Search Results (137)

This paper presents an experimental study validating the feasibility of Radio Frequency Identification (RFID) marker systems as a complementary solution for autonomous vehicle (AV) localization in Global Navigation Satellite System (GNSS)-degraded urban environments. A novel synchronized dynamic testbed featuring hardware-level integration with wheel revolution tracking enables precise correlation of RFID marker reads with vehicle angular position. Experimental results demonstrate that multi-antenna configurations achieve consistently high read success rates (up to 99.6% at 0.5 m distance), sub-meter localization accuracy (~55 cm marker spacing), and reliable performance at average urban speeds (36 km/h simulated velocity). Spatial diversity from four strategically positioned antennas overcomes multipath interference and orientation challenges inherent to high-speed RFID reading. Processing latency remains well within the 58 ms time budget critical for autonomous navigation. These findings validate RFID’s potential for smart road infrastructure integration and demonstrate a scalable, cost-effective solution for enhancing AV safety and decision-making capabilities through contextual information transmission. Full article

To enhance grid stability with high renewable energy penetration, this paper proposes an adaptive virtual synchronous generator (VSG) control using a backpropagation neural network (BPNN). Traditional VSG control methods exhibit limitations in handling nonlinear dynamics and suppressing power oscillations. Distinguishing from existing studies that apply BPNN solely for damping adjustment, this paper proposes a novel strategy where BPNN simultaneously regulates both VSG virtual inertia and damping coefficients by learning nonlinear relationships among inertia, angular velocity deviation, and its rate of change. A key innovation is redesigning the error function to minimize angular acceleration changes rather than frequency deviations, aligning with rotational inertia’s physical role and preventing excessive adjustments. Additionally, an adaptive damping coefficient is introduced based on optimal damping ratio principles to further suppress power oscillations. Simulation under load disturbances and grid frequency perturbations demonstrates that the proposed BPNN strategy significantly outperforms constant inertia, bang–bang, and radial basis function neural network methods. Full article

Background: Ground contact (GC) detection is essential for sprint performance analysis. Inertial measurement units (IMUs) enable field-based assessment, but their reliability during sprint acceleration remains limited when using heuristic and recently used machine learning algorithms. This study introduces a deep learning one-dimensional convolutional neural network (1D-CNN) to improve GC event and GC times detection in sprint acceleration. Methods: Twelve sprint-trained athletes performed 60 m sprints while bilateral shank-mounted IMUs (1125 Hz) and synchronized high-speed video (250 Hz) captured the first 15 m. Video-derived GC events served as reference labels for model training, validation, and testing, using resultant acceleration and angular velocity as model inputs. Results: The optimized model (18 inception blocks, window = 100, stride = 15) achieved mean Hausdorff distances ≤ 6 ms and 100% precision and recall for both validation and test datasets (Rand Index ≥ 0.977). Agreement with video references was excellent (bias < 1 ms, limits of agreement ± 15 ms, r > 0.90, p < 0.001). Conclusions: The 1D-CNN surpassed heuristic and prior machine learning approaches in the sprint acceleration phase, offering robust, near-perfect GC detection. These findings highlight the promise of deep learning-based time-series models for reliable, real-world biomechanical monitoring in sprint acceleration tasks. Full article

Monitoring the complex, three-dimensional flow within centrifugal compressor diffusers remains a major challenge due to geometric confinement, high rotational speeds, and strong unsteadiness near surge and stall. This review provides a comprehensive assessment of contemporary instrumentation strategies for diffuser flow characterization, spanning pressure, temperature, velocity, vibration, and acoustic measurements. The article outlines the standards governing compressor instrumentation, compares conventional probes with emerging high-resolution and high-bandwidth sensor technologies, and evaluates the effectiveness of pressure- and temperature-based diagnostics, optical methods, and advanced dynamic sensing in capturing diffuser behavior. Case studies from industrial compressors, research rigs, and high-speed experimental facilities illustrate how sensor layout, bandwidth, and synchronization influence the interpretation of flow stability, performance degradation, and surge onset. Collectively, these examples demonstrate that high-frequency pressure and temperature probes remain indispensable for instability detection, while optical techniques such as PIV, LDV, and PSP/TSP offer unprecedented spatial resolution for understanding flow structures. The findings highlight the growing integration of hybrid sensing architectures, digital acquisition systems, and data-driven analysis in diffuser research. Overall, the review identifies current limitations in measurement fidelity and accessibility while outlining promising paths toward more robust, real-time monitoring solutions for reliable centrifugal compressor operation. Full article

An automatic optimization approach to the non-periodic (NP) folded-waveguide slow-wave structure (FW-SWS) is proposed for the high efficiency traveling wave tube (TWT). Considering the beam-wave synchronism condition, the data of the beam velocity distribution are analyzed and utilized for automatic optimization. For concise expression, a W-band concentric arc NP FW-SWS TWT is automatically optimized as an example, where the beam voltage is set as 6000 V, the beam current is 0.12 A, the magnet field is 0.5 T, and the input power is 0.4 W. Without any training data or previous given datasets, the output power (electronic efficiency) can be optimized to reach 238.7 W (33.1%) at 94 GHz by the automatic optimization approach code within 22.7 h. Full article

To maintain a high standard of environmental quality, industrial plants must be able to foresee and control the impacts resulting from their activities. One of the most challenging issues for the metallurgical and mining industry when it comes to protecting the environment is the measurement of particulate matter emissions generated by the wind action over the erodible surfaces of stockpiles of granular materials. It is known that the emissive phenomenon starts from a specific threshold friction velocity, which is an inherent characteristic of each material. This parameter can be derived from relationships available in the scientific and technical literature, which, however, only provide qualitative estimations. Therefore, the threshold friction velocity of the specific materials under investigation must be assessed through laboratory tests. This article discusses the results obtained for nine raw materials sampled in a metallurgical plant by applying three different procedures, (1) the sieve-based analysis suggested by U.S. EPA; (2) the laboratory tests performed with an Environmental Wind Tunnel; and (3) the PI-SWERL tests (i.e., tests performed with a Portable In-Situ Wind ERosion Lab), and presents a comparative analysis of the three methods. Findings indicate that the EPA methodology tends to be less accurate than the wind tunnel and PI-SWERL tests, though its accuracy can be slightly improved by adding an additional sieve size for materials with finer aggregates. The wind tunnel and PI-SWERL provided comparable results, with PI-SWERL offering practical advantages due to its portability and an effective synchronization between its data acquisition systems. Full article

Steatotic liver diseases are increasing globally, with metabolic dysfunction-associated steatohepatitis potentially causing irreversible fibrosis progression. This study focuses on an ultrasonic diagnostic method for steatotic liver disease based on temperature dependence of sound velocity for tissue characterization. Since the temperature coefficient of sound velocity in liver is expected to decrease with increasing lipid accumulation, the temperature coefficient of sound velocity in tissue-mimicking material as a function of glycerol concentration was measured. It decreased as glycerol concentration increased, changing from positive to negative value at 37.5% glycerol concentration. Change rates in sound velocity by ultrasonic heating were then measured in vitro on liver left lobes of mice with steatotic liver induced by choline-deficient, L-amino acid-defined, and high-fat diet. There were positive values in the control group, whereas there were negative values in the steatotic liver group. In vivo measurements of mouse livers using an electrocardiogram-synchronized system showed similar results, with positive values in the control group and negative values in the steatotic liver group. Thermophysical properties can determine whether the liver is normal or steatotic. However, to estimate the lipid accumulation rate from the change rate in sound velocity, it is necessary to reduce the measurement variation. Full article

Hundred-micron-sized microdroplets are widely used in microbial culture, chemical investigations and industrial processes. The size, velocity and frequency of microdroplets significantly affect the cultivation and processing effects. The detections of droplets mainly rely on capacitance detection or imaging, but it requires expensive and complex systems for capacitance detection, and high-throughput imaging detections are challenging. In this study, four-way microfluid structure (FWMS) is proposed for microdroplets generation and detection. FWMS, fixed on a 3D-printed holder, is designed to generate microdroplets (100–500 µm), with optical fibers embedded to collect double photoresist method pulses of scattering light by fast-moving microdroplets. The size and volume of the microdroplets are retrieved by tracking the double pulse signal in the time sequence. In the experiments, 50 groups of microdroplets (a total of 10⁵ microdroplets) with size ranging from 100 to 450 µm were generated and detected. Compared with traditional imaging detection, this method has a better sampling rate and detection error of less than 1.42%, which can provide a simple and accurate integrated microfluid system for microdroplet generation and synchronous detection. Full article

Countermovement jump (CMJ) is a key test for evaluating lower-limb neuromuscular function, and accurate measurement of countermovement depth (CMD) and countermovement velocity (CMV) is critical for determining optimal performance. However, the measurement validity and reliability of CMD and CMV—particularly when obtained from force plates (FP) and linear position transducers (LPT)—have remained uncertain. This study determined the validity and reliability of FP and LPT for measuring CMD and CMV. Twenty-eight male recreational athletes performed the CMJ test, and the variables were synchronously acquired by Motion Capture (MC), FP, and LPT. The test was divided into two sessions, with participants completing three maximal effort CMJs per session, and the second session occurred more than 48 h after the first. The reliability was evaluated using the intraclass correlation coefficient (ICC), and the validity was evaluated with linear Pearson’s correlation coefficient (r), one-way ANOVA with repeated measures, and Bland–Altman plots. The reliability results for FP and LPT indicated good to excellent (ICC = 0.809–0.900). Compared with MC, the FP showed a high to very high correlation (r = 0.894–0.937), and the LPT showed a high correlation (r = 0.721–0.726). When precise quantification of CMD/CMV is required, FP should be preferred. If only an LPT is available, it is best used for within-athlete longitudinal monitoring with a consistent setup, and cross-device comparisons should be avoided. Full article

Understanding vehicle acceleration behavior during intersection departures is critical for advancing traffic safety, sustainable mobility, and intelligent transport systems. This study presents a high-resolution kinematic analysis of 714 vehicle departures from signalized intersections, encompassing straight crossings, left turns, and right turns, and involving a diverse sample of internal combustion engine (ICE), hybrid electric (HEV), and battery electric vehicles (BEV). Using synchronized Micro Electro-Mechanical Systems (MEMS) accelerometers and Real-Time Kinematic (RTK)-GPS systems, the study captures longitudinal acceleration and velocity profiles over fixed distances. Results indicate that BEVs exhibit significantly higher acceleration and final speeds than ICE and HEV vehicles, particularly during straight crossings and longer left-turn maneuvers. Several mathematical models—including polynomial, arctangent, and Akçelik functions—were calibrated to describe acceleration and velocity dynamics. Findings contribute by modeling jerk and delay propagation, supporting better calibration of AV acceleration profiles and the optimization of intersection control strategies. Moreover, the study provides validated acceleration benchmarks that enhance the accuracy of forensic engineering and road accident reconstruction, particularly in scenarios involving intersection dynamics, and demonstrates that BEVs accelerate more rapidly than ICE and HEV vehicles, especially in straight crossings, with direct implications for traffic simulation, ADAS calibration, and urban crash analysis. Full article

Accurate and reliable localization is crucial for autonomous systems operating in dynamic and semi-structured environments, such as precision agriculture and outdoor robotics. Advances in Global Navigation Satellite System (GNSS) technologies, particularly Differential GPS (DGPS) and Real-Time Kinematic (RTK) positioning, have significantly enhanced position estimation precision, achieving centimeter-level accuracy. However, GNSS-based localization continues to encounter inherent limitations due to signal degradation and intermittent data loss, known as GNSS outages. This paper proposes a novel complementary RTK-like position increment prediction model with the purpose of mitigating challenges posed by GNSS outages and RTK signal discontinuities. This model can be integrated with a Dual Extended Kalman Filter (Dual EKF) sensor fusion framework, widely utilized in robotic navigation. The proposed model uses time-synchronized inertial measurement data combined with the velocity inputs to predict GNSS position increments during periods of outages and RTK disengagement, effectively substituting for missing GNSS measurements. The model demonstrates high accuracy, as the total aDTW across 180 s trajectories averages at 1.6 m while the RMSE averages at 3.4 m. The 30 s test shows errors below 30 cm. We leave the actual Dual EKF fusion to future work, and here, we evaluate the standalone deep network. Full article

Horizontal wells in shale formations, such as those in the South Pars gas field (Iran) and the Bakken shale (Canada/USA), are essential for production from ultralow-permeability reservoirs but remain limited by poor hole cleaning, high torque, and unstable fluid transport. This study integrates real-time field data from South Pars with Drillbench simulations in the Bakken to develop practical strategies for improving drilling efficiency. A water-based mud system (9–10.2 ppg, 29–35 cP) supplemented with 2 wt.% sulphonated asphalt was applied to mitigate shale hydration, enhance cuttings transport, and preserve near-wellbore injectivity. Field implementation in South Pars demonstrated that adjusting drillstring rotation to 90 RPM and circulation rates to 1100 GPM reduced torque by ~70% (24 to 7 klbf·ft) and increased the rate of penetration (ROP) by ~25% (8 to 10 m/h) across a 230 m interval. Simulations in the Bakken confirmed these improvements, showing consistent torque and pressure trends, with cuttings transport efficiency above 95%. Inducing controlled synchronous whirl further improved sweep efficiency by ~15% and stabilized annular velocities at 0.7 m/s. Overall, these optimizations enhanced drilling efficiency by up to 25%, reduced operational risks, and created better well conditions for field development and EOR applications. The results provide clear, transferable guidelines for designing and drilling shale wells that balance immediate operational gains with long-term reservoir recovery. Full article

Background: A single lumbar-mounted inertial sensor offers a practical alternative to optoelectronic systems for gait analysis, simplifying measurements and improving usability in the clinical field. However, its validity can be influenced by sensor placement and signal choice. This study aimed to develop and validate a novel algorithm for estimating spatiotemporal parameters using anteroposterior linear acceleration and angular velocity around the sagittal axis using a single inertial measurement unit (IMU) placed on the lumbar region. The proposed algorithm was validated comparing the parameters computed by the algorithm with the ones computed using a commercial wearable system based on a two-foot-mounted IMU configuration. Thirty healthy subjects underwent a 2 min walk test, and five spatiotemporal parameters were computed using the two methodologies. Study results showed that cadence and gait cycle time exhibited very high agreement, with only a small, statistically significant bias in cadence negligible for practical purposes. In contrast, swing, stance, and double-support parameters showed disagreement due to the presence of systematic proportional errors. This work introduces a novel algorithm for gait event detection and spatiotemporal parameter estimation, addressing uncertainties related to sensor placement, metric models, processing techniques, and signal selection, while avoiding synchronization issues associated with using multiple sensors. Full article

This paper presents a W-band low-voltage traveling-wave tube (TWT) incorporating a spoof surface plasmon polariton (SSPP) slow-wave structure (SWS) and a dual-sheet beam. The SSPP-based SWS adopts a periodic double-F-groove configuration, which provides strong field localization, increases the interaction impedance, and reduces the phase velocity, thereby enabling a low synchronization voltage. Owing to its symmetric open geometry, the SWS naturally forms a dual-sheet beam tunnel, which enhances the effective beam current without increasing the aperture size. Eigenmode calculations indicate that, within the 92–97 GHz band, the normalized phase velocity is between 0.198 and 0.208, and the interaction impedance exceeds 2.65 Ω. Moreover, an energy-coupling structure was developed to ensure efficient signal transmission. Three-dimensional particle-in-cell (PIC) simulations predict a peak output power of 366.1 W and an electronic efficiency of 6.15% at 95.5 GHz for a 2 × 250 mA dual-sheet beam at 11.9 kV, with stable amplification and without self-oscillation observed. The proposed low-voltage, high-efficiency W-band TWT offers a manufacturable and easily integrable solution for next-generation millimeter-wave systems, supporting high-capacity wireless backhaul, airborne communication, radar imaging, and sensing platforms where compactness and reduced power-supply demands are critical. Full article

Precise and robust localization for micro Unmanned Aerial Vehicles (UAVs) in GNSS-denied environments is hindered by the lack of diverse datasets and the limited real-world performance of existing visual matching methods. To address these gaps, we introduce two contributions: (1) the SatLoc dataset, a new benchmark featuring synchronized, multi-source data from varied real-world scenarios tailored for UAV-to-satellite image matching, and (2) SatLoc-Fusion, a hierarchical localization framework. Our proposed pipeline integrates three complementary layers: absolute geo-localization via satellite imagery using DinoV2, high-frequency relative motion tracking from visual odometry with XFeat, and velocity estimation using optical flow. An adaptive fusion strategy dynamically weights the output of each layer based on real-time confidence metrics, ensuring an accurate and self-consistent state estimate. Deployed on a 6 TFLOPS edge computer, our system achieves real-time operation at over 2 Hz, with an absolute localization error below 15 m and effective trajectory coverage exceeding $90\%$, demonstrating state-of-the-art performance. The SatLoc dataset and fusion pipeline provide a robust and comprehensive baseline for advancing UAV navigation in challenging environments. Full article