Yue Gao

Professor Yue Gao


Biography

Affiliations and memberships

IEEE Transactions on Cognitive Communications and Networking
Editor
IEEE Transactions on Vehicular Technology
Editor
IEEE Internet of Things Journal
Editor
Vehicular Technology Society
IEEE Distinguished Lecturer
IEEE ComSoc Technical Committee on Cognitive Networks
Chair
IEEE WoWMoM and iWEM 2017
General Chair
IEEE GLOBECOM 2017
Cognitive Radio Symposium Co-Chair
IEEE ICCC 2016
Signal Processing for Communications Symposium Co-Chair
IEEE GLOBECOM 2016
Publicity Co-Chair

Research

Research interests

Research projects

My publications

Publications

Chun Xu Mao, Long Zhang, Mohsen Khalily, Yue Gao, Pei Xiao (2021)A Multiplexing Filtering Antenna, In: IEEE Transactions on Antennas and Propagationpp. 1-1 IEEE
In this paper, a compact, highly integrated multiplexing filtering antenna operating at 4.7/5.2/6.0/6.6 GHz is proposed for the first time. Different from traditional antennas, the proposed antenna has one shared radiator but four ports working in different frequency bands and thus, it can simultaneously support four different transmission channels. The proposed multiplexing antenna is composed of a patch with a U-shaped slot, two substrate integrated waveguide (SIW) cavities, and four resonator-based frequency-selective paths. The resonator-based paths can not only enhance the inter-channel isolations but also improve the impedance bandwidth. The design principles and the methods of controlling the four operating bands are studied. Measurement results agree reasonably well with the simulations, showing four channels from 4.5 to 4.8 GHz, 5.1 to 5.3 GHz, 5.85 to 6.3 GHz, and 6.4 to 6.6 GHz, respectively. The antenna also exhibits a high isolation of over 25 dB between the channels. In addition, the proposed antenna has a consistent broadside radiation pattern and polarization in the four bands, manifesting the proposed multiplexing filtering antenna can be a promising candidate for multi-service wireless communication systems.
Khaled Alqurashi, James R. Kelly, Zhengpeng Wang, Carol Crean, Raj Mittra, Mohsen Khalily, Yue Gao (2020)Liquid Metal Bandwidth-Reconfigurable Antenna, In: IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS19(1)pp. 218-222 Institute of Electrical and Electronics Engineers
This letter shows how slugs of liquid metal can be used to connect/disconnect large areas of metalization and achieve a radiation performance not possible by using conventional switches. The proposed antenna can switch its operating bandwidth between ultrawideband and narrowband by connecting/disconnecting the ground plane for the feedline from that of the radiator. This could be achieved by using conventional semiconductor switches. However, such switches provide point-like contacts. Consequently, there are gaps in electrical contact between the switches. Surface currents, flowing around these gaps, lead to significant back radiation. In this letter, the slugs of a liquid metal are used to completely fill the gaps. This significantly reduces the back radiation, increases the bore-sight gain, and produces a pattern identical to that of a conventional microstrip patch antenna. Specifically, the realized gain and total efficiency are increased by 2 dBi and 24%, respectively. The antenna has potential applications in wireless systems employing cognitive radio (CR) and spectrum aggregation.
Shaker Alkaraki, Yue Gao, Samuel Stremsdoerfer, Edouard Gayets, Clive G Parini (2020)3D Printed Corrugated Plate Antennas With High Aperture Efficiency and High Gain at X-Band and Ka-Band, In: IEEE access8pp. 30643-30654 IEEE
A design criterion for a compact 3D printed high gain corrugated plate antenns that has high aperture efficiency and wide bandwidth is presented in this paper. The proposed design criterion is validated numerically and experimentally by fabricating 3D printed and Aluminium prototypes for X-band and Ka-band applications. The proposed antenna structure consists of two layers, where the electromagnetic energy (EM) is launched into a cavity that exists between both layers and the EM energy is coupled to the surface of the second layer. The second layer is the radiating structure which consists of three slots surrounded by a rectangular cavity and periodic corrugations that significantly improve the gain of the antennas. The 3D printed prototypes of the proposed antennas are fabricated and tested to validate the proposed design criterion, and their performance is compared to the Aluminium metallic counterparts. Using 3D printing technology, to fabricate the proposed antennas offer low cost and low weight alternatives to the Aluminium metallic prototypes. The measured results of the fabricated prototypes show high gain, high aperture efficiency, low side lobe level, and low cross polarization performance over a wide bandwidth.
Shaker Alkaraki, Yue Gao (2020)mm-Wave Low-Cost 3D Printed MIMO Antennas With Beam Switching Capabilities for 5G Communication Systems, In: IEEE access8pp. 32531-32541 IEEE
This paper presents designs and prototypes of low cost multiple input multiple output (MIMO) antennas for 5G and millimetre-wave (mm-wave) applications. The proposed MIMOs are fabricated using 3D printing and are able deliver beams in multiple directions that provide continuous and real time coverage in the elevation of up to {\mp }30^{\circ } without using phase shifters. This equips the proposed MIMO with a superior advantage of being an attractive low cost technology for 5G and mm-wave applications. The proposed MIMO antennas operate at the 28 GHz 5G band, with wide bandwidth performance exceeds 4 GHz and with beam switching ability of up to {\mp }30^{\circ } in the elevation plane. The direction of the main beam of the single element antenna in the MIMO is steered over the entire bandwidth through introducing 3D printed walls with different heights on the side of the 3D printed radiating antenna. Unlike all other available beam steering techniques; the proposed wall is not only able to change the direction of the beam of the antenna, but also it is able to increase the overall directivity and gain of the proposed antenna and MIMO at the same time over the entire bandwidth.
Zhijin Qin, Xiangwei Zhou, Lin Zhang, Yue Gao, Ying-Chang Liang, Geoffrey Ye Li (2020)20 Years of Evolution From Cognitive to Intelligent Communications, In: IEEE transactions on cognitive communications and networking6(1)pp. 6-20 IEEE
It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an efficient approach to provide more access opportunities to connect massive wireless devices. To improve the spectrum efficiency, CR enables unlicensed usage of licensed spectrum resources. It has been regarded as the key enabler for intelligent communications. In this article, we will provide an overview on the intelligent communication in the past two decades to illustrate the revolution of its capability from cognition to artificial intelligence (AI). Particularly, this article starts from a comprehensive review of typical spectrum sensing and sharing, followed by the recent achievements on the AI-enabled intelligent radio. Moreover, research challenges in the future intelligent communications will be discussed to show a path to the real deployment of intelligent radio. After witnessing the glorious developments of CR in the past 20 years, we try to provide readers a clear picture on how intelligent radio could be further developed to smartly utilize the limited spectrum resources as well as to optimally configure wireless devices in the future communication systems.
Yue Gao, Ekram Hossain, Geoffrey Ye Li, Kevin Sowerby, Carlo Regazzoni, Lin Zhang (2020)IEEE TCCN Special Section Editorial: Evolution of Cognitive Radio to AI-Enabled Radio and Networks, In: IEEE transactions on cognitive communications and networking6(1)pp. 1-5 IEEE
We are delighted to introduce this special section of the IEEE Transactions on Cognitive Communications and Networking (TCCN), which aims at addressing the evolution of cognitive radio (CR) to intelligence radio and networks by exploring recent advances in artificial intelligence (AI) and machine learning (ML). We have selected 14 articles for this special section after a rigorous review process, which are briefly discussed as follows.
Xiaolan Liu, Jiadong Yu, Jian Wang, Yue Gao (2020)Resource Allocation With Edge Computing in IoT Networks via Machine Learning, In: IEEE internet of things journal7(4)pp. 3415-3426 IEEE
In this article, we investigate resource allocation with edge computing in Internet-of-Things (IoT) networks via machine learning approaches. Edge computing is playing a promising role in IoT networks by providing computing capabilities close to users. However, the massive number of users in IoT networks requires sufficient spectrum resource to transmit their computation tasks to an edge server, while the IoT users were developed to have more powerful computation ability recently, which makes it possible for them to execute some tasks locally. Then, the design of computation task offloading policies for such IoT edge computing systems remains challenging. In this article, centralized user clustering is explored to group the IoT users into different clusters according to users' priorities. The cluster with the highest priority is assigned to offload computation tasks and executed at the edge server, while the lowest priority cluster executes computation tasks locally. For the other clusters, the design of distributed task offloading policies for the IoT users is modeled by a Markov decision process, where each IoT user is considered as an agent which makes a series of decisions on task offloading by minimizing the system cost based on the environment dynamics. To deal with the curse of high dimensionality, we use a deep Q -network to learn the optimal policy in which deep neural network is used to approximate the Q -function in Q -learning. Simulations show that users are grouped into clusters with optimal number of clusters. Moreover, our proposed computation offloading algorithm outperforms the other baseline schemes under the same system costs.
Jiadong Yu, Xiaolan Liu, Yue Gao, Xuemin Shen (2020)3D Channel Tracking for UAV-Satellite Communications in Space-Air-Ground Integrated Networks, In: IEEE journal on selected areas in communications38(12)pp. 2810-2823 IEEE
The space-air-ground integrated network (SAGIN) aims to provide seamless wide-area connections, high throughput and strong resilience for 5G and beyond communications. Acting as a crucial link segment of the SAGIN, unmanned aerial vehicle (UAV)-satellite communication has drawn much attention. However, it is a key challenge to track dynamic channel information due to the low earth orbit (LEO) satellite orbiting and three-dimensional (3D) UAV trajectory. In this paper, we explore the 3D channel tracking for a Ka-band UAV-satellite communication system. We firstly propose a statistical dynamic channel model called 3D two-dimensional Markov model (3D-2D-MM) for the UAV-satellite communication system by exploiting the probabilistic insight relationship of both hidden value vector and joint hidden support vector. Specifically, for the joint hidden support vector, we consider a more realistic 3D support vector in both azimuth and elevation direction. Moreover, the spatial sparsity structure and the time-varying probabilistic relationship between degree patterns named the spatial and temporal correlation, respectively, are studied for each direction. Furthermore, we derive a novel 3D dynamic turbo approximate message passing (3D-DTAMP) algorithm to recursively track the dynamic channel with the 3D-2D-MM priors. Numerical results show that our proposed algorithm achieves superior channel tracking performance to the state-of-the-art algorithms with lower pilot overhead and comparable complexity.
M-T Pham, Y Gao, V-DD Hoang, T-J Cham (2010)Fast polygonal integration and its application in extending haar-like features to improve object detection, In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognitionpp. 942-949
Z Qin, Y Gao, MD Plumbley, CG Parini, LG Cuthbert (2013)Low-rank Matrix Completion based Malicious User Detection in Cooperative Spectrum Sensing, In: 2013 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP)pp. 1186-1189 IEEE
In a cognitive radio (CR) system, cooperative spectrum sensing (CSS) is the key to improving sensing performance in deep fading channels. In CSS networks, signals received at the secondary users (SUs) are sent to a fusion center to make a final decision of the spectrum occupancy. In this process, the presence of malicious users sending false sensing samples can severely degrade the performance of the CSS network. In this paper, with the compressive sensing (CS) technique being implemented at each SU, we build a CSS network with double sparsity property. A new malicious user detection scheme is proposed by utilizing the adaptive outlier pursuit (AOP) based low-rank matrix completion in the CSS network. In the proposed scheme, the malicious users are removed in the process of signal recovery at the fusion center. The numerical analysis of the proposed scheme is carried out and compared with an existing malicious user detection algorithm.
Z Qin, Y Gao, MD Plumbley, CG Parini (2015)Wideband spectrum sensing on real-time signals at sub-Nyquist sampling rates in single and cooperative multiple nodes, In: IEEE Transactions on Signal Processing64(12)pp. 3106-3117 IEEE
This paper presents two new algorithms for wideband spectrum sensing at sub-Nyquist sampling rates, for both single nodes and cooperative multiple nodes. In single-node spectrum sensing, a two-phase spectrum sensing algorithm based on compressive sensing is proposed to reduce the computational complexity and improve the robustness at secondary users (SUs). In the cooperative multiple nodes case, the signals received at SUs exhibit a sparsity property that yields a low-rank matrix of compressed measurements at the fusion center. This therefore leads to a two-phase cooperative spectrum sensing algorithm for cooperative multiple SUs based on low-rank matrix completion. In addition, the two proposed spectrum sensing algorithms are evaluated on the TV white space (TVWS), in which pioneering work aimed at enabling dynamic spectrum access into practice has been promoted by both the Federal Communications Commission and the U.K. Office of Communications. The proposed algorithms are tested on the real-time signals after they have been validated by the simulated signals in TVWS. The numerical results show that our proposed algorithms are more robust to channel noise and have lower computational complexity than the state-of-the-art algorithms.
S. Hu, Q. Luo, J. Zhang, Z. Liu, D. Huang, Y. Gao (2018)Practical Implementation of MIMO-FBMC System, In: Proceedings of The International Conference on Communications, Signal Processing, and Systems (CSPS 2018) Institute of Electrical and Electronics Engineers (IEEE)
Y Gao, MJ Er (2003)Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems, In: IEEE TRANSACTIONS ON FUZZY SYSTEMS11(4)pp. 462-477 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
A Zafar, MA Imran, P Xiao, A Cao, Y Gao (2015)Performance Evaluation and Comparison of Different Multicarrier Modulation Schemes, In: 2015 IEEE 20TH INTERNATIONAL WORKSHOP ON COMPUTER AIDED MODELLING AND DESIGN OF COMMUNICATION LINKS AND NETWORKS (CAMAD)pp. 49-53
JM Delfa, N Policella, M Gallant, O Stryk, A Donati, Y Gao (2012)Metrics for Planetary Rover Planning & Scheduling Algorithms, In: Proceedings of the Workshop on Performance Metrics for Intelligent Systemspp. 47-52
In addition to its utility in terrestrial-based applications, Automated Planning and Scheduling (P&S) has had a growing impact on space exploration. Such applications require an influx of new technologies to improve performance while not comprimising safety. As a result, a reliable method to rapidly assess the effectiveness of new P&S algorithms would be desirable to ensure the fulfillment of of all software requirements. This paper introduces RoBen, a mission-independent benchmarking tool that provides a standard framework for the evaluation and comparison of P&S algorithms. RoBen considers metrics derived from the model (the system on which the P&S algorithm will operate) as well as user input (e.g., desired problem complexity) to automatically generate relevant problems for quality assessment. A thorough description of the algorithms and metrics used in RoBen is provided, along with the preliminary test results of a P&S algorithm solving RoBen-generated problems.
JM Delfa, Y Gao, N Policella, A Donati (2011)A Domain-Independent Pattern Recognition System to Support Space Mission Planning, In: Proceedings of 11th Symposium on Advanced Space Technologies in
S Hu, J Zhang, W Tang, Zilong Liu, Pei Xiao, Y Gao (2018)Real-Valued Orthogonal Sequences for Ultra-Low Overhead Channel Estimation in MIMO-FBMC Systems, In: Machine Learning and Intelligent Communications. MLICOM 2018pp. 162-171 Springer
Multiple-input multiple-output filterbank multicarrier communication (MIMO-FBMC) is a promising technique to achieve very tight spectrum confinement (thus, higher spectral efficiency) as well as strong robustness against dispersive channels. In this paper, we present a novel training design for MIMO-FBMC system which enables efficient estimate of frequency-selective channels (associated to multiple transmit antennas) with only one non-zero FBMC symbol. Our key idea is to design real-valued orthogonal training sequences (in the frequency domain) which displaying zero-correlation zone properties in the time-domain. Compared to our earlier proposed training scheme requiring at least two non-zero FBMC symbols (separated by several zero guard symbols), the proposed scheme features ultra-low training overhead yet achieves channel estimation performance comparable to our earlier proposed complex training sequence decomposition(CTSD). Our simulations validate that the proposed method is an efficient channel estimation approach for practical preamble-based MIMO-FBMC systems.
Y Gao, MJ Er (2005)An intelligent adaptive control scheme for postsurgical blood pressure regulation, In: IEEE TRANSACTIONS ON NEURAL NETWORKS16(2)pp. 475-483 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
G Burroughes, Y Gao (2016)Ontology-Based Self-Reconfiguring Guidance, Navigation, and Control for Planetary Rovers, In: Journal of Aerospace Information Systems13(8)pp. 316-328 American Institute of Aeronautics and Astronautics
Certain limitations exist in autonomous software and guidance, navigation, and control architectures developed for extraterrestrial planetary exploration rovers in regard to fault tolerance, changes in environment, and changes in rover capabilities. To address these limitations, this paper outlines a self-reconfiguring guidance, navigation, and control architecture, using an ontology-based rational agent to enable autonomous reconfiguration of mission goals, software architecture, software components, and the control of hardware components during the run time. This new architecture was evaluated through implementation onboard a rover and tested in challenging, Mars-like environments, both simulated and real world, and was found to be highly reliable, fault tolerant, and adaptable.
A Shaukat, Y Gao, JA Kuo, BA Bowen, PE Mort (2016)Visual classification of waste material for nuclear decommissioning, In: Robotics and Autonomous Systems75(Part B)pp. 365-378 Elsevier
Redundant and nonoperational buildings at nuclear sites go through the process of ‘decommissioning’, involving decontamination of nuclear waste material and demolition of physical infrastructure. One challenging problem currently faced by the nuclear industry during this process is the segregation of redundant waste material into a choice of ‘post-processes’ based upon the nature and extent of its radioactivity that may pose a serious threat to the environment. Following an initial inspection, waste materials are subjected to treatment, disruption and consigned to various types of export containers. To date, the process of objects (waste) classification is performed manually. In order to automate this process, robotic platforms can be deployed that utilise robust and fast vision systems for visual classification of nuclear waste material. This paper proposes a novel solution incorporating a machine vision system for autonomous identification of waste material from decommissioned nuclear plants. Rotation and scale invariant moments are used to describe object shapes in the visual scene whereas a random forest learning algorithm performs object classification. Using nuclear waste simulants (from the nuclear plant decommissioning process), an exhaustive ‘proof-of-concept’ quantitative assessment of the proposed technique is performed, in order to test its applicability within the current problem domain.
C-Q Yu, H-H Ju, Y Gao, P-Y Cui (2009)A bilateral teleoperation system for planetary rovers, In: Proceedings of International Conference on Computational Intelligence and Software Engineering
In this paper, a bilateral teleoperation system (BTS) is proposed for planetary rover remote control and operation. This is a continuation of the work presented in on computer-simulated virtual environment, that can be used as a predictive display module in the BTS. In the proposed system, a haptic device is used by the human operators to control planetary rovers as driving a car. Dynamic model of the rover is used and a passive-based bilateral teleoperation scheme is adopted to achieve the stability of the close-loop control. Kinematic model of the rover is used to get the torque distribution algorithm and applied to calculate the corresponding torque for each wheel based on the desired linear speed and heading angle of the rover. Buffer unit is designed to address the issue of variable time-delay caused by the Internet. Lab-based experiments have been carried out using the BH2 rover. The test results demonstrate good performance of the proposed system.
JM Delfa, N Policella, Y Gao, O Stryk (2012)Design Concepts for a new Temporal Planning Paradigm
Jing Zhang, Su Hu, Zilong Liu, Pei Wang, Pei Xiao, Yuan Gao (2019)Real-Valued Orthogonal Sequences for Iterative Channel Estimation in MIMO-FBMC Systems, In: IEEE Access4pp. 1-10 Institute of Electrical and Electronics Engineers (IEEE)
In this paper, we present a novel sequence design for efficient channel estimation in multiple input multiple output filterbank multicarrier (MIMO-FBMC) system with offset QAM modulation. Our proposed sequences, transmitted over one FBMC/OQAM symbol, are real-valued in the frequency domain and display zero-correlation zone properties in the time-domain. The latter property enables optimal channel estimation for a least-square estimator in frequency-selective fading channels. To further improve the system performance, we mitigate the data interference by an iterative feedback loop between channel estimation and FBMC demodulation. Simulation results validate that our proposed real-valued orthogonal sequences and the iterative channel estimation and demodulation scheme provide a practical solution for enhanced performance in preamble-based MIMO-FBMC systems.
Su Hu, Qu Luo, Fan Li, Zilong Liu, Yuan Gao, Jenming Wu (2018)Practical Implementation of Multi-User Transform Domain Communication System for Control Channels in Cloud-Based Cognitive Radio Networks, In: IEEE Access6pp. 17010-17021
Evolving from cognitive radio networks (CRNs), the concept has developed into a new paradigm of cloud and big data-based next-generation CRNs due to huge amount of data processing, complicated spectrum resource scheduling, and real-time information exchange. In cloud-based CRNs, control channels are needed for CR nodes to perform certain handshakings for network self-organization, spectrum sensing, network coordination, and flexible data connections. This paper investigates a transmission scheme for control channel (CC) in cloud-based CRNs, which is over several noncontiguous spectral holes. Transform domain communication system (TDCS)-based transmission scheme with spectrally-constrained sequence design is presented for CC. A practical testbed design for TDCS-based CC with multiple National Instruments PXIe devices and six universal software defined radio reconfigurable input/output devices is presented. Details of system design as well as main implementation challenges are described. Bit-error rate of the system is validated through both theoretical analysis and simulation results under realistic channel conditions.
Aijun Cao, Y Gao, Pei Xiao, Rahim Tafazolli (2015)Performance Analysis of an Ultra Dense Network with and without Cell Cooperation, In: 2015 12th International Symposium on Wireless Communication Systems (ISWCS) Procedingspp. 51-55 IEEE
This paper presents an analysis on performance of an ultra dense network (UDN) with and without cell cooperation from the perspective of network information theory. We propose a UDN performance metric called Total Average Geometry Throughput which is independent from the user distribution or scheduler etc. This performance metric is analyzed in detail for UDN with and without cooperation. The numerical results from the analysis show that under the studied system model, the total average geometry throughput reaches its maximum when the inter-cell distance is around 6 ~ 8 meters, both without and with cell cooperation. Cell cooperation can significantly reduce inter-cell interference but not remove it completely. With cell cooperation and an optimum number of the cooperating cells the maximum performance gain can be achieved. Furthermore, the results also imply that there is an optimum aggregate transmission power if considering the energy cost per bit.
Su Hu, Fan Li, Huiting Guo, Pei Wang, Guoan Bi, Yuan Gao, Zilong Liu, Bin Yu (2018)TDCS-IDMA System for Cognitive Radio Networks With Cloud, In: IEEE Access6pp. 20520-20530 Institute of Electrical and Electronics Engineers (IEEE)
With increasing demand of wireless radio spectrum, fixed spectrum assignment policy leads to spectrum scarcity worldwide. However, most portion of spectrum is inefficiently used, which urges the development of dynamic spectrum access techniques [1]. The concept of cognitive radio (CR) is proposed as a possible solution to solve the spectral congestion problem. It provides the capability to utilize spectrum bands more efficiently in an opportunistic manner without much interruptions to primary users [2]–[4]. In the cognitive radio networks (CRNs), sensors are used to detect the presence of licensed users and find spectrum holes for dynamic spectrum access. Traditional spectrum sensing is usually carried out by CR nodes. This procedure requires complex computation and sufficient storage space to download software packages.
A Shaukat, PC Blacker, C Spiteri, Y Gao (2016)Towards Camera-LIDAR Fusion-Based Terrain Modelling for Planetary Surfaces: Review and Analysis, In: Sensors16(11) MDPI
: In recent decades, terrain modelling and reconstruction techniques have increased research interest in precise short and long distance autonomous navigation, localisation and mapping within field robotics. One of the most challenging applications is in relation to autonomous planetary exploration using mobile robots. Rovers deployed to explore extraterrestrial surfaces are required to perceive and model the environment with little or no intervention from the ground station. Up to date, stereopsis represents the state-of-the art method and can achieve short-distance planetary surface modelling. However, future space missions will require scene reconstruction at greater distance, fidelity and feature complexity, potentially using other sensors like Light Detection And Ranging (LIDAR). LIDAR has been extensively exploited for target detection, identification, and depth estimation in terrestrial robotics, but is still under development to become a viable technology for space robotics. This paper will first review current methods for scene reconstruction and terrain modelling using cameras in planetary robotics and LIDARs in terrestrial robotics; then we will propose camera-LIDAR fusion as a feasible technique to overcome the limitations of either of these individual sensors for planetary exploration. A comprehensive analysis will be presented to demonstrate the advantages of camera-LIDAR fusion in terms of range, fidelity, accuracy and computation.
The book provides a comprehensive introduction to the key techniques and technologies that help to achieve autonomous space systems for cost-effective, high performing planetary robotic missions. Main topics covered include robotic vision, surface navigation, manipulation, mission operations and autonomy, being explained in both theoretical principles and practical use cases. The book recognizes the importance of system design hence discusses practices and tools that help take mission concepts to baseline design solutions, making it a practical piece of scientific reference suited to a variety of practitioners in planetary robotics.
Z Qin, Yue Gao, Mark D. Plumbley (2018)Malicious User Detection Based on Low-Rank Matrix Completion in Wideband Spectrum Sensing, In: IEEE Transactions on Signal Processing66(1)pp. 5-17 IEEE
In cognitive radio networks, cooperative spectrum sensing (CSS) has been a promising approach to improve sensing performance by utilizing spatial diversity of participating secondary users (SUs). In current CSS networks, all cooperative SUs are assumed to be honest and genuine. However, the presence of malicious users sending out dishonest data can severely degrade the performance of CSS networks. In this paper, a framework with high detection accuracy and low costs of data acquisition at SUs is developed, with the purpose of mitigating the influences of malicious users. More specifically, a low-rank matrix completion based malicious user detection framework is proposed. In the proposed framework, in order to avoid requiring any prior information about the CSS network, a rank estimation algorithm and an estimation strategy for the number of corrupted channels are proposed. Numerical results show that the proposed malicious user detection framework achieves high detection accuracy with lower data acquisition costs in comparison with the conventional approach. After being validated by simulations, the proposed malicious user detection framework is tested on the real-world signals over TV white space spectrum.
Y Gao, MJ Er, S Yang (2001)Adaptive control of robot manipulators using fuzzy neural networks, In: IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS48(6)pp. 1274-1278 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
MJ Er, Y Gao (2003)Robust adaptive control of robot manipulators using generalized fuzzy neural networks, In: IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS50(3)pp. 620-628 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Y Gao, MJ Er (2003)Modelling, control, and stability analysis of non-linear systems using generalized fuzzy neural networks, In: INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE34(6)pp. 427-438 TAYLOR & FRANCIS LTD
P Davies, A Phipps, M Taylor, ADS Curiel, A Baker, Y Gao, M Sweeting, D Parker, IA Crawford, AJ Ball, L Wilson (2007)Uk lunar science missions: Moonlite & moonraker, In: 2007 3RD INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN SPACE TECHNOLOGIES, VOLS 1 AND 2pp. 774-779
SQ Wu, MJ Er, Y Gao (2001)A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks, In: IEEE TRANSACTIONS ON FUZZY SYSTEMS9(4)pp. 578-594 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

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