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"abstract": "In this paper, we present a new method of using Support Vector Machine (SVM) for multiclass classification. In our method, we use a tree based SVM classifier for classification. Compared with the other SVM multi-class classification methods in literature (i.e. One-Against-One, DAGSVM), our proposed SVM tree classifier is more efficient in both training/classification. Our new SVM tree classifier requires o(n) SVM training during the training stage and o(log(n)) SVM testing during the test stage, while other methods require o(n^2) or at best o(n) SVM training during the training and o(n^2) or at best o(n) SVM testing during testing. Experimental results on digital library document classification demonstrate that our methods is not only significantly more efficient but also achieves the similar precision of classification.",
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"abstract": "The orthogonal projection iteration for the solutions of a class of constrained matrix equation and the related optimal approximation problem is considered. The iteration method for the symmetric ortho-anti-symmetric solution of the matrix equation AX=B is constructed. The convergence of the method is proved and the estimation of the convergence rate is given. The method converges to its least-norm solution when the equation is consistent. Make slight changes of this iteration method, the related optimal approximation solution can also be obtained.",
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"abstract": "High data rate requirement has necessitated the use of optical wireless links from satellite-to-ground that invariably pass through the inhomogeneous cloud layers. It has thus become necessary to accurately model the scattering channel and estimate attenuation suffered by the information bearing optical signal traversing the multiple scattering inhomogeneous clouds. This paper introduces the utilization of three dimensional radiative transfer equation RTE for prediction of attenuation in the optical signal traversing the cloud layers. The three dimensional radiative transfer equation explicitly handles the spatial complexity and multiple scattering effects of inhomogeneous clouds and provides accurate propagation channel estimation without invoking mathematical assumptions. Cloud droplets mainly composed of liquid water attenuates the beam of light through absorption and scattering in forward direction. Therefore, it is necessary to generate three dimensional cloud fields that exhibit realistic spatial distribution of the cloud structure. This statistically generated cloud field from experimental data is used as an input to three dimensional radiative transfer model to calculate transmitted irradiance at the bottom of cloud to predict attenuation in cumulus cloud scene. This paper employs the numerical solution of three dimensional radiative transfer equation using Monte Carlo simulation that uses stochastic methods to simulate physical processes of scattering and absorption within inhomogeneous cloud layers. The open source I3RC Monte Carlo code has been chosen to perform simulations at commonly employed optical wavelengths of 850 nm and 1550 nm. The 10 µm has been specifically analyzed to gain insight of wavelength dependence in optical propagation through clouds.",
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"abstract": "Recent research has shown the poor accuracy of widely used simulators in the area of wireless networks. Beside many other parameters two are of particular interest: (i) the mobility model, and (ii) the radio wave propagation model. The first is responsible for the network topology and the latter for the perception of transmitted data. Both have strong impact on the performance of mobile ad-hoc networks, e.g. the performance of routing protocols changes with these models. We developed a framework combining a realistic mobility model and radio wave propagation model. To generate mobility multiple well understood random mobility models are combined in a scenario graph. The graph also includes obstacles which restrict the movement and the radio wave propagation. The radio wave propagation is calculated using a ray-tracing approach. We present an extensive simulation study on the effect of the mobility and radio wave propagation on simulation results.",
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"content": "Recent research has shown the poor accuracy of widely used simulators in the area of wireless networks. Beside many other parameters two are of particular interest: (i) the mobility model, and (ii) the radio wave propagation model. The first is responsible for the network topology and the latter for the perception of transmitted data. Both have strong impact on the performance of mobile ad-hoc networks, e.g. the performance of routing protocols changes with these models. We developed a framework combining a realistic mobility model and radio wave propagation model. To generate mobility multiple well understood random mobility models are combined in a scenario graph. The graph also includes obstacles which restrict the movement and the radio wave propagation. The radio wave propagation is calculated using a ray-tracing approach. We present an extensive simulation study on the effect of the mobility and radio wave propagation on simulation results.",
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"affiliation": "Department of Computer Science, Informatik 4, RWTH Aachen University, Germany. guenes@i4.informatik.rwth-aachen.de",
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"doi": "10.1109/CMC.2010.57",
"title": "Study on a Sea Radio-Wave Propagation Loss Model",
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"abstract": "In mobile communication, the covering range and the interference is considered to be so important in each base station that it is necessary to know the regional transmission loss. Currently, there is a high margin of error in actual transmission loss model on the sea. This paper presents a propagation loss model. After doing many experiments, the results show that the error between the models predicted levels and measured levels of models is less than 3dB within the radio line of sight. Once over the radio line of sight, the model prediction accuracy will decrease, which is to be considered the Earth's curvature on the impact of propagation loss.",
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"content": "In mobile communication, the covering range and the interference is considered to be so important in each base station that it is necessary to know the regional transmission loss. Currently, there is a high margin of error in actual transmission loss model on the sea. This paper presents a propagation loss model. After doing many experiments, the results show that the error between the models predicted levels and measured levels of models is less than 3dB within the radio line of sight. Once over the radio line of sight, the model prediction accuracy will decrease, which is to be considered the Earth's curvature on the impact of propagation loss.",
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"fno": "3989a428",
"keywords": [
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"fullName": "Mingyi Zhu",
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"content": "Visibility culling has the potential to accelerate large data visualization in significant ways. Unfortunately, existing algorithms do not scale well when parallelized, and require full re-computation whenever the opacity transfer function is modified. To address these issues, we have designed a Plenoptic Opacity Function (POF) scheme to encode the view-dependent opacity of a volume block. POFs are computed off-line during a pre-processing stage, only once for each block. We show that using POFs is (i) an efficient, conservative and effective way to encode the opacity variations of a volume block for a range of views, (ii) flexible for re-use by a family of opacity transfer functions without the need for additional off-line processing, and (iii) highly scalable for use in massively parallel implementations. Our results confirm the efficacy of POFs for visibility culling in large-scale parallel volume rendering; we can interactively render the Visible Woman dataset using software ray-casting on 32 processors, with interactive modification of the opacity transfer function on-the-fly.",
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"abstract": "Volume rendering is a flexible technique for visualizing dense 3D volumetric datasets. A central element of volume rendering is the conversion between data values and observable quantities such as color and opacity. This process is usually realized through the use of transfer functions that are precomputed and stored in lookup tables. For multidimensional transfer functions applied to multivariate data, these lookup tables become prohibitively large. We propose the direct evaluation of a particular type of transfer functions based on a sum of Gaussians. Because of their simple form (in terms of number of parameters), these functions and their analytic integrals along line segments can be evaluated efficiently on current graphics hardware, obviating the need for precomputed lookup tables. We have adopted these transfer functions because they are well suited for classification based on a unique combination of multiple data values that localize features in the transfer function domain. We apply this technique to the visualization of several multivariate datasets (CT, cryosection) that are difficult to classify and render accurately at interactive rates using traditional approaches.",
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"abstract": "The availability of massive volumes of trajectory data has made it convenient for the study of different types of movement behaviors. Among them, bi-directional movement behaviors exist ubiquitously in our daily life, from urban traffic to animal migration, and from sports to wars. To analyze bi-directional movement behaviors, people need to compare movements in two directions simultaneously for detecting similarities or differences in the movement patterns. If the movement involves tens of thousands items like vehicles or bird migration during a ten-year time span, the comparisons need to be done at both macro level and micro level. Due to the complexities of data and the challenges of analytical tasks, visual analytics is often used to take full advantage of machines' computational power as well as human's domain knowledge and cognitive abilities. In this paper, we present a comprehensive visual analytics system with three major visualization modules, including Global View, OD-pair Flow View and Isotime Storyline View, to depict bi-directional movement behaviors in a novel way, which enables a three-level exploration to help users gain insights into both macro and micro patterns. Quantitative analyses (e.g. movement model construction, modular Dol specification and key node extraction) and intuitive visualizations (e.g. parallelized flow map, bidirectional storyline chart with contour map and multi-layer heat map) are integrated into our system to provide an efficient and intuitive solution to the analysis of bi-directional movement behaviors based on big movement data. Case studies with two real-world datasets and expert interviews are carried out to demonstrate the effectiveness and usefulness of our system.",
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"content": "The availability of massive volumes of trajectory data has made it convenient for the study of different types of movement behaviors. Among them, bi-directional movement behaviors exist ubiquitously in our daily life, from urban traffic to animal migration, and from sports to wars. To analyze bi-directional movement behaviors, people need to compare movements in two directions simultaneously for detecting similarities or differences in the movement patterns. If the movement involves tens of thousands items like vehicles or bird migration during a ten-year time span, the comparisons need to be done at both macro level and micro level. Due to the complexities of data and the challenges of analytical tasks, visual analytics is often used to take full advantage of machines' computational power as well as human's domain knowledge and cognitive abilities. In this paper, we present a comprehensive visual analytics system with three major visualization modules, including Global View, OD-pair Flow View and Isotime Storyline View, to depict bi-directional movement behaviors in a novel way, which enables a three-level exploration to help users gain insights into both macro and micro patterns. Quantitative analyses (e.g. movement model construction, modular Dol specification and key node extraction) and intuitive visualizations (e.g. parallelized flow map, bidirectional storyline chart with contour map and multi-layer heat map) are integrated into our system to provide an efficient and intuitive solution to the analysis of bi-directional movement behaviors based on big movement data. Case studies with two real-world datasets and expert interviews are carried out to demonstrate the effectiveness and usefulness of our system.",
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"normalizedAbstract": "The availability of massive volumes of trajectory data has made it convenient for the study of different types of movement behaviors. Among them, bi-directional movement behaviors exist ubiquitously in our daily life, from urban traffic to animal migration, and from sports to wars. To analyze bi-directional movement behaviors, people need to compare movements in two directions simultaneously for detecting similarities or differences in the movement patterns. If the movement involves tens of thousands items like vehicles or bird migration during a ten-year time span, the comparisons need to be done at both macro level and micro level. Due to the complexities of data and the challenges of analytical tasks, visual analytics is often used to take full advantage of machines' computational power as well as human's domain knowledge and cognitive abilities. In this paper, we present a comprehensive visual analytics system with three major visualization modules, including Global View, OD-pair Flow View and Isotime Storyline View, to depict bi-directional movement behaviors in a novel way, which enables a three-level exploration to help users gain insights into both macro and micro patterns. Quantitative analyses (e.g. movement model construction, modular Dol specification and key node extraction) and intuitive visualizations (e.g. parallelized flow map, bidirectional storyline chart with contour map and multi-layer heat map) are integrated into our system to provide an efficient and intuitive solution to the analysis of bi-directional movement behaviors based on big movement data. Case studies with two real-world datasets and expert interviews are carried out to demonstrate the effectiveness and usefulness of our system.",
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"abstract": "The widespread use of positioning technologies ranging from GSM and GPS to WiFi devices, tend to produce large-scale datasets of trajectories, representing the movement of travelling entities. Several applications may benefit from mining such datasets. However, mining results only become truly useful and meaningful for the end user when the intrinsically complex nature of the movement data in terms of context is taken into account during the discovery process. For this reason we propose a pattern interpretation framework that consists of three main steps, namely, pattern discovery, semantic annotation and pattern analysis. The framework supports the understanding of movement patterns that were extracted using some trajectory mining algorithm. To demonstrate the feasibility and effectiveness of the framework, we performed an experiment with the Dwingelderveld National Park (DNP) dataset, which contains records of observations on the movement of visitors in the park. As a result, some forms of interaction, such as certain groups of visitors following the most popular path in the park, were inferred after completing the steps of the framework.",
"abstracts": [
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"content": "The widespread use of positioning technologies ranging from GSM and GPS to WiFi devices, tend to produce large-scale datasets of trajectories, representing the movement of travelling entities. Several applications may benefit from mining such datasets. However, mining results only become truly useful and meaningful for the end user when the intrinsically complex nature of the movement data in terms of context is taken into account during the discovery process. For this reason we propose a pattern interpretation framework that consists of three main steps, namely, pattern discovery, semantic annotation and pattern analysis. The framework supports the understanding of movement patterns that were extracted using some trajectory mining algorithm. To demonstrate the feasibility and effectiveness of the framework, we performed an experiment with the Dwingelderveld National Park (DNP) dataset, which contains records of observations on the movement of visitors in the park. As a result, some forms of interaction, such as certain groups of visitors following the most popular path in the park, were inferred after completing the steps of the framework.",
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"abstract": "We propose the use of Partial Differential Equations (PDEs) for shape modelling within visual cyberworlds. PDEs, especially those that are elliptic in nature, enable surface modelling to be defined as boundary-value problems. Here we show how the PDE based on the Biharmonic equation subject to suitable boundary conditions can be used for shape modelling within visual cyberworlds. We discuss an analytic solution formulation for the Biharmonic equation which allows us to define a function based geometry whereby the resulting geometry can be visualised efficiently at arbitrary levels of shape resolutions. In particular, we discuss how function based PDE surfaces can be readily integrated within VRML and X3D environments.",
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"abstract": "Previous image based relighting methods require capturing multiple images to acquire high frequency lighting effect under different lighting conditions, which needs nontrivial effort and may be unrealistic in certain practical use scenarios. While such approaches rely entirely on cleverly sampling the color images under different lighting conditions, little has been done to utilize geometric information that crucially influences the high-frequency features in the images, such as glossy highlight and cast shadow. We therefore propose a framework for image relighting from a single flash photograph with its corresponding depth map using deep learning. By incorporating the depth map, our approach is able to extrapolate realistic high-frequency effects under novel lighting via geometry guided image decomposition from the flashlight image, and predict the cast shadow map from the shadow-encoding transformed depth map. Moreover, the single-image based setup greatly simplifies the data capture process. We experimentally validate the advantage of our geometry guided approach over state-of-the-art image-based approaches in intrinsic image decomposition and image relighting, and also demonstrate our performance on real mobile phone photo examples.",
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"content": "Previous image based relighting methods require capturing multiple images to acquire high frequency lighting effect under different lighting conditions, which needs nontrivial effort and may be unrealistic in certain practical use scenarios. While such approaches rely entirely on cleverly sampling the color images under different lighting conditions, little has been done to utilize geometric information that crucially influences the high-frequency features in the images, such as glossy highlight and cast shadow. We therefore propose a framework for image relighting from a single flash photograph with its corresponding depth map using deep learning. By incorporating the depth map, our approach is able to extrapolate realistic high-frequency effects under novel lighting via geometry guided image decomposition from the flashlight image, and predict the cast shadow map from the shadow-encoding transformed depth map. Moreover, the single-image based setup greatly simplifies the data capture process. We experimentally validate the advantage of our geometry guided approach over state-of-the-art image-based approaches in intrinsic image decomposition and image relighting, and also demonstrate our performance on real mobile phone photo examples.",
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"abstract": "This paper address the problem of synthesizing realistic crowd behavior animation in virtual environment, which usually requires setting a lot of empirical parameters and interacting with the users frequently. A new method is proposed in this paper, which extracts the typical pedestrian velocity fields from video data, and then synthesizes the macroscopic crowd behavior animation based on these fields. The video data used to extract velocity fields was generated from a specifically designed experiment. With these velocity fields, we could design the macroscopic crowd behavior interactively in a 2D behavior editor, and synthesized the corresponding real time 3D crowd animation. Experiments results have proved that this new method can get a good performance of realistic crowd animation synthesis with little user interaction, and can be applied in many ways.",
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"abstract": "Finding one's way through a complex virtual environment is a standard task in 3D graphics and virtual reality applications. Constrained navigation is a method that appropriately restricts the user's degrees of freedom when there is a poor match between the goal of an exploration activity, the control device, and the user's familiarity with the exploration domain. The fundamental prerequisite for the adoption of constrained navigation is that the designer can significantly improve the quality of the user's experience by choosing a predetermined parametric set of viewing parameters or algorithms. We discuss families of constrained navigation methods appropriate to desktop and immersive virtual reality applications. We illustrate the approach with a variety of examples, emphasizing the possibility of topologically nontrivial navigation spaces, and present the results of a preliminary user study.",
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"abstract": "This paper addresses attention to a research regarding the design of an exoskeleton used for children walking rehabilitation. The research core is focused on chain transmissions implementation in order to command and control each joint by six servomotors. Thus, by using design techniques, especially modeling and simulation on MSC Adams environment, it will be demonstrate the engineering feasibility of the proposed design.",
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"content": "This paper proposes a method to distinguish distracted pedestrians from normal pedestrians, using the acceleration and the angular velocity while walking. This method uses an acceleration sensor attached on the back of the pedestrian. The acceleration and the angular velocity are obtained while the pedestrian is walking. In addition to that, walking features are calculated based on the obtained data. Some studies points out distraction of the pedestrian relates to consumption of working memory. We assume considering the relationship between consumption of working memory and walking behavior suggest the effectiveness to estimate distraction of the pedestrian. When each pedestrian is walking while consuming working memory, for example thinking about something, their walk deviates from normal. Machine Learning method, Random Forest, is applied to classify whether the pedestrian is distracted using features of walking. An experiment suggests we can distinguish walking features represent both distracted state and normal state completely. The result indicates the method can find distracted pedestrians whose working memory is highly consumed. We discuss why we can distinguish the distraction of the pedestrian from walking feature components with the variable importance. In addition, we have conducted regression analysis on the significant feature components to figure out the reasons. Finally, we discuss the feasibility of our proposed method.",
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"title": "Statistical and Machine Learning Approach to Assessing the Environmental Impact on Walking Patterns",
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"abstract": "It has been recongised that subjects may change their gait pattern when walking in different environments. This paper investigated the impact of walking environments on gait monitoring and analysis. A tri-axial accelerometer attached to subject's lower back was used to record gait pattern while walking in 5 different urban environments (quiet street, busy street, cobbled street, dark street and checkerboard floor). Forty-one young students participated the experiment. For each trial, a total of 33 gait features were extracted, of which 11 were derived from the entire walking trial and 22 were computed for each stride cycle. Statistics analysis showed that 7 out of 11 features extracted from each trial were significantly different across the five environments. The obtained results suggested that different environments have various impacts on gait features extracted from accelerometer data. To further access the impact, a multi-layer perceptrons based hierarchical classification approach was proposed to discriminate stride cycles taken from different walking environments. The classification accuracy of each level ranged from 98.26% to 65.62% with the discrimination of walking in quiet environment achieving the best performance.",
"abstracts": [
{
"abstractType": "Regular",
"content": "It has been recongised that subjects may change their gait pattern when walking in different environments. This paper investigated the impact of walking environments on gait monitoring and analysis. A tri-axial accelerometer attached to subject's lower back was used to record gait pattern while walking in 5 different urban environments (quiet street, busy street, cobbled street, dark street and checkerboard floor). Forty-one young students participated the experiment. For each trial, a total of 33 gait features were extracted, of which 11 were derived from the entire walking trial and 22 were computed for each stride cycle. Statistics analysis showed that 7 out of 11 features extracted from each trial were significantly different across the five environments. The obtained results suggested that different environments have various impacts on gait features extracted from accelerometer data. To further access the impact, a multi-layer perceptrons based hierarchical classification approach was proposed to discriminate stride cycles taken from different walking environments. The classification accuracy of each level ranged from 98.26% to 65.62% with the discrimination of walking in quiet environment achieving the best performance.",
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"normalizedAbstract": "It has been recongised that subjects may change their gait pattern when walking in different environments. This paper investigated the impact of walking environments on gait monitoring and analysis. A tri-axial accelerometer attached to subject's lower back was used to record gait pattern while walking in 5 different urban environments (quiet street, busy street, cobbled street, dark street and checkerboard floor). Forty-one young students participated the experiment. For each trial, a total of 33 gait features were extracted, of which 11 were derived from the entire walking trial and 22 were computed for each stride cycle. Statistics analysis showed that 7 out of 11 features extracted from each trial were significantly different across the five environments. The obtained results suggested that different environments have various impacts on gait features extracted from accelerometer data. To further access the impact, a multi-layer perceptrons based hierarchical classification approach was proposed to discriminate stride cycles taken from different walking environments. The classification accuracy of each level ranged from 98.26% to 65.62% with the discrimination of walking in quiet environment achieving the best performance.",
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{
"proceeding": {
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"article": {
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"doi": "10.1109/VR.2016.7504742",
"title": "Simultaneous mapping and redirected walking for ad hoc free walking in virtual environments",
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"abstract": "Providing real walking in virtual environments remains a challenge because of space, setup, and tracking system requirements. With the help of redirected walking (RDW), natural and unconstrained walking in virtual environments has become possible — without using mechanical locomotion devices — by manipulating the user's real world trajectory such that he remains within the boundaries of the walkable space. Nevertheless, typical home or office environments do not provide sufficient space or a suitable room which is free of obstacles. This paper presents an approach that combines RDW with a low-cost and user-worn tracking approach based on simultaneous localization and mapping (SLAM). I.e. learning the environment with the walkable area, tracking the user's viewpoint, and RDW is done on the fly without any prior setup, without preparing a room, and without setting up a tracking system. This allows ad hoc free walking in virtual environments even within dynamic and cluttered physical rooms, where the walkable area is of arbitrary shape. Furthermore, by combining SLAM and a planning RDW controller, this approach has the potential to provide the best free walking experience for any given physical environment.",
"abstracts": [
{
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"content": "Providing real walking in virtual environments remains a challenge because of space, setup, and tracking system requirements. With the help of redirected walking (RDW), natural and unconstrained walking in virtual environments has become possible — without using mechanical locomotion devices — by manipulating the user's real world trajectory such that he remains within the boundaries of the walkable space. Nevertheless, typical home or office environments do not provide sufficient space or a suitable room which is free of obstacles. This paper presents an approach that combines RDW with a low-cost and user-worn tracking approach based on simultaneous localization and mapping (SLAM). I.e. learning the environment with the walkable area, tracking the user's viewpoint, and RDW is done on the fly without any prior setup, without preparing a room, and without setting up a tracking system. This allows ad hoc free walking in virtual environments even within dynamic and cluttered physical rooms, where the walkable area is of arbitrary shape. Furthermore, by combining SLAM and a planning RDW controller, this approach has the potential to provide the best free walking experience for any given physical environment.",
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"fno": "07504742",
"keywords": [
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"Simultaneous Localization And Mapping",
"Virtual Environments",
"Cameras",
"Three Dimensional Displays",
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"affiliation": "ICVR - IWF - ETH Zurich",
"fullName": "Thomas Nescher",
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"affiliation": "ICVR - IWF - ETH Zurich",
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"title": "Auditory Feedback to Make Walking in Virtual Reality More Accessible",
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"abstract": "The objective of this study is to investigate the impact of several auditory feedback modalities on gait (i.e., walking patterns) in virtual reality (VR). Prior research has substantiated gait disturbances in VR users as one of the primary obstacles to VR usability. However, minimal research has been done to mitigate this issue. We recruited 39 participants (with mobility impairments: 18, without mobility impairments: 21) who completed timed walking tasks in a real-world environment and the same tasks in a VR environment with various types of auditory feedback. Within-subject results showed that each auditory condition significantly improved gait performance while in VR Z_$(p \\lt$_Z.001) compared to the no auditory condition in VR for both groups of participants with and without mobility impairments. Moreover, spatial audio improved gait performance significantly Z_$(p \\lt$_Z.001) compared to other auditory conditions for both groups of participants. This research could help to make walking in VR more accessible for people with and without mobility impairments.",
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"content": "The objective of this study is to investigate the impact of several auditory feedback modalities on gait (i.e., walking patterns) in virtual reality (VR). Prior research has substantiated gait disturbances in VR users as one of the primary obstacles to VR usability. However, minimal research has been done to mitigate this issue. We recruited 39 participants (with mobility impairments: 18, without mobility impairments: 21) who completed timed walking tasks in a real-world environment and the same tasks in a VR environment with various types of auditory feedback. Within-subject results showed that each auditory condition significantly improved gait performance while in VR $(p \\lt$.001) compared to the no auditory condition in VR for both groups of participants with and without mobility impairments. Moreover, spatial audio improved gait performance significantly $(p \\lt$.001) compared to other auditory conditions for both groups of participants. This research could help to make walking in VR more accessible for people with and without mobility impairments.",
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"title": "2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)",
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"title": "Estimation of Walking Direction with Vibration Sensor based on Piezoelectric Device",
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"abstract": "Recently, device-free indoor positioning and tracking of persons is attracting attention. Some approaches use vibration sensors installed on the floor to identify the position of the vibration source by measuring and analyzing vibration strength and vibration fingerprints. However, vibration sensors can give the output information only on the strength of the vibration which shows that the vibration source is near or far from the sensor. Since the moving direction cannot be measured, it is difficult to apply the vibration sensor to track persons walking around in indoor space. In this paper, we propose a method for estimating the walking direction of a person using two vibration sensors. In the proposed method, we capture floor vibration using a pair of piezoelectric vibration sensors installed at a certain distance, calculate frequency domain features and estimate walking direction by machine learning. We conducted an experiment in a smart home where we installed our vibration sensors on the floor, asked four participants to pass near the sensors 20 times in two directions and estimated the walking direction by the proposed method. As a result, the walking direction was estimated with a maximum accuracy of 90%.",
"abstracts": [
{
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"content": "Recently, device-free indoor positioning and tracking of persons is attracting attention. Some approaches use vibration sensors installed on the floor to identify the position of the vibration source by measuring and analyzing vibration strength and vibration fingerprints. However, vibration sensors can give the output information only on the strength of the vibration which shows that the vibration source is near or far from the sensor. Since the moving direction cannot be measured, it is difficult to apply the vibration sensor to track persons walking around in indoor space. In this paper, we propose a method for estimating the walking direction of a person using two vibration sensors. In the proposed method, we capture floor vibration using a pair of piezoelectric vibration sensors installed at a certain distance, calculate frequency domain features and estimate walking direction by machine learning. We conducted an experiment in a smart home where we installed our vibration sensors on the floor, asked four participants to pass near the sensors 20 times in two directions and estimated the walking direction by the proposed method. As a result, the walking direction was estimated with a maximum accuracy of 90%.",
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"affiliation": "Nara Institute of Science and Technology,Ikoma,Nara,Japan,630-0192",
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"abstract": "A novel efficient skeleton extraction algorithm is proposed, which is based on feature points extraction and Reeb graph theories. Because of the topological facility of feature points, a model can be divided into several branches according to them. One feature point can present one branch. So we just extract the other point of the branch – that could be skeleton point, connect these points with their corresponding feature points, then we can get all branch skeletons. Finally, connecting branch skeletons through connecting skeleton points according the topological relationship of each skeleton point preserving, then 3D model’s skeleton can be extracted.Without pre-processing stages and without input parameters, this algorithm can automatically extract the skeleton of 3D models. Theoretical analyses and experimental results show that our method has a lower computing complexity, and meets the requirement of extracting nice-looking and affine-invariant skeletons efficiently.",
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|
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