Article
Automation & Control Systems
Jian Pan, Sunde Liu, Jun Shu, Xiangkui Wan
Summary: This paper proposes a hierarchical least squares algorithm for parameter identification problems of a Volterra nonlinear system. By decomposing the Volterra system into three subsystems with a smaller number of parameters and estimating the parameters of each subsystem separately, the proposed algorithm overcomes the excessive calculation amount of the Volterra systems. The calculation analysis shows that the proposed algorithm has lower computational cost compared to the recursive least squares algorithm, and simulation results demonstrate its effectiveness in identifying Volterra systems.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Automation & Control Systems
Michael Ruderman
Summary: A novel nonlinear damping control scheme is proposed for second-order systems, demonstrating global asymptotic stability, passivity, and fast convergence without transient overshoots. Control saturation case is also explicitly analyzed in the study.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Engineering, Electrical & Electronic
Zhao Zhang, Jiashu Zhang, Defang Li
Summary: Recently, a widely nonlinear quaternion recursive least square algorithm was proposed to enhance the performance of quaternion-valued second-order Volterra LMS algorithms. Additionally, a novel widely nonlinear quaternion Volterra recursive least square dichotomous coordinate descent filtering model was introduced to reduce computational complexity.
Article
Automation & Control Systems
Shiling Li, Xiaohong Nian, Zhenhua Deng, Zhao Chen, Qing Meng
Summary: This article investigates a resource allocation problem of second-order nonlinear multiagent systems, proposing a distributed protocol for agents based on gradient descent and analyzing the global convergence of the algorithm by constructing a suitable Lyapunov function. Examples are provided to illustrate the results.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Shantanu Singh, George Weiss
Summary: We investigate a special class of nonlinear infinite dimensional systems obtained by modifying the second order differential equation that describes conservative linear systems. This modification introduces a new nonlinear damping term that is maximal monotone and possibly set-valued. We show that this new class of systems is incrementally scattering passive, and our approach uses the theory of maximal monotone operators and the Crandall-Pazy theorem.
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
(2023)
Article
Automation & Control Systems
Wenhui Dou, Shihong Ding, Xinghuo Yu
Summary: This article proposes a novel event-triggered second-order sliding mode (SOSM) control method for uncertain nonlinear systems. A new switched triggering mechanism is constructed using three predesigned saturated-like functions. An event-triggered SOSM controller is designed under the proposed triggering mechanism to ensure finite-time convergence of the SOSM system states to a domain of the origin, which prevents escape from the domain. To avoid Zeno behavior, two positive minimum inter-execution intervals are obtained based on different triggering conditions. The effectiveness of the control strategy is verified through a simulation study.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Bin-Bin Hu, Hai-Tao Zhang, Jun Wang
Summary: This article proposes an equal-distance surrounding control method for second-order nonlinear multiagent systems, which uses a distributed estimator and adaptive distributed control law to achieve collaborative surrounding of multiple moving targets while meeting stability conditions. Experimental results with unmanned surface vessels confirm the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Physics, Multidisciplinary
Marco S. Kirsch, Yiqi Zhang, Mark Kremer, Lukas J. Maczewsky, Sergey K. Ivanov, Yaroslav V. Kartashov, Lluis Torner, Dieter Bauer, Alexander Szameit, Matthias Heinrich
Summary: Higher-order topological insulators represent a novel topological phase with boundary modes characterized by a unique co-dimension of at least two. Despite promising preliminary considerations, experimental research on these systems has been limited to linear evolution of topological states, with observation of the interplay between nonlinearity and dynamics of higher-order topological phases remaining elusive. However, experimental demonstration of nonlinear higher-order topological corner states and observation of soliton formation in such structures could pave the way for exploring topological properties of matter in the nonlinear regime and potentially lead to the development of compact devices harnessing the intriguing features of topology.
Article
Mathematics
Feliz Minhos, Gracino Rodrigues
Summary: This paper investigates two types of second-order differential equation systems with parameters: coupled systems with Sturm-Liouville type boundary conditions and classical systems with Dirichlet boundary conditions. We discuss the Ambosetti-Prodi alternative for each system. We provide sufficient conditions for the existence and non-existence of solutions for both types of systems using the lower and upper solutions method and the properties of the Leary-Schauder topological degree theory. This study is the first to obtain the Ambrosetti-Prodi alternative for such systems with different parameters.
Article
Automation & Control Systems
Maobin Lu, Lu Liu
Summary: This study addresses the consensus problem of heterogeneous second-order nonlinear uncertain multiagent systems under switching networks using a distributed control approach. A nonlinear distributed dynamic controller is developed based on the internal model principle to achieve consensus in the presence of system uncertainties, disturbances, and communication delays. The consensus is achieved through Lyapunov analysis in the presence of uniformly connected topologies and nonuniform time-varying communication delays.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Jiaxin Zhang, Yue Fu, Jun Fu
Summary: This article addresses the adaptive backstepping finite-time optimal formation control problem for second-order multiagent systems with unknown nonlinear dynamics. Neural networks are used for identifying unknown uncertain terms, and a novel optimal performance index function based on the framework of identifier-actor-critic is constructed for designing the finite-time optimal formation control. It is proved that all signals in the system are bounded in finite time, and the formation control is achieved at minimum cost. The effectiveness and superiority of the proposed control algorithm are verified through simulation comparisons and data analyses.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Yue Yang, Jiangshuai Huang, Xiaojie Su, Kai Wang, Guoqi Li
Summary: This article discusses the adaptive control for a class of strict-feedback nonlinear systems with uncertainties under injection and deception attacks. It proposes an adaptive control scheme to deal with the attacks while ensuring that regulation errors can be made arbitrarily small by adjusting control parameters.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Lina Huang, Jin-Liang Wang, Tingwen Huang
Summary: This paper investigates the output consensus of a second-order nonlinear multiagent system with mismatched input and output vectors by exploiting the output strict passivity (OSP). The authors derive an OSP criterion for the system using the Lyapunov functional approach and state feedback control method. Based on the obtained criterion and the relationship between output consensus and OSP, a sufficient condition to ensure the output consensus of the system is obtained. The merits of the theoretical results are substantiated through numerical simulations.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Review
Chemistry, Inorganic & Nuclear
Yaoguo Shen, Wenyue Tang, Xiaoxin Lin
Summary: This review provides a comprehensive overview of NLO sulfates, including classification, synthesis methods, crystal structure features, optical performances, and structure-property relationships. The key structure-property relationships are summarized to assist new researchers in designing superior sulfate NLO materials.
COORDINATION CHEMISTRY REVIEWS
(2022)
Article
Automation & Control Systems
Zhenxing Li, Jun Yan, Wenwu Yu, Jianlong Qiu
Summary: This article investigates adaptive control problems for unknown second-order nonlinear multiagent systems using an event-triggered approach. It provides adaptive event-triggered consensus and tracking controllers for different types of MASs, demonstrating their distributed nature and effectiveness in controlling systems with unknown dynamics.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Engineering, Electrical & Electronic
Yu Zhang, Yue Wang, Zhi Tian, Geert Leus, Gong Zhang
Summary: This paper proposes an efficient solution for super-resolution 2D harmonic retrieval from multiple measurement vectors (MMV). By performing a redundancy reduction (RR) transformation, the problem size is effectively reduced without losing useful frequency information. The transformed 2D covariance matrices in the RR domain allow for a sparse representation using decoupled 1D frequency components, enabling super-resolution 2D frequency estimation. The resulting RR-enabled D-ANM technique, RR-D-ANM, achieves low computational complexity comparable to the 1D case. Simulation results confirm the superiority of our solutions in terms of computational efficiency and detectability for 2D harmonic retrieval.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Qiuling Yang, Mario Coutino, Geert Leus, Georgios B. Giannakis
Summary: Graph-based learning and estimation are important problems in various applications, but higher-order interactions in network data have not been fully explored. This paper proposes autoregressive graph Volterra models (AGVMs) to capture both connectivity between nodes and higher-order interactions. The model inherits the identifiability and expressiveness of the Volterra series. Two algorithms based on AGVM for topology identification and link prediction are introduced, and experiments on real-world collaboration networks demonstrate the impact of higher-order interactions.
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
(2023)
Editorial Material
Engineering, Electrical & Electronic
Geert Leus, Antonio G. Marques, Jose M. F. Moura, Antonio Ortega, David Shuman
IEEE SIGNAL PROCESSING MAGAZINE
(2023)
Article
Engineering, Electrical & Electronic
Pim van der Meulen, Mario Coutino, Johannes G. Bosch, Pieter Kruizinga, Geert Leus
Summary: In this study, a method is proposed for blindly estimating the transfer function of an aberrating layer in front of a receiving ultrasound array, assuming a separate non-aberrated transmit source, without exact knowledge of the ultrasound sources or acoustic contrast image, and without directly measuring the transfer function using a separate controlled calibration experiment. The proposed approach utilizes the measurement data of many unknown random images, such as blood flow, and exploits their second-order statistics to formulate a measurement model that defines the layer's transfer function. Through manifold-based optimization, the layer's transfer function is solved for by defining and solving a covariance domain problem that eliminates the image variable. The proposed algorithm is evaluated using realistic simulations and is found to accurately estimate the transfer function, leading to increased imaging performance in various aberrating layers, including a skull layer.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2023)
Article
Engineering, Electrical & Electronic
Lingya Liu, Cunqing Hua, Jing Xu, Geert Leus, Yiyin Wang
Summary: This article proposes greedy approaches to select informative sensors to maximize the Fisher information, and introduces a new metric called the Fisher information intensity (FII). The volume ratio between the information ellipsoid corresponding to the selected subset and the ground set is optimized. A cost function based on the volume ratio is developed and proven to be monotone submodular. A greedy algorithm and its fast version are proposed to obtain near-optimal solutions. Numerical results demonstrate the superiority of the proposed algorithms.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Krishnaprasad Nambur Ramamohan, Sundeep Prabhakar Chepuri, Daniel Fernandez Comesana, Geert Leus
Summary: In this work, the self-calibration problem of joint calibration and direction-of-arrival (DOA) estimation using acoustic sensor arrays is addressed. Novel solvers are proposed for both linear and non-linear arrays, capable of estimating the sensor gain, phase errors, and the source DOAs. The algorithms are derived for conventional element-space and covariance data models and are applicable to both sparse and regular arrays formed using scalar and vector sensors. Identifiability conditions for a unique solution are derived, and numerical experiments and comparisons are provided to demonstrate the effectiveness of the developed techniques. Experimental results using an acoustic vector sensor array in an anechoic chamber further showcase the usefulness of the proposed self-calibration techniques.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Proceedings Paper
Computer Science, Theory & Methods
Hamed Masoumi, Nitin Jonathan Myers, Geert Leus, Sander Wahls, Michel Verhaegen
Summary: In this paper, an in-sector compressed sensing-based mmWave channel estimation technique is proposed to deal with the low SNR problem caused by wide beams. By focusing the energy on the sector of interest and using a new class of structured CS matrices, the proposed approach achieves better channel estimates with reduced aliasing artifacts in the sector of interest compared to benchmark algorithms.
2022 IEEE 23RD INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATION (SPAWC)
(2022)
Proceedings Paper
Acoustics
Costas A. Kokke, Mario Coutino, Richard Heusdens, Geert Leus, Laura Anitori
Summary: This work presents variance bounds on the estimation of velocity using the Doppler shift as it appears in the array model. An efficient method of performing the velocity estimation is proposed and its performance is verified using Monte Carlo simulations.
2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022)
(2022)
Proceedings Paper
Acoustics
Yanbin He, Mario Coutino, Elvin Isufi, Geert Leus
Summary: In this study, we focus on partitioning dynamic graphs with two types of nodes, and propose solutions based on the generalized eigenvalue problem for static partition problems. We also introduce an eigenvector update-based method for adaptive partition. Numerical experiments demonstrate the effectiveness of the proposed approaches.
2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022)
(2022)
Proceedings Paper
Acoustics
Yuyang Hu, Michael Brown, Didem Dogan, Geert Leus, Pieter Kruizinga, Antonius F. W. Van der Steen, Johannes G. Bosch
Summary: We aim to develop an ultrasound compressive imaging device for carotid artery (CA) function and flow monitoring/imaging using a few single element transducers with spatial coding masks. The unique impulse responses can be utilized in compressive reconstructions. In this study, we emulated such a device using a linear array system to explore different configurations. The results suggest that our spatial coding mask approach based on reconstructions regularized with a least squares method has potential for CA monitoring with only 10 to 12 sensors.
2022 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS)
(2022)
Proceedings Paper
Acoustics
Maosheng Yang, Elvin Isufi, Geert Leus
Summary: Graphs can represent networked data using nodes and edges, and methods in signal processing and neural networks have been developed to process and learn from graph data. However, these methods are limited to data defined on nodes. This paper proposes a simplicial convolutional neural network (SCNN) architecture for learning from data defined on simplices, and studies its properties and performance on a coauthorship complex.
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2022)
Article
Engineering, Electrical & Electronic
Maosheng Yang, Elvin Isufi, Michael T. Schaub, Geert Leus
Summary: This study investigates linear filters for processing signals on abstract topological spaces modeled as simplicial complexes. The study develops simplicial convolutional filters and examines their properties and frequency responses. The research also discusses different procedures for designing these filters and demonstrates their applications in various fields.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Alberto Natali, Elvin Isufi, Mario Coutino, Geert Leus
Summary: This work proposes an algorithmic framework for learning time-varying graphs from online data, which can be applied to various model-dependent graph learning problems. The framework formulates graph learning as a composite optimization problem, utilizing the empirical covariance matrix to represent data dependence. It incorporates time-varying optimization tools and temporal regularization to improve convergence speed and solution accuracy.
IEEE OPEN JOURNAL OF SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Samuel Fernandez-Menduina, Felix Krahmer, Geert Leus, Ayush Bhandari
Summary: This paper aims to address the problem of information loss caused by sensor saturation and clipping. By using a co-design approach with computational arrays, we can overcome the barriers between sensor array hardware and algorithms, enabling encoding and decoding of high-dynamic-range information for various signal processing tasks.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Yu Zhang, Yue Wang, Zhi Tian, Geert Leus, Gong Zhang
Summary: This paper proposes an efficient method for estimating DOD and DOA in MIMO systems. By reducing redundancy, the covariance matrix is transformed into a smaller one without losing useful angle information. This method achieves efficient estimation on a reduced-size problem.
IEEE SIGNAL PROCESSING LETTERS
(2022)
