With network developing and virtualization rising, more and more indoor environment (POIs) such as care, library, office, even bus and subway can provide plenty of bandwidth and computing resources. Meanwhile many peo...With network developing and virtualization rising, more and more indoor environment (POIs) such as care, library, office, even bus and subway can provide plenty of bandwidth and computing resources. Meanwhile many people daily spending much time in them are still suffering from the mobile device with limited resources. This situation implies a novel local cloud computing paradigm in which mobile device can leverage nearby resources to facilitate task execution. In this paper, we implement a mobile local computing system based on indoor virtual cloud. This system mainly contains three key components: 1)As to application, we create a parser to generate the "method call and cost tree" and analyze it to identify resource- intensive methods. 2) As to mobile device, we design a self-learning execution controller to make offtoading decision at runtime. 3) As to cloud, we construct a social scheduling based application-isolation virtual cloud model. The evaluation results demonstrate that our system is effective and efficient by evaluating CPU- intensive calculation application, Memory- intensive image translation application and I/ O-intensive image downloading application.展开更多
Based on salient visual regions for mobile robot navigation in unknown environments, a new place recognition system was presented. The system uses monocular camera to acquire omni-directional images of the environment...Based on salient visual regions for mobile robot navigation in unknown environments, a new place recognition system was presented. The system uses monocular camera to acquire omni-directional images of the environment where the robot locates. Salient local regions are detected from these images using center-surround difference method, which computes opponencies of color and texture among multi-scale image spaces. And then they are organized using hidden Markov model (HMM) to form the vertex of topological map. So localization, that is place recognition in our system, can be converted to evaluation of HMM. Experimental results show that the saliency detection is immune to the changes of scale, 2D rotation and viewpoint etc. The created topological map has smaller size and a higher ratio of recognition is obtained.展开更多
This study addresses the joint state and fault fusion estimation problem for mobile robot localization under the energy-harvesting sensors.Under such a circumstance,the sensors can harvest energy from the external env...This study addresses the joint state and fault fusion estimation problem for mobile robot localization under the energy-harvesting sensors.Under such a circumstance,the sensors can harvest energy from the external environment and then consume an amount of energy when transmitting measurements to the corresponding estimator.Based on the energy harvesting mechanism's probability distribution,the probability of measurement loss is computed at each step.The main objective of this study is to tackle the mobile robot localization problem by designing local estimators for each sensor node,where the upper bound of the local estimation error covariance is guaranteed and then minimized by appropriately tuning the estimator parameters.Furthermore,the local estimates are fused using the covariance intersection(CI)fusion approach.Finally,a numerical experiment is presented to demonstrate the effectiveness of the proposed fusion estimation algorithm.展开更多
A new distributed node localization algorithm named mobile beacons-improved particle filter （MB-IPF） was proposed. In the algorithm, the mobile nodes equipped with globe position system （GPS） move around in the wi...A new distributed node localization algorithm named mobile beacons-improved particle filter （MB-IPF） was proposed. In the algorithm, the mobile nodes equipped with globe position system （GPS） move around in the wireless sensor network （WSN） field based on the Gauss-Markov mobility model, and periodically broadcast the beacon messages Each unknown node estimates its location in a fully distributed mode based on the received mobile beacons. The localization algorithm is based on the IPF and several refinements, including the proposed weighted centroid algorithm, the residual resampling algorithm, and the markov chain monte carlo （MCMC） method etc., which were also introduced for performance improvement. The simulation results show that our proposed algorithm is efficient for most applications.展开更多
The clock operator U and shift operator V are higher-dimensional Pauli operators. Just recently, tighter uncertainty relations with respect to U and V were derived, and we apply them to study the electron localization...The clock operator U and shift operator V are higher-dimensional Pauli operators. Just recently, tighter uncertainty relations with respect to U and V were derived, and we apply them to study the electron localization properties in several typical one-dimensional nonuniform lattice systems. We find that uncertainties △U^2 are less than, equal to, and greater than uncertainties △V^2 for extended, critical, and localized states, respectively. The lower bound LB of the uncertainty relation is relatively large for extended states and small for localized states. Therefore, in combination with traditional quantities,for instance inverse participation ratio, these quantities can be as novel indexes to reflect Anderson localization.展开更多
Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we ...Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone＇s sensors only without any infrastructure support. It does not require any prior knowledge of the building such as floor height or floor levels. Through crowdsourcing, FTrack builds a mapping table which contains the magnetic field signature of users taking the elevator/escalator or walking on the stairs between any two floors. The table can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the eiTiciency, scalability and robustness of FTrack. Our field trial shows that FTrack achieves an accuracy of over 96% in three different buildings.展开更多
基金ACKNOWLEDGEMENTS This work was supported by the Research Fund for the Doctoral Program of Higher Education of China(No.20110031110026 and No.20120031110035)the National Natural Science Foundation of China(No.61103214)the Key Project in Tianjin Science&Technology Pillar Program(No.13ZCZDGX01098).
文摘With network developing and virtualization rising, more and more indoor environment (POIs) such as care, library, office, even bus and subway can provide plenty of bandwidth and computing resources. Meanwhile many people daily spending much time in them are still suffering from the mobile device with limited resources. This situation implies a novel local cloud computing paradigm in which mobile device can leverage nearby resources to facilitate task execution. In this paper, we implement a mobile local computing system based on indoor virtual cloud. This system mainly contains three key components: 1)As to application, we create a parser to generate the "method call and cost tree" and analyze it to identify resource- intensive methods. 2) As to mobile device, we design a self-learning execution controller to make offtoading decision at runtime. 3) As to cloud, we construct a social scheduling based application-isolation virtual cloud model. The evaluation results demonstrate that our system is effective and efficient by evaluating CPU- intensive calculation application, Memory- intensive image translation application and I/ O-intensive image downloading application.
基金Projects(60234030 ,60404021) supported by the National Natural Science Foundation of China
文摘Based on salient visual regions for mobile robot navigation in unknown environments, a new place recognition system was presented. The system uses monocular camera to acquire omni-directional images of the environment where the robot locates. Salient local regions are detected from these images using center-surround difference method, which computes opponencies of color and texture among multi-scale image spaces. And then they are organized using hidden Markov model (HMM) to form the vertex of topological map. So localization, that is place recognition in our system, can be converted to evaluation of HMM. Experimental results show that the saliency detection is immune to the changes of scale, 2D rotation and viewpoint etc. The created topological map has smaller size and a higher ratio of recognition is obtained.
基金supported in part by the National Natural Science Foundation of China(Nos.62403259,62431014,and 62001254)the Natural Science Foundation of Jiangsu Province(No.BK20240945)+1 种基金Fujian Province Special Fund Project for Promoting High-Quality Development of Marine and Fisheries Industries(No.FJHYF-ZH-2023-03)the Natural Science Foundation of Nantong(No.JC2023074).
文摘This study addresses the joint state and fault fusion estimation problem for mobile robot localization under the energy-harvesting sensors.Under such a circumstance,the sensors can harvest energy from the external environment and then consume an amount of energy when transmitting measurements to the corresponding estimator.Based on the energy harvesting mechanism's probability distribution,the probability of measurement loss is computed at each step.The main objective of this study is to tackle the mobile robot localization problem by designing local estimators for each sensor node,where the upper bound of the local estimation error covariance is guaranteed and then minimized by appropriately tuning the estimator parameters.Furthermore,the local estimates are fused using the covariance intersection(CI)fusion approach.Finally,a numerical experiment is presented to demonstrate the effectiveness of the proposed fusion estimation algorithm.
文摘A new distributed node localization algorithm named mobile beacons-improved particle filter （MB-IPF） was proposed. In the algorithm, the mobile nodes equipped with globe position system （GPS） move around in the wireless sensor network （WSN） field based on the Gauss-Markov mobility model, and periodically broadcast the beacon messages Each unknown node estimates its location in a fully distributed mode based on the received mobile beacons. The localization algorithm is based on the IPF and several refinements, including the proposed weighted centroid algorithm, the residual resampling algorithm, and the markov chain monte carlo （MCMC） method etc., which were also introduced for performance improvement. The simulation results show that our proposed algorithm is efficient for most applications.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61475075 and 61170321)
文摘The clock operator U and shift operator V are higher-dimensional Pauli operators. Just recently, tighter uncertainty relations with respect to U and V were derived, and we apply them to study the electron localization properties in several typical one-dimensional nonuniform lattice systems. We find that uncertainties △U^2 are less than, equal to, and greater than uncertainties △V^2 for extended, critical, and localized states, respectively. The lower bound LB of the uncertainty relation is relatively large for extended states and small for localized states. Therefore, in combination with traditional quantities,for instance inverse participation ratio, these quantities can be as novel indexes to reflect Anderson localization.
基金This work was supported by the National High Technology Research and Development 863 Program of China under Grant No. 2013AA01A213 and the National Natural Science Foundation of China under Grant Nos. 91318301, 61373011 and 61321491.
文摘Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone＇s sensors only without any infrastructure support. It does not require any prior knowledge of the building such as floor height or floor levels. Through crowdsourcing, FTrack builds a mapping table which contains the magnetic field signature of users taking the elevator/escalator or walking on the stairs between any two floors. The table can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the eiTiciency, scalability and robustness of FTrack. Our field trial shows that FTrack achieves an accuracy of over 96% in three different buildings.
