Distance Estimation Research Articles

Seshadri constants and AG codes of vector bundles

We give estimates for the minimum distances of the algebraic geometric codes on certain fibered varieties. By means of the Seshadri constants, we also obtain similar results for the codes defined from vector bundles of higher rank.

Estimation of Distance and Rotation With an Optical Correlator

Estimation of Distance and Rotation With an Optical Correlator

Travel Distance Between Participants in US Telemedicine Sessions With Estimates of Emissions Savings: Observational Study.

Digital health and telemedicine are potentially important strategies to decrease health care's environmental impact and contribution to climate change by reducing transportation-related air pollution and greenhouse gas emissions. However, we currently lack robust national estimates of emissions savings attributable to telemedicine. This study aimed to (1) determine the travel distance between participants in US telemedicine sessions and (2) estimate the net reduction in carbon dioxide (CO2) emissions attributable to telemedicine in the United States, based on national observational data describing the geographical characteristics of telemedicine session participants. We conducted a retrospective observational study of telemedicine sessions in the United States between January 1, 2022, and February 21, 2023, on the doxy.me platform. Using Google Distance Matrix, we determined the median travel distance between participating providers and patients for a proportional sample of sessions. Further, based on the best available public data, we estimated the total annual emissions costs and savings attributable to telemedicine in the United States. The median round trip travel distance between patients and providers was 49 (IQR 21-145) miles. The median CO2 emissions savings per telemedicine session was 20 (IQR 8-59) kg CO2). Accounting for the energy costs of telemedicine and US transportation patterns, among other factors, we estimate that the use of telemedicine in the United States during the years 2021-2022 resulted in approximate annual CO2 emissions savings of 1,443,800 metric tons. These estimates of travel distance and telemedicine-associated CO2 emissions costs and savings, based on national data, indicate that telemedicine may be an important strategy in reducing the health care sector's carbon footprint.

Molecular Data Confirm Interspecific Limits of Four Alloxysta and One Phaenoglyphis Species of Parasitic Wasps within the Subfamily Charipinae (Cynipoidea: Figitidae).

The hymenopteran subfamily Charipinae (Cynipoidea: Figitidae) consist of a group of parasitic wasps that are exclusive hyperparasitoids of Hemipteran. The species boundaries in Charipinae have historically been unclear. While diagnostic morphological features have been established for the stepwise separation of species, it is recommended to confirm those limits using molecular data. Here, we focus on the genera Alloxysta Förster, 1869 and Phaenoglyphis Förster, 1869, both of which contain species that are hyperparasitoids of aphids. We sequenced three genes (mitochondrial COI and 16S rDNA, and nuclear ITS2 rDNA) from specimens that were identified as belonging to five species: Alloxysta brevis (Thomson, 1862), A. castanea (Hartig, 1841), A. ramulifera (Thomson, 1862), A. victrix (Westwood, 1833), and Phaenoglyphis villosa (Hartig, 1841). The phylogeny resulting from concatenating these genes supported the species status of the five morphologically identified taxa, with P. villosa nested within Alloxysta. Our study thus indicates that these molecular markers can successfully distinguish charipine species, and also indicates that the genera Alloxysta and Phaenoglyphis may be more closely related than previously hypothesized. We also present the first estimates of genetic distances for these species. Future studies that include more species, loci, and/or genomic data will complement our research and help determine species relationships within the Charipinae subfamily.

Design and Initiation of Lidar Based Visibility Distance Estimation Under Adverse Weather for Collision Avoidance in Fog

The vehicle detection in fog using LIDAR offers a practical solution for staying safe from the road accidents in the foggy condition. The purpose of our endeavor is to develop a vehicle detection system using LIDAR technology to identify and track vehicles within foggy conditions. The system is implemented on Raspberry pi for real-time testing. A camera captures the images and the computer vision is applied to identify whether fog and vehicle is present. If fog and vehicle is presented it send signal for LIDAR to turn on in which sensor fixed on a vehicle to look through the surrounding environment and generate a 3D point cloud depiction of the environment and which give the distance between vehicles. ultrasonic sensor which signals driver to concentrate when vehicle is close to the sides of road divider to avoid hitting them. It gives signal to DC motor which control the movement of the vehicle and motor driver which control the DC motor from giving the reverse current to the PCB of the Raspberry Pi. An electrical source supplies the necessary power for the working of various components. The software tool used is OpenCV with python programming. Finally, when LIDAR and laser module senses the vehicle and detects the separation between them it displays the result on the OLED display screen and indicate through the buzzer.

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

SummaryIn wireless sensor networks (WSNs), target tracking has been prominently raised in recent days. Because of the frequent utilization of WSN, the attention on target tracking is greatly increased. The estimated optimal value derived from the earlier moment is rarely taken into consideration in traditional target‐tracking algorithms. One of the most crucial uses of WSNs is mobile target tracking, and it is especially used for spying. Precision surveillance is heavily dependent on localization or distance estimation, and extensive study has been done in this area. This research aims to develop a new target‐tracking network‐assisted target movement prediction model in WSN with reduced energy consumption. The major phases involved in the proposed target movement prediction in WSN are (a) mobility target tracking and (b) target movement prediction. Initially, the mobility target tracking is done with the help of adaptive distributed extended Kalman filtering (ADEKF). The mobility target tracking performance is improved by optimally tuning the parameters from ADEKF with the support of the improved squid game optimizer (ISGO). Then, the target movement prediction phase is executed with the help of input parameters like “Angle of Arrival (AoA) and Received Signal Strength (RSS),” and the optimal progress of the mobile node is predicted. The implementation outcome of the proposed tracking network‐assisted target movement prediction model is validated concerning various performance metrics. Overall performance analysis shows that the developed model offers 2.5% in terms of RMSE measures. The developed model shows better analysis while validating with existing approaches.

Refined von Neumann-type trace inequality and its application

The purpose of this note is to establish logarithmic submajorization inequalities that are associated with von Neumann's trace inequality for operators in finite von Neumann algebras. As an application, we extend some of of the results in Carlen and Lieb [Some matrix rearrangement inequalities. Ann Mat Pura Appl. 2006;185:S315–S324. doi: 10.1007/s10231-004-0147-z] and Chayes [Matrix rearrangement inequalities revisited; 2021. arXiv:2009.04032; Reverse Hölder, Minkowski, and Hanner inequalities for matrices; 2021. arXiv:2103.09915] to non-commutative L p spaces associated with finite von Neumann algebras. Finally, we provide an estimation of the geodesic distance on P .

Orbital Tolerance and Intrinsic Orbital Capacity for Electric Propulsion Constellations

Large constellations are typically designed using sets of periodic orbits and satellite control boxes sized to ensure compatible phasing between coorbital constellation planes to prevent conjunction events. This work investigates how the control action for orbit maintenance influences feasible intrashell and intershell distances in a satellite constellation. Two-dimensional lattice flower constellations are simulated in an environment inclusive of orbital perturbations, with onboard sensors and an electric propulsion system constituting the satellite guidance, navigation, and control system. An analysis to define estimates for minimum separation distances is performed as a function of control, propulsion, orbit, environmental, and spacecraft characteristics. An estimate of the intrinsic (geometric) orbital capacity is proposed, based on current and advanced technologies, in order to quantify the number of admissible spacecraft and orbital shells in a selected altitude range. Simulation results are used to improve the fidelity of intrinsic orbital capacity estimates and to understand factors that influence the number of admissible satellite locations in low Earth orbit.

A parsec-scale Galactic 3D dust map out to 1.25 kpc from the Sun

Context. High-resolution 3D maps of interstellar dust are critical for probing the underlying physics shaping the structure of the interstellar medium, and for foreground correction of astrophysical observations affected by dust. Aims. We aim to construct a new 3D map of the spatial distribution of interstellar dust extinction out to a distance of 1.25 kpc from the Sun. Methods. We leveraged distance and extinction estimates to 54 million nearby stars derived from the Gaia BP/RP spectra. Using the stellar distance and extinction information, we inferred the spatial distribution of dust extinction. We modeled the logarithmic dust extinction with a Gaussian process in a spherical coordinate system via iterative charted refinement and a correlation kernel inferred in previous work. In total, our posterior has over 661 million degrees of freedom. We probed the posterior distribution using the variational inference method MGVI. Results. Our 3D dust map has an angular resolution of up to 14′ (Nside = 256), and we achieve parsec-scale distance resolution, sampling the dust in 516 logarithmically spaced distance bins spanning 69 pc to 1250 pc. We generated 12 samples from the variational posterior of the 3D dust distribution and release the samples alongside the mean 3D dust map and its corresponding uncertainty. Conclusions. Our map resolves the internal structure of hundreds of molecular clouds in the solar neighborhood and will be broadly useful for studies of star formation, Galactic structure, and young stellar populations. It is available for download in a variety of coordinate systems online and can also be queried via the publicly available dustmaps Python package.

High-resolution Pan-STARRS and SMA Observations of IRAS 23077+6707: A Giant Edge-on Protoplanetary Disk

We present resolved images of IRAS 23077+6707 (“Dracula’s Chivito”) in 1.3 mm/225 GHz thermal dust and CO gas emission with the Submillimeter Array (SMA) and optical (0.5–0.8 μm) scattered light with the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). The Pan-STARRS data show a bipolar distribution of optically scattering dust that is characteristic for disks observed at high inclinations. Its scattered light emission spans ∼14″, with two highly asymmetric filaments extending along the upper bounds of each nebula by ∼9″. The SMA data measure 1.3 mm continuum dust as well as 12CO, 13CO, and C18O J = 2 − 1 line emission over 12″–14″ extents, with the gas presenting the typical morphology of a disk in Keplerian rotation, in both position–velocity space and in each CO line spectrum. IRAS 23077+6707 has no reported distance estimate, but if it is located in the Cepheus star-forming region (180–800 pc), it would have a radius spanning thousands of astronomical units. Taken together, we infer IRAS 23077+6707 to be a giant and gas-rich edge-on protoplanetary disk, which to our knowledge is the largest in extent so far discovered.

Empowering the Blind: An AI Driven Indoor Assistance for Visually Impaired

Abstract: This review paper presents the design and development of a comprehensive mobile application aimed at enhancing the daily lives of visually impaired individuals. The proposed mobile app offers real-time assistance for various visual recognition tasks, including object detection, distance estimation, currency recognition, barcode detection, color recognition, and emotion analysis. The primary objective of this application is to provide visually impaired users with a powerful tool to navigate their surroundings, identify objects and currency, scan barcodes, discern colors, and even gauge the emotions of those they interact with. The app integrates pre-trained deep learning models for object detection and facial emotion analysis, computer vision techniques for distance estimation, and optical character recognition for currency recognition. User feedback and engagement are central to the ongoing improvement of the application, ensuring that it remains a valuable resource for the community it serves. Ethical considerations and privacy concerns are also addressed, with a focus on data security and user privacy. By presenting the development and functionalities of this app, this review paper not only contributes to the field of assistive technology but also underscores the importance of harnessing cutting-edge technology to improve the quality of life for visually impaired individuals.

X-ray counterpart detection and γ-ray analysis of the supernova remnant G279.0+01.1 with eROSITA and Fermi-LAT

A thorough inspection of known Galactic supernova remnants (SNRs) along the Galactic plane with SRG/eROSITA yielded the detection of the X-ray counterpart of the SNR G279.0+01.1. The SNR is located just 1.5° above the Galactic plane. Its X-ray emission emerges as an incomplete, partial shell of ~3° angular extension. It is strongly correlated to the fragmented shell-type morphology of its radio continuum emission. The X-ray spatial morphology of the SNR seems to be linked to the presence of dust clouds in the surroundings. The majority of its X-ray emission is soft (exhibiting strong O, Ne, and Mg lines), and it occurs in a narrow range of energies between 0.3 and 1.5 keV. Above 2.0 keV the remnant remains undetected. The remnant’s X-ray spectrum is purely of a thermal nature. Constraining the X-ray absorption column to values which are consistent with optical extinction data from the remnant’s location favors nonequilibrium over equilibrium models. A nonequilibrium two-temperature plasma model of kT ~ 0.3 keV and kT ~ 0.6 keV, as well as an absorption column density of NH ~ 0.3 cm−2 describe the spectrum of the entire remnant well. Significant temperature variations across the remnant have been detected. Employing 14.5 yr of Fermi-LAT data, we carried out a comprehensive study of the extended giga-electronvolt source 4FGL J1000.0-5312e. By refining and properly modeling the giga-electronvolt excess originating from the location of the remnant, we conclude that the emission is likely related to the remnant itself rather than being colocated by chance. The remnant’s properties as determined by the X-ray spectra are consistent with the ~2.5 kpc distance estimates from the literature, which implies a source diameter of ~140 pc and an old age of >7 × 105 yr. However, if the source is associated with any of the pulsars previously considered to be associated with the SNR, then the updated nearby pulsar distance estimates from the YMW16 electron density model rather place the SNR at a distance of ~0.4 kpc. This would correspond to a ~20 pc linear size and a younger age of 104− < 7 × 105 yr, which would be more in line with the nonequilibrium state of the plasma.

Velocity-based rack storage location assignment for the unidirectional robotic mobile fulfillment system

Nowadays, the robotic mobile fulfillment system (RMFS) has been increasingly used by online retailers. Compared with traditional picker-to-parts warehouses, the racks of RMFS do not have to return to the same location after picking, thus we can dynamically change their locations, which brings great potential to efficiently fulfill orders. Aiming at minimizing the sum of rack travel distances, the key question is how to reassign a rack to an unoccupied storage location after picking items from the rack. However, the issue involves two challenges for unidirectional RMFS, one is that huge differences may exist between the classical Manhattan distance estimation and the actual distance for the unidirectional aisles in RMFS, and the other one is that we need to account for the frequency of rack moving for multi-item orders. We thus first propose closed-form formulas to optimally estimate the cycle travel distance for each rack. Then, by overcoming the repeated counting issue for multi-item orders, we propose a novel SKU (Stock Keeping Units)-correlation-based algorithm to choose high-velocity racks, which can better fulfill multi-item orders. Finally, embedding the cycle travel distance and SKU-correlation-based velocity, we propose a Velocity-based Rack Storage Location Assignment method (VRSLA) to solve the rack storage location assignment problem by assigning high-velocity racks to the nearest storage locations. Collaborating with a large online retailer in China, we demonstrate the performance of VRSLA by using both small-scale and large-scale datasets. The computational results show that VRSLA not only can achieve near-best solutions compared with an integer programming model solved by Gurobi, but also outperforms four state-of-the-art assignment methods in literature (random, velocity-based class, shortest path, and sale-based) by reducing the rack travel distance up to 43.32%. We also found that the stronger the correlation between SKUs on the racks or the larger the size of the RMFS, the shorter the rack travel distance by the proposed VRSLA method.

Large-scale motions and growth rate from forward-modelling Tully–Fisher peculiar velocities

ABSTRACT Peculiar velocities are an important probe of the mass distribution in the Universe and the growth rate of structure, directly measuring the effects of gravity on the largest scales and providing a test for theories of gravity. Comparing peculiar velocities predicted from the density field mapped by a galaxy redshift survey with peculiar velocities measured using a distance estimator such as the Tully–Fisher relation yields the growth factor for large-scale structure. We present a method for forward modelling a sample of galaxy magnitudes and velocity widths that simultaneously determines the parameters of the Tully–Fisher relation and the peculiar velocity field. We apply this to the Cosmicflows-4 Tully–Fisher data set, using the peculiar velocities predicted from the 2M++ redshift survey. After validating the method on mock surveys, we measure the product of the growth rate and mass fluctuation amplitude to be fσ8 = 0.35 ± 0.03 at an effective redshift of z = 0.017. This is consistent at 3σ with the Planck CMB prediction, even though the uncertainty does not fully account for all sources of sample variance. We find the residual bulk flow from gravitational influences outside the 2M++ survey volume to be |V| = 227 ± 11 km s−1, (l, b) = (303°, −1°) in Galactic polar coordinates and the CMB frame. Using simulations, we show that applying our methodology to the large new sample of Tully–Fisher peculiar velocities expected from the WALLABY H I survey of the southern sky can improve the constraints on the growth rate by a factor of 2–3.

Testing versus estimation of graph properties, revisited

AbstractA graph on vertices is ‐far from property if one should add/delete at least edges to turn into a graph satisfying . A distance estimator for is an algorithm that given and distinguishes between the case that is ‐close to and the case that is ‐far from . If has a distance estimator whose query complexity depends only on , then is said to be estimable. Every estimable property is clearly also testable, since testing corresponds to estimating with . A central result in the area of property testing is the Fischer–Newman theorem, stating that an inverse statement also holds, that is, that every testable graph property is in fact estimable. The proof of Fischer and Newman was highly ineffective, since it incurred a tower‐type loss when transforming a testing algorithm for into a distance estimator. This raised the natural problem, studied recently by Fiat–Ron and by Hoppen–Kohayakawa–Lang–Lefmann–Stagni, whether one can find a transformation with a polynomial loss. We obtain the following results. We show that if 𝒫 is hereditary, then one can turn a tester for 𝒫 into a distance estimator with an exponential loss. This is an exponential improvement over the result of Hoppen et al., who obtained a transformation with a double exponential loss. We show that for every 𝒫, one can turn a testing algorithm for 𝒫 into a distance estimator with a double exponential loss. This improves over the transformation of Fischer–Newman that incurred a tower‐type loss. Our main conceptual contribution in this work is that we manage to turn the approach of Fischer–Newman, which was inherently ineffective, into an efficient one. On the technical level, our main contribution is in establishing certain properties of Frieze–Kannan Weak Regular partitions that are of independent interest.

Distance mis-estimations can be reduced with specific shadow locations

Shadows in physical space are copious, yet the impact of specific shadow placement and their abundance is yet to be determined in virtual environments. This experiment aimed to identify whether a target’s shadow was used as a distance indicator in the presence of binocular distance cues. Six lighting conditions were created and presented in virtual reality for participants to perform a perceptual matching task. The task was repeated in a cluttered and sparse environment, where the number of cast shadows (and their placement) varied. Performance in this task was measured by the directional bias of distance estimates and variability of responses. No significant difference was found between the sparse and cluttered environments, however due to the large amount of variance, one explanation is that some participants utilised the clutter objects as anchors to aid them, while others found them distracting. Under-setting of distances was found in all conditions and environments, as predicted. Having an ambient light source produced the most variable and inaccurate estimates of distance, whereas lighting positioned above the target reduced the mis-estimation of distances perceived.

Design and Implementation of Bluetooth Low Energy Based Access Control System

This paper discusses the design of a Bluetooth Low Energy (BLE) based access control system intended to make access control more practical to implement on areas with high personnel turnover rate by making access rules easy to set and making access keys relatively safe to distribute compared to existing key-based access control systems. The use of BLE technology allows the system to estimate key position within the system by utilizing the curve fitting method for distance estimation and the trilateral method for positioning. The proposed system consists of a server, an admin panel, electronic locks, access keys in the form of a wearable, and access keys in the form of an Android application. The system is found to be capable of implementing access control functionalities and capable of implementing indoor positioning based on sections.

A quantum k-nearest neighbors algorithm based on the Euclidean distance estimation

The k-nearest neighbors (k-NN) is a basic machine learning (ML) algorithm, and several quantum versions of it, employing different distance metrics, have been presented in the last few years. Although the Euclidean distance is one of the most widely used distance metrics in ML, it has not received much consideration in the development of these quantum variants. In this article, a novel quantum k-NN algorithm based on the Euclidean distance is introduced. Specifically, the algorithm is characterized by a quantum encoding requiring a low number of qubits and a simple quantum circuit not involving oracles, aspects that favor its realization. In addition to the mathematical formulation and some complexity observations, a detailed empirical evaluation with simulations is presented. In particular, the results have shown the correctness of the formulation, a drop in the performance of the algorithm when the number of measurements is limited, the competitiveness with respect to some classical baseline methods in the ideal case, and the possibility of improving the performance by increasing the number of measurements.

Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT.

As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.

Genotyping of Croatian Olive Germplasm with Consensus SSR Markers

Leaf samples of 226 cultivated olive trees were collected from traditionally managed olive orchards and genotyped with eleven consensual SSR markers. The proportion of shared allele distance was used for the estimation of distances between olive genotypes. Cluster analyses were performed using a Fitch–Margoliash least-squares algorithm. The number of different genetic subgroups of olive genotypes (K) was investigated using STRUCTURE analysis. The standardization of allele lengths was performed to enable the comparison SSR profiles of Croatian olive genotypes with olive profiles obtained with the same SSR primers in OleaDB and WOGB databases. Overall, 73 SSR profiles of known Croatian varieties and 53 profiles of unknown olive genotypes were differentiated. Synonyms were detected in 18 varieties, and we found intra-varietal differences in 15 varieties. Three genetic subgroups of olive genotypes were determined. Following allele length standardization achieved using nine referral samples, the genetic profiles of 126 cultivated olive genotypes were compared to OleaDB and WOGB databases, out of which 92 genotypes were found to be unique to Croatian olive germplasm. The results revealed the wide genetic diversity of olive germplasm beyond the known, registered varieties. The FAZ_oliveDB database containing the profiles of 126 Croatian olive genotypes was created and made available for public use.