Relevant bibliographies by topics / Localization density

Academic literature on the topic 'Localization density'

Author: Grafiati
Published: 7 July 2024
Last updated: 7 July 2024

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Contents

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

Journal articles on the topic "Localization density":

1

Gadre, Shridhar R., Sudhir A. Kulkarni, and Rajeev K. Pathak. "Density‐based electron localization function via nonlocal density approximation." Journal of Chemical Physics 98, no. 4 (February 15, 1993): 3574–76. http://dx.doi.org/10.1063/1.464082.

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Movaghar, B. "Localization and the density of states." Philosophical Magazine B 65, no. 5 (May 1992): 1097–108. http://dx.doi.org/10.1080/13642819208217923.

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Balan, Radu, Peter G. Casazza, Christopher Heil, and Zeph Landau. "Density, overcompleteness, and localization of frames." Electronic Research Announcements of the American Mathematical Society 12, no. 10 (July 7, 2006): 71–86. http://dx.doi.org/10.1090/s1079-6762-06-00163-6.

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Hutník, Ondrej, Egor A. Maximenko, and Anna Mišková. "Toeplitz Localization Operators: Spectral Functions Density." Complex Analysis and Operator Theory 10, no. 8 (May 20, 2016): 1757–74. http://dx.doi.org/10.1007/s11785-016-0564-1.

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Pilmé, Julien. "Electron localization function from density components." Journal of Computational Chemistry 38, no. 4 (November 17, 2016): 204–10. http://dx.doi.org/10.1002/jcc.24672.

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Schroer, Bert. "Area density of localization entropy: I. The case of wedge localization." Classical and Quantum Gravity 23, no. 17 (August 7, 2006): 5227–48. http://dx.doi.org/10.1088/0264-9381/23/17/008.

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Bouhdid, Badia, Wafa Akkari, and Sofien Gannouni. "Low Cost Recursive Localization scheme for High Density Wireless Sensor Networks." International Journal on Semantic Web and Information Systems 13, no. 3 (July 2017): 68–88. http://dx.doi.org/10.4018/ijswis.2017070104.

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While existing localization approaches mainly focus on enhancing the accuracy, particular attention has recently been given to reducing the localization algorithm implementation costs. To obtain a tradeoff between location accuracy and implementation cost, recursive localization approaches are being pursued as a cost-effective alternative to the more expensive localization approaches. In the recursive approach, localization information increases progressively as new nodes compute their positions and become themselves reference nodes. A strategy is then required to control and maintain the distribution of these new reference nodes. The lack of such a strategy leads, especially in high density networks, to wasted energy, important communication overhead and even impacts the localization accuracy. In this paper, the authors propose an efficient recursive localization approach that reduces the energy consumption, the execution time, and the communication overhead, yet it increases the localization accuracy through an adequate distribution of reference nodes within the network.
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Suslov, Igor' M. "Density of states near the localization threshold." Uspekhi Fizicheskih Nauk 166, no. 8 (1996): 907. http://dx.doi.org/10.3367/ufnr.0166.199608x.0907.

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Marsh, Richard J., Karin Pfisterer, Pauline Bennett, Liisa M. Hirvonen, Mathias Gautel, Gareth E. Jones, and Susan Cox. "Artifact-free high-density localization microscopy analysis." Nature Methods 15, no. 9 (July 30, 2018): 689–92. http://dx.doi.org/10.1038/s41592-018-0072-5.

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Suslov, Igor' M. "Density of states near the localization threshold." Physics-Uspekhi 39, no. 8 (August 31, 1996): 848–49. http://dx.doi.org/10.1070/pu1996v039n08abeh001549.

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Journal articles Dissertations / Theses

Dissertations / Theses on the topic "Localization density":

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Lee, Chee Sing. "Simultaneous localization and mapping using single cluster probability hypothesis density filters." Doctoral thesis, Universitat de Girona, 2015. http://hdl.handle.net/10803/323637.

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The majority of research in feature-based SLAM builds on the legacy of foundational work using the EKF, a single-object estimation technique. Because feature-based SLAM is an inherently multi-object problem, this has led to a number of suboptimalities in popular solutions. We develop an algorithm using the SC-PHD filter, a multi-object estimator modeled on cluster processes. This algorithm hosts capabilities not typically seen with feature-base SLAM solutions such as principled handling of clutter measurements and missed detections, and navigation with a mixture of stationary and moving landmarks. We present experiments with the SC-PHD SLAM algorithm on both synthetic and real datasets using an autonomous underwater vehicle. We compare our method to the RB-PHD SLAM, showing that it requires fewer approximations in its derivation and thus achieves superior performance.
En aquesta tesis es desenvolupa aquest algoritme a partir d’un filtre PHD amb un únic grup (SC-PHD), una tècnica d’estimació multi-objecte basat en processos d’agrupació. Aquest algoritme té unes capacitats que normalment no es veuen en els algoritmes de SLAM basats en característiques, ja que és capaç de tractar falses característiques, així com característiques no detectades pels sensors del vehicle, a més de navegar en un entorn amb la presència de característiques estàtiques i característiques en moviment de forma simultània. Es presenten els resultats experimentals de l’algoritme SC-PHD en entorns reals i simulats utilitzant un vehicle autònom submarí. Els resultats són comparats amb l’algoritme de SLAM Rao-Blackwellized PHD (RB-PHD), demostrant que es requereixen menys aproximacions en la seva derivació i en conseqüència s’obté un rendiment superior.
2

Heinicke, Christiane. "Lithospheric-Scale Stresses and Shear Localization Induced by Density-Driven Instabilities." Thesis, Uppsala universitet, Geofysik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183725.

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The initiation of subduction requires the formation of lithospheric plates which mostly deform at their edges. Shear heating is a possible candidate for producing such localized deformation. In this thesis we employ a 2D model of the mantle with a visco-elasto-plastic rheology and enabled shear heating. We are able to create a shear heating instability both in a constant strain rate and a constant stress boundary condition setup. For the rst case, localized deformation in our specic setup is found for strain rates of 10-15 1/s and mantle temperatures of 1300°C. For constant stress boundaries, the conditions for a setup to localize are more restrictive. Mantle motion is induced by large cold and hot temperature perturbations. Lithospheric stresses scale with the size of these perturbations; maximum stresses are on the order of the yield stress (1 GPa). Adding topography or large inhomogeneities does not result in lithospheric-scale fracture in our model. However, localized deformation does occur for a restricted parameter choice presented in this thesis. The perturbation size has little effect on the occurrence of localization, but large perturbations shorten its onset time.
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Torab, Leili. "The forward problem of EEG source localization using Current Density Imaging." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0020/MQ53445.pdf.

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López, Villafuerte Freddy [Verfasser]. "Localization of wireless sensor nodes based on local network density / Freddy López Villafuerte." Berlin : Freie Universität Berlin, 2010. http://d-nb.info/1024104060/34.

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De, Santis Lorenzo. "Theory of electron Localization Function and its Applications: Surfaces, Impurities and Enzymatic Catalysis." Doctoral thesis, SISSA, 1999. http://hdl.handle.net/20.500.11767/4428.

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Mazzarello, Riccardo. "Localization and density of states of disordered low-dimensional systems in a magnetic field." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=971652023.

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Dihidar, Souvik. "Applications of Low Density Parity Check Codes for Wiretap Channels and Congestion Localization in Networks." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/13969.

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Error control coding in some form is present in virtually every communication system today. Recently, Low Density Parity Check (LDPC) codes have been proposed along with a simple iterative decoding algorithm. These codes have been demonstrated to perform very close to the Shannon Limit. The simplicity of LDPC codes have also led to many interesting asymptotic and finite-length properties of these codes. The techniques for designing good LDPC codes over a wide variety of channels have been studied. LDPC codes are being used in a wide variety of applications, such as fading channels, Orthogonal Frequency Division Multiplexing (OFDM) systems, source compression etc. This proposal investigates the use of LDPC codes in wiretap channel systems such as quantum key distribution and for congestion localization in large networks. Quantum Key Distribution (QKD) is secure key exchange method where the two legitimate parties first transmit information over a quantum channel, which can be eavesdropped on by the eavesdropper. The QKD system can be modeled as a special case of an wiretap channel system. An wiretap chanel system is a broadcast system, where the sender has to send a message to a legitimate party over a main channel. The wiretapper also receives the message through another channel called the wiretap channel. The sender has to code the transmitted message in such a way so that the legitimate party is able to recover the message without errors, whereas the wiretapper essentially has no information about the message. As we will see, the encoder for such a system is stochastic as opposed to a deterministic encoder in error correction coding. In this research, we propose a coding scheme using LDPC codes for such systems. Congestion in a network occurs when some nodes receive more traffic than they can process. It leads to dropping packets and thus lowering the throughput. On the contrary, if other nodes in the network are aware of the congested nodes, new packets can be dynamically routed through less congested routes. We developed a congestion detection mechanism wherein a few high priority probe packets are routed through the network. A central entity collects the contents of all the probe packets and estimates the state (congested or not) of every node in the network. One important parameter of congeston localization schemes is scalability, i.e. how the number of measurements scales with the size of network as the size of the network grows. We have shown that it is possible to do congestion detection using our scheme for a properly designed network with the number of measurements required growing linearly with the size of the network.
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Elesev, Aleksandr. "Robot Localization Using Inertial and RF Sensors." Miami University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=miami1218571607.

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Maffei, Renan de Queiroz. "Translating sensor measurements into texts for localization and mapping with mobile robots." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/158403.

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Localização e Mapeamento Simultâneos (SLAM), fundamental para robôs dotados de verdadeira autonomia, é um dos problemas mais difíceis na Robótica e consiste em estimar a posição de um robô que está se movendo em um ambiente desconhecido, enquanto incrementalmente constrói-se o mapa de tal ambiente. Provavelmente o requisito mais importante para localização e mapeamento adequados seja um preciso reconhecimento de local, isto é, determinar se um robô estava no mesmo lugar em diferentes ocasiões apenas analizando as observações feitas pelo robô em cada ocasião. A maioria das abordagens da literatura são boas quando se utilizam sensores altamente expressivos, como câmeras, ou quando o robô está situado em ambientes com pouco ambiguidade. No entanto, este não é o caso, por exemplo, quando o robô equipado apenas com sensores de alcance está em ambientes internos estruturados altamente ambíguos. Uma boa estratégia deve ser capaz de lidar com tais ambientes, lidar com ruídos e erros nas observações e, especialmente, ser capaz de modelar o ambiente e estimar o estado do robô de forma eficiente. Nossa proposta consiste em traduzir sequências de medições de laser em uma representação de texto eficiente e compacta, para então lidar com o problema de reconhecimento de local usando técnicas de processamento lingüísticos. Nós traduzimos as medições dos sensores em valores simples computados através de um novo modelo de observação baseado em estimativas de densidade de kernel chamado de Densidade de Espaço Livre (FSD). Estes valores são quantificados permitindo a divisão do ambiente em regiões contíguas de densidade homogênea, como corredores e cantos. Regiões são representadas de forma compacta por simples palavras descrevendo o valor de densidade espacial, o tamanho e a variação da orientação daquela região. No final, as cadeias de palavras compõem um texto, no qual se buscam casamentos de n-gramas (isto é, sequências de palavras). Nossa técnica também é aplicada com sucesso em alguns cenários de operação de longo-prazo, onde devemos lidar com objetos semi-estáticos (i.e. que se movem ocasionalmente, como portas e mobílias). Todas as abordagens foram avaliadas em cenários simulados e reais obtendo-se bons resultados.
Simultaneous Localization and Mapping (SLAM), fundamental for building robots with true autonomy, is one of the most difficult problems in Robotics and consists of estimating the position of a robot that is moving in an unknown environment while incrementally building the map of such environment. Arguably the most crucial requirement to obtain proper localization and mapping is precise place recognition, that is, determining if the robot is at the same place in different occasions just by looking at the observations taken by the robot. Most approaches in literature are good when using highly expressive sensors such as cameras or when the robot is situated in low ambiguous environments. However this is not the case, for instance, using robots equipped only with range-finder sensors in highly ambiguous indoor structured environments. A good SLAM strategy must be able to handle these scenarios, deal with noise and observation errors, and, especially, model the environment and estimate the robot state in an efficient way. Our proposal in this work is to translate sequences of raw laser measurements into an efficient and compact text representation and deal with the place recognition problem using linguistic processing techniques. First, we translate raw sensor measurements into simple observation values computed through a novel observation model based on kernel-density estimation called Free-Space Density (FSD). These values are quantized into significant classes allowing the division of the environment into contiguous regions of homogeneous spatial density, such as corridors and corners. Regions are represented in a compact form by simple words composed of three syllables – the value of spatial density, the size and the variation of orientation of that region. At the end, the chains of words associated to all observations made by the robot compose a text, in which we search for matches of n-grams (i.e. sequences of words), which is a popular technique from shallow linguistic processing. The technique is also successfully applied in some scenarios of long-term operation, where we must deal with semi-static objects (i.e. that can move occasionally, such as doors and furniture). All approaches were evaluated in simulated and real scenarios obtaining good results.
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MERICO, DAVIDE. "Tracking with high-density, large-scale wireless sensor networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/7785.

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Given the continuous technological advances in computing and communication, it seems that we are rapidly heading towards the realization of paradigms commonly described as ubiquitous computing, pervasive computing, ambient intelligence, or, more recently, "everyware". These paradigms envision living environments pervaded by a high number of invisible technological devices affecting and improving all aspects of our lives. Therefore, it is easy to justify the need of knowing the physical location of users. Outdoor location-aware applications are already widespread today, their growing popularity showing that location-awareness is indeed a very useful functionality. Less obvious is how the growing availability of these locations and tracks will be exploited for providing more intelligent "situation-understanding" services that help people. My work is motivated by the fact that, thanks to location-awareness systems, we are more and more aware of the exact positions of the users but unfortunately we are rarely capable of exactly understanding what they are doing. Location awareness should rapidly evolve and become "situation-awareness" otherwise the ubiquitous-computing vision will become impracticable. The goal of this thesis is devising alternative and innovative approaches to the problem of indoor position estimation/assessment and evaluating them in real environments. These approaches are be based on: (i) a low-cost and energy-aware localization infrastructure; (ii) multi-sensor, statistically-based, localization algorithms; (iii) logic-based situation assessment techniques. The algorithms and techniques that are the outcome of this thesis have all been tested by implementing them and measuring (both in a quantitative sense and in a qualitative sense) the performance in the field.
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Books on the topic "Localization density":

1

Torab, Leili. The forward problem of EEG source localization using current density imaging. Ottawa: National Library of Canada, 2000.

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Schomer, Andrew, Margitta Seeck, Andres M. Kanner, and Donald L. Schomer. Anterotemporal, Basal Temporal, Nasopharyngeal, and Sphenoidal Electrodes and High-Density Arrays. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0006.

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Temporal lobe epilepsy is the most frequent type of epilepsy of focal origin in adults. Electroencephalographic evaluation for surgical treatment requires accurate localization of epileptic foci. The yield of detection with scalp electrodes depends on three variables: source and extent of the epileptogenic area relative to the scalp electrodes’ position; electric field generated by the epileptiform activity and the electric vectors’ orientation; and extent of propagation of the epileptiform activity from mesial to temporal lateral regions. Recordings of epileptiform activity of presumed mesial-temporal origin should include additional electrodes such as anterior temporal or basal temporal electrodes or a subtemporal chain. Nasopharyngeal electrodes appear to yield no advantage over anterior temporal or basal temporal electrodes or a subtemporal chain and are associated with discomfort. Sphenoidal electrodes should be considered in special circumstances; reliability is improved if placed under fluoroscopy. High-density scalp recordings allow for even greater resolution and improved spatial sampling.
3

Michel, Christoph M., and Bin He. EEG Mapping and Source Imaging. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0045.

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This chapter describes methods to analyze the scalp electric field recorded with multichannel electroencephalography (EEG). With advances in high-density EEG, systems now allow fast and easy recording from 64 to 256 channels simultaneously. Pattern-recognition algorithms can characterize the topography of scalp electric fields and detect changes in topography over time and between experimental or clinical conditions. Methods for estimating the sources underlying the recorded scalp potential maps have increased the spatial resolution of EEG. The use of anatomical information in EEG source reconstruction has increased the precision of EEG source localization. Algorithms of functional connectivity applied to the source space allow determination of communication between large-scale brain networks in certain frequencies and identification of the directionality of this information flow and detection of crucial drivers in these networks. These methods have boosted the use of EEG as a functional neuroimaging method in experimental and clinical applications.
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Hermans, Hubert J. M. The Dynamics of Society-in-the-Self. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190687793.003.0002.

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In the field of tension between globalization and localization, a set of new phenomena is emerging showing that society is not simply a social environment of self and identity but works in their deepest regions: self-radicalization, self-government, self-cure, self-nationalization, self-internationalization, and even self-marriage. The consequence is that the self is faced with an unprecedented density of self-parts, called I-positions in this theory. In the field of tension between boundary-crossing developments in the world and the search for an identity in a local niche, a self emerges that is characterized by a great variety of contradicting and heterogeneous I-positions and by large and unexpected jumps between different positions as the result of rapid and unexpected changes in the world. The chapter argues that such developments require a new vision of the relationship between self and society.

Book chapters on the topic "Localization density":

1

March, N. H. "Localization via Density Functionals." In Topics in Current Chemistry, 201–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48972-x_11.

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Wegner, Franz. "Density Correlations Near the Mobility Edge." In Localization and Metal-Insulator Transitions, 337–46. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2517-8_27.

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Contreras-García, Julia, Miriam Marqués, Bernard Silvi, and José M. Recio. "Bonding Changes Along Solid-Solid Phase Transitions Using the Electron Localization Function Approach." In Modern Charge-Density Analysis, 625–58. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-3836-4_18.

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Fang, Sheng En, Ricardo Perera, and Maria Consuelo Huerta. "Damage Localization Based on Power Spectral Density Analysis." In Damage Assessment of Structures VII, 589–94. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-444-8.589.

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Borghesani, A. F., and M. Santini. "Excess Electron Localization in High-Density Neon Gas." In Linking the Gaseous and Condensed Phases of Matter, 281–301. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2540-0_18.

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Luo, Ye, Junsong Yuan, Ping Xue, and Qi Tian. "Saliency Density Maximization for Object Detection and Localization." In Computer Vision – ACCV 2010, 396–408. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19318-7_31.

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Dunstan, Rhys A., Iain D. Hay, and Trevor Lithgow. "Defining Membrane Protein Localization by Isopycnic Density Gradients." In Methods in Molecular Biology, 81–86. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7033-9_6.

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Dunstan, Rhys A., Iain D. Hay, and Trevor Lithgow. "Defining Membrane Protein Localization by Isopycnic Density Gradients." In Methods in Molecular Biology, 91–98. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3445-5_6.

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Chen, J., T. C. Chung, F. Moraes, and A. J. Heeger. "First-Order Phase Transition to the Metallic State in Doped Polyacetylene: Solitons at High Density." In Localization and Metal-Insulator Transitions, 367–78. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2517-8_30.

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Dattola, Serena, Fabio La Foresta, Lilla Bonanno, Simona De Salvo, Nadia Mammone, Silvia Marino, and Francesco Carlo Morabito. "Effect of Sensor Density on eLORETA Source Localization Accuracy." In Neural Approaches to Dynamics of Signal Exchanges, 403–14. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8950-4_36.

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Conference papers on the topic "Localization density":

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Kusy, Branislav, Akos Ledeczi, Miklos Maroti, and Lambert Meertens. "Node density independent localization." In the fifth international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1127777.1127844.

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Kusy, B., A. Ledeczi, M. Maroti, and L. Meertens. "Node-density independent localization." In The Fifth International Conference on Information Processing in Sensor Networks. IEEE, 2006. http://dx.doi.org/10.1109/ipsn.2006.243912.

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Karoliny, Julian, Bernhard Etzlinger, and Andreas Springer. "Mixture Density Networks for WSN Localization." In 2020 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2020. http://dx.doi.org/10.1109/iccworkshops49005.2020.9145035.

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Zaarour, Nour, Nadir Hakem, and NahiKandil. "Anchor Density Minimization for Localization in Wireless Sensor Network (WSN)." In 7th International Conference on Computer Science and Information Technology (CSTY 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112201.

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In wireless sensor networks (WSN) high-accuracy localization is crucial for both of WNS management and many other numerous location-based applications. Only a subset of nodes in a WSN is deployed as anchor nodes with their locations a priori known to localize unknown sensor nodes. The accuracy of the estimated position depends on the number of anchor nodes. Obviously, increasing the number or ratio of anchors will undoubtedly increase the localization accuracy. However, it severely constrains the flexibility of WSN deployment while impacting costs and energy. This paper aims to drastically reduce anchor number or ratio of anchor in WSN deployment and ensures a good trade-off for localization accuracy. Hence, this work presents an approach to decrease the number of anchor nodes without compromising localization accuracy. Assuming a random string WSN topology, the results in terms of anchor rates and localization accuracy are presented and show significant reduction in anchor deployment rates from 32% to 2%.
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Lu, Ya, Ji Zhao, and Jiayi Ma. "Object localization by density-based spatial clustering." In 2016 Visual Communications and Image Processing (VCIP). IEEE, 2016. http://dx.doi.org/10.1109/vcip.2016.7805515.

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Bahi, Jacques M., Abdallah Makhoul, and Ahmed Mostefaoui. "Localization and Coverage for High Density Sensor Networks." In Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07). IEEE, 2007. http://dx.doi.org/10.1109/percomw.2007.61.

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Maffei, Renan, Vitor A. M. Jorge, Vitor F. Rey, Mariana Kolberg, and Edson Prestes. "Fast Monte Carlo Localization using spatial density information." In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015. http://dx.doi.org/10.1109/icra.2015.7140091.

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Ribacki, Arthur, Vitor A. M. Jorge, Mathias Mantelli, Renan Maffei, and Edson Prestes. "Vision-Based Global Localization Using Ceiling Space Density." In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. http://dx.doi.org/10.1109/icra.2018.8460515.

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Dogan, Refika Sultan, and Bulent Yilmaz. "Polyp Localization in Colonoscopy Images Using Vessel Density." In 2018 Medical Technologies National Congress (TIPTEKNO). IEEE, 2018. http://dx.doi.org/10.1109/tiptekno.2018.8597166.

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Kemper, Jurgen, and Daniel Hauschildt. "Passive infrared localization with a Probability Hypothesis Density filter." In 2010 7th Workshop on Positioning, Navigation and Communication (WPNC). IEEE, 2010. http://dx.doi.org/10.1109/wpnc.2010.5653529.

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Reports on the topic "Localization density":

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Gillespie, Douglas. 6th International workshop on the Detection, Classification, Localization and Density Estimation of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada602542.

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Mellinger, David K. Fifth International Workshop on Detection, Classification, Localization and Density Estimation of Marine Mammals using Passive Acoustics. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada573558.

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Mellinger, David K. Fifth International Workshop on Detection, Classification, Localization and Density Estimation of Marine Mammals using Passive Acoustics. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada598544.

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Christie, Benjamin, Osama Ennasr, and Garry Glaspell. ROS integrated object detection for SLAM in unknown, low-visibility environments. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42385.

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Abstract:
Integrating thermal (or infrared) imagery on a robotics platform allows Unmanned Ground Vehicles (UGV) to function in low-visibility environments, such as pure darkness or low-density smoke. To maximize the effectiveness of this approach we discuss the modifications required to integrate our low-visibility object detection model on a Robot Operating System (ROS). Furthermore, we introduce a method for reporting detected objects while performing Simultaneous Localization and Mapping (SLAM) by generating bounding boxes and their respective transforms in visually challenging environments.
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Rahmani, Mehran, Xintong Ji, and Sovann Reach Kiet. Damage Detection and Damage Localization in Bridges with Low-Density Instrumentations Using the Wave-Method: Application to a Shake-Table Tested Bridge. Mineta Transportation Institute, September 2022. http://dx.doi.org/10.31979/mti.2022.2033.

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Abstract:
This study presents a major development to the wave method, a methodology used for structural identification and monitoring. The research team tested the method for use in structural damage detection and damage localization in bridges, the latter being a challenging task. The main goal was to assess capability of the improved method by applying it to a shake-table-tested prototype bridge with sparse instrumentation. The bridge was a 4-span reinforced concrete structure comprising two columns at each bent (6 columns total) and a flat slab. It was tested to failure using seven biaxial excitations at its base. Availability of a robust and verified method, which can work with sparse recording stations, can be valuable for detecting damage in bridges soon after an earthquake. The proposed method in this study includes estimating the shear (cS) and the longitudinal (cL) wave velocities by fitting an equivalent uniform Timoshenko beam model in impulse response functions of the recorded acceleration response. The identification algorithm is enhanced by adding the model’s damping ratio to the unknown parameters, as well as performing the identification for a range of initial values to avoid early convergence to a local minimum. Finally, the research team detect damage in the bridge columns by monitoring trends in the identified shear wave velocities from one damaging event to another. A comprehensive comparison between the reductions in shear wave velocities and the actual observed damages in the bridge columns is presented. The results revealed that the reduction of cS is generally consistent with the observed distribution and severity of damage during each biaxial motion. At bents 1 and 3, cS is consistently reduced with the progression of damage. The trends correctly detected the onset of damage at bent 1 during biaxial 3, and damage in bent 3 during biaxial 4. The most significant reduction was caused by the last two biaxial motions in bents 1 and 3, also consistent with the surveyed damage. In bent 2 (middle bent), the reduction trend in cS was relatively minor, correctly showing minor damage at this bent. Based on these findings, the team concluded that the enhanced wave method presented in this study was capable of detecting damage in the bridge and identifying the location of the most severe damage. The proposed methodology is a fast and inexpensive tool for real-time or near real-time damage detection and localization in similar bridges, especially those with sparsely deployed accelerometers.

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