Distance Estimation Research Articles

Dark dust

Distance estimates derived from spectroscopy or parallax have been unified by considering extinction by large grains. The addition of such a population of what is called dark dust to models of the diffuse interstellar medium is tested against a contemporary set of observational constraints. By respecting representative solid-phase element abundances, the dark dust model simultaneously explains the typical wavelength-dependent reddening, extinction, and emission of polarised and unpolarised light by interstellar dust particles between far-UV and millimeter wavelengths. The physical properties of dark dust were derived. Dark dust consists of micrometer-sized particles. These particles have recently been detected in situ. Dark dust provides significant wavelength-independent reddening from the far-UV to the near-infrared. Light absorbed by dark dust is re-emitted in the submillimeter region by grains at dust temperatures of 8–12 K. This very cold dust has frequently been observed in external galaxies. Dark dust contributes to the polarisation at ≳1 mm to ~35% and marginally at shorter wavelengths. Optical constants for silicate dust analogous were investigated. By mixing 3% in mass of Mg0.8Fe0.22+ SiO3 to MgO−0.5 SiO2, a good fit to the data was derived that can still accommodate up to 5–10% of mass in dark dust. The additional diming of light by dark dust is unexplored when supernova Ia light curves are discussed and in other research. Previous models that ignored dark dust do not account for the unification of the distance scales.

An autocovariance-based learning framework for high-dimensional functional time series

Many scientific and economic applications involve the statistical learning of high-dimensional functional time series, where the number of functional variables is comparable to, or even greater than, the number of serially dependent functional observations. In this paper, we model observed functional time series, which are subject to errors in the sense that each functional datum arises as the sum of two uncorrelated components, one dynamic and one white noise. Motivated from the fact that the autocovariance function of observed functional time series automatically filters out the noise term, we propose a three-step framework by first performing autocovariance-based dimension reduction, then formulating a novel autocovariance-based block regularized minimum distance estimation to produce block sparse estimates, and based on which obtaining the final functional sparse estimates. We investigate theoretical properties of the proposed estimators, and illustrate the proposed estimation procedure with the corresponding convergence analysis via three sparse high-dimensional functional time series models. We demonstrate via both simulated and real datasets that our proposed estimators significantly outperform their competitors.

Determination of Distance, Extinction, Mass, and Age for Stars in LAMOST DR7

Large scale spectroscopic surveys such as that using Large-sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) have collected spectra of millions stars in the Milky Way. Utilizing this huge sample of stars to study the assembling history and structure of our Galaxy requires accurate estimates of distance, extinction, age, and mass for individual stars. Combining the parallax constraint from Gaia EDR3 with Bayesian inference, we have estimated the distance and extinction for stars observed in LAMOST DR7, as well as the stellar mass and age for evolved stars in this data release. We validated the accuracies of the stellar parameters by comparing our results against various measurements, including the star-pair technique, asteroseismology, globular clusters, and isochrone fits to main sequence stars and subgiants. This is a valuable catalog of stellar parameters under a Bayesian framework estimated using the data from Gaia EDR3 and LAMOST spectroscopic data. With this data set we explored the stellar population of the Galactic massive substructure Gaia-Sausage-Enceladus (GSE). The kinematically selected members of GSE have a median metallicity of [Fe/H] = −1.29 and a median age of 11.6 Gyr.

Redder than Red: Discovery of an Exceptionally Red L/T Transition Dwarf

We present the discovery of CWISE J050626.96+073842.4 (CWISE J0506+0738), an L/T transition dwarf with extremely red near-infrared colors discovered through the Backyard Worlds: Planet 9 citizen science project. Photometry from UKIRT and CatWISE give a (J − K)MKO color of 2.97 ± 0.03 mag and a J MKO − W2 color of 4.93 ± 0.02 mag, making CWISE J0506+0738 the reddest known free-floating L/T dwarf in both colors. We confirm the extremely red nature of CWISE J0506+0738 using Keck/NIRES near-infrared spectroscopy and establish that it is a low-gravity, late-type L/T transition dwarf. The spectrum of CWISE J0506+0738 shows possible signatures of CH4 absorption in its atmosphere, suggesting a colder effective temperature than other known, young, red L dwarfs. We assign a preliminary spectral type for this source of L8γ–T0γ. We tentatively find that CWISE J0506+0738 is variable at 3–5 μm based on multiepoch WISE photometry. Proper motions derived from follow-up UKIRT observations combined with a radial velocity from our Keck/NIRES spectrum and a photometric distance estimate indicate a strong membership probability in the β Pic moving group. A future parallax measurement will help to establish a more definitive moving group membership for this unusual object.

Difference in Incisal sum analysis in STL models and DICOM files of University of Cuenca students

A cross-sectional study was carried out among the students of the 7th cycle of the State University of Cuenca where the incisal sum of each student was analyzed by using STL models with the Nemocast program and by using tomographies with the Sidexis 4 program. It was possible to use a wide universe previously obtained from a database of 40 students, provided by the University of Cuenca, of which 19 were randomly chosen to be able to analyze the data without the aim of distorting and manipulating the data released. Through a meticulous evaluation of the analyzed data of the incisal sum of the 19 people, we proceeded to order the values ​​and standardize them, however, for a greater impact and veracity in the study, we proceeded to analyze the data and propose an alternative hypothesis: which would be corroborated through the "T Student" method. Finally, it was possible to find the values ​​of the mean and variance of each data grouping (format and incisal sum). Theoretical framework: To carry out this study, it is important to know that different orthodontic measurement parameters and different software used were used. These elements will be disclosed below. Mayoral analysis: This method can be used in permanent occlusion and allows estimation of the relative distances between the grooves that separate the buccal cusps from the lingual cusps of maxillary first and second premolars, and between the midline grooves where the mandibular cusps meet, the first molars of the maxilla. converge and normal patients should have dimensions of 35, 41, and 47 mm, respectively. When the value obtained is lower than the norm, a diagnosis of transverse micrognathism is obtained, and when a higher value is obtained, a diagnosis of transverse micrognathism is. [1] The disadvantage of this method is that the cross-sectional assessment is only performed in the maxilla and not in the mandible. [1] Nemoceph: NemoCast is an orthodontic software that allows you to perform digital cast analysis, digital diagnosis and planning, and export and print products.

Population-level estimate of bicycle use and fatality risk in a data-poor setting

Lack of data on exposure for walking and cycling poses a significant barrier to understanding the injury risk of these road users. Though this data paucity is most prevalent across low-and-middle-income countries (LMICs), it remains a challenge in many high-income countries as well. A new and simple method has been proposed to estimate population-level cycling distance travelled, with New Delhi, India as a case study. I used two independent estimates to calculate this distance. First, a ratio of motorcycle volume counts to that of cycle volume counts across major roads, and second, the total annual distance travelled by motorcycles. I validate this method using data from London, where cycling distance estimates are available from city-wide traffic volume counts as well as household travel survey. Combining the distance estimates with annual fatalities of corresponding road users, I found that cyclists have about 2 times greater fatality risk per kilometre than motorcycle occupants and about 40 times greater risk than car occupants. To encourage greater use of cycling, there is an urgent need to narrow this gap between the safety of cyclists and that of car occupants. The proposed method can be used to monitor cycling usage and its risk for many settings where traffic surveillance systems do not exist.

Route effects in city-based survey knowledge estimates

When studying wayfinding in urban environments, researchers are often interested in obtaining measures of participants’ survey knowledge, i.e., their estimate of distant locations relative to other places. Previous work showed that distance estimations are consistently biased when no direct route is available to the queried target or when participants follow a detour. Here we investigated whether a corresponding bias is manifested in two other popular measures of survey knowledge: a pointing task and a sketchmapping task. The aim of this study was to investigate whether there is a systematic bias in pointing/sketchmapping performance associated with the preferred route choice in an applied urban setting. The results were mixed. We found moderate evidence for the presence of a systematic bias, but only for a subset of urban locations. When two plausible routes to the target were available, survey knowledge estimates were significantly biased in the direction of the route chosen by the participant. When only one plausible route was available, we did not find a statistically significant pattern. The results may have methodological implications for spatial cognition studies in applied urban settings that might be obtaining systematically biased survey knowledge estimates at some urban locations. Researchers should be aware that the choice of urban locations from which pointing and sketchmapping are performed might systematically distort the results, in particular when two plausible but diverging routes to the target are visible from the location.

Chronic stress is associated with specific path integration deficits

Repeated exposure to stress (chronic stress) can cause excess levels of circulating cortisol and has detrimental influences on various cognitive functions including long-term memory and navigation. However, it remains an open question whether chronic stress affects path integration, a navigational strategy that presumably relies on the functioning of grid cells in the medial entorhinal cortex. The entorhinal cortex is a brain region in the medial temporal lobe, which contains multiple cell types involved in spatial navigation (and episodic memory), and a high number of corticosteroid receptors, predisposing it as a potential target of cortisol effects. Here, our goal was to investigate the association between chronic stress and path integration performance. We assessed chronic stress via hair cortisol concentration (physiological measure) and the Perceived Stress Questionnaire (subjective measure) in 52 female participants aged 22–65 years. Path integration was measured using a virtual homing task. Linear mixed models revealed selective impairments associated with chronic stress that depended on error type and environmental features. When focusing on distance estimations in the path integration task, we observed a significant relationship to hair cortisol concentrations indicating impaired path integration particularly during trials with higher difficulty in participants with high hair cortisol concentrations. This relationship especially emerged in the absence of spatial cues (a boundary or a landmark), and particularly in participants who reported high levels of subjectively experienced chronic stress. The findings are in line with the hypothesis that chronic stress compromises path integration, possibly via an effect on the entorhinal grid cell system.

Fast safety distance warning framework for proximity detection based on oriented object detection and pinhole model

Unauthorized approaching dangerous devices can cause serious dangers in electricity industry. Estimation on distances between human and these devices can effectively reduce the probabilities of various accidents, but there are limited studies focusing on it due to the complexity. In this paper, we propose a fast safety distance warning framework to detect proximity to dangerous devices in electrical operations. The framework consists of a customized oriented object detection model to extract precise pixel widths of objects, and a distance estimation method based on monocular camera and pinhole model to estimate distance. A tracking algorithm is applied to achieve targeted warning and fewer false alarms. The framework has been put into use in transformer stations in Yuxi power supply bureau, Yunnan Province, and distance estimation errors can be restricted to 0.5 meters. In experiments, the framework achieves 34 frames per second and 49.5% average precision, which are both state-of-the-art performances.

Photoplethysmography-Based Distance Estimation for True Wireless Stereo.

Recently, supplying healthcare services with wearable devices has been investigated. To realize this for true wireless stereo (TWS), which has limited resources (e.g. space, power consumption, and area), implementing multiple functions with one sensor simultaneously is required. The Photoplethysmography (PPG) sensor is a representative healthcare sensor that measures repeated data according to the heart rate. However, since the PPG data are biological, they are influenced by motion artifact and subject characteristics. Hence, noise reduction is needed for PPG data. In this paper, we propose the distance estimation algorithm for PPG signals of TWS. For distance estimation, we designed a waveform adjustment (WA) filter that minimizes noise while maintaining the relationship between before and after data, a lightweight deep learning model called MobileNet, and a PPG monitoring testbed. The number of criteria for distance estimation was set to three. In order to verify the proposed algorithm, we compared several metrics with other filters and AI models. The highest accuracy, precision, recall, and f1 score of the proposed algorithm were 92.5%, 92.6%, 92.8%, and 0.927, respectively, when the signal length was 15. Experimental results of other algorithms showed higher metrics than the proposed algorithm in some cases, but the proposed model showed the fastest inference time.

A distance Protection/Fault location algorithm for Double-Circuit transmission lines with asymmetrical circuits during Cross-Country faults

The impedance-measuring elements and fault locators in distance relays suffer from measuring the correct fault impedance and fault location when facing cross-country faults (CCFs). During such atypical faults, this paper proposes a distance protection along with a fault location algorithm for double-circuit lines with asymmetrical circuits. This is fulfilled through instantaneous fault-branch currents innovatively derived from the line six-phase circuit model analysis on a time-domain basis. The currents derived lead to the relations that are mathematically linear combinations of designated vectors. This makes the possibility of establishing vector-input adaptive linear combiners (VI-ALC) in accordance with the relations derived. Being fed from the vectors of instantaneous measurements at the relaying side, the VI-ALCs with a distinctive capability of online training would swiftly give an accurate estimation of the fault distances during CCFs. The estimations are exploited in a logical procedure for both of the distance protection and fault location functions, taking account of different position status of the CCFs’ comprising LG faults. The proposed algorithm is feasibly implementable and is independent of the system configuration. It operates robustly in high-ground resistance and noisy conditions. Through extensive simulation studies in PSCAD/EMTDC, performance of the proposed algorithm would be investigated and verified in a variety of fault scenarios.

Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm

In the recent age of technological advancements, wireless sensor networks are an important application for smart modernized environments. In WSNs, node localization has been an issue for over a decade in the research community. One of the goals of localization in a wireless sensor network is to localize sensor nodes in a two-dimensional plane. Localization in wireless sensor networks helps to supply information to aid decision-making from the aggregated data that are sent from packets to base stations. Internet of Things with the use of Global Positioning Systems for tracking sensor zones is not a cost-effective means of solution. In the extant literature, there have been a variety of algorithms to identify unknown sensor locations in wireless sensor networks. This research paper aims to address the problem of determining the location of the sensor node at the base station with minimum localization error when the data between the nodes is transmitted wirelessly. To detect the location of an unknown sensor node packets sent to the destinations, the total number of anchor nodes, location error and distance estimation error were considered. The DEEC-Gauss Gradient Distance Algorithm has a lower localization error than the Weighted Centroid Localizations algorithm, Compensation Coefficient algorithm, DV-Hop algorithm, Weighted Hyperbolic algorithm and Weighted Centroid algorithm for the same ratio of anchor nodes and WSN configuration. According to the study's findings, the DGGDEA has an average localization error of 11% for anchor nodes (20-80), and an average localization error of 11.3% for anchor nodes 200-450. Hence, the DEEC-Gaussian Gradient Distance Elimination Algorithm (DGGDEA) showed higher accuracy with comparison to the modern-day approaches.

Automated measurement of inter-arytenoid distance on 4D laryngeal CT: A validation study.

Changes to the voice are prevalent and occur early in Parkinson's disease. Correlates of these voice changes on four-dimensional laryngeal computed-tomography imaging, such as the inter-arytenoid distance, are promising biomarkers of the disease's presence and severity. However, manual measurement of the inter-arytenoid distance is a laborious process, limiting its feasibility in large-scale research and clinical settings. Automated methods of measurement provide a solution. Here, we present a machine-learning module which determines the inter-arytenoid distance in an automated manner. We obtained automated inter-arytenoid distance readings on imaging from participants with Parkinson's disease as well as healthy controls, and then validated these against manually derived estimates. On a modified Bland-Altman analysis, we found a mean bias of 1.52 mm (95% limits of agreement -1.7 to 4.7 mm) between the automated and manual techniques, which improves to a mean bias of 0.52 mm (95% limits of agreement -1.9 to 2.9 mm) when variability due to differences in slice selection between the automated and manual methods are removed. Our results demonstrate that estimates of the inter-arytenoid distance with our automated machine-learning module are accurate, and represents a promising tool to be utilized in future work studying the laryngeal changes in Parkinson's disease.

Estimating population size: The importance of model and estimator choice.

We consider estimator and model choice when estimating abundance from capture-recapture data. Our work is motivated by a mark-recapture distance sampling example, where model and estimator choice led to unexpectedly large disparities in the estimates. To understand these differences, we look at three estimation strategies (maximum likelihood estimation, conditional maximum likelihood estimation, and Bayesian estimation) for both binomial and Poisson models. We show that assuming the data have a binomial or multinomial distribution introduces implicit and unnoticed assumptions that are not addressed when fitting with maximum likelihood estimation. This can have an important effect in finite samples, particularly if our data arise from multiple populations. We relate these results to those of restricted maximum likelihood in linear mixed effectsmodels.

Self-Organized Ad Hoc Mobile (SOAM) Underwater Sensor Networks

The need of underwater wireless sensor networks (UWSNs) having mobile sensor nodes has been there for a long time in form of underwater warfare or explorations by autonomous underwater vehicles (AUVs) or remote unmanned vehicles (ROVs). There are very few protocols for ad hoc mobile UWSNs (AMUWSNs). Designing a protocol for AMUWSN is quite challenging because of continuous random movement of the sensor nodes. In addition to random movement, the challenges to design a routing protocol for AMUWSN are more demanding than terrestrial ad hoc networks due to acoustic communications, which has large propagation delay in water. In this article, we present a self-organized ad hoc mobile (SOAM) routing protocol for AMUWSN. The sensor nodes may need to communicate with each other to the gateway (GW). The protocol, which we also refer to as SOAM, is a reactive, self-configuring, and self-organizing cluster-based routing protocol that uses received signal strength (RSS) for distance estimation. A beacon (BCN) packet will be sent by the GW, which will traverse through all the cluster heads (CHs) to form forwarding paths between the GW and the CHs. The ordinary sensor nodes (OSNs) will select the CHs every time they intend to forward a packet based on the BCN and they will receive from CHs. The formation of the forwarding path between the GW and the CHs and the selection CHs by OSN is explained in <xref ref-type="sec" rid="sec4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Section IV</xref> .

One estimation method of road slope and vehicle distance

Aiming at the problem that the intelligent vehicle cannot accurately obtain the vehicle distance information when the front and rear vehicles are located on the road surface with different slopes, this paper proposes a road slope and vehicle distance estimation method based on vision’s and lidar’s information fusion. Firstly, the road lane line is detected by combining vision detection and lidar positioning ability, and the slope of the lane line is calculated. Then, we judge and analyze the slope ahead according to the change of the lane line slope, estimate the slope value according to the vanishing point coordinates. Finally, an improved distance estimation method adapted to the conditions of horizontal road surface and slope road surface is designed based on the combination of Lidar and camera. The experimental results show that the improved distance estimation model has good performance and can reach the accuracy of more than 95%.

Variational Bayesian Inference Time Delay Estimation for Passive Sonars

In passive sonars, distance and depth estimation of underwater targets is often limited by the accuracy of time delay estimations. The estimation accuracy of the existing methods of time delay estimation is limited by the uniform discrete grid (signal sampling rate). When a true time delay is out of the grid, the estimation accuracy deteriorates due to the mismatch between the real-time delay and the discrete grid. This paper proposes a new method for time delay estimation, which realizes the time delay estimation under the framework of variational Bayesian inference. The proposed method is grid-less, that is, continuous in the time domain. Unlike the popular grid-less compressive time delay estimation method, this method does not require parameter adjustment, and can automatically estimate the number of time delays, noise variance, and amplitude variance. The simulation results showed that the performance of the proposed method was superior to the reference state-of-the-art time delay estimation methods.

Usage of LDPC Codes in a Gilbert Channel

Although low-density parity-check (LDPC) codes in modern communication standards have been extensively studied over a memoryless channel, their burst error correction capacity in channels with memory has yet to be thoroughly analyzed. The conventional approach to transmission in channels with memory uses interleaving within a buffer of several codewords. However, such an approach reduces the efficiency of the redundancy embedded by the error-correcting code. It is known from information theory that considering channel memory during decoding allows the transmission rate to be increased. An evaluation of the decoding error probability of different types of low-density parity-check codes in channels with memory is presented along with estimates of minimum distance and burst error correction capability for the considered codes. The decoding error probability is estimated for conventional decoding with deinterleaving and decoding taking channel memory into account. The decoding error probability is estimated for several parameters of a channel with memory and different buffer lengths. The obtained results reveal the absence of the unique decoding approach for all parameters of the channel with memory. The best decoding error probability is determined by the degree of channel memory correlation.

Numerical and theoretical modeling for transmission of droplet carrying virus

Safe social distance is an important parameter for the prevention and treatment of a virus transmitted through droplets. However, the distance selected is not suitable for all air environments, and the calculation method of fluid research is time-consuming. Therefore, rapid and accurate prediction of safe social distance is the key to epidemic prevention and control. However, it is difficult for the existing fluid research to obtain the safe social distance rapidly. In this study, we set up a simple and effective numerical model and develop codes that combine the effects of evaporation, drag force, and gravity. We further conducted numerical simulations to investigate the motion of droplets in various air conditions. We completed a single case simulation with only one core and within several minutes, and determined that the resistance time and velocity evolution directly impact the transmission distance. We also observed two-stage regularity of motions in both the vertical and horizontal directions, and competition between evaporation and vertical falling. In addition, we derived a set of analytical solutions to describe the evaporation time and vertical and horizontal distances. The results demonstrated the good accuracy of the predicted data. Herein, we obtained an approximate rather than an accurate solution and, together with empirical coefficients, fitted it based on our numerical simulation. The proposed method can provide a rapid and accurate estimation of safe social distances for various environmental conditions. Common clinical cases were also analyzed, and prevention and control recommendations were provided based on the outcomes of the study.

A New Method of Fault Localization for 500 kV Transmission Lines Based on FRFT-SVD and Curve Fitting

The paper presents the Fractional Fourier Transform-Singular Value Decomposition (FRFT-SVD) method for the localization of various power system faults in a 200 km long, 500 kV Egyptian transmission line using sent end-line current signals. Transient simulations are carried out using Alternating Transient Program/Electromagnetic Transient Program (ATP-EMTP), and the outcomes are then examined in MATLAB to carry out a sensitivity analysis against measurement noises, sampling frequency, and fault characteristics. The proposed work employs current fault signals of five distinct kinds at nineteen intermediate points throughout the length of the line. The approach utilized to construct the localizer model is FRFT-SVD. It is much more effortless, precise, and effective. The FRFT-SVD is utilized in this technique to calculate 19 sets of indices of the greatest S value throughout the length of the line. The FRFT-SVD localizer model is also designed to be realistic, with power system noise corrupting fault signals. To generate fault curves, the curve fitting technique is applied to these 19 sets of indices. Reduced chi-squared and modified R-squared criteria are used to choose the best-suited curve. The proposed work results in a very precise localization, with only a 0.0016% average percentage error for fault localization and a maximum percentage error of 0.002% for the 200 km Egyptian transmission line. Finally, this work can be employed as a proper link between the nuclear power plant and the grid. The proposed method is an efficient fault distance estimation method that might contribute to creating a dependable transient-based approach to power system protection.