Three-dimensional Spatial Information Research Articles

Enhancing Terrain Analysis from Digital Elevation Models Using 2-D Kalman Filtering Technique

Three-dimensional spatial information, particularly elevation, is crucial for understanding terrain characteristics essential for meaningful development, often expressed as a Digital Elevation Model (DEM). To achieve reliable and accurate DEM values for terrain analysis, modeling uncertainties is necessary. The primary objective of this study is to determine improved terrain variables from the Digital Elevation Model of the study area. The recursive 2-D Kalman filtering technique was applied four times at different orientations to 121 elevation values extracted from a 30-meter resolution ALOS DEM of the study area using QGIS Desktop 3.22.7 software of an area covering approximately 10.80 Hectares using QGIS, the process involved 144 iterations. MATLAB was used for the computations. The terrain variables (elevation, first partial derivatives along the X and Y axes) of the central point of the DEM were obtained as a linear combination of the four filtering results. The final estimated values for the central point were 26.5589m for elevation, 0.0002m and 0.0011m for partial derivatives along the X and Y directions, with standard errors of ±0.0001m, ±0.0005m, and ±0.0007m, respectively. A 3-D plot of the terrain surface of the study area using Surfer10 software showed that the recursive 2-D Kalman filtering significantly improved the quality of the terrain surface when applied over the DEM. Therefore, the adopted recursive 2-D Kalman filter is well-suited for terrain surface modeling using grid DEMs. Its use is encouraged for determining improved values of terrain topographic variables, leading to more accurate terrain interpretation. In addition, when compared with ground survey data confirmed the technique's efficiency in reducing DEM noise. These results are promising as they are necessary information for flood route modelling, land use allocation and enhance functionality of the urban space domain of the study area.

Redefining Accuracy: Underwater Depth Estimation for Irregular Illumination Scenes.

Acquiring underwater depth maps is essential as they provide indispensable three-dimensional spatial information for visualizing the underwater environment. These depth maps serve various purposes, including underwater navigation, environmental monitoring, and resource exploration. While most of the current depth estimation methods can work well in ideal underwater environments with homogeneous illumination, few consider the risk caused by irregular illumination, which is common in practical underwater environments. On the one hand, underwater environments with low-light conditions can reduce image contrast. The reduction brings challenges to depth estimation models in accurately differentiating among objects. On the other hand, overexposure caused by reflection or artificial illumination can degrade the textures of underwater objects, which is crucial to geometric constraints between frames. To address the above issues, we propose an underwater self-supervised monocular depth estimation network integrating image enhancement and auxiliary depth information. We use the Monte Carlo image enhancement module (MC-IEM) to tackle the inherent uncertainty in low-light underwater images through probabilistic estimation. When pixel values are enhanced, object recognition becomes more accessible, allowing for a more precise acquisition of distance information and thus resulting in more accurate depth estimation. Next, we extract additional geometric features through transfer learning, infusing prior knowledge from a supervised large-scale model into a self-supervised depth estimation network to refine loss functions and a depth network to address the overexposure issue. We conduct experiments with two public datasets, which exhibited superior performance compared to existing approaches in underwater depth estimation.

Redefining Accuracy: Underwater Depth Estimation for Irregular Illumination Scenes

Acquiring underwater depth maps is essential as they provide indispensable three-dimensional spatial information for visualizing the underwater environment. These depth maps serve various purposes, including underwater navigation, environmental monitoring, and resource exploration. While most of the current depth estimation methods can work well in ideal underwater environments with homogeneous illumination, few consider the risk caused by irregular illumination, which is common in practical underwater environments. On the one hand, underwater environments with low-light conditions can reduce image contrast. The reduction brings challenges to depth estimation models in accurately differentiating among objects. On the other hand, overexposure caused by reflection or artificial illumination can degrade the textures of underwater objects, which is crucial to geometric constraints between frames. To address the above issues, we propose an underwater self-supervised monocular depth estimation network integrating image enhancement and auxiliary depth information. We use the Monte Carlo image enhancement module (MC-IEM) to tackle the inherent uncertainty in low-light underwater images through probabilistic estimation. When pixel values are enhanced, object recognition becomes more accessible, allowing for a more precise acquisition of distance information and thus resulting in more accurate depth estimation. Next, we extract additional geometric features through transfer learning, infusing prior knowledge from a supervised large-scale model into a self-supervised depth estimation network to refine loss functions and a depth network to address the overexposure issue. We conduct experiments with two public datasets, which exhibited superior performance compared to existing approaches in underwater depth estimation.

Underground Target Extraction by Local Entropy Feature of Voxelized 3D Ground Penetrating Radar

Abstract. To solve the current problem of insufficient exploration of three-dimensional spatial information detected by 3D ground penetrating radar (3D GPR) and the data processing mainly based on the analysis and interpretation of two-dimensional slice images, a method is proposed to extract underground target based on the local entropy feature of discrete point clouds after the voxelization of 3D GPR data. First, the acquired 3D GPR data was voxelized into discrete three-dimensional point clouds. Then the local entropy feature of the voxelized point clouds over the entire region were calculated. The soil background and underground targets were distinguished by classifying them from multiple dimensions through Support Vector Machine (SVM). Finally, the urban road underground environment was taken as the research object, and this method was used for experimental analysis using measured data. The experimental results show that the accuracy of this method in extracting underground targets is as high as 90.1%, and the missing detection rate of missing underground targets is as low as 7.8%. The proposed method is accurate and effective, providing a new approach for 3D GPR to extract underground target.

A system for the study of roots 3D kinematics in hydroponic culture: a study on the oscillatory features of root tip

BackgroundThe root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics.ResultsThe system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of < 1 mm between computed and manually measured root length.ConclusionsThe system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analysing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.

Application of Three-dimensional Coding Network in Screening and Diagnosis of Cervical Precancerous Lesions

Globally, the incidence of cervical cancer ranks fourth among female malignant tumors, seriously threatening the physical and mental health of women. Early detection and early treatment can greatly reduce the mortality of cervical cancer "cytology /HPV test, colposcopy and cervical biopsy" is the main method for clinical diagnosis of cervical cancer. The progress of medical technology has significantly improved the early diagnosis of cervical cancer, but due to various factors, there are still many cases of missed diagnosis and misdiagnosis. In recent years, artificial intelligence has developed rapidly in the medical field, and has also shown good applicability in the screening and diagnosis of cervical cancer. In this paper, we design an mcs-SEM structure that contains both channels and space compression excitation modules, which can retain comprehensive spatial information at a low computational cost. Then, the structure is embedded in a three-dimensional coding network to realize the combination of two-dimensional convolutional neural network and three-dimensional spatial information, so as to predict the DVH in the complex distribution scenario of multi-endangered organs with higher accuracy.

ACCURACY EVALUATION OF AUTOMATIC SEGMENTATION IN BIOMEDICAL IMAGES: 3D SPATIAL MAPPING OF SEGMENTATION ERRORS

Currently implemented accuracy metrics in open-source libraries for segmentation by supervised machine learning are typically one-dimensional scores [1]. While extremely relevant to evaluate applicability in clinics, anatomical location of segmentation errors is often neglected.This study aims to include the three-dimensional (3D) spatial information in the development of a novel framework for segmentation accuracy evaluation and comparison between different methods.Predicted and ground truth (manually segmented) segmentation masks are meshed into 3D surfaces. A template mesh of the same anatomical structure is then registered to all ground truth 3D surfaces. This ensures all surface points on the ground truth meshes to be in the same anatomically homologous order. Next, point-wise surface deviations between the registered ground truth mesh and the meshed segmentation prediction are calculated and allow for color plotting of point-wise descriptive statistics. Statistical parametric mapping includes point-wise false discovery rate (FDR) adjusted p-values (also referred to as q-values).The framework reads volumetric image data containing the segmentation masks of both ground truth and segmentation prediction. 3D color plots containing descriptive statistics (mean absolute value, maximal value,…) on point-wise segmentation errors are rendered. As an example, we compared segmentation results of nnUNet [2], UNet++ [3] and UNETR [4] by visualizing the mean absolute error (surface deviation from ground truth) as a color plot on the 3D model of bone and cartilage of the mean distal femur.A novel framework to evaluate segmentation accuracy is presented. Output includes anatomical information on the segmentation errors, as well as point-wise comparative statistics on different segmentation algorithms. Clearly, this allows for a better informed decision-making process when selecting the best algorithm for a specific clinical application.

Multi-echo hyperspectral reflectance extraction method based on full waveform hyperspectral LiDAR

Full waveform hyperspectral LiDAR can simultaneously obtain three-dimensional spatial information and spectral information of targets. As a result of it, target attributes in the vertical direction can be accurately assessed. However, spectral reflectance extraction from full waveform hyperspectral LiDAR data, especially for multi-echo full waveforms, is still challenging due to the indeterminate backscattering cross-sectional area and unseparated reflectance. This challenge hampers the extraction accuracy of multi-echo reflectance and then causes underestimation consequently. Here, we proposed a new method to extract reflectance of multi-echo full waveforms, namely multi-echo reflectance correction using neighbourhood single-echo reflectance (MCNS), and verified its validation by using the coefficient of determination (R2) and the root mean square error (RMSE) based on simulated and measured data. Results indicated that R2 and RMSE between the corrected simulated multi-echo reflectance and the spectrometer reflectance were more than 0.95 and less than 0.042 respectively, while that between the corrected measured multi-echo reflectance and the spectrometer reflectance were above 0.56 and below 0.15 respectively. These results demonstrated the feasibility of the proposed method in extracting multi-echo reflectance. This study lays a foundation for subsequent forest attribute assessments more accurately, making it possible to characterise multiple target features vertically using high-resolution spectral reflectance.

Evaluation of aggregate segregation in self-consolidating concrete using 3D point cloud analysis

Self-consolidating concrete (SCC) is a widely used construction material known for its high workability. In general, the proportion of chemical admixture in SCC is higher than that of standard concrete, making it more susceptible to the segregation due to its low aggregate bearing capacity. To test the aggregate segregation, a variety of qualitative and quantitative methods have been proposed for either fresh or hardened SCC; however, the conventional methods are difficult to accurately investigate the segregation in advance of the construction. This study presents a novel method for evaluating the degree of segregation in fresh SCC at an early stage using point cloud data obtained from scanning the surface of SCC slump flow. The acquired point cloud provides three-dimensional (3D) spatial information, which can be used to calculate various parameters, including diameter, maximum height, and curvature. In particular, the volume of the segregation suspicious region (VSSR) is proposed to quantify the aggregate segregation of the fresh SCC. The reliability of the proposed method is evaluated by comparing the obtained spread diamters with those of the manual inspection, in which the average error is minimal (i.e., approximately 2.4%). Furthermore, the overall performance is compared with standardized qualitative tests and validated through digital image processing on hardened SCC samples, showing good agreement between the VSSR and the degree of segregation obtained from the image analysis. This method offers a comprehensive and efficient tool to assess segregation in the fresh SCC, contributing to improved quality control and optimization of SCC mix designs.

Research and Implementation of Three-Dimensional Spatial Information Characterization and Visualization of Fractures in Deteriorated Sandstone

Primary fractures significantly impacted the stability of surrounding rock in underground projects. Therefore, it is vital to find a solution for the problem of performing a non-destructive detection of rocks and extracting the internal three-dimensional (3D) data field of rocks for visualization analysis. To address this problem, this paper proposed a method of 3D reconstruction for complex cracks in deteriorated sandstone and developed a program based on MATLAB. This work carried out image recognition on the CT scan images of deteriorated sandstone, then implemented a surface reconstruction technique based on object cross-section information, a contour reconstruction technique based on object contour information, a point cloud reconstruction technique for extracting point cloud data of internal cracks in deteriorated sandstone, and a Graphical User Interface (GUI) control system that combines these three reconstruction techniques. The results showed that the 3D reconstruction techniques and the GUI control system proposed in this paper were capable of precisely marking the location of the cracks on a 3D coordinate system and accurately describing their shape with a vector. With only 10 CT scan images, the point cloud reconstruction technique constructed the digital core, and the digital core can quantitatively characterize the influence of primary fractures on the stability of surrounding rock. Additionally, the calculated results of the proposed method were very close to that of Avizo. This method realized the visualization and quantitative characterization of the internal structure of rocks and offered a model for analyzing the stress-fracture-seepage field change during excavation.

Improving Estimation of Tree Parameters by Fusing ALS and TLS Point Cloud Data Based on Canopy Gap Shape Feature Points

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) are two ways to obtain forest three-dimensional (3D) spatial information. Due to canopy occlusion and the features of different scanning methods, some of the forest point clouds acquired by a single scanning platform may be missing, resulting in an inaccurate estimation of forest structure parameters. Hence, the registration of ALS and TLS point clouds is an alternative for improving the estimation accuracy of forest structure parameters. Currently, forest point cloud registration is mainly conducted based on individual tree attributes (e.g., location, diameter at breast height, and tree height), but the registration is affected by individual tree segmentation and is inefficient. In this study, we proposed a method to automatically fuse ALS and TLS point clouds by using feature points of canopy gap shapes. First, the ALS and TLS canopy gap boundary vectors were extracted by the canopy point cloud density model, and the turning or feature points were obtained from the canopy gap vectors using the weighted effective area (WEA) algorithm. The feature points were then aligned, the transformation parameters were solved using the coherent point drift (CPD) algorithm, and the TLS point clouds were further aligned using the recovery transformation matrix and refined by utilizing the iterative closest point (ICP) algorithm. Finally, individual tree segmentations were performed to estimate tree parameters using the TLS and fusion point clouds, respectively. The results show that the proposed method achieved more accurate registration of ALS and TLS point clouds in four plots, with the average distance residuals of coarse and fine registration of 194.83 cm and 2.14 cm being much smaller compared with those from the widely used crown feature point-based method. Using the fused point cloud data led to more accurate estimates of tree height than using the TLS point cloud data alone. Thus, the proposed method has the potential to improve the registration of ALS and TLS point cloud data and the accuracy of tree height estimation.

İHA Sistemleri ile Elde Edilen Ortofoto Haritaların Doğruluk Değerlendirmesi

Unmanned Aerial Vehicles (UAV), which is one of the most important gains of the developing technology, was firstly used for military purposes, but soon became an important issue of our daily life. In addition to the ability to produce three-dimensional spatial information with the UAV, it provides less labor, time and cost advantage compared to conventional measurement methods, playing an important role in the spread of UAV usage. UAV systems, whose usage are becoming widespread and diverse day by day, are in a continuous development to meet the high accuracy requirements, especially for surveying activities. For this reason, UAV hardware and software features are also constantly developing. In this study, firstly, the accuracy comparisons of the data sets based on the UAV with GNSS-RTK and the UAV without this feature were studied. Later, the accuracy comparisons of the data sets processed by Pix4D Mapper and Agisoft FotoScan photogrammetry software. As a result of the comparisons, it was seen that the horizontal and vertical accuracy values obtained for the UAV data with GNSS-RTK were better than the horizontal and vertical accuracy values obtained for other UAV data. When the software were evaluated among themselves, it was seen that the horizontal and vertical accuracy of the data sets obtained with Pix4D Mapper software was better. On the other hand, while Agisoft FotoScan software gives better results in horizontal position accuracy of data sets based on UAV without GNSS-RTK, Pix4D Mapper software gives better results in vertical position accuracy of those.

Experimental study on the spatial fracture behaviour of red sandstone containing weak inclusions with non-unitary geometric features under uniaxial compression

The failure of weakly filled rock masses is an important factor in the safety and stability of rock engineering. To investigate the spatial fracture behaviour of red sandstone with non-unitary geometric weakly filled joints, uniaxial compression experiments were carried out. The 3D DIC and acoustic emission (AE) technology were used to obtain the three-dimensional spatial deformation information and AE parameters of specimens containing both planar and circular weak inclusions, respectively. The main results are as follows: (1) The results show that the ductility of planar weak inclusions has a certain deterioration effect on the bearing capacity of the specimens, and the extent of this deterioration effect is not constant. (2) When the ductile length of the planar weak inclusions increases, the three-dimensional spatial displacement and strain fields of the specimens are divided by the planar weak inclusions, showing different degrees of deformation behaviour and different distribution patterns in the upper and lower parts. (3) The three-dimensional spatial fracture characteristics of specimens with different ductile lengths of planar weak inclusions will be dominated by the ductility, and the main failure mode of the specimen will gradually evolve from shear failure to splitting failure. The “guiding effect” of circular hole weak inclusions on the main crack is no longer evident when the ductility of planar weak inclusions is at its maximum. (4) The growth trend of the cumulative AE counts can be divided into four stages, and the stage characteristics are significantly influenced by the ductile length of the planar weak inclusions; different failure modes show different AE signal distribution characteristics. In addition, the key points of stability control of rock masses containing weak inclusions in practical engineering are discussed.

MISF: A Method for Measurement of Standing Tree Size via Multi-Vision Image Segmentation and Coordinate Fusion

With the development of computer vision technology, its applications in forestry are steadily becoming wider. To address the problems of inconvenience in transporting unmanned aerial vehicles (UAVs), as well as the complex operation of large instruments for measurement, a new method based on multi-vision image segmentation and coordinate fusion (MISF) is proposed in this paper for measuring the size of standing trees. In MISF, after images of a standing tree are captured using a camera from multiple angles, a semantic segmentation method based on deep learning is used to segment the main body of the standing tree and automatically detect the edge feature points. Next, the effects of visual field splicing and fusion are analyzed collaboratively using the correlations among images, so as to restore the three-dimensional spatial information of the feature points of the tree to be measured. Lastly, the size attributes of the standing tree, such as height, diameter at breast height (DBH), and crown width, are automatically measured. The urban environment measurement experiment showed that the relative errors of tree height, DBH, and crown width measured using the proposed method, i.e., MISF, were 1.89%, 2.42%, and 3.15%, respectively, representing a significant enhancement compared with binocular measurement. On the one hand, the experimental results exhibited a high degree of measurement accuracy; therefore, MISF can be used for the management inventory of typical forests. On the other hand, MISF cannot be used if a tree’s images cannot be acquired due to environmental or other reasons.

GND-PCA Method for Identification of Gene Functions Involved in Asymmetric Division of C. elegans

Due to the rapid development of imaging technology, a large number of biological images have been obtained with three-dimensional (3D) spatial information, time information, and spectral information. Compared with the case of two-dimensional images, the framework for analyzing multidimensional bioimages has not been completely established yet. WDDD is an open biological image database. It dynamically records 3D developmental images of 186 samples of nematodes C. elegans. In this study, based on WDDD, we constructed a framework to analyze the multidimensional dataset, which includes image segmentation, image registration, size registration by the length of main axes, time registration by extracting key time points, and finally, using generalized N-dimensional principal component analysis (GND-PCA) to analyze the phenotypes of bioimages. As a data-driven technique, GND-PCA can automatically extract the important factors involved in the development of P1 and AB in C. elegans. A 3D bioimage can be regarded as a third-order tensor. Therefore, GND-PCA was applied to the set of third-order tensors, and a set of third-order tensor bases was iteratively learned to linearly approximate the set. For each tensor base, a corresponding characteristic image is built to reveal its geometric meaning. The results show that different bases can be used to express different vital factors in development, such as the asymmetric division within the two-cell stage of C. elegans. Based on selected bases, statistical models were built by 50 wild-type (WT) embryos in WDDD, and were applied to RNA interference (RNAi) embryos. The results of statistical testing demonstrated the effectiveness of this method.

SmartGate is a spatial metabolomics tool for resolving tissue structures.

Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing the internal microenvironment of organisms. It has dramatically advanced the understanding of the structure of biological tissues and the drug treatment of diseases. However, the complexity of IMS data hinders the further acquisition of biomarkers and the study of certain specific activities of organisms. To this end, we introduce an artificial intelligence tool, SmartGate, to enable automatic peak selection and spatial structure identification in an iterative manner. SmartGate selects discriminative m/z features from the previous iteration by differential analysis and employs a graph attention autoencoder model to perform spatial clustering for tissue segmentation using the selected features. We applied SmartGate to diverse IMS data at multicellular or subcellular spatial resolutions and compared it with four competing methods to demonstrate its effectiveness. SmartGate can significantly improve the accuracy of spatial segmentation and identify biomarker metabolites based on tissue structure-guided differential analysis. For multiple consecutive IMS data, SmartGate can effectively identify structures with spatial heterogeneity by introducing three-dimensional spatial neighbor information.

Development of Geospatial Data Acquisition, Modeling, and Service Technology for Digital Twin Implementation of Underground Utility Tunnel

In the maintenance domain of the construction industry, digital twins have been actively introduced based on the technologies of the Fourth Industrial Revolution with growing interest in three-dimensional spatial information facility management and disaster response service using digital twin technology. In particular, disasters or abnormal situations in an underground utility tunnel (UTU) can cause serious casualties and property damage since key elements of a city such as power, communications, water supply, and heating facilities are collectively accommodated in a certain underground space. This study established the methodology for implementing an underground utility tunnel through a digital twin. The novelty of this study is demonstrating a sequential procedure of implementing digital twin technology by configuring major layers such as data acquisition, modeling, and service. This methodology can serve as reference material or a training aid for implementing a digital twin of underground facilities or underground utility tunnels in the future; the proposed methodology will be verified when an actual digital twin service is provided.

DMU-Net: A Dual-Stream Multi-Scale U-Net Network Using Multi-Dimensional Spatial Information for Urban Building Extraction.

Automatically extracting urban buildings from remote sensing images has essential application value, such as urban planning and management. Gaofen-7 (GF-7) provides multi-perspective and multispectral satellite images, which can obtain three-dimensional spatial information. Previous studies on building extraction often ignored information outside the red-green-blue (RGB) bands. To utilize the multi-dimensional spatial information of GF-7, we propose a dual-stream multi-scale network (DMU-Net) for urban building extraction. DMU-Net is based on U-Net, and the encoder is designed as the dual-stream CNN structure, which inputs RGB images, near-infrared (NIR), and normalized digital surface model (nDSM) fusion images, respectively. In addition, the improved FPN (IFPN) structure is integrated into the decoder. It enables DMU-Net to fuse different band features and multi-scale features of images effectively. This new method is tested with the study area within the Fourth Ring Road in Beijing, and the conclusions are as follows: (1) Our network achieves an overall accuracy (OA) of 96.16% and an intersection-over-union (IoU) of 84.49% for the GF-7 self-annotated building dataset, outperforms other state-of-the-art (SOTA) models. (2) Three-dimensional information significantly improved the accuracy of building extraction. Compared with RGB and RGB + NIR, the IoU increased by 7.61% and 3.19% after using nDSM data, respectively. (3) DMU-Net is superior to SMU-Net, DU-Net, and IEU-Net. The IoU is improved by 0.74%, 0.55%, and 1.65%, respectively, indicating the superiority of the dual-stream CNN structure and the IFPN structure.

Polymer modelling accurately predicts three-dimensional chromosome reorganization with a seasonal coronavirus infection.

Many vital processes, such as DNA transcription, replication, and repair, depending on the cell's structural organization of chromosomes. Three-dimensional genome spatial information is essential for understanding gene regulation and epigenetic effects, which can regulate the 3D architecture of the whole genome. Various advances in experimental techniques such as chromosome conformation capture (Hi-C), Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) and other related methods have led to the finding of topologically associating domains (TADs), chromatin loops, and different compartments existing in the chromosomes. However, less progress has been made in modelling chromosomes based on experimentally-driven approaches. Previously, polymer simulations were employed, incorporating one-dimension information and generating connectivity profiles for the chromosomes. Our method (4DHiC) extracts 2D information from their corresponding Hi-C maps and implements it as a harmonic constraint in the polymer model. Here, different Hi-C maps for MRC cell lines infected with and without seasonal coronavirus infection at varying stages of infection were generated to test the model's applicability. We performed Langevin dynamics to simulate the polymer model with HiC-map constraints using the 4DHiC method. On comparing the simulated and experimental HiC map, a strong correlation was observed (point-to-point RPearson∼0.75). Along with the model development, this study also revealed the critical structural difference that arises from viral infection in the chromosome organization. One important chromosome organization feature, A/B compartmentalization, was also studied. A clear separation was observed based on the principal component analysis, predicting the change in compartment contacts. Another advantage of the method is the scalability, i.e. can vary the number of beads based on the resolution of the HiC map. Lastly, we emphasize that the protocol is general and can be used to model the whole genome or individual chromosomes for any system.

Spatiotemporal Processing for Remote Sensing of Trapped Victims Using 4-D Imaging Radar

It is of great importance to remotely sense trapped victims with radio signals in modern search and rescue after natural disasters like earthquakes, avalanches, building collapses, and so on. Various radio sensors have been developed to date; however, they are hardly deployed to recognize efficiently the vital sign in long-distance, deep-coverage, and multi-subject situations because the back-scattered victim-critical radio signals are really weak and are nearly drowned in the ambient nonstationary noise and clutter. To tackle the formidable difficulty, we present a four-dimensional wideband microwave radar operating at 1.7 GHz to 2.7 GHz and develop a spatio-temporal processing algorithm to fully explore the vital knowledge of victims in the three-dimensional spatial and one-dimensional temporal information. We conducted comprehensive field experiments in real post-disaster environments and demonstrated experimentally that our radio sensor can continuously monitor multiple survivors trapped under mounds of debris in real urban environments. Moreover, we demonstrate that the presented method can achieve a signal-to-noise-and-clutter ratio improvement of more than 20 dB even in the case of deep burial, which enables localizing the victims trapped in the order of ten meters and recognizing the survivors’ vital states. We expect that the presented strategy may open an avenue for future remote life-rescuing and beyond in practical applications.