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1

Naruse, Keitaro, Shigekazu Fukui, and Jie Luo. "Mutual Localization of Multiple Sensor Node Robots." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (2011): 1269–76. http://dx.doi.org/10.20965/jaciii.2011.p1269.

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The objective of this paper is to develop a localization systemof cooperativemultiple mobile robots, in which each robot is assumed to observe a set of known landmarks and equipped with an omnidirectional camera. In this paper, it is assumed that a robot can detect other robots by using the omnidirectional camera, share its estimated position with others, and utilize shared positions for its localization. In other words, each robot can be viewed as an additional mobile landmark to a set of stationary landmarks. A foremost concern is how well this system performs localization under a limited am
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2

Wang, Kai, Ling Wang, Jian Xie, and Yuexian Wang. "Classification and Localization of Mixed Sources after Blind Calibration of Unknown Mutual Coupling." International Journal of Antennas and Propagation 2019 (June 2, 2019): 1–13. http://dx.doi.org/10.1155/2019/5943956.

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In order to deal with the problem of passive mixed source localization under unknown mutual coupling, the authors propose an effective algorithm. This algorithm provides array blind calibration as well as classification and localization of mixed sources in this paper. In practice, an ideal sensor array without the effects of unknown mutual coupling is rarely satisfied, which degrades the performance of most high-resolution algorithms. Firstly, the directions of arrival of far-field sources and the number of nonzero mutual coupling coefficients are estimated directly through the rank-reduction
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3

Jung, Kwang-Min, and Kwee-Bo Sim. "Mutual Localization of swarm robot using Particle Filter." Journal of Korean Institute of Intelligent Systems 20, no. 2 (2010): 298–303. http://dx.doi.org/10.5391/jkiis.2010.20.2.298.

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4

Chen, Hua, Wei Liu, Wei-Ping Zhu, M. N. S. Swamy, and Qing Wang. "Mixed rectilinear sources localization under unknown mutual coupling." Journal of the Franklin Institute 356, no. 4 (2019): 2372–94. http://dx.doi.org/10.1016/j.jfranklin.2019.01.019.

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5

Song, Jiahui, Haitao Wu, Xiaochen Guo, et al. "GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System." Applied Sciences 12, no. 3 (2022): 1386. http://dx.doi.org/10.3390/app12031386.

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A smoothing localization method for Global Navigation Satellite System (GNSS) and visual Simultaneous Localization and Mapping (SLAM) system is proposed to identify GNSS spoofing, optimize the cumulative error of the GNSS/visual SLAM system, and obtain smoothing localization results. The proposed method analyzes the joint error distribution of the GNSS/visual SLAM system, uses the visual frame to invert the relative error offset of the GNSS from the dimensions of time and localization, performs error analysis and mutual verification based on the verification threshold. According to the mutual
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6

Xu, S., Z. Ji, D. T. Pham, and F. Yu. "Simultaneous localization and mapping: swarm robot mutual localization and sonar arc bidirectional carving mapping." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 3 (2010): 733–44. http://dx.doi.org/10.1243/09544062jmes2239.

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This work primarily aims to study robot swarm global mapping in a static indoor environment. Due to the prerequisite estimation of the robots' own poses, it is upgraded to a simultaneous localization and mapping (SLAM) problem. Five techniques are proposed to solve the SLAM problem, including the extended Kalman filter (EKF)-based mutual localization, sonar arc bidirectional carving mapping, grid-oriented correlation, working robot group substitution, and termination rule. The EKF mutual localization algorithm updates the pose estimates of not only the current robot, but also the landmark-func
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7

Fujiwara, Ryosuke, Takeshi Tsuchiya, and Daisuke Kubo. "1306 Development of UAV mutual localization with relative position." Proceedings of the Transportation and Logistics Conference 2013.22 (2013): 241–44. http://dx.doi.org/10.1299/jsmetld.2013.22.241.

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8

Liu, Daizong, Xiang Fang, Xiaoye Qu, et al. "Unsupervised Domain Adaptative Temporal Sentence Localization with Mutual Information Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3567–75. http://dx.doi.org/10.1609/aaai.v38i4.28145.

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Temporal sentence localization (TSL) aims to localize a target segment in a video according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on abundant yet expensive manual annotations for training. Moreover, these trained data-dependent models usually can not generalize well to unseen scenarios because of the inherent domain shift. To facilitate this issue, in this paper, we target another more practical but challenging setting: unsupervised domain adaptative temporal sentence localization (UDA-TSL), which explores whether the
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9

Wu, Xiang, Dumidu S. Talagala, Wen Zhang, and Thushara D. Abhayapala. "Individualized Interaural Feature Learning and Personalized Binaural Localization Model." Applied Sciences 9, no. 13 (2019): 2682. http://dx.doi.org/10.3390/app9132682.

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The increasing importance of spatial audio technologies has demonstrated the need and importance of correctly adapting to the individual characteristics of the human auditory system, and illustrates the crucial need for humanoid localization systems for testing these technologies. To this end, this paper introduces a novel feature analysis and selection approach for binaural localization and builds a probabilistic localization mapping model, especially useful for the vertical dimension localization. The approach uses the mutual information as a metric to evaluate the most significant frequenci
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10

Lee, Yang-Weon. "Automatic Mutual Localization of Swarm Robot Using a Particle Filter." Journal of information and communication convergence engineering 10, no. 4 (2012): 390–95. http://dx.doi.org/10.6109/jicce.2012.10.4.390.

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11

Lee, Joa-Hyoung, Dong-Sun Lim, and In-Bum Jung. "Mutual Exclusion based Localization Technique in Mobile Wireless Sensor Networks." Journal of the Korean Institute of Information and Communication Engineering 14, no. 6 (2010): 1493–504. http://dx.doi.org/10.6109/jkiice.2010.14.6.1493.

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12

Franchi, Antonio, Giuseppe Oriolo, and Paolo Stegagno. "Mutual localization in multi-robot systems using anonymous relative measurements." International Journal of Robotics Research 32, no. 11 (2013): 1302–22. http://dx.doi.org/10.1177/0278364913495425.

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13

BOSCO, Domenico, Paolo MEDA, and Patrick B. IYNEDJIAN. "Glucokinase and glucokinase regulatory protein: mutual dependence for nuclear localization." Biochemical Journal 348, no. 1 (2000): 215. http://dx.doi.org/10.1042/0264-6021:3480215.

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14

BOSCO, Domenico, Paolo MEDA, and Patrick B. IYNEDJIAN. "Glucokinase and glucokinase regulatory protein: mutual dependence for nuclear localization." Biochemical Journal 348, no. 1 (2000): 215–22. http://dx.doi.org/10.1042/bj3480215.

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Conditional expression of the glucokinase regulatory protein in insulinoma cells, under control of the reverse tetracycline-dependent transactivator, was used to investigate whether expression of this protein de novo would alter the intracellular distribution of glucokinase. The regulatory protein, which was undetectable in the basal state, could be induced by doxycycline to levels comparable to those of liver and was detected mostly in the nucleus. Concomitantly, glucokinase accumulated in the nucleus. Human embryonic kidney cells were transiently transfected to express glucokinase and the re
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15

Stegagno, Paolo, Marco Cognetti, Giuseppe Oriolo, Heinrich H. Bulthoff, and Antonio Franchi. "Ground and Aerial Mutual Localization Using Anonymous Relative-Bearing Measurements." IEEE Transactions on Robotics 32, no. 5 (2016): 1133–51. http://dx.doi.org/10.1109/tro.2016.2593454.

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16

He, Suining, S. H. Gary Chan, Lei Yu, and Ning Liu. "Maxlifd: Joint Maximum Likelihood Localization Fusing Fingerprints and Mutual Distances." IEEE Transactions on Mobile Computing 18, no. 3 (2019): 602–17. http://dx.doi.org/10.1109/tmc.2018.2841842.

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17

Liu, Ma, Wang, Zhang, Yang, and Wang. "Passive multiple target indoor localization based on joint interference cancellation in an RFID System." Electronics 8, no. 4 (2019): 426. http://dx.doi.org/10.3390/electronics8040426.

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Radio frequency identification (RFID) provides a simple and effective solution to the passive indoor localization. The conventional wisdom about RFID localization is using reference tags. It performs well in tag or passive single target localization. However, in the passive multiple target scenario, reference tag based localization suffers from some limitations, including the array aperture, mutual coupling of reference tags, and coherent superimposition signals. These problems are harmless and ignored in tag or passive single target localization, but degrade the performance severely in passiv
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18

Li, Haoran, Hongxun Yao, and Yuxin Hou. "HPnet: Hybrid Parallel Network for Human Pose Estimation." Sensors 23, no. 9 (2023): 4425. http://dx.doi.org/10.3390/s23094425.

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Hybrid models which combine the convolution and transformer model achieve impressive performance on human pose estimation. However, the existing hybrid models on human pose estimation, which typically stack self-attention modules after convolution, are prone to mutual conflict. The mutual conflict enforces one type of module to dominate over these hybrid sequential models. Consequently, the performance of higher-precision keypoints localization is not consistent with overall performance. To alleviate this mutual conflict, we developed a hybrid parallel network by parallelizing the self-attenti
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19

Kumar, Gulshan, Mritunjay Kumar Rai, Hye-jin Kim, and Rahul Saha. "A Secure Localization Approach Using Mutual Authentication and Insider Node Validation in Wireless Sensor Networks." Mobile Information Systems 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/3243570.

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Localization is a concerning issue in the applications of wireless sensor networks. Along with the accuracy of the location estimation of the sensor nodes, the security of the estimation is another priority. Wireless sensor networks often face various attacks where the attackers try to manipulate the estimated location or try to provide false beacons. In this paper, we have proposed a methodology that will address this problem of security aspects in localization of the sensor nodes. Moreover, we have considered the network environment with random node deployment and mobility as these two condi
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20

Yamauchi, Yohei, Kaoru Wada, Fumi Goshima, et al. "The UL14 protein of herpes simplex virus type 2 translocates the minor capsid protein VP26 and the DNA cleavage and packaging UL33 protein into the nucleus of coexpressing cells." Journal of General Virology 82, no. 2 (2001): 321–30. http://dx.doi.org/10.1099/0022-1317-82-2-321.

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The herpes simplex virus type 2 (HSV-2) gene UL14 encodes a 32 kDa protein which is a minor component of the virion tegument and is expressed late in infection. The UL14 protein shows varied localization patterns in HSV-2-infected and singly expressing cells, suggesting the possibility that it is multifunctional. We have investigated the influence of the UL14 protein on the intracellular localization of capsid proteins and DNA cleavage and packaging proteins in coexpressing cells. VP26 is the minor capsid protein; it binds to hexons of the outer capsid shell and is predominantly cytoplasmic up
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21

Cognetti, Marco, Paolo Stegagno, Antonio Franchi, and Giuseppe Oriolo. "Two Measurement Scenarios for Anonymous Mutual Localization in Multi-UAV Systems." IFAC Proceedings Volumes 45, no. 28 (2012): 13–18. http://dx.doi.org/10.3182/20121003-3-sf-4024.00006.

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22

Hollis, Andrew N., Ralph C. Smith, and Alyson G. Wilson. "SURROGATE BASED MUTUAL INFORMATION APPROXIMATION AND OPTIMIZATION FOR URBAN SOURCE LOCALIZATION." International Journal for Uncertainty Quantification 11, no. 5 (2021): 39–55. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2021034400.

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23

Hao, Ning, Fenghua He, Yi Hou, and Yu Yao. "Graph-based observability analysis for mutual localization in multi-robot systems." Systems & Control Letters 161 (March 2022): 105152. http://dx.doi.org/10.1016/j.sysconle.2022.105152.

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24

Grimnes, Sverre, Ørjan G. Martinsen, and Gorm K. Johnsen. "Mutual localization of electrode pairs in a 4-electrode measuring system." Journal of Physics: Conference Series 224 (April 1, 2010): 012074. http://dx.doi.org/10.1088/1742-6596/224/1/012074.

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25

Czaja, Bartosz, Krzysztof Maslanka, Pawel Skokowski, and Jan Kelner. "Overview of Mutual Localization Techniques Between Unmanned Aerial Vehicles in Swarm." TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation 19, no. 1 (2025): 317–23. https://doi.org/10.12716/1001.19.01.37.

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26

Wang, Tian, Yuzhu Liang, Yaxin Mei, Muhammad Arif, and Chunsheng Zhu. "High-accuracy localization for indoor group users based on extended Kalman filter." International Journal of Distributed Sensor Networks 14, no. 11 (2018): 155014771881272. http://dx.doi.org/10.1177/1550147718812722.

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Indoor localization has attracted increasing research attentions in the recent years. However, many important issues still need to be further studied to keep pace with new requirements and technical progress, such as real-time operation, high accuracy, and energy efficiency. In order to meet the high localization accuracy requirement and the high localization dependable requirement in some scenarios, we take the users as a group to utilize the mutual distance information among them to get better localization performance. Moreover, we design a mobile group localization method based on extended
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27

Wang, Kai, Ling Wang, Jian Xie, Yuexian Wang, and Zhanolin Zhang. "Joint Space and Time Processing for Unknown Mutual Coupling Blind Calibration and Mixed Sources Identification Using Uniform Circular Array." Electronics 8, no. 5 (2019): 525. http://dx.doi.org/10.3390/electronics8050525.

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In classification and localization of mixed far-field and near-field sources, the unknown mutual coupling degrades the performance of most high-resolution algorithms. In practice, the assumption of an ideal receiving sensor array is rarely satisfied. This paper proposes an effective algorithm of mixed sources identification using uniform circular array under unknown mutual coupling. Firstly, according to rank reduction and joint space–time processing, the directions of arrival of far-field sources is estimated directly without mutual coupling elimination. Addition, the joint space–time process
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28

Wen, Feng, Xiaohu Chu, Qiang Li, Wenhan Zhao, Xueqiong Zhu, and Yuwei Wu. "Receiver Localization Strategy of Wireless Charging System Based on Mutual Inductance Disturbance." IEEE Transactions on Applied Superconductivity 31, no. 8 (2021): 1–4. http://dx.doi.org/10.1109/tasc.2021.3091121.

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29

Fang, Shih-Hau, Ying-Tso Hsu, Yao Shiao, and Fan-Yu Sung. "An Enhanced Device Localization Approach Using Mutual Signal Strength in Cellular Networks." IEEE Internet of Things Journal 2, no. 6 (2015): 596–603. http://dx.doi.org/10.1109/jiot.2015.2451997.

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30

Marín, Marta, Carlos Moya, and Salvador Máñez. "Mutual Influences between Nitric Oxide and Paraoxonase 1." Antioxidants 8, no. 12 (2019): 619. http://dx.doi.org/10.3390/antiox8120619.

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One of the best consolidated paradigms in vascular pharmacology is that an uncontrolled excess of oxidizing chemical species causes tissue damage and loss of function in the endothelial and subendothelial layers. The fact that high-density lipoproteins play an important role in preventing such an imbalance is integrated into that concept, for which the expression and activity of paraoxonases is certainly crucial. The term paraoxonase (aryldialkyl phosphatase, EC 3.1.8.1) encompasses at least three distinct isoforms, with a wide variation in substrate affinity, cell and fluid localization, and
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31

Du, Zeyu, Luoyu Liu, Hangyu Zhou, Ziyi Chen, and Yusen Yao. "Optimization of Distributed UAV Swarm Placement for Target Localization Using Multiple Heuristic Algorithms Based on Compressive Sensing." Applied and Computational Engineering 132, no. 1 (2025): 43–54. https://doi.org/10.54254/2755-2721/2024.20529.

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Collaborative UAV swarms are increasingly deployed as temporary base stations in emergency situations to relay communications. This paper designs intelligent optimization algorithms, which minimizing mutual coherence within a region of interest (ROI) aims to solving challenge in these scenarios is optimizing the UAVs' locations to avoid mutual signal interference and ensure high communication quality. Minimizing mutual coherence reduces the likelihood of signal interference between UAVs. We explore various optimize algorithum, including Heuristic Search (HS) and Ant Colony Optimization (ACO) a
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32

Ji, Hongxin, Chao Zheng, Zijian Tang, Xinghua Liu та Liqing Liu. "Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-β Generalized Cross-Correlation". Sensors 24, № 15 (2024): 4937. http://dx.doi.org/10.3390/s24154937.

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In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise int
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33

Kondakova, Irina V., Elena E. Shashova, Evgenia A. Sidenko, Tatiana M. Astakhova, Liudmila A. Zakharova, and Natalia P. Sharova. "Estrogen Receptors and Ubiquitin Proteasome System: Mutual Regulation." Biomolecules 10, no. 4 (2020): 500. http://dx.doi.org/10.3390/biom10040500.

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This review provides information on the structure of estrogen receptors (ERs), their localization and functions in mammalian cells. Additionally, the structure of proteasomes and mechanisms of protein ubiquitination and cleavage are described. According to the modern concept, the ubiquitin proteasome system (UPS) is involved in the regulation of the activity of ERs in several ways. First, UPS performs the ubiquitination of ERs with a change in their functional activity. Second, UPS degrades ERs and their transcriptional regulators. Third, UPS affects the expression of ER genes. In addition, th
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34

LAGUNA, HUMBERTO G., and ROBIN P. SAGAR. "SHANNON ENTROPY OF THE WIGNER FUNCTION AND POSITION-MOMENTUM CORRELATION IN MODEL SYSTEMS." International Journal of Quantum Information 08, no. 07 (2010): 1089–100. http://dx.doi.org/10.1142/s0219749910006484.

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Shannon entropies of the Wigner function are calculated for ground and excited stationary states of the Particle-In-A-Box and Harmonic Oscillator model systems and examined as a measure of the localization of the phase-space distribution. We show that their behavior is consistent with that of the sum of the position and momentum space entropies as a function of quantum number. Position-momentum correlation is then analyzed in these systems by defining mutual information between position and momentum variables. This mutual information yields non-zero values, in contrast to the quantum covarianc
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35

Zhao, Jianchun, Guangyue Zhang, Jierui Qu, et al. "A Sound Source Localization Method Based on Frequency Divider and Time Difference of Arrival." Applied Sciences 13, no. 10 (2023): 6183. http://dx.doi.org/10.3390/app13106183.

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In recent years, sound source localization, as a passive localization technique with higher safety and convenience compared with other localization techniques such as active emission of electromagnetic waves, has received more and more attention in academia. This paper researches and improves the far-field sound source localization algorithm based on the generalized cross-correlation method (GCC) Time Difference of Arrival (TDOA) estimation algorithm and completes the design and implementation of the microphone array sound source localization system. This paper adds a frequency divider to the
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36

Xie, Xiang, Quan Zhou, Dibo Hou, and Hongjian Zhang. "Compressed sensing based optimal sensor placement for leak localization in water distribution networks." Journal of Hydroinformatics 20, no. 6 (2017): 1286–95. http://dx.doi.org/10.2166/hydro.2017.145.

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Abstract The performance of model-based leak detection and localization techniques heavily depends on the configuration of a limited number of sensors. This paper presents a sensor placement optimization strategy that guarantees sufficient diagnosability while satisfying the budget constraint. Based on the theory of compressed sensing, the leak localization problem could be transformed into acquiring the sparse leak-induced demands from the available measurements, and the average mutual coherence is devised as a diagnosability criterion for evaluating whether the measurements contain enough in
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37

Wilmeth, Lori Jo, Sanjay Shrestha, Gilbert Montaño, Jennifer Rashe, and Charles Bradley Shuster. "Mutual Dependence of Mob1 and the Chromosomal Passenger Complex for Localization during Mitosis." Molecular Biology of the Cell 21, no. 3 (2010): 380–92. http://dx.doi.org/10.1091/mbc.e09-06-0471.

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The spatial and temporal coordination of chromosome segregation with cytokinesis is essential to ensure that each daughter cell receives the correct complement of chromosomal and cytoplasmic material. In yeast, mitotic exit and cytokinesis are coordinated by signaling cascades whose terminal components include a nuclear Dbf2-related family kinase and a noncatalytic subunit, Mps one binding (Mob) 1. There are five human Mob1 isoforms, all of which display redundant localization patterns at the spindle poles and kinetochores in early mitosis, and the spindle midzone during cytokinesis. Mob1 shar
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38

Rand, S. C. "Strong localization of light and photonic atoms." Canadian Journal of Physics 78, no. 5-6 (2000): 625–37. http://dx.doi.org/10.1139/p00-042.

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In the strong localization regime, light confined to a well-defined volume of space by the mutual interference of its countless constituent scattered waves can be expected to acquire unusual characteristics. These include a vanishing momentum, an effective tensorial rest mass, and "photonic atom recoil." Impurity atoms that emit light under these conditions should consist of coupled atom-cavity systems with energy densities extending over regions larger than the atoms, but smaller than a cubic wavelength, in which radiant transport is suppressed by near-field correlated scattering. This unusua
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39

Chen, Peng, Zhenxin Cao, Zhimin Chen, and Chunhua Yu. "Sparse DOD/DOA Estimation in a Bistatic MIMO Radar With Mutual Coupling Effect." Electronics 7, no. 11 (2018): 341. http://dx.doi.org/10.3390/electronics7110341.

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The unknown mutual coupling effect between antennas significantly degrades the target localization performance in the bistatic multiple-input multiple-output (MIMO) radar. In this paper, the joint estimation problem for the direction of departure (DOD) and direction of arrival (DOA) is addressed. By exploiting the target sparsity in the spatial domain and formulating a dictionary matrix with discretizing the DOD/DOA into grids, compressed sensing (CS)-based system model is given. However, in the practical MIMO radar systems, the target cannot be precisely on the grids, and the unknown mutual c
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40

Cowan, D. P. "Group Living in the European Rabbit (Oryctolagus cuniculus): Mutual Benefit or Resource Localization?" Journal of Animal Ecology 56, no. 3 (1987): 779. http://dx.doi.org/10.2307/4948.

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Schalk, Stefan G., Libertario Demi, Nabil Bouhouch, et al. "Contrast-Enhanced Ultrasound Angiogenesis Imaging by Mutual Information Analysis for Prostate Cancer Localization." IEEE Transactions on Biomedical Engineering 64, no. 3 (2017): 661–70. http://dx.doi.org/10.1109/tbme.2016.2571624.

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42

Liu, Han, Xuehong Deng, Y. John Shyu, Jian Jian Li, Elizabeth J. Taparowsky, and Chang-Deng Hu. "Mutual regulation of c-Jun and ATF2 by transcriptional activation and subcellular localization." EMBO Journal 25, no. 12 (2006): 2952. http://dx.doi.org/10.1038/sj.emboj.7601183.

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43

Liu, Han, Xuehong Deng, Y. John Shyu, Jian Jian Li, Elizabeth J. Taparowsky, and Chang-Deng Hu. "Mutual regulation of c-Jun and ATF2 by transcriptional activation and subcellular localization." EMBO Journal 25, no. 5 (2006): 1058–69. http://dx.doi.org/10.1038/sj.emboj.7601020.

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44

Xie, Jian, Haihong Tao, Xuan Rao, and Jia Su. "Localization of mixed far-field and near-field sources under unknown mutual coupling." Digital Signal Processing 50 (March 2016): 229–39. http://dx.doi.org/10.1016/j.dsp.2015.10.012.

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45

Arkhipova, I. V. "MUTUAL RELATIONS IN THE SPHERE OF TEMPORAL CATEGORIES (based on the German language)." Siberian Philological Forum 21, no. 4 (2022): 4–10. http://dx.doi.org/10.25146/2587-7844-2022-21-4-133.

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Statement of the problem. Due to the lack of coverage of issues describing inter-categorial relations in the sphere of interaction between taxis and other related semantic categories involved in the verbalization of the idea of time, this problem is relevant today. The purpose of this paper is to describe inter-categorial relations in the field of integrative interaction of inter-connected functional and semantic categories: taxis, temporality, aspectuality, and temporal localization. Methodology (materials and methods). The research is carried out within the framework of a functional and sema
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Momin, Shadab, Yang Lei, Zhen Tian, et al. "Cascaded mutual enhancing networks for brain tumor subregion segmentation in multiparametric MRI." Physics in Medicine & Biology 67, no. 8 (2022): 085015. http://dx.doi.org/10.1088/1361-6560/ac5ed8.

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Abstract Accurate segmentation of glioma and its subregions plays an important role in radiotherapy treatment planning. Due to a very populated multiparameter magnetic resonance imaging image, manual segmentation tasks can be very time-consuming, meticulous, and prone to subjective errors. Here, we propose a novel deep learning framework based on mutual enhancing networks to automatically segment brain tumor subregions. The proposed framework is suitable for the segmentation of brain tumor subregions owing to the contribution of Retina U-Net followed by the implementation of a mutual enhancing
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Otsuka, Takuma, Katsuhiko Ishiguro, Hiroshi Sawada, and Hiroshi Okuno. "Bayesian Unification of Sound Source Localization and Separation with Permutation Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 2038–45. http://dx.doi.org/10.1609/aaai.v26i1.8376.

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Sound source localization and separation with permutation resolution are essential for achieving a computational auditory scene analysis system that can extract useful information from a mixture of various sounds. Because existing methods cope separately with these problems despite their mutual dependence, the overall result with these approaches can be degraded by any failure in one of these components. This paper presents a unified Bayesian framework to solve these problems simultaneously where localization and separation are regarded as a clustering problem. Experimental results confirm tha
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Zhao, Jianwei, Qiang Zhai, Pengbo Zhao, Rui Huang, and Hong Cheng. "Co-Visual Pattern-Augmented Generative Transformer Learning for Automobile Geo-Localization." Remote Sensing 15, no. 9 (2023): 2221. http://dx.doi.org/10.3390/rs15092221.

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Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographic location of the ground-level camera by matching against enormous geo-tagged aerial (e.g., satellite) images, has received a lot of attention but remains extremely challenging due to the drastic appearance differences across aerial–ground views. In existing methods, global representations of different views are extracted primarily using Siamese-like architectur
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Smola, H., G. Thiekötter, and NE Fusenig. "Mutual induction of growth factor gene expression by epidermal-dermal cell interaction." Journal of Cell Biology 122, no. 2 (1993): 417–29. http://dx.doi.org/10.1083/jcb.122.2.417.

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Epithelial-mesenchymal interactions control epidermal growth and differentiation, but little is known about the mechanisms of this interaction. We have examined the effects of human dermal microvascular endothelial cells (DMEC) and fibroblasts on keratinocytes in conventional (feeder layer) and organotypic cocultures (lifted collagen gels) and demonstrated the induction of paracrine growth factor gene expression. Clonal keratinocyte growth was similarly stimulated in cocultures with irradiated DMEC and fibroblasts as feeder cells. This effect is most probably caused by induction of growth fact
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Pu, Henglin, Chao Cai, Menglan Hu, Tianping Deng, Rong Zheng, and Jun Luo. "Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming." Sensors 21, no. 2 (2021): 532. http://dx.doi.org/10.3390/s21020532.

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Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose
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