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"abstract": "With advancement in sensors and Internet of Things, gathering spatiotemporal information from one's surroundings has become easier, to an extent that we can start to use sensor data to infer indoor occupancy patterns. This paper aims to identify which ambient sensor is the most dominant in recognising human presence. Four different types of off-the-shelf sensors from two manufacturers were deployed to ensure that we could collect the following data reliably: illumination, temperature, humidity, levels of carbon dioxide, pressure and sound from within one staff office. Motion, power consumption, door opening and closing data and annotations from a self-developed mobile app were also collected as ground truth. We present our methods to preprocess the data and compute the number of people in the room with different classifiers, and identify sensors with strong and weak correlations. We explain our methodology for integrating large amounts of sensor data, discuss our experiments and findings in relation to the binary occupancy of a single person office, providing a baseline for recognising human occupancy.",
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"abstract": "There lies a geometric relationship (homography) between two frames in the video sequences captured by pan-tilt (PT) cameras due to their constrained movement, and the geometric relationship is valuable for reducing the spatial redundancy in video coding. In this paper, we propose a novel homography-based search (HBS) algorithm for block motion estimation in coding the sequences captured by PT cameras, which well utilizes the homography between two frames. In addition, adaptive thresholds are adopted in our method to classify different kinds of blocks. Compared with other traditional fast algorithms, the proposed HBS algorithm is proved more efficient for the sequences captured by PT cameras.",
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"content": "There lies a geometric relationship (homography) between two frames in the video sequences captured by pan-tilt (PT) cameras due to their constrained movement, and the geometric relationship is valuable for reducing the spatial redundancy in video coding. In this paper, we propose a novel homography-based search (HBS) algorithm for block motion estimation in coding the sequences captured by PT cameras, which well utilizes the homography between two frames. In addition, adaptive thresholds are adopted in our method to classify different kinds of blocks. Compared with other traditional fast algorithms, the proposed HBS algorithm is proved more efficient for the sequences captured by PT cameras.",
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"affiliation": "ISN National Key Lab, School of Telecommunication Engineering, Xidian University, Xi'an, China",
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"abstract": "The authors describe their efforts in developing sensorimotor chips which contain arrays of sensing elements, circuitry for processing the raw sensor data into forms relevant to motion-related tasks, circuitry for generating motion signals based on the processed sensor data and the goals of the system, and an operating system which selects a unique motor command from a set of usually conflicting motion signals. Sensors of this kind are intended for use in robots. They would greatly reduce the computational burden from that imposed with standard sensing techniques that use high bandwidth video cameras or even tactile sensing arrays, since they would generate motor signals directly rather than sensor signals from which another computer would have to generate motor signals.<>",
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"abstractType": "Regular",
"content": "The authors describe their efforts in developing sensorimotor chips which contain arrays of sensing elements, circuitry for processing the raw sensor data into forms relevant to motion-related tasks, circuitry for generating motion signals based on the processed sensor data and the goals of the system, and an operating system which selects a unique motor command from a set of usually conflicting motion signals. Sensors of this kind are intended for use in robots. They would greatly reduce the computational burden from that imposed with standard sensing techniques that use high bandwidth video cameras or even tactile sensing arrays, since they would generate motor signals directly rather than sensor signals from which another computer would have to generate motor signals.<>",
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"abstract": "With increasing physical threats in recent years targeted at critical infrastructures, it is crucial to establish a reliable threat monitoring system integrating video surveillance and digital sensors based on cutting-edge technologies. A physical threat monitoring solution unifying the floorplan, cameras, and sensors for smart buildings has been set up in our study. Computer vision and deep learning models are used for video streams analysis. When a threat is detected by a rule engine based on the real-time analysis results combining with feedback from related digital sensors, an alert is sent to the Video Management System so that human operators can take further action.A physical threat monitoring system typically needs to address complex and even destructive incidents, such as fire, which is unrealistic to simulate in real life. Restrictions imposed during the Covid-19 pandemic and privacy concerns have added to the challenges. Our study utilises the Unreal Engine to simulate some typical suspicious and intrusion scenes with photorealistic qualities in the context of a virtual building. Add-on programs are implemented to transfer the video stream from virtual PTZ cameras to the Milestone Video Management System and enable users to control those cameras from the graphic client application. Virtual sensors such as fire alarms, temperature sensors and door access controls are implemented similarly, fulfilling the same programmatic VMS interface as real-life sensors. Thanks to this simulation system’s extensibility and repeatability, we have consolidated this unified physical threat monitoring system and verified its effectiveness and user-friendliness. Both the simulated Unreal scenes and the software add-ons developed during this study are highly modulated and thereby are ready for reuse in future projects in this area.",
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"abstract": "Mashups are prevalent Service-Oriented Architecture (SOA) based applications consisting of multiple Web Application Programming Interfaces (APIs) and content. Tags have been extensively used to organize and index mashup services. However, people favor manual tags creation in the past. This approach demands user intervention, which is extremely time-consuming and probes to errors. In this paper we propose a novel Mashup-API-Tag model for automatic mashup tag recommendation. The model simultaneously incorporates the composition relationships between mashups and APIs as well as the annotation relationships between APIs and tags to discover the latent topics. Then the semantic similarity between Web APIs and mashups can be acquired. Subsequently, tags of chosen APIs are recommended to a mashup where the mashup and the APIs are most similar. In addition, we develop a tag filtering algorithm to select the most relevant tags for recommendation. The experimental results on a real world dataset prove that our approach outperforms other methods, including frequency-based methods and the methods that only consider the composition relationships and the annotation relationships separately.",
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"abstract": "Because of the excellent performance of Mashup service in the service composition, Mashup service is used more and more. It is meaningful for service management, discovery and composition that how to achieve effective Mashup service classification and recommendation. We analyze the service network consisted of Mashup applications, Web API services and Tag functions, basing on the rule that there are connections among those Mashups if some Mashups call the same APIs and are marked by the same Tags, and the degree of the connection can be described by similarity, and build 13 kinds of networks and visualize them. Based on built service network, this paper proposes an automatic service classification algorithm that each connected sub-graph is justly a classification in the network consisted of a same kind of service node, and a service recommendation method based on the similarity sorting. We use the Web API data crawled from ProgrammableWeb. The result of our experiment shows the composite index of precision rate and recall rate is up to 87.44%.",
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"abstract": "We present MixMash, an interactive tool to assist users in the creation of music mashups based on cross-modal associations between musical content analysis and information visualisation. Our point of departure is a harmonic mixing method for musical mashups by Bernardes et al. [1]. To surpass design limitations identified in the previous method, we propose a new interactive visualisation of multidimensional musical attributes-hierarchical harmonic compatibility, onset density, spectral region, and timbral similarity-extracted from a large collection of audio tracks. All tracks are represented as nodes whose distances and edge connections indicate their harmonic compatibility as a result of a force-directed graph. In addition, we provide a visual language that aims to enhance the tool usability and foster creative endeavour in the search for meaningful music mixes.",
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"title": "Towards a Reinforcement Learning-based Exploratory Search for Mashup Tag Recommendation",
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"title": "Understanding SPARQL Endpoints through Targeted Exploration and Visualization",
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"abstract": "The Resource Description Framework (RDF) has provided a unified way for everyone to publish their data. The SPARQL query language has been developed to facilitate the exploration of this information. The RDF format, however, is addressed mainly to machines and it is not easily comprehended by humans. Due to the value of the information available through the SPARQL endpoints many efforts have been dedicated to facilitate the access and exploration of this information from users with limited knowledge of the Semantic Web. The main challenge of such approaches is the diversity of the information contained the endpoints, which renders holistic or schema specific solutions obsolete. We present here an integrated platform that supports the users to the querying, exploration and visualization of information contained in SPARQL endpoints. The platform handles each query result independently based only on its characteristics, offering an endpoint and data schema agnostic solution. This is achieved through a Decision Support System, developed based on knowledge base containing raw data from many endpoints, that allows us to provide case-specific visualization strategies for SPARQL query results based exclusively on features extracted from the result.",
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"content": "The Resource Description Framework (RDF) has provided a unified way for everyone to publish their data. The SPARQL query language has been developed to facilitate the exploration of this information. The RDF format, however, is addressed mainly to machines and it is not easily comprehended by humans. Due to the value of the information available through the SPARQL endpoints many efforts have been dedicated to facilitate the access and exploration of this information from users with limited knowledge of the Semantic Web. The main challenge of such approaches is the diversity of the information contained the endpoints, which renders holistic or schema specific solutions obsolete. We present here an integrated platform that supports the users to the querying, exploration and visualization of information contained in SPARQL endpoints. The platform handles each query result independently based only on its characteristics, offering an endpoint and data schema agnostic solution. This is achieved through a Decision Support System, developed based on knowledge base containing raw data from many endpoints, that allows us to provide case-specific visualization strategies for SPARQL query results based exclusively on features extracted from the result.",
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"abstract": "Association rule mining often leads the analyst into a rough rummaging process to identify rules that are relevant to understand specific problems. We propose a visualization interface to assist the rule selection process and evaluate it on an RDF knowledge graph derived from the COVID-19 Open Research Dataset. The user interface supports data exploration with focus on the overview of rules through a scatter plot, subsets of rules through a chord diagram chart, and itemsets through an association graph which is dynamically created by entering an item of interest (i.e. a named entity). Further, the analyst can interactively recover a list of publications containing the named entities involved in a particular rule. Among the original aspects of our approach, we highlight the representation of attributes describing measures of interest (i.e. confidence and interestingness), a visual indication of existence (or not) of symmetry in association rules, the exploration of subsets of rules according to clusters of publications and named entities, and an interactive prompting that aims at expanding the discovery of named entities within selected association rules. We assess our approach through a semi-structured interview involving experts in the domains of data mining and biomedicine, whose feedback could assist the refinement of the visual and interaction tools.",
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"content": "Association rule mining often leads the analyst into a rough rummaging process to identify rules that are relevant to understand specific problems. We propose a visualization interface to assist the rule selection process and evaluate it on an RDF knowledge graph derived from the COVID-19 Open Research Dataset. The user interface supports data exploration with focus on the overview of rules through a scatter plot, subsets of rules through a chord diagram chart, and itemsets through an association graph which is dynamically created by entering an item of interest (i.e. a named entity). Further, the analyst can interactively recover a list of publications containing the named entities involved in a particular rule. Among the original aspects of our approach, we highlight the representation of attributes describing measures of interest (i.e. confidence and interestingness), a visual indication of existence (or not) of symmetry in association rules, the exploration of subsets of rules according to clusters of publications and named entities, and an interactive prompting that aims at expanding the discovery of named entities within selected association rules. We assess our approach through a semi-structured interview involving experts in the domains of data mining and biomedicine, whose feedback could assist the refinement of the visual and interaction tools.",
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"abstract": "Users of hypertext systems like the World Wide Web (WWW) often find themselves following hypertext links deeper and deeper, only to find themselves \"lost\" and unable to find their way back to the previously visited pages. We have implemented a Web browser companion called Domain Tree Browser (DTB) that builds a tree structured visual navigation history while browsing the Web. The Domain Tree Browser organizes the URLs visited based on the domain name of each URL and shows thumbnails of each page in a zoomable window.",
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"abstract": "Serial animated cartoons, also called animations, is a kind of popular video documents that describe narratives by videos of cartoons. By using digital techniques, illegal users can divide animations into fragments and distribute them without any copyright permissions. In this paper we focus on the copyright problem of animations and try to retrieve copyright infringement fragments based on key frames. Because of the huge volumes and rapid release of animations, it is impossible to store all the episodes into the database. We propose applying bag-of-features model to retrieve similar fragments based on the visual words extracted from a limited data set. In the experiments, 12 titles of animations are employed and the similar fragments outside database are applied as queries. Our method has achieved above 98% precision at 80% recall for fragments whose durations are over 140 seconds. From the results, we show that a latest release can be retrieved based on the features of the former ones from the same title.",
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"abstract": "Discovering causal relationships in large databases of observational data is challenging. The pioneering work in this area was rooted in the theory of Bayesian network (BN) learning, which however, is a NP-complete problem. Hence several constraint-based algorithms have been developed to efficiently discover causations in large databases. These methods usually use the idea of BN learning, directly or indirectly, and are focused on causal relationships with single cause variables. In this paper, we propose an approach to mine causal rules in large databases of binary variables. Our method expands the scope of causality discovery to causal relationships with multiple cause variables, and we utilise partial association tests to exclude noncausal associations, to ensure the high reliability of discovered causal rules. Furthermore an efficient algorithm is designed for the tests in large databases. We assess the method with a set of real-world diagnostic data. The results show that our method can effectively discover interesting causal rules in large databases.",
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"abstract": "Building tall structures behind an aesthetic and historical building tends to destroy the good landscape. To avoid such situations, public agencies must regulate height of buildings and other structures near the landscape target. In order to check the visibility of portions of high, future structures, in this research, a new method using Augmented Reality (AR) was proposed. In this method, a number of virtual rectangular objects with a scale are located on the grid of 3D geographical model. And then, the virtual rulers are shown in an overlapping manner with the actual landscape from multiple viewpoints using the AR technology. The user measures the maximum skyline-preserving height for each rectangular object at a grid point. Using the measured data, the government or public agencies can establish appropriate height regulations for all surrounding areas of the target structures. To verify the proposed method, a system was developed deploying AR Toolkit and was applied to a scenic building. The performance of the system was checked and then, the errors of the obtained data were evaluated. In conclusion, the proposed method was evaluated feasible and effective.",
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"content": "Building tall structures behind an aesthetic and historical building tends to destroy the good landscape. To avoid such situations, public agencies must regulate height of buildings and other structures near the landscape target. In order to check the visibility of portions of high, future structures, in this research, a new method using Augmented Reality (AR) was proposed. In this method, a number of virtual rectangular objects with a scale are located on the grid of 3D geographical model. And then, the virtual rulers are shown in an overlapping manner with the actual landscape from multiple viewpoints using the AR technology. The user measures the maximum skyline-preserving height for each rectangular object at a grid point. Using the measured data, the government or public agencies can establish appropriate height regulations for all surrounding areas of the target structures. To verify the proposed method, a system was developed deploying AR Toolkit and was applied to a scenic building. The performance of the system was checked and then, the errors of the obtained data were evaluated. In conclusion, the proposed method was evaluated feasible and effective.",
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"title": "2012 IEEE International Conference on Granular Computing",
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"doi": "10.1109/GrC.2012.6468678",
"title": "Determination of ICESat sea surface height and comparing with TP data",
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"abstract": "ICESat's Geoscience Laser Altimeter System (GLAS)emits 3.4 million laser pulses per day. Typically one third of ICESat's laser pulses occur over land and ice, and about half of the ocean surface is obstructed by opaque cloud cover, yielding 1 million valid ocean elevations per day, ICESat bring a new perspective for marine altimetry. It given ICESat determined the sea surface height and the mesoscale sea surface anomalies through ICESAT comparison with the TOPEX in this paper. Major global ocean sea level anomaly and mesoscale ocean features can be detected with the TOPEX United ICESat. ICESat GLA15_026 sea surface height accuracy can be tested, the data obtained through the use of marine ICESat high compared with the TOPEX found, We found height difference of about 10.0cm between sea surface height ICESat identified with sea surface height TOPEX identified relative to the same mean sea surface height through the ICESat altimeter data comparison with the TOPEX altimeter data the deviation along the track mean is − 4.4cm by Comparison of binary stars. Although the initial value of this deviation may be closely related to sea conditions, although ICESat GLA15_026 has improved due to the difference between day and night temperature variation, difference in the size of about 1 ∼ 2cm.",
"abstracts": [
{
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"content": "ICESat's Geoscience Laser Altimeter System (GLAS)emits 3.4 million laser pulses per day. Typically one third of ICESat's laser pulses occur over land and ice, and about half of the ocean surface is obstructed by opaque cloud cover, yielding 1 million valid ocean elevations per day, ICESat bring a new perspective for marine altimetry. It given ICESat determined the sea surface height and the mesoscale sea surface anomalies through ICESAT comparison with the TOPEX in this paper. Major global ocean sea level anomaly and mesoscale ocean features can be detected with the TOPEX United ICESat. ICESat GLA15_026 sea surface height accuracy can be tested, the data obtained through the use of marine ICESat high compared with the TOPEX found, We found height difference of about 10.0cm between sea surface height ICESat identified with sea surface height TOPEX identified relative to the same mean sea surface height through the ICESat altimeter data comparison with the TOPEX altimeter data the deviation along the track mean is − 4.4cm by Comparison of binary stars. Although the initial value of this deviation may be closely related to sea conditions, although ICESat GLA15_026 has improved due to the difference between day and night temperature variation, difference in the size of about 1 ∼ 2cm.",
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"Sea Measurements",
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"TOPEX",
"Sea Surface Height"
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"affiliation": "Data Center, National Disaster Reduction Center of China, NDRCC, Beijing, China",
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"givenName": "Chunbo",
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"affiliation": "Data Center, National Disaster Reduction Center of China, NDRCC, Beijing, China",
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"affiliation": "Data Center, National Disaster Reduction Center of China, NDRCC, Beijing, China",
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"affiliation": "School of Information, Shandong Polytechnic University, Jinan, China",
"fullName": "Chuanlu Liu",
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{
"affiliation": "Information Center, Surveying and Mapping Institute of Guangdong, SMIG, Guangzhou, China",
"fullName": "Xiaochun Shi",
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"proceeding": {
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"title": "2016 IEEE Conference on Visual Analytics Science and Technology (VAST)",
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"doi": "10.1109/VAST.2016.7883507",
"title": "DocuCompass: Effective exploration of document landscapes",
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"abstract": "The creation of interactive visualization to analyze text documents has gained an impressive momentum in recent years. This is not surprising in the light of massive and still increasing amounts of available digitized texts. Websites, social media, news wire, and digital libraries are just few examples of the diverse text sources whose visual analysis and exploration offers new opportunities to effectively mine and manage the information and knowledge hidden within them. A popular visualization method for large text collections is to represent each document by a glyph in 2D space. These landscapes can be the result of optimizing pairwise distances in 2D to represent document similarities, or they are provided directly as meta data, such as geo-locations. For well-defined information needs, suitable interaction methods are available for these spatializations. However, free exploration and navigation on a level of abstraction between a labeled document spatialization and reading single documents is largely unsupported. As a result, vital foraging steps for task-tailored actions, such as selecting subgroups of documents for detailed inspection, or subsequent sense-making steps are hampered. To fill in this gap, we propose DocuCompass, a focus+context approach based on the lens metaphor. It comprises multiple methods to characterize local groups of documents, and to efficiently guide exploration based on users' requirements. DocuCompass thus allows for effective interactive exploration of document landscapes without disrupting the mental map of users by changing the layout itself. We discuss the suitability of multiple navigation and characterization methods for different spatializations and texts. Finally, we provide insights generated through user feedback and discuss the effectiveness of our approach.",
"abstracts": [
{
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"content": "The creation of interactive visualization to analyze text documents has gained an impressive momentum in recent years. This is not surprising in the light of massive and still increasing amounts of available digitized texts. Websites, social media, news wire, and digital libraries are just few examples of the diverse text sources whose visual analysis and exploration offers new opportunities to effectively mine and manage the information and knowledge hidden within them. A popular visualization method for large text collections is to represent each document by a glyph in 2D space. These landscapes can be the result of optimizing pairwise distances in 2D to represent document similarities, or they are provided directly as meta data, such as geo-locations. For well-defined information needs, suitable interaction methods are available for these spatializations. However, free exploration and navigation on a level of abstraction between a labeled document spatialization and reading single documents is largely unsupported. As a result, vital foraging steps for task-tailored actions, such as selecting subgroups of documents for detailed inspection, or subsequent sense-making steps are hampered. To fill in this gap, we propose DocuCompass, a focus+context approach based on the lens metaphor. It comprises multiple methods to characterize local groups of documents, and to efficiently guide exploration based on users' requirements. DocuCompass thus allows for effective interactive exploration of document landscapes without disrupting the mental map of users by changing the layout itself. We discuss the suitability of multiple navigation and characterization methods for different spatializations and texts. Finally, we provide insights generated through user feedback and discuss the effectiveness of our approach.",
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"normalizedAbstract": "The creation of interactive visualization to analyze text documents has gained an impressive momentum in recent years. This is not surprising in the light of massive and still increasing amounts of available digitized texts. Websites, social media, news wire, and digital libraries are just few examples of the diverse text sources whose visual analysis and exploration offers new opportunities to effectively mine and manage the information and knowledge hidden within them. A popular visualization method for large text collections is to represent each document by a glyph in 2D space. These landscapes can be the result of optimizing pairwise distances in 2D to represent document similarities, or they are provided directly as meta data, such as geo-locations. For well-defined information needs, suitable interaction methods are available for these spatializations. However, free exploration and navigation on a level of abstraction between a labeled document spatialization and reading single documents is largely unsupported. As a result, vital foraging steps for task-tailored actions, such as selecting subgroups of documents for detailed inspection, or subsequent sense-making steps are hampered. To fill in this gap, we propose DocuCompass, a focus+context approach based on the lens metaphor. It comprises multiple methods to characterize local groups of documents, and to efficiently guide exploration based on users' requirements. DocuCompass thus allows for effective interactive exploration of document landscapes without disrupting the mental map of users by changing the layout itself. We discuss the suitability of multiple navigation and characterization methods for different spatializations and texts. Finally, we provide insights generated through user feedback and discuss the effectiveness of our approach.",
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"proceeding": {
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"title": "2022 2nd International Signal Processing, Communications and Engineering Management Conference (ISPCEM)",
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"title": "Three-dimensional Image and Terrain Matching Based on Interferometric Synthetic Aperture Radar Technology",
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"abstract": "In recent years, due to underground resource extraction and underground space construction, the internal structure of the ground strata has been seriously damaged, leading to frequent disasters such as road collapse, bridge collapse and landslide, which pose serious threats to people's lives and safety. In order to improve the spatial resolution and deformation measurement accuracy, this paper also adopts the interferometric synthetic aperture radar (InSAR) technology to match the image with the topographic data in three dimensions by geometric mapping, and the organic combination of the two technologies with complementary advantages greatly improves the accuracy and reliability of surface deformation data in the monitoring area. Finally, the measured results are analyzed by comparing whether the difference of coordinate changes at the same point in a short period of time exceeds the threshold value for disaster pre-disaster judgment, and disaster prediction is made by wireless network or Beidou short message function. Several sets of experimental results show that the measurement accuracy of deformation in the horizontal direction of the ground surface is better than 3 mm and the measurement accuracy of elevation settlement deformation is better than 5 mm by using the method of this paper, which greatly improves the accuracy and reliability of deformation monitoring.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In recent years, due to underground resource extraction and underground space construction, the internal structure of the ground strata has been seriously damaged, leading to frequent disasters such as road collapse, bridge collapse and landslide, which pose serious threats to people's lives and safety. In order to improve the spatial resolution and deformation measurement accuracy, this paper also adopts the interferometric synthetic aperture radar (InSAR) technology to match the image with the topographic data in three dimensions by geometric mapping, and the organic combination of the two technologies with complementary advantages greatly improves the accuracy and reliability of surface deformation data in the monitoring area. Finally, the measured results are analyzed by comparing whether the difference of coordinate changes at the same point in a short period of time exceeds the threshold value for disaster pre-disaster judgment, and disaster prediction is made by wireless network or Beidou short message function. Several sets of experimental results show that the measurement accuracy of deformation in the horizontal direction of the ground surface is better than 3 mm and the measurement accuracy of elevation settlement deformation is better than 5 mm by using the method of this paper, which greatly improves the accuracy and reliability of deformation monitoring.",
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