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Xianghua Xu
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

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Journal article
Published: 28 April 2021 in IEEE Sensors Journal
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Energy harvesting technology has been applied to power nodes in a wireless sensor networks (WSNs). However, it suffers from the dynamic nature of the ambient energy. Wireless charging, on the contrary, offers an additional choice to charge the sensor nodes (SNs) with a mobile charger (MC) which is often implemented to move within the network. Nevertheless, the energy spent on moving among sensors accounts for a considerable proportion of the whole energy the MC can take. To this end, we consider combining these two sources of energy to carry out the energy neutral operation for the WSNs. In this paper, we formulate the problem of minimizing the number of nodes for perpetual coverage of targets under the constraint of the energy capacity of a MC and the distribution of the energy harvesting rate across the monitoring field which generates great difficulty. We designed two approximation algorithms to tackle this problem. We conduct extensive simulations to evaluate the performance of our proposed algorithms. The results showed performance of these two algorithms with respect to impact of different parameters.

ACS Style

Ran Wang; Xianghua Xu; Xianyuan Ran; Yongpan Liu; Liang Xue. Minimum Nodes Deployment for Mixed Energy Replenishment in Rechargeable WSNs. IEEE Sensors Journal 2021, 21, 16282 -16290.

AMA Style

Ran Wang, Xianghua Xu, Xianyuan Ran, Yongpan Liu, Liang Xue. Minimum Nodes Deployment for Mixed Energy Replenishment in Rechargeable WSNs. IEEE Sensors Journal. 2021; 21 (14):16282-16290.

Chicago/Turabian Style

Ran Wang; Xianghua Xu; Xianyuan Ran; Yongpan Liu; Liang Xue. 2021. "Minimum Nodes Deployment for Mixed Energy Replenishment in Rechargeable WSNs." IEEE Sensors Journal 21, no. 14: 16282-16290.

Journal article
Published: 22 June 2020 in IEEE Access
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Short videos are popular information carriers on the Internet, and detecting events from them can well benefit widespread applications, e.g., video browsing, management, retrieval and recommendation. Existing video analysis methods always require decoding all frames of videos in advance, which is very costly in time and computation power. These short videos are often untrimmed, noisy and even incomplete, adding much difficulty to event analysis. Unlike previous works focusing on actions, we target short video event detection and propose Recurrent Compressed Convolutional Networks (RCCN) for discovering the underlying event patterns within short videos possibly including a large proportion of non-event videos. Instead of using the whole videos, RCCN performs representation learning at much lower cost within the compressed domain where the encoded motion information reflecting the spatial relations among frames can be easily obtained to capture dynamic tendency of event videos. This alleviates the information incompleteness problem that frequently emerges in user-generated short videos. In particular, RCCN leverages convolutional networks as the backbone and the Long Short-Term Memory components to model the variable-range temporal dependency among untrimmed video frames. RCCN not only learns the common representation shared by the short videos of the same event, but also obtains the discriminative ability to detect dissimilar videos. We benchmark the model performance on a set of short videos generated from publicly available event detection database YLIMED, and compare RCCN with several baselines and state-of-the-art alternatives. Empirical studies have verified the preferable performance of RCCN.

ACS Style

Ping Li; Xianghua Xu. Recurrent Compressed Convolutional Networks for Short Video Event Detection. IEEE Access 2020, 8, 114162 -114171.

AMA Style

Ping Li, Xianghua Xu. Recurrent Compressed Convolutional Networks for Short Video Event Detection. IEEE Access. 2020; 8 (99):114162-114171.

Chicago/Turabian Style

Ping Li; Xianghua Xu. 2020. "Recurrent Compressed Convolutional Networks for Short Video Event Detection." IEEE Access 8, no. 99: 114162-114171.

Journal article
Published: 07 January 2020 in IEEE Transactions on Services Computing
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Recommendation technology is an important part of the Internet of Things (IoT) services, which can provide better service for users and help users get information anytime, anywhere. However, the traditional recommendation algorithms cannot meet user's fast and accurate recommended requirements in the IoT environment. In the face of a large-volume data, the method of finding neighborhood by comparing whole user information will result in a low recommendation efficiency. In addition, the traditional recommendation system ignores the inherent connection between user's preference and time. In reality, the interest of the user varies over time. Recommendation system should provide users accurate and fast with the change of time. To address this, we propose a novel recommendation model based on time correlation coefficient and an improved K-means with cuckoo search (CSK-means), called TCCF. The clustering method can cluster similar users together for further quick and accurate recommendation. Moreover, an effective and personalized recommendation model based on preference pattern (PTCCF) is designed to improve the quality of TCCF. It can provide a higher quality recommendation by analyzing the user's behaviors. The extensive experiments are conducted on two real datasets of MovieLens and Douban, and the precision of our model have improved about 5.2% compared with the MCoC model. Systematic experimental results have demonstrated our models TCCF and PTCCF are effective for IoT scenarios.

ACS Style

Zhihua Cui; Xianghua Xu; Fei Xue; Xingjuan Cai; Yang Cao; Wensheng Zhang; Jinjun Chen. Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios. IEEE Transactions on Services Computing 2020, 13, 685 -695.

AMA Style

Zhihua Cui, Xianghua Xu, Fei Xue, Xingjuan Cai, Yang Cao, Wensheng Zhang, Jinjun Chen. Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios. IEEE Transactions on Services Computing. 2020; 13 (4):685-695.

Chicago/Turabian Style

Zhihua Cui; Xianghua Xu; Fei Xue; Xingjuan Cai; Yang Cao; Wensheng Zhang; Jinjun Chen. 2020. "Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios." IEEE Transactions on Services Computing 13, no. 4: 685-695.

Journal article
Published: 24 September 2019 in IEEE Systems Journal
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Sensing coverage has attracted considerable attention in wireless sensor networks. Existing work focuses mainly on the 0/1 disk model which provides only coarse approximation to real scenarios. In this article, we study the connected target coverage problem which concerns both coverage and connectivity. We use directional probabilistic sensors, and combine probabilistic and directional sensing model features to characterize the quality of coverage more accurately in an energy efficient manner. Based on the analysis of the collaborative detection probability with multiple sensors, we formulate the minimum energy connected target ϵ-probability coverage problem, aiming at minimizing the total energy cost while satisfying the requirements of both coverage and connectivity. By a reduction from a unit disk cover, we prove that the problem is nondeterministic polynomial (NP)-hard, and present an approximation algorithm with provable time complexity and approximation ratio. To evaluate our design, we analyze the performance of our algorithm theoretically and also conduct extensive evaluations to demonstrate its effectiveness.

ACS Style

Xianghua Xu; Zhixiang Dai; Anxing Shan; Tao Gu. Connected Target ϵ-probability Coverage in WSNs With Directional Probabilistic Sensors. IEEE Systems Journal 2019, 14, 3399 -3409.

AMA Style

Xianghua Xu, Zhixiang Dai, Anxing Shan, Tao Gu. Connected Target ϵ-probability Coverage in WSNs With Directional Probabilistic Sensors. IEEE Systems Journal. 2019; 14 (3):3399-3409.

Chicago/Turabian Style

Xianghua Xu; Zhixiang Dai; Anxing Shan; Tao Gu. 2019. "Connected Target ϵ-probability Coverage in WSNs With Directional Probabilistic Sensors." IEEE Systems Journal 14, no. 3: 3399-3409.

Article
Published: 14 June 2019 in World Wide Web
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Barrier Coverage is an important sensor deployment issue in many industrial, consumer and military applications.The barrier coverage in bistatic radar sensor networks has attracted many researchers recently. The Bistatic Radars (BR) consist of radar signal transmitters and radar signal receivers. The effective detection area of bistatic radar is a Cassini oval area that determined by the distance between transmitter and receiver and the predefined detecting SNR threshold. Many existing works on bistatic radar barrier coverage mainly focus on homogeneous radar sensor networks. However, cooperation among different types or different physical parameters of sensors is necessary in many practical application scenarios. In this paper, we study the optimal deployment problem in heterogeneous bistatic radar networks.The object is how to maximize the detection ability of bistatic radar barrier with given numbers of radar sensors and barrier’s length. Firstly, we investigate the optimal placement strategy of single transmitter and multiple receivers, and propose the patterns of aggregate deployment. Then we study the optimal deployment of heterogeneous transmitters and receivers and introduce the optimal placement sequences of heterogeneous transmitters and receivers. Finally, we design an efficient greedy algorithm, which realize optimal barrier deployment of M heterogeneous transmitters and N receivers on a L length boundary, and maximizing the detection ability of the barrier. We theoretically proved that the placement sequence of the algorithm construction is optimal deployment solution in heterogeneous bistatic radar sensors barrier. And we validate the algorithm effectiveness through comprehensive simulation experiments.

ACS Style

Xianghua Xu; Chengwei Zhao; Zichen Jiang; Zongmao Cheng; Jinjun Chen. Optimal placement of barrier coverage in heterogeneous bistatic radar sensor networks. World Wide Web 2019, 23, 1361 -1380.

AMA Style

Xianghua Xu, Chengwei Zhao, Zichen Jiang, Zongmao Cheng, Jinjun Chen. Optimal placement of barrier coverage in heterogeneous bistatic radar sensor networks. World Wide Web. 2019; 23 (2):1361-1380.

Chicago/Turabian Style

Xianghua Xu; Chengwei Zhao; Zichen Jiang; Zongmao Cheng; Jinjun Chen. 2019. "Optimal placement of barrier coverage in heterogeneous bistatic radar sensor networks." World Wide Web 23, no. 2: 1361-1380.

Journal article
Published: 12 June 2019 in Sensors
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Wireless Power Transfer (WPT) is a promising technology to replenish energy of sensors in Rechargeable Wireless Sensor Networks (RWSN). In this paper, we investigate the mobile directional charging optimization problem in RWSN. Our problem is how to plan the moving path and charging direction of the Directional Charging Vehicle (DCV) in the 2D plane to replenish energy for RWSN. The objective is to optimize energy charging efficiency of the DCV while maintaining the sensor network working continuously. To the best of our knowledge, this is the first work to study the mobile directional charging problem in RWSN. We prove that the problem is NP-hard. Firstly, the coverage utility of the DCV's directional charging is proposed. Then we design an approximation algorithm to determine the docking spots and their charging orientations while minimizing the number of the DCV's docking spots and maximizing the charging coverage utility. Finally, we propose a moving path planning algorithm for the DCV's mobile charging to optimize the DCV's energy charging efficiency while ensuring the networks working continuously. We theoretically analyze the DCV's charging service capability, and perform the comprehensive simulation experiments. The experiment results show the energy efficiency of the DCV is higher than the omnidirectional charging model in the sparse networks.

ACS Style

Xianghua Xu; Lu Chen; Zongmao Cheng. Optimizing Charging Efficiency and Maintaining Sensor Network Perpetually in Mobile Directional Charging. Sensors 2019, 19, 2657 .

AMA Style

Xianghua Xu, Lu Chen, Zongmao Cheng. Optimizing Charging Efficiency and Maintaining Sensor Network Perpetually in Mobile Directional Charging. Sensors. 2019; 19 (12):2657.

Chicago/Turabian Style

Xianghua Xu; Lu Chen; Zongmao Cheng. 2019. "Optimizing Charging Efficiency and Maintaining Sensor Network Perpetually in Mobile Directional Charging." Sensors 19, no. 12: 2657.

Journal article
Published: 26 May 2019 in Sensors
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Heterogeneous Bistatic Radars (BR) have different sensing ranges and couplings of sensing regions, which provide more flexible coverage for the boundary at complex terrain such as across rivers and valleys. Due to the Cassini oval sensing region of a BR and the coupling of sensing regions among different BRs, the coverage problem of BR sensor networks is very challenging. Existing works in BR barrier coverage focus mainly on homogeneous BR sensor networks. This paper studies the heterogeneous BR placement problem on a line barrier to achieve optimal coverage. 1) We investigate coverage differences of the basic placement sequences of heterogeneous BRs on the line barrier, and prove the optimal basic placement spacing patterns of heterogeneous BRs. 2) We study the coverage coupling effect among adjacent BRs on the line barrier, and determine that different placement sequences of heterogeneous BR transmitters will affect the barrier’s coverage performance and length. The optimal placement sequence of heterogeneous BR barrier cannot be solved through the greedy algorithm. 3) We propose an optimal BRs placement algorithm on a line barrier when the heterogeneous BR transmitters’ placement sequence is predetermined on the barrier, and prove it to be optimal. Through simulation experiments, we determine that the different placement sequences of heterogeneous BR transmitters have little influence on the barrier’s maximum length. Then, we propose an approximate algorithm to optimize the BR placement spacing sequence on the heterogeneous line barrier. 4) As a heterogeneous barrier case study, a minimum cost coverage algorithm of heterogeneous BR barrier is presented. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.

ACS Style

Xianghua Xu; Chengwei Zhao; Zongmao Cheng; Tao Gu. Approximate Optimal Deployment of Barrier Coverage on Heterogeneous Bistatic Radar Sensors. Sensors 2019, 19, 2403 .

AMA Style

Xianghua Xu, Chengwei Zhao, Zongmao Cheng, Tao Gu. Approximate Optimal Deployment of Barrier Coverage on Heterogeneous Bistatic Radar Sensors. Sensors. 2019; 19 (10):2403.

Chicago/Turabian Style

Xianghua Xu; Chengwei Zhao; Zongmao Cheng; Tao Gu. 2019. "Approximate Optimal Deployment of Barrier Coverage on Heterogeneous Bistatic Radar Sensors." Sensors 19, no. 10: 2403.

Special issue paper
Published: 09 May 2019 in Software: Practice and Experience
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The abnormal visual event detection is an important subject in Smart City surveillance where a lot of data can be processed locally in edge computing environment. Real‐time and detection effectiveness are critical in such an edge environment. In this paper, we propose an abnormal event detection approach based on multi‐instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places, focusing on real‐time and detection effectiveness. We propose an unsupervised method for abnormal event detection by combining multi‐instance visual feature selection and the autoregressive integrated moving average model. In the proposed method, each video clip is modeled as a visual feature bag containing several subvideo clips, each of which is regarded as an instance. The time‐transform characteristics of the optical flow characteristics within each subvideo clip are considered as a visual feature instance, and time‐series modeling is carried out for multiple visual feature instances related to all subvideo clips in a surveillance video clip. The abnormal events in each surveillance video clip are detected using the multi‐instance fusion method. This approach is verified on publically available urban surveillance video datasets and compared with state‐of‐the‐art alternatives. Experimental results demonstrate that the proposed method has better abnormal event detection performance for crowded scene of urban public places with an edge environment.

ACS Style

Xianghua Xu; LiQiming Liu; Lingjun Zhang; Ping Li; Jinjun Chen. Abnormal visual event detection based on multi‐instance learning and autoregressive integrated moving average model in edge‐based Smart City surveillance. Software: Practice and Experience 2019, 50, 476 -488.

AMA Style

Xianghua Xu, LiQiming Liu, Lingjun Zhang, Ping Li, Jinjun Chen. Abnormal visual event detection based on multi‐instance learning and autoregressive integrated moving average model in edge‐based Smart City surveillance. Software: Practice and Experience. 2019; 50 (5):476-488.

Chicago/Turabian Style

Xianghua Xu; LiQiming Liu; Lingjun Zhang; Ping Li; Jinjun Chen. 2019. "Abnormal visual event detection based on multi‐instance learning and autoregressive integrated moving average model in edge‐based Smart City surveillance." Software: Practice and Experience 50, no. 5: 476-488.

Journal article
Published: 28 January 2019 in IEEE Transactions on Knowledge and Data Engineering
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Big sensing data is commonly encountered from various surveillance or sensing systems. Sampling and transferring errors are commonly encountered during each stage of sensing data processing. How to recover from these errors with accuracy and efficiency is quite challenging because of high sensing data volume and unrepeatable wireless communication environment. While Cloud provides a promising platform for processing big sensing data, however scalable and accurate error recovery solutions are still need. In this paper, we propose a novel approach to achieve fast error recovery in a scalable manner on cloud. This approach is based on the prediction of a recovery replacement data by making multiple data sources based approximation. The approximation process will use coverage information carried by data units to limit the algorithm in a small cluster of sensing data instead of a whole data spectrum. Specifically, in each sensing data cluster, a Euclidean distance based approximation is proposed to calculate a time series prediction. With the calculated time series, a detected error can be recovered with a predicted data value. Through the experiment with real world meteorological data sets on cloud, we demonstrate that the proposed error recovery approach can achieve high accuracy in data approximation to replace the original data error. At the same time, with MapReduce based implementation for scalability, the experimental results also show significant efficiency on time saving.

ACS Style

Chi Yang; Xianghua Xu; Kotagiri Ramamohanarao; Jinjun Chen. A Scalable Multi-Data Sources Based Recursive Approximation Approach for Fast Error Recovery in Big Sensing Data on Cloud. IEEE Transactions on Knowledge and Data Engineering 2019, 32, 841 -854.

AMA Style

Chi Yang, Xianghua Xu, Kotagiri Ramamohanarao, Jinjun Chen. A Scalable Multi-Data Sources Based Recursive Approximation Approach for Fast Error Recovery in Big Sensing Data on Cloud. IEEE Transactions on Knowledge and Data Engineering. 2019; 32 (5):841-854.

Chicago/Turabian Style

Chi Yang; Xianghua Xu; Kotagiri Ramamohanarao; Jinjun Chen. 2019. "A Scalable Multi-Data Sources Based Recursive Approximation Approach for Fast Error Recovery in Big Sensing Data on Cloud." IEEE Transactions on Knowledge and Data Engineering 32, no. 5: 841-854.

Journal article
Published: 09 January 2019 in Sensors
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Perimeter barriers can provide intrusion detection for a closed area. It is efficient for practical applications, such as coastal shoreline monitoring and international boundary surveillance. Perimeter barrier coverage construction in some regions of interest with irregular boundaries can be represented by its minimum circumcircle and every point on the perimeter can be covered. This paper studies circle barrier coverage in Bistatic Radar Sensor Network (BRSN) which encircles a region of interest. To improve the coverage quality, it is required to construct a circle barrier with a predefined width. Firstly, we consider a BR deployment problem to constructing a single BR circular barrier with minimum threshold of detectability. We study the optimized BR placement patterns on the single circular ring. Then the unit costs of the BR sensor are taken into account to derive the minimum cost placement sequence. Secondly, we further consider a circular BR barrier with a predefined width, which is wider than the breadth of Cassini oval sensing area with minimum threshold of detectability. We propose two segment strategies to efficiently divide a circular barrier to several adjacent sub-ring with some appropriate width: Circular equipartition strategy and an adaptive segmentation strategy. Finally, we propose approximate optimization placement algorithms for minimum cost placement of BR sensor for circular barrier coverage with required width and detection threshold. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.

ACS Style

Xianghua Xu; Chengwei Zhao; Tingcong Ye; Tao Gu. Minimum Cost Deployment of Bistatic Radar Sensor for Perimeter Barrier Coverage. Sensors 2019, 19, 225 .

AMA Style

Xianghua Xu, Chengwei Zhao, Tingcong Ye, Tao Gu. Minimum Cost Deployment of Bistatic Radar Sensor for Perimeter Barrier Coverage. Sensors. 2019; 19 (2):225.

Chicago/Turabian Style

Xianghua Xu; Chengwei Zhao; Tingcong Ye; Tao Gu. 2019. "Minimum Cost Deployment of Bistatic Radar Sensor for Perimeter Barrier Coverage." Sensors 19, no. 2: 225.

Journal article
Published: 03 December 2018 in Future Generation Computer Systems
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Detection and recognition of road traffic signs constitute an important element in Advanced Driver Assistance Systems (ADAS), which can provide real-time road sign perception information to vehicles. In this paper, we proposed a new traffic sign detection method based on adaptive color threshold segmentation and the hypothesis testing of shape symmetry by leveraging smart traffic signs and image data. First, we calculated an adaptive color threshold using the cumulative distribution function of the image histogram. Based on this, we designed an approximate maximum and minimum normalization method, which is used to suppress the interference of high brightness area and background in image smart thresholding processes. Secondly, we transformed the highlight shape feature of thresholding image into a connected domain feature vector. And we formulated a shape symmetry detection algorithm based on statistical hypothesis testing to efficiently extract the ROI of traffic signs based on smart traffic data analysis. We performed some comprehensive experiments on the GTSDB (German Traffic Sign Detection Benchmark) dataset. The accuracy of traffic sign detection exceeded 94%. This method has higher detection accuracy and time efficiency than other methods, and better robustness under complex traffic environment.

ACS Style

Xianghua Xu; Jiancheng Jin; Shanqing Zhang; Lingjun Zhang; Shiliang Pu; Zongmao Chen. Smart data driven traffic sign detection method based on adaptive color threshold and shape symmetry. Future Generation Computer Systems 2018, 94, 381 -391.

AMA Style

Xianghua Xu, Jiancheng Jin, Shanqing Zhang, Lingjun Zhang, Shiliang Pu, Zongmao Chen. Smart data driven traffic sign detection method based on adaptive color threshold and shape symmetry. Future Generation Computer Systems. 2018; 94 ():381-391.

Chicago/Turabian Style

Xianghua Xu; Jiancheng Jin; Shanqing Zhang; Lingjun Zhang; Shiliang Pu; Zongmao Chen. 2018. "Smart data driven traffic sign detection method based on adaptive color threshold and shape symmetry." Future Generation Computer Systems 94, no. : 381-391.

Journal article
Published: 20 August 2018 in IEEE Transactions on Neural Networks and Learning Systems
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Tensor data (i.e., the data having multiple dimensions) are quickly growing in scale in many practical applications, which poses new challenges for data modeling and analysis approaches, such as high-order relations of large complexity, gross noise, and varying data scale. Existing low-rank data analysis methods, which are effective at analyzing matrix data, may fail in the regime of tensor data due to these challenges. A robust and scalable low-rank tensor modeling method is heavily desired. In this paper, we develop an online robust low-rank tensor modeling (ORLTM) method to address these challenges. The ORLTM method leverages the high-order correlations among all tensor modes to model an intrinsic low-rank structure of streaming tensor data online and can effectively analyze data residing in a mixture of multiple subspaces by virtue of dictionary learning. ORLTM consumes a very limited memory space that remains constant regardless of the increase of tensor data size, which facilitates processing tensor data at a large scale. More concretely, it models each mode unfolding of streaming tensor data using the bilinear formulation of tensor nuclear norms. With this reformulation, ORLTM employs a stochastic optimization algorithm to learn the tensor low-rank structure alternatively for online updating. To capture the final tensors, ORLTM uses an average pooling operation on folded tensors in all modes. We also provide the analysis regarding computational complexity, memory cost, and convergence. Moreover, we extend ORLTM to the image alignment scenario by incorporating the geometrical transformations and linearizing the constraints. Extensive empirical studies on synthetic database and three practical vision tasks, including video background subtraction, image alignment, and visual tracking, have demonstrated the superiority of the proposed method.

ACS Style

Ping Li; Jiashi Feng; Xiaojie Jin; Luming Zhang; Xianghua Xu; Shuicheng Yan. Online Robust Low-Rank Tensor Modeling for Streaming Data Analysis. IEEE Transactions on Neural Networks and Learning Systems 2018, 30, 1061 -1075.

AMA Style

Ping Li, Jiashi Feng, Xiaojie Jin, Luming Zhang, Xianghua Xu, Shuicheng Yan. Online Robust Low-Rank Tensor Modeling for Streaming Data Analysis. IEEE Transactions on Neural Networks and Learning Systems. 2018; 30 (4):1061-1075.

Chicago/Turabian Style

Ping Li; Jiashi Feng; Xiaojie Jin; Luming Zhang; Xianghua Xu; Shuicheng Yan. 2018. "Online Robust Low-Rank Tensor Modeling for Streaming Data Analysis." IEEE Transactions on Neural Networks and Learning Systems 30, no. 4: 1061-1075.

Journal article
Published: 23 April 2018 in Solar Energy
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In this paper, we propose a method for modeling and analyzing the performance degradation of GaInP/GaAs/Ge solar cells which work in complex and changing space environment. By analyzing the on-track monitoring data, we select the output power as a key parameter for solar cell performance changes. According to the observation data of the power, the performance of the solar cell was analyzed by the time point of the failure of the lithium battery as the dividing point, and the degradation of the solar cell before and after the dividing point was calculated. The rationality of the model is verified and the predicted value is calculated. It is found that it is basically the same as the actual value, the accuracy are 3.3%, 0.71% respectively. These results show that the performance degradation model made in this paper is effective for the performance analysis of GaInP/GaAs/Ge solar cells.

ACS Style

Wentao Xu; Zongmao Cheng; Xianghua Xu. The model of performance change of GaInP/GaAs/Ge triple-junction solar cells in pico-satellite. Solar Energy 2018, 169, 105 -110.

AMA Style

Wentao Xu, Zongmao Cheng, Xianghua Xu. The model of performance change of GaInP/GaAs/Ge triple-junction solar cells in pico-satellite. Solar Energy. 2018; 169 ():105-110.

Chicago/Turabian Style

Wentao Xu; Zongmao Cheng; Xianghua Xu. 2018. "The model of performance change of GaInP/GaAs/Ge triple-junction solar cells in pico-satellite." Solar Energy 169, no. : 105-110.

Journal article
Published: 04 July 2017 in Sensors
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Wireless charging is an important issue in wireless sensor networks, since it can provide an emerging and effective solution in the absence of other power supplies. The state-of-the-art methods employ a mobile car and a predefined moving path to charge the sensor nodes in the network. Previous studies only consider a factor of the network (i.e., residual energy of sensor node) as a constraint to design the wireless charging strategy. However, other factors, such as the travelled distance of the mobile car, can also affect the effectiveness of wireless charging strategy. In this work, we study wireless charging strategy based on the analysis of a combination of two factors, including the residual energy of sensor nodes and the travelled distance of the charging car. Firstly, we theoretically analyze the limited size of the sensor network to match the capability of a charging car. Then, the networked factors are selected as the weights of traveling salesman problem (TSP) to design the moving path of the charging car. Thirdly, the charging time of each sensor node is computed based on the linear programming problem for the charging car. Finally, a charging period for the network is studied. The experimental results show that the proposed approach can significantly maximize the lifetime of the wireless sensor network.

ACS Style

Weijian Tu; Xianghua Xu; Tingcong Ye; Zongmao Cheng. A Study on Wireless Charging for Prolonging the Lifetime of Wireless Sensor Networks. Sensors 2017, 17, 1560 .

AMA Style

Weijian Tu, Xianghua Xu, Tingcong Ye, Zongmao Cheng. A Study on Wireless Charging for Prolonging the Lifetime of Wireless Sensor Networks. Sensors. 2017; 17 (7):1560.

Chicago/Turabian Style

Weijian Tu; Xianghua Xu; Tingcong Ye; Zongmao Cheng. 2017. "A Study on Wireless Charging for Prolonging the Lifetime of Wireless Sensor Networks." Sensors 17, no. 7: 1560.

Journal article
Published: 25 May 2017 in Sensors
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Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ-connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm.

ACS Style

Anxing Shan; Xianghua Xu; Zongmao Cheng; Wensheng Wang. A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors. Sensors 2017, 17, 1208 .

AMA Style

Anxing Shan, Xianghua Xu, Zongmao Cheng, Wensheng Wang. A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors. Sensors. 2017; 17 (6):1208.

Chicago/Turabian Style

Anxing Shan; Xianghua Xu; Zongmao Cheng; Wensheng Wang. 2017. "A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors." Sensors 17, no. 6: 1208.

Journal article
Published: 27 August 2016 in Sensors
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Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm.

ACS Style

Anxing Shan; Xianghua Xu; Zongmao Cheng. Target Coverage in Wireless Sensor Networks with Probabilistic Sensors. Sensors 2016, 16, 1372 .

AMA Style

Anxing Shan, Xianghua Xu, Zongmao Cheng. Target Coverage in Wireless Sensor Networks with Probabilistic Sensors. Sensors. 2016; 16 (9):1372.

Chicago/Turabian Style

Anxing Shan; Xianghua Xu; Zongmao Cheng. 2016. "Target Coverage in Wireless Sensor Networks with Probabilistic Sensors." Sensors 16, no. 9: 1372.

Journal article
Published: 27 June 2011 in Sensors
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This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms.

ACS Style

Xianghua Xu; Xueyong Gao; Jian Wan; Naixue Xiong. Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs. Sensors 2011, 11, 6555 -6574.

AMA Style

Xianghua Xu, Xueyong Gao, Jian Wan, Naixue Xiong. Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs. Sensors. 2011; 11 (7):6555-6574.

Chicago/Turabian Style

Xianghua Xu; Xueyong Gao; Jian Wan; Naixue Xiong. 2011. "Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs." Sensors 11, no. 7: 6555-6574.