Point Mapping Research Articles

Identification of reentry vulnerable targets during substrate mapping for ablation in scar related VT: the "visual" reentry vulnerability index

Abstract Introduction Conduction abnormalities including zones of deceleration (DZ) & late activation are functionally important targets for ablation in scar-related ventricular tachycardias (VT). Repolarisation heterogeneities are equally important but are not routinely assessed. Visualisation of a combination of both conduction & repolarisation metrics whilst in sinus in the form of a re-entry vulnerability index (RVI) may enable more precise identification of critical ablation targets. Rationale: To test accuracy of a novel functional substrate mapping strategy combining conduction & repolarisation metrics to "visually" estimate reentry vulnerability sites (RVIv) during scar related VT ablation without the need for offline processing required for "computational" RVI. Methods VT cases using a multipolar grid-catheter for contact mapping were reviewed. DZ &areas of late activation were tagged on omnipolar LAT substrate maps. The corresponding unipolar repolarisation maps depicting local repolarisation time (LRT) based on the Wyatt method (WOI over T-Wave annotating to +dV/dt, UEGM-Filter 0.05-100Hz) were then displayed side-by-side with the LAT map. Timing reference was set to onset of surface QRS. Colour scale of LRT maps was set to start at or ahead of the timing of latest LAT. "Visual RVI" zones were identified if the tagged zone of late activation was overlapping and/or directly adjacent to an area of "early" repolarisation (defined as before or within 100ms of latest LAT point) based on colour. In visually identified RVI areas, temporal (timing difference in ms) & spatial relationship (distance in mm) of EGMs with earliest RT (RTe) to EGM with latest AT (ATL) separated by max. 20mm were measured & RVI calculated (RTe subtracted by ATL). Number of RVIv zones per map, RVI metrics & distance from closest RVI to VT Exit site (defined per activation±entrainment or pace mapping) were assessed. The lower/negative the numeric RVI, the higher the vulnerability for reentry. Results 20 patients with scar-related reentry VT were included (90% male, 63.8±11.6 years, LVEF 36±16%, 60% ischemic/40% non-ischemic). LAT & LRT map point counts were 4835±2625 and 3596±2805. In 17 (85%) patients at least 1 RVIv zone was identified, with on avg. 3.3±1.7 RVIv zones/patient. Manual RVIs were calculated to 10.4±78.9ms ( "most vulnerable" RVI was -227ms) with on avg. 9.5±5.3mm distance between RTe & ATL. All maps with no RVIv zone on the mapped surface were of non-ischemic etiology. The shortest distance of RVIs to functionally critical sites during VT was 8.9±7.4mm (range 0-33mm). Conclusion Visual reentry vulnerability estimation based on a combination of established conduction and novel repolarisation maps is feasible & may help to identify functionally critical zones during substrate mapping. Future efforts should include dedicated automated repolarisation mapping tools to facilitate integration of repolarisation dynamics in functional VT substrate mapping workflows.Figure 1Example Visual RVI Mapping

“Reinforcement learning particle swarm optimization based trajectory planning of autonomous ground vehicle using 2D LiDAR point cloud”

The advent of autonomous mobile robots has spurred research into efficient trajectory planning methods, particularly in dynamic environments with varied obstacles. This study focuses on optimizing trajectory planning for an Autonomous Ground Vehicle (AGV) using a novel Reinforcement Learning Particle Swarm Optimization (RLPSO) algorithm. Real-time mobile robot localization and map generation are introduced through the utilization of the Hector-SLAM algorithm within the Robot Operating System (ROS) platform, resulting in the creation of a binary occupancy grid. The present research thoroughly investigates the performance of the RLPSO algorithm, juxtaposed against five established Particle Swarm Optimization (PSO) variants, within the context of four distinct physical environments. The experimental design is tailored to emulate real-world scenarios, encompassing a spectrum of challenges posed by static and dynamic obstacles. The AGV, equipped with LiDAR sensors, navigates through diverse environments characterized by obstacles of different geometries. The RLPSO algorithm dynamically adapts its strategies based on feedback, enabling adaptable trajectory planning while effectively avoiding obstacles. Numerical results obtained from extensive experimentation highlight the algorithm's efficacy. The navigational model's validation is achieved within a MATLAB 2D virtual environment, employing 2D Lidar mapping point data. Transitioning to physical experiments with an AGV, RLPSO continues to demonstrate superior performance, showcasing its potential for real-world applications in autonomous navigation. On average, RLPSO achieves a 10–15 % reduction in path distances and traversal time compared to the following best-performing PSO variant across diverse scenarios. The adaptive nature of RLPSO, informed by feedback from the environment, distinguishes it as a promising solution for autonomous navigation in dynamic settings, with implications for practical implementation in real-world scenarios.

Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points Analysis

Under a distributed information system, the scale of various operational components such as applications, operating systems, databases, servers, and networks is immense, with intricate access relationships. The silo effect of each professional is prominent, and the linkage mechanism is insufficient, making it difficult to locate the infrastructure components that cause exceptions under a particular application. Current research only plays a role in local scenarios, and its accuracy and generalization are still very limited. This paper proposes a novel fault location method based on dynamic operation maps and alarm common point analysis. During the fault period, various alarm entities are associated with dynamic operation maps, and alarm common points are obtained based on graph search addressing methods, covering deployment relationship common points, connection common points (physical and logical), and access flow common points. This method, compared with knowledge graph approaches, eliminates the complex process of knowledge graph construction, making it more concise and efficient. Furthermore, in contrast to indicator correlation analysis methods, this approach supplements with configuration correlation information, resulting in more precise positioning. Through practical validation, its fault hit rate exceeds 82%, which is significantly better than the existing main methods.

Non-rigid scene reconstruction of deformable soft tissue with monocular endoscopy in minimally invasive surgery.

The utilization of image-guided surgery has demonstrated its ability to improve the precision and safety of minimally invasive surgery (MIS). Non-rigid scene reconstruction is a challenge in image-guided system duo to uniform texture, smoke, and instrument occlusion, etc. METHODS: In this paper, we introduced an algorithm for 3D reconstruction aimed at non-rigid surgery scenes. The proposed method comprises two main components: firstly, the front-end process involves the initial reconstruction of 3D information for deformable soft tissues using embedded deformation graph (EDG) on the basis of dual quaternions, enabling the reconstruction without the need for prior knowledge of the target. Secondly, the EDG is integrated with isometric nonrigid structure from motion (Iso-NRSFM) to facilitate centralized optimization of the observed map points and camera motion across different time instances in deformable scenes. For the quantitative evaluation of the proposed method, we conducted comparative experiments with both synthetic datasets and publicly available datasets against the state-of-the-art 3D reconstruction method, DefSLAM. The test results show that our proposed method achieved a maximum reduction of 1.6mm in average reconstruction error compared to method DefSLAM across all datasets. Additionally, qualitative experiments were performed on video scene datasets involving surgical instrument occlusions. Our method proved to outperform DefSLAM on both synthetic datasets and public datasets through experiments, demonstrating its robustness and accuracy in the reconstruction of soft tissues in dynamic surgical scenes. This success highlights the potential clinical application of our method in delivering surgeons with critical shape and depth information for MIS.

Spatial and temporal clustering of anti-SARS-CoV-2 antibodies in Illinois household cats, 2021-2023.

This study aimed to evaluate the seroprevalence and spatial and temporal clustering of SARS-CoV-2 antibodies in household cats within 63 counties in Illinois from October 2021 to May 2023. The analysis followed a stepwise approach. First, in a choropleth point map, we illustrated the distribution of county-level seroprevalence of SARS-CoV-2 antibodies. Next, spatial interpolation was used to predict the seroprevalence in counties without recorded data. Global and local clustering methods were used to identify the extent of clustering and the counties with high or low seroprevalence, respectively. Next, temporal, spatial, and space-time scan statistic was used to identify periods and counties with higher-than-expected seroprevalence. In the last step, to identify more distinct areas in counties with high seroprevalence, city-level analysis was conducted to identify temporal and space-time clusters. Among 1,715 samples tested by serological assays, 244 samples (14%) tested positive. Young cats had higher seropositivity than older cats, and the third quarter of the year had the highest odds of seropositivity. Three county-level space-time clusters with higher-than-expected seroprevalence were identified in the northeastern, central-east, and southwest regions of Illinois, occurring between June and October 2022. In the city-level analysis, 2 space-time clusters were identified in Chicago's downtown and the southwestern suburbs of Chicago between June and September 2022. Our results suggest that the high density of humans and cats in large cities such as Chicago, might play a role in the transmission and clustering of SARS-CoV-2. Our study provides an in-depth analysis of SARS-CoV-2 epidemiology in Illinois household cats, which will aid in COVID-19 control and prevention.

Identifying critical success factors and risks in the academic course development process through the application of project management methodology

Improving the educational process through evaluation requires adopting new concepts and approaches. In addition to the traditional methodologies applied in this field, such as observation or surveys, business methods are increasingly being implemented, including the Deming cycle or selected elements of project management methodologies. Their application allows for a holistic and unbiased view of the quality of teaching and learning. This article aims to illustrate and evaluate the usefulness of project management methodologies in identifying Critical Success Factors (CSFs) and risks in creating and evaluating academic courses. The subject of the research was the course “Creativity and Decision-making”, and the main method of the research, which was conducted in four cycles from 2020 to 2023, was the Deming cycle (PDCA). Data collection was based on data triangulation, including unstructured interviews with students and experts, as well as participant observation. Content analysis with a categorisation key was used to analyse the data, with the results presenting lists of extracted critical success and risk factors determining the quality of the course. The implemented procedure represents an innovative approach to the evaluation of academic courses, at the core of which is the creation and updating of two substantively opposing lists: the determinants of learning successes and failures constituting of a map of strategic control points, and a basis for the improvement of the educational process.

‘Conga lines’ of Ediacaran fronds: insights into the reproductive biology of early metazoans

Late Ediacaran strata from Newfoundland, Canada (~574–560 Ma) document near-census palaeocommunities of some of the earliest metazoans. Such preservation enables reproductive strategies to be inferred from the spatial distribution of populations of fossilized benthic organisms, previously revealing the existence of both propagule and stoloniferous reproductive modes among Ediacaran frondose taxa. Here, we describe ‘conga lines’: linear arrangements of more than three closely spaced fossil specimens. We calculate probabilistic models of point maps of 13 fossil-bearing bedding surfaces and show that four surfaces contain conga lines that are not the result of chance alignments. We then test whether these features could result from passive pelagic propagules settling in the lee of an existing frond, using computational fluid dynamics and discrete phase modelling. Under Ediacaran palaeoenvironmental conditions, preferential leeside settlement at the spatial scale of the conga lines is unlikely. We therefore conclude that these features are novel and do not reflect previously described reproductive strategies employed by Ediacaran organisms, suggesting the use of mixed reproductive strategies in the earliest animals. Such strategies enabled Ediacaran frondose taxa to act as reproductive generalists and may be an important facet of early metazoan evolution.

GEODESY IN SOLVING PROBLEMS OF SUSTAINABLE CITY DEVELOPMENT: ANALYSIS OF URBAN TERRITORY PLANNING

The article focuses on the creation and implementation of a geodetic planning system aimed at ensuring the sustainable development of urban areas. The main emphasis is on the use of open and accessible software, integration of full-fledged GIS and DBMS components. The process of determining the most suitable GIS platform for GSP included a detailed analysis of various GIS systems taking into account their functionality, which allowed to choose QGIS as the optimal solution due to its high functionality in the field of geodata management, support for various data formats, user interface and dynamic development. The structure of the GSP is based on two main platforms: the QGIS GIS system for cartographic data presentation and analysis, and the Microsoft Access DBMS for performing all data operations. Interaction between these platforms is provided through the eVis module built into QGIS, which allows for efficient planning of urban area surveys and land use management. The main tasks solved with the help of GSP are the planning of primary surveys to identify target plantations, detailed taxation of plantations with the creation of samples according to specified parameters, obtaining point maps of the distribution of various types of vegetation, planning sample surveys with specific geospatial conditions, as well as geo-information and tax analysis in in solving the problems of sustainable development of cities. The implementation of GSP provides significant advantages for planning the sustainable development of urban areas, allowing more efficient use of land resources, optimizing urban management processes and intensifying scientific research in this field. The development and integration of modern digital technologies in the field of urban planning play a key role in achieving the goals of sustainable development.

Group concept mapping for health professions education scholarship.

While explicit conceptual models help to inform research, they are left out of much of the health professions education (HPE) literature. One reason may be the limited understanding about how to develop conceptual models with intention and rigor. Group concept mapping (GCM) is a mixed methods conceptualization approach that has been used to develop frameworks for planning and evaluation, but GCM has not been common in HPE. The purpose of this article is to describe GCM in order to make it more accessible for HPE scholars. We recount the origins and evolution of GCM and summarize its core features: GCM can combine multiple stakeholder perspectives in a systematic and inclusive manner to generate explicit conceptual models. Based on the literature and prior experience using GCM, we detail seven steps in GCM: (1) brainstorming ideas to a specific "focus prompt," (2) preparing ideas by removing duplicates and editing for consistency, (3) sorting ideas according to conceptual similarity, (4) generating the point map through quantitative analysis, (5) interpreting cluster map options, (6) summarizing the final concept map, and (7) reporting and using the map. We provide illustrative examples from HPE studies and compare GCM to other conceptualization methods. GCM has great potential to add to the myriad of methodologies open to HPE researchers. Its alignment with principles of diversity and inclusivity, as well as the need to be systematic in applying theoretical and conceptual frameworks to practice, make it a method well suited for the complexities of contemporary HPE scholarship.

LTA‐OM: Long‐term association LiDAR–IMU odometry and mapping

AbstractThis paper focuses on the Light Detection and Ranging (LiDAR)–Inertial Measurement Unit (IMU) simultaneous localization and mapping (SLAM) problem: How to fuse the sensor measurement from the LiDAR and IMU to online estimate robot's poses and build a consistent map of the environment. This paper presents LTA‐OM: an efficient, robust, and accurate LiDAR SLAM system. Employing fast direct LiDAR‐inertial odometry (FAST‐LIO2) and Stable Triangle Descriptor as LiDAR–IMU odometry and the loop detection method, respectively, LTA‐OM is implemented to be functionally complete, including loop detection and correction, false‐positive loop closure rejection, long‐term association (LTA) mapping, and multisession localization and mapping. One novelty of this paper is the real‐time LTA mapping, which exploits the direct scan‐to‐map registration of FAST‐LIO2 and employs the corrected history map to provide direct global constraints to the LIO mapping process. LTA mapping also has the notable advantage of achieving drift‐free odometry at revisit places. Besides, a multisession mode is designed to allow the user to store the current session's results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life‐long mapping. Furthermore, LTA‐OM has valuable features for robot control and path planning, including high‐frequency and real‐time odometry, driftless odometry at revisit places, and fast loop closing convergence. LTA‐OM is versatile as it is applicable to both multiline spinning and solid‐state LiDARs, mobile robots and handheld platforms. In experiments, we exhaustively benchmark LTA‐OM and other state‐of‐the‐art LiDAR systems with 18 data sequences. The results show that LTA‐OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA‐OM is validated in a challenging scene—a multilevel building having similar structures at different levels. To demonstrate our system, we created a video which can be found on https://youtu.be/DVwppEKlKps.

SPECTRAL CLUSTERING AND LONG TIMESERIES CLASSIFICATION

AbstractClustering is a method of allocating data points in various groups, known as clusters, based on similarity. The notion of expressing similarity mathematically and then maximizing it (minimize dissimilarity) can be formulated as an optimization problem. Spectral clustering is an example of such an approach to clustering, and it has been successfully applied to visualization of clustering and mapping of points into clusters in two and three dimensions. Higher dimension problems remained untouched due to complexity and, most importantly, lack of understanding what “similarity” means in higher dimensions. In this paper, we apply spectral clustering to long timeseries EEG (electroencephalogram) data. We developed several models, based on different similarity functions and different approaches for spectral clustering itself. The results of the numerical experiment demonstrate that the created models are accurate and can be used for timeseries classification.

Perspective deformation correction for circular pointer meter based on prior indication structure feature

The meter verification and industrial inspection robots often use automatic reading of pointer meters. Due to tilt and rotation of circular pointer meter, the perspective deformation may be induced to the captured image, so it will extremely affect the accuracy of automatic reading algorithm based on machine vision. Some existing deformation correction methods suffer from poor universality or low accuracy. The paper bases on prior indication structure feature of circular pointer meter and presents a new deformation correction method. First, the MobileNetV3-UNet network is used to detect the outer-endpoints of long scale lines in deformed meter as the key points. Second, with the help of prior information, the positions of mapping points in standard image are constructed. Third, according to the one-to-one mapping relationship of key points from deformed and standard images, a perspective transform matrix is calculated. Finally, the deformation and rotation correction are performed through inverse perspective transform. The experimental results show that the proposed method has better precision and robustness and works well under different conditions.

学前教育师范生实习认知及需求多维尺度的实证研究

Internship courses are an important part of cultivating students' professional skills, and the level of internship awareness of teacher trainees can have a certain impact on their internship attitude, efficiency, and results. This study focuses on students majoring in preschool education at a certain university in Guangxi as the survey subjects. A Likert five-point scale was designed to investigate the cognitive status of students during internships based on dimensions such as childcare knowledge, childcare ability, class management, and comprehensive education. The data was analyzed at multiple scales using SPSS 25.0 to generate a knowledge map of internship cognitive points. Analyze the internship cognitive preferences and needs of preschool education teacher trainees through visual graphs, and propose targeted countermeasures and suggestions based on the analysis results.

Minimal configuration point cloud odometry and mapping

Simultaneous Localization and Mapping (SLAM) refers to the common requirement for autonomous platforms to estimate their pose and map their surroundings. There are many robust and real-time methods available for solving the SLAM problem. Most are divided into a front-end, which performs incremental pose estimation, and a back-end, which smooths and corrects the results. A low-drift front-end odometry solution is needed for robust and accurate back-end performance. Front-end methods employ various techniques, such as point cloud-to-point cloud (PC2PC) registration, key feature extraction and matching, and deep learning-based approaches. The front-end algorithms have become increasingly complex in the search for low-drift solutions and many now have large configuration parameter sets. It is desirable that the front-end algorithm should be inherently robust so that it does not need to be tuned by several, perhaps many, configuration parameters to achieve low drift in various environments. To address this issue, we propose Simple Mapping and Localization Estimation (SiMpLE), a front-end LiDAR-only odometry method that requires five low-sensitivity configurable parameters. SiMpLE is a scan-to-map point cloud registration algorithm that is straightforward to understand, configure, and implement. We evaluate SiMpLE using the KITTI, MulRan, UrbanNav, and a dataset created at the University of Queensland. SiMpLE performs among the top-ranked algorithms in the KITTI dataset and outperformed all prominent open-source approaches in the MulRan dataset whilst having the smallest configuration set. The UQ dataset also demonstrated accurate odometry with low-density point clouds using Velodyne VLP-16 and Livox Horizon LiDARs. SiMpLE is a front-end odometry solution that can be integrated with other sensing modalities and pose graph-based back-end methods for increased accuracy and long-term mapping. The lightweight and portable code for SiMpLE is available at: https://github.com/vb44/SiMpLE.

Atlas Florae Europaeae notes, 34. Distributions and two conservation profiles of East European species of the Cytisusratisbonensis group (Fabaceae).

The Cytisusratisbonensis group (Fabaceae) includes small shrubs with attractive yellow flowers, used in ornamental cultivation. It is widely distributed in southern forest, forest steppe and steppe zones of Eastern Europe, both in the lowlands and low mountains. This group is notorious for its taxonomic complexity and difficulties in identification, which accounted for incongruent taxonomic treatments and common identification errors, and resulted in a poor understanding of the distribution areas. The increasing availability of herbarium collections and accumulation of human observations through digital resources require their critical taxonomic revision and update in order to provide reliable data for plant species mapping, conservation and analysis of distribution areas. This paper describes a distributional dataset of East European species of the Cytisusratisbonensis group, which was prepared for the pan-European grid mapping project, Atlas Florae Europaeae. The taxonomic revision includes seven species and two interspecific hybrids and is based on the critical evaluation of diagnostic characters, nomenclature and synonymy. The territorial scope of this study is Eastern Europe, but it also includes some data from Central Europe, the Caucasus and the neighbouring territories of Siberia and Kazakhstan, which are included in order to trace eastern and south-eastern limits of the species distributions and to establish reliable synonymy. We report 3699 native occurrence records included in the dataset; these records are based on major herbarium collections which are known to hold specimens from Eastern Europe (DNZ, KRA, KRAM, KW, LE, LW, LWKS, MHA, MSK, MSKH, MSKU, MW), complemented by selected specimens from other herbaria (BUNS, CSAU, H, NNSU, NS, NSK, OXF, PVB, RWBG, TUL). The herbarium collections were examined largely de visu, but partly online. Besides the herbarium specimens, recognisable photographs documenting human observations on online platforms (florafauna.by, iNaturalist, Plantarium, UkrBIN) were also examined and included. All specimen records are accompanied with textual data and georeferences, which were produced for this dataset. Point and contour distribution maps are created for each accepted species. According to the distributional data, the Cytisusratisbonensis group may be classified as temperate lowland and steppic in Eastern Europe. Among European endemic shrubs, C.cinereus (as C.paczoskii) was assessed in 2017 as Near Threatened, whereas its new IUCN protection status is proposed as Least Concern because the new data demonstrated that its distribution area is much greater than previously believed. A species restricted to the Crimea, C.wulffii, is assessed here for the first time as Vulnerable because of its restricted area and small population size (criteria D1,2).

IDENTIFIKASI BAHAYA DAN PENILAIAN RISIKO DI KAWASAN WISATA LEUWI KENIT, CILETUH PALABUHANRATU UGG

Ciletuh Palabuhanratu as the first geopark in West Java which has been recognized by UNESCO since 2018, is famous as the first land in western Java. One of the natural tourist object in the Geopark area with the beauty of ancient rock structures aged more than 60 million years ago is the Leuwi Kenit natural tourist destination in Pasirpanjang Village, which has a very large number of tourists, but in certain periods there are often visitor accidents. This research aims to identify hazards and risk assessment, as well as risk control from hazards, with research objects including parking areas, entrance gates, access routes, play facilities, and exit routes. The research method is a qualitative Hazard Identification and Risk Assessment and mapping of danger points, literature studies, field observations, interviews with the community, tour guides and tourist attraction guards. Based on the results of the study, there are fifteen danger points in the Leuwi Kenit tourist attraction, some of which can occur when the activity is running with a range of risk relative status, namely low to high. Risk relative high status is recorded to have dominated with seven danger points, risk relative moderate status with six danger points, and risk relative low with two danger points. According to the results of the study, there are fifteen danger points in the Leuwi Kenit tourist attraction, some of which can occur when the activity is running with a range of risk relative status, namely low to high. Risk relative high status is recorded as dominant with seven danger points, risk relative moderate status with six danger points, and risk relative low status with two danger points. The results of the risk assessment show that the risk status is included in potential hazards with high and medium hazard risk levels, management attention and control planning are needed. Alternative risk controls that can be implemented to minimize the occurrence of risk are administrative, engineering, and elimination approaches. Forms of risk avoidance applications such as making paths according to safe standards, making information boards for hazard location points, and rearranging amusement ride according to safety standards at Leuwi Kenit.

Point Projection Mapping System for Tracking, Registering, Labeling, and Validating Optical Tissue Measurements.

The validation of newly developed optical tissue-sensing techniques for tumor detection during cancer surgery requires an accurate correlation with the histological results. Additionally, such an accurate correlation facilitates precise data labeling for developing high-performance machine learning tissue-classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with the histopathology results is proposed and validated on a case study. The proposed framework provides a more-robust and accurate method for the tracking and validation of optical tissue-sensing techniques, which saves time and resources compared to the available conventional techniques.

Q-RBSA: high-resolution 3D EBSD map generation using an efficient quaternion transformer network

Gathering 3D material microstructural information is time-consuming, expensive, and energy-intensive. Acquisition of 3D data has been accelerated by developments in serial sectioning instrument capabilities; however, for crystallographic information, the electron backscatter diffraction (EBSD) imaging modality remains rate limiting. We propose a physics-based efficient deep learning framework to reduce the time and cost of collecting 3D EBSD maps. Our framework uses a quaternion residual block self-attention network (QRBSA) to generate high-resolution 3D EBSD maps from sparsely sectioned EBSD maps. In QRBSA, quaternion-valued convolution effectively learns local relations in orientation space, while self-attention in the quaternion domain captures long-range correlations. We apply our framework to 3D data collected from commercially relevant titanium alloys, showing both qualitatively and quantitatively that our method can predict missing samples (EBSD information between sparsely sectioned mapping points) as compared to high-resolution ground truth 3D EBSD maps.

Speed and Accuracy Comparison of Person Detection Using Pretrained CenterNet and Yolov3

Implementations of object detection by using common devices for general purpose application is not widely used for speed and prone to errors. Most of research in speed detection using known method such as Centernet, Yolov3, Fast-RNN has variant result since the computer used are different. This experiment try to conduct experiments for person detection with Centernet and Yolov3 method using commonly used computer only using CPU. Based on the experiments, Yolov3 can give a much better precision for person detection by 98.42% of mAP point while Centernet only 97%. In terms of processing speed, Centernet can give much better speed where it can detect a person in average 370ms better than Yolov3 with average of 1050 ms or 1 second.

Self-adaptive learning particle swarm optimization-based path planning of mobile robot using 2D Lidar environment

AbstractThe loading and unloading operations of smart logistic application robots depend largely on their perception system. However, there is a paucity of study on the evaluation of Lidar maps and their SLAM algorithms in complex environment navigation system. In the proposed work, the Lidar information is finetuned using binary occupancy grid approach and implemented Improved Self-Adaptive Learning Particle Swarm Optimization (ISALPSO) algorithm for path prediction. The approach makes use of 2D Lidar mapping to determine the most efficient route for a mobile robot in logistical applications. The Hector SLAM method is used in the Robot Operating System (ROS) platform to implement mobile robot real-time location and map building, which is subsequently transformed into a binary occupancy grid. To show the path navigation findings of the proposed methodologies, a navigational model has been created in the MATLAB 2D virtual environment using 2D Lidar mapping point data. The ISALPSO algorithm adapts its parameters inertia weight, acceleration coefficients, learning coefficients, mutation factor, and swarm size, based on the performance of the generated path. In comparison to the other five PSO variants, the ISALPSO algorithm has a considerably shorter path, a quick convergence rate, and requires less time to compute the distance between the locations of transporting and unloading environments, based on the simulation results that was generated and its validation using a 2D Lidar environment. The efficiency and effectiveness of path planning for mobile robots in logistic applications are validated using Quanser hardware interfaced with 2D Lidar and operated in environment 3 using proposed algorithm for production of optimal path.