resolution, the array aperture should be increased, and this is often not allowed in practical applications. A new narrow beam forming (NBF) method for beam sharpening based on the generalised oblique projection (GOP) filter with a flexible parameter is proposed. This method uses a GOP filter bank to form deep nulls in the undesired azimuth range on the pattern and utilises the logic product process to synthesise the GOP filters' outputs and thus obtains a narrow beam. Compared to traditional beamforming methods, the result of NBF has the characteristics of narrower beam width and bigger side lobe suppression ratio (SLSR). Especially, a narrower beam can be obtained in the case of a small array aperture, which is valuable for practical applications. Experimental results of the range‐Doppler spectrum of short wave radar show that this narrow beam forming method can achieve super resolution of targets within a wide beam and greatly suppress clutter. Therefore, NBF can improve the azimuth resolution and achieve interference suppression in a conventional beam. [ABSTRACT FROM AUTHOR]

Forward‐looking radar imaging is a top priority due to a variety of applications. An airborne forward‐looking radar imaging approach via a modified propagator method (MPM) based on the planar phased array is proposed. It can obtain a better focusing effect without range migration correction. Through the MPM algorithm, the azimuth resolution can be enhanced greatly. Compared with the conventional synthetic aperture radar imaging algorithm, the left‐right ambiguity process can be avoided by generating two‐dimensional spatial spectra. Furthermore, the imaging results of different numbers of elements are given to provide an assessment of the imaging performance. Additionally, the authors proposed to apply the scanning planar phased array configuration to airborne forward‐looking two‐dimensional imaging for the first time. The validity of the method is proven by experiments. The image is more focused. Furthermore, the results of the experiments verify that the proposed method is fit for high‐speed situation and aircraft height variation condition. [ABSTRACT FROM AUTHOR]

Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use. [ABSTRACT FROM AUTHOR]

Robustness against Electronic Warfare/Electronic Defence attacks represents an important advantage of Noise Radar Technology (NRT). An evaluation of the related Low Probability of Detection (LPD) and of Intercept (LPI) is presented for Continuous Emission Noise Radar (CE‐NR) waveforms with different operational parameters, that is, "tailored", and with various "degrees of randomness". In this frame, three different noise radar waveforms, a phase Noise (APCN) and two "tailored" noise waveforms (FMeth and COSPAR), are compared by time–frequency analysis. Using a correlator (i.e. a two antennas) receiver, assuming a complete knowledge of the band (B) and duration (T) of the coherent emission of these waveforms, it will be shown that the LPD features of a CE‐NR do not significantly differ from those of any CE radar transmitting deterministic waveforms. However, in real operations, B and T are unknown; hence, assuming an instantaneous bandwidth estimation will show that the duration T can be estimated only for some specific "tailored" waveforms (of course, not to be operationally used). The effect of "tailoring" is analysed with prospects for future work. Finally, some limitations in the classification of these radar signals are analysed. [ABSTRACT FROM AUTHOR]

A novel method is proposed for rail fastener detection based on millimetre‐wave (mmWave) radar, mmWave radar fastener counter (MMW‐FC), which can accurately detect and record the fasteners in real‐time as the train traverses its route. Under circumstances where GNSS signals remain unavailable for prolonged durations, precise train localisation can be accomplished by correlating the number of fasteners derived from this method with the corresponding track map. Initially, MMW‐FC utilises fast Fourier transform and adaptive beamforming to focus the energy reflected from fasteners. Subsequently, it applies an adaptive template‐matching algorithm to detect each fastener. Furthermore, by leveraging known fastener spacing and the average time for trains to pass adjacent fasteners, the Kalman filter can execute precise speed tracking, used as a speed reference when adjusting the matching template adaptively. The experimental results indicate that the proposed method can precisely count the fasteners the train encounters in diverse road and speed conditions. The fastener counter maintains the Counting Error less than 0.067%, the speed error stays below 1.8 km/h, and the maximum values of the mean absolute error and root mean square error for speed are 0.7337 and 0.9584 km/h, respectively. [ABSTRACT FROM AUTHOR]

The utilisation of continuous random waveforms for radar, that is, noise radar, has been extensively studied as a candidate for low probability of intercept operation. However, compared with the more traditional pulse‐Doppler radar, noise radar systems are significantly more complicated to implement, which is likely why few systems exist. If noise radar systems are to see the light of day, system design, implementation, limitations etc., must be investigated. Therefore, the authors examine and detail the implementation of a real‐time noise radar system on a field programmable gate array. The system is capable of operating with 100% duty cycle, 200 MHz bandwidth, and 268 ms integration time while processing a range of about 8.5 km. Additionally, the system can perform real‐time moving target compensation to reduce cell migration. System performance is primarily limited by the memory bandwidth of the off‐chip dynamic random access memory. [ABSTRACT FROM AUTHOR]

A compact three-band symmetrical triangular linearly frequency-modulated (TLFM) continuous-wave radar based on the gain-switched DFB laser is proposed in this paper. By introducing the low central-frequency TLFM signal with high-power to modulate the DFB laser, the DFB laser would enter into the gain-switching state and a coherent and stable three-band broadband TLFM signals (S-/C-/X-band) with a bandwidth of 0.4*N GHz (N = 1, 2, 3) would be generated after the photodetector. Based on this condition, one three-band TLFM continuous-wave radar system can be realized. The system does not require an external modulator in multi-band generation, and uses a single-channel transceiver module to achieve simultaneous transmission and echo dechirp processing of multi-band radar, which greatly reduces the deployment difficulty of the multi-band radar system, so the cost, size, weight and other characteristics of the system are expected to be reduced. In the current proof-of-concept experiment, the range resolution of the three-band TLFM signal can reach 13.8 cm, compared with the single-band TLFM signal which cannot distinguish the targets. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vibration measurements play a critical role in troubleshooting mechanical equipment and assessing the structural integrity of buildings. However, conventional vibration measurement methods rely on contact-based approaches, such as the attachment of accelerometers to the target object, leading to complex equipment deployment. Therefore, non-contact vibration measurement has attracted great attention but has yet to be fully addressed. In this paper, we propose DeepVib, a non-contact vibration measurement system that enables accurate micron-level vibration monitoring. First, we introduce a series of signal processing algorithms to extract the vibration object motion from mmWave reflection signals. Then, we design a deep neural network to effectively suppress noise interference and achieve outputting higher signal-to-noise ratio data. Finally, we eliminate static reflections with geometric-based method to recover the vibrations of the target. The experimental results show that our non-contact measurement method can accurately measure the vibration at the micron level with the average error of vibration frequency less than 0.1%. For the amplitude below 100μm, the median error of estimation is 7.23%. In addition, DeepVib reduces the estimation error of 80th-percentile amplitude by 56.60% compared with the conventional method. [ABSTRACT FROM AUTHOR]

In remote dynamic hand-gesture recognition, uncertainties in timing and distance of gesture occurrences, coupled with the subtle bodily perturbations induced by arm movements, pose substantial challenges to the accurate extraction of gesture features. In this paper, we propose a lightweight real-time gesture recognition system based on support vector machines. By analyzing the Doppler features of different motion states, a Doppler weighting factor was constructed to suppress bodily micro-motion interference in the range-time spectrum, and achieve foreground extraction of gesture signals concurrently. Furthermore, prior to the extraction of HOG features, we employ Gaussian filtering to suppress abrupt transitions and noise inherent in the gesture signals. This preprocessing significantly enhances the stability of feature extraction. Subsequently, the extracted features are input into an SVM for training and classification. Experimental results demonstrate that, for five distinct gestures exhibited in two different states –– standing and seated –– within a range of 1 to 5 meters, the recognition accuracy reaches 96%. This proves the feasibility of the proposed methodology, and its potential to realize real-time gesture recognition. [ABSTRACT FROM AUTHOR]

Reconfigurable Intelligent Surface (RIS) has been recognized as the core technology for 6G. However, transmission signals may be blocked by obstacles due to mobility and complex transmission in vehicular scenario. In order to solve the above issue, this paper researches the RIS assisted dual function radar communication system (DFRC) in the Internet of Vehicles (IoV), and proposes an alternating optimization algorithm, named joint guaranteed radar communication (JGRC) algorithm, with sensed power and semidefinite relaxation to maximize the spectral efficiency of the communication vehicle while simultaneously ensuring the radar sensing performance of the target vehicle. Specifically, the proposed JGRC algorithm can be divided into two stages. Stage one, active beamforming of Base Station (BS) transmission is optimized with the power constraint of the sensed target vehicle. Stage two, the optimized active beamforming is applied, and the phase shift matrix is optimized. Then, RIS phase shift optimization problem is transformed into Quadratically Constrained Quadratic Programming (QCQP), and rank-1 non-convex constraint is relaxed by semidefinite relaxation, which can be transformed into a standard semidefinite programming solved by Matlab toolbox. Simulation results demonstrate that compared to the RIS element random reflection, the proposed algorithm achieves higher spectral efficiency by 9.5%. [ABSTRACT FROM AUTHOR]

The quality of surface water is closely related to human's production and livelihood. Water salinity is one of the key indicators of water quality assessment. Recently, there has been an increased salinization problem of surface water in many regions of the world, making it necessary to timely monitor the salinity of surface water. Water salinity sensing could be challenging when it comes to surface water with complicated basin and tributaries, where existing methods fail to satisfy both efficiency and accuracy requirements. To address this problem, we propose a novel water salinity sensing system USalt, which leverages the high mobility of UAV and the contactless sensing ability of IR-UWB radar, and realizes fast and accurate water salinity sensing for surface water. Specifically, we design novel methods to eliminate the contamination in raw received radar signals and extract salinity-related features from radar signals. Furthermore, we adopt a neural network model ssNet to precisely estimate water salinity using the extracted features. To efficiently adapt ssNet to different environments, we customize meta learning and design a meta-learning framework mssNet. Extensive real-world experiments carried out by our UAV-based system illustrate that USalt can accurately sense the salinity of water with an MAE of 0.39 g/100 mL. [ABSTRACT FROM AUTHOR]

The successful operation of autonomous vehicles hinges on their ability to accurately identify objects in their vicinity, particularly living targets such as bikers and pedestrians. However, visual interference inherent in real-world environments, such as omnipresent billboards, poses substantial challenges to extant vision-based detection technologies. These visual interference exhibit similar visual attributes to living targets, leading to erroneous identification. We address this problem by harnessing the capabilities of mmWave radar, a vital sensor in autonomous vehicles, in combination with vision technology, thereby contributing a unique solution for liveness target detection. We propose a methodology that extracts features from the mmWave radar signal to achieve end-to-end liveness target detection by integrating the mmWave radar and vision technology. This proposed methodology is implemented and evaluated on the commodity mmWave radar IWR6843ISK-ODS and vision sensor Logitech camera. Our extensive evaluation reveals that the proposed method accomplishes liveness target detection with a mean average precision of 98.1%, surpassing the performance of existing studies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As one of the fundamental vehicular perception technologies, millimeter-wave radar's accuracy in angle measurement affects the decision-making and control of vehicles. In order to enhance the accuracy and efficiency of the Direction of Arrival (DoA) estimation of radar systems, a super-resolution angle measurement strategy based on the Fast Deterministic Maximum Likelihood (FDML) algorithm is proposed in this paper. This strategy sequentially uses Digital Beamforming (DBF) and Deterministic Maximum Likelihood (DML) in the Field of View (FoV) to perform a rough search and precise search, respectively. In a simulation with a signal-to-noise ratio of 20 dB, FDML can accurately determine the target angle in just 16.8 ms, with a positioning error of less than 0.7010. DBF, the Iterative Adaptive Approach (IAA), DML, Fast Iterative Adaptive Approach (FIAA), and FDML are subjected to simulation with two targets, and their performance is compared in this paper. The results demonstrate that under the same angular resolution, FDML reduces computation time by 99.30 % and angle measurement error by 87.17 % compared with the angular measurement results of two targets. The FDML algorithm significantly improves computational efficiency while ensuring measurement performance. It provides more reliable technical support for autonomous vehicles and lays a solid foundation for the advancement of autonomous driving technology. [ABSTRACT FROM AUTHOR]

The University of Southern Mississippi has developed the coastal CUBEnet environment. Coastal CUBEnet is a high-resolution, coastal ocean sensor, modeling, and data sharing web-based network that provides the environmental intelligence needed to support the complex modeling of the interlinked processes in the northern Gulf of Mexico. With near-real time data delivery via a common infrastructure, CUBEnet uses state of the art sensors to provide a set of networked measurements systems, visualization tools, and model developments to gain an understanding of the Gulf of Mexico's marine environments. CUBEnet is also a mechanism for improved human engagement with Gulf of Mexico resources and provides stake holders with the data needed to make informed coastal, environmental, and economic decisions. The Coastal CUBEnet's data environment utilizes both stationary and uncrewed mobile systems and high-resolution distributed sensors to create a networked platform across the northern coastal Gulf of Mexico. CUBEnet's modeling environment has developed an implementation of The Coupled Ocean Atmosphere Wave Sediment Transport (COAWST) Model for the Mississippi Bight region that has been applied to investigate shore to shelf advective exchange processes, and their influence on coastal water quality conditions that support the region's prolific marine ecosystem. CUBEnet's modeling environment provides prototype modeling applications that are supported by real-time observations of key coastal environmental variables. CUBEnet's Web accessible visualization tools provide parameter fields and vertical profiles from hydrodynamic models and field observations. Nowcasts and forecast results are available for the Eastern LA coastline, MS coastline, Mobile Bay, and the West coast of Florida. [ABSTRACT FROM AUTHOR]

This study addresses the challenges that high-noise environments and complex multi-speaker scenarios present in civil aviation radio communications. A novel radiotelephone communications speaker diffraction network is developed specifically for these circumstances. To improve the precision of the speaker diarization network, three core modules are designed: voice activity detection (VAD), end-to-end speaker separation for air–ground communication (EESS), and probabilistic knowledge-based text clustering (PKTC). First, the VAD module uses attention mechanisms to separate silence from irrelevant noise, resulting in pure dialogue commands. Subsequently, the EESS module distinguishes between controllers and pilots by levying voice print differences, resulting in effective speaker segmentation. Finally, the PKTC module addresses the issue of pilot voice print ambiguity using text clustering, introducing a novel flight prior knowledge-based text-related clustering model. To achieve robust speaker diarization in multi-pilot scenarios, this model uses prior knowledge-based graph construction, radar data-based graph correction, and probabilistic optimization. This study also includes the development of the specialized ATCSPEECH dataset, which demonstrates significant performance improvements over both the AMI and ATCO2 PROJECT datasets. [ABSTRACT FROM AUTHOR]

Aerodynamic/stealth optimization is a key issue during the design of a stealth UAV. Balancing the weight of different incident angles of the RCS and combining stealth characteristics with aerodynamic characteristics are hotspots of aerodynamic/stealth optimization. To address this issue, this paper introduces a radar detection probability model to solve the weight balance problem of incident angles of the RCS and a penetration efficiency model to transfer the multi-object optimization into single-objective optimization. In this paper, a parameterized model of a flying-wing UAV is selected as the research object. A gradient-free optimization algorithm based on the genetic algorithm is used for maximizing efficiency. The optimization model balances the influence of the RCS mean value and RCS peak value on stealth performance. Moreover, the model achieves an optimal entire life cycle penetration efficiency coefficient by balancing aerodynamic and stealth optimization. The results show that the optimized model improves the penetration efficiency coefficient by 13.84% and increases maximum flight sorties by 1.8%. These results prove that the model has a reasonable combination of aerodynamic and stealth optimization for UAVs undertaking penetration missions. [ABSTRACT FROM AUTHOR]

Over 9 years of hourly surface current data from high‐frequency radar (HFR) off the US West Coast are analyzed using a Bayesian least‐squares fit for tidal components. The spatial resolution and geographic extent of HFR data allow us to assess the spatial structure of the non‐phase‐locked component of the tide. In the frequency domain, the record length and sampling rate allow resolution of discrete tidal lines corresponding to well‐known constituents and the near‐tidal broadband elevated continuum resulting from amplitude and phase modulation of the tides, known as cusps. The FES2014 tide model is used to remove the barotropic component of tidal surface currents in order to evaluate its contribution to the phase‐locked variance and spatial structure. The mean time scale of modulation is 243 days for the M2 constituent and 181 days for S2, with overlap in their range of values. These constituents' modulated amplitudes are significantly correlated in several regions, suggesting shared forcing mechanisms. Within the frequency band M2 ± 5 cycles per year, an average of 48% of energy is not at the phase‐locked frequency. When we remove the barotropic model, this increases to 64%. In both cases there is substantial regional variability. This indicates that a large fraction of tidal energy is not easily predicted (e.g., for satellite altimeter applications). The spatial autocorrelation of the non‐phase‐locked variance fraction drops to zero over a distance of 150 km, a scale that is comparable to the swath width of the Surface Water and Ocean Topography altimeter. Plain Language Summary: Tides in the ocean encompass both the highly predictable daily changes in sea level seen from the shore as well as a less predictable component that changes over time depending on seasonal conditions and wind. Tidal signals are detectable at the ocean surface, for example, via satellite or land‐based radar antenna. The time‐evolving tide signals interact with other processes in the ocean, like currents, and can become harder to predict and describe. Many studies have examined this aspect of tides. In this work, we use land‐based radar observations of ocean currents off the US West Coast to examine this process with high detail, using mathematical techniques to separate the tides from everything else and then evaluating how much the tide has been altered by other processes. This is useful because ocean‐observing satellites can observe a single part of the ocean only when the satellite passes overhead, approximately every 10–20 days, while the data we analyze are sampled hourly and thus more easily allow us to draw conclusions about how tides behave. Key Points: In the California Current System, High‐Frequency Radar resolves stationary and non‐stationary tidesBroadband cusps occur at tidal constituent frequencies, with M2 exhibiting annual modulationWe analyze the tidal modulation to assess physics governing non‐stationary tidal variability [ABSTRACT FROM AUTHOR]

This paper introduces a broadband triple-pole triple-throw (3P3T) RF MEMS switch with a frequency range from DC to 380 GHz. The switch achieves precise signal control and efficient modulation through its six-port design. It achieves an insertion loss of −0.66 dB across its frequency range, with isolation and return loss metrics of −32 dB and −15 dB, respectively. With its low actuation voltage of 6.8 V and rapid response time of 2.28 μs, the switch exemplifies power-efficient and prompt switching performance. The compact design is ideal for integration into space-conscious systems. This switch is pivotal for 6G research and has potential applications in satellite communications, military radar systems, and next-generation radio applications that require multi-antenna access. [ABSTRACT FROM AUTHOR]

Extremely severe cyclonic storm (ESCS) 'Fani' formed in the North Indian Ocean and crossed at Puri in Orissa State on the east coast of India on 03 May 2019. In this study, we examine the sensitivity of convection permitting WRF simulations (3 km) of 'Fani' to cloud microphysics (CMP) schemes using radar and multi-satellite data products. Five CMP schemes, namely Thompson, Goddard, WSM6, Morrison and Lin are tested in WRF. Results show that the changes in the CMP schemes primarily affect the simulated intensity and have lesser impact on the track predictions. Simulations with Thompson followed by Goddard produced the best predictions for both track and intensity estimates. Our analysis reveals significant variations in vertical motions associated with Fani across different CMP schemes; the WSM6, Goddard and Lin schemes produced relatively stronger vertical motions. The explicit WRF simulations could reproduce the wind profiler radar observed intense convective motions during the transit of Fani between 1 and 2 May 2019 at Gadanki station. Experiments with Thompson and Goddard schemes simulated the mean vertical velocities in lower, middle and upper layers in better agreement with radar data. The Lin, WSM6 and Goddard CMP predicted stronger updraft velocities (~ 0.35 m/s); Thompson produced moderate updraft velocities (~ 0.25 m/s) in the upper troposphere over a relatively wider area of high theta-e (385–390 K) indicating the simulation of a convectively stronger and warmer core compared to Morrison. Our analysis suggests that the differences in vertical motions in various CMP simulations are mainly due to the variations in the warming in simulations. It has been found that WSM6, Lin and Goddard produced a deeper core (up to 200 hPa) with a stronger diabatic heating of ~ 6° C followed by Thompson, which simulated a moderately deep core extending to ~ 250 hPa with moderate heating of ~ 5 °C whereas Morrison produced a relatively weak core with a heating of ~ 4 °C limited to 300 hPa. The stronger simulated diabatic heating in Lin, WSM6 and Goddard produces stronger inflow, moisture convergence in the lower levels and stronger outflow and divergence in the upper levels leading to stronger convection in the core region in these cases. The Lin, WSM6 and Goddard mixed phase schemes with more solid hydrometeors simulated stronger radar reflectivities, and stronger eyewalls, due to more latent heat release leading to the development of a strong warm core in the upper troposphere and thus a stronger TC. [ABSTRACT FROM AUTHOR]

Delirium is highly prevalent among hospitalized older adults and is associated with unfavorable outcomes. However, delirium often remains undiagnosed in the hospital context. Having a valid, simple, and fast screening tool could help in limiting the additional workload for healthcare professionals, without leaving delirium undetected. The aim of this study was to estimate the sensitivity and specificity of the Recognizing Acute Delirium As part of your Routine (RADAR) scale in an Italian hospital. An observational cross-sectional study was conducted. A total of 150 patients aged ≥70 years were enrolled. Receiver operating characteristic (ROC) curves using the Confusion Assessment Method (CAM) criterion-defined delirium as the gold standard were plotted to evaluate the performance of the RADAR scale. The cut-off suggested by previous research was used to estimate the sensitivity, specificity, and positive and negative predictive values of the RADAR scale. The involved patients were mostly females (60%; n = 90), with a median age of 84 years (I–III quartiles: 80–88). According to the CAM and the RADAR scale, 37 (25%) and 58 (39%) patients were classified as experiencing delirium, respectively. The area under the ROC curve of the RADAR scale was 0.916. Furthermore, the RADAR scale showed robust sensitivity (95%), specificity (80%), and positive (60%) and negative predictive values (98%). The RADAR scale is thus suggested to be a valid tool for screening assessment of delirium in hospitalized older adults. [ABSTRACT FROM AUTHOR]

This article introduces a comprehensive Mode-S radar simulation, designed to evaluate interrogation and reply signal functionality in various uplink formats (UFs) and downlink formats (DFs). Mode-S radar, crucial in modern aviation, facilitates communication between aircraft and ground systems, thereby enhancing aircraft tracking, situational awareness, and air traffic safety. Our simulation focuses on optimizing interrogation and reply sequences in the 1030- and 1090-MHz channels, respectively. It provides a practical solution for testing complex air flight scenarios, which may be impractical or costly in real life, by replicating challenging conditions in a computerized environment. This approach aids in developing advanced radar signaling algorithms for air traffic control. The simulator’s user-friendly interface displays real-time aircraft data, demonstrating its realworld applicability. Its successful operation underscores its potential to advance Mode-S radar technology and its value for research in the field. [ABSTRACT FROM AUTHOR]

T he interferometric Shuttle Radar Topography Mission (SRTM) satellite's digital elevation model (DEM) is an important tool for studying topographic features on a medium-spacing scale. Data were collected and processed using the satellite's orbital and navigation parameters with selected global GPS stations for verification. Distortion may be expressed by surveying measurements, such as position, distance, area, and shape. This study focuses on this distortion and proposes a new registration method to reduce its effect. Because of generality, the purpose shapes were excluded from this study. The proposed registration method depends on precise GPS control points that act as the ground truth for describing the considered surveying measurements. The processing was carried out using deep artificial neural networks (DANN) to produce a new registered DEM. A comparison was made between the original DEM and the new one, focusing on the selected surveying measurements. Another comparison was made between the GPS coordinates and SRTM polynomials to determine the potential of the proposed system. Some statistical investigations were applied to determine the level of significance of the distortion in each surveying measurement. The study shows that the distortion is highly significant; therefore, the proposed registration method is recommended to fix the distortion. An important finding is the enhancement in local coordinates scope. [ABSTRACT FROM AUTHOR]