Search Results (31)

The rapid increase in global data generation has intensified the demand for magnetic storage systems with substantially higher areal density. Double-layer bit-patterned media recording (DLBPMR), which integrates the benefits of bit-patterned media recording (BPMR) and double-layer magnetic recording (DLMR), provides a promising pathway by combining nanoscale patterned islands with multilayer recording structures. However, severe two-dimensional intersymbol interference (ISI) within each layer, together with interlayer interference (ILI) between stacked layers, continues to present significant challenges for reliable data detection. To address these issues, this work investigates and advances the structure of DLMR to improve signal separation and recovery. In particular, we emphasize that detection plays a crucial role in mitigating both ISI and ILI. Accordingly, we propose a maximum a posteriori (MAP) detection scheme derived for a newly developed generalized two-layer partial-response (PR) model that accurately characterizes intra-layer ISI and cross-layer interference coupling. A parallel detection architecture is designed and employed for the upper and lower layers of the DLMR system, enabling the exchange of extrinsic information and enhancing MAP detection performance. Simulation results demonstrate that the proposed PR modeling and MAP-based detection framework achieves significant bit error rate (BER) improvements over existing detection methods, highlighting its strong potential for next-generation ultra-high-density DLBPMR systems. Full article

Ancient underground voids present non-trivial hazards to urban redevelopment, particularly where groundwater conditions change during construction. We propose a staged, groundwater-aware workflow that integrates in-void mapping with area-scale geophysics and explicitly links water state to imaging performance. Following exposure of an undocumented masonry tunnel in a foundation pit in Wuhan (China), we acquired underwater CCTV and sonar during water-filled conditions, and, after drainage, collected ground-penetrating radar (GPR, 75–150 MHz) and ultra-high-density electrical resistivity tomography (UHD-ERT, 1 m electrode spacing) data. Calibration lines over the breach anchored the depth/geometry and reduced interpretational non-uniqueness. Analytical estimates using Archie-type and CRIM relations, together with observed signatures, indicate that drainage increased resistivity and reduced electromagnetic attenuation, improving UHD-ERT contrast and GPR penetration. The merged evidence resolves a straight-walled arch (~1.8 m wide × ~1.9 m high) at ~4–5 m depth with a sealed end 4 m south of the breach. Sonar confirms a northward segment measuring 45 ± 2 m to a sealed wall; a GPR void-type anomaly at ~57 m along trend represents a candidate continuation that remains unverified with current access. Within the resolution and sensitivity of the 2D survey, no additional voids were detected elsewhere on site. This case demonstrates that coupling in-void CCTV/sonar with post-drainage GPR and UHD-ERT, organized by hydrologic stage, yields engineering-grade constraints for risk control. The workflow and boundary conditions provide a transferable template for water-influenced, urban environments. Full article

To address critical limitations of ultra-low viscosity supercritical CO₂ fracturing fluids, including excessive fluid loss and inadequate proppant transport capacity, a series of thickeners designed to significantly enhance CO₂ viscosity were synthesized. Initially, FT-IR and ¹H NMR characterization confirmed successful chemical reactions and incorporation of both solvation-enhancing and -thickening functional groups. Subsequently, dissolution and thickening performance were evaluated using a custom-designed high-pressure vessel featuring visual observation capability, in-line viscosity monitoring, and high-temperature operation. All thickener systems exhibited excellent solubility, with 5 wt% loading elevating CO₂ viscosity to 3.68 mPa·s. Ultimately, molecular simulations performed in Materials Studio elucidated the mechanistic basis, electrostatic potential (ESP) mapping, cohesive energy density analysis, intermolecular interaction energy, and radial distribution function comparisons. These computational approaches revealed dissolution and thickening mechanisms of polymeric thickeners in CO₂. Full article

Electroencephalography (EEG) communication technology with ultra-low power consumption, high transmission data rates, and low latency plays a significant role in mHealth, telemedicine, and Internet of Medical Things (IoMT). In this paper, generalized frequency division multiplexing (GFDM)-based direct mapping (DM) multi-input—multi-output (MIMO) mobile EEG communication technology (MECT) is proposed for implementation with the above-mentioned applications. The (2000, 1000) low-density parity-check (LDPC) code, four-quadrature amplitude modulation (4-QAM), a power assignment mechanism, and the 3rd Generation Partnership Project (3GPP) cluster delay line (CDL) channel model D were integrated into the proposed EEGCT. The transmission bit error rates (BERs), mean square errors (MSEs), and Pearson-correlation coefficients (PCCs) of the original and received EEG signals were evaluated. Simulation results show that, with a signal to noise ratio (SNR) of 14.51 dB, with a channel estimation error (CEE) of 5%, the BER, MSE, and PCC of the original and received EEG signals were 9.9777 × 10⁻⁸, 1.440 × 10⁻⁵ and 0.999999998, respectively, whereas, with an SNR of 15.0004 dB and a CEE of 10%, they were 9.9777 × 10⁻⁸, 1.4368 × 10⁻⁵, and 0.999999997622151, respectively. As the BER value, and PS saving are 9.9777 × 10⁻⁸, and 40%, respectively. With the CEE changes from 0% to 5%, and 5% to 10%, the N₀ values of the proposed MECT decrease by approximately 0.0022 and 0.002, respectively. The MECT has excellent EEG signal transmission performance. Full article

Background: Atrial fibrosis is a key contributor to atrial cardiomyopathy and can be assessed invasively using mean left atrial voltage (MLAV) from electroanatomical mapping. However, the invasive nature of this procedure limits its clinical applicability. Machine learning (ML), particularly regression tree-based models, may offer a non-invasive approach for predicting MLAV using clinical and echocardiographic data, improving non-invasive atrial fibrosis characterisation beyond current dichotomous classifications. Methods: We prospectively included and followed 113 patients with paroxysmal or persistent atrial fibrillation (AF) undergoing pulmonary vein isolation (PVI) with ultra-high-density voltage mapping (uHDvM), from whom MLAV was estimated. Standardised two-dimensional transthoracic echocardiography was performed before ablation, and clinical and echocardiographic variables were analysed. A regression tree model was constructed using the Classification and Regression Trees—CART-algorithm to identify key predictors of MLAV. Results: The regression tree model exhibited moderate predictive accuracy (R² = 0.63; 95% CI: 0.55–0.71; root mean squared error = 0.90; 95% CI: 0.82–0.98), with indexed minimum LA volume and passive emptying fraction emerging as the most influential variables. No significant differences in AF recurrence-free survival were found among MLAV tertiles or model-based generated groups (log-rank p = 0.319 and p = 0.126, respectively). Conclusions: We present a novel ML-based regression tree model for non-invasive prediction of MLAV, identifying minimum LA volume and passive emptying fraction as the most significant predictors. This model offers an accessible, non-invasive tool for refining atrial cardiomyopathy characterisation by reflecting the fibrotic substrate as a continuum, a crucial advancement over existing dichotomous approaches to guide tailored therapeutic strategies. Full article

Camera-centric perception has matured into a cornerstone of modern autonomy, from self-driving cars and factory cobots to underwater and planetary exploration. This review synthesizes more than a decade of progress in vision-based robotic navigation through an engineering lens, charting the full pipeline from sensing to deployment. We first examine the expanding sensor palette—monocular and multi-camera rigs, stereo and RGB-D devices, LiDAR–camera hybrids, event cameras, and infrared systems—highlighting the complementary operating envelopes and the rise of learning-based depth inference. The advances in visual localization and mapping are then analyzed, contrasting sparse and dense SLAM approaches, as well as monocular, stereo, and visual–inertial formulations. Additional topics include loop closure, semantic mapping, and LiDAR–visual–inertial fusion, which enables drift-free operation in dynamic environments. Building on these foundations, we review the navigation and control strategies, spanning classical planning, reinforcement and imitation learning, hybrid topological–metric memories, and emerging visual language guidance. Application case studies—autonomous driving, industrial manipulation, autonomous underwater vehicles, planetary rovers, aerial drones, and humanoids—demonstrate how tailored sensor suites and algorithms meet domain-specific constraints. Finally, the future research trajectories are distilled: generative AI for synthetic training data and scene completion; high-density 3D perception with solid-state LiDAR and neural implicit representations; event-based vision for ultra-fast control; and human-centric autonomy in next-generation robots. By providing a unified taxonomy, a comparative analysis, and engineering guidelines, this review aims to inform researchers and practitioners designing robust, scalable, vision-driven robotic systems. Full article

The accurate detection and mapping of subsurface utilities are critical for ensuring safety and efficiency in excavation and construction projects. Among various technologies, Ground-Penetrating Radar (GPR) has been widely used for locating underground infrastructure due to its non-destructive nature and ability to detect both metallic and non-metallic materials. However, many GPR systems struggle to meet the practical depth requirements in real-world conditions, especially when identifying non-metallic facilities such as PVC and PE pipes. In South Korea, the legal regulations require underground utility locators to meet specific accuracy standards, including a minimum detectable depth of 3 m. These regulations also mandate periodic performance testing of surveying equipment at authorized inspection centers. Despite this, most GPR systems tested at the official performance evaluation site at Sungkyunkwan University demonstrated limited effectiveness, with an average detection range of only 1.5 m. This study evaluates the performance of a handheld All Materials Locator (AML) device developed by SubSurface Instruments, Inc., (Janesville, WI, USA) which uses ultra-high radio frequencies to detect subsurface density variations. Experimental results from both the certified test facility and field conditions indicate that the AML meets South Korea’s legal requirements for minimum depth and accuracy, by successfully identifying a wide range of subsurface utilities including non-metallic materials. The findings suggest that the AML is a viable alternative to conventional GPR systems for utility detection in regulated environments. Full article

This study introduces an innovative and scalable approach for automated road surface assessment by integrating Mobile Mapping System (MMS)-based LiDAR data analysis with an open-source WebGIS platform. In a U.S.-based case study, over 20 datasets were collected along Interstate I-65 in West Lafayette, Indiana, using the Purdue Wheel-based Mobile Mapping System—Ultra High Accuracy (PWMMS-UHA), following Indiana Department of Transportation (INDOT) guidelines. Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. DBSCAN parameter tuning was guided by silhouette scores, while model performance was evaluated using precision, recall, F1-score, and the Jaccard Index, benchmarked against reference data. Results demonstrate that RF consistently outperformed DBSCAN, particularly under intensity normalization, achieving Jaccard Index values of 94% for longitudinal and 88% for transverse cracks. A key contribution of this work is the integration of geospatial analytics into an interactive, open-source WebGIS environment—developed using Blender, QGIS, and Lizmap—to support predictive maintenance planning. Moreover, intervention thresholds were defined based on crack surface area, aligned with the Pavement Condition Index (PCI) and FHWA standards, offering a data-driven framework for infrastructure monitoring. This study emphasizes the practical advantages of comparing clustering and machine learning techniques on 3D LiDAR point clouds, both with and without intensity normalization, and proposes a replicable, computationally efficient alternative to deep learning methods, which often require extensive training datasets and high computational resources. Full article

Zebrafish (Danio rerio) is an important animal model for a wide range of neurodegenerative diseases. However, obtaining the cellular resolution that is essential for studying the zebrafish brain remains challenging as it requires high spatial resolution and signal-to-noise ratios (SNR). In the current study, we present the first MRI results of the zebrafish brain at the state-of-the-art magnetic field strength of 28.2 T. The performance of MRI at 28.2 T was compared to 17.6 T. A 20% improvement in SNR was observed at 28.2 T as compared to 17.6 T. Excellent contrast, resolution, and SNR allowed the identification of several brain structures. The normative T₁ and T₂ relaxation values were established over different zebrafish brain structures at 28.2 T. To zoom into the white matter structures, we applied diffusion tensor imaging (DTI) and obtained axial, radial, and mean diffusivity, as well as fractional anisotropy, at a very high spatial resolution. Visualisation of white matter structures was achieved by short-track track-density imaging by applying the constrained spherical deconvolution method (stTDI CSD). For the first time, an algorithm for stTDI with multi-shell multi-tissue (msmt) CSD was tested on zebrafish brain data. A significant reduction in false-positive tracks from grey matter signals was observed compared to stTDI with single-shell single-tissue (ssst) CSD. This allowed the non-invasive identification of white matter structures at high resolution and contrast. Our results show that ultra-high field DTI and tractography provide reproducible and quantitative maps of fibre organisation from tiny zebrafish brains, which can be implemented in the future for a mechanistic understanding of disease-related microstructural changes in zebrafish models of various brain diseases. Full article

Soil salinity poses a serious threat to rice production. The salt tolerance of rice at the germination stage is one of the major determinants of stable stand establishment, which is very important for direct seeding in saline soil. The complexity and polygenic nature of salt tolerance have limited the efficiency of discovering and cloning key genes in rice. In this study, an RIL population with an ultra-high-density genetic map was employed to investigate the salt-tolerant genetic basis in rice, and a total of 20 QTLs were detected, including a major and stable QTL (qRCL3-1). Subsequently, salt-specific DEGs from a comparative transcriptome analysis were overlaid onto annotated genes located on a stable QTL interval, and eight putative candidate genes were further identified. Finally, from the sequence alignment and variant analysis, OsCam1-1 was confirmed to be the most promising candidate gene for regulating salinity tolerance in rice. This study provides important information for elucidating the genetic and molecular basis of rice salt tolerance at the germination stage, and the genes detected here will be useful for improvements in rice salt tolerance. Full article

Background: Understanding the dynamics of conduction velocity (CV) and voltage amplitude (VA) is crucial in cardiac electrophysiology, particularly for substrate-based catheter ablations targeting slow conduction zones and low voltage areas. This study utilizes ultra-high-density mapping to investigate the impact of heart rate and pacing location on changes in the wavefront direction, CV, and VA of healthy pig hearts. Methods: We conducted in vivo electrophysiological studies on four healthy juvenile pigs, involving various pacing locations and heart rates. High-resolution electroanatomic mapping was performed during intrinsic normal sinus rhythm (NSR) and electrical pacing. The study encompassed detailed analyses at three levels: entire heart cavities, subregions, and localized 5-mm-diameter circular areas. Linear mixed-effects models were used to analyze the influence of heart rate and pacing location on CV and VA in different regions. Results: An increase in heart rate correlated with an increase in conduction velocity and a decrease in voltage amplitude. Pacing influenced conduction velocity and voltage amplitude. Pacing also influenced conduction velocity and voltage amplitude, with varying effects observed based on the pacing location within different heart cavities. Pacing from the right atrium (RA) decreased CV in all heart cavities. The overall CV and VA changes in the whole heart cavities were not uniformly reflected in all subregions and subregional CV and VA changes were not always reflected in the overall analysis. Overall, there was a notable variability in absolute CV and VA changes attributed to pacing. Conclusions: Heart rate and pacing location influence CV and VA within healthy juvenile pig hearts. Subregion analysis suggests that specific regions of the heart cavities are more susceptible to pacing. High-resolution mapping aids in detecting regional changes, emphasizing the substantial physiological variations in CV and VA. Full article

The problem of the interferometric phase unwrapping in radar remote sensing of Earth systems is considered. Such interferograms are widely used in the problems of creating and updating maps of the relief of the Earth’s surface in geodesy, cartography, environmental monitoring, geological, hydrological and glaciological studies, and for monitoring transport communications. Modern radar systems have ultra-high spatial resolution and a wide band, which leads to the need to unwrap large interferograms from several tens of millions of elements. The implementation of calculations by these methods requires a processing time of several days. In this paper, an effective method for equalizing the inverse vortex field for phase unwrapping is proposed, which allows solving a problem with quasi-linear computational complexity depending on the interferogram size and the number of singular points on it. To implement the method, a parallel algorithm for solving the problem on a multi-core processor using OpenMP technology was developed. Numerical experiments on radar data models were carried out to investigate the effectiveness of the algorithm depending on the size of the source data, the density of singular points and the number of processor cores. Full article

Genetic maps facilitate an understanding of genome organization and the mapping of genes and QTLs for traits of interest. Our objective was to develop a high-density genetic map of Jatropha and anchoring scaffolds from genome assemblies. We developed two ultra-high-density genetic linkage maps of Jatropha curcas × Jatropha intergerrima using a backcross (BC₁) population using SNP, AFLP and SSR markers. First, SNPs were identified through genotyping-by-sequencing (GBS). The polymorphic SNPs were mapped to 3267 Jat_r4.5 scaffolds and 484 Wu_JatCur_1.0 scaffolds, and then these genomic scaffolds were mapped/anchored to the genetic linkage groups along with the AFLP and SSR markers for each genome assembly separately. We successfully mapped 7284 polymorphic SNPs, and 54 AFLP and SSR markers on 11 linkage groups using the Jat_r4.5 genomic scaffolds, resulting in a genome length of 1088 cM and an average marker interval of 0.71 cM. We mapped 7698 polymorphic SNPs, and 99 AFLP and SSR markers on 11 linkage groups using the Wu_JatCur_1.0 genomic scaffolds, resulting in a genome length of 870 cM and an average marker interval of 1.67 cM. The mapped SNPs were annotated to various regions of the genome, including exon, intron and intergenic regions. We developed two ultra-high-density linkage maps anchoring a high number of genome scaffolds to linkage groups, which provide an important resource for the structural and functional genomics as well as for molecular breeding of Jatropha while also serving as a framework for assembling and ordering whole genome scaffolds. Full article

Background: Ultra-high-density mapping systems allow more precise measurement of the heart chambers at corresponding conduction velocities (CVs) and voltage amplitudes (VAs). Our aim for this study was to define and compare a basic value set for unipolar CV and VA in all four heart chambers and their separate walls in healthy, juvenile porcine hearts using ultra-high-density mapping. Methods: We used the Rhythmia Mapping System to create electroanatomical maps of four pig hearts in sinus rhythm. CVs and VAs were calculated for chambers and wall segments with overlapping circular areas (radius of 5 mm). Results: We analysed 21 maps with a resolution of 1.4 points/mm². CVs were highest in the left atrium (LA), followed by the left ventricle (LV), right ventricle (RV), and right atrium (RA). As for VA, LV was highest, followed by RV, LA, and RA. The left chambers had a higher overall CV and VA than the right. Within the chambers, CV varied more in the right than in the left chambers, and VA varied in the ventricles but not in the atria. There was a slightly positive correlation between CVs and VAs at velocity values of <1.5 m/s. Conclusions: In healthy porcine hearts, the left chambers showed higher VAs and CVs than the right. CV differs mainly within the right chambers and VA differs only within the ventricles. A slightly positive linear correlation was found between slow CVs and low VAs. Full article

Oat products are significant parts of a healthy diet. Pure oat is gluten-free, which makes it an excellent choice for people with celiac disease. Elimination of alien cereals is important not only in gluten-free oat production but also in seed production. Detecting gluten-rich crops such as wheat, rye, and barley in an oat production field is an important initial processing step in gluten-free food industries; however, this particular step can be extremely time consuming. This article demonstrates the potential of emerging drone techniques for identifying alien barleys in an oat stand. The primary aim of this study was to develop and assess a novel machine-learning approach that automatically detects and localizes barley plants by employing drone images. An Unbiased Teacher v2 semi-supervised object-detection deep convolutional neural network (CNN) was employed to detect barley ears in drone images with a 1.5 mm ground sample distance. The outputs of the object detector were transformed into ground coordinates by employing a photogrammetric technique. The ground coordinates were analyzed with the kernel density estimate (KDE) clustering approach to form a probabilistic map of the ground locations of barley plants. The detector was trained using a dataset from a reference data production site (located in Ilmajoki, Finland) and tested using a 10% independent test data sample from the same site and a completely unseen dataset from a commercial gluten-free oats production field in Seinäjoki, Finland. In the reference data production dataset, 82.9% of the alien barley plants were successfully detected; in the independent farm test dataset, 60.5% of the ground-truth barley plants were correctly recognized. Our results establish the usefulness and importance of the proposed drone-based ultra-high-resolution red–green–blue (RGB) imaging approach for modern grain production industries. Full article