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Journal articles on the topic 'Localization analysis'

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Author: Grafiati

Published: 10 March 2023

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1

Suwannawach, Piyapan, and Sorawat Chivapreecha. "Reduce RSSI Variance for Indoor Localization System Using Frequency Analysis." International Journal of Future Computer and Communication 8, no. 2 (June 2019): 34–38. http://dx.doi.org/10.18178/ijfcc.2019.8.2.536.

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2

Lei, Lili, and Jeffrey S. Whitaker. "Model Space Localization Is Not Always Better Than Observation Space Localization for Assimilation of Satellite Radiances." Monthly Weather Review 143, no. 10 (October 1, 2015): 3948–55. http://dx.doi.org/10.1175/mwr-d-14-00413.1.

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Abstract Covariance localization is an essential component of ensemble-based data assimilation systems for large geophysical applications with limited ensemble sizes. For integral observations like the satellite radiances, where the concepts of location or vertical distance are not well defined, vertical localization in observation space is not as straightforward as in model space. The detailed differences between model space and observation space localizations are examined using a real radiance observation. Counterintuitive analysis increments can be obtained with model space localization; the magnitude of the increment can increase and the increment can change sign when the localization scale decreases. This occurs when there are negative background-error covariances and a predominately positive forward operator. Too narrow model space localization can neglect the negative background-error covariances and result in the counterintuitive analysis increments. An idealized 1D model with integral observations and known true error covariance is then used to compare errors resulting from model space and observation space localizations. Although previous studies have suggested that observation space localization is inferior to model space localization for satellite radiances, the results from the 1D model reveal that observation space localization can have advantages over model space localization when there are negative background-error covariances. Differences between model space and observation space localizations disappear as ensemble size, observation error variance, and localization scale increase. Thus, large ensemble sizes and vertical localization length scales may be needed to more effectively assimilate radiance observations.

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3

Cordero, Elena, and Karlheinz Gröchenig. "Time–Frequency analysis of localization operators." Journal of Functional Analysis 205, no. 1 (December 2003): 107–31. http://dx.doi.org/10.1016/s0022-1236(03)00166-6.

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4

von Brecht, James H. "Localization and vector spherical harmonics." Journal of Differential Equations 260, no. 2 (January 2016): 1622–55. http://dx.doi.org/10.1016/j.jde.2015.09.041.

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5

Bindel, David, and Amanda Hood. "Localization Theorems for Nonlinear Eigenvalue Problems." SIAM Journal on Matrix Analysis and Applications 34, no. 4 (January 2013): 1728–49. http://dx.doi.org/10.1137/130913651.

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6

Pushnitski, Alexander, and Dmitri Yafaev. "Localization principle for compact Hankel operators." Journal of Functional Analysis 270, no. 9 (May 2016): 3591–621. http://dx.doi.org/10.1016/j.jfa.2015.10.018.

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7

Elgart, Alexander, and Abel Klein. "An eigensystem approach to Anderson localization." Journal of Functional Analysis 271, no. 12 (December 2016): 3465–512. http://dx.doi.org/10.1016/j.jfa.2016.09.008.

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8

Faris, William G. "A localization principle for multiplicative perturbations." Journal of Functional Analysis 67, no. 1 (June 1986): 105–14. http://dx.doi.org/10.1016/0022-1236(86)90045-5.

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9

Taliaferro, J. Matthew, Eric T. Wang, and Christopher B. Burge. "Genomic analysis of RNA localization." RNA Biology 11, no. 8 (August 3, 2014): 1040–50. http://dx.doi.org/10.4161/rna.32146.

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10

Wallace, John S., Donald L. Fisher, and John Collura. "Sound Localization: Information Theory Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 18 (October 1996): 905–9. http://dx.doi.org/10.1177/154193129604001808.

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Three experiments were performed which examined the applicability of the Hick-Hyman law to the design of an auditory interface for a vehicle collision avoidance warning system. All trials used a single broadband noise signal emanating from one of a subset of six loudspeakers equally spaced around the subject in the azimuthal plane. Both the size of the sub-set and the balance of relative probabilities from speaker to speaker were altered to evaluate the relationship between information content and the dependent variable, choice reaction time. Choice reaction time was found to be related to the information content of the sound stimulus in all cases. It was also found to be related to the presence of pairs of speakers which were symmetrically opposed to one another in front of and behind the subject.

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11

Jirásek, Milan. "Mathematical analysis of strain localization." Revue Européenne de Génie Civil 11, no. 7-8 (August 2007): 977–91. http://dx.doi.org/10.1080/17747120.2007.9692973.

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12

Farrer, Lindsay A. "Gene Localization By Linkage Analysis." Otolaryngologic Clinics of North America 25, no. 5 (October 1992): 907–22. http://dx.doi.org/10.1016/s0030-6665(20)30914-2.

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13

Jirásek, Milan. "Mathematical analysis of strain localization." Revue européenne de génie civil 11, no. 7-8 (October 1, 2007): 977–91. http://dx.doi.org/10.3166/regc.11.977-991.

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14

Wallace, John S., and Donald L. Fisher. "Sound Localization: Information Theory Analysis." Human Factors: The Journal of the Human Factors and Ergonomics Society 40, no. 1 (March 1998): 50–68. http://dx.doi.org/10.1518/001872098779480532.

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15

Barata, J. C. A., and D. A. Cortez. "Perturbative analysis of dynamical localization." Journal of Mathematical Physics 44, no. 5 (May 2003): 1937–60. http://dx.doi.org/10.1063/1.1562750.

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16

Wharton, John. "Localization and analysis of receptors." Histochemical Journal 28, no. 11 (November 1996): 727–28. http://dx.doi.org/10.1007/bf02272146.

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17

Joshi, Dr Shreedhar A. "Analysis of RSSI and CLS based Localization Algorithms in Wireless Sensor Networks." Bonfring International Journal of Research in Communication Engineering 6, Special Issue (November 30, 2016): 16–19. http://dx.doi.org/10.9756/bijrce.8192.

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18

Arakaki, Atsushi, Daiki Kikuchi, Masayoshi Tanaka, Ayana Yamagishi, Takuto Yoda, and Tadashi Matsunaga. "Comparative Subcellular Localization Analysis of Magnetosome Proteins Reveals a Unique Localization Behavior of Mms6 Protein onto Magnetite Crystals." Journal of Bacteriology 198, no. 20 (August 1, 2016): 2794–802. http://dx.doi.org/10.1128/jb.00280-16.

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ABSTRACTThe magnetosome is an organelle specialized for inorganic magnetite crystal synthesis in magnetotactic bacteria. The complex mechanism of magnetosome formation is regulated by magnetosome proteins in a stepwise manner. Protein localization is a key step for magnetosome development; however, a global study of magnetosome protein localization remains to be conducted. Here, we comparatively analyzed the subcellular localization of a series of green fluorescent protein (GFP)-tagged magnetosome proteins. The protein localizations were categorized into 5 groups (short-length linear, middle-length linear, long-length linear, cell membrane, and intracellular dispersing), which were related to the protein functions. Mms6, which regulates magnetite crystal growth, localized along magnetosome chain structures under magnetite-forming (microaerobic) conditions but was dispersed in the cell under nonforming (aerobic) conditions. Correlative fluorescence and electron microscopy analyses revealed that Mms6 preferentially localized to magnetosomes enclosing magnetite crystals. We suggest that a highly organized spatial regulation mechanism controls magnetosome protein localization during magnetosome formation in magnetotactic bacteria.IMPORTANCEMagnetotactic bacteria synthesize magnetite (Fe3O4) nanocrystals in a prokaryotic organelle called the magnetosome. This organelle is formed using various magnetosome proteins in multiple steps, including vesicle formation, magnetosome alignment, and magnetite crystal formation, to provide compartmentalized nanospaces for the regulation of iron concentrations and redox conditions, enabling the synthesis of a morphologically controlled magnetite crystal. Thus, to rationalize the complex organelle development, the localization of magnetosome proteins is considered to be highly regulated; however, the mechanisms remain largely unknown. Here, we performed comparative localization analysis of magnetosome proteins that revealed the presence of a spatial regulation mechanism within the linear structure of magnetosomes. This discovery provides evidence of a highly regulated protein localization mechanism for this bacterial organelle development.

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19

Damanik, David, and Zheng Gan. "Limit-periodic Schrödinger operators onZd: Uniform localization." Journal of Functional Analysis 265, no. 3 (August 2013): 435–48. http://dx.doi.org/10.1016/j.jfa.2013.05.020.

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20

Stolz, Günter. "Localization for Schrödinger Operators with Effective Barriers." Journal of Functional Analysis 146, no. 2 (June 1997): 416–29. http://dx.doi.org/10.1006/jfan.1996.3043.

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21

Naor, Assaf, and Terence Tao. "Random martingales and localization of maximal inequalities." Journal of Functional Analysis 259, no. 3 (August 2010): 731–79. http://dx.doi.org/10.1016/j.jfa.2009.12.009.

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22

Chulaevsky, Victor. "From Fixed-Energy Localization Analysis to Dynamical Localization: An Elementary Path." Journal of Statistical Physics 154, no. 6 (February 13, 2014): 1391–429. http://dx.doi.org/10.1007/s10955-014-0937-7.

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23

Aladro, G. "Localization of the Kobayashi Distance." Journal of Mathematical Analysis and Applications 181, no. 1 (January 1994): 200–204. http://dx.doi.org/10.1006/jmaa.1994.1014.

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24

Requejo, B. "Topological Localization in Fréchet Algebras." Journal of Mathematical Analysis and Applications 189, no. 1 (January 1995): 160–78. http://dx.doi.org/10.1006/jmaa.1995.1010.

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25

Liimatainen, Kaisa, Riku Huttunen, Leena Latonen, and Pekka Ruusuvuori. "Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns." Biomolecules 11, no. 2 (February 11, 2021): 264. http://dx.doi.org/10.3390/biom11020264.

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Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

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26

Patil, Miss Prajakta B., and Dr A. N. Jadhav. "Comparative Analysis of AODV Base and RSSI Base Wireless Sensor Node Localization Techniques." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1348–53. http://dx.doi.org/10.31142/ijtsrd14301.

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27

Hyun, Yoonsuk, and Doory Kim. "Development of Deep-Learning-Based Single-Molecule Localization Image Analysis." International Journal of Molecular Sciences 23, no. 13 (June 21, 2022): 6896. http://dx.doi.org/10.3390/ijms23136896.

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Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is significantly restricted by the image analysis method, as the final super-resolution image is reconstructed from identified localizations through computational analysis. With recent advancements in deep learning, many researchers have employed deep learning-based algorithms to analyze SMLM image data. This review discusses recent developments in deep-learning-based SMLM image analysis, including the limitations of existing fitting algorithms and how the quality of SMLM images can be improved through deep learning. Finally, we address possible future applications of deep learning methods for SMLM imaging.

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28

Cvetković, LJ, V. Kostić, and J. M. Peña. "Eigenvalue Localization Refinements for Matrices Related to Positivity." SIAM Journal on Matrix Analysis and Applications 32, no. 3 (July 2011): 771–84. http://dx.doi.org/10.1137/100807077.

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29

Beattie, Christopher, and David W. Fox. "Localization Criteria and Containment for Rayleigh Quotient Iteration." SIAM Journal on Matrix Analysis and Applications 10, no. 1 (January 1989): 80–93. http://dx.doi.org/10.1137/0610006.

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30

Criado, Alberto, and Fernando Soria. "Localization and dimension free estimates for maximal functions." Journal of Functional Analysis 265, no. 10 (November 2013): 2553–83. http://dx.doi.org/10.1016/j.jfa.2013.06.015.

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31

Kachkovskiy, Ilya. "Localization for quasiperiodic operators with unbounded monotone potentials." Journal of Functional Analysis 277, no. 10 (November 2019): 3467–90. http://dx.doi.org/10.1016/j.jfa.2019.03.017.

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32

Damanik, David, Robert Sims, and Günter Stolz. "Localization for discrete one-dimensional random word models." Journal of Functional Analysis 208, no. 2 (March 2004): 423–45. http://dx.doi.org/10.1016/j.jfa.2003.07.011.

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33

Fang, Quanlei, and Jingbo Xia. "Commutators and localization on the Drury–Arveson space." Journal of Functional Analysis 260, no. 3 (February 2011): 639–73. http://dx.doi.org/10.1016/j.jfa.2010.10.013.

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34

TAKEMASA, Fumio, and Akihiko SUZUKI. "Preliminary investigation for damage localization analysis." Proceedings of The Computational Mechanics Conference 2000.13 (2000): 531–32. http://dx.doi.org/10.1299/jsmecmd.2000.13.531.

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35

Jin, Yunye, Wee-Seng Soh, and Wai-Choong Wong. "Error analysis for fingerprint-based localization." IEEE Communications Letters 14, no. 5 (May 2010): 393–95. http://dx.doi.org/10.1109/lcomm.2010.05.092152.

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36

Zurek, Patrick M., and Barbara G. Shinn‐Cunningham. "Analysis of phantom source localization cues." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3085. http://dx.doi.org/10.1121/1.418802.

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37

Mourikis, A. I., and S. I. Roumeliotis. "Performance analysis of multirobot Cooperative localization." IEEE Transactions on Robotics 22, no. 4 (August 2006): 666–81. http://dx.doi.org/10.1109/tro.2006.878957.

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38

Aharonov, Ranit, Lior Segev, Isaac Meilijson, and Eytan Ruppin. "Localization of Function via Lesion Analysis." Neural Computation 15, no. 4 (April 1, 2003): 885–913. http://dx.doi.org/10.1162/08997660360581949.

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This article presents a general approach for employing lesion analysis to address the fundamental challenge of localizing functions in a neural system. We describe functional contribution analysis (FCA), which assigns contribution values to the elements of the network such that the ability to predict the network's performance in response to multilesions is maximized. The approach is thoroughly examined on neurocontroller networks of evolved autonomous agents. The FCA portrays a stable set of neuronal contributions and accurate multilesion predictions that are significantly better than those obtained based on the classical single lesion approach. It is also used for a detailed synaptic analysis of the neurocontroller connectivity network, delineating its main functional backbone. The FCA provides a quantitative way of measuring how the network functions are localized and distributed among its elements. Our results question the adequacy of the classical single lesion analysis traditionally used in neuroscience and show that using lesioning experiments to decipher even simple neuronal systems requires a more rigorous multilesion analysis.

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39

Noachtar, S. "ME7 Movement analysis for seizure localization." Clinical Neurophysiology 119 (May 2008): S3. http://dx.doi.org/10.1016/s1388-2457(08)60014-0.

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40

Mazzaferri, Javier, and Silvia Ledesma. "Optical multiresolution analysis with spatial localization." Optics Communications 283, no. 10 (May 2010): 2056–60. http://dx.doi.org/10.1016/j.optcom.2010.01.018.

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41

Mass-Sanchez, Joaquin, Erica Ruiz-Ibarra, Ana Gonzalez-Sanchez, Adolfo Espinoza-Ruiz, and Joaquin Cortez-Gonzalez. "Factorial Design Analysis for Localization Algorithms." Applied Sciences 8, no. 12 (December 17, 2018): 2654. http://dx.doi.org/10.3390/app8122654.

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Localization is a fundamental problem in Wireless Sensor Networks, as it provides useful information regarding the detection of an event. There are different localization algorithms applied in single-hop or multi-hop networks; in both cases their performance depends on several factors involved in the evaluation scenario such as node density, the number of reference nodes and the log-normal shadowing propagation model, determined by the path-loss exponent (η) and the noise level (σdB) which impact on the accuracy and precision performance metrics of localization techniques. In this paper, we present a statistical analysis based on the 2k factorial methodology to determine the key factors affecting the performance metrics of localization techniques in a single-hop network to concentrate on such parameters, thus reducing the amount of simulation time required. For this proposal, MATLAB simulations are carried out in different scenarios, i.e., extreme values are used for each of the factors of interest and the impact of the interaction among them in the performance metrics is observed. The simulation results show that the path-loss exponent (η) and noise level (σdB) factors have the greatest impact on the accuracy and precision metrics evaluated in this study. Based on this statistical analysis, we recommend estimating the propagation model as close to reality as possible to consider it in the design of new localization techniques and thus improve their accuracy and precision metrics.

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42

Magowe, Kagiso, Andrea Giorgetti, Sithamparanathan Kandeepan, and Xinghuo Yu. "Accurate Analysis of Weighted Centroid Localization." IEEE Transactions on Cognitive Communications and Networking 5, no. 1 (March 2019): 153–64. http://dx.doi.org/10.1109/tccn.2018.2874452.

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43

Chen, Liyong, Lu Qian, Zhiyi Zhang, Ming Shi, Yuhua Song, Guogang Yuan, Hao Zhang, et al. "Mutational analysis of ErbB2 intracellular localization." Histochemistry and Cell Biology 128, no. 5 (September 12, 2007): 473–83. http://dx.doi.org/10.1007/s00418-007-0329-z.

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44

Benallal, Ahmed, and Claudia Comi. "Localization analysis via a geometrical method." International Journal of Solids and Structures 33, no. 1 (January 1996): 99–119. http://dx.doi.org/10.1016/0020-7683(95)00018-6.

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45

Fornasier, Massimo, and Karlheinz Gröchenig. "Intrinsic Localization of Frames." Constructive Approximation 22, no. 3 (April 8, 2005): 395–415. http://dx.doi.org/10.1007/s00365-004-0592-3.

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46

Kovács, Bálint Barna H., Dániel Varga, Dániel Sebők, Hajnalka Majoros, Róbert Polanek, Tibor Pankotai, Katalin Hideghéty, Ákos Kukovecz, and Miklós Erdélyi. "Application of Lacunarity for Quantification of Single Molecule Localization Microscopy Images." Cells 11, no. 19 (October 2, 2022): 3105. http://dx.doi.org/10.3390/cells11193105.

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The quantitative analysis of datasets achieved by single molecule localization microscopy is vital for studying the structure of subcellular organizations. Cluster analysis has emerged as a multi-faceted tool in the structural analysis of localization datasets. However, the results it produces greatly depend on the set parameters, and the process can be computationally intensive. Here we present a new approach for structural analysis using lacunarity. Unlike cluster analysis, lacunarity can be calculated quickly while providing definitive information about the structure of the localizations. Using simulated data, we demonstrate how lacunarity results can be interpreted. We use these interpretations to compare our lacunarity analysis with our previous cluster analysis-based results in the field of DNA repair, showing the new algorithm’s efficiency.

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47

Wang, Zhuo, Wei Ming Yan, and Lie Ping Ye. "Experimental Analysis on Mode Localization of Damaged Reticulated Shell Structures." Advanced Materials Research 243-249 (May 2011): 1301–4. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.1301.

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The damage of reticulated shell structure will arouse structural mode localization. The quantitative assessment index of mode localization is defined. A scale model experiment for mode localization is carried out on a Kiewit single-layer spherical reticulated shell structure, and the features of mode localization are studied. Four cases about structural stiffness’ change are constructed in the experiment, and three structural modals are obtained at random. The localization of the three modes is analyzed under each case. The results show that slight changes of physical parameters are likely to arouse obvious localization of some modes for reticulated shell structure.

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48

Hayward, Thomas J. "Bayesian localization of acoustic sources with information-theoretic analysis of localization performance." Journal of the Acoustical Society of America 132, no. 3 (September 2012): 2055. http://dx.doi.org/10.1121/1.4755565.

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49

Damanik, D., and P. Stollmann. "Multi-scale analysis implies strong dynamical localization." Geometric and Functional Analysis 11, no. 1 (April 2001): 11–29. http://dx.doi.org/10.1007/pl00001666.

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50

Li, Jinzhou, Shouye Lv, Liujie Lv, Sheng Wu, Yang Liu, Jing Nie, Ying Jin, and Chenglin Wang. "Joint TDOA, FDOA and PDOA Localization Approaches and Performance Analysis." Remote Sensing 15, no. 4 (February 7, 2023): 915. http://dx.doi.org/10.3390/rs15040915.

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Multi-station joint localization has important practical significance. In this paper, phase difference of arrival (PDOA) information is introduced into the joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) localization method to improve the target localization accuracy. First, the Cramer–Rao lower bound (CRLB) of the joint TDOA, FDOA and PDOA localization approach with multi-station precise phase synchronization is derived. Then, the CRLB of the joint TDOA, FDOA and differential PDOA (dPDOA) localization method for the case of phase asynchronization between observation stations is also presented. Furthermore, the authors analyze the influence of the phase wrapping problem on localization accuracy and propose solutions to solve the phase wrapping problem based on cost functions of grid search. Finally, iterative localization algorithms based on maximum likelihood (ML) are proposed for both TDOA/FDOA/PDOA and TDOA/FDOA/dPDOA scenarios, respectively. Simulation results demonstrate the localization performance of the proposed approaches.

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