Distance Information Research Articles

Atomistic descriptor optimization using complementary Euclidean and geodesic distance information

Descriptors are physically-inspired, symmetry-preserving schemes for representing atomistic systems that play a central role in the construction of models of potential energy surfaces. Although physical intuition can be flexibly encoded into descriptor schemes, they are generally ultimately guided only by the spatial or topological arrangement of atoms in the system. However, since interatomic potential models aim to capture the variation of the potential energy with respect to atomic configurations, it is conceivable that they would benefit from descriptor schemes that implicitly encode both structural and energetic information rather than structural information alone. Therefore, we propose a novel approach for the optimisation of descriptors based on encoding information about geodesic distances along potential energy manifolds into the hyperparameters of commonly used descriptor schemes. To accomplish this, we combine two ideas: (1) a differential-geometric approach for the fast estimation of approximate geodesic distances [Zhu et al., J. Chem. Phys. 150, 164103 (2019)]; and (2) an information-theoretic evaluation metric – information imbalance – for measuring the shared information between two distance measures [Glielmo et al. PNAS Nexus, 1, 1 (2022)]. Using three example molecules – ethanol, malonaldehyde, and aspirin – from the MD22 dataset, we first show that Euclidean (in Cartesian coordinates) and geodesic distances are inequivalent distance measures, indicating the need for updated ground-truth distance measures that go beyond the Euclidean (or, more broadly, spatial) distance. We then utilize a Bayesian optimisation framework to show that descriptors (in this case, atom-centred symmetry functions) can be optimized to maximally express a certain type of distance information, such as Euclidean or geodesic information. We also show that modifying the Bayesian optimisation algorithm to minimise a combined objective function – the sum of the descriptor ↔ Euclidean and descriptor ↔ geodesic information imbalances – can yield descriptors that not only optimally express both Euclidean and geodesic distance information simultaneously, but in fact resolve substantial disagreements between descriptors optimized to encode only one type of distance measure. We discuss the relevance of our approach to the design of more physically rich and informative descriptors that can encode useful, alternative information about molecular systems.

2D/3D deformable registration for endoscopic camera images using self-supervised offline learning of intraoperative pneumothorax deformation

Shape registration of patient-specific organ shapes to endoscopic camera images is expected to be a key to realizing image-guided surgery, and a variety of applications of machine learning methods have been considered. Because the number of training data available from clinical cases is limited, the use of synthetic images generated from a statistical deformation model has been attempted; however, the influence on estimation caused by the difference between synthetic images and real scenes is a problem. In this study, we propose a self-supervised offline learning framework for model-based registration using image features commonly obtained from synthetic images and real camera images. Because of the limited number of endoscopic images available for training, we use a synthetic image generated from the nonlinear deformation model that represents possible intraoperative pneumothorax deformations. In order to solve the difficulty in estimating deformed shapes and viewpoints from the common image features obtained from synthetic and real images, we attempted to improve the registration error by adding the shading and distance information that can be obtained as prior knowledge in the synthetic image. Shape registration with real camera images is performed by learning the task of predicting the differential model parameters between two synthetic images. The developed framework achieved registration accuracy with a mean absolute error of less than 10 mm and a mean distance of less than 5 mm in a thoracoscopic pulmonary cancer resection, confirming improved prediction accuracy compared with conventional methods.

Single-shot dual-wavelength telecentric in-line-and-off-axis hybrid digital holography with non-prior reconstruction

Dual-wavelength in-line-and-off-axis hybrid digital holography (iohDH) can achieve high-resolution holographic dynamic imaging. However, it requires the prediction of the diffraction distance and the complex amplitude of the reference beam, which is time consuming and results in complications and accuracy limitations. While telecentric imaging technique can obtain nondiffractive images without predicting the diffraction distance, it also can even eliminate spherical aberration and astigmatic aberration. Therefore, a dual-wavelength telecentric iohDH is proposed to realize non-prior high-resolution reconstruction in a single shot. Employing the dual-wavelength telecentric iohDH, our approach acquires the focused in-line-and-off-axis hologram using a color camera in a single shot. In this case, we perform wavelength conversion on the phase and low-frequency information about the off-axis hologram as constraints for in-line iteration. Then, the in-line amplitude constraints are performed in the spatial and frequency domains until the algorithm converges. Compared to the state-of-the-art dual-wavelength iohDH, our approach can streamline the reconstructed processes without demanding a priori information of the diffraction distance and the complex amplitude of the reference beam. More importantly, our approach enables higher quality and efficient reconstruction under the telecentric system. We verified our approach using simulations and experiments, and the results indicate that our approach can allow the amplitude and phase reconstruction with high resolution in a single shot.

On the mathematical properties of spatial Rao’s Q to compute ecosystem heterogeneity

Spatio-ecological heterogeneity has a significant impact on various ecosystem properties, such as biodiversity patterns, variability in ecosystem resources, and species distributions. Given this perspective, remote sensing has gained widespread recognition as a powerful tool for assessing the spatial heterogeneity of ecosystems by analyzing the variability among different pixel values in both space and, potentially, time. Several measures of spatial heterogeneity have been proposed, broadly categorized into abundance-related measures (e.g., Shannon’s H) and dispersion-related measures (e.g., Variance). A measure that integrates both abundance and distance information is the Rao’s quadratic entropy (Rao’s Q index), mainly used in ecology to measure plant diversity based on in-situ based functional traits. The question arises as to why one should use a complex measure that considers multiple dimensions and couples abundance and distance measurements instead of relying solely on simple dispersion-based measures of heterogeneity. This paper sheds light on the spatial version of the Rao’s Q index, based on moving windows for its calculation, with a particular emphasis on its mathematical and statistical properties. The main objective is to theoretically demonstrate the strength of Rao’s Q index in measuring heterogeneity, taking into account all its potential facets and applications, including (i) integrating multivariate data, (ii) applying differential weighting to pixels, and (iii) considering differential weighting of distances among pixel reflectance values in spectral space.

An Improved Underwater Visual SLAM through Image Enhancement and Sonar Fusion

To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness-consistency recovery method is proposed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from images. After image-level processing, a combined underwater acousto–optic feature-association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for its performance against the OKVIS framework. The system achieves a better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

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.

Anchor-Based Multi-Scale Deep Grasp Pose Detector With Encoded Angle Regression

An intelligent robot grasping system should be able to automatically grasp a variety of objects that have never been seen, which requires accurate and efficient grasp pose detection. To this end, we propose a deep grasp detector designed for the robot equipped with a parallel gripper. The deep model consumes RGB or depth data and extracts features via a feature pyramid network (FPN), followed by multiple grasp prediction units to output grasp parameters in a single stage without refining process. Attaching grasp prediction units to different FPN stages increases the model capability to predict different-size grasps. Furthermore, in each prediction unit, the grasp parameters are regressed with the horizontal anchor as a reference to overcome the challenges posed by the various shapes of the grasp regions. We improve the accuracy and efficiency of grasp rotation estimation by regressing the angle directly and encoding the angle with a continuous Gaussian-like curve during training. This encoded angle regression strategy provides distance information of different angle predictions without introducing additional computational costs. Evaluations on three datasets prove the superior performance of our method than state of the arts. The experiments in real scenarios further validate the effectiveness of our grasping system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper proposes a robot system that can automatically grasp novel objects with a parallel gripper and RGB-D camera. We focus on generating accurate grasp configurations for various objects using the captured color or depth image, which is the cornerstone of a successful grasp. To obtain effective and efficient grasp pose detection, we present a deep model that generates robust grasp poses represented by rotated bounding boxes for multiple novel objects. The first step of the grasp detector is to capture the image features through a feature pyramid network (FPN). Then, we attach separate grasp prediction units to each layer of the FPN stage and adopt anchors as references to make the model robust to variable grasp rectangle sizes. In each grasp prediction unit, two separate subnetworks are used to directly output the grasp rectangles and their probabilities, without using an extra second stage to refine the predicted grasp areas. For the prediction of rotation angle, we encode the rectangle angles with a continuous Gaussian-like curve during training to improve the prediction accuracy. Our grasp detector is trained and tested on three datasets and validated on real-scene grasp experiments. Comparisons with state-of-the-art methods show that our model is more accurate while maintaining high efficiency. The proposed grasp detection model can be applied to generate stable grasps for novel objects with different shapes, colors, and materials. Our grasping system is capable of working in multiple scenarios, including homes, factories, and warehouses.

Enhanced Real-Time Target Detection for Picking Robots Using Lightweight CenterNet in Complex Orchard Environments

The rapid development of artificial intelligence and remote sensing technologies is indispensable for modern agriculture. In orchard environments, challenges such as varying light conditions and shading complicate the tasks of intelligent picking robots. To enhance the recognition accuracy and efficiency of apple-picking robots, this study aimed to achieve high detection accuracy in complex orchard environments while reducing model computation and time consumption. This study utilized the CenterNet neural network as the detection framework, introducing gray-centered RGB color space vertical decomposition maps and employing grouped convolutions and depth-separable convolutions to design a lightweight feature extraction network, Light-Weight Net, comprising eight bottleneck structures. Based on the recognition results, the 3D coordinates of the picking point were determined within the camera coordinate system by using the transformation relationship between the image’s physical coordinate system and the camera coordinate system, along with depth map distance information of the depth map. Experimental results obtained using a testbed with an orchard-picking robot indicated that the proposed model achieved an average precision (AP) of 96.80% on the test set, with real-time performance of 18.91 frames per second (FPS) and a model size of only 17.56 MB. In addition, the root-mean-square error of positioning accuracy in the orchard test was 4.405 mm, satisfying the high-precision positioning requirements of the picking robot vision system in complex orchard environments.

ADVANTAGES AND DISADVANTAGES OF STUDENTS’ GROUP WORK IN TEACHING FOREIGN LANGUAGES AT UNIVERSITY

The aim of the article is analysing the advantages and disadvantages of group work for students while teaching foreign languages at university, showing the feasibility of using group work in higher edu- cation. Methods of analysis and systematization of primary sources, interviews, questionnaires, and focus groups were used during the consideration of the problem. The process of teaching English in higher education has certain features that are associated with dif- ferent initial levels of language training of freshmen; the number of hours to study the subject; the number of groups; and sometimes absent enthusiasm for learning a foreign language. We emphasize that group work plays an important role in the educational process, and its introduction into the educational process should be one of the main tasks for students of philological specialties. Practice has proved that high-quality training in foreign languages for philological specialties can be carried out through the introduction of mod- ern educational technologies, such as: vocational training, project methodology, technology, such as: voca- tional training, project methodology, technology of fixing and distance learning, information and commu- nication technologies, and educational and control work. The study found that the use of interactive learn- ing technology involves seeking help in communication, including cognitive communication and constructiv- ist methods of teaching a foreign language. The facts proved that the use of innovative methods of teach- ing English and multimedia teaching methods can enhance the motivation of students to study foreign lan- guages, provide access to new alternative sources of information, develop independent psychological activ- ity, and develop communication skills, intercultural and professional abilities. Interactive technologies and online resources play an important role in group work while learning for- eign languages, providing an effective and engaging learning process. They promote active interaction be- tween students, stimulate involvement in language learning and improve language skills. To determine the advantages and disadvantages of group work, we conducted interviews with teach- ers and students to collect their impressions and thoughts about group work in the context of teaching for- eign languages. Subsequently, the reviews of teachers and students about their experience in groups dur- ing the study of foreign languages were analysed. Questionnaires were compiled, which included questions about impressions and thoughts about group work in the context of studying foreign languages. The questions concerned the advantages and dis- advantages of this training method, the efficiency from group work, the influence on motivation, etc. Fo- cus groups were held with students and separately with teachers to discuss their impressions and thoughts about group work. This allowed us to gain a deeper understanding of different points of view and identi- fy general trends. In group work during the study of foreign languages, the teacher and students play different roles, each of which has its own importance for successful learning. We will discuss the role of the teacher-facili- tator. The teacher acts as an organizer and leader of group work, promoting structured learning and prob- lem solving. Students actively interact with each other and with the teacher, exchanging ideas and opin- ions, completing tasks and solving problems. Scientists have demonstrated that the active participation of students in group work contributes to the improvement of critical thinking and the development of com- munication skills. The use of group work while teaching foreign languages at university has its advantages and disadvantages. On the one hand, it promotes the development of communication skills, and stimulates cooperation and motivation of students. On the other hand, there may be a risk of social blind steering, problems with the distribution of responsibilities and conflicts between group members. It has been concluded that it is important to properly organize and control the process of group work to achieve maximum efficiency in teaching foreign languages.

SDN-based reliable emergency message routing schema using Digital Twins for adjusting beacon transmission in VANET

Digital Twin (DT) has revolutionized the contextualized digital environment. This advancement enables real-time monitoring and simulation of events, leading to more effective decision-making. In smart transportation, DT plays a crucial role in enhancing various aspects of road decision-making, including optimizing routing decisions for Emergency Message (EM) forwarding in Vehicular Ad hoc Networks (VANETs). In this study, DT has integrated with cutting-edge technologies such as Software-Defined-Network (SDN) and geographically distributed computing (fog computing) to elevate the position-based routing behavior in two directions: selecting the optimal next-hop and taking control of periodic messages (beacon transmission). The nature of routing decisions can be complex due to constant movement and frequent position changes of vehicles in the VANET system. Merely selecting the next-hop vehicle without considering its stability and predictability can result in suboptimal routing choices. To this end, SDN based on Reliable Emergency Message Routing Schema (SDN-REMR) is introduced. This schema utilizes DT and aims to leverage the behaviors of moving vehicles to enhance the delivery of EM by providing a reliable routing mechanism. To eliminate unstable neighbors from the list of potential next-hops, SDN-REMR forecasts the relative positions of surrounding vehicles using Euclidean distance and position information. Moreover, SDN-REMR uses the movement information collected from vehicles (such as position, speed, and power consumption) to reduce the possibility of a link disruption and select the best next-hop for reliable communication. SDN-REMR enables the Store-Carry-Forward (SCF) approach to ensure packets are successfully forwarded to the destination, even in challenging network conditions. This control strategy aims to strike a balance between maintaining reliable communication and minimizing congestion in the network. The experimental results prove that there are 42.6%, 76.22%, 76.22%, and 22.2% enhancement in terms of network throughput, total delivered packets, number of dropped packets, and routing overhead.

A 3D local feature description algorithm based on point distribution

It is well known that the local feature descriptor is playing an important role in 3D image recognition. But the current local feature descriptors have poor ability to describe target features in situations such as noise interference, be obscured, and changes in data resolution. On the basis of point distribution, we propose a new 3D local feature descriptor, the Point Distribution Signature Histogram(PDSH), which is relatively simple and easy to implement and has a fast identification speed by simplifying Local Reference Frame(LRF). We also rotate the local surface of the point cloud from multiple angles and project them onto three coordinate planes of the LRF. The projection planes are segmented at the same angle, and the distance information of the edge points and the count of projected points in each segment are statistically accumulated. Finally, the sub-feature vectors of the three coordinate planes are concatenated to obtain the feature descriptor of the key points. Experimental results show that our PDSH descriptor achieves a descriptiveness of 98 % on the pollution-free point cloud data set and better performance than the classical descriptors.

Exploring how stay-at-home orders during the COVID-19 pandemic impedes engagement along the HIV/AIDS care continuum in public hospitals of Southwest Ethiopia: a qualitative study.

COVID-19 has rapidly spread across the world. In March 2020, shortly after the first confirmed case of COVID-19 in Ethiopia in March 2020, the government of Ethiopia took several measures. This study aims to explore how stay-at-home orders during the COVID-19 pandemic hinder engagement with HIV/AIDS care in public hospitals in Southwest Ethiopia. Additionally, we aim to explore the psychosocial challenges faced in accessing services during stay-at-home orders. A descriptive qualitative study was conducted from 20 May to 3 June 2020, using semi-structured, in-depth interviews. In total, 27 study participants were recruited from purposively selected people living with HIV/AIDS (PLWHA) who had experienced delays, declines, or discontinuation of care after COVID-19 was confirmed in Ethiopia on 13 March 2020. The participants were interviewed over the phone and their responses were audio-recorded. Data were transcribed verbatim, translated, and analyzed using inductive thematic analysis in the Atlas ti.7.1 software package. The main themes and sub-themes that emerged were psychosocial issues (such as depression, hopelessness, and fear), risk perception (including high risk, susceptibility, and severity), forceful enforcement of stay-at-home orders (such as police beatings, community leaders disgracing, and influence of families and relatives), socioeconomic factors (such as stigma, religion, and transportation costs), misinformation about COVID-19 (such as lockdowns and ART stock-outs), and healthcare factors (such as inadequate health information and long distances to healthcare facilities). Overall, these findings were similar to the challenges experienced by PLWHA in adhering to the recommended continuum of care. However, there are additional factors due to COVID-19, such as misinformation and the forceful implementation of the stay-at-home-orders, that impede the continuum of care. Therefore, it is important to strengthen information, education, and communication.

Online course as a means of developing master's professional competencies: results of a pedagogical experiment

The article proposes a solution to a scientific problem, which is to substantiate the feasibility of using online courses in the professional training of masters in the pedagogical university, to develop an online course and experimentally test its effectiveness as a means of developing the professional competencies of masters in the speciality 014~Secondary Education (Mathematics). The author used the following methods of scientific research: pedagogical experiment, observation (for systematic perception and analysis of learning activities, motivation and personal growth of students in the context of blended learning), testing (to determine the level of logical thinking and development of students’ professional competences), questionnaire (to monitor the ergonomics and efficiency of the online course and the Mathematics teacher’s e-portfolio), expert evaluation of the developed online course, and methods of mathematical statistics (for proper processing of experimental data and verification of the online course’ effectiveness). The results of the control stage of the study indicate a positive trend in the development of mathematical, information and educational, methodological, psychological and pedagogical, and professional and technological competencies of students in the experimental group. The final test of the online course was chosen as a tool for determining the level of mathematical competencies of the masters in the experimental group. The reliability of the assessment of the masters’ professional competencies was confirmed by testing the hypothesis of the normal distribution of test results according to the Shapiro-Wilk test. In addition, a close correlation between students' academic achievement level in an online course and the level of logical thinking development was substantiated using the Student’s t-test. Prospects for further research include the development of online courses on Mathematics teaching methods with an emphasis on methodical, professional, and technological competencies; a detailed study of the impact of the latest information technologies (artificial intelligence, virtual and augmented reality, web programming, etc.) on the development of students’ information and educational professional competences in distance and blended learning.

How Virtual are We? Introducing the Team Perceived Virtuality Scale

AbstractWith the strong proliferation of virtual teams across various organizations and contexts, understanding how virtuality affects teamwork has become fundamental to team and organizational effectiveness. However, current conceptualizations of virtuality rely almost exclusively on more or less fixed, structural features, such as the degree of technology reliance. In this paper, we take a socio-constructivist perspective on team virtuality, focusing on individuals’ experience of team virtuality, which may vary across teams and time points with similar structural features. More specifically, we develop and validate a scale that captures the construct of Team Perceived Virtuality (Handke et al., 2021). Following a description of item development and content validity, we present the results of four different studies that demonstrate the construct’s structural, discriminant, and criterion validity with an overall number of 2,294 teams. The final instrument comprises 10 items that measure the two dimensions of Team Perceived Virtuality (collectively-experienced distance and collectively-experienced information deficits) with five items each. This final scale showed a very good fit to a two-dimensional structure both at individual and team levels and adequate psychometric properties including aggregation indices. We further provide evidence for conceptual and empirical distinctiveness of the two TPV dimensions based on related team constructs, and for criterion validity, showing the expected significant relationships with leader-rated interaction quality and team performance. Lastly, we generalize results from student project teams to an organizational team sample. Accordingly, this scale can enhance both research and practice as a validated instrument to address how team virtuality is experienced.

Monocular Vision Guidance for Unmanned Surface Vehicle Recovery

The positioning error of the GPS method at close distances is relatively large, rendering it incapable of accurately guiding unmanned surface vehicles (USVs) back to the mother ship. Therefore, this study proposes a near-distance recovery method for USVs based on monocular vision. By deploying a monocular camera on the USV to identify artificial markers on the mother ship and subsequently leveraging the geometric relationships among these markers, precise distance and angle information can be extracted. This enables effective guidance for the USVs to return to the mother ship. The experimental results validate the effectiveness of this approach, with positioning distance errors of less than 40 mm within a 10 m range and positioning angle errors of less than 5 degrees within a range of ±60 degrees.

COMPARATIVE ANALYSIS OF SPECTRAL ANOMALIES DETECTION METHODS ON IMAGES FROM ON-BOARD REMOTE SENSING SYSTEMS

The subject matter of the article is methods of detecting spectral anomalies on images from remote sensing systems. The goal is to conduct a comparative analysis of methods for detecting spectral anomalies on images from remote sensing systems. The tasks are: analysis of the main methods of detecting spectral anomalies on images from remote sensing systems; processing of images from remote sensing systems using basic methods of detecting spectral anomalies; comparative assessment of the quality of methods for detecting spectral anomalies on images from remote monitoring systems. The methods used are: methods of digital image processing, mathematical apparatus of matrix theory, methods of mathematical modeling, methods of optimization theory, analytical and empirical methods of image comparison. The following results are obtained. The main methods of detecting spectral anomalies on images from remote sensing systems were analyzed. Processing of images from remote sensing systems using the basic methods of detecting spectral anomalies was carried out. A comparative assessment of the quality of methods for detecting spectral anomalies on images from remote monitoring systems was carried out. Conclusions. The spectral difference of the considered methods is revealed by the value of information indicators - Euclidean distance, Mahalanobis distance, brightness contrast, and Kullback-Leibler information divergence. Mathematical modeling of the considered methods of detecting spectral anomalies of images with a relatively “simple” and complicated background was carried out. It was established that when searching for a spectral anomaly on an image with a complicated background, the method based on the Kullback-Leibler divergence can be more effective than the other considered methods, but is not optimal. When determining several areas of the image with high divergence indicators, they should be additionally investigated using the specified methods in order to more accurately determine the position of the spectral anomaly.

Symmetry-Enhanced Fuzzy Logic Analysis in Parallel and Cross-Road Scenarios: Optimizing Direction and Distance Weights for Map Matching

This study addresses the challenges of setting segmentation points in the membership function and determining appropriate weights for different types of information within a fuzzy logic algorithm for map matching. We use linear fitting to derive an empirical formula for setting segmentation points for the information membership function. Furthermore, we evaluate the effects of various weights for direction and distance information in parallel and cross-road scenarios. The research identified the optimal distance that achieves the highest matching accuracy and provided insights into how the weights of connection, direction, and distance information affect this accuracy. The simulations confirmed the critical importance of precise segmentation point settings and weight determinations in enhancing the accuracy of fuzzy logic algorithms for map matching. The results underscore the potency of our tailored parameter-setting strategy and contribute to knowledge of symmetry, offering practical insights for implementing fuzzy logic in map matching with a particular emphasis on the principle of symmetry in algorithm design and information processing.

Use of directed quasi-metric distances for quantifying the information of gene families

A large hindrance to analyzing information in genetic or protein sequence data has been a lack of a mathematical framework for doing so. In this paper, we present a multinomial probability space X as a general foundation for multicategory discrete data, where categories refer to variants/alleles of biosequences. The external information that is infused in order to generate a sample of such data is quantified as a distance on X between the prior distribution of data and the empirical distribution of the sample. A number of distances on X are treated. All of them have an information theoretic interpretation, reflecting the information that the sampling mechanism provides about which variants that have a selective advantage and therefore appear more frequently compared to prior expectations. This includes distances on X based on mutual information, conditional mutual information, active information, and functional information. The functional information distance is singled out as particularly useful. It is simple and has intuitive interpretations in terms of 1) a rejection sampling mechanism, where functional entities are retained, whereas non-functional categories are censored, and 2) evolutionary waiting times. The functional information is also a quasi-metric on X, with information being measured in an asymmetric, mountainous landscape. This quasi-metric property is also retained for a robustified version of the functional information distance that allows for mutations in the sampling mechanism. The functional information quasi-metric has been applied with success on bioinformatics data sets, for proteins and sequence alignment of protein families.

Ultraviolet collaborative networking method based on a novel neighbor discovery algorithm

Ultraviolet (UV) optical communication presents several advantages, such as non-line-of-sight propagation, high confidentiality, reliability, and the absence of precise alignment requirements. UV optical communication networks find application in various scenarios in complex electromagnetic environments. In this study, we propose a UV collaborative networking method based on a neighbor discovery algorithm (UVCN–NDA), which establishes neighbor tables within nodes and selects the shortest transmission path between the nodes for frame transmission. During data transmission, nodes dynamically adjust the transmission time of each bit to enhance throughput and use the distance information in the neighbor table for path selection. This strategy prevents ineffective data forwarding and subsequently reduces network power consumption. Simulation results demonstrate that the neighbor discovery algorithm implemented in this protocol exhibits higher efficiency in neighbor discovery and lower message load than existing algorithms. Furthermore, when compared to UV networks based on bit–simultaneous–transmission, the UVCN–NDA–based UV networks achieve an approximate 86.2% reduction in average power consumption and approximately 52% increase in throughput, thereby providing a considerable performance advantage. Finally, the conducted network experiments prove the shortest path selection capability of UVCN–NDA.

Accurate distances measures and machine learning of the texture-property relation for crystallographic textures represented by one-point statistics

The crystallographic texture of metallic materials is a key microstructural feature that is responsible for the anisotropic behavior, e.g. important in forming operations. In materials science, crystallographic texture is commonly described by the orientation distribution function, which is defined as the probability density function of the orientations of the monocrystal grains conforming a polycrystalline material. For representing the orientation distribution function, there are several approaches such as using generalized spherical harmonics, orientation histograms, and pole figure images. Measuring distances between crystallographic textures is essential for any task that requires assessing texture similarities, e.g. to guide forming processes. Therefore, we introduce novel distance measures based on (i) the Earth Movers Distance that takes into account local distance information encoded in histogram-based texture representations and (ii) a distance measure based on pole figure images. For this purpose, we evaluate and compare existing distance measures for selected use-cases. The present study gives insights into advantages and drawbacks of using certain texture representations and distance measures with emphasis on applications in materials design and optimal process control.