Search Results (40)

This paper presents a near real-time implementation of the Karhunen–Loève Transform (KLT) for Radio Frequency Interference (RFI) mitigation in microwave radiometry. KLT is a powerful, data-adaptive technique capable of adjusting to various signal types by estimating the covariance matrix of the incoming signal and segmenting its eigenvectors to form an effective RFI basis. In this paper, the KLT is evaluated with real signals in laboratory conditions, aiming to characterize its performance in realistic conditions. To that effect, the dual Rx/Tx capability of a Pluto SDR is used to generate and capture RFI. The main mitigation metrics are computed for the KLT and other commonly used mitigation methods. In addition, while previous studies have shown the effectiveness of offline processing of recorded I/Q data, real-time mitigation is often necessary. Given the computational cost of eigendecomposition, this work introduces a low-complexity solution using the “economy covariance” approach alongside asynchronous covariance decomposition. The proposed implementation, realized within the GNU Radio framework, demonstrates the practical feasibility of real-time KLT-based mitigation and underscores its potential for improving signal integrity in digital radiometers operating under dynamic RFI conditions. Full article

In complex urban electromagnetic environments, wireless positioning signals are subject to various types of interference, including narrowband, chirp, and pulse jamming. Traditional generic suppression methods struggle to achieve global optimization tailored to specific interference mechanisms. This paper proposes a classification-driven differentiated jamming suppression (CDDJ) method, which adaptively selects the optimal mitigation strategy by pre-identifying interference types and integrating classification confidence levels. First, the theoretical bounds of the output carrier-to-noise ratio ($C/N_{0_{out}}$) under typical interference scenarios are derived, characterizing the performance distribution of anti-jamming efficiency ($\mathsf{\Gamma }$). Then, a mapping relationship between interference categories and their corresponding suppression strategies is established, along with decision criteria for strategy switching based on signal quality evaluation metrics. Finally, an OpenMax-Lite rejection layer is designed to handle low-confidence inputs, identify unknown jamming using the Weibull distribution, and implement a broadband conservative suppression policy. Simulation results demonstrate that the proposed method exhibits significant advantages across different interference types. Under high JSR conditions, the signal recovery rate improves by over 10% and 8% compared to that of the WPT and KLT methods, respectively. In terms of SINR performance, the proposed approach outperforms the AFF, TDPB, and FDPB methods by 1.5 dB, 1.1 dB, and 5.3 dB, respectively, thereby enhancing the reliability of wireless positioning in complex environments. Full article

Three-dimensional reconstruction using a two-dimensional inverse synthetic aperture radar (ISAR) faces dual challenges: geometric distortion in initial point clouds caused by accumulated feature-matching errors and degraded reconstruction accuracy due to point cloud outlier interference. This paper proposes an optimized method to delete the error points based on motion vector features and local spatial point cloud density. Before reconstruction, feature point extraction and matching for ISAR sequence images are performed using Harris corner detection and the improved Kanade–Lucas–Tomasi (KLT) algorithm. To address the issue of mismatched points, a method based on motion vector features is proposed. This method applies the dual constraints of motion distance and direction thresholds and deletes mismatched points based on local motion consistency. After point cloud reconstruction, a clustering method based on local spatial point cloud density is employed to effectively remove outliers. To validate the effectiveness of the proposed method, simulation experiments comparing the performance of different approaches are conducted. The experimental results demonstrate the effectiveness and robustness of the proposed method in the 3D reconstruction of moving targets. Full article

This study compares dosimetric approaches for lung dosimetry in ¹⁶⁶ radioembolization (Ho-TARE) with direct Monte Carlo (MC) simulations on a voxelized anthropomorphic phantom derived from a real patient’s CT scan, preserving the patient’s lung density distribution. Lung dosimetry was assessed for five lung shunt (LS) scenarios with conventional methods: the mono-compartmental organ-level approach (MIRD), voxel S-value convolution for soft tissue (kST, ICRU soft tissue with 1.04 g/cm³) and lung tissue (kLT, ICRU lung tissue with 0.296 g/cm³), local density rescaling (kST_L and kLT_L, respectively, for soft tissue and lung tissue), or global rescaling for a lung mean density of 0.221 g/cm³ (kLT₂₂₁). Significant underestimations in the mean absorbed dose (AD) were observed, with relative differences with respect to the reference (MC) of −64% for MIRD, −93% for kST, −56% for kST_L, −76% for kLT, −68% for kLT₂₂₁, and −60% for kLT_L. Given the high heterogeneity of lung tissue, standard dosimetric approaches cannot accurately estimate the AD. Additionally, MC results for ¹⁶⁶Ho showed notable spatial absorbed dose inhomogeneity, highlighting the need for tailored lung dosimetry in Ho-TARE accounting for the patient-specific lung density distribution. MC-based dosimetry thus proves to be essential for safe and effective radioembolization treatment planning in the presence of LS. Full article

The ATLAZUL project is an Interreg effort among 18 partners from Spain and Portugal along the Atlantic Iberian coasts. One of its objectives is the development of new methods and data processing for oceanic information to produce useful products for private and public stakeholders. This study proposes a new insight on the sea surface dynamic of the ATLAZUL area based on almost two years of multiscale high resolution sea surface temperature imagery. The use of techniques such as the Karhunen–Loève transform (Empirical Orthogonal Function) and the Maximum Entropy Spectral Analysis were applied to study long- and short-term features in the sea surface temperature imagery. Mathematical Morphology and the Geometrical Theory of Measure are utilized to compute the Medial Axis Transform and the Hausdorff dimension. The results can be summarized as follows: (i) the tow upwelling areas are identified along the Galician–Portugal coast as indicated in the second and third modes of KLT/EOF analysis, and they are partially affected by wind; (ii) the tow warm water outflows from the Bay of Cádiz to the Gulf of Cádiz are identified as the second and third modes of KLT/EOF analysis, which are also influenced by wind; (iii) the skeletons of the surface signature of the upwelling and of the warmer water outflow, along with their fractal dimensions, indicate a chaotic pattern of spatial distribution and (iv) the harmonic prediction model should be combined with the wind prediction. Full article

The present study aimed to investigate the impact on nutritional and functional properties of dried kiwifruit (Actinidia chinensis cv. Sungold) slices during conservation for 120 days in sealed containers in the dark at 25 °C. For this purpose, kiwifruits slices were dried at two different temperatures, 40 and 55 °C, for 30 and 25 h, respectively. Fresh and dried kiwi slices were analyzed for their pH, activity water, total solid soluble (TSS), color, titratable acidity, total phenols (TPC) and flavonoids content (TFC), organic acids, and radical scavenging activities. Analysis carried out on the dehydrated samples showed a good aptitude of kiwi material towards the drying process. Particularly, it has been observed that the drying treatment at low temperature helped to preserve the nutraceutical properties of the fruits. In fact, samples treated at 40 °C (KLT) showed at day 0 (T0) the highest TPC and TFC with values of 979.42 Gallic Acid Equivalents (GAE)/100 g of dried weight (dw) and 281.84 mg catechin equivalents (CTE)/100 g dw even if compared with fresh kiwi slices sample (FKF). Moreover, KLT also exhibited the highest values of antioxidant activity (1657 mmol Trolox/100 g dw). After 120 days storage, all dried samples showed a high ascorbic acid content (429–339 mg/100 g dw fruits) and only a slight variation of physicochemical parameters. Textural Parameters (hardness, springiness, cohesiveness, gumminess, and chewiness), apart from resilience results, showed significant differences between kiwifruit dried at 55 °C and at 50 °C (KLT and KHT, respectively). Color and aroma intensity were the main sensory descriptors with higher scores. Full article

The solution of the nonlinear (NL) vibration problem of the interaction of laminated plates made of exponentially graded orthotropic layers (EGOLs) with elastic foundations within the Kirchhoff–Love theory (KLT) is developed using the modified Lindstedt–Poincaré method for the first time. Young’s modulus and the material density of the orthotropic layers of laminated plates are assumed to vary exponentially in the direction of thickness, and Poisson’s ratio is assumed to be constant. The governing equations are derived as equations of motion and compatibility using the stress–strain relationship within the framework of KLT and von Karman-type nonlinear theory. NL partial differential equations are reduced to NL ordinary differential equations by the Galerkin method and solved by using the modified Lindstedt–Poincaré method to obtain unique amplitude-dependent expressions for the NL frequency. The proposed solution is validated by comparing the results for laminated plates consisting of exponentially graded orthotropic layers with the results for laminated homogeneous orthotropic plates. Finally, a series of examples are presented to illustrate numerical results on the nonlinear frequency of rectangular plates composed of homogeneous and exponentially graded layers. The effects of the exponential change in the material gradient in the layers, the arrangement and number of the layers, the elastic foundations, the plate aspect ratio and the nonlinearity of the frequency are investigated. Full article

The accurate measurement of river velocity is essential due to its multifaceted significance. In response to this demand, remote measurement techniques have emerged, including large-scale particle image velocimetry (LSPIV), which can be implemented through cameras or unmanned aerial vehicles (UAVs). This study conducted water surface velocity measurements in the Xihu River, situated in Miaoli County, Taiwan. These measurements were subjected to analysis using five distinct algorithms (PIVlab, Fudaa-LSPIV, OpenPIV, KLT-IV, and STIV) and were compared with surface velocity radar (SVR) results. In the quest for identifying the optimal parameter configuration, it was found that an IA size of 32 pixels × 32 pixels, an image acquisition frequency of 12 frames per second (fps), and a pixel size of 20.5 mm/pixel consistently yielded the lowest values for mean error (ME) and root mean squared error (RMSE) in the performance of Fudaa-LSPIV. Among these algorithms, Fudaa-LSPIV consistently demonstrated the lowest mean error (ME) and root mean squared error (RMSE) values. Additionally, it exhibited the highest coefficient of determination (R² = 0.8053). Subsequent investigations employing Fudaa-LSPIV delved into the impact of various water surface velocity calculation parameters. These experiments revealed that alterations in the size of the interrogation area (IA), image acquisition frequency, and pixel size significantly influenced water surface velocity. This parameter set was subsequently employed in an experiment exploring the incorporation of artificial particles in image velocimetry analysis. The results indicated that the introduction of artificial particles had a discernible impact on the calculation of surface water velocity. Inclusion of these artificial particles enhanced the capability of Fudaa-LSPIV to detect patterns on the water surface. Full article

Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing platforms and the high computational cost of compression algorithms for hyperspectral data, especially when the number of bands is large. To address these issues, a channel clusterisation strategy is proposed, which reduces the computational demands of learned compression methods for real scenarios and is scalable for different sources of data with varying numbers of bands. The proposed method is compatible with an embedded implementation for state-of-the-art on board hardware, a first for a ML hyperspectral data compression method. In terms of coding performance, our proposal surpasses established lossy methods such as JPEG 2000 preceded by a spectral Karhunen-Loève Transform (KLT), in clusters of 3 to 7 bands, achieving a PSNR improvement of, on average, 9 dB for AVIRIS and 3 dB for Hyperion images. Full article

The dynamic measurement and identification of structural deformation are essential for structural health monitoring. Traditional contact-type displacement monitoring inevitably requires the arrangement of measurement points on physical structures and the setting of stable reference systems, which limits the application of dynamic displacement measurement of structures in practice. Computer vision-based structural displacement monitoring has the characteristics of non-contact measurement, simple installation, and relatively low cost. However, the existing displacement identification methods are still influenced by lighting conditions, image resolution, and shooting-rate, which limits engineering applications. This paper presents a data fusion method for contact acceleration monitoring and non-contact displacement recognition, utilizing the high dynamic sampling rate of traditional contact acceleration sensors. It establishes and validates an accurate estimation method for dynamic deformation states. The structural displacement is obtained by combining an improved KLT algorithm and asynchronous multi-rate Kalman filtering. The results show that the presented method can help improve the displacement sampling rate and collect high-frequency vibration information compared with only the vision measurement technique. The normalized root mean square error is less than 2% for the proposed method. Full article

Most power plants, particularly those that burn fossil fuels such as coal, oil, and gas, create CO₂, a greenhouse gas that contributes to climate change. By 2060, the Indonesian government has committed to reach net zero emissions. With the lowest CO₂ emissions, nuclear power plants are dependable sources of energy. Small modular reactors (SMRs) are a particular kind of nuclear power plant that has the potential to be Indonesia’s first commercial nuclear power plant because of their small size, low capacity, uncomplicated design, and modular characteristics. The purpose of this study is to examine the economics and technological feasibility of SMRs. In this analysis, the levelized cost of electricity (LCOE) comparative method and the technology readiness level (TRL) approach are both applied. The SMRs with a minimum TRL value of 7 were CAREM-25 (TRL7), KLT-40S (TRL8), and HTR-PM (TRL 8), according to the results of this research. Although CAREM-25 and KLT-40S are still in the demonstration stage and have not yet entered the market, their LCOE estimates are greater than 0.07 USD/kWh with a 5% discount rate. Whereas CAREM 100 MW is an economy scale from CAREM-25 and VBER 300 MW is a commercial size from KLT-40S, HTR-PM is already an economy scale. With discount rates between 5% and 10%, the LCOE values of HTR-PM, CAREM 100 MW, and VBER 300 MW range from 0.06 USD to 0.12 USD per kWh. Other than hydropower and coal-fired power plants, these LCOE figures can compete with the local LCOE in Indonesia and the LCOE of a variety of other types of power plants. Full article

The KLT-40S is a small modular reactor developed by Russia based on the KLT-40 reactor with two fuel assembly designs: a four-ring and a five-ring. Few studies have been published on fuel assembly and power-flattening designs for the KLT-40S. In this paper, the effects of different fuel assembly designs on burnup and power flattening are investigated. This paper compares the effects of the two fuel assembly designs of the KLT-40S on its burnup characteristics, analyzes the effects of fuel rod diameter on burnup characteristics, and conducts a computational study on the ideal power-flattening design. The results show that the five-ring fuel assembly design has better burnup characteristics than the four-ring fuel assembly design. At a fuel rod diameter of 0.62 cm, the optimal burnup lattice is obtained. The 15.84% + 19.75% power-flattening design (uranium enrichment in the innermost and outermost rings + uranium enrichment in inner rings) reduces the local power peaking factor of the five-ring fuel assembly below 1.11 throughout the lifetime. Therefore, the KLT-40S five-ring fuel assembly has better burnup characteristics, and its optimal burnup lattice is at the 0.62 cm fuel rod diameter. The use of power-flattening designs can effectively reduce the local power peaking factor. Full article

Transcriptomics and phosphoproteomics were carried out in the cerebral cortex of B6.Cg-Mapttm1(EGFP)Klt (tau knockout: tau-KO) and wild-type (WT) 12 month-old mice to learn about the effects of tau ablation. Compared with WT mice, tau-KO mice displayed reduced anxiety-like behavior and lower fear expression induced by aversive conditioning, whereas recognition memory remained unaltered. Cortical transcriptomic analysis revealed 69 downregulated and 105 upregulated genes in tau-KO mice, corresponding to synaptic structures, neuron cytoskeleton and transport, and extracellular matrix components. RT-qPCR validated increased mRNA levels of col6a4, gabrq, gad1, grm5, grip2, map2, rab8a, tubb3, wnt16, and an absence of map1a in tau-KO mice compared with WT mice. A few proteins were assessed with Western blotting to compare mRNA expression with corresponding protein levels. Map1a mRNA and protein levels decreased. However, β-tubulin III and GAD1 protein levels were reduced in tau-KO mice. Cortical phosphoproteomics revealed 121 hypophosphorylated and 98 hyperphosphorylated proteins in tau-KO mice. Deregulated phosphoproteins were categorized into cytoskeletal (n = 45) and membrane proteins, including proteins of the synapses and vesicles, myelin proteins, and proteins linked to membrane transport and ion channels (n = 84), proteins related to DNA and RNA metabolism (n = 36), proteins connected to the ubiquitin-proteasome system (UPS) (n = 7), proteins with kinase or phosphatase activity (n = 21), and 22 other proteins related to variegated pathways such as metabolic pathways, growth factors, or mitochondrial function or structure. The present observations reveal a complex altered brain transcriptome and phosphoproteome in tau-KO mice with only mild behavioral alterations. Full article

Coherent motions depict the individuals’ collective movements in widely existing moving crowds in physical, biological, and other systems. In recent years, similarity-based clustering algorithms, particularly the Coherent Filtering (CF) clustering approach, have accomplished wide-scale popularity and acceptance in the field of coherent motion detection. In this work, a tracklet-before-clustering initialization strategy is introduced to enhance coherent motion detection. Moreover, a Hierarchical Tracklet Association (HTA) algorithm is proposed to address the disconnected KLT tracklets problem of the input motion feature, thereby making proper trajectories repair to optimize the CF performance of the moving crowd clustering. The experimental results showed that the proposed method is effective and capable of extracting significant motion patterns taken from crowd scenes. Quantitative evaluation methods, such as Purity, Normalized Mutual Information Index (NMI), Rand Index (RI), and F-measure (Fm), were conducted on real-world data using a huge number of video clips. This work has established a key, initial step toward achieving rich pattern recognition. Full article

In the last few years, uncrewed aerial systems (UASs) have been broadly employed for many applications including urban traffic monitoring. However, in the detection, tracking, and geolocation of moving vehicles using UAVs there are problems to be encountered such as low-accuracy sensors, complex scenes, small object sizes, and motion-induced noises. To address these problems, this study presents an intelligent, self-optimised, real-time framework for automated vehicle detection, tracking, and geolocation in UAV-acquired images which enlist detection, location, and tracking features to improve the final decision. The noise is initially reduced by applying the proposed adaptive filtering, which makes the detection algorithm more versatile. Thereafter, in the detection step, top-hat and bottom-hat transformations are used, assisted by the Overlapped Segmentation-Based Morphological Operation (OSBMO). Following the detection phase, the background regions are obliterated through an analysis of the motion feature points of the obtained object regions using a method that is a conjugation between the Kanade–Lucas–Tomasi (KLT) trackers and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. The procured object features are clustered into separate objects on the basis of their motion characteristics. Finally, the vehicle labels are designated to their corresponding cluster trajectories by employing an efficient reinforcement connecting algorithm. The policy-making possibilities of the reinforcement connecting algorithm are evaluated. The Fast Regional Convolutional Neural Network (Fast-RCNN) is designed and trained on a small collection of samples, then utilised for removing the wrong targets. The proposed framework was tested on videos acquired through various scenarios. The methodology illustrates its capacity through the automatic supervision of target vehicles in real-world trials, which demonstrates its potential applications in intelligent transport systems and other surveillance applications. Full article