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All Outputs (159)

Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges (2023)
Journal Article
Liu, Q., Yang, Z., Ji, R., Zhang, Y., Bilal, M., Liu, X., Vimal, S., & Xu, X. (2023). Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges. IEEE Systems, Man, and Cybernetics Magazine, 9(4), 4-12. https://doi.org/10.1109/msmc.2022.3216943

Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this article, recent relevant scientific investigation and practical efforts using deep learning (DL) models for weather radar data analy... Read More about Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges.

High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network (2022)
Presentation / Conference Contribution
Zhang, Z., Li, Y., Liu, Q., & Liu, X. (2022, September). High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network. Presented at 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Falerna, Italy

A basic stage of hydrological research is to automatically extract water body information from high-resolution remote sensing images. Various methods based on deep learning convolutional neural networks have been proposed in recent studies to achieve... Read More about High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network.

Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract) (2022)
Presentation / Conference Contribution
Sampath Kalutharage, C., Liu, X., & Chrysoulas, C. (2022, September). Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract). Presented at 27th European Symposium on Research in Computer Security (ESORICS) 2022, Copenhagen, Denmark

Over the past few decades, Machine Learning (ML)-based intrusion detection systems (IDS) have become increasingly popular and continue to show remarkable performance in detecting attacks. However, the lack of transparency in their decision-making pro... Read More about Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract).

A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars (2022)
Journal Article
Yang, Z., Wu, H., Liu, Q., Liu, X., Zhang, Y., & Cao, X. (2023). A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars. ISA Transactions, 132, 155-166. https://doi.org/10.1016/j.isatra.2022.06.046

In recent years, the number of weather-related disasters significantly increases across the world. As a typical example, short-range extreme precipitation can cause severe flooding and other secondary disasters, which therefore requires accurate pred... Read More about A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars.

CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets (2022)
Journal Article
Yang, Z., Liu, Q., Wu, H., Liu, X., & Zhang, Y. (2023). CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets. Computer Modeling in Engineering and Sciences, 135(1), 45-64. https://doi.org/10.32604/cmes.2022.022045

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain. Recent relevant research activities have shown their conc... Read More about CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets.

Near-Data Prediction Based Speculative Optimization in a Distribution Environment (2022)
Journal Article
Liu, Q., Wu, X., Liu, X., Zhang, Y., & Hu, Y. (2022). Near-Data Prediction Based Speculative Optimization in a Distribution Environment. Mobile Networks and Applications, 27(6), 2339-2347. https://doi.org/10.1007/s11036-021-01793-7

Hadoop is an open source from Apache with a distributed file system and MapReduce distributed computing framework. The current Apache 2.0 license agreement supports on-demand payment by consumers for cloud platform services, helping users leverage th... Read More about Near-Data Prediction Based Speculative Optimization in a Distribution Environment.

Intelligent Question Answering System Based on Knowledge Graph (2022)
Presentation / Conference Contribution
Feng, X., Liu, Q., & Liu, X. (2021, December). Intelligent Question Answering System Based on Knowledge Graph. Presented at IEEE SmartCity-2021, Hainan, China

In order to build a smart city and pursue more efficient city management, various industries have introduced intelligent question answering into process management. The intelligent question answering system based on the knowledge graph is dedicated t... Read More about Intelligent Question Answering System Based on Knowledge Graph.

A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a Distributed Environment (2022)
Presentation / Conference Contribution
Lu, H., Kamoto, K. M., Liu, Q., Zhang, Y., Liu, X., Xu, X., & Qi, L. (2021, December). A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a Distributed Environment. Presented at 11th EAI International Conference, CloudComp 2021, Online

It is of great significance to study the positive characteristics of concrete bearing cracks, fire and other adverse environment for the safety of human life and property and the protection of environmental resources. However, there are still some ch... Read More about A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a Distributed Environment.

Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach (2022)
Presentation / Conference Contribution
Zhang, J., Sun, J., Gan, J., Liu, Q., & Liu, X. (2021, October). Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach. Presented at The 6th IEEE Cyber Science and Technology Congress (2021) (CyberSciTech 2021), AB, Canada [Online]

The past decade have seen a growth in Internet technology, the overlap of cyberspace and social space provides great convenience for people's life. The in-depth study of non-intrusive load management (NILM) promotes the development of multi-integrati... Read More about Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach.

An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data (2022)
Presentation / Conference Contribution
Wu, Z., Wu, X., Liu, Q., & Liu, X. (2021, October). An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data. Presented at The 6th IEEE Cyber Science and Technology Congress (2021) (CyberSciTech 2021), AB, Canada [Online]

There are more than 10 million new stroke cases worldwide every year, and stroke has become one of the main causes of death and disability. In recent years, with the rapid development of computer science and technology, through the combination of Int... Read More about An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data.

SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction (2022)
Journal Article
Li, Y., Lu, H., Liu, Q., Zhang, Y., & Liu, X. (2022). SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction. Remote Sensing, 14(3), Article 768. https://doi.org/10.3390/rs14030768

In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for the statistical work of a building area. Recent... Read More about SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction.

Tracking Stream Quality Issues in Combined Physical and Radar Sensors for IoT-based Data-driven Actuation (2021)
Presentation / Conference Contribution
Bamgboye, O., Liu, X., Cruickshank, P., Liu, Q., & Zhang, Y. (2021, December). Tracking Stream Quality Issues in Combined Physical and Radar Sensors for IoT-based Data-driven Actuation. Presented at 2021 CIE International Conference on Radar (Radar), Haikou, Hainan, China

In this paper, a stream quality tracking for measurements from combined radar and physical sensors is developed. The authors proposed the use of RDF stream processing system and semantic rules to provide semantic reasoning for tracking erroneous data... Read More about Tracking Stream Quality Issues in Combined Physical and Radar Sensors for IoT-based Data-driven Actuation.

Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks (2021)
Journal Article
Liu, Q., Zhang, J., Liu, X., Zhang, Y., Xu, X., Khosravi, M., & Bilal, M. (2022). Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks. Physical Communication, 51, Article 101584. https://doi.org/10.1016/j.phycom.2021.101584

The intensification of the greenhouse effect is driving the implementation of energy saving and emission reduction policies, which lead to a wide variety of energy saving solutions benefiting from the advancement of emerging technologies such as Wire... Read More about Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks.

An Edge-Assisted Cloud Framework Using a Residual Concatenate FCN Approach to Beam Correction in the Internet of Weather Radars (2021)
Journal Article
Wu, H., Liu, Q., Liu, X., Zhang, Y., & Yang, Z. (2022). An Edge-Assisted Cloud Framework Using a Residual Concatenate FCN Approach to Beam Correction in the Internet of Weather Radars. World Wide Web, 25, 1923-1949. https://doi.org/10.1007/s11280-021-00988-y

Internet of Things (IoT) has been rapidly developed in recent years, being well applied in the fields of Environmental Surveillance, Smart Grid, Intelligent Transportation, and so on. As one of the typical earth-based meteorological observation metho... Read More about An Edge-Assisted Cloud Framework Using a Residual Concatenate FCN Approach to Beam Correction in the Internet of Weather Radars.

A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data (2021)
Journal Article
Lu, H., Liu, Q., Liu, X., & Zhang, Y. (2021). A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data. Journal of Organizational and End User Computing, 33(6), Article 6. https://doi.org/10.4018/joeuc.20211101.oa6

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing dat... Read More about A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data.

Architecting Green Mobile Cloud Apps (2021)
Book Chapter
Jaachimma Chinenyeze, S., & Liu, X. (2021). Architecting Green Mobile Cloud Apps. In C. Calero, M. Á. Moraga, & M. Piattini (Eds.), Software Sustainability (183-214). Cham: Springer. https://doi.org/10.1007/978-3-030-69970-3_8

With the resource-constrained nature of mobile devices, and the resource-abundant offerings of the cloud, several promising optimization techniques have been proposed by the green computing research community. Prominent techniques and unique methods... Read More about Architecting Green Mobile Cloud Apps.

RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction (2021)
Journal Article
Zhu, L., Liu, Q., Liu, X., & Zhang, Y. (2021). RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction. EURASIP Journal on Wireless Communications and Networking, 2021, Article 171 (2021). https://doi.org/10.1186/s13638-021-02044-9

For the purpose of exploring the long-term variation of regional sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST atmospher... Read More about RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction.

Blockchain-based identity and authentication scheme for MQTT protocol (2021)
Presentation / Conference Contribution
Abdelrazig Abubakar, M., Jaroucheh, Z., Al-Dubai, A., & Liu, X. (2021, March). Blockchain-based identity and authentication scheme for MQTT protocol. Presented at ICBCT '21: 2021 The 3rd International Conference on Blockchain Technology, Shanghai, China

The publish and subscribe messaging model has proven itself as a dominant messaging paradigm for IoT systems. An example of such is the commonly used Message Queuing Telemetry Transport (MQTT) protocol. However, the security concerns with this protoc... Read More about Blockchain-based identity and authentication scheme for MQTT protocol.