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Статті в журналах з теми "Mutual localization"

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Автор: Grafiati

Опубліковано: 8 червня 2024

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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 (November 20, 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 amount of information. This paper presents an investigation of self localization error of each robot in a group using Extended Kalman Filter to solve the localization problem with the insufficient landmarks and inaccurate position information.

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 type method. Then, these estimates are adopted to reconstruct the mutual coupling matrix. In addition, the fourth-order cumulant technique is required to eliminate the Gauss colored noise effects caused by mutual coupling calibration of the raw received data vector. Finally, in an algebraic way, the results of rapid classification and localization of near-field sources are obtained without any spectral search. The proposed algorithm is described in detail, and its behavior is illustrated by numerical examples.

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 (April 25, 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 (March 2019): 2372–94. http://dx.doi.org/10.1016/j.jfranklin.2019.01.019.

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5

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 (September 10, 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-functioned robots. The arc-carving mapping algorithm is to increase the azimuth resolution of sonar readings by using their freespace regions to shrink the possible regions. It is further improved in both accuracy and efficiency by the creative ideas of bidirectional carving, grid-orientedly correlated-arc carving, working robot group substitution, and termination rule. Software simulation and hardware experiment have verified the feasibility of the proposed SLAM philosophy when implemented in a typical medium-cluttered office by a team of three robots. Besides the combined effect, individual algorithm components have also been investigated.

6

Song, Jiahui, Haitao Wu, Xiaochen Guo, Dehuai Jiang, Xuqiang Guo, Tong Lv, and Hanze Luo. "GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System." Applied Sciences 12, no. 3 (January 27, 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 verification results, the GNSS spoofing is identified, and the corresponding back-end optimization strategy is selected to obtain a smoothing localization result. Through simulation, the time verification threshold and localization verification threshold of the proposed method are obtained under the condition that the sensors frequency and accuracy are set. The KITTI datasets in rural and urban scenes are used for verification. The simulation results show that our method can identify GNSS spoofing and provide credible and smoothing localization results in the case of GNSS spoofing occurs.

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, Jianfeng Dong, He Yan, Yang Yang, Pan Zhou, and Yu Cheng. "Unsupervised Domain Adaptative Temporal Sentence Localization with Mutual Information Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (March 24, 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 localization knowledge can be transferred from a fully-annotated data domain (source domain) to a new unannotated data domain (target domain). Particularly, we propose an effective and novel baseline for UDA-TSL to bridge the multi-modal gap across different domains and learn the potential correspondence between the video-query pairs in target domain. We first develop separate modality-specific domain adaptation modules to smoothly balance the minimization of the domain shifts in cross-dataset video and query domains. Then, to fully exploit the semantic correspondence of both modalities in target domain for unsupervised localization, we devise a mutual information learning module to adaptively align the video-query pairs which are more likely to be relevant in target domain, leading to more truly aligned target pairs and ensuring the discriminability of target features. In this way, our model can learn domain-invariant and semantic-aligned cross-modal representations. Three sets of migration experiments show that our model achieves competitive performance compared to existing methods.

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 (June 30, 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 frequencies of the interaural phase difference and interaural level difference. Then, by using the random forest algorithm and embedding the mutual information as a feature selection criteria, the feature selection procedures are encoded with the training of the localization mapping. The trained mapping model is capable of using interaural features more efficiently, and, because of the multiple-tree-based model structure, the localization model shows robust performance to noise and interference. By integrating the direct path relative transfer function estimation, we propose to devise a novel localization approach that has improved performance in the presence of noise and reverberation. The proposed mapping model is compared with the state-of-the-art manifold learning procedure in different acoustical configurations, and a more accurate and robust output can be observed.

10

Lee, Yang-Weon. "Automatic Mutual Localization of Swarm Robot Using a Particle Filter." Journal of information and communication convergence engineering 10, no. 4 (December 31, 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 (June 30, 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 (September 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 (May 15, 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 (May 9, 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 regulatory protein, either separately or together. Each protein localized predominantly to the cytoplasm when expressed alone. On co-expression, however, both proteins localized virtually entirely to the nucleus. The enzymic activity of glucokinase was not required for promoting nuclear import of the two proteins, as shown with a glucose-phosphorylation-deficient mutant. Finally, in embryonic kidney cells expressing the regulatory protein alone, treatment with leptomycin B resulted in a partial redistribution of the protein from the cytoplasm to the nucleus, suggesting that this protein can shuttle between the two compartments.

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 (October 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 (March 1, 2019): 602–17. http://dx.doi.org/10.1109/tmc.2018.2841842.

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17

Li, Haoran, Hongxun Yao, and Yuxin Hou. "HPnet: Hybrid Parallel Network for Human Pose Estimation." Sensors 23, no. 9 (April 30, 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-attention modules and the convolution modules, which conduce to leverage the complementary capabilities effectively. The parallel network ensures that the self-attention branch tends to model the long-range dependency to enhance the semantic representation, whereas the local sensitivity of the convolution branch contributes to high-precision localization simultaneously. To further mitigate the conflict, we proposed a cross-branches attention module to gate the features generated by both branches along the channel dimension. The hybrid parallel network achieves 75.6% and 75.4%AP on COCO validation and test-dev sets and achieves consistent performance on both higher-precision localization and overall performance. The experiments show that our hybrid parallel network is on par with the state-of-the-art human pose estimation models.

18

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 (April 11, 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 passive multiple target scenario. Therefore, in this paper, the authors propose a joint interference cancellation method to mitigate the effect of these limitations. Uniform circular array (UCA) of reference tags were used to reduce the interference of the array aperture. A carefully designed relative position of adjacent reference tags and a modified channel model were combined to reduce the mutual coupling. A virtual distributed reader antenna array was used to reduce the false positive and false negative estimations. The system was evaluated in real indoor environment using noodles and colas as targets and can work well in a smoky environment that is similar to some real industrial environments. The accuracy of target number estimation is 97.5%. The spatial resolution is about 30 cm, and the median error of 2-D multiple target localization is about 5.5 cm.

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 conditions are less addressed in previous research works. Further, our proposed algorithm provides low overhead due to the usage of less control messages in a limited transmission range. In addition, we have also proposed an algorithm to detect the malicious anchor nodes inside the network. The simulated results show that our proposed algorithm is efficient in terms of time consumption, localization accuracy, and localization ratio in the presence of malicious nodes.

20

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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21

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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22

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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23

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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24

Yamauchi, Yohei, Kaoru Wada, Fumi Goshima, Hiroki Takakuwa, Tohru Daikoku, Masao Yamada, and Yukihiro Nishiyama. "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 (February 1, 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 upon sole expression. We have found that VP26 coexpressed with the UL14 protein showed mutual and predominant relocation into the nucleus. At least seven viral genes encode proteins (UL6, UL15, UL17, UL25, UL28, UL32 and UL33) that are required for DNA cleavage and packaging. We have found that the UL33 protein, which was also cytoplasmic by sole expression, was relocated to the nucleus upon expression with the UL14 protein, which again seemed to be a result of mutual influence. Coexpression experiments also suggested the possibility of a mutual influence between the UL14 and UL17 proteins, and the UL17 protein and VP26. Our results suggest that the UL14 protein can influence the intracellular localization patterns of a number of proteins belonging to the capsid or the DNA encapsidation machinery.

25

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 (November 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 kalman filter and believable factor of non-localized nodes, which can alleviate the influence caused by environmental noisy and unstable wireless signals to improve the localization accuracy. Besides, we implement a real system based on ZigBee technique and perform experiments on the campus of Huaqiao University. Experimental results and theoretical analysis validate the effectiveness of the proposed method.

26

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 (May 10, 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 processing can improve the estimation results in the case of low signal noise ratio of incoming signal sources and small number of snapshots. Then, these estimates are adopted to reconstruct the mutual coupling matrix. Finally, both direction and range parameters of near-field sources are obtained through spatial search after mutual coupling effects and far-field components elimination. The proposed algorithm is described in detail, and its behavior is illustrated by numerical examples.

27

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 (November 2021): 1–4. http://dx.doi.org/10.1109/tasc.2021.3091121.

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28

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 (December 2015): 596–603. http://dx.doi.org/10.1109/jiot.2015.2451997.

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29

Marín, Marta, Carlos Moya, and Salvador Máñez. "Mutual Influences between Nitric Oxide and Paraoxonase 1." Antioxidants 8, no. 12 (December 5, 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 biased expression of polymorphism. The purpose of this review is to determine the interactions that paraoxonase 1 has with nitric oxide synthase, its reaction product, nitric oxide (nitrogen monoxide, NO), and its derived reactive species generated in an oxidative medium, with a special focus on its pathological implications.

30

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 (March 26, 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, the opportunity of the regulation of proteasome functioning by ERs—in particular, the expression of immune proteasomes—is discussed. Understanding the complex mechanisms underlying the regulation of ERs and proteasomes has great prospects for the development of new therapeutic agents that can make a significant contribution to the treatment of diseases associated with the impaired function of these biomolecules.

31

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 (October 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 covariance, and increases with quantum number.

32

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 (February 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 shares similar localization patterns to Polo-like kinase (Plk1) and the chromosomal passenger complex (CPC), and although depletion of Plk1 resulted in a loss of Mob1 from the spindle poles, Mob1 recruitment to kinetochores was unaffected. Conversely, disruption of CPC signaling resulted in a loss of Mob1 from kinetochores without disrupting recruitment to the spindle poles. In Mob1-depleted cells, the relocalization of the CPC and mitotic kinesin-like protein (MKLP) 2 to the spindle midzone was delayed during early anaphase, and as a consequence, the midzone recruitment of MKLP1 also was affected. Together, these results suggest that Mob1 and the other mammalian orthologues of the mitotic exit network regulate mitotic progression by facilitating the timely mobilization of the CPC to the spindle midzone.

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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 (August 24, 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 information for identifying the potential leaks. The optimal sensor placement problem is then reformulated as a {0, 1} quadratic knapsack problem, seeking an optimal sensor placement scheme by minimizing the average mutual coherence to maximize the degree of diagnosability. To effectively handle the complicated real-life water distribution networks, a validated binary version of artificial bee colony algorithm enhanced by genetic operators, including crossover and swap, is introduced to solve the binary knapsack problem. The proposed strategy is illustrated and validated through a real-life water distribution network with synthetically generated field data.

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Rand, S. C. "Strong localization of light and photonic atoms." Canadian Journal of Physics 78, no. 5-6 (April 5, 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 unusual regime is now accessible over broad wavelength ranges in random nanoparticle media, opening the way to studies of novel phenomena such as strong localization phase transitions, lasers without cavities, and optical energy storage. PACS Nos.: 78.45+h, 78.60Hk, 42.55Rz, 42.25Fx, 78.35+c, 78.20Dj, 42.50Gy

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

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Schalk, Stefan G., Libertario Demi, Nabil Bouhouch, Maarten P. J. Kuenen, Arnoud W. Postema, Jean J. M. C. H. de la Rosette, Hessel Wijkstra, Tjalling J. Tjalkens, and Massimo Mischi. "Contrast-Enhanced Ultrasound Angiogenesis Imaging by Mutual Information Analysis for Prostate Cancer Localization." IEEE Transactions on Biomedical Engineering 64, no. 3 (March 2017): 661–70. http://dx.doi.org/10.1109/tbme.2016.2571624.

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37

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 (June 21, 2006): 2952. http://dx.doi.org/10.1038/sj.emboj.7601183.

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38

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 (March 2, 2006): 1058–69. http://dx.doi.org/10.1038/sj.emboj.7601020.

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39

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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40

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 (November 21, 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 coupling effect degrades the estimation performance. Therefore, a novel CS-based DOD/DOA estimation model with both the off-grid and mutual coupling effect is proposed, and a novel sparse reconstruction method is proposed to estimate DOD/DOA with updating both the off-grid and mutual coupling parameters iteratively. Moreover, to describe the estimation performance, the corresponding Cramér–Rao lower bounds (CRLBs) with all the unknown parameters are theoretically derived. Simulation results show that the proposed method can improve the DOD/DOA estimation in the scenario with unknown mutual coupling effect, and outperform state-of-the-art methods.

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Zhao, Jianchun, Guangyue Zhang, Jierui Qu, Jiayi Chen, Shuang Liang, Kaiqiang Wei, and Guan Wang. "A Sound Source Localization Method Based on Frequency Divider and Time Difference of Arrival." Applied Sciences 13, no. 10 (May 18, 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 traditional generalized correlation time delay estimation algorithm for pre-processing, sampling, and localization of sound source acoustic waves and adopts a low-cost microphone array deployment scheme as far as possible to improve the flexibility and practicality of the localization system; at the same time, the “Minimum Sphere Method” is used at the back end of the algorithm to classify the localization coordinates at different frequencies and, finally, output reasonable sound source coordinates. In the back-end of the algorithm, the “Minimum Sphere Method” is used to classify the localization coordinates at different frequencies and, finally, output the reasonable sound source coordinates. The experimental results show that the sound source localization system designed in this paper has good performance in terms of localization accuracy and cost-effectiveness and overcomes the failure of the generalized mutual correlation algorithm in the original application of high noise environment and multi-source environment localization.

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Arkhipova, I. V. "MUTUAL RELATIONS IN THE SPHERE OF TEMPORAL CATEGORIES (based on the German language)." Siberian Philological Forum 21, no. 4 (November 30, 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 semantic approach to the study and description of semantic categories. The main methods used are: the method of directed sampling, hypothetical-deductive, inductive, descriptive methods, classification method, as well as a method of generalization and interpretation of linguistic material. The research material includes statements with taxis prepositions obtained by the method of directed sampling from the Leipzig National Corpus, the Russian National Corpus and the German Electronic Dictionary. Research results. In the course of the study, it was found out that due to the inter-categorial crossing of functional and semantic categories of taxis, temporality, aspectuality and temporal localization in German statements with prepositional deverbatives, various variants of primary-taxis categorial situations of simultaneity, precedence and consecution are actualized.

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Momin, Shadab, Yang Lei, Zhen Tian, Justin Roper, Jolinta Lin, Shannon Kahn, Hui-Kuo Shu, Jeffrey Bradley, Tian Liu, and Xiaofeng Yang. "Cascaded mutual enhancing networks for brain tumor subregion segmentation in multiparametric MRI." Physics in Medicine & Biology 67, no. 8 (April 11, 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 strategy between the classification localization map (CLM) module and segmentation module. Retina U-Net is trained to accurately identify view-of-interest and feature maps of the whole tumor (WT), which are then transferred to the CLM module and segmentation module. Subsequently, CLM generated by the CLM module is integrated with the segmentation module to bring forth a mutual enhancing strategy. In this way, our proposed framework first focuses on WT through Retina U-Net, and since WT consists of subregions, a mutual enhancing strategy then further aims to classify and segment subregions embedded within WT. We implemented and evaluated our proposed framework on the BraTS 2020 dataset consisting of 369 cases. We performed a 5-fold cross-validation on 200 datasets and a hold-out test on the remaining 169 cases. To demonstrate the effectiveness of our network design, we compared our method against the networks without Retina U-Net, mutual enhancing strategy, and a recently published Cascaded U-Net architecture. Results of all four methods were compared to the ground truth for segmentation and localization accuracies. Our method yielded significantly (P < 0.01) better values of dice-similarity-coefficient, center-of-mass-distance, and volume difference compared to all three competing methods across all tumor labels (necrosis and non-enhancing, edema, enhancing tumor, WT, tumor core) on both validation and hold-out dataset. Overall quantitative and statistical results of this work demonstrate the ability of our method to both accurately and automatically segment brain tumor subregions.

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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 (April 22, 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 architectures, but their interactive benefits are seldom taken into account. In this paper, we present a novel approach using cross-view knowledge generative techniques in combination with transformers, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, by taking the initial representations produced by the backbone network, MGTL develops two separate generative sub-modules—one for aerial-aware knowledge generation from ground-view semantics and vice versa—and fully exploits the entirely mutual benefits through the attention mechanism. Moreover, to better capture the co-visual relationships between aerial and ground views, we introduce a cascaded attention masking algorithm to further boost accuracy. Extensive experiments on challenging public benchmarks, i.e., CVACT and CVUSA, demonstrate the effectiveness of the proposed method, which sets new records compared with the existing state-of-the-art models. Our code will be available upon acceptance.

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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 (July 15, 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 factor expression in cocultured dermal cells. Keratinocytes stimulated mRNA levels for KGF and IL-6 in both mesenchymal cell types and GM-CSF in fibroblasts. The feeder effect could not be replaced by conditioned media or addition of isolated growth factors. In organotypic cocultures with keratinocytes growing on collagen gels (repopulated with dermal cells), a virtually normal epidermis was formed within 7 to 10 d. Keratinocyte proliferation was drastically stimulated by dermal cells (histone 3 mRNA expression and BrdU labeling) which continued to proliferate as well in the gel. Expression of all typical differentiation markers was provoked in the reconstituted epithelium, though with different localization as compared to normal epidermis. Keratins K1 and K10 appeared coexpressed but delayed, reflecting conditions in epidermal hyperplasia. Keratin localization and proliferation were normalized under in vivo conditions, i.e., in surface transplants on nude mice. From these data it is concluded that epidermal homeostasis is in part controlled by complex reciprocally induced paracrine acting factors in concert with cell-cell interactions and extracellular matrix influences.

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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 (September 20, 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 that our method outperforms state-of-the-art methods in terms of the separation quality with various setups including practical reverberant environments.

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Wang, K., L. Wang, J. Xie, and M. Tao. "Classification and Localization of Mixed Sources Using Uniform Circular Array under Unknown Mutual Coupling." Radioengineering 27, no. 1 (April 12, 2019): 220–29. http://dx.doi.org/10.13164/re.2019.0220.

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Zheng, Zhidong, Jianyun Zhang, and Yuebo Wu. "Multi-target localization for bistatic MIMO radar in the presence of unknown mutual coupling." Journal of Systems Engineering and Electronics 23, no. 5 (October 2012): 708–14. http://dx.doi.org/10.1109/jsee.2012.00087.

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Wen, Kelei, Ye Tian, and Zhiyan Dong. "Mixed source localization considering mutual coupling and unknown nonuniform noise under exact spatial geometry." Signal Processing 210 (September 2023): 109066. http://dx.doi.org/10.1016/j.sigpro.2023.109066.

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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 (January 13, 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 a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.