Coordinate Information Research Articles

Triangle-Mesh-Rasterization-Projection (TMRP): An Algorithm to Project a Point Cloud onto a Consistent, Dense and Accurate 2D Raster Image.

The projection of a point cloud onto a 2D camera image is relevant in the case of various image analysis and enhancement tasks, e.g., (i) in multimodal image processing for data fusion, (ii) in robotic applications and in scene analysis, and (iii) for deep neural networks to generate real datasets with ground truth. The challenges of the current single-shot projection methods, such as simple state-of-the-art projection, conventional, polygon, and deep learning-based upsampling methods or closed source SDK functions of low-cost depth cameras, have been identified. We developed a new way to project point clouds onto a dense, accurate 2D raster image, called Triangle-Mesh-Rasterization-Projection (TMRP). The only gaps that the 2D image still contains with our method are valid gaps that result from the physical limits of the capturing cameras. Dense accuracy is achieved by simultaneously using the 2D neighborhood information (rx,ry) of the 3D coordinates in addition to the points P(X,Y,V). In this way, a fast triangulation interpolation can be performed. The interpolation weights are determined using sub-triangles. Compared to single-shot methods, our algorithm is able to solve the following challenges. This means that: (1) no false gaps or false neighborhoods are generated, (2) the density is XYZ independent, and (3) ambiguities are eliminated. Our TMRP method is also open source, freely available on GitHub, and can be applied to almost any sensor or modality. We also demonstrate the usefulness of our method with four use cases by using the KITTI-2012 dataset or sensors with different modalities. Our goal is to improve recognition tasks and processing optimization in the perception of transparent objects for robotic manufacturing processes.

A novel method for measurement of the angle of repose of granular seeds in discrete element methods

Discrete element numerical simulations can help researchers find potential problems in the design phase, shortening the development cycle and reducing costs. In the field of agricultural engineering, more and more researchers are using discrete element methods (DEM) to assist in designing and optimising equipment parameters. Model parameters calibration is a prerequisite for discrete element numerical calculations, and the angle of repose (AoR) is commonly used to calibrate the parameters. However, the measurement of AoR in DEM was not seriously considered in industrial or academic fields. In practice, AoR is measured manually, using 2D digital image processing or using a 3D scan. However, reliable and consistent measurements of AoR in DEM are rarely mentioned. This study suggests an accurate and consistent way to measure AoR in DEM using a novel method to read particle coordinate information directly from the data file; then, the AoR is calculated by linearly fitting the centre coordinates of the outermost particles. Influences of input variables on AoR acquisition are discussed through several examples using customised templates with known angles. Then a comparative study of the accuracy of the measurement of AoR in DEM and the reliability of the parameter calibration results by the manual measurement, 2D digital image processing, and algorithm proposed in this paper was conducted. In case studies with four seed materials, this method prevented the subjective selection of AoR, improved the identification accuracy, and increased the precision and accuracy of DEM calibration. In addition, the time consumption for obtaining AoR using the novel method for measurement is much less than that of 2D.

Short Distance Detection and Control System Based on Photoelectric Sensor and Its Application in Target Detection of Handling Robot

The photoelectric sensor is an electronic device that is widely used in industrial automation processes. The device uses light to detect whether an object exists. The photoelectric sensor detection control system is the core part of the photoelectric sensor. The system includes sensor processing chip, control circuit, detection optical structure and automatic teaching control algorithm. In this research, the core control chip of the automatic teaching system and the core part of the control circuit of the short-distance photoelectric sensor are set up. The modular circuit around the above chip is designed. The optical structure of the sensor is designed by Position sensitive sensor (PSD) photoelectric accessories and triangulation method. Meanwhile, the core circuit control chip (CSP) is applied to complete the basic functions of the short-distance photoelectric sensor. Among them, the optical structure is designed to collect and process the optical signal of the detected object. Based on this, the I/V conversion is needed after collecting the optical signal by PSD, and then the signal processing is carried out. The fuzzy PID control model is used to solve the problem that the control object cannot accurately establish the mathematical model. The designed photoelectric sensor is used for the handling robot, and the D-H mathematical model of the handling robot is established. The Robotics toolbox provided by MatLab is used to simulate the movement of the robot, and the sensor is used to detect the target of the robot grabbing. The experimental results show that the sensor uploads the coordinate information of the object to be grabbed to the software interface, and the handling robot can accurately grab the object with the help of the photoelectric sensor, and place it in the corresponding position according to the appearance color of different objects to be grabbed.

Formal local government coordination to mitigate climate change

The effectiveness of municipal climate change mitigation policies depends partly on local governments' ability to coordinate with each other regionally. Yet coordination on climate policies beyond network participation and information sharing is rare. This study investigates how local governments can work together on climate policies, focusing on formal horizontal policy coordination. The study examines multi-government coordination to enact Community Choice Aggregation (CCA) in California, an impactful climate policy that lets local governments control energy procurement. The study surveys all multi-government CCA programs in the state, evaluating their characteristics and governance structures. Three regional areas are examined in-depth through policymaker and stakeholder interviews, media coverage, and decision-making outcomes. The study identifies several strategies for success and finds that formal horizontal policy coordination on climate mitigation can provide a path forward in multilevel governance systems for local governments to enact ambitious climate policies together.

Differential expression of GABAA receptor subunits δ and α6 mediates tonic inhibition in parvalbumin and somatostatin interneurons in the mouse hippocampus.

Inhibitory γ-aminobutyric acid (GABA)-ergic interneurons mediate inhibition in neuronal circuitry and support normal brain function. Consequently, dysregulation of inhibition is implicated in various brain disorders. Parvalbumin (PV) and somatostatin (SST) interneurons, the two major types of GABAergic inhibitory interneurons in the hippocampus, exhibit distinct morpho-physiological properties and coordinate information processing and memory formation. However, the molecular mechanisms underlying the specialized properties of PV and SST interneurons remain unclear. This study aimed to compare the transcriptomic differences between these two classes of interneurons in the hippocampus using the ribosome tagging approach. The results revealed distinct expressions of genes such as voltage-gated ion channels and GABAA receptor subunits between PV and SST interneurons. Gabrd and Gabra6 were identified as contributors to the contrasting tonic GABAergic inhibition observed in PV and SST interneurons. Moreover, some of the differentially expressed genes were associated with schizophrenia and epilepsy. In conclusion, our results provide molecular insights into the distinct roles of PV and SST interneurons in health and disease.

PointCNT: A One-Stage Point Cloud Registration Approach Based on Complex Network Theory

Inspired by the parallel visual pathway model of the human neural system, we propose an efficient and high-precision point cloud registration method based on complex network theory (PointCNT). A deep learning network (DNN) design method based on complex network theory is proposed, and a multipath feature extraction network, namely, Complex Kernel Point Convolution Neural Network (ComKP-CNN) for point clouds is designed based on the design method. Self-supervision is introduced to improve the feature extraction ability of the model. A feature embedding module is proposed to explicitly embed the transformation-variant coordinate information and transformation-invariant distance information into features. A feature fusion module is proposed to enable the source and template point clouds to perceive each other’s nonlocal features. Finally, a Multilayer Perceptron (MLP) with prominent fitting characteristics is utilized to estimate the transformation matrix. The experimental results show that the Registration Recall (RR) of PointCNT on ModelNet40 dataset reached 96.4%, significantly surpassing one-stage methods such as Feature-Metric Registration (FMR) and approaching two-stage methods such as Geometric Transformer (GeoTransformer). The computation speed is faster than two-stage methods, and the registration run time is 0.15 s. In addition, ComKP-CNN is universal and can improve the registration accuracy of other point cloud registration methods.

Continuity and care coordination of primary health care: a scoping review

BackgroundHealthcare coordination and continuity of care conceptualize all care providers and organizations involved in health care to ensure the right care at the right time. However, systematic evidence synthesis is lacking in the care coordination of health services. This scoping review synthesizes evidence on different levels of care coordination of primary health care (PHC) and primary care.MethodsWe conducted a scoping review of published evidence on healthcare coordination. PubMed, Scopus, Embase, CINAHL, Cochrane, PsycINFO, Web of Science and Google Scholar were searched until 30 November 2022 for studies that describe care coordination/continuity of care in PHC and primary care. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines to select studies. We analysed data using a thematic analysis approach and explained themes adopting a multilevel (individual, organizational, and system) analytical framework.ResultsA total of 56 studies were included in the review. Most studies were from upper-middle-income or high-income countries, primarily focusing on continuity/care coordination in primary care. Ten themes were identified in care coordination in PHC/primary care. Four themes under care coordination at the individual level were the continuity of services, linkage at different stages of health conditions (from health promotion to rehabilitation), health care from a life-course (conception to elderly), and care coordination of health services at places (family to hospitals). Five themes under organizational level care coordination included interprofessional, multidisciplinary services, community collaboration, integrated care, and information in care coordination. Finally, a theme under system-level care coordination was related to service management involving multisectoral coordination within and beyond health systems.ConclusionsContinuity and coordination of care involve healthcare provisions from family to health facility throughout the life-course to provide a range of services. Several issues could influence multilevel care coordination, including at the individual (services or users), organizational (providers), and system (departments and sectors) levels. Health systems should focus on care coordination, ensuring types of care per the healthcare needs at different stages of health conditions by a multidisciplinary team. Coordinating multiple technical and supporting stakeholders and sectors within and beyond health sector is also vital for the continuity of care especially in resource-limited health systems and settings.

Beyond neurons and spikes: cognon, the hierarchical dynamical unit of thought

AbstractFrom the dynamical point of view, most cognitive phenomena are hierarchical, transient and sequential. Such cognitive spatio-temporal processes can be represented by a set of sequential metastable dynamical states together with their associated transitions: The state is quasi-stationary close to one metastable state before a rapid transition to another state. Hence, we postulate that metastable states are the central players in cognitive information processing. Based on the analogy of quasiparticles as elementary units in physics, we introduce here the quantum of cognitive information dynamics, which we term “cognon”. A cognon, or dynamical unit of thought, is represented by a robust finite chain of metastable neural states. Cognons can be organized at multiple hierarchical levels and coordinate complex cognitive information representations. Since a cognon is an abstract conceptualization, we link this abstraction to brain sequential dynamics that can be measured using common modalities and argue that cognons and brain rhythms form binding spatiotemporal complexes to keep simultaneous dynamical information which relate the ‘what’, ‘where’ and ‘when’.

Differential processing of intrinsic vs. extrinsic coordinates in wrist movement: connectivity and chronometry perspectives.

This study explores brain-network differences between the intrinsic and extrinsic motor coordinate frames. A connectivity model showing the coordinate frames difference was obtained using brain fMRI data of right wrist isometric flexions and extensions movements, performed in two forearm postures. The connectivity model was calculated by machine-learning-based neural representation and effective functional connectivity using psychophysiological interaction and dynamic causal modeling analyses. The model indicated the network difference wherein the inferior parietal lobule receives extrinsic information from the rostral lingual gyrus through the superior parietal lobule and transmits intrinsic information to the Handknob, whereas extrinsic information is transmitted to the Handknob directly from the rostral lingual gyrus. A behavioral experiment provided further evidence on the difference between motor coordinate frames showing onset timing delay of muscle activity of intrinsic coordinate-directed wrist movement compared to extrinsic one. These results suggest that, if the movement is externally directed, intrinsic coordinate system information is bypassed to reach the primary motor area.

A Multi-Scale Object Detector Based on Coordinate and Global Information Aggregation for UAV Aerial Images

Unmanned aerial vehicle (UAV) image object detection has great application value in the military and civilian fields. However, the objects in the captured images from UAVs have problems of large-scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Feature Fusion Module (FFM) is proposed, which can better fuse features by learning the importance of different scale features and improve the representation ability of multi-scale features by reusing feature maps to help models better detect multi-scale objects. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, we tested the CGMDet on VisDrone and UAVDT datasets. The proposed CGMDet improves mAP0.5 by 1.9% on the VisDrone dataset and 3.0% on the UAVDT dataset.

CHAN: Skeleton based action recognition by multi‐level feature learning

AbstractSkeleton‐based action recognition has been continuously and intensively studied. However, dynamic 3D skeleton data are difficult to be popularized in practical applications due to the restricted data acquisition conditions. Although the action recognition method based on 2D pose information extracted from RGB video can effectively avoid the influence of complex background, it is susceptible to factors such as video jitter and joint overlap. To reduce the interference of the aforementioned factors, we use two‐dimensional skeletal joint coordinate modal information to represent the changes in human body posture. First, we use a target detector and pose estimation algorithm to obtain the joint coordinates of each frame sample from RGB video. Then the feature extraction network is combined to perform multi‐level feature learning to establish correspondence between actions and corresponding multi‐level features. Finally, the hierarchical attention mechanism is introduced to design the model named CHAN. By calculating the association between elements, the weight of the action classification is redistributed. Extensive experiments on three datasets demonstrate the effectiveness of our proposed method.

Learnable interpolation and extrapolation network for fuzzy pulmonary lobe segmentation

AbstractPulmonary lobe segmentation is an important prerequisite for accurately quantifying pulmonary damage in many pulmonary diseases and planning treatment. However, due to the incomplete lobar structures and morphological changes caused by diseases, the lobe segmentation still encounters great challenges. In this study, a Learnable Interpolation and Extrapolation Network (LIE‐Net) is proposed to form complete and consecutive fissure surfaces by learning to extract information of the fissures from existing fissure points and absent points (unsegmented points belonging to fissures) to predict the z coordinate of the absent fissure points. The completed pulmonary fissures are further used for accurate pulmonary lobe segmentation. Specifically, LIE‐Net takes the coordinate information of existing fissure points (their (x, y, z) coordinates) and absent fissure points (their (x, y) coordinates) as two independent inputs, and predicts the z coordinates of absent points. The proposed LIE‐Net makes voxel‐wise predictions based on the spatial structure characteristics of the lung fissure, and is able to provide a consecutive fissure surface in space. According to the evaluation of radiologists, the lobe segmentation performance was remarkably enhanced in approximately 76% of patients in our additional dataset after the application of LIE‐Net, especially for those cases with large‐scale missing fissures.

Research on azimuth-only passive positioning of UAV formation: A case study of circular formation

In order to maintain the formation, the position of Drones cluster can be adjusted by the method of azimuth-only passive positioning. In this paper, the circular formation is taken as an example. Firstly, when the coordinate information of Drones at the center of the circle and around the circle is known, the coordinates of other Drones are solved according to the Angle signal. By using polar coordinate programming, the Angle between different Drone lines is calculated, and using this information as known information, the nonlinear equations are solved by inverse derivation of their own coordinates. Secondly, in order to determine that at least several Drones need to transmit signals to realize effective positioning of all Drones in the circular formation, this paper concludes that only three Drones at most are needed to realize azimuth-only passive positioning based on the established model.

Improving point cloud classification and segmentation via parametric veronese mapping

Deep learning based 3D point cloud classification and segmentation has achieved remarkable success. Existing methods are usually implemented in the original space with 3D coordinates as inputs. However, we find that point networks taking only information of first-order coordinates hardly learn geometric features of higher order, such as point cloud normals or poses. In this study, we propose to map the input point clouds into a non-linear space to facilitate networks learning and leveraging high-order features. Firstly, we design the Parametric Veronese Mapping (PVM) function which automatically learns to map point clouds into a non-linear space. As a result, the mapped point clouds are enriched with high-order elements and maintain the basic point set properties as in the original 3D space. We can then exploit existing networks to learn high-order features from mapped point clouds. Secondly, we contribute a two-stage transformation learning module that modifies the previous one-stage module to better leverage high-order features for aligning point clouds in the projective space. Finally, an interaction module is designed to learn more discriminative features by aggregating information from both the original and projective space. Extensive experiments demonstrate that our method successfully improves the ability of most existing networks to learn high-order features and thus contributing to more accurate classification and segmentation. Moreover, the resulting models show stronger robustness to affine transformations and real-world perturbations.

SGReg: segmentation guided 3D/2D rigid registration for orthogonal X-ray and CT images in spine surgery navigation

Objective. One of the essential technologies in various image-guided spine surgeries is the rigid registration of 3D pre-operative CT and 2D intra-operative X-ray images. The 3D/2D registration is patterned as two essential tasks, that is, dimensional correspondence establishment and estimation of the 3D pose. 3D data is projected to 2D for dimensional correspondence by most of the existing methods, which makes pose parameters difficult to estimate caused by the loss of spatial information. This work aims to develop a reconstruction based 3D/2D registration method for spine surgery navigation. Approach. A novel segmentation-guided 3D/2D registration (SGReg) method for orthogonal X-ray and CT images was proposed based on reconstruction. SGReg consists of a bi-path segmentation network and an inter-path multi-scale pose estimation module. The X-ray segmentation path in the bi-path segmentation network reconstructs 3D spatial information from 2D orthogonal X-ray images to segmentation masks; meanwhile, the CT segmentation path predicts segmentation masks from 3D CT images, thereby bringing the 3D/2D data into dimensional correspondence. In the inter-path multi-scale pose estimation module, the features from the two segmentation paths are integrated, and the pose parameters are directly regressed under the guidance of the coordinate information. Main result. We evaluated SGReg using a public dataset CTSpine1k and compared the registration performance with other methods. SGReg achieved considerable improvement over other methods with great robustness. Significance We have proposed an end-to-end 3D/2D registration framework named SGReg. Based on the idea of reconstruction, SGReg performs a unified framework between dimensional correspondence establishment and direct pose estimation in 3D space, showing significant potential in spine surgery navigation.

BiGA-YOLO: A Lightweight Object Detection Network Based on YOLOv5 for Autonomous Driving

Object detection in autonomous driving scenarios has become a popular task in recent years. Due to the high-speed movement of vehicles and the complex changes in the surrounding environment, objects of different scales need to be detected, which places high demands on the performance of the network model. Additionally, different driving devices have varying performance capabilities, and a lightweight model is needed to ensure the stable operation of devices with limited computing power. To address these challenges, we propose a lightweight network called BiGA-YOLO based on YOLOv5. We design the Ghost-Hardswish Conv module to simplify the convolution operations and incorporate spatial coordinate information into feature maps using Coordinate Attention. We also replace the PANet structure with the BiFPN structure to enhance the expression ability of features through different weights during the process of fusing multi-scale feature maps. Finally, we conducted extensive experiments on the KITTI dataset, and our BiGA-YOLO achieved a mAP@0.5 of 92.2% and a mAP@0.5:0.95 of 68.3%. Compared to the baseline model YOLOv5, our proposed model achieved improvements of 1.9% and 4.7% in mAP@0.5 and mAP@0.5:0.95, respectively, while reducing the model size by 15.7% and the computational cost by 16%. The detection speed was also increased by 6.3 FPS. Through analysis and discussion of the experimental results, we demonstrate that our proposed model is superior, achieving a balance between detection accuracy, model size, and detection speed.

An AR Application for the Efficient Construction of Water Pipes Buried Underground

Unlike other civil engineering works, water pipe works require digging out before construction because the construction site is buried. The AR application is a system that displays buried objects in the ground in three dimensions when users hold a device such as a smartphone over the ground, using images from the smartphone. The system also registers new buried objects when they are updated. The target of this project is water pipes, which are the most familiar of all buried structures. The system has the following functions: “registration and display of new water pipe information” and “acquisition and display of current location coordinate information.” By applying the plane detection function to data acquired from a camera mounted on a smartphone, the system can easily register and display a water pipe model horizontally to the ground. The system does not require a reference marker because it uses GPS and the plane detection function. In the future, the system will support the visualization and registration of not only water pipes but also other underground infrastructures and will play an active role in the rapid restoration of infrastructure after a large-scale disaster through the realization of a buried-object 3D MAP platform.

Bluetooth-Based Healthcare Information and Medical Resource Management System.

This paper presents a healthcare information and medical resource management platform utilizing wearable devices, physiological sensors, and an indoor positioning system (IPS). This platform provides medical healthcare information management based on the physiological information collected by wearable devices and Bluetooth data collectors. The Internet of Things (IoT) is constructed for this medical care purpose. The collected data are classified and used to monitor the status of patients in real time with a Secure MQTT mechanism. The measured physiological signals are also used for developing an IPS. When the patient is out of the safety zone, the IPS will send an alert message instantly by pushing the server to remind the caretaker, easing the caretaker's burden and offering extra protection for the patient. The presented system also provides medical resource management with the help of IPS. The medical equipment and devices can be tracked by IPS to tackle some equipment rental problems, such as lost and found. A platform for the medical staff work coordination information exchange and transmission is also developed to expedite the maintenance of medical equipment, providing the shared medical information to healthcare and management staff in a timely and transparent manner. The presented system in this paper will finally reduce the loading of medical staff during the COVID-19 pandemic period.

Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity.

Background: With the rapid development of high-throughput sequencing technology and the explosive growth of genomic data, storing, transmitting and processing massive amounts of data has become a new challenge. How to achieve fast lossless compression and decompression according to the characteristics of the data to speed up data transmission and processing requires research on relevant compression algorithms. Methods: In this paper, a compression algorithm for sparse asymmetric gene mutations (CA_SAGM) based on the characteristics of sparse genomic mutation data was proposed. The data was first sorted on a row-first basis so that neighboring non-zero elements were as close as possible to each other. The data were then renumbered using the reverse Cuthill-Mckee sorting technique. Finally the data were compressed into sparse row format (CSR) and stored. We had analyzed and compared the results of the CA_SAGM, coordinate format (COO) and compressed sparse column format (CSC) algorithms for sparse asymmetric genomic data. Nine types of single-nucleotide variation (SNV) data and six types of copy number variation (CNV) data from the TCGA database were used as the subjects of this study. Compression and decompression time, compression and decompression rate, compression memory and compression ratio were used as evaluation metrics. The correlation between each metric and the basic characteristics of the original data was further investigated. Results: The experimental results showed that the COO method had the shortest compression time, the fastest compression rate and the largest compression ratio, and had the best compression performance. CSC compression performance was the worst, and CA_SAGM compression performance was between the two. When decompressing the data, CA_SAGM performed the best, with the shortest decompression time and the fastest decompression rate. COO decompression performance was the worst. With increasing sparsity, the COO, CSC and CA_SAGM algorithms all exhibited longer compression and decompression times, lower compression and decompression rates, larger compression memory and lower compression ratios. When the sparsity was large, the compression memory and compression ratio of the three algorithms showed no difference characteristics, but the rest of the indexes were still different. Conclusion: CA_SAGM was an efficient compression algorithm that combines compression and decompression performance for sparse genomic mutation data.

Key Technologies of Collaborative Target Detection between Self-propelled Anti-aircraft Weapons

In the case of a lack of target information, a self-propelled anti-aircraft weapon can complete its own combat functions by using the detection information of other vehicles through detection coordination technology. When using the detection coordination technology to process the detection information of other vehicles into information that can be used by the vehicle, the relative position and attitude of the vehicle and data transmission delay will affect the accuracy of the processed target information. With the system of detection collaborative technology expounded, emphasis is attached to the research on the detection coordination method based on pure angles without considering distance. A mathematical model of detection coordination technology is created, the approximate statistical law of the processed detection coordination information is obtained through the Monte Carlo method, and the influence of various factors on transmission error analyzed. In addition, the polynomial curve fitting method is used for time alignment, and the unscented Kalman filter method based on singular value decomposition adopted to reduce the information transfer error. The simulation results show the the method is effective for that end.