Search Results (319)

At present, it is a real challenge to measure brain signals outside of the lab with portable systems that are robust, comfortable and easy to use. We propose in this article a bimodal electroencephalography–functional near-infrared spectroscopy (EEG-fNIRS) sensor whose spatial geometry allows the robust estimation of colocated electrical and hemodynamic brain activity. The geometry allows for the correction of extra-cerebral activity (short-channel distance) as well as the computation of the spatial gradient of absorbance required in the spatially resolved spectroscopy (SRS) method. The complete system is described, detailing the technical solutions implemented to provide signals at 250 Hz for both synchronized modalities and without crosstalk. The system performances are validated during an N-Back mental workload protocol. Full article

This paper presents a non-intrusive method for estimating the distance between a camera and a human subject using a monocular vision system and statistically derived interpupillary distance (IPD) values. The proposed approach eliminates the need for individual calibration by utilizing average IPD values based on biological sex, enabling accurate, scalable distance estimation for diverse users. The algorithm, implemented in Python 3.12.11 using the MediaPipe Face Mesh framework, extracts pupil coordinates from facial images and calculates IPD in pixels. A sixth-degree polynomial calibration function, derived from controlled experiments using a uniaxial displacement system, maps pixel-based IPD to real-world distances across three intervals (20–80 cm, 80–160 cm, and 160–240 cm). Additionally, a geometric correction is applied to compensate for in-plane facial rotation. Experimental validation with 26 participants (15 males, 11 females) demonstrates the method’s robustness and accuracy, as confirmed by relative error analysis against ground truth measurements obtained with a Bosch GLM120C laser distance meter. Males exhibited lower relative errors across the intervals (3.87%, 4.75%, and 5.53%), while females recorded higher mean relative errors (6.0%, 6.7%, and 7.27%). The results confirm the feasibility of the proposed method for real-time applications in human–computer interaction, augmented reality, and camera-based proximity sensing. Full article

In order to enhance the performance of 112 Gb/s polarization-multiplexed quadrature phase-shift keying (PM-QPSK) coherent optical receivers, a novel digital signal processing (DSP) framework is presented in this study. The suggested method combines cutting-edge signal processing techniques to address important constraints in long-distance, high data rate coherent systems. The framework uses overlap frequency domain equalization (OFDE) for chromatic dispersion (CD) compensation, which offers a cheaper computational cost and higher dispersion control precision than traditional time-domain equalization. An adaptive carrier phase recovery (CPR) technique based on mean-squared differential phase (MSDP) estimation is incorporated to manage phase noise induced by cross-phase modulation (XPM), providing dependable correction under a variety of operating situations. When combined, these techniques significantly increase Q factor performance, and optimum systems can handle transmission distances of up to 2400 km. The suggested DSP approach improves phase stability and dispersion tolerance even in the presence of nonlinear impairments, making it a viable and effective choice for contemporary coherent optical networks. The framework’s competitiveness was evaluated by comparing it against the most recent, cutting-edge DSP methods that were released after 2021. These included CPR systems that were based on kernels, transformers, and machine learning. The findings show that although AI-driven approaches had the highest absolute Q factors, they also required a large amount of computing power. On the other hand, the suggested OFDE in conjunction with adaptive CPR achieved Q factors of up to 11.7 dB over extended distances with a significantly reduced DSP effort, striking a good balance between performance and complexity. Its appropriateness for scalable, long-haul 112 Gb/s PM-QPSK systems is confirmed by a complexity versus performance trade-off analysis, providing a workable and efficient substitute for more resource-intensive alternatives. Full article

Unmanned Surface Vehicles (USVs) play a critical role in maritime monitoring, environmental protection, and emergency response, necessitating accurate scene understanding in complex aquatic environments. Conventional semantic segmentation methods often fail to capture global context and lack physical boundary consistency, limiting real-world performance. This paper proposes USV-Seg, a unified segmentation framework integrating a vision-language model, the Segment Anything Model (SAM), DINOv2-based visual features, and a physically constrained refinement module. We design a task-specific <Describe> Token to enable fine-grained semantic reasoning of navigation scenes, considering USV-to-shore distance, landform complexity, and water surface texture. A mask selection algorithm based on multi-layer Intersection-over-Prediction (IoP) heads improves segmentation precision across sky, water, and obstacle regions. A boundary-aware correction module refines outputs using estimated sky-water and land-water boundaries, enhancing robustness and realism. Unlike prior works that simply apply vision-language or geometric post-processing in isolation, USV-Seg integrates structured scene reasoning and scene-aware boundary constraints into a unified and physically consistent framework. Experiments on a real-world USV dataset demonstrate that USV-Seg outperforms state-of-the-art methods, achieving 96.30% mIoU in challenging near-shore scenarios. Full article

The pipe-jacking inertial guidance method is a key technology to solve the guidance problems of complex pipe-jacking projects, such as long distances and curves. However, since its guidance information is obtained by gyroscope integration. System errors will accumulate over time and affect the guidance accuracy. To address the above issues, this study proposes an intelligent online system error correction scheme based on single-axis rotation and data backtracking. The method enhances system observability by actively exciting the sensor states and introducing data reuse technology. Then, a Bayesian optimization algorithm is incorporated to construct a multi-objective function. The algorithm autonomously searches for the optimal values of three key control parameters, thereby constructing an optimal correction strategy. The results show that the inclination accuracy improving by 99.36%. The tool face accuracy improving by 94.05%. The azimuth accuracy improving by 94.42% improvement. By comparing different correction schemes, the proposed method shows better performance in estimating gyro bias. In summary, the proposed method uses single-axis rotation and data backtracking, and can correct system errors in inertial navigation effectively. It has better value for engineering and provides a technical foundation for high-accuracy navigation in tunnel, pipe-jacking, and other complex tasks with low-cost inertial systems. Full article

This systematic review and meta-analysis evaluated refractive outcomes, particularly astigmatic correction, in keratoconus following toric intraocular lens (tIOL) implantation. A systematic search identified eligible studies reporting pre- and postoperative refractive cylinder, spherical equivalent (SE), uncorrected distance visual acuity (UDVA), and corrected distance visual acuity (CDVA). Eight studies, comprising 135 eyes, were included. Outcomes were pooled using a random-effects model with restricted maximum likelihood as the estimator for tau². Methodological quality was assessed using the MINORS tool for non-comparative studies and the JBI checklist for case series. Postoperative refractive cylinder and SE improved by 2.28 dioptres (95% CI, 1.60–2.96) and 4.17 dioptres (95% CI, 2.32–6.01), respectively. UDVA and CDVA also improved substantially, with pooled gains of 0.87 logMAR (95% CI, 0.71–1.03) and 0.19 logMAR (95% CI, 0.12–0.26), respectively. Most tIOL rotations did not exceed 10 degrees, with only one case requiring realignment surgery. Complications were infrequent and mostly minor. tIOL implantation is effective in reducing astigmatism and improving vision in stable keratoconus patients. However, limitations in vector analysis and methodology heterogeneity underscore the need for standardised reporting to optimise outcomes. Full article

Accurate localization plays a critical role in enabling underwater vehicle autonomy. In this work, we develop a robust infrastructure-based localization framework that estimates the position and orientation of underwater vehicles using only range measurements from long baseline (LBL) acoustic beacons to multiple on-board receivers. The proposed framework integrates three key components, each formulated as a convex optimization problem. First, we introduce a robust calibration function that unifies multiple sources of measurement error—such as range-dependent degradation, variable sound speed, and latency—by modeling them through a monotonic function. This function bounds the true distance and defines a convex feasible set for each receiver location. Next, we estimate the receiver positions as the center of this feasible region, using two notions of centrality: the Chebyshev center and the maximum volume inscribed ellipsoid (MVE), both formulated as convex programs. Finally, we recover the vehicle’s full 6-DOF pose by enforcing rigid-body constraints on the estimated receiver positions. To do this, we leverage the known geometric configuration of the receivers in the vehicle and solve the Orthogonal Procrustes Problem to compute the rotation matrix that best aligns the estimated and known configurations, thereby correcting the position estimates and determining the vehicle orientation. We evaluate the proposed method through both numerical simulations and field experiments. To further enhance robustness under real-world conditions, we model beacon-location uncertainty—due to mooring slack and water currents—as bounded spherical regions around nominal beacon positions. We then mitigate the uncertainty by integrating the modified range constraints into the MVE position estimation formulation, ensuring reliable localization even under infrastructure drift. Full article

Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 10⁶ Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm³) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R² = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. Full article

With the rise of the Internet of Things (IoT), smart sensors are increasingly being deployed as compact edge processing units, necessitating continuously writable memory with low power consumption and fast access times. Magnetic random-access memory (MRAM) has emerged as a promising non-volatile alternative to conventional DRAM and SDRAM, offering advantages such as faster access speeds, reduced power consumption, and enhanced endurance. However, MRAM is subject to challenges including process variations and thermal fluctuations, which can induce random bit errors and result in imbalanced probabilities of 0 and 1 bits. To address these issues, we propose a novel sparse coding scheme characterized by a minimum Hamming distance of three. During the encoding process, three check bits are appended to the user data and processed using a generator matrix. If the resulting codeword fails to satisfy the sparsity constraint, it is inverted to comply with the coding requirement. This method is based on the error characteristics inherent in MRAM to facilitate effective error correction. Furthermore, we introduce a dynamic threshold detection technique that updates bit probability estimates in real time during data transmission. Simulation results demonstrate substantial improvements in both error resilience and decoding accuracy, particularly as MRAM density increases. Full article

Electromagnetic radiation measurement and management emerge as crucial factors in the economical deployment of fifth-generation (5G) infrastructure, as the new 5G network emerges as a network of services. By installing many base stations in strategic locations that operate in the millimeter-wave range, 5G services are able to meet serious demands for bandwidth. To evaluate the ground-plane radiation level of electromagnetics close to 5G base stations, we propose a unique machine-learning-based approach. Because a machine learning algorithm is trained by utilizing data obtained from numerous 5G base stations, it exhibits the capability to estimate the strength of the electric field effectively at every point of arbitrary radiation, while the base station generates a network and serves various numbers of 5G terminals running in different modes of service. The model requires different numbers of inputs, including the antenna’s transmit power, antenna gain, terminal service modes, number of 5G terminals, distance between the 5G terminals and 5G base station, and environmental complexity. Based on experimental data, the estimation method is both feasible and effective; the machine learning model’s mean absolute percentage error is about 5.89%. The degree of correctness shows how dependable the developed technique is. In addition, the developed approach is less expensive when compared to measurements taken on-site. The results of the estimates can be used to save test costs and offer useful guidelines for choosing the best location, which will make 5G base station electromagnetic radiation management or radio wave coverage optimization easier. Full article

In recent years, Kazakhstan has faced the problem of sustainable development in the field of operation of a number of reservoirs: periods of drought lead to a systematic decrease in accumulated fresh water reserves, and the flood of 2024 led to the flooding of a number of settlements. The article raises questions about the real state of the region’s reservoirs (using the example of the Karatomar reservoir), the accuracy of the conducted bathymetric studies, and the correctness of estimating the required step (or distance between the control points being taken) of the tacks (trajectory lines) of the measurement, which was carried out using the Apache 3 bathymetric drone. The study of the patterns of modeling accuracy from the frequency of tacks (trajectory lines) was carried out using kriging methods. Reservoir models were built in QGis and Surfe. When analyzing the coastline, Sentinel-2 space images and Kazvodkhoz (Kazakhstani state enterprise) data were used. The result of the study was an algorithm for determining the step of tacks (trajectory lines) for modern bottom geomorphology. The conducted research has shown that over 78 years of use, the reservoir’s parameters have undergone significant changes. A similar situation of significant deterioration in parameters is characteristic of other hydrotechnical structures in the region. Full article

Rolling bearing is an indispensable part of mechanical rotating parts, which plays an important role in reducing friction and ensuring the rotation accuracy of rotating parts. It is necessary to carry out a health assessment of the bearing. Current health assessment methods for rolling bearings only extract strongly related feature indicators and input them into the health assessment model without considering the profound impact external conditions have on the fluctuation of feature indicators, which will lead to an inaccurate health assessment. Besides, most methods evaluating the health of rolling bearings only consider the real-time index data but do not make full use of bearing maintenance data for reliability modeling and analysis, actually reducing the hierarchy and rationality of the health assessment. Therefore, this paper combines multivariate state estimation (MSET) and reliability analysis to evaluate the health of rolling bearings. Firstly, the health baseline of the rolling bearing under multi-speed conditions is established based on MSET, which collects the history health data of rolling bearings under various working conditions and learns the impact of working conditions on health data. Subsequently, Mahalanobis distance is used to measure the degree of deviation from the health baseline, and calculated Mahalanobis distance is input into the health mapping function to get the initial health score. Finally, combined with the reliability analysis correcting the initial score, the final health score is obtained, which can provide data support for intelligent operation and maintenance and a decision-making basis for equipment maintenance. The proposed health assessment method is validated using the bearing dataset from Case Western Reserve University and historical failure data of rolling bearings. The proposed method reduces speed-related influences in bearing health evaluation, dynamically adjusting the health assessment result through the reliability model to track performance degradation throughout the bearing’s service life. Full article

Optical window freeform surfaces have emerged as a critical research focus in advanced optical engineering owing to their extensive surface degrees of freedom. These surfaces enable the simultaneous correction of on-axis and off-axis aberrations while satisfying stringent requirements for high-performance, lightweight, and compact optical systems. In the initial metrological characterization of these surfaces, coordinate measuring machines (CMMs) are conventionally employed for target point cloud acquisition. However, the achievable measurement accuracy (>2 μm) inherently constrained by CMM precision imposes fundamental limitations for subsequent optical inspections requiring sub-micron to nanometer-level resolution. Meanwhile, although optical measurement methods can result in higher measurement accuracy, they also lead to an increase in costs and testing difficulties. To overcome these limitations, we propose an accelerated point cloud registration methodology based on point-to-triangulation distance estimation. In simulation, using optimal coordinate transformation enabled good capabilities for exceptional surface characterization: peak-to-valley (PV) surface error of 10⁻⁶ nm, residual error of 5 nm, and registration accuracy of log₁₀ (mm/°). Further, in the experiment, the PV surface error was reduced from 27.3 μm to 6.9 μm, equivalent to a reduction of 3.95 times. These results confirm that the point-to-triangulation distance approximation maintains sufficient fidelity to the nominal point-to-surface distance, thereby empirically validating the efficacy of our proposed methodology. Notably, compared with conventional 3D alignment methods, our novel 2D estimation registration approach with point-to-triangulation surface normal vectors demonstrates significant advantages in computational complexity, which achieved a 78% reduction from O(n³) to O(n) while maintaining sub-millisecond alignment times. We believe that the method has potential for use as a low-cost optical precision measurement in manufacturing technology. Full article

Online shopping has become popular due to its convenience and potential cost savings. However, clothing size cannot be accurately estimated, particularly when buying shirts. Many shoppers provide size choices but with inaccurate fits. To assist users in selecting the correct size when purchasing t-shirts online, we estimated shirt size using calculated upper body dimensions. Computer vision algorithms, including YOLO, PoseNet, body contour detection, and a trained convolutional neural network (CNN) model were employed to estimate shirt sizes from 2D images. The model was tested using images of 30 participants taken at a distance of 180–185 cm away from a Raspberry Pi camera. The estimation accuracy was 70%. Inaccurate predictions were attributed to the precision of body measurements from computer vision and image quality, which needs to be solved in further studies. Full article

Intersection sight distance (ISD) requirements, currently designed for driver-operated vehicles (DVs), will be affected once automated vehicles (AVs) enter the driving environment. This paper examines the ISD for intersections with a yield control on a minor road in a mixed DV-AV environment. Five potential conflict types with different ISD requirements are modeled as a minor-road vehicle proceeds to cross the intersection, turns right, or turns left. Furthermore, different models are developed for each conflict type depending on the vehicle types on the minor and major roads. These models, along with the intersection geometry, establish the system demand and supply models for ISD reliability analysis. A surrogate safety measure is developed and used to measure ISD non-compliance and is denoted by the probability of unresolved conflicts (PUC). The models are applied to a case study intersection, where PUC values are estimated using Monte Carlo Simulation and compared to an established target value relating to the DV-only traffic of 0.00674. The results show that AV-related traffic has higher overall PUC values than those of DV-only traffic. A corrective measure, reducing the AV speed limit on the minor-road approaches by 3 to 4 km/h, decreases the overall PUC to values below those of the target PUC. Full article