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Journal articles on the topic 'Topological change detection'
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KUBOTA, NAOYUKI, HIROYUKI KOJIMA, NAOHIDE AIZAWA, and DALAI TANG. "DYNAMIC TOPOLOGICAL VISUALIZATION OF CHANGE IN PERCEPTUAL INFORMATION OF PARTNER ROBOTS." International Journal of Information Acquisition 05, no. 03 (September 2008): 247–58. http://dx.doi.org/10.1142/s0219878908001673.
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This paper proposes a method for topologically visualizing the perceptual information of a partner robot. First, we explain the methods for human detection, human motion extraction, and object recognition. Next, we explain the perceptual system of the robot based on the detected human and objects. We propose a topological visualization method based on a spring-mass-damper system according to the perceptual information. Finally, we show several experimental results of the proposed method, and the proposed method enables a human to understand what the robot perceives in the interaction with the human and environment.
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Wang, Xianghai, Wei Cheng, Yining Feng, and Ruoxi Song. "TSCNet: Topological Structure Coupling Network for Change Detection of Heterogeneous Remote Sensing Images." Remote Sensing 15, no. 3 (January 20, 2023): 621. http://dx.doi.org/10.3390/rs15030621.
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With the development of deep learning, convolutional neural networks (CNNs) have been successfully applied in the field of change detection in heterogeneous remote sensing (RS) images and achieved remarkable results. However, most of the existing methods of heterogeneous RS image change detection only extract deep features to realize the whole image transformation and ignore the description of the topological structure composed of the image texture, edge, and direction information. The occurrence of change often means that the topological structure of the ground object has changed. As a result, these algorithms severely limit the performance of change detection. To solve these problems, this paper proposes a new topology-coupling-based heterogeneous RS image change detection network (TSCNet). TSCNet transforms the feature space of heterogeneous images using an encoder–decoder structure and introduces wavelet transform, channel, and spatial attention mechanisms. The wavelet transform can obtain the details of each direction of the image and effectively capture the image’s texture features. Unnecessary features are suppressed by allocating more weight to areas of interest via channels and spatial attention mechanisms. As a result of the organic combination of a wavelet, channel attention mechanism, and spatial attention mechanism, the network can focus on the texture information of interest while suppressing the difference of images from different domains. On this basis, a bitemporal heterogeneous RS image change detection method based on the TSCNet framework is proposed. The experimental results on three public heterogeneous RS image change detection datasets demonstrate that the proposed change detection framework achieves significant improvements over the state-of-the-art methods.
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Feilhauer, J., M. Zelent, Zhiwang Zhang, J. Christensen, and M. Mruczkiewicz. "Unidirectional spin-wave edge modes in magnonic crystal." APL Materials 11, no. 2 (February 1, 2023): 021104. http://dx.doi.org/10.1063/5.0134099.
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We present a numerical demonstration of magnonic crystals hosting unidirectional, topologically protected edge states. The magnonic crystal is formed of dipolarly coupled Permalloy triangles. We show that due to the geometry of the block, the size of the structure can be scaled up. In addition, edge states can be found over a wide frequency range. Experimental detection of edge excitations in the considered system can be done with state-of-the-art techniques. Thus, we demonstrate a proof-of-concept magnonic Chern topological insulator nanostructure with simple geometry feasible for experimental realization. Furthermore, by tuning the strength of the perpendicular magnetic field, we induce a topological phase transition, which results in the change of direction of the topological edge state. Then, we demonstrate the magnonic switch based on this effect.
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Wang, Xiaodong, Dongbao Zhao, Xingze Li, Nan Jia, and Li Guo. "Change Detection and Incremental Updates for Multi-Source Road Networks Considering Topological Consistency." ISPRS International Journal of Geo-Information 14, no. 1 (December 24, 2024): 2. https://doi.org/10.3390/ijgi14010002.
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Vector road networks are vital components of intelligent transportation systems and electronic navigation maps. There is a pressing need for efficient and rapid dynamic updates for road network data. In this paper, we propose a series of methods designed specifically for geometric change detection and the topological consistency updating of multi-source vector road networks without relying on complicated road network matching. For geometric change detection, we employ buffer analysis to compare various sources of vector road networks, differentiating between newly added, deleted, and unchanged road features. Furthermore, we utilize road shape similarity analysis to detect and recognize partial matching relationships between different road network sources. For incremental updates, we define topology consistency and propose three distinct methods for merging road nodes, aiming to preserve the topological integrity of the road network to the greatest extent possible. To address geometric conflicts and topological inconsistencies, we present a fusion and update method specifically tailored for partially matched road features. In order to verify the proposed methods, a road central line network with a scale of 1:10000 from the official institution is employed to geometrically update the commercial navigation road network of a similar scale in the remote area. The experiment results indicate that our method achieves an impressive 91.7% automation rate in detecting geometric changes for road features. For the remaining 8.3% of road features, our method provides suggestions on potential geometric changes, albeit necessitating manual verification and assessment. In terms of the incremental updating of the road network, approximately 89.2% of the data can be seamlessly updated automatically using our methods, while a minor 10.8% requires manual intervention for road updates. Collectively, our methods expedite the updating cycle of vector road network data and facilitate the seamless sharing and integrated utilization of multi-source road network data.
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Yu, Lanlan, Biao Wang, Luojie Huang, Zhen Dai, Yang Yang, Yan Chen, and Ping Li. "Detecting change points in dynamic networks by measuring cluster stability." International Journal of Modern Physics C 32, no. 09 (May 18, 2021): 2150123. http://dx.doi.org/10.1142/s0129183121501230.
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Clustering patterns are ubiquitously present in a variety of networked systems, and may change with the evolution of network topology. Probing into the cluster structures can shed light on the change of the entire network, especially those sudden changes emerging in the process of network evolution. Though abundant researches have been done in detecting the changes of dynamic networks, more precisely, change points at which the network topology experiences abrupt changes, most of the existing methods focus on local changes (e.g. edges change) that are commonly mixed with noise, giving rise to high false positive reports. Different from the previous work, here we inspect the topological changes from mesoscale clusters of dynamic networks, which will reduce the perturbation of link variation to detection accuracy. Towards this end, we look for the invariant clusters of nodes during the observation window in dynamic networks and propose a new measure to quantify the stability of node clusters with respect to the invariant clustering patterns. Then the change of dynamic networks at mesoscale can be captured by comparing the variations of stability measures. In the light of the proposed measurement, we design a change-point detection algorithm and conduct extensive experiments on synthetic and real-life datasets to demonstrate the effectiveness of our method. The results show the outperformance of our method in identifying change points, compared to several baseline methods.
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Gu, Kongjing, Liang Yan, Xiang Li, Xiaojun Duan, and Jingjie Liang. "Change point detection in multi-agent systems based on higher-order features." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 11 (November 2022): 111102. http://dx.doi.org/10.1063/5.0126848.
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Change point detection (CPD) for multi-agent systems helps one to evaluate the state and better control the system. Multivariate CPD methods solve the [Formula: see text] time series well; however, the multi-agent systems often produce the [Formula: see text] dimensional data, where [Formula: see text] is the dimension of multivariate observations, [Formula: see text] is the total observation time, and [Formula: see text] is the number of agents. In this paper, we propose two valid approaches based on higher-order features, namely, the Betti number feature extraction and the Persistence feature extraction, to compress the [Formula: see text]-dimensional features into one dimension so that general CPD methods can be applied to higher-dimensional data. First, a topological structure based on the Vietoris–Rips complex is constructed on each time-slice snapshot. Then, the Betti number and persistence of the topological structures are obtained to separately constitute two feature matrices for change point estimates. Higher-order features primarily describe the data distribution on each snapshot and are, therefore, independent of the node correspondence cross snapshots, which gives our methods unique advantages in processing missing data. Experiments in multi-agent systems demonstrate the significant performance of our methods. We believe that our methods not only provide a new tool for dimensionality reduction and missing data in multi-agent systems but also have the potential to be applied to a wider range of fields, such as complex networks.
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Shao, Pan, Wenzhong Shi, Zhewei Liu, and Ting Dong. "Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting." Remote Sensing 13, no. 16 (August 10, 2021): 3171. http://dx.doi.org/10.3390/rs13163171.
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Remote sensing change detection (CD) plays an important role in Earth observation. In this paper, we propose a novel fusion approach for unsupervised CD of multispectral remote sensing images, by introducing majority voting (MV) into fuzzy topological space (FTMV). The proposed FTMV approach consists of three principal stages: (1) the CD results of different difference images produced by the fuzzy C-means algorithm are combined using a modified MV, and an initial fusion CD map is obtained; (2) by using fuzzy topology theory, the initial fusion CD map is automatically partitioned into two parts: a weakly conflicting part and strongly conflicting part; (3) the weakly conflicting pixels that possess little or no conflict are assigned to the current class, while the pixel patterns with strong conflicts often misclassified are relabeled using the supported connectivity of fuzzy topology. FTMV can integrate the merits of different CD results and largely solve the conflicting problem during fusion. Experimental results on three real remote sensing images confirm the effectiveness and efficiency of the proposed method.
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Qu, Song, Yuqing Du, Mu Zhu, Guan Yuan, Jining Wang, Yanmei Zhang, and Xiangyu Duan. "Dynamic Community Detection Based on Evolutionary DeepWalk." Applied Sciences 12, no. 22 (November 11, 2022): 11464. http://dx.doi.org/10.3390/app122211464.
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To fully characterize the evolution process of the topological structure of dynamic communities, we propose a dynamic community detection based on Evolutionary DeepWalk (DEDW) for the high-dimensional data and dynamic characteristics. First, DEDW solves the problem of data sparseness in the process of dynamic network data representation through graph embedding. Then, DEDW uses the DeepWalk algorithm to generate node embedding feature vectors based on the characteristics of the stable change of the community structure; finally, DEDW integrates historical network structure information to generate evolutionary graph features and implements dynamic community detection with the K-means algorithm. Experiments show that DEDW can mine the time-smooth change characteristics of dynamic communities, solve the problem of data sparseness in the process of node embedding, fully consider historical structure information, and improve the accuracy and stability of dynamic community detection.
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Tribot-La-Spiere, M. "Естественнонаучные и философские аспекты единства живописи и музыки: взгляд через призму искусственного интеллекта." Studia Culturae, no. 57 (December 26, 2023): 97. http://dx.doi.org/10.31312/2310-1245-2023-57-97-106.
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This article discusses a philosophical description of the unity of painting and music through the prism of the natural-science phenomenon of the generation of phonon oscillations by the surface of a painting surface. A method is proposed for using artificial intelligence in the automated detection of phonon oscillations, which consists in a matrix recording of dynamic two-dimensional light scattering, convolution of the matrix into topological indices, and sonification of the change in indices over time. We have developed the new method for controlling surface phonon oscillations based on diffuse reflection kinetics. The specialized software “Vidan” records the kinetics of changes with 10 topological indices, which reflects the propagation of a phonon wave on the surface during capture and inelastic scattering of secondary cosmic rays [3]. To control the state of the surface, either the fingerprint of 10 topological descriptors is used (this analysis is automated through the “Atrium” remote software), or a spectral analysis of the distribution of descriptor values in time is carried out. In art the method was used for generation the "digital" passport (including the topological descriptors) of new paintings in order to control the appearance of copies. The algorithm for converting phonon oscillations of a painting leads to the generation of a unique melody. In this case, the main part of the creative activity belongs not to the composer, but to artificial intelligence, which generates a melody based on the chemical composition of the paint material of the picture and the method of applying the painting layer (that is, the artist's "hands"). The material heritage of the painter's work continues to generate new works of art “offline”, while artificial intelligence acts only as a way of detecting and sonification of the natural vibrations of the surface of the picture. It is shown that each painting generates its own unique melody. This uniqueness made it possible to propose an original system for checking the authenticity of works of art: for new works of artists. A cloud-based automated control system "Atrium" has been implemented, which allows tracking any logistical movements of the picture and the change of owner according to the topological musical digital passport of the picture generated by its surface.
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Soroush, M. Z., K. Maghooli, N. F. Pisheh, M. Mohammadi, P. Z. Soroush, and P. Tahvilian. "Detection of Change to SSVEPs Using Analysis of Phase Space Topological Features: A Novel Approach." Neurophysiology 51, no. 3 (May 2019): 180–90. http://dx.doi.org/10.1007/s11062-019-09811-x.
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Khasawneh, Firas A., and Elizabeth Munch. "Topological data analysis for true step detection in periodic piecewise constant signals." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2218 (October 2018): 20180027. http://dx.doi.org/10.1098/rspa.2018.0027.
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This paper introduces a simple yet powerful approach based on topological data analysis for detecting true steps in a periodic, piecewise constant (PWC) signal. The signal is a two-state square wave with randomly varying in-between-pulse spacing, subject to spurious steps at the rising or falling edges which we call digital ringing. We use persistent homology to derive mathematical guarantees for the resulting change detection which enables accurate identification and counting of the true pulses. The approach is tested using both synthetic and experimental data obtained using an engine lathe instrumented with a laser tachometer. The described algorithm enables accurate and automatic calculations of the spindle speed without any choice of parameters. The results are compared with the frequency and sequency methods of the Fourier and Walsh–Hadamard transforms, respectively. Both our approach and the Fourier analysis yield comparable results for pulses with regular spacing and digital ringing while the latter causes large errors using the Walsh–Hadamard method. Further, the described approach significantly outperforms the frequency/sequency analyses when the spacing between the peaks is varied. We discuss generalizing the approach to higher dimensional PWC signals, although using this extension remains an interesting question for future research.
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Oueslati, Rania, Yu Jiang, Jiangang Chen, and Jayne Wu. "Rapid and Sensitive Point of Care Detection of MRSA Genomic DNA by Nanoelectrokinetic Sensors." Chemosensors 9, no. 5 (April 29, 2021): 97. http://dx.doi.org/10.3390/chemosensors9050097.
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Biosensors have shown great potential in realizing rapid, low cost, and portable on-site detection for diseases. This work reports the development of a new bioelectronic sensor called AC electrokinetics-based capacitive (ABC) biosensor, for the detection of genomic DNA (gDNA) of methicillin-resistant Staphylococcus aureus (MRSA). The ABC sensor is based on interdigitated microelectrodes biofunctionalized with oligonucleotide probes. It uses a special AC signal for direct capacitive monitoring of topological change on nanostructured sensor surface, which simultaneously induces dielectrophoretic enrichment of target gDNAs. As a result, rapid and specific detection of gDNA/probe hybridization can be realized with high sensitivity. It requires no signal amplification such as labeling, hybridization chain reaction, or nucleic acid sequence-based amplification. This method involves only simple sample preparation. After optimization of nanostructured sensor surface and signal processing, the ABC sensor demonstrated fast turnaround of results (~10 s detection), excellent sensitivity (a detection limit of 4.7 DNA copies/µL MRSA gDNA), and high specificity, suitable for point of care diagnosis. As a bioelectronic sensor, the developed ABC sensors can be easily adapted for detections of other infectious agents.
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Silva, Nilton Correia da, Osmar Abílio de Carvalho Júnior, Antonio Nuno de Castro Santa Rosa, Renato Fontes Guimarães, and Roberto Arnaldo Trancoso Gomes. "CHANGE DETECTION SOFTWARE USING SELF-ORGANIZING FEATURE MAPS." Revista Brasileira de Geofísica 30, no. 4 (December 1, 2012): 505. http://dx.doi.org/10.22564/rbgf.v30i4.237.
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Os mapas auto-organizáveis (SOFM) consistem em um tipo de rede neural artificial que permite a conversão de dados de alta dimensão, complexos e não lineares, em simples relações geométricas com baixa dimensionalidade. Este método também pode ser utilizado para a classificação de imagens de sensoriamento remoto, pois permite a compressão de dados de alta dimensão preservando as relações topológicas dos dados primários. Este trabalho objetiva desenvolver uma metodologia eficaz para a utilização de mapas auto-organizáveis na detecção de mudanças. No presente estudo o SOFM é utilizado para a classificação não supervisionada de dados de sensoriamento remoto, considerando os seguintes atributos: espaciais (x, y), espectrais e temporais. O método é empregado na região oeste da Bahia, que teve recentemente um aumento significativo em monoculturas. Testes foram realizados com os parâmetros do SOFM com o objetivo de refinar o mapa de detecção demudanças. O SOFM possibilita uma melhor seleção de células e dos correspondentes vetores de peso, que mostram o processo de ordenação e agrupamento hierárquicodos dados. Esta informação é essencial para identificar mudanças ao longo do tempo. Um programa em linguagem C ++ do método proposto foi desenvolvido. ABSTRACT. Self-organizing feature maps (SOFM) consist of a type of artificial neural network that allows the conversion from high-dimensional data into simple geometric relationships with low-dimensionality. This method can also be used for classification of remote sensing images because it allows the compression of high dimensional data while preserving the most important topological and metric relationships of the primary data. This paper aims to develop an effective methodology forusing self-organizing maps in change detection. In this study, SOFM is used for unsupervised classification of remote sensing data, considering the following attributes: spatial (x and y), spectral and temporal. The method is tested and simulated in the western region of Bahia that has observed a significant increase in mechanized agriculture. Tests were performed with the SOFM parameters for the purpose of fine tuning a change detection map. The SOFM provides the best selection of cell and corresponding adjustment of weight vectors, which show the process of ordering and hierarchical clustering of the data. This information is essential to identify changes over time. All algorithms were implemented in C++ language.Keywords: unsupervised classification; land cover; multitemporal analysis; remote sensing
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Jimenez-Sierra, David Alejandro, Hernán Darío Benítez-Restrepo, Hernán Darío Vargas-Cardona, and Jocelyn Chanussot. "Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops." Remote Sensing 12, no. 17 (August 19, 2020): 2683. http://dx.doi.org/10.3390/rs12172683.
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The complementary nature of different modalities and multiple bands used in remote sensing data is helpful for tasks such as change detection and the prediction of agricultural variables. Nonetheless, correctly processing a multi-modal dataset is not a simple task, owing to the presence of different data resolutions and formats. In the past few years, graph-based methods have proven to be a useful tool in capturing inherent data similarity, in spite of different data formats, and preserving relevant topological and geometric information. In this paper, we propose a graph-based data fusion algorithm for remotely sensed images applied to (i) data-driven semi-unsupervised change detection and (ii) biomass estimation in rice crops. In order to detect the change, we evaluated the performance of four competing algorithms on fourteen datasets. To estimate biomass in rice crops, we compared our proposal in terms of root mean squared error (RMSE) concerning a recent approach based on vegetation indices as features. The results confirm that the proposed graph-based data fusion algorithm outperforms state-of-the-art methods for change detection and biomass estimation in rice crops.
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Chen, J., J. L. Hou, and M. Deng. "AN APPROACH TO ALLEVIATE THE FALSE ALARM IN BUILDING CHANGE DETECTION FROM URBAN VHR IMAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 459–65. http://dx.doi.org/10.5194/isprs-archives-xli-b7-459-2016.
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Building change detection from very-high-resolution (VHR) urban remote sensing image frequently encounter the challenge of serious false alarm caused by different illumination or viewing angles in bi-temporal images. An approach to alleviate the false alarm in urban building change detection is proposed in this paper. Firstly, as shadows casted by urban buildings are of distinct spectral and shape feature, it adopts a supervised object-based classification technique to extract them in this paper. Secondly, on the opposite direction of sunlight illumination, a straight line is drawn along the principal orientation of building in every extracted shadow region. Starting from the straight line and moving toward the sunlight direction, a rectangular area is constructed to cover partial shadow and rooftop of each building. Thirdly, an algebra and geometry invariant based method is used to abstract the spatial topological relationship of the potential unchanged buildings from all central points of the rectangular area. Finally, based on an oriented texture curvature descriptor, an index is established to determine the actual false alarm in building change detection result. The experiment results validate that the proposed method can be used as an effective framework to alleviate the false alarm in building change detection from urban VHR image.
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Chen, J., J. L. Hou, and M. Deng. "AN APPROACH TO ALLEVIATE THE FALSE ALARM IN BUILDING CHANGE DETECTION FROM URBAN VHR IMAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 459–65. http://dx.doi.org/10.5194/isprsarchives-xli-b7-459-2016.
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Building change detection from very-high-resolution (VHR) urban remote sensing image frequently encounter the challenge of serious false alarm caused by different illumination or viewing angles in bi-temporal images. An approach to alleviate the false alarm in urban building change detection is proposed in this paper. Firstly, as shadows casted by urban buildings are of distinct spectral and shape feature, it adopts a supervised object-based classification technique to extract them in this paper. Secondly, on the opposite direction of sunlight illumination, a straight line is drawn along the principal orientation of building in every extracted shadow region. Starting from the straight line and moving toward the sunlight direction, a rectangular area is constructed to cover partial shadow and rooftop of each building. Thirdly, an algebra and geometry invariant based method is used to abstract the spatial topological relationship of the potential unchanged buildings from all central points of the rectangular area. Finally, based on an oriented texture curvature descriptor, an index is established to determine the actual false alarm in building change detection result. The experiment results validate that the proposed method can be used as an effective framework to alleviate the false alarm in building change detection from urban VHR image.
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Nguyen, S. H., and T. H. Kolbe. "MODELLING CHANGES, STAKEHOLDERS AND THEIR RELATIONS IN SEMANTIC 3D CITY MODELS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences VIII-4/W2-2021 (October 7, 2021): 137–44. http://dx.doi.org/10.5194/isprs-annals-viii-4-w2-2021-137-2021.
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Abstract. Urban digital twins have been increasingly adopted by cities worldwide. Digital twins, especially semantic 3D city models as key components, have quickly become a crucial platform for urban monitoring, planning, analyses and visualization. However, as the massive influx of data collected from cities accumulates quickly over time, one major problem arises as how to handle different temporal versions of a virtual city model. Many current city modelling deployments lack the capability for automatic and efficient change detection and often replace older city models completely with newer ones. Another crucial task is then to make sense of the detected changes to provide a deep understanding of the progresses made in the cities. Therefore, this research aims to provide a conceptual framework to better assist change detection and interpretation in virtual city models. Firstly, a detailed hierarchical model of all potential changes in semantic 3D city models is proposed. This includes appearance, semantic, geometric, topological, structural, Level of Detail (LoD), auxiliary and scoped changes. In addition, a conceptual approach to modelling most relevant stakeholders in smart cities is presented. Then, a model - reality graph is used to represent both the different groups of stakeholders and types of changes based on their relative interest and relevance. Finally, the study introduces two mathematical methods to represent the relevance relations between stakeholders and changes, namely the relevance graph and the relevance matrix.
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Shi, X., L. Lu, S. Yang, G. Huang, and Z. Zhao. "Object-oriented change detection based on weighted polarimetric scattering differences on POLSAR images." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (June 26, 2015): 149–54. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-149-2015.
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For wide application of change detection with SAR imagery, current processing technologies and methods are mostly based on pixels. It is difficult for pixel-based technologies to utilize spatial characteristics of images and topological relations of objects. Object-oriented technology takes objects as processing unit, which takes advantage of the shape and texture information of image. It can greatly improve the efficiency and reliability of change detection. Recently, with the development of polarimetric synthetic aperture radar (PolSAR), more backscattering features on different polarization state can be available for usage of object-oriented change detection study. In this paper, the object-oriented strategy will be employed. Considering the fact that the different target or target's state behaves different backscattering characteristics dependent on polarization state, an object-oriented change detection method that based on weighted polarimetric scattering difference of PolSAR images is proposed. The method operates on the objects generated by generalized statistical region merging (GSRM) segmentation processing. The merit of GSRM method is that image segmentation is executed on polarimetric coherence matrix, which takes full advantages of polarimetric backscattering features. And then, the measurement of polarimetric scattering difference is constructed by combining the correlation of covariance matrix and the difference of scattering power. Through analysing the effects of the covariance matrix correlation and the scattering echo power difference on the polarimetric scattering difference, the weighted method is used to balance the influences caused by the two parts, so that more reasonable weights can be chosen to decrease the false alarm rate. The effectiveness of the algorithm that proposed in this letter is tested by detection of the growth of crops with two different temporal radarsat-2 fully PolSAR data. First, objects are produced by GSRM algorithm based on the coherent matrix in the pre-processing. Then, the corresponding patches are extracted in two temporal images to measure the differences of objects. To detect changes of patches, a difference map is created by means of weighted polarization scattering difference. Finally, the result of change detection can be obtained by threshold determining. The experiments show that this approach is feasible and effective, and a reasonable choice of weights can improve the detection accuracy significantly.
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Muszynski, Grzegorz, Karthik Kashinath, Vitaliy Kurlin, and Michael Wehner. "Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets." Geoscientific Model Development 12, no. 2 (February 7, 2019): 613–28. http://dx.doi.org/10.5194/gmd-12-613-2019.
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Abstract. Identifying weather patterns that frequently lead to extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Here, we propose an automated method for recognizing atmospheric rivers (ARs) in climate data using topological data analysis and machine learning. The method provides useful information about topological features (shape characteristics) and statistics of ARs. We illustrate this method by applying it to outputs of version 5.1 of the Community Atmosphere Model version 5.1 (CAM5.1) and the reanalysis product of the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). An advantage of the proposed method is that it is threshold-free – there is no need to determine any threshold criteria for the detection method – when the spatial resolution of the climate model changes. Hence, this method may be useful in evaluating model biases in calculating AR statistics. Further, the method can be applied to different climate scenarios without tuning since it does not rely on threshold conditions. We show that the method is suitable for rapidly analyzing large amounts of climate model and reanalysis output data.
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Awrangjeb, Mohammad, Syed Gilani, and Fasahat Siddiqui. "An Effective Data-Driven Method for 3-D Building Roof Reconstruction and Robust Change Detection." Remote Sensing 10, no. 10 (September 21, 2018): 1512. http://dx.doi.org/10.3390/rs10101512.
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Three-dimensional (3-D) reconstruction of building roofs can be an essential prerequisite for 3-D building change detection, which is important for detection of informal buildings or extensions and for update of 3-D map database. However, automatic 3-D roof reconstruction from the remote sensing data is still in its development stage for a number of reasons. For instance, there are difficulties in determining the neighbourhood relationships among the planes on a complex building roof, locating the step edges from point cloud data often requires additional information or may impose constraints, and missing roof planes attract human interaction and often produces high reconstruction errors. This research introduces a new 3-D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the roof planes. It identifies any missing planes through an analysis using the 3-D plane intersection lines between adjacent planes. Then, it generates new planes to fill gaps of missing planes. Finally, it obtains complete building models through insertion of approximate wall planes and building floor. The reported research in this paper then uses the generated building models to detect 3-D changes in buildings. Plane connections between neighbouring planes are first defined to establish relationships between neighbouring planes. Then, each building in the reference and test model sets is represented using a graph data structure. Finally, the height intensity images, and if required the graph representations, of the reference and test models are directly compared to find and categorise 3-D changes into five groups: new, unchanged, demolished, modified and partially-modified planes. Experimental results on two Australian datasets show high object- and pixel-based accuracy in terms of completeness, correctness, and quality for both 3-D roof reconstruction and change detection techniques. The proposed change detection technique is robust to various changes including addition of a new veranda to or removal of an existing veranda from a building and increase of the height of a building.
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Saputra, Azhar Aulia, János Botzheim, and Naoyuki Kubota. "Neuro-Cognitive Locomotion with Dynamic Attention on Topological Structure." Machines 11, no. 6 (June 3, 2023): 619. http://dx.doi.org/10.3390/machines11060619.
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This paper discusses a mechanism for integrating locomotion with cognition in robots. We demonstrate an attentional ability model that can dynamically change the focus of its perceptual area by integrating attention and perception to generate behavior. The proposed model considers both internal sensory information and also external sensory information. We also propose affordance detection that identifies different actions depending on the robot’s immediate possibilities. Attention is represented in a topological structure generated by a growing neural gas that uses 3D point-cloud data. When the robot faces an obstacle, the topological map density increases in the suspected obstacle area. From here, affordance information is processed directly into the behavior pattern generator, which comprises interconnections between motor and internal sensory neurons. The attention model increases the density associated with the suspected obstacle to produce a detailed representation of the obstacle. Then, the robot processes the cognitive information to enact a short-term adaptation to its locomotion by changing its swing pattern or movement plan. To test the effectiveness of the proposed model, it is implemented in a computer simulation and also in a medium-sized, four-legged robot. The experiments validate the advantages in three categories: (1) Development of attention model using topological structure, (2) Integration between attention and affordance in moving behavior, (3) Integration of exteroceptive sensory information to lower-level control of locomotion generator.
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Li, Jia, Huan Lin, Duo Qiang Zhang, and Xiao Lu Xue. "Extracting Geometric Edges from 3D Point Clouds Based on Normal Vector Change." Applied Mechanics and Materials 571-572 (June 2014): 729–34. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.729.
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Normal vector of 3D surface is important differential geometric property over localized neighborhood, and its abrupt change along the surface directly reflects the variation of geometric morphometric. Based on this observation, this paper presents a novel edge detection algorithm in 3D point clouds, which utilizes the change intensity and change direction of adjacent normal vectors and is composed of three steps. First, a two-dimensional grid is constructed according to the inherent data acquisition sequence so as to build up the topology of points. Second, by this topological structure preliminary edge points are retrieved, and the potential directions of edges passing through them are estimated according to the change of normal vectors between adjacent points. Finally, an edge growth strategy is designed to regain the missing edge points and connect them into complete edge lines. The results of experiment in a real scene demonstrate that the proposed algorithm can extract geometric edges from 3D point clouds robustly, and is able to reduce edge quality’s dependence on user defined parameters.
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Larson, Danelle Marie, Wako Bungula, Casey McKean, Alaina Stockdill, Amber Lee, Frederick Forrest Miller, and Killian Davis. "Quantifying ecosystem states and state transitions of the Upper Mississippi River System using topological data analysis." PLOS Computational Biology 19, no. 6 (June 7, 2023): e1011147. http://dx.doi.org/10.1371/journal.pcbi.1011147.
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Aquatic systems worldwide can exist in multiple ecosystem states (i.e., a recurring collection of biological and chemical attributes), and effectively characterizing multidimensionality will aid protection of desirable states and guide rehabilitation. The Upper Mississippi River System is composed of a large floodplain river system spanning 2200 km and multiple federal, state, tribal and local governmental units. Multiple ecosystem states may occur within the system, and characterization of the variables that define these ecosystem states could guide river rehabilitation. We coupled a long-term (30-year) highly dimensional water quality monitoring dataset with multiple topological data analysis (TDA) techniques to classify ecosystem states, identify state variables, and detect state transitions over 30 years in the river to guide conservation. Across the entire system, TDA identified five ecosystem states. State 1 was characterized by exceptionally clear, clean, and cold-water conditions typical of winter (i.e., a clear-water state); State 2 had the greatest range of environmental conditions and contained most the data (i.e., a status-quo state); and States 3, 4, and 5 had extremely high concentrations of suspended solids (i.e., turbid states, with State 5 as the most turbid). The TDA mapped clear patterns of the ecosystem states across several riverine navigation reaches and seasons that furthered ecological understanding. State variables were identified as suspended solids, chlorophyll a, and total phosphorus, which are also state variables of shallow lakes worldwide. The TDA change detection function showed short-term state transitions based on seasonality and episodic events, and provided evidence of gradual, long-term changes due to water quality improvements over three decades. These results can inform decision making and guide actions for regulatory and restoration agencies by assessing the status and trends of this important river and provide quantitative targets for state variables. The TDA change detection function may serve as a new tool for predicting the vulnerability to undesirable state transitions in this system and other ecosystems with sufficient data. Coupling ecosystem state concepts and TDA tools can be transferred to any ecosystem with large data to help classify states and understand their vulnerability to state transitions.
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Yatsyshen, V. V., and A. Yu Gordeev. "Electrodynamic target selection techniques – gradient analysi." Journal of «Almaz – Antey» Air and Space Defence Corporation, no. 3 (September 30, 2016): 3–10. http://dx.doi.org/10.38013/2542-0542-2016-3-3-10.
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We offer a new method for analyzing the electromagnetic field scattered from the objects. The method is based on calculating the field gradients in the incidence and scattering space in a bistatic radar scheme. The analysis of the differences between the real target and artificial jam-producing object showed the high sensitivity and efficiency of the method used, because the topology of the two-dimensional field scattering gradients pattern varies significantly for these two objects. We detected substantial polarization dependence of the scattered field gradients, which together with the topological portraits of the scattered field itself make it possible to find a new approach to the target discrimination. The analysis we did allows us to develop a target detection strategy for an artificial object by the controlled change of the incident and scattered angles (viewing angles) in accordance with the laws obtained in topological portraits of the gradients of electromagnetic fields scattered over a wide angular range from the objects. Findings of the research could be helpful in developing specific strategies of polarization bistatic radiolocation based on the gradient analysis method.
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Uren, Philip, Jonathon Torchia, Daniel Hwang, Mark Wadolkowski, Natalie Fredriksson, Marco Blanchette, and Lisa Munding. "Topolink™: Advancing Cancer Genomics with a High-Resolution, High-Throughput Approach for Low Sequencing and Highly Sensitive Detection of Structural Variants." Blood 142, Supplement 1 (November 28, 2023): 7162. http://dx.doi.org/10.1182/blood-2023-187888.
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Despite numerous technological advances, including the widespread adoption of massively parallel genome sequencing, many clinically relevant cancer driver mutations go undetected. Additionally, even with the most comprehensive cancer profiling using a combination of whole genome and whole transcriptome sequencing, a driver mutation goes undetected in approximately 20% of cancers, making targeted treatment of these patients challenging. The reason for this gap in understanding is presumably two-fold: 1) current technologies do not have the required sensitivity for detection of the causative variants; or 2) causative variants are epigenetic or regulatory in nature, meaning the driving alteration does not result in a change to the core DNA sequence. An explanation for 1) is that structural variants (SVs) are particularly challenging to detect using shotgun-based approaches, since these depends on the presence of specific chimeric molecules within the library that directly span or bridge the breakpoint. Long-read technologies circumvent this limitation but have significant drawbacks with respect to cost and strict sample requirements. Another possibility is that standard sequencing - which results in hundreds or thousands of variants of unknown significance (VUS) - may in fact contain causative variants, but our understanding of function of these variants is limited. For 2), it is increasingly appreciated that epigenetic and chromatin topological features are fundamental in the gene regulation and disease. To address this gap in our understanding of cancer, we utilized TopoLink™ proximity ligation library protocol that yields high-quality, high-resolution, unbiased HiC libraries and that can be performed in under 8 hours. To our knowledge, TopoLink™ is the first and only assay of its kind. Proximity ligation offers enhanced sensitivity for detection of SVs, and the restriction enzyme-free digestion method ensures the uniform coverage needed for accurate detection of single nucleotide variations (SNVs) and copy number variants (CNVs). To our knowledge, TopoLink™ is the first and only assay combine the speed, throughput, and unbiased primary base coverage of WGS with the improved detection of large SVs in Hi-C data. Detection of SVs provides a critical basis for personalized therapies in hematological cancers. To test the ability of TopoLink™ to detect clinically relevant SVs, we used the BCR-ABL1 positive CML cell line K562 to determine the limit of detection of interchromosomal translocations. In addition, we calculated the sensitivity of SV detection relative to current industry-leading sequencing technologies using the breast carcinoma cell line HCC1187 gold standard. We noted enhanced sensitivity for SV detection for TopoLink™ relative to both long-read technologies and WGS. Using TopoLink™, sensitivity was greater than 95% at a genome coverage of less than 1x, whereas both long-read and WGS sensitivity drops below 95% sensitivity at approximately 10x genome coverage. Similarly, using hybridization capture of BCR-ABL1 in a TopoLink™ library of K562 cells reduced the required sequencing burden by 10-fold relative to standard shotgun approaches. Importantly, we demonstrate that TopoLink™ libraries maintain topological features consistent with biologically relevant topologically-associated domains (TADs) and chromatin loops, thus enabling insight into novel epigenetic cancer drivers. Therefore, we demonstrate TopoLink™ proximity ligation libraries as a complementary technique that offers enhanced sensitivity of clinically relevant structural variants, with the added benefit of improving discovery of novel epigenetic mechanisms. Finally, the reduced sequencing costs needed to detect clinically relevant SVs allows for improved diagnostics and personalized medicine in a clinical or translational research setting.
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Leng, Liang, Guodong Yang, and Shengbo Chen. "A Combinatorial Reasoning Mechanism with Topological and Metric Relations for Change Detection in River Planforms: An Application to GlobeLand30’s Water Bodies." ISPRS International Journal of Geo-Information 6, no. 1 (January 12, 2017): 13. http://dx.doi.org/10.3390/ijgi6010013.
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WANG Kejian, 王柯俭, 张大成 ZHANG Dacheng, 杨云霄 YANG Yunxiao, 刘旭阳 LIU Xuyang, 余璇 YU Xuan, 雷建廷 LEI Jianting, 张少锋 ZHANG Shaofeng, and 朱江峰 ZHU Jiangfeng. "飞秒涡旋光拓扑荷数的检测方法研究." ACTA PHOTONICA SINICA 50, no. 10 (2021): 1026001. http://dx.doi.org/10.3788/gzxb20215010.1026001.
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Maggiore, Giuseppe, Teodoro Semeraro, Roberta Aretano, Luigi De Bellis, and Andrea Luvisi. "GIS Analysis of Land-Use Change in Threatened Landscapes by Xylella fastidiosa." Sustainability 11, no. 1 (January 7, 2019): 253. http://dx.doi.org/10.3390/su11010253.
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Land-use/land-cover analysis using Geographic Information System (GIS) application can describe and quantify the transformation of the landscape, evaluating the effectiveness of municipal planning in driving urban expansion. This approach was applied in the municipality of Spongano (Salento, South Italy) in order to evaluate the spatial heterogeneity and the transformations of the land use/land cover from 1988 to 2016. This approach was also used to examine the spread of Xylella fastidiosa, which is a plant pathogen of global importance that is reshaping the Salento landscape. The land-use maps are based on the CORINE Land Cover project classification, while the topological consistency was verified through field surveys. A change detection analysis was carried out using the land-use maps of 1988 and 2016. The most extensive land-use class is olive groves (34–36%), followed by non-irrigated arable lands and shrub and/or herbaceous vegetation associations. The main transition of land involved non-irrigated arable lands, which lost 76 ha and 23 ha to shrub and olive areas, respectively. Meanwhile, the artificial surfaces class doubled its extension, which involved mainly the transition from shrub and heterogeneous agricultural areas. However, the olive groves class is threatened by the dramatic phytosanitary condition of the area, indicating a compromised agroecosystem, which is causing a de facto transition into unproductive areas. The results highlight the inconsistency between what was planned by the urban plan in the past and how the landscape of Spongano has been changed over time. This evidence suggests that it is necessary to develop a plan based on learning by doing, in order to shape and adapt the processes of territorial transformation to the unpredictability of the ecologic, social, and economic systems, as well as ensure that these processes are always focused on environmental issues.
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Fetcko-Fayad, Kaleigh, Xiaojun Zheng, Devon Godfrey, Henry Kirveslahti, Sayan Mukherjee, Kyle Lafata, Jennifer Thomas, Katherine Peters, and Marc Ryser. "NIMG-47. LONGITUDINAL TRAJECTORIES FROM SERIAL SURVEILLANCE MRI IMAGING OF PROGRESSIVE IDH MUTANT LOW-GRADE GLIOMA PATIENTS." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v196. http://dx.doi.org/10.1093/neuonc/noad179.0743.
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Abstract BACKGROUND Most patients with isocitrate dehydrogenase mutant (IDHm) low-grade glioma (LGG) undergo active MRI surveillance after initial surgery. Timely biopsy referral during surveillance is a complex trade-off between avoiding the harms of unnecessary surgery and early detection of transformation. Serial surveillance MRIs could be leveraged to develop dynamic imaging markers, yet prior research has primarily focused on the prognostication of pre-treatment images. We piloted the feasibility of an automated pipeline using patient-specific imaging trajectories. METHODS We retrospectively identified 12 progressive IDHm LGG patients and collected serial surveillance MRIs. A previously developed segmentation model was fine-tuned on a subset of T2/FLAIR images (n=49) before automated tumor segmentation was performed on all MRIs. We extracted geometric and topological tumor features, including volume, cross product, and Euler Characteristic Transform, from each MRI. We used piecewise linear regression to quantify feature trajectories and identify change points in feature growth rates. RESULTS Twelve patients received 14 biopsies/repeat resections (10 with transformation, 4 without) and 282 MRIs (median per patient: 17, range: 10-40). All biopsies/repeat resections were preceded by a change point in tumor volume trajectory, indicating an increase in tumor growth rate preceding clinician-determined MRI progression and biopsy referral. The mean increase in volume growth rate, pre to post change point, was 2.3cm3/month (SE: 0.9) in biopsies/repeat resections with transformation, compared to 1.1cm3/month (SE: 0.6) in biopsies/repeat resections without transformation. Among the ten patients with transformation, the median time from IDHm LGG diagnosis to transformation was 64.8 months (range: 38.6-141.1), and the median time between volume change point and transformation was 15.6 months (range: 4.6-28.2). DISCUSSION This pilot study suggests that feature trajectories from serial MRIs of IDHm LGG patients may advance the detection and treatment of transformed lesions by 12-18 months. Larger studies can corroborate these findings and inform clinical decision-making.
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Zang, Yufu, Bisheng Yang, Jianping Li, and Haiyan Guan. "An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas." Remote Sensing 11, no. 6 (March 16, 2019): 647. http://dx.doi.org/10.3390/rs11060647.
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Deformation detection determines the quantified change of a scene’s geometric state, which is of great importance for the mitigation of hazards and property loss from earth observation. Terrestrial laser scanning (TLS) provides an efficient and flexible solution to rapidly capture high precision three-dimensional (3D) point clouds of hillside areas. Most existing methods apply multi-temporal TLS surveys to detect deformations depending on a variety of ground control points (GCPs). However, on the one hand, the deployment of various GCPs is time-consuming and labor-intensive, particularly for difficult terrain areas. On the other hand, in most cases, TLS stations do not form a closed loop, such that cumulative errors cannot be corrected effectively by the existing methods. To overcome these drawbacks, this paper proposes a deformation detection method with limited GCPs based on a novel registration algorithm that accurately registers TLS stations to the UAV (Unmanned Aerial Vehicle) dense image points. First, the proposed method extracts patch primitives from smoothed hillside points, and adjacent TLS scans are pairwise registered by comparing the geometric and topological information of or between patches. Second, a new multi-station adjustment algorithm is proposed, which makes full use of locally closed loops to reach the global optimal registration. Finally, digital elevation models (DEMs, a DEM is a numerical representation of the terrain surface, formed by height points to represent the topography), slope and aspect maps, and vertical sections are generated from multi-temporal TLS surveys to detect and analyze the deformations. Comprehensive experiments demonstrate that the proposed deformation detection method obtains good performance for the hillside areas with limited (few) GCPs.
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Wang, Jing, Lian-Liang Sun, Feng Chi, and Zhen-Guo Fu. "Thermoelectric Transport in a Double-Quantum-Dot Coupled to Majorana Zero Modes." Journal of Nanoelectronics and Optoelectronics 16, no. 5 (May 1, 2021): 753–61. http://dx.doi.org/10.1166/jno.2021.3025.
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Thermoelectric transport through a double-quantum-dot (DQD) connected to the left and right leads is theoretically investigated in the framework of non-equilibrium Green’s function technique. We consider that the dots are also coupled to Majorana zero modes (MZMs) prepared at the two ends of a topological superconductor nanowire. It is found that the sign change of thermopower, which is promising in the detection of MZMs, can be realized by tuning several system’s parameters related to the MZMs, such as the coupling strength between the dots and the MZMs, the direct coupling between the MZMs, or even the magnetic flux penetrating through the structure. The above parameters also lead to significant enhancement of the thermopower and thermoelectric figure of merit (FOM), which indicates the conversion efficiency between heat and electrical energies. We also find that in this DQD system, both the thermopower and FOM are simultaneously enhanced by the MZMs around the electron-hole symmetric point, an ideal phenomenon in applications of thermoelectric effect. In addition, the thermoelectric effect is remarkably enhanced by the direct hybridization between the MZMs, which is very different from the case in single-dot structure.
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Lee, Hyojin, Jong A. Chun, Hyun-Hee Han, and Sung Kim. "Prediction of Frost Occurrences Using Statistical Modeling Approaches." Advances in Meteorology 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/2075186.
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We developed the frost prediction models in spring in Korea using logistic regression and decision tree techniques. Hit Rate (HR), Probability of Detection (POD), and False Alarm Rate (FAR) from both models were calculated and compared. Threshold values for the logistic regression models were selected to maximize HR and POD and minimize FAR for each station, and the split for the decision tree models was stopped when change in entropy was relatively small. Average HR values were 0.92 and 0.91 for logistic regression and decision tree techniques, respectively, average POD values were 0.78 and 0.80 for logistic regression and decision tree techniques, respectively, and average FAR values were 0.22 and 0.28 for logistic regression and decision tree techniques, respectively. The average numbers of selected explanatory variables were 5.7 and 2.3 for logistic regression and decision tree techniques, respectively. Fewer explanatory variables can be more appropriate for operational activities to provide a timely warning for the prevention of the frost damages to agricultural crops. We concluded that the decision tree model can be more useful for the timely warning system. It is recommended that the models should be improved to reflect local topological features.
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Borst, Alexander. "Connectivity Matrix Seriation via Relaxation." PLOS Computational Biology 20, no. 2 (February 20, 2024): e1011904. http://dx.doi.org/10.1371/journal.pcbi.1011904.
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Volume electron microscopy together with computer-based image analysis are yielding neural circuit diagrams of ever larger regions of the brain. These datasets are usually represented in a cell-to-cell connectivity matrix and contain important information about prevalent circuit motifs allowing to directly test various theories on the computation in that brain structure. Of particular interest are the detection of cell assemblies and the quantification of feedback, which can profoundly change circuit properties. While the ordering of cells along the rows and columns doesn’t change the connectivity, it can make special connectivity patterns recognizable. For example, ordering the cells along the flow of information, feedback and feedforward connections are segregated above and below the main matrix diagonal, respectively. Different algorithms are used to renumber matrices such as to minimize a given cost function, but either their performance becomes unsatisfying at a given size of the circuit or the CPU time needed to compute them scales in an unfavorable way with increasing number of neurons. Based on previous ideas, I describe an algorithm which is effective in matrix reordering with respect to both its performance as well as to its scaling in computing time. Rather than trying to reorder the matrix in discrete steps, the algorithm transiently relaxes the integer program by assigning a real-valued parameter to each cell describing its location on a continuous axis (‘smooth-index’) and finds the parameter set that minimizes the cost. I find that the smooth-index algorithm outperforms all algorithms I compared it to, including those based on topological sorting.
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Liu Xuelian, 刘雪莲, 陈旭东 Chen Xudong, 林志立 Lin Zhili, 刘卉 Liu Hui, 朱香渝 Zhu Xiangyu, and 张晓雪 Zhang Xiaoxue. "深度学习辅助测量强散射涡旋光束拓扑荷数." Acta Optica Sinica 42, no. 14 (2022): 1426001. http://dx.doi.org/10.3788/aos202242.1426001.
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Ma, Jingzhen, Qun Sun, Zhao Zhou, Bowei Wen, and Shaomei Li. "A Multi-Scale Residential Areas Matching Method Considering Spatial Neighborhood Features." ISPRS International Journal of Geo-Information 11, no. 6 (May 31, 2022): 331. http://dx.doi.org/10.3390/ijgi11060331.
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Residential areas is one of the basic geographical elements on the map and an important content of the map representation. Multi-scale residential areas matching refers to the process of identifying and associating entities with the same name in different data sources, which can be widely used in map compilation, data fusion, change detection and update. A matching method considering spatial neighborhood features is proposed to solve the complex matching problem of multi-scale residential areas. The method uses Delaunay triangulation to divide complex matching entities in different scales into closed domains through spatial neighborhood clusters, which can obtain many-to-many matching candidate feature sets. At the same time, the geometric features and topological features of the residential areas are fully considered, and the Relief-F algorithm is used to determine the weight values of different similarity features. Then the similarity and spatial neighborhood similarity of the polygon residential areas are calculated, after which the final matching results are obtained. The experimental results show that the accuracy rate, recall rate and F value of the matching method are all above 90%, which has a high matching accuracy. It can identify a variety of matching relationships and overcome the influence of certain positional deviations on matching results. The proposed method can not only take account of the spatial neighborhood characteristics of residential areas, but also identify complex matching relationships in multi-scale residential areas accurately with a good matching effect.
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Wang, Maolin, Hongyu Wang, Zhi Wang, and Yumeng Li. "A UAV Visual Relocalization Method Using Semantic Object Features Based on Internet of Things." Wireless Communications and Mobile Computing 2022 (February 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/7299309.
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Unmanned Air Vehicle (UAV) has the advantages of high autonomy and strong dynamic deployment capabilities. At the same time, with the rapid development of the Internet of Things (IoT) technology, the construction of the IoT based on UAVs can break away from the traditional single-line communication mode of UAVs and control terminals, which makes the UAVs more intelligent and flexible when performing tasks. When using UAVs to perform IoT tasks, it is necessary to track the UAVs’ position and pose at all times. When the position and pose tracking fails, relocalization is required to restore the current position and pose. Therefore, how to perform UAV relocalization accurately by using visual information has attracted much attention. However, the complex changes in light conditions in the real world have brought huge challenges to the visual relocalization of UAV. Traditional visual relocalization algorithms mostly rely on artificially designed low-level geometric features which are sensitive to light conditions. In this paper, oriented to the UAV-based IoT, a UAV visual relocalization method using semantic object features is proposed. Specifically, the method uses YOLOv3 as the object detection framework to extract the semantic information in the picture and uses the semantic information to construct a topological map as a sparse description of the environment. With prior knowledge of the map, the random walk algorithm is used on the association graphs to match the semantic features and the scenes. Finally, the EPnP algorithm is used to solve the position and pose of the UAV which will be returned to the IoT platform. Simulation results show that the method proposed in this paper can achieve robust real-time UAVs relocalization when the scene lighting conditions change dynamically and provide a guarantee for UAVs to perform IoT tasks.
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Nalimov, A. G. "A metalens for detecting fractional-order optical vortices." Computer Optics 48, no. 3 (June 2024): 342–48. http://dx.doi.org/10.18287/2412-6179-co-1435.
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In this work, a metalens for detecting an incident field with a fractional topological charge ranging from – 2 to 0 is proposed. The metalens is based on a spiral zone plate with a topological charge of 1.5. A change in the topological charge of the incident beam is numerically shown to lead to an off-axis shift of the focal spot from the center, with the intensity maximum value also changing. This results in a 6.9-fold change in the on-axis intensity while the topological charge of the incident beam changes from –0.6 to –1.5. The on-axis intensity at the focus is also shown to be affected by the rotation of the fractional vortex beam. This makes it possible to use the proposed metalens for measuring the angle of rotation of the incident beam in the range from 0 to 110°.
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Cavé, Tiphanie, Rebecka Desmarais, Chloé Lacombe-Burgoyne, and Guylain Boissonneault. "Genetic Instability and Chromatin Remodeling in Spermatids." Genes 10, no. 1 (January 14, 2019): 40. http://dx.doi.org/10.3390/genes10010040.
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The near complete replacement of somatic chromatin in spermatids is, perhaps, the most striking nuclear event known to the eukaryotic domain. The process is far from being fully understood, but research has nevertheless unraveled its complexity as an expression of histone variants and post-translational modifications that must be finely orchestrated to promote the DNA topological change and compaction provided by the deposition of protamines. That this major transition may not be genetically inert came from early observations that transient DNA strand breaks were detected in situ at chromatin remodeling steps. The potential for genetic instability was later emphasized by our demonstration that a significant number of DNA double-strand breaks (DSBs) are formed and then repaired in the haploid context of spermatids. The detection of DNA breaks by 3’OH end labeling in the whole population of spermatids suggests that a reversible enzymatic process is involved, which differs from canonical apoptosis. We have set the stage for a better characterization of the genetic impact of this transition by showing that post-meiotic DNA fragmentation is conserved from human to yeast, and by providing tools for the initial mapping of the genome-wide DSB distribution in the mouse model. Hence, the molecular mechanism of post-meiotic DSB formation and repair in spermatids may prove to be a significant component of the well-known male mutation bias. Based on our recent observations and a survey of the literature, we propose that the chromatin remodeling in spermatids offers a proper context for the induction of de novo polymorphism and structural variations that can be transmitted to the next generation.
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Lucchese, Riccardo, Damiano Varagnolo, and Karl H. Johansson. "Distributed detection of topological changes in communication networks." IFAC Proceedings Volumes 47, no. 3 (2014): 1928–34. http://dx.doi.org/10.3182/20140824-6-za-1003.00913.
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Bernaschi, M., M. Lulli, and M. Sbragaglia. "GPU based detection of topological changes in Voronoi diagrams." Computer Physics Communications 213 (April 2017): 19–28. http://dx.doi.org/10.1016/j.cpc.2016.11.005.
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Chan, Kevin, Clive Yik-Sham Chung, and Vivian Wing-Wah Yam. "Parallel folding topology-selective label-free detection and monitoring of conformational and topological changes of different G-quadruplex DNAs by emission spectral changes via FRET of mPPE-Ala–Pt(ii) complex ensemble." Chemical Science 7, no. 4 (2016): 2842–55. http://dx.doi.org/10.1039/c5sc04563k.
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Boucher, C., and J.-C. Noyer. "Automatic Detection of Topological Changes for Digital Road Map Updating." IEEE Transactions on Instrumentation and Measurement 61, no. 11 (November 2012): 3094–102. http://dx.doi.org/10.1109/tim.2012.2203873.
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Abdullahi, Muhammad Sirajo, Apichat Suratanee, Rosario Michael Piro, and Kitiporn Plaimas. "Persistent Homology Identifies Pathways Associated with Hepatocellular Carcinoma from Peripheral Blood Samples." Mathematics 12, no. 5 (February 29, 2024): 725. http://dx.doi.org/10.3390/math12050725.
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Topological data analysis (TDA) methods have recently emerged as powerful tools for uncovering intricate patterns and relationships in complex biological data, demonstrating their effectiveness in identifying key genes in breast, lung, and blood cancer. In this study, we applied a TDA technique, specifically persistent homology (PH), to identify key pathways for early detection of hepatocellular carcinoma (HCC). Recognizing the limitations of current strategies for this purpose, we meticulously used PH to analyze RNA sequencing (RNA-seq) data from peripheral blood of both HCC patients and normal controls. This approach enabled us to gain nuanced insights by detecting significant differences between control and disease sample classes. By leveraging topological descriptors crucial for capturing subtle changes between these classes, our study identified 23 noteworthy pathways, including the apelin signaling pathway, the IL-17 signaling pathway, and the p53 signaling pathway. Subsequently, we performed a comparative analysis with a classical enrichment-based pathway analysis method which revealed both shared and unique findings. Notably, while the IL-17 signaling pathway was identified by both methods, the HCC-related apelin signaling and p53 signaling pathways emerged exclusively through our topological approach. In summary, our study underscores the potential of PH to complement traditional pathway analysis approaches, potentially providing additional knowledge for the development of innovative early detection strategies of HCC from blood samples.
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Giustolisi, Orazio, Zoran Kapelan, and Dragan Savic. "Algorithm for Automatic Detection of Topological Changes in Water Distribution Networks." Journal of Hydraulic Engineering 134, no. 4 (April 2008): 435–46. http://dx.doi.org/10.1061/(asce)0733-9429(2008)134:4(435).
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Wang, Yuwei, Yuanying Qiu, Peitao Cheng, and Xuechao Duan. "Robust Loop Closure Detection Integrating Visual–Spatial–Semantic Information via Topological Graphs and CNN Features." Remote Sensing 12, no. 23 (November 27, 2020): 3890. http://dx.doi.org/10.3390/rs12233890.
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Loop closure detection is a key module for visual simultaneous localization and mapping (SLAM). Most previous methods for this module have not made full use of the information provided by images, i.e., they have only used the visual appearance or have only considered the spatial relationships of landmarks; the visual, spatial and semantic information have not been fully integrated. In this paper, a robust loop closure detection approach integrating visual–spatial–semantic information is proposed by employing topological graphs and convolutional neural network (CNN) features. Firstly, to reduce mismatches under different viewpoints, semantic topological graphs are introduced to encode the spatial relationships of landmarks, and random walk descriptors are employed to characterize the topological graphs for graph matching. Secondly, dynamic landmarks are eliminated by using semantic information, and distinctive landmarks are selected for loop closure detection, thus alleviating the impact of dynamic scenes. Finally, to ease the effect of appearance changes, the appearance-invariant descriptor of the landmark region is extracted by a pre-trained CNN without the specially designed manual features. The proposed approach weakens the influence of viewpoint changes and dynamic scenes, and extensive experiments conducted on open datasets and a mobile robot demonstrated that the proposed method has more satisfactory performance compared to state-of-the-art methods.
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Damanjani, Amin, Mohamad Hosseini Abardeh, Azita Azarfar, and Mehrdad Hojjat. "A comprehensive fuzzy-based scheme for online detection of operational and topological changes." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (August 1, 2022): 3396. http://dx.doi.org/10.11591/ijece.v12i4.pp3396-3409.
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<span>Operational modes and topological changes affect power flow in the power systems. As a result, a broad spectrum of protection issues may happen in the power system. So, both the operational and topological changes should be detected fast to prevent blackouts. On the other hand, the existing detection schemes are complex in analyzing and implementation. Therefore, there is a need for an online scheme to identify the network's topology and operation mode simultaneously without complex computations and additional communication infrastructures. To this end, a comprehensive scheme is proposed in which the changes are detected by analyzing the power flow obtained from the network. For this purpose, line outage contingencies and operation modes are defined in rules to be used in a fuzzy inference system (FIS) as a decision-making tool. The proposed scheme can be implemented on existing lines as a communication infrastructure and determines the network’s status in an online manner. Also, in comparison to the existing schemes, the proposed scheme reduces the complexity and the computational burden. The proposed scheme is implemented on IEEE 8-bus system and the results proved its efficiency.</span>
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Barsi, M., and A. Barsi. "TOPOLOGICAL ANOMALY DETECTION IN AUTOMOTIVE SIMULATOR MAPS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 1, 2022): 343–48. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-343-2022.
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Abstract. Autonomous driving went through numerous significant improvements over the past couple of years, including driver assistants that are already capable of executing an increasing number of complex tasks without the need for any human intervention. As a result of these changes, manufacturers are relying more and more on fast, cheap, and often better-quality simulations over real-world tests. To create these environments, the real world needs to be transformed to a digital, high-definition model. HD maps – for example, the XML-based, hierarchic OpenDRIVE format – aim to serve this purpose.The most important element of any realistic map format is the ability to check connectivity on the map in a convenient way, hence the need for topology. In HD maps, the description of junctions poses a significant challenge to the designers of the format, since they are essential yet complex topological elements. The representation of these junctions is still in progress, however, according to our analysis, the use of the current tools in OpenDRIVE can result in anomalies in the map.In the most recent release of OpenDRIVE (version 1.7), road-road and lane-lane connections are described using links consisting of a predecessor and a successor. These however, has to be described multiple times when the junction tag is used, resulting in duplicates in the model which can be easily exploited. Our proposed solution for this issue is the elimination of the junction tag, which not only gets rid of the anomalies without any loss of information, but it also significantly reduces the size of the model. In this paper, a detailed explanation is provided of this issue and the proposed solution with examples using OpenDRIVE models.
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Guo, Wei, Han Qiu, Zimian Liu, Junhu Zhu, and Qingxian Wang. "GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion." Computational Intelligence and Neuroscience 2022 (August 16, 2022): 1–20. http://dx.doi.org/10.1155/2022/4611331.
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Distributed denial of service (DDoS) attacks are the most common means of cyberattacks against infrastructure, and detection is the first step in combating them. The current DDoS detection mainly uses the improvement or fusion of machine learning and deep learning methods to improve classification performance. However, most classifiers are trained with statistical flow features as input, ignoring topological connection changes. This one-sidedness affects the detection accuracy and cannot provide a basis for the distribution of attack sources for defense deployment. In this study, we propose a topological and flow feature-based deep learning method (GLD-Net), which simultaneously extracts flow and topological features from time-series flow data and exploits graph attention network (GAT) to mine correlations between non-Euclidean features to fuse flow and topological features. The long short-term memory (LSTM) network connected behind GAT obtains the node neighborhood relationship, and the fully connected layer is utilized to achieve feature dimension reduction and traffic type mapping. Experiments on the NSL-KDD2009 and CIC-IDS2017 datasets show that the detection accuracy of the GLD-Net method for two classifications (normal and DDoS flow) and three classifications (normal, fast DDoS flow, and slow DDoS flow) reaches 0.993 and 0.942, respectively. Compared with the existing DDoS attack detection methods, its average improvement is 0.11 and 0.081, respectively. In addition, the correlation coefficient between the detection accuracy of attack flow and the four source distribution indicators ranges from 0.7 to 0.83, which lays a foundation for the inference of attack source distribution. Notably, we are the first to fuse topology and flow features and achieve high-performance DDoS attack intrusion detection through graph-style neural networks. This study has important implications for related research and development of network security systems in other fields.
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Jude, Kevin M., Abbey Hartland, and James M. Berger. "Real-time detection of DNA topological changes with a fluorescently labeled cruciform." Nucleic Acids Research 41, no. 13 (May 16, 2013): e133-e133. http://dx.doi.org/10.1093/nar/gkt413.
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Kong, Dexu, Anping Zhang, and Yang Li. "Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (March 24, 2024): 8617–26. http://dx.doi.org/10.1609/aaai.v38i8.28706.
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Dynamic community detection methods often lack effective mechanisms to ensure temporal consistency, hindering the analysis of network evolution. In this paper, we propose a novel deep graph clustering framework with temporal consistency regularization on inter-community structures, inspired by the concept of minimal network topological changes within short intervals. Specifically, to address the representation collapse problem, we first introduce MFC, a matrix factorization-based deep graph clustering algorithm that preserves node embedding. Based on static clustering results, we construct probabilistic community networks and compute their persistence homology, a robust topological measure, to assess structural similarity between them. Moreover, a novel neural network regularization TopoReg is introduced to ensure the preservation of topological similarity between inter-community structures over time intervals. Our approach enhances temporal consistency and clustering accuracy on real-world datasets with both fixed and varying numbers of communities. It is also a pioneer application of TDA in temporally persistent community detection, offering an insightful contribution to field of network analysis. Code and data are available at the public git repository: https://github.com/kundtx/MFC-TopoReg.