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Inertial estimator learning automata

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2012年第6期E95-A卷 1041-1048页

作者： Zhang, Junqi Ni, Lina Xie, Chen Gao, Shangce Tang, Zheng Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 200092 China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China Department of Intellectual Information Systems Engineering University of Toyama Toyama-shi 930-8555 Japan

This paper presents an inertial estimator learning automata scheme by which both the short-term and long-term perspectives of the environment can be incorporated in the stochastic estimator-the long term information crystallized in terms of the running reward-probability estimates, and the short term information used by considering whether the most recent response was a reward or a penalty. Thus, when the shortterm perspective is considered, the stochastic estimator becomes pertinent in the context of the estimator algorithms. The proposed automata employ an inertial weight estimator as the short-term perspective to achieve a rapid and accurate convergence when operating in stationary random environments. According to the proposed inertial estimator scheme, the estimates of the reward probabilities of actions are affected by the last response from environment. In this way, actions that have gotten the positive response from environment in the short time, have the opportunity to be estimated as "optimal", to increase their choice probability and consequently, to be selected. The estimates become more reliable and consequently, the automaton rapidly and accurately converges to the optimal action. The asymptotic behavior of the proposed scheme is analyzed and it is proved to be Ε-optimal in every stationary random environment. Extensive simulation results indicate that the proposed algorithm converges faster than the traditional stochastic-estimator-based SERI scheme, and the deterministicestimator- based DGPA and DPRI schemes when operating in stationary random environments. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers.

关键词： Probability

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New tight upper bounds on the capacity for general deterministic dissemination in wireless ad hoc networks

New tight upper bounds on the capacity for general determini...

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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Cheng Wang Jieren Zhou Tianci Liu Lu Shao Huiya Yan Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science Tongji University Shanghai China School of Software Tsinghua University Beijing China Mathematics Department University of Wisconsin-La Crosse La Crosse WI

In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdress this issue is two-fold: Firstly, we aim to propose a more general result to unify the network capacity for general homogeneous random models by investigating the impacts of different parameters of the system on the network capacity. Secondly, we target to close the open gaps between the upper and the lower bounds on the network capacity in the literature. We derive the general upper bounds on the capacity for the arbitrary case of (λ, nd, ns) by introducing the Poisson Boolean model of continuum percolation, where λ, nd, and ns are the general node density, the number of destinations for each session, and the number of sessions, respectively. We prove that the derived upper bounds are tight according to the existing general lower bounds constructed in the literature.

关键词： Roads Mathematics Unicast Signal to noise ratio Interference

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Design issues in ZigBee-based sensor network for healthcare applications

Design issues in ZigBee-based sensor network for healthcare ...

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IEEE International Conference on Networking, Sensing and Control

作者： Jin Soo Choi Meng Chu Zhou Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji Shanghai China ECE Department New Jersey Institute of Technology Newark NJ USA

IEEE 802.15.4/ZigBee sensor networks support small power consumption and node expansion compared to other network standards for WSN. Body sensor networks (BSN) require a number of sensors for sensing medical information from human body, and low power consumption to monitor a patient's status for a long time. However, ZigBee has limited bandwidth and is thus hard to support real time data transmission because of the adoption of CSMA-CA as its medium access control (MAC) protocol. In this paper, we will analyze the reasonable number of nodes, size of payload and packet interval for best QoS for such network. It is found that an appropriate MAC parameters setting can improve the QoS compared to the default setting in IEEE 802.15.4 specification. The effective data rate, average end-to-end delay and packet delivery ratio are found via simulation for various network settings. The results are useful for the construction of ZigBee networks for patient monitoring and care.

关键词： Delay IEEE 802.15 Standards Zigbee Monitoring Payloads Medical services Data models

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SelectCast: Scalable data aggregation scheme in wireless sensor networks

SelectCast: Scalable data aggregation scheme in wireless sen...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Cheng Wang Shaojie Tang Xiang-Yang Li Changjun Jiang Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL USA TNLIST School of Software Tsinghua University China

In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density λ ∈ [1, n], we study the tradeoffs between the aggregation throughput and gathering efficiency. The gathering efficiency refers to the ratio of the number of the sensors whose data have been gathered to the total number of sensors. Specifically, we design two efficient aggregation schemes, called single-hop-length (SLH) scheme and multiple-hop-length (MLH) scheme. By novelly integrating these two schemes, we theoretically prove that our protocol achieves the optimal tradeoffs, and derive the optimal aggregation throughput depending on a given threshold value (lower bound) on gathering efficiency. Particularly, we show that under the MLH scheme, for a practically important set of symmetric functions called perfectly compressible functions, including the mean, max, or various kinds of indicator functions, etc., the data from Θ(n) sensors can be aggregated to the sink at the throughput of a constant order Θ(1), implying that our MLH scheme is indeed scalable.

关键词： TV Wireless sensor networks Power capacitors Blogs Lattices

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Predictive Resilience Assessment Featuring Diffusion Reconstruction for Road Networks Under Rainfall Disturbances

SSRN

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SSRN 2024年

作者： Zang, Di Wei, Chenguang Zhao, Jiayi Zhang, Junqi Tang, Keshuang Zhu, Hong Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University No. 4800 Cao’an Road Shanghai200092 China Key Laboratory of Road and Traffic Engineering Ministry of Education College of Transportation Engineering Tongji University No. 4800 Cao’an Road Shanghai200092 China

The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into the system's resistance and recovery capabilities. In the face of unforeseen resilience disturbance events, predictive and accurate assessment of road network resilience is essential for better traffic regulation and emergency response management. However, existing resilience assessment methods of road networks are insufficient: they lack reliable real-time big-data analysis, do not possess predictive capabilities for guiding decision-making, and have a narrow view with single-dimensional resilience indicators. To address these issues, focusing on rainfall disturbance scenarios, this work introduces a novel resilience assessment method, which is predictive and real-time, consisting of two components: a traffic indicator prediction model and a comprehensive resilience assessment model. Firstly, we propose a two-stage traffic indicator prediction model, namely the Conditional Diffusion-Reconstruction-based Graph Neural Network (CDRGNN), which particularly enhances disturbance-scenario prediction accuracy, thereby providing reliable foresight in aid of the following assessments. Subsequently, we develop a resilience assessment model featuring structural-functional comprehensive resilience indicators established on the shortest-path aggregation, and the overall resilience assessment is performed through comparative analysis using indicators obtained in real time with historical non-disruptive resilience benchmarks. In a case study focusing on heavy rainfall disturbances on a road network in California, USA, abundant experiments and visualizations are conducted to demonstrate the rationality of our proposed comprehensive resilience indicators as well as the precision and reliability of these predictive resilience assessment outcomes. © 2024, The Authors. All

关键词： Diffusion

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Construction-and-extraction based index for images retrieval

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2011年第7期131卷 1377-1383页

作者： Zhang, Junqi Ni, Lina Tian, Chunqi Gao, Shangce Tang, Zheng Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 200092 China College of Info Sci and Engi Shandong University of Science and Technology Qingdao 266510 China Department of Intellectual Information Systems Engineering University of Toyama Toyama-shi 930-8555 Japan

The past recent years have witnessed more and more applications on image retrieval. As searching a large image database is often costly, to improve the efficiency, high dimensional indexes may help. This paper proposes an adaptive hybrid index (AHI) supported by a construction-and-extraction technique to support image retrieval. First, the image clusters are further partitioned into sub-clusters to reduce the overlap between clusters and indexed into an iDistance index. Then, the query sampling statistically extracts some sub-cluster from the iDistance index into a sequential file. Finally, the users' queries are accurately returned by searching both the iDistance index and the sequential file. It's proved that the proposed AHI never performs worse than the sequential scan. Particularly, the experimental results demonstrate that the proposed index AHI is beneficial and achieves better performance than some exiting methods. It is about 2 times faster than iDistance, almost three times than Omni-sequential, more than four times faster than sequential file and more than 10 times faster than M-tree on the benchmark images set. The effect of the proposed AHI is also investigated by our implemented content based images retrieval system. © 2011 The Institute of Electrical Engineers of Japan.

关键词： Extraction

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Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology

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Neural Regeneration Research 2011年第28期6卷 2198-2202页

作者： Hongyu Sun Yang Xiang Mingdao Yang Department of Computer Science and Technology Tongji University Shanghai 201804 China College of Information and Electrical Engineering Shandong University of Science and Technology China Key Laboratory of Embedded System and Service Computing Ministry of Education of the People＇s Republic of China Tongji University Shanghai 201804 China Qingdao Economic ＆ Technical Development Area First People＇s Hospital Qingdao 266555 Shangdong Province China

The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment （P 〈 0.01）, which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.

关键词： brain-computer interface motor cortex neuronal plasticity rehabilitation stroke neural regeneration

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LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation

arXiv

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arXiv 2023年

作者： Huang, Kai Zhao, Junqiao Zhu, Zhongyang Ye, Chen Feng, Tiantian The School of Surveying and Geo-Informatics Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Local geometric information, i.e., normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localization. However, estimating normal and distribution of points are time-consuming tasks even with the assistance of kdtree or volumetric maps. To achieve fast normal estimation, we look into the structure of LiDAR scan and propose a ring-based fast approximate least squares (Ring FALS) method. With the Ring structural information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update the distribution of points in each voxel incrementally while maintaining its consistency with the normal estimation. We further fix the distribution after its convergence to balance the time consumption and the correctness of representation. Based on the extracted and maintained local geometric information, we devise a robust and accurate hierarchical data association scheme where point-to-surfel association is prioritized over point-to-plane. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods. Our open source implementation is available at https://***/tiev-tongji/LOG-LIO. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical radar

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Predictive Resilience Assessment Featuring Diffusion Reconstruction for Road Networks Under Rainfall Disturbances

SSRN

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SSRN 2024年

The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into the system's resistance and recovery capabilities. In the face of unforeseen resilience disturbance events, predictive and accurate assessment of road network resilience is essential for better traffic regulation and emergency response management. However, existing resilience assessment methods of road networks are insufficient: they lack reliable real-time big-data analysis, do not possess predictive capabilities for guiding decision-making, and have a narrow view with single-dimensional resilience indicators. To address these issues, focusing on rainfall disturbance scenarios, this work introduces a novel resilience assessment method, which is predictive and real-time, consisting of two components: a deep learning traffic indicator prediction model and a comprehensive resilience assessment model. Firstly, we propose a two-stage traffic indicator prediction model, namely the Conditional Diffusion-Reconstruction-based Graph Neural Network (CDRGNN), which particularly enhances disturbance-scenario prediction accuracy, thereby providing reliable foresight in aid of the following assessments. Subsequently, we develop a resilience assessment model featuring structural-functional comprehensive resilience indicators established through shortest-path aggregation, and the overall resilience assessment is performed through comparative analysis using indicators obtained in real-time with historical non-disruptive resilience benchmarks. In a case study focusing on heavy rainfall disturbances on a road network in California, the United States, abundant experiments and visualizations are conducted to demonstrate the rationality of our proposed comprehensive resilience indicators as well as the precision and reliability of these predictive resilience assessment outcom

关键词： Diffusion

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LIMOT: A Tightly-Coupled system for LiDAR-Inertial Odometry and Multi-Object Tracking

arXiv

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arXiv 2023年

作者： Zhu, Zhongyang Zhao, Junqiao Huang, Kai Tian, Xuebo Lin, Jiaye Ye, Chen Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China The School of Surveying and Geo-Informatics Tongji University Shanghai China

Simultaneous localization and mapping (SLAM) is critical to the implementation of autonomous driving. Most LiDAR-inertial SLAM algorithms assume a static environment, leading to unreliable localization in dynamic environments. Moreover, the accurate tracking of moving objects is of great significance for the control and planning of autonomous vehicles. This study proposes LIMOT, a tightly-coupled multi-object tracking and LiDAR-inertial odometry system that is capable of accurately estimating the poses of both ego-vehicle and objects. We propose a trajectory-based dynamic feature filtering method, which filters out features belonging to moving objects by leveraging tracking results before scan-matching. Factor graph-based optimization is then conducted to optimize the bias of the IMU and the poses of both the ego-vehicle and surrounding objects in a sliding window. Experiments conducted on the KITTI tracking dataset and self-collected dataset show that our method achieves better pose and tracking accuracy than our previous work DL-SLOT and other baseline methods. Our open-source implementation is available at https://***/tiev-tongji/LIMOT. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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