Bibliografías temáticas / Node Localization

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Literatura académica sobre el tema "Node Localization"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 1 de febrero de 2022

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Artículos de revistas sobre el tema "Node Localization"

Li, Xiao Qin y Guang Rong Chen. "A Sensor Node Localization Algorithm Based on Fuzzy RSSI Distance". Applied Mechanics and Materials 543-547 (marzo de 2014): 989–92. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.989.

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The node self-localization is the basis of target localization for wireless sensor network (WSN), the WSN nodes localization algorithms have two types based on distance and non distance. The node localization based on RSSI is simple and widely used in application. According to the traditional WSN nodes localization algorithm, the RSSI signal intensity changes greatly and with nonlinearity. And it is converted into distance feature with a large deviation, which leads to inaccurate positioning and localization. In order to solve this problem, a sensor node localization algorithm is proposed based on fuzzy RSSI distance. The nodes information is collected based on RSSI ranging method. And the location information is processed with fuzzy operation. The disturbance from the environmental factors for the positioning is solved. The accuracy of the node localization is improved. Simulation result shows that this algorithm can locate the sensor nodes accurately. The localization accuracy is high, and the performance of nodes localization is better than the traditional algorithm. It has good application value in the WSN nodes distribution and localization design.

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Khajuria, Vinayak y Manjot Kaur. "Advancement in range based scheme for node localization of underwater acoustic networks". International Journal of Engineering & Technology 7, n.º 2.27 (6 de agosto de 2018): 122. http://dx.doi.org/10.14419/ijet.v7i2.27.12730.

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The underwater acoustic network is the type of network which is deployed under the deep sea or ocean to gather underwater information. The sensor node position estimation is a major issue of the underwater acoustic network. The process of estimating node position is called node localization. In the existing RSSI based approach for the node localization has a high delay which reduces its efficiency. The technique needs to be designed which localize a number of nodes in less amount of time. This research is based on the advancement of the range-based scheme for node localization. In the proposed scheme mobile beacons are responsible for the node localization. The beacon nodes send beacon message in the network and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node. The sensor nodes which are already localized will not respond back to the beacon messages which reduce delay in the network for node localization. The simulation of proposed modal is performed in MATLAB and it shows that proposed scheme performs well in terms of a number of nodes localized.

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An, Peng. "An Algorithm of Mobile Robot Node Location Based on Wireless Sensor Network". International Journal of Online Engineering (iJOE) 13, n.º 05 (14 de mayo de 2017): 4. http://dx.doi.org/10.3991/ijoe.v13i05.7044.

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In the wireless sensor network, there is a consistent one-to-one match between the information collected by the node and the location of the node. Therefore, it attempts to determine the location of unknown nodes for wireless sensor networks. At present, there are many kinds of node localization methods. Because of the distance error, hardware level, application environment and application costs and other factors, the positioning accuracy of various node positioning methods is not in complete accord. The objective function is established and algorithm simulation experiments are carried out to make a mobile ronot node localization. The experimnettal results showed that the proposed algorithm can achieve higher localization precision in fewer nodes. In addition, the localization algorithm was compared with the classical localization algorithm. In conclusion, it is verified that the localization algorithm proposed in this paper has higher localization accuracy than the traditional classical localization algorithm when the number of nodes is larger than a certain number

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Puneetpal Kaur, Mohit Marwaha y Baljinder Singh. "Design and Implement Beacon based scheme for Node Localization in Underwater Acoustic Network". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 20 (5 de junio de 2020): 53–64. http://dx.doi.org/10.24297/ijct.v20i.8756.

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A network that can sense the surroundings and collected all the information from the sensor nodes and passed it to the base station is known as a wireless sensor network. The underwater acoustic networks are the type of network deployed under the oceans and passed information to the base station. Due to the dynamic nature of the network, nodes change their location at any time. To maximum aggregate information from the sensor nodes, to estimate exact node location is very important. The sensor node position estimation is a major issue of the underwater acoustic network. The process of estimating node position is called node localization. In the existing RSSI based approach for the node, localization has a high delay, which reduces its efficiency. The technique needs to be designed, which localizes more nodes in less amount of time. This research is based on the advancement of the range based scheme for node localization. In the proposed scheme, mobile beacons are responsible for node localization. The beacon nodes send beacon messages in the network, and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node, the sensor nodes which are already localized will not respond back to the beacon messages, which reduce delay in the network for node localization.

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Xia, Xin Jiang, Gang Hu y Qin Wei Wei. "A Research on Circular Localization Algorithm of Wireless Sensor Network". Applied Mechanics and Materials 58-60 (junio de 2011): 1657–63. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1657.

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This paper based on several common wireless sensor node localization algorithms. According to the concentric localization algorithm principle, we proposed an annular localization algorithm and its improved algorithm .The algorithm uses the anchor node to do node ring through certain rules, narrows unknown nodes estimate area continually, and until finally gets the minimum area contains unknown nodes. Then taking the minimum area centroid position as unknown node’s estimate coordinates. Through the simulation of concentric localization algorithm and its improved algorithm, circular localization algorithm and its improved algorithm, can conclude that: When the proportion of anchor node increases from 5% to 10%, the positioning accuracy is obviously improved in the situation of low energy consumption.

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Mittal, Rachit y Manik Lal Das. "Secure Node Localization in Mobile Sensor Networks". International Journal of Wireless Networks and Broadband Technologies 3, n.º 1 (enero de 2014): 18–33. http://dx.doi.org/10.4018/ijwnbt.2014010102.

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Secure node localization in wireless sensor networks (WSN) has become an important research topic. Although, Global Positioning System (GPS) based node localization has got significant attention from researchers, GPS-free node localization trend is evolving in recent times. GPS-free node localization in mobile sensor networks can be constructed in two ways: Beacon based (BB) and Without Beacon based (WBB). The BB approach has been studied extensively under adversarial model and many algorithms based on BB approach have been proposed in literature in order to localize nodes in a secure manner. In contrast, WBB approach for node localization under adversarial model has not received substantial attention from researchers. In this paper, the authors discuss WBB approach for node localization under adversarial model. The authors discuss static and dynamic key settings for node localization using WBB approach. The authors present an improved protocol for node localization in mobile sensor networks, aiming at minimizing the impact of node capture threats. The authors consider the LEAP (Localized Encryption and Authentication Protocol) (Zhu, Setia, & Jajodia, 2003) and the LOCK (Localized Combinatorial Keying) (Eltoweissy, Moharrum, & Mukkamala, 2006) as the building blocks of their proposed scheme. The authors show that the improved protocol provides effective node localization in a secure manner with minimal node capture threats.

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Rashid, Mofeed Turky, Abdulmuttalib Rashid y Ammar Aldair. "Multi-Node Localization and Identity Estimation Based Multi-Beacon Searching Algorithm". Information Technology And Control 49, n.º 4 (19 de diciembre de 2020): 511–29. http://dx.doi.org/10.5755/j01.itc.49.4.24902.

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The accuracy of multi-nodes localization and identity estimation algorithms directly affected the performance of multi-agent systems like WSN, multi-robot, cellular phone and so on. In this paper, a novel algorithm is introduced in order to achieve high accuracy for multi-nodes localization and identity estimation, this algorithm is named multi-beacon searching algorithm. In this algorithm, the concept of the grid is employed for estimating the location and identity of nodes, in which the environment represented by a grid of reference beacons, for each beacon, light-emitting diode (LED) is used. Whereas, each node in the environment is equipped with four LDR sensors which are used to sense the lighting of LEDs according to a proposed searching algorithm. The localization process achieved based on three proposed algorithms: Firstly, a modified binary search algorithm is utilized to estimate the approximate location of the node by a group of neighbor LEDs. Secondly, the accurate localization algorithm is used to find the accurate location of each node by reducing the number of neighbor LEDs. Finaly, two algorithms are introduced to evaluate the location and identification of each node: the centroid algorithm and the minimum bounded circle algorithm. In the minimum bounded circle algorithm, a new faster algorithm called the maxima boundaries convex hull algorithm for polygon convex hull construction is introduced instead of the Chan's algorithm. Several simulation processes have been implemented for testing the proposed algorithms. The obtained results show that the proposed algorithms have very good performance in estimating the accurate localizations of the nodes.

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Santhosh, M. y P. Sudhakar. "Nelder Mead with Grasshopper Optimization Algorithm for Node Localization in Wireless Sensor Networks". Journal of Computational and Theoretical Nanoscience 17, n.º 12 (1 de diciembre de 2020): 5409–21. http://dx.doi.org/10.1166/jctn.2020.9434.

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Node localization in wireless sensor network (WSN) becomes essential to calculate the coordinate points of the unknown nodes with the use of known or anchor nodes. The efficiency of the WSN has significant impact on localization accuracy. Node localization can be considered as an optimization problem and bioinspired algorithms finds useful to solve it. This paper introduces a novel Nelder Mead with Grasshopper Optimization Algorithm (NMGOA) for node localization in WSN. The Nelder-Mead simplex search method is employed to improve the effectiveness of GOA because of its capability of faster convergence. At the beginning, the nodes in WSN are arbitrarily placed in the target area and then nodes are initialized. Afterwards, the node executes the NMGOA technique for estimating the location of the unknown nodes and become localized nodes. In the subsequent round, the localized nodes will be included to the collection of anchor nodes to perform the localization process. The effectiveness of the NMGOA model is validated using a series of experiments and results indicated that the NMGOA model has achieved superior results over the compared methods.

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Zhang, Kai Sheng, Ya Ming Xu, Wu Yang y Qian Zhou. "Improved Localization Algorithm Based on Proportion of Differential RSSI". Applied Mechanics and Materials 192 (julio de 2012): 401–5. http://dx.doi.org/10.4028/www.scientific.net/amm.192.401.

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How to enhance the accuracy of sensor node self-localization for limited energy resource networks is an important problem in the study of wireless sensor networks (WSNs). Concerning the advantages and disadvantages of some main algorithms for senor node self-localization, an easy and simple algorithm is proposed to locate the unknown node itself. The algorithm is to improve weight centroid localization (WCL), by the way of determining weight through using the proportion of differential received signal strength indicator (RSSI) that are derived from unknown node and criterion nodes. In contrast to WCL, the algorithm has the strengths of less computation and better determination of weight, and the determination of weight shows more distinguished distinction in the effect on the localization of unknown node, which is caused by various beacon nodes. Simulations demonstrate that the algorithm has a higher localization accuracy than WCL

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Song, Ling, Xiaoyu Jiang, Liying Wang y Xiaochun Hu. "Monte Carlo Node Localization Based on Improved QUARTE Optimization". Journal of Sensors 2021 (5 de abril de 2021): 1–12. http://dx.doi.org/10.1155/2021/6670061.

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Wireless sensor network (WSN) is a research hot spot of scholars in recent years, in which node localization technology is one of the key technologies in the field of wireless sensor network. At present, there are more researches on static node localization, but relatively few on mobile node localization. The Monte Carlo mobile node localization algorithm utilizes the mobility of nodes to overcome the impact of node velocity on positioning accuracy. However, there are still several problems: first, the demand for anchor nodes is large, which makes the positioning cost too high; second, the sampling efficiency is low, and it is easy to fall into the infinite loop of sampling and filtering; and third, the positioning accuracy and positioning coverage are not high. In order to solve the above three problems, this paper proposes a Monte Carlo node location algorithm based on improved QUasi-Affine TRansformation Evolutionary (QUATRE) optimization. The algorithm firstly selects the high-quality common nodes in the range of one hop of unknown nodes as temporary anchor nodes, and takes the temporary anchor nodes and anchor nodes as the reference nodes for positioning, so as to construct a more accurate sampling area; then, the improved QUATRE optimization algorithm is used to obtain the estimated location of unknown nodes in the sampling area. Simulation experiments show that the Monte Carlo node positioning algorithm based on the improved QUATRE optimization has higher positioning accuracy and positioning coverage, especially when the number of anchor nodes is relatively small.

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Tesis sobre el tema "Node Localization"

Zinkhon, David C. "Undersea node localization using node-to-node acoustic ranges in a distributed Seaweb network". Thesis, Monterey, Calif. : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/March/09Mar%5FZinkhon.pdf.

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Thesis (M.S. in Engineering Acoustics)--Naval Postgraduate School, March 2009.
Thesis Advisor(s): Rice, Joseph A. "March 2009." Description based on title screen as viewed on April 24, 2009. Author(s) subject terms: Underwater acoustics, Seaweb, Localization, Difference linearization. Includes bibliographical references (p. 97-98). Also available in print.

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Pandey, Santosh Agrawal Prathima. "Secure localization and node placement strategies for wireless networks". Auburn, Ala., 2007. http://hdl.handle.net/10415/1388.

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Kaiser, Tashniba. "Node Localization using Fractal Signal Preprocessing and Artificial Neural Network". WorldComp, International Conference on Security and Management, 2012, 2012. http://hdl.handle.net/1993/22730.

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This thesis proposes an integrated artificial neural network based approach to classify the position of a wireless device in an indoor protected area. Our experiments are conducted in two different types of interference affected indoor locations. We found that the environment greatly influences the received signal strength. We realized the need of incorporating a complexity measure of the Wi-Fi signal as additional information in our localization algorithm. The inputs to the integrated artificial neural network were comprised of an integer dimension representation and a fractional dimension representation of the Wi-Fi signal. The integer dimension representation consisted of the raw signal strength, whereas the fractional dimension consisted of a variance fractal dimension of the Wi-Fi signal. The results show that the proposed approach performed 8.7% better classification than the “one dimensional input” ANN approach, achieving an 86% correct classification rate. The conventional Trilateration method achieved only a 47.97% correct classification rate.

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Pettersson, Christopher. "Automatic fault detection and localization in IPnetworks : Active probing from a single node perspective". Thesis, Linköpings universitet, Programvara och system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-120414.

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Fault management is a continuously demanded function in any kind of network management. Commonly it is carried out by a centralized entity on the network which correlates collected information into likely diagnoses of the current system states. We survey the use of active-on-demand-measurement, often called active probes, together with passive readings from the perspective of one single node. The solution is confined to the node and is isolated from the surrounding environment. The utility for this approach, to fault diagnosis, was found to depend on the environment in which the specific node was located within. Conclusively, the less environment knowledge, the more useful this solution presents. Consequently this approach to fault diagnosis offers limited opportunities in the test environment. However, greater prospects was found for this approach while located in a heterogeneous customer environment.

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Lo, Geoffrey S. "Wireless body area network node localization using small-scale spatial information". Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43256.

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Deploying wireless body area networks (WBANs) in the long-term at-home monitoring of a patient’s physiological and bio-kinetic conditions has become increasingly prevalent. However, such WBANs do not typically incorporate mechanisms to detect and correct for the possibility of accidentally switching up wearable wireless sensor nodes (W²SNs), where a node assigned to one limb is placed on another, and vice-versa, leading to possible incorrect prognoses from interpreting the data. In this thesis, we present a new scheme to automatically identify and verify the locations of W²SNs in a WBAN. Using small-scale geospatial information, instantaneous atmospheric air pressures at each node are examined and compared to map and match them in physical space. By enhancing the context-awareness of WBANs, this enhancement enables unassisted sensor node placement, providing a practical solution to obtain and continuously monitor node locations at a sufficient resolution to recognize limb placement, without multidimensional fine-grain position information. Only a single atmospheric air pressure sensor (A²PS) is added to each W²SN; compared to existing localization techniques, no beacons or extra nodes are required, enabling an inexpensive and self-contained solution. To quantify and validate the accuracy, consistency and reliability of this localization scheme, a statistical analysis on a set of commercially-available air pressure sensors and an experimental prototype WBAN is conducted to examine the scheme’s performance and limitations. This study has verified that this approach is indeed capable of distinguishing between positions indicative of expected separation between different limbs of the patient’s body. Based on a 60cm separation between nodes, the statistical analysis consistently exceeded 95% accuracy within the confidence interval (CI), demonstrating great promise for incorporation into commercial WBANs. We also present and experimentally demonstrate an enhancement aiming to reduce false-positive (Type I) errors in conventional accelerometer-based on-body fall detection schemes. Our statistical analysis has shown that by continuously monitoring the patient’s limb positions, the W²SN position information would enable the WBAN to better classify ‘fall-like’ motion from actual falls, where the patient requires remote caregiver assistance.

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Dong, Shaoqiang Agrawal Prathima. "Node placement, routing and localization algorithms for heterogeneous wireless sensor networks". Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Electrical_and_Computer_Engineering/Thesis/Dong_Shaoqiang_40.pdf.

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Smolau, Siarhei. "Evaluation of the Received Signal Strength Indicator for Node Localization in Wireless Sensor Networks". Thesis, Université Laval, 2009. http://www.theses.ulaval.ca/2009/26078/26078.pdf.

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A wireless sensor network (WSN) consists of a large number of sensor nodes that are capable of detecting many types of information from the environment, including temperature, light, humidity, radiation and seismic vibrations. Current applications of WSNs include: physical security, air traffic control, video surveillance, environment and building monitoring. Such applications require that each sensor node knows its exact location. In this context, the received signal strength indicator (RSSI) is often used for distance measurements between the sensor nodes. This thesis presents a method for the evaluation of the RSSI properties in application to node localization in WSN. More specifically, a WSN application is implemented for collecting RSSI measurement in different conditions. The application consists of two parts: an experiment control script which runs on a computer, and an experiment mote firmware which runs on each WSN node. Statistical analysis of variance (ANOVA) was performed to determine the factors affecting the RSSI measurements. Result analysis shows that: the relation between RSSI values and distances depends on the environment; the used WSN motes are manufactured with enough precision, as the differences between the motes are insignificant; even if the RSSI measurements have significant variation, the mean RSSI values correlate with the distances; using different transmission power levels can provide additional information about the distances.

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Vícha, Tomáš. "Lokalizace objektů v prostředí bezdrátové senzorové sítě". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445530.

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This thesis deals with the use of spatial awareness methods within technologies designed for creation of short-range wireless sensor networks. The thesis analyzes several techniques that can be used to estimate position of objects within the sensor network. For a practical solution, a method based on measuring the time differences of the sent messages was chosen. A circuit implementation of a network node based on the DW1000 chip, which works on ultra-wideband transmission technology, was implemented. A sensor network with the appropriate user application for its operation and display of localization results was also implemented.

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Martynek, Tomáš. "Implementace bezkotevní lokalizační techniky do simulačního prostředí ns2". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218279.

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This thesis deals with the issue of localization in wireless sensor networks. It focuses on the implementation Anchor-Free localization techniques to NS2 and evaluation of its effectiveness. In the theoretical introduction is summarized IEEE802.15.4 and ZigBee technology. The following chapter describes the Anchor-Based and Anchor-Free localization methods. Furthermore, existing methods of measuring the distance of network nodes are described. Also pay attention NS2 environment, especially the inclusion of standard IEEE802.15.4. The section devoted to Anchor-Free algorithm describes how to search and select anchor modes. It also described the method of optimization by measuring the distance of neighboring nodes using the Mass-Spring Algorithm. Next chapter describe the inclusion of a new protocol called AFLOCAL into NS2 and detailed analysis of its function. Next described the process of localization and calculating the coordinates. Described was aslo a method of simulation and evaluation of results. In conclusion, the results are summarized and formulated the characteristics of designed protocol AFLOCAL.

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Maňák, Jiří. "Energetická náročnost lokalizačních algoritmů". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219344.

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This masters thesis deals with technics of localization for wireless senzor networks with a focus on anchor free location algorithm by authors G. Jianquan and Z. Wei. It also describes the available simulations frameworks based on OMNeT++ platform and implementation of the anchor free algorithm in the simulation program Castalia. Furthermore, it deals with the energy demands of this algorithm based on results of simulations that were done for the communication unit IRIS.

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Libros sobre el tema "Node Localization"

Agarwal, Anil, Neil Borley y Greg McLatchie. Breast surgery. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199608911.003.0005.

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This chapter provides essential information for common breast operations. Techniques of fine-needle aspiration cytology and core biopsy are described. Operations include incision and drainage of breast abscess, excision of benign breast lump, and wide local excision. Mastectomy, sentinel node biopsy, axillary clearance, and fine wire localization are described. Other operations include microdochectomy and the Hadfield–Adair procedure. Breast reconstruction operations include implant-based reconstruction, latissimus dorsi myocutaneous flap, transvers rectus abdominis myocutaneous pedicled flap (TRAM) and free TRAM, and deep inferior epigastric perforation (DIEP) free flap. Reduction mammoplasty operations described are inferior pedicle mammoplasty and medial pedicle breast reduction (Hall Findlay or Snowman technique).

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Sunderkötter, Cord y Luis Requena. Panniculitides. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0165.

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Panniculitis is an inflammation that originates primarily in the subcutaneous fatty tissue (panniculus adiposus). It is associated with rheumatological diseases and with adverse events to rheumatological therapies (e.g. poststeroid panniculitis, erythema nodosum, infective panniculitis). The panniculitides are classified histopathologically into mostly septal panniculitis and mostly lobular panniculitis, according to the major or denser localization of the infiltrate, and also into those with or without vasculitis. Additional criteria involve the composition of the inflammatory infiltrate, the cause, and an underlying or associated disease. The clinical hallmarks of panniculitis are subcutaneous nodules or plaques, often located on the lower limb. A deep excisional biopsy is often required for a more precise diagnosis, given the often sparse and monotonous clinical symptoms. Erythema nodosum is the most common form and a typical example of septal panniculitis. It occurs in response to many different provoking factors, the most common trigger in children being a 'strep throat', in adults sarcoidosis. Clinically, it presents with a sudden symmetrical appearance of painful, tender, warm, erythematous nodes or plaques, usually on the shins, which resemble bruises. Classical and cutaneous polyarteriitis nodosa present a mostly septal panniculitis associated with vasculitis. Here subcutaneous, partially ulcerating nodules are surrounded by livedo racemosa. The mostly lobular panniculitides not associated with vasculitis include lupus panniculitis (lupus erythematosus profundus, typically with ensuing lipoatrophy and predilection for the upper part of the body), panniculitis in dermatomyositis (often calcifiying), cold panniculitis, pancreatic panniculitis, panniculitis due toα‎-antitrypsin deficiency, poststeroid panniculitis (in children after rapid withdrawal of corticosteroids), calciphylaxis (with and without renal failure), and factitious panniculitis (after mechanical, physical, or chemical injuries to the subcutaneous tissue, often self-inflicted). Nodular vasculitis (formerly erythema induratum Bazin) is a lobular panniculitis with vasculitis involving mostly the small blood vessels of the fat lobule. It appears to present a (hyper)reactive response to certain infections (tuberculosis, streptococci, candida) or to cold exposure or chronic venous insufficiency in susceptible females. In conclusion, the panniculitides are a heterogenous group of diseases requiring a systematic work-up and knowledge of certain histological or clinical criteria.

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Capítulos de libros sobre el tema "Node Localization"

Miao, Chunyu, Guoyong Dai, Lina Chen, Hongbo Jin y Qingzhang Chen. "A Node Localization Verification Model for WSN". En Human Centered Computing, 296–309. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31854-7_27.

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Jiang, Tao, Yourui Huang y Yanlin Wang. "Study on Improved LANDMARC Node Localization Algorithm". En Advances in Intelligent and Soft Computing, 415–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27951-5_62.

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Jauregui, Salvador, Michel Barbeau, Evangelos Kranakis, Edson Scalabrin y Mario Siller. "Localization of a Mobile Node in Shaded Areas". En Ad-hoc, Mobile, and Wireless Networks, 93–106. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19662-6_7.

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Mondal, Kaushik y Partha Sarathi Mandal. "Range-Free Mobile Node Localization Using Static Anchor". En Wireless Algorithms, Systems, and Applications, 269–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39701-1_23.

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Li, Yang, Weixiao Meng, Yingbo Zhao y Shuai Han. "Improvement of a Single Node Indoor Localization System". En Ad Hoc Networks, 208–19. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37262-0_17.

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Hoefnagel, Cornelis A. "Lymphoscintigraphy and Sentinel Node Localization in Breast Cancer". En Lobar Approach to Breast Ultrasound, 215–26. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-61681-0_11.

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Huang, Hai, Lei Tian, Wei Wu, Songlin Sun y Xiaojun Jing. "Node Localization for Distributed Simulation Based on Logical Node Group in Simulation Grid". En Entertainment for Education. Digital Techniques and Systems, 298–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14533-9_30.

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Li, Jinbao, Jianzhong Li, Longjiang Guo y Peng Wang. "Power-Efficient Node Localization Algorithm in Wireless Sensor Networks". En Lecture Notes in Computer Science, 420–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11610496_55.

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Radeke, Rico y Jorge Juan Robles. "Node Degree Improved Localization Algorithms for Ad-Hoc Networks". En Ad-hoc, Mobile, and Wireless Networks, 290–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22450-8_22.

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Tuba, Eva, Milan Tuba y Marko Beko. "Two Stage Wireless Sensor Node Localization Using Firefly Algorithm". En Lecture Notes in Networks and Systems, 113–20. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6916-1_10.

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Actas de conferencias sobre el tema "Node Localization"

Liu, Zemin, Yuan Fang, Chenghao Liu y Steven C. H. Hoi. "Node-wise Localization of Graph Neural Networks". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/210.

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Resumen

Graph neural networks (GNNs) emerge as a powerful family of representation learning models on graphs. To derive node representations, they utilize a global model that recursively aggregates information from the neighboring nodes. However, different nodes reside at different parts of the graph in different local contexts, making their distributions vary across the graph. Ideally, how a node receives its neighborhood information should be a function of its local context, to diverge from the global GNN model shared by all nodes. To utilize node locality without overfitting, we propose a node-wise localization of GNNs by accounting for both global and local aspects of the graph. Globally, all nodes on the graph depend on an underlying global GNN to encode the general patterns across the graph; locally, each node is localized into a unique model as a function of the global model and its local context. Finally, we conduct extensive experiments on four benchmark graphs, and consistently obtain promising performance surpassing the state-of-the-art GNNs.

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Kusy, Branislav, Akos Ledeczi, Miklos Maroti y Lambert Meertens. "Node density independent localization". En 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 y L. Meertens. "Node-density independent localization". En 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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Liu, Zhenyu, Wenhan Dai y Moe Z. Win. "Node placement for localization networks". En ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7997097.

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Bui, ThiOanh y Pingping Xu. "Energy considerations for node localization". En 2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM). IEEE, 2017. http://dx.doi.org/10.1109/pacrim.2017.8121902.

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Hsiao, Chun-Chieh y Yi-Jhong Tsai. "Node deployment strategy for WSN-based node-sequence localization". En 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146536.

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Peng, Jing, Junxia Jia y Tao Liu. "WSN Node Localization Algorithm Based On Range Free Localization". En 2015 International Industrial Informatics and Computer Engineering Conference. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/iiicec-15.2015.305.

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Ukawa, Shohei, Tatsuya Shinada, Masanori Hashimoto, Yuichi Itoh y Takao Onoye. "3D node localization from node-to-node distance information using cross-entropy method". En 2015 IEEE Virtual Reality (VR). IEEE, 2015. http://dx.doi.org/10.1109/vr.2015.7223416.

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Hukui, Shigekazu, Jie Luo, Takuma Daicho, Keigo Suenaga y Keitaro Naruse. "Mutual localization of sensor node robots". En 2010 2nd International Symposium on Aware Computing (ISAC). IEEE, 2010. http://dx.doi.org/10.1109/isac.2010.5670463.

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Namin, Parham H. y Mohammad A. Tinati. "Node localization using Particle Swarm Optimization". En 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146558.

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Informes sobre el tema "Node Localization"

Mattrey, Robert F. Tagging of Breast Tumors for Excision and Specimen Radiography and of Sentinel Nodes for Ultrasound-Guided Localization Using Novel Particulate Agents. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1999. http://dx.doi.org/10.21236/ada382950.

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