Relevant bibliographies by topics / Estimation de la distance

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Estimation de la distance'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Estimation de la distance"

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Kronenfeld, Barry J. "A Plotless Density Estimator Based on the Asymptotic Limit of Ordered Distance Estimation Values." Forest Science 55, no. 4 (August 1, 2009): 283–92. http://dx.doi.org/10.1093/forestscience/55.4.283.

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Abstract Estimation of tree density from point-tree distances is an attractive option for quick inventory of new sites, but estimators that are unbiased in clustered and dispersed situations have not been found. Noting that bias of an estimator derived from distances to the kth nearest neighbor from a random point tends to decrease with increasing k, a method is proposed for estimating the limit of an asymptotic function through a set of ordered distance estimators. A standard asymptotic model is derived from the limiting case of a clustered distribution. The proposed estimator is evaluated against 13 types of simulated generating processes, including random, clustered, dispersed, and mixed. Performance is compared with ordered distance estimation of the same rank and with fixed-area sampling with the same number of trees tallied. The proposed estimator consistently performs better than ordered distance estimation and nearly as well as fixed-area sampling in all but the most clustered situations. The estimator also provides information regarding the degree of clustering or dispersion.
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Makaremi, Masrour, Rafael Ristor, François de Brondeau, Agathe Choquart, Camille Mengelle, and Bernard N’Kaoua. "Estimation of Distances within Real and Virtual Dental Models as a Function of Task Complexity." Diagnostics 13, no. 7 (March 30, 2023): 1304. http://dx.doi.org/10.3390/diagnostics13071304.

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Orthodontists have seen their practices evolve from estimating distances on plaster models to estimating distances on non-immersive virtual models. However, if the estimation of distance using real models can generate errors (compared to the real distance measured using tools), which remains acceptable from a clinical point of view, is this also the case for distance estimation performed on digital models? To answer this question, 50 orthodontists (31 women and 19 men) with an average age of 36 years (σ = 12.84; min = 23; max = 63) participated in an experiment consisting of estimating 3 types of distances (mandibular crowding, inter-canine distance, and inter-molar distance) on 6 dental models, including 3 real and 3 virtual models. Moreover, these models were of three different levels of complexity (easy, medium, and difficult). The results showed that, overall, the distances were overestimated (compared to the distance measured using an instrument) regardless of the situation (estimates on real or virtual models), but this overestimation was greater for the virtual models than for the real models. In addition, the mental load associated with the estimation tasks was considered by practitioners to be greater for the estimation tasks performed virtually compared to the same tasks performed on plaster models. Finally, when the estimation task was more complex, the number of estimation errors decreased in both the real and virtual situations, which could be related to the greater number of therapeutic issues associated with more complex models.
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Wartenberg, Constanze, and Per Wiborg. "Precision of Exocentric Distance Judgments in Desktop and Cube Presentation." Presence: Teleoperators and Virtual Environments 12, no. 2 (April 2003): 196–206. http://dx.doi.org/10.1162/105474603321640941.

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Accuracy of space perception and distance estimation in virtual environments is an important precondition for the reliable use of virtual techniques in the design of products, workplaces, architecture, and production systems. The present study compares the accuracy of exocentric 1 distance estimations that a static perceiver achieves with two virtual presentation techniques: a desktop and an immersive cube presentation. Estimation accuracy in a physical mock-up is used as a point of reference. Subjects estimated exocentric distances in detailed models of a workplace previously unknown to them. All distances to be judged were located in the subjects' personal space (less than 1.5 m from the subject). Major differences between the two virtual presentation modes are that stereo information is available in the cube but not in desktop environment, and that, in the cube, changes in perspective are achieved by actually moving inside the cube instead of using a mouse. Furthermore, the cube provides a wider absolute field of view than the desktop environment. The experiment showed advantages of the cube over desktop presentation when estimating exocentric distances in “personal space” from a static position. The magnitude of distance estimation errors was significantly higher in the desktop than in the cube environment. However, estimation errors tended to be overestimations in the cube presentation, whereas over- and underestimation occurred with equal frequency in the desktop environment. In the discussion it is argued that the higher estimation accuracy in the cube environment may mainly be due to the availability of stereoscopic depth cues. According to Cutting (1997), these cues are especially relevant for spatial perception in “personal space.” 1 The term exocentric distance is used for distances between two points external to the perceiver indicating (for example) interobject distances or distances colinear with the side length of an object. These distances are to be distinguished from egocentric distances, those distances between the perceiver and one point in the environment (Waller, 1999).
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Nam, Gyeong-Mo, and Eui-Rim Jeong. "Distance Estimation Based on Deep Convolutional Neural Network Using Ultra-Wideband Signals." Journal of Computational and Theoretical Nanoscience 17, no. 7 (July 1, 2020): 3212–17. http://dx.doi.org/10.1166/jctn.2020.9163.

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Recently, high accuracy localization technique is required to provide indoor location services. The purpose of this paper is to propose a distance estimation technique based on deep convolutional neural network (DCNN) for indoor environments. Among distance estimation techniques based on wireless communication signals, the use of ultra-wideband (UWB) signals has the advantage of high accuracy in the time domain. The proposed distance estimation method uses UWB signals and proposes a new DCNN-based distance estimator. The superiority of the proposed method is confirmed through computer simulation. Widely used conventional distance estimators are based on the power threshold. The threshold is determined by signal to noise ratio (SNR) of the received signal. The arrival time of the received signal that exceeds the threshold is considered as the time-of-arrival (ToA) and the distance between transmitter and receiver is obtained from the ToA. On the other hand, the proposed distance estimator requires only the received signal without SNR estimation, which make the proposed technique simpler to implement. According to computer simulation, the conventional method is highly sensitive to SNR and distance. In contrast, the proposed method shows less than 2 m root mean square error (RMSE) performance in a wide range of SNR and the RMSE performance is not degraded in long distances. The proposed distance estimator shows excellent distance estimation performance at low SNR and long distance, so it can be applied to indoor localization system of large indoor space and can be used for precise location service.
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Nadeau, Christopher P., and Courtney J. Conway. "Field evaluation of distance-estimation error during wetland-dependent bird surveys." Wildlife Research 39, no. 4 (2012): 311. http://dx.doi.org/10.1071/wr11161.

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Context The most common methods to estimate detection probability during avian point-count surveys involve recording a distance between the survey point and individual birds detected during the survey period. Accurately measuring or estimating distance is an important assumption of these methods; however, this assumption is rarely tested in the context of aural avian point-count surveys. Aims We expand on recent bird-simulation studies to document the error associated with estimating distance to calling birds in a wetland ecosystem. Methods We used two approaches to estimate the error associated with five surveyor’s distance estimates between the survey point and calling birds, and to determine the factors that affect a surveyor’s ability to estimate distance. Key results We observed biased and imprecise distance estimates when estimating distance to simulated birds in a point-count scenario (error = –9 m, s.d.error = 47 m) and when estimating distances to real birds during field trials (error = 39 m, s.d.error = 79 m). The amount of bias and precision in distance estimates differed among surveyors; surveyors with more training and experience were less biased and more precise when estimating distance to both real and simulated birds. Three environmental factors were important in explaining the error associated with distance estimates, including the measured distance from the bird to the surveyor, the volume of the call and the species of bird. Surveyors tended to make large overestimations to birds close to the survey point, which is an especially serious error in distance sampling. Conclusions Our results suggest that distance-estimation error is prevalent, but surveyor training may be the easiest way to reduce distance-estimation error. Implications The present study has demonstrated how relatively simple field trials can be used to estimate the error associated with distance estimates used to estimate detection probability during avian point-count surveys. Evaluating distance-estimation errors will allow investigators to better evaluate the accuracy of avian density and trend estimates. Moreover, investigators who evaluate distance-estimation errors could employ recently developed models to incorporate distance-estimation error into analyses. We encourage further development of such models, including the inclusion of such models into distance-analysis software.
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Allen, Robert C., Daniel P. McDonald, and Michael J. Singer. "Landmark Direction and Distance Estimation in Large Scale Virtual Environments." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 2 (October 1997): 1213–17. http://dx.doi.org/10.1177/1071181397041002109.

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The current paper describes our classification of errors participants made when estimating direction and distances in a large scale (2000 m × 2000 m) Virtual Environment (VE). Two VE configuration groups (Low or High Interactivity) traversed a 400 m route through one of two Virtual Terrain's (Distinctive or Non-Distinctive or Terrain 1 and 2, respectively) in 100 m increments. The High VE group used a treadmill to move through the VE with head tracked visual displays; the Low VE group used a joystick for movement and visual display control. Results indicate that as experience within either terrain increased, participants demonstrated an improved ability to directionally locate landmarks. Experience in the environment did not affect distance estimation accuracy. Terrain 1 participants were more accurate in locating proximal, as opposed to distal, landmarks. They also overestimated distances to near landmarks and underestimated distances to far landmarks. In Terrain 2, the Low VE group gave more accurate distance estimations. We believe this result can be explained in terms of increased task demands placed on the High VE Group.
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Witmer, Bob G., and Paul B. Kline. "Judging Perceived and Traversed Distance in Virtual Environments." Presence: Teleoperators and Virtual Environments 7, no. 2 (April 1998): 144–67. http://dx.doi.org/10.1162/105474698565640.

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The ability to accurately estimate distance is an essential component of navigating large-scale spaces. Although the factors that influence distance estimation have been a topic of research in real-world environments for decades and are well known, research on distance estimation in virtual environments (VEs) has only just begun. Initial investigations of distance estimation in VEs suggest that observers are less accurate in estimating distance in VEs than in the real world (Lampton et al., 1995). Factors influencing distance estimates may be divided into those affecting perceived distance (visual cues only) and those affecting traversed distance to include visual, cognitive, and proprioceptive cues. To assess the contribution of the various distance cues in VEs, two experiments were conducted. The first required a static observer to estimate the distance to a cylinder placed at various points along a 130-foot hallway. This experiment examined the effects of floor texture, floor pattern, and object size on distance estimates in a VE. The second experiment required a moving observer to estimate route segment distances and total route distances along four routes, each totaling 1210 feet. This experiment assessed the effects of movement method, movement speed, compensatory cues, and wall texture density. Results indicate that observers underestimate distances both in VEs and in the real world, but the underestimates are more extreme in VEs. Texture did not reliably affect the distance estimates, providing no compensation for the gross underestimates of distance in VE. Traversing a distance improves the ability to estimate that distance, but more natural means of moving via a treadmill do not necessarily improve distance estimates over traditional methods of moving in VE (e.g., using a joystick). The addition of compensatory cues (tone every 10 feet traversed on alternate route segments) improves VE distance estimation to almost perfect performance.
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Yoon, Jeonghyeon, Jisoo Oh, and Seungku Kim. "Transfer Learning Approach for Indoor Localization with Small Datasets." Remote Sensing 15, no. 8 (April 17, 2023): 2122. http://dx.doi.org/10.3390/rs15082122.

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Indoor pedestrian localization has been the subject of a great deal of recent research. Various studies have employed pedestrian dead reckoning, which determines pedestrian positions by transforming data collected through sensors into pedestrian gait information. Although several studies have recently applied deep learning to moving object distance estimations using naturally collected everyday life data, this data collection approach requires a long time, resulting in a lack of data for specific labels or a significant data imbalance problem for specific labels. In this study, to compensate for the problems of the existing PDR, a method based on transfer learning and data augmentation is proposed for estimating moving object distances for pedestrians. Consistent high-performance moving object distance estimation is achieved using only a small training dataset, and the problem of the concentration of training data only on labels within a certain range is solved using window warping and scaling methods. The training dataset consists of the three-axes values of the accelerometer sensor and the pedestrian’s movement speed calculated based on GPS coordinates. All data and GPS coordinates are collected through the smartphone. A performance evaluation of the proposed moving pedestrian distance estimation system shows a high distance error performance of 3.59 m with only approximately 17% training data compared to other moving object distance estimation techniques.
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Lampton, Donald R., Daniel P. McDonald, Michael Singer, and James P. Bliss. "Distance Estimation in Virtual Environments." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 39, no. 20 (October 1995): 1268–72. http://dx.doi.org/10.1177/154193129503902006.

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This paper describes an experiment to evaluate a procedure for measuring distance perception in immersive VEs. Forty-eight subjects viewed a VE with a Head Mounted Display (HMD), a Binocular Omni-Oriented Monitor (BOOM), or a computer monitor. Subjects estimated the distance to a figure of known height that was initially 40 ft away. As the figure moved forward, subjects indicated when the figure was perceived to be 30, 20, 10, 5, and 2.5 ft away. A separate group of 36 subjects performed the task in a real-world setting roughly comparable to the VE. VE distance estimation was highly variable across subjects. For distance perception involving a moving figure, in the VE conditions most subjects called out before the figure had closed to the specified distances. Distance estimation was least accurate with the monitor. In the real world, most subjects called out after the figure had closed to or passed the specified distances. Ways to improve the procedure are discussed.
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Kim, Sunyong, Sun Young Park, Daehoon Kwon, Jaehyun Ham, Young-Bae Ko, and Hyuk Lim. "Two-hop distance estimation in wireless sensor networks." International Journal of Distributed Sensor Networks 13, no. 2 (February 2017): 155014771668968. http://dx.doi.org/10.1177/1550147716689683.

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In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.
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Dissertations / Theses on the topic "Estimation de la distance"

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Lowe, Karsten. "Distance estimation between transceivers over short distances." Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1202417257/.

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Tekaya, Souhaiel Ben. "Distance estimation using handheld devices." Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/34753.

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Approved for public release; distribution is unlimited
The different capabilities mobile devices can offer in the field of distance estimation for military applications are explored in this thesis. Of particular interest is the potential for using computer vision techniques to estimate distance in an operational military environment. The methods used for this investigation include a review of past literature on computer vision techniques in this domain, as well as an exploration of the different capabilities mobile devices offer in terms of sensors and networking. We present two potential solutions. The first is a simulation of a distance estimation algorithm that gives the distance to the target using a pair of hyper stereo images. The second solution is a web-based mobile application prototype developed in HTML5. This prototype is intended for the use of untrained forward observers. It goes through the basic steps of a call for fire mission as required by a forward observer, with a focus on distance estimation.
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Andrieu, Guillaume. "Estimation par intervalle d'une distance évolutive." Montpellier 2, 1997. http://www.theses.fr/1997MON20233.

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L'objet de cette these est l'etude de la variabilite d'une estimation de la distance evolutive entre deux sequences moleculaires. Apres un bref apercu de l'historique des theories de l'evolution, nous presentons les differentes methodes de reconstruction phylogenetiques. Nous detaillons ensuite l'expression et l'estimation d'une distance evolutive entre deux sequences dans le cadre d'un modele stochastique de l'evolution. Deux methodes sont communement utilisees pour mesurer l'incertitude d'une estimation de la distance evolutive : l'estimation de la variance et le bootstrap. Nous proposons l'application de la methode statistique d'estimation par intervalle. Nous presentons les methodes et les algorithmes de construction d'intervalles de confiance que nous avons developpe dans le cadre de deux modeles : celui de jukes et cantor (1969) et celui de kimura (1980). Les resultats obtenus par simulation ou avec des donnees reelles et artificielles montrent la validite de notre demarche et nous permettent d'observer le comportement des autres methodes dans diverses conditions quant a la taille des sequences et la proportion de difference entre les sequences.
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Lammi, J. (Jouni), P. (Petteri) Moilanen, and A. (Aleksi) Sierilä. "Size and distance estimation in virtual reality." Bachelor's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906132541.

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Abstract. Interest in virtual reality (VR) has been on the rise in the recent few years. However, it is difficult to create virtual environments which provide realistic perception of scale for their users. We wanted to study how humans perceive scale in VR and ways to improve VR scale perception. We did a pilot test to see how design choices affect distance and height estimation in VR. For pilot tests we had nine test participants. Based on the experience gathered from the pilot test, we designed the main test. For the main test we had 44 participants. The main test showed similar results for distance estimating as earlier studies. Humans underestimated distances and heights in VR. Having a familiar size object cue, a VR model of a milk carton, next to the object improved height estimations.Koon ja etäisyyden arviointi virtuaalitodellisuudessa. Tiivistelmä. Kiinnostus virtuaalitodellisuuteen (VT) on ollut kasvavaa muutaman viime vuoden aikana. On kuitenkin vaikeaa tehdä virtuaalinen ympäristö, joka luo käyttäjilleen luonnollisen aistimuksen mittakaavasta. Halusimme tutkia, miten ihmiset hahmottavat skaalaa VR:ssä ja kuinka sitä voi parantaa. Teimme pilottitestejä, jotta näkisimme kuinka erilaiset suunnittelupäätökset vaikuttavat etäisyyden ja korkeuden arviointiin virtuaalitodellisuudessa. Pilottitesteissä oli yhdeksän osallistujaa. Pilottitestien pohjalta teimme laajemman testin, jossa hyödynsimme pilottitesteistä saatuja kokemuksia. Laajemmassa testissä oli 44 osallistujaa. Tämä testi tuotti samankaltaisia tuloksia etäisyyden arvioinnissa kuin aikaisemmat tutkimukset. Ihmiset aliarvioivat etäisyyksiä ja korkeuksia virtuaalitodellisuudessa. Kun esineen vieressä oli tunnetun kokoinen esine, maitopurkin virtuaalimalli, korkeuden arviointi parani.
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Warwick, Jane. "Selecting tuning parameters in minimum distance estimators." n.p, 2001. http://ethos.bl.uk/.

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Eriksson, Nils. "Estimation of Distance to empty for heavy vehicles." Thesis, Linköping University, Vehicular Systems, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56905.

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The distance to empty (DTE) for a heavy vehicle is valuable information both forthe driver and the hauler company. The DTE is estimated as the ratio between the current fuel level and a representative mean fuel consumption. This means the fuel consumption is a prediction of the most likely future mean fuel consumption based on earlier data. It is calculated by applying a forgetting filter on the signal of the momentary fuel consumption in the engine. The filter parameter control how many values that contributes to the output. This is a balance between desired robustness and adaptability of the estimate.

Initially, a pre-stored value is used as an estimate of the mean fuel consumption. By this, the driver gets a first hint of the DTE value and the estimation of the DTE gets a good starting point. Stored values will adapt continuously with an online algorithm using vehicle data from previous runs. An alternative to showing the DTE is to present the time to empty when the vehicle speed is close to zero.

The accuracy of the proposed algorithm depends on the quality of the input signals. With the current input signals, it is possible to get a DTE estimate that, over a longer time period, decrease in the same pace as the distance meter increase. This is considered as a good validation measurement. If altitude data for the current route would be used, a more accurate DTE estimate could be obtained. The sample distance for this altitude data could however be set to a 1000 meter without affecting the estimate significantly.


Sträckan till tom tank för ett tungt fordon är värdefull information, både för den enskilde föraren och åkeriet. Förkortad som DTE (Distance to empty) kan detta värde estimeras som kvoten av den nuvarande bränslenivån i tanken och en genomsnittlig bränsleförbrukning.

Denna genomsnittliga bränsleförbrukning är en prediktion av den troligaste framtida snittförbrukningen baserad på tidigare värden. Detta görs genom att ett glömskefilter appliceras på signalen för den aktuella bränsleförbrukningen i motorn. Filterparametern avgör hur snabbt gamla värden på insignalen ska klinga av och när den anpassas så måste önskad stabilitet vägas mot önskad känslighet hos skattningen.

Initialt så används förlagrade värden som skattning för den genomsnittliga bränsleförbrukningen. Detta gör att föraren får en första aning om hur långt fordonet kan köras samt ger DTE estimeringen en bra utgångspunkt. Dessa lagrade värden uppdateras under drift med information från det aktuella fordonet. För att hanterade problem som kan uppstå vid låga hastigheter eller tomgång kan istället tiden till tom tank visas.

Resultatet av DTE skattningen beror på kvalitén på insignalerna. Med de nuvarande insignalerna fås en DTE skattning som över en längre tidsperiod minskar sitt värde i samma takt som avståndsmätaren ökar sitt, vilket är ett önskvärt uppförande.

Om höjddata för en den aktuella rutten skulle användas skulle DTE estimeringen kunna göras mer noggrant. Det skulle dock räcka med att använda höjdinformation var 1000:e meter och ändå få en tillräckligt noggrann skattning.

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Gelman, Geoffrey M. (Geoffrey Michael) 1977. "Distance estimation through wavefront curvature in cellular systems." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80537.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (leaves 78-79).
by Geoffrey M. Gelman.
S.B.and M.Eng.
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Vichare, Nitin Shrikrishna. "Robust Mahalanobis distance in power systems state estimation." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/40024.

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Huang, Kyman. "DISTANCE ESTIMATION USING OFDM SIGNALS FOR ULTRASONIC POSITIONING." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2147.

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This paper describes a method of estimating distance via Time-of-Flight (TOF) measurement using ultrasonic Orthogonal Frequency Division Multiplexing (OFDM) signals. Using OFDM signals allows the signals and their sub-carriers to remain orthogonal to each other while continuously transmitting. This estimation method is based on the change of phase of a traveling wave as it propagates through a medium (air for ultrasonic signals). By using signals containing multiple tones, the phase change between each frequency component is slightly different. This phase difference is dependent on the distance traveled and can thus be used to estimate distance. This paper studies the impact of tone (OFDM sub-carriers) separation on accuracy, maximum distance, and computation for two-tone and three-tone systems. The effects of the transducer channel bandwidth and channel noise are accounted for to build an accurate model for a single-transmitter single-receiver system. This study found that each additional tone provides one extra independent distance measurement which improves accuracy in the presence of noise. The inclusion of an additional tone while maintaining the same overall signal strength shows improved performance with a reduction in standard deviation of estimated distance from 5.64 mm to 3.42 mm in simulation. A four-tone system is also examined to show that this effect holds for additional tones.
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Campos, Jennifer L. Sun Hong-Jin. "Multisensory integration in the estimation of distance travelled." *McMaster only, 2007.

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Books on the topic "Estimation de la distance"

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1945-, Kauffman Louis H., and Sandin Daniel J, eds. Hypercomplex iterations: Distance estimation and higher dimensional fractals. River Edge, NJ: World Scientific, 2002.

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Schiffer, Robert G. Long-Distance and Rural Travel Transferable Parameters for Statewide Travel Forecasting Models. WASHINGTON, D.C: TRANSPORTATION RESEARCH BOARD, 2012.

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Koenker, Roger. Momentary lapses: Moment expansions and the robustness of minimum distance estimation. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1991.

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1931-, Saridis George N., and United States. National Aeronautics and Space Administration., eds. Distance estimation and collision prediction for on-line robotic motion planning. Troy, N.Y: Center for Intelligent Robotic Systems for Space Exploration, Rensselaer Polytechnic Institute, 1991.

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K, Niall Keith, Armstrong Laboratory (U.S.), and Armstrong Laboratory (U.S.). Aircrew Training Research Division., eds. Distance estimation with night vision goggles: A direct feedback training method. Brooks Air Force Base, TX: Air Force Materiel Command, Armstrong Laboratory, Human Resources Directorate, 1997.

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Lehtonen, Matti. Transient analysis for ground fault distance estimation in electrical distribution networks. Espoo [Finland]: Technical Research Centre of Finland, 1992.

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Hiroyuki, Shioya, and Park Chanseok, eds. Statistical inference: The minimum distance approach. Boca Raton: Taylor & Francis, 2011.

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Vassilopoulos, V. An image analysis system for identification and distance estimation of road vehicles. Manchester: UMIST, 1995.

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Ehrlich, Jennifer A. Effect of viewing conditions on sickness and distance estimation in a virtual environment. Alexandria, Va: U.S. Army Research Institute for the Behavioral and Social Sciences, 2000.

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Wesselman, A. M. The population-sample decomposition method: A distribution-free estimation technique for minimum distance parameters. Dordrecht: M. Nijhoff, 1987.

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Book chapters on the topic "Estimation de la distance"

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Nissen, Ivor. "Distance Estimation." In SpringerBriefs in Electrical and Computer Engineering, 41–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61658-1_3.

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Kutoyants, Yu. "Minimum Distance Estimation." In Identification of Dynamical Systems with Small Noise, 217–83. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1020-4_8.

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Canter, David, and Stephen K. Tagg. "Distance estimation in cities." In Readings on the Psychology of Place, 56–69. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003313052-7.

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Devroye, Luc, and Gábor Lugosi. "The Minimum Distance Estimate: Examples." In Combinatorial Methods in Density Estimation, 70–78. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0125-7_8.

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Rezaei, Mahdi, and Reinhard Klette. "Vehicle Detection and Distance Estimation." In Computer Vision for Driver Assistance, 147–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50551-0_7.

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Marsh, William E., Jean-Rémy Chardonnet, and Frédéric Merienne. "Virtual Distance Estimation in a CAVE." In Spatial Cognition IX, 354–69. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11215-2_25.

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Buckland, S. T., E. A. Rexstad, T. A. Marques, and C. S. Oedekoven. "Design-Based Estimation of Animal Density and Abundance." In Distance Sampling: Methods and Applications, 105–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19219-2_6.

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Huang, Ge, Flávia C. Delicato, Paulo F. Pires, and Albert Y. Zomaya. "Probabilistic Distance Estimation in Wireless Sensor Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 353–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29154-8_39.

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Benlamine, Kaoutar, Younès Bennani, Ahmed Zaiou, Mohamed Hibti, Basarab Matei, and Nistor Grozavu. "Distance Estimation for Quantum Prototypes Based Clustering." In Neural Information Processing, 561–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36718-3_47.

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Wilson, R. E., H. Raichur, and B. Paul. "Distance Estimation for Eclipsing X-ray Pulsars." In Astrophysics and Space Science Library, 179–89. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6544-6_10.

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Conference papers on the topic "Estimation de la distance"

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Pelka, Mathias, Martin Mackenberg, Christian Funda, and Horst Hellbruck. "Optical underwater distance estimation." In OCEANS 2017 - Aberdeen. IEEE, 2017. http://dx.doi.org/10.1109/oceanse.2017.8084898.

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Patil, Nishad, Sandeep Menon, Diganta Das, and Michael Pecht. "Evaluation of Robust Covariance Estimation Techniques for Anomaly Detection of Insulated Gate Bipolar Transistors (IGBT)." In ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2010. http://dx.doi.org/10.1115/smasis2010-3861.

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An approach to detect anomalies in IGBTs is to monitor the collector-emitter current and voltage in application. These current and voltage parameters can then be reduced to a univariate distance measure called the Mahalanobis Distance (MD). The MD values with the use of an appropriate threshold enable anomaly detection of these devices. Mahalanobis distances (MD) are weighted Euclidean distances; the distance of each point from the center of the distribution is weighted by the inverse of the sample variance-covariance matrix. The presence of outliers in the monitored data can lead to the overestimation of the covariance matrix that in turn affects the anomaly detection results. This issue can be addressed by the use of robust covariance estimation techniques. In this study, the minimum volume ellipsoid (MVE) estimator, the minimum covariance determinant estimator (MCD) and the nearest neighbor variance estimator (NNVE) were used for anomaly detection of IGBTs. IGBTs were aged under a resistive load until failure. The monitored collector-emitter current and voltage values were used as input parameters for the MD calculation. The three robust covariance estimation techniques were used to compute the MD values and the anomaly detection times were compared to the results obtained by the classical covariance estimation technique.
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Anderson, Paul W., and Pavel Zahorik. "Auditory and visual distance estimation." In 161st Meeting Acoustical Society of America. Acoustical Society of America, 2011. http://dx.doi.org/10.1121/1.3656353.

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Fabrizio, Jonathan, and Severine Dubuisson. "Motion Estimation using Tangent Distance." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4378998.

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Morawiec, Garret, Keith K. Niall, and Kathleen Scullion. "Distance Estimation and Simulation Training." In Tenth International Conference on Computer Modeling and Simulation (uksim 2008). IEEE, 2008. http://dx.doi.org/10.1109/uksim.2008.66.

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Zhang, Yi, Madeline J. Goh, and Vidya Nariyambut Murali. "Vision Based Object Distance Estimation." In WCX™ 17: SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2017. http://dx.doi.org/10.4271/2017-01-0109.

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Wuxiang, Cao, Xia Yong, and Yu Shidong. "Estimation of maximum interference distance." In SEG Technical Program Expanded Abstracts 2007. Society of Exploration Geophysicists, 2007. http://dx.doi.org/10.1190/1.2793037.

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Amagata, Daichi, Yusuke Arai, Sumio Fujita, and Takahiro Hara. "Learned k-NN distance estimation." In SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3557915.3560935.

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Qin, Lianke, Aravind Reddy, and Zhao Song. "Online Adaptive Mahalanobis Distance Estimation." In 2023 IEEE International Conference on Big Data (BigData). IEEE, 2023. http://dx.doi.org/10.1109/bigdata59044.2023.10386869.

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Strauss, Mark, and James V. Carnahan. "Observed Errors in Distance Estimation." In SAE 2010 World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2010. http://dx.doi.org/10.4271/2010-01-0046.

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Reports on the topic "Estimation de la distance"

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Fluitt, Kim F., Timothy Mermagen, and Tomasz Letowski. Auditory Perception in Open Field: Distance Estimation. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada588823.

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Eslinger, Paul W., and Wayne A. Woodward. Minimum Hellinger Distance Estimation for Normal Models. Fort Belvoir, VA: Defense Technical Information Center, October 1990. http://dx.doi.org/10.21236/ada228714.

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Reising, Jack D., and Elizabeth L. Martin. Distance Estimation Training with Night Vision Goggles Under Low Illumination. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada291338.

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Traina, Caetano, Traina Jr., Faloutsos Agma J., and Christos. Distance Exponent: A New Concept for Selectivity Estimation in Metric Trees. Fort Belvoir, VA: Defense Technical Information Center, March 1999. http://dx.doi.org/10.21236/ada363780.

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Niall, Keith K., Jack D. Reising, Elizabeth L. Martin, and Marcus H. Gregory. Distance Estimation with Night Vision Goggles: A Direct Feedback Training Method. Fort Belvoir, VA: Defense Technical Information Center, June 1997. http://dx.doi.org/10.21236/ada328758.

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Ehrlich, Jennifer A. Effect of Viewing Conditions on Sickness and Distance Estimation in a Virtual Environment. Fort Belvoir, VA: Defense Technical Information Center, February 2000. http://dx.doi.org/10.21236/ada393953.

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Jerome, Christian J., and Bob G. Witmer. The Perception and Estimation of Egocentric Distance in Real and Augmented Reality Environments. Fort Belvoir, VA: Defense Technical Information Center, May 2008. http://dx.doi.org/10.21236/ada493544.

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Ozeki, Toru, and Donald E. Irish. Estimation of the Concentration-Distance Profile within the Electrochemical Diffusion Layer by Raman Microprobe Spectroscopy. Fort Belvoir, VA: Defense Technical Information Center, March 1990. http://dx.doi.org/10.21236/ada219521.

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Stastny, Petr, Robert Roczniok, Daniel Cleather, Martin Musalek, Dominik Novak, and Michal Vagner. Straight speed and acceleration optimal distances and reference values. A systematic review, and meta-analyses. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0010.

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Review question / Objective: To summarize the sprint reference acceleration and speed values for different sprint distances and suggest optimal unification of ice-hockey straight sprint testing. Eligibility criteria: The title and abstract screening was done by two researchers (PS and RR) who selected a set of articles for full text screening, where the inclusion criteria were: 1) male or female ice-hockey players; 2) any cross-sectional or intervention study; 3) tests of ice-hockey sprinting over any distance or any battery of conditioning tests that included straight-line sprints; and, 4) results reported straight-line sprint distance, speed, time, or acceleration. In the case of disagreement between the evaluating authors, the final decision was made by a third author (MV).The full text screening exclusion criteria were: 1) if the article was not in English; 2) the testing did not include straight-line sprinting; 3) the reported values did not include data distribution; 4) the study reported only maximum speed without skating time or average speed; 5) the end of the sprint was defined by the point the player stopped sprinting; 6) the measurement was made with a stopwatch; and, 7) the study had high bias estimation. The maximum speed test was not included due to the uncertain velocity conditions at beginning of testing distance. The bias estimation was performed using the JBI (Joanna Briggs Institute) Critical Appraisal Checklist for Analytical Cross Sectional Studies (supplementary material 1).
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Herrin, Eugene, and Tom Goforth. Development of Seismic Signal Processing and Yield Estimation Techniques for Use at Regional to Teleseismic Distance. Fort Belvoir, VA: Defense Technical Information Center, December 1986. http://dx.doi.org/10.21236/ada182349.

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