Xinheng Wang

Prof. Xinheng Wang

Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool Univesity, Suzhou 215123, China
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Academic Career Length

26Years (2001-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 February 2012 in IEEE Transactions on Industrial Informatics
Verified
Journal Article
Open access
SLShancang Li
Shancang Li
LXLi Da Xu
Li Da Xu
XWXinheng Wang
Xinheng Wang
The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment.
29 March 2022 in IEEE Transactions on Network Science and Engineering
Verified
Journal Article
Closed access
Honghao GaoHonghao Gao
Honghao Gao
BQBinyang Qiu
Binyang Qiu
Ramón J. Durán BarrosoRamón J. Durán Barroso
Ramón J. Durán Barroso
WHWalayat Hussain
Walayat Hussain
YXYueshen Xu
Yueshen Xu
XWXinheng Wang
Xinheng Wang
With the development of the Internet of Things, it has been widely studied and deployed in industrial manufacturing, intelligent transportation, and healthcare systems. The time-series feature of the IoT makes the data density and the data dimension higher, where anomaly detection is important to ensure hardware and software security. However, the traditional anomaly detection algorithm has difficulty meeting this demand, not only in complexity but also accuracy. Sometimes the anomaly can be well reconstructed, resulting in a low reconstruction error. In this paper, we propose a memory-augmented autoencoder approach for detecting anomalies in IoT data, which aims to use reconstruction errors to determine data anomalies. First, a memory mechanism is introduced to suppress the generalization ability of the model, and a memory-augmented autoencoder TSMAE is designed for time-series data anomaly detection. Second, by adding penalties and derivable rectifier functions to loss to make the addressing vector sparse, memory modules are encouraged to extract typical normal patterns, thus inhibiting model generalization ability. Finally, through experiments on ECG and Wafer datasets, the validity of TSMAE is verified, and the rationality of hyperparameter setting is discussed through visualizing the memory module addressing vector.
1 September 2003 in Telemedicine and e-Health
Verified
Journal Article
Closed access
STSapal Tachakra
Sapal Tachakra
XWXinheng Wang
Xinheng Wang
RIRobert S.H. Istepanian
Robert S.H. Istepanian
YSY.H. Song
Y.H. Song
The movement of telemedicine from desktop platforms to wireless and mobile configurations may have a significant impact on future health care. This paper reviews some of the latest technologies in wireless communication and their application in health care. The new technologies can make the remote medical monitoring, consulting, and health care more flexible and convenient. But, there are challenges for successful wireless telemedicine, which are addressed in this paper.
2 December 2019 in IEEE Internet of Things Journal
Verified
Journal Article
Closed access
Honghao GaoHonghao Gao
Honghao Gao
YXYueshen Xu
Yueshen Xu
YYYuyu Yin
Yuyu Yin
WZWeipeng Zhang
Weipeng Zhang
RLRui Li
Rui Li
XWXinheng Wang
Xinheng Wang
With the prevalent application of Internet-of-Things (IoT) in real world, services have become a widely used means of providing configurable resources. As the number of services is large and is also increasing fast, it is an inevitable mission to determine the suitability of a service to a user. Two typical tasks are needed, which are service recommendation and service selection. The prediction for quality-of-service (QoS) is an important way to accomplish the two tasks, and there have been a series of methods proposed to predict QoS values. However, few methods have been used to study QoS prediction in IoT environments, where contextual information is vital. In this paper, we develop a holistic framework to attack the QoS prediction in IoT environment, which is based on neural collaborative filtering (NCF) and fuzzy clustering. We design a fuzzy clustering algorithm that is capable of clustering contextual information and then propose a new combined similarity computation method. Next, a new NCF model is designed that can leverage local and global features. Sufficient experiments are implemented on two real-world datasets, and the experimental results verify the effectiveness of the proposed framework.
31 January 2012 in Enterprise Information Systems
Verified
Journal Article
Closed access
SLShan Li
Shan Li
LXLida Xu
Lida Xu
XWXinheng Wang
Xinheng Wang
JWJue Wang
Jue Wang
This article presents a hybrid wireless network integration scheme in cloud services-based enterprise information systems (EISs). With the emerging hybrid wireless networks and cloud computing technologies, it is necessary to develop a scheme that can seamlessly integrate these new technologies into existing EISs. By combining the hybrid wireless networks and computing in EIS, a new framework is proposed, which includes frontend layer, middle layer and backend layers connected to IP EISs. Based on a collaborative architecture, cloud services management framework and process diagram are presented. As a key feature, the proposed approach integrates access control functionalities within the hybrid framework that provide users with filtered views on available cloud services based on cloud service access requirements and user security credentials. In future work, we will implement the proposed framework over SwanMesh platform by integrating the UPnP standard into an enterprise information system.
1 December 2003 in IEEE Transactions on Nanobioscience
Verified
Journal Article
Closed access
XWXinheng Wang
Xinheng Wang
RIR.S.H. Istepanian
R.S.H. Istepanian
YSYong Hua Song
Yong Hua Song
Microarray imaging is considered an important tool for large scale analysis of gene expression. The accuracy of the gene expression depends on the experiment itself and further image processing. It's well known that the noises introduced during the experiment will greatly affect the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Traditionally, statistical methods are used to estimate the noises while the microarray images are being processed. In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. That is, to denoise the image noises before further image processing using stationary wavelet transform (SWT). The time invariant characteristic of SWT is particularly useful in image denoising. The testing result on sample microarray images has shown an enhanced image quality. The results also show that it has a superior performance than conventional discrete wavelet transform and widely used adaptive Wiener filter in this procedure.