Distance Estimation Research Articles

Deep learning estimation of scalar source distance for different turbulent and molecular diffusion environments

Deep learning estimation of scalar source distance for different turbulent and molecular diffusion environments

Sketched reference databases for genome-based taxonomy and comparative genomics.

The analysis of curated genomic, metagenomic and proteomic data is of paramount importance in the fields of biology, medicine, education, and bioinformatics. Although this type of data is usually hosted in raw format on free international repositories, the full access requires lots of computing power and large storage disk space for the domestic user. The purpose of the study is to offer a comprehensive set of microbial genomic and proteomic reference databases in an accessible and easy-to-use form to the scientific community and demonstrate its advantages and usefulness. Also, we present a case study on the applicability of the sketched data, for the determination of overall genomic coherence between two members of the Brucellacea family, which suggests they belong to the same genomospecies that remain as discrete ecotypes. A representative set of genomes, proteomes (from type material), and metagenomes were directly collected from the NCBI Assembly database and Genome Taxonomy Database (GTDB), associated with the major groups of Bacteria, Archaea, Virus, and Fungi. Sketched databases were subsequently created and stored on handy reduced representations by using the MinHash algorithm implemented in Mash software. The obtained dataset contains more than 133 GB of space disk reduced to 883.25 MB and represents 125,110 genomics/proteomic records from eight informative contexts, which have been prefiltered to make them accessible, usable, and user-friendly with limited computational resources. Potential uses of these sketched databases are discussed, including but not limited to microbial species delimitation, estimation of genomic distances and genomic novelties, paired comparisons between proteomes, genomes, and metagenomes; phylogenetic neighbor's exploration and selection, among others.

Distribution of the intraosseous branch of the posterior superior alveolar artery relative to the posterior maxillary teeth.

Preoperative identification of the intraosseous posterior superior alveolar artery (PSAA) is critical when planning sinus surgery. This study was conducted to determine the distance between the cementoenamel junction and the PSAA, as well as to identify factors influencing the detection of the PSAA on cone-beam computed tomography (CBCT). In total, 254 CBCT scans of maxillary sinuses, acquired with 2 different scanners, were examined to identify the PSAA. The distance from the cementoenamel junction (CEJ) to the PSAA was recorded at each maxillary posterior tooth position. Binomial logistic regression and multiple linear regression were employed to evaluate the effects of scanner type, CBCT parameters, sex, and age on PSAA detection and CEJ-PSAA distance, respectively. P-values less than 0.05 were considered to indicate statistical significance. The mean CEJ-PSAA distances at the second molar, first molar, second premolar, and first premolar positions were 17.0±4.0 mm, 21.8±4.1 mm, 19.5±4.7 mm, and 19.9±4.9 mm for scanner 1, respectively, and 17.3±3.5 mm, 16.9±4.3 mm, 18.5±4.1 mm, and 18.4±4.3 mm for scanner 2. No independent variable significantly influenced PSAA detection. However, tooth position (b=-0.67, P<0.05) and scanner type (b=-1.3, P<0.05) were significant predictors of CEJ-PSAA distance. CBCT-based estimates of CEJ-PSAA distance were comparable to those obtained in previous studies involving cadavers, CT, and CBCT. The type of CBCT scanner may slightly influence this measurement. No independent variable significantly impacted PSAA detection.

Distance Estimation for Early Three-core Cable Fault Based on Multiple Amplitude Ratio Fusion of Grounding Line Current Selected Frequency Band

Distance Estimation for Early Three-core Cable Fault Based on Multiple Amplitude Ratio Fusion of Grounding Line Current Selected Frequency Band

Accuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development.

Goal: Distance information is highly requested in assistive smartphone Apps by people who are blind or low vision (PBLV). However, current techniques have not been evaluated systematically for accuracy and usability. Methods: We tested five smartphone-based distance-estimation approaches in the image center and periphery at 1-3 meters, including machine learning (CoreML), infrared grid distortion (IR_self), light detection and ranging (LiDAR_back), and augmented reality room-tracking on the front (ARKit_self) and back-facing cameras (ARKit_back). Results: For accuracy in the image center, all approaches had <±2.5 cm average error, except CoreML which had ±5.2-6.2 cm average error at 2-3 meters. In the periphery, all approaches were more inaccurate, with CoreML and IR_self having the highest average errors at ±41 cm and ±32 cm respectively. For usability, CoreML fared favorably with the lowest central processing unit usage, second lowest battery usage, highest field-of-view, and no specialized sensor requirements. Conclusions: We provide key information that helps design reliable smartphone-based visual assistive technologies to enhance the functionality of PBLV.

Appreciation and Dissemination of Chinese Paintings from a Cross-cultural Perspective Based on Information Diffusion Modeling

Abstract With a long history, profound cultural deposits, and diversified art types, Chinese painting is both an important part of traditional Chinese culture and a typical representative of traditional oriental art and culture. This article starts with the spirit of Chinese painting and aesthetic appreciation and then explores the significance of Chinese painting’s foreign dissemination and dissemination path. For the appreciation of Chinese paintings, the article designs an index system for assessing the appreciation ability of Chinese paintings, solves the weights of the indexes by using the optimal assignment method of distance estimation, and constructs a model for assessing the appreciation ability of Chinese paintings by combining with the cloud model. The Hidden Markov Model is used to establish an information diffusion model for Chinese painting dissemination, and the parameters of the model are estimated using the Great Likelihood Method. The quantitative analyses of Chinese painting appreciation ability, information diffusion effect, and satisfaction with communication effect were carried out with students from University S as the research subjects. The main factor of Chinese painting appreciation ability is emotional ability, which has a weight share of 26.74%. The comprehensive assessment cloud eigenvalue is (3.4714, 0.1462, 0.0348), which shows that the appreciation ability is good. When the state transfer probability is raised from 0.3 to 0.9, the increase in the number of Chinese painting communication nodes in 25s is 93.59%, and the satisfaction index of the audience for the communication effect of Chinese painting is 3.899. Combining with the information diffusion model, we can understand the change of communication nodes in Chinese paintings, which can provide a guide to enhance the communication effect of Chinese paintings.

Abnormal sample detection based on robust Mahalanobis distance estimation in adversarial machine learning

Abnormal sample detection based on robust Mahalanobis distance estimation in adversarial machine learning

Advance Path Loss Model for Distance Estimation Using LoRaWAN Network’s Received Signal Strength Indicator (RSSI)

Advance Path Loss Model for Distance Estimation Using LoRaWAN Network’s Received Signal Strength Indicator (RSSI)

Count-Free Single-Photon 3D Imaging with Race Logic.

Single-photon cameras (SPCs) have emerged as a promising new technology for high-resolution 3D imaging. A single-photon 3D camera determines the round-trip time of a laser pulse by precisely capturing the arrival of individual photons at each camera pixel. Constructing photon-timestamp histograms is a fundamental operation for a single-photon 3D camera. However, in-pixel histogram processing is computationally expensive and requires large amount of memory per pixel. Digitizing and transferring photon timestamps to an off-sensor histogramming module is bandwidth and power hungry. Can we estimate distances without explicitly storing photon counts? Yes-here we present an online approach for distance estimation suitable for resource-constrained settings with limited bandwidth, memory and compute. The two key ingredients of our approach are (a) processing photon streams using race logic, which maintains photon data in the time-delay domain, and (b) constructing count-free equi-depth histograms as opposed to conventional equi-width histograms. Equi-depth histograms are a more succinct representation for "peaky" distributions, such as those obtained by an SPC pixel from a laser pulse reflected by a surface. Our approach uses a binner element that converges on the median (or, more generally, to another k-quantile) of a distribution. We cascade multiple binners to form an equi-depth histogrammer that produces multi-bin histograms. Our evaluation shows that this method can provide at least an order of magnitude reduction in bandwidth and power consumption while maintaining similar distance reconstruction accuracy as conventional histogram-based processing methods.

Preserved tactile distance estimation despite body representation distortions in individuals with fibromyalgia.

Our mental representation of our body depends on integrating various sensory modalities, such as tactile information. In tactile distance estimation (TDE) tasks, participants must estimate the distance between two tactile tips applied to their skin. This measure of tactile perception has been linked to body representation assessments. Studies in individuals with fibromyalgia (FM), a chronic widespread pain syndrome, suggest the presence of body representation distortions and tactile alterations, but TDE has never been examined in this population. Twenty participants with FM and 24 pain-free controls performed a TDE task on three Body regions (upper limb, trunk, lower limb), in which they manually estimated the interstimuli distance on a tablet. TDE error, the absolute difference between the estimation and the interstimuli distance, was not different between the Groups, on any Body region. Drawings of their body as they felt it revealed clear and frequent distortions of body representation in the group with FM, compared to negligible perturbations in controls. This contrast between distorted body drawings and unaltered TDE suggests a preserved integration of tactile information but an altered integration of this information with other sensory modalities to generate a precise and accurate body representation. Future research should investigate the relative contribution of each sensory information and prior knowledge about the body in body representation in individuals with FM to shed light on the observed distortions.

Haar cascade-based deep learning model to predict in/out bound passenger flow and distance estimation for intelligent transport system

Haar cascade-based deep learning model to predict in/out bound passenger flow and distance estimation for intelligent transport system

TTNN: A Physically Guided Deep Learning Model for Focal Depth and Epicenter Distance Estimation Based on Multistation Waveforms

TTNN: A Physically Guided Deep Learning Model for Focal Depth and Epicenter Distance Estimation Based on Multistation Waveforms

Feasibility Analysis of Applying Deep Neural Network on Driving Distance Estimation

Feasibility Analysis of Applying Deep Neural Network on Driving Distance Estimation

Haar cascade-based deep learning model to predict in/out bound passenger flow and distance estimation for intelligent transport system

Haar cascade-based deep learning model to predict in/out bound passenger flow and distance estimation for intelligent transport system

Impact of Background, Foreground, and Manipulated Object Rendering on Egocentric Depth Perception in Virtual and Augmented Indoor Environments.

This research investigated how the similarity of the rendering parameters of background and foreground objects affected egocentric depth perception in indoor virtual and augmented environments. We refer to the similarity of the rendering parameters as visual 'congruence'. Study participants manipulated the depth of a sphere to match the depth of a designated target peg. In the first experiment, the sphere and peg were both virtual, while in the second experiment, the sphere is virtual and the peg is real. In both experiments, depth perception accuracy was found to depend on the levels of realism and congruence between the sphere, pegs, and background. In Experiment 1, realistic backgrounds lead to overestimation of depth, but resulted in underestimation when the background was virtual, and when depth cues were applied to the sphere and target peg. In Experiment 2, background and target pegs were real but matched with the virtual sphere; in comparison to Experiment 1, realistically rendered targets prompted an underestimation and more accuracy with the manipulated object. These findings suggest that congruence can affect distance estimation and the underestimation effect in the AR environment resulted from increased graphical fidelity of the foreground target and background.

Speaker Distance Estimation in Enclosures From Single-Channel Audio

Speaker Distance Estimation in Enclosures From Single-Channel Audio

Assesment of Cognitive Skills in Billiards Players

In this study, it is aimed to reveal differences between the cognitive skills of the brain such as reaction time, speed distance estimation, nonverbal intelligence between different grade billiards players with numerical data. Research population consisted of professional and amateur male billiards players between the ages of 18-35. Cognitive abilities such as reaction time, speed-distance estimation, and judgment were measured with Vienna Test System (Schuhfried GmbH Moedling, Austria). The data was analyzed by Mann-Whitney U Test and Pearson correlation test. All statistical analysis were calculated using SPSS version 15.0 (SPSS Inc, USA). Reaction time and Speed-Distance Estimates of professional billiards players were statistically significantly higher than amateur billiards players (p=0.034, p=0.012). It was observed that the ability to make decisions under stress was higher in amateur players than in professional billiards players (p=0.155). Furthermore, a positive correlation was found between billiards scores and speed-distance estimation skills in professional billiards players (r=0.102, p=0.766). The test results indicate a positive correlation between players' cognitive skills and billiards scores, while concurrently revealing that stress negatively impacts test scores. It was observed that sport of billiards had a positive effect on cognitive skills, which are upper functions of the brain. It has been determined that billiards may be beneficial for brain development. As a result, it was concluded that it would be beneficial to expand the billiards in educational institutions and throughout country by cooperating with non-governmental organizations such as billiard federation.

Inspection Operations and Hole Detection in Fish Net Cages through a Hybrid Underwater Intervention System Using Deep Learning Techniques

Net inspection in fish-farm cages is a daily task for divers. This task represents a high cost for fish farms and is a high-risk activity for human operators. The total inspection surface can be more than 1500 m2, which means that this activity is time-consuming. Taking into account the severe restrictions for human operators in such hostile underwater conditions, this activity represents a significant area for improvement. A platform for net inspection is proposed in this work. This platform includes a surface vehicle, a ground control station, and an underwater vehicle (BlueROV2 heavy) which incorporates artificial intelligence, trajectory control procedures, and the necessary communications. In this platform, computer vision was integrated, involving a convolutional neural network trained to predict the distance between the net and the robot. Additionally, an object detection algorithm was developed to recognize holes in the net. Furthermore, a simulation environment was established to evaluate the inspection trajectory algorithms. Tests were also conducted to evaluate how underwater wireless communications perform in this underwater scenario. Experimental results about the hole detection, net distance estimation, and the inspection trajectories demonstrated robustness, usability, and viability of the proposed methodology. The experimental validation took place in the CIRTESU tank, which has dimensions of 12 × 8 × 5 m, at Universitat Jaume I.

Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation

Maritime traffic monitoring systems are particularly important in Mediterranean ports, as they provide more comprehensive data collection compared to traditional systems such as the Automatic Identification System (AIS), which is not mandatory for all vessels. This paper improves the existing real-time maritime traffic monitoring systems by introducing a distance estimation algorithm for monocular cameras, which aims to provide high quality maritime traffic metadata collection for traffic density analysis. Two distance estimation methods based on a pinhole camera model are presented: the Vessel-Focused Distance Estimation (VFDE) and the novel Vessel Object-Focused Distance Estimation (VOFDE). While VFDE uses the predefined height of a vessel for distance estimation, VOFDE uses standardized dimensions of objects on the vessel, detected with a Convolutional Neural Network (CNN) for instance segmentation to enhance estimation accuracy. Our evaluation covers distances up to 414 m, which is significantly beyond the scope of previous studies. When compared to the distances measured with a precise instrument, VOFDE achieves a Percentage Deviation Index (PDI) of 1.34% to 9.45%. This advance holds significant potential for improving maritime surveillance with monocular cameras and is also applicable in other areas, such as low-cost maritime vehicles equipped with single cameras.

Path Inference based on Voronoi Graph for Unmanned Maritime Vehicles

Design/methodology/approachWe propose a novel path inference approach based on Voronoi graph for unmanned maritime vehicles (UMV). We model the two-dimensional space with a flexible construction scheme of Voronoi graph, represent the inter-cell shortest distance with a delicate calculation of cell diameters, and infer the potential qualified navigation paths of UMVs w.r.t. the specified distance constraint based on a diameter path model with an efficient depth-first exploration algorithm. PurposeExisting path planning approaches are generally focused on finding a single optimal path. However, in many practical scenarios, such as UMV patrolling and search and rescue, it is the comprehensiveness rather than the optimality that matters for the UMV paths. Inspired by the above problem, we propose a path inference method based on Voronoi graph to infer all the possible UMV paths satisfying the distance constraint. FindingsExperimental results indicate that our path inference approach based on Voronoi graph is able to perform more delicately and efficiently than the traditional path planning approach in terms of both space modeling and path exploration. The paths identified by our path inference method were more precise in distance estimation as well as more comprehensive when compared with the grid-based path planning method. In the simulated scenario of UMV search & rescue, we obtained the coverage rates of 63.14% and 100% against those of 14.5% and 81.07% for the grid-based method. Originality/valueOur path inference approach is able to provide valuable insights in various practical UMV applications.