In this article, an adaptive distributed predefined-time and bounded (PTB) sliding mode controller based on a novel super-twisting-like estimator is proposed for the formation control problem of underactuated uncrewed surface vehicles (USVs) subject to unknown disturbances. To leverage the advantages of the super-twisting estimator and achieve ultimate PTB stability in the formation control system, a novel distributed predefined-time super-twisting-like estimator is first proposed. This estimator achieves real-time estimation of the leader's position and velocity within a predefined-time by collecting the neighboring USVs' estimates of the leader's position and velocity through switching interaction topologies, and it does not require the leader's acceleration information. Next, an improved nonsingular PTB sliding surface is designed. By integrating this sliding surface with adaptive control techniques, a distributed PTB sliding mode formation controller is designed for underactuated USVs subject to unknown disturbances. Finally, the PTB stability of the formation control system is rigorously proven. Numerical simulations of both fixed and time-varying formations validate the effectiveness and robustness of the proposed method.

Personal sound zone (PSZ) refers to the technique that uses an array of loudspeakers and digital signal processing tools to achieve spatial soundfield control. To generate the target sound zones, this technique generally requires to know the acoustic transfer functions (ATFs) between the loudspeakers and the spots where soundfields are to be controlled. In practical applications, however, the true ATFs are never accessible and they have to be measured or estimated. Due to many sophisticated reasons, the measured ATFs generally deviate from the true ones, which may lead to significant degradation in performance of sound zone reproduction. In this work, a robust pressure matching (RPM) algorithm is presented for sound zone generation. It exploits a complex Gaussian mixture model (CGMM) to model the ATFs and their perturbations. The CGMM parameters are estimated using the expectation-maximization (EM) algorithm. To improve the robustness of the pressure matching method, an uncertainty constraint is applied to the ATF estimates and the pressure matching problem is then formulated as one of biconvex optimization. The coordinate descent algorithm is subsequently used to solve the optimization problem, thereby obtaining the optimal control filter. In comparison with the existing pressure matching methods without considering the effect of ATF perturbations, the presented algorithm is able to achieve lower normalized signal distortion energy and higher signal to interference ratio. Numerical simulations justify the effectiveness of the presented algorithm as well as its advantages over the traditional methods.

Sound field control systems deployed in room acoustic environments require knowing the acoustic channel impulse responses between the loudspeakers and matching microphones, which are challenging to estimate accurately due to perturbations caused by such factors as temperature changes and sensors’ position mismatches. To deal with this issue, a robust pressure matching algorithm is developed in this work where a perturbation term of the acoustic transfer function (ATF) is modeled as a Gaussian process, based on which an uncertainty constraint is applied to limit the impact of perturbation on pressure matching. This constrained problem is formulated as one of biconvex optimization, and a coordinate descent algorithm is adopted to estimate the optimal control filter. Simulations are performed and results show that the proposed method is able to achieve more accurate control as compared to the standard pressure matching algorithm in the presence of ATF perturbations.

Spectral unmixing is an effective tool to mine information at the subpixel level from complex hyperspectral images. To consider the spatially correlated materials distributions in the scene, many algorithms unmix the data in a spatial–spectral fashion; however, existing models are usually unable to model spectral variability simultaneously. In this article, we present a variational autoencoder-based deep generative model for spatial–spectral unmixing (DGMSSU) with endmember variability, by linking the generated endmembers to the probability distributions of endmember bundles extracted from the hyperspectral imagery via discriminators. Besides the convolutional autoencoder-like architecture that can only model the spatial information within the regular patch inputs, DGMSSU is able to alternatively choose graph convolutional networks or self-attention mechanism modules to handle the irregular but more flexible data—superpixel. Experimental results on a simulated dataset, as well as two well-known real hyperspectral images, show the superiority of our proposed approach in comparison with other state-of-the-art spatial–spectral unmixing methods. Compared to the conventional unmixing methods that consider the endmember variability, our proposed model generates more accurate endmembers on each subimage by the adversarial training process. The codes of this work will be available athttps://github.com/shuaikaishi/DGMSSUfor the sake of reproducibility.

The complex nature of hyperspectral images makes the analysis of spectral signatures a challenging task in remote sensing. For quantitative analysis, spectral unmixing is a well-established and effective tool to analyze the spectra and spatial distribution of substances in the scene. The classical unmixing algorithms usually fail to tackle spectral variability caused by variations in environmental conditions. Many variants based on the linear mixing process have been proposed to tackle this problem; however, the spectral variability modeling capacity of these algorithms is usually insufficient. In this article, we present a probabilistic generative model to address endmember variability and provide more accurate abundance and endmember estimates. The proposed model simultaneously extracts the endmembers and estimates abundances in an unsupervised manner. In particular, it allows fitting arbitrary endmember distributions through the nonlinear modeling capability of neural networks compared to other methods that use parametric endmember variability models. The performance of the proposed approach is evaluated on both synthetic and real datasets. Experimental results show its superiority in comparison with other state-of-the-art methods. The code of this work is available athttps://github.com/shuaikaishi/PGMSUfor the sake of reproducibility.

Spectral signatures are usually affected by variations in environmental conditions. The spectral variability is thus one of the most important and challenging problems to be addressed in hyperspectral unmixing. Generally, it is a non-trivial task to model the endmember variability, and existing spectral unmixing methods that address the spectral variability have different limitations. This paper presents a variational autoencoder (VAE) framework for hyperspectral unmixing accounting for the endmember variability. The endmembers are generated using the posterior distributions of the latent variables to describe their variability in the image. Compared with other existing distribution based methods, the proposed method is able to fit an arbitrary distribution of endmembers for each material through the representation capacity of deep neural networks. Evaluated with both synthetic and real datasets, the proposed method shows superior unmixing results compared with other state-of-the-art unmixing methods.

Personal sound zones provide users to experience independent listening and quiet areas in the same acoustic environment using multiple loudspeakers. The generalized eigenvalue decomposition (GEVD) has been proposed for sound zones generation, allowing user to control the trade-off between acoustic contrast and signal distortion by adjusting some parameters. Unfortunately, these parameters are not physically meaningful, and the user has to tune them for different source materials and acoustic environments. Moreover, performing a high dimensional GEVD is computational complex. In this article, we first propose various strategies to control the reproduced sound zones as precisely and accurately as possible by reformulating the problem using physically meaningful constraints using regularization approach. Then, a hybrid approach of combining the conjugate gradient method and GEVD is proposed to reduce the computational complexity and signal distortion when the subspace dimension is small. The proposed methods show precise control over the reproduced sound zone via extensive numerical simulations in reverberant environments for different physically meaningful constraints.

All spatial active noise control (ANC) systems, when deployed in typical room environments, have time-varying acoustic channels between the secondary sources and the error microphones. The conventional online secondary path modeling techniques, which introduces additive auxiliary random noise to estimate the secondary paths, become challenging especially in a multichannel setup. In this work, we propose to use higher-order variable-directivity sound sources as secondary sources for spatial ANC, in which both the interior residual noise field within the control region and exterior sound field due to secondary source radiation are jointly controlled. The aim of controlling the exterior sound field is to minimize room reverberation generated by the secondary sources so that the secondary paths in the proposed algorithm can be approximated as free-field propagation and thus can be pre-calibrated. The system is implemented in an adaptive manner to track noise variations. The results show that the proposed method can effectively cancel spatial noise field and control exterior sound field at an acceptable low level in time-varying room environments.

Sound zone control enables different users to enjoy different audio contents in the same acoustic environment. Generalized eigenvalue decomposition (GEVD)-based methods allow us to control the tradeoff between the acoustic contrast (AC) and signal distortion (SD). However, such methods have a high computational complexity. In this paper, we propose a fast reduced-rank sound zone control algorithm using the conjugate gradient (CG) method. Instead of using the eigenvectors as the basis for the solution space, the search directions in the CG method are used to reduce the computational complexity. Then, a low dimensional EVD is applied to obtain the sub-optimal control filter coefficients. The dark zone power can be adjusted by a parameter, which implicitly controls the trade-off between the AC and SD. Compared with GEVD-based methods, experimental results show that the proposed algorithm has a degradation of performance (4-5 dB) in terms of AC or SD but a high improvement on computational efficiency.

Multizone reproduction has been focused on reproducing sounds in an empty listening space. However, there are always scatterers such as human heads in sound zones, generating scattered sound fields and causing degraded system performance. In this work, we develop a modal-domain method for 2.5D multizone reproduction with a solid object in the bright zone. Analytical expressions of the incident and scattered fields are developed. We then propose an active control strategy to correct the scattering effect. In the reproduction stage, we use the weighted mode matching approach to achieve the optimal control over the entire region. Simulation results show that in comparison with the conventional method which does not consider the scattering effect, the proposed method can achieve higher acoustic contrast performance over a broadband frequency range.

The mode matching based multizone reproduction has mainly been focused on a purely 2D theory which is inadequate to fit the 3D reality. Its extension to the 3D theory however requires many secondary sources and a high computational complexity. In this paper, a weighted mode matching approach is developed for 2.5D multizone reproduction. The multizone soundfield is reproduced in the horizontal plane within a circular control region using the loudspeakers modelled as 3D point sources. We propose weighting the Bessel-spherical harmonic modes for 2.5D reproduction and a matching between the desired and reproduced soundfields over the entire control region. Simulation results show that in comparison with the conventional 2.5D reproduction method a more accurate reproduction is achieved using the proposed weighting approach.

In Fauré et al. (2006), the dynamics of the core network regulating the mammalian cell cycle is formulated as a Boolean control network (BCN) model consisting of nine proteins as state nodes and a tenth protein (protein CycD) as the control input node. In this model, one of the state nodes, protein Cdc20, plays a central role in the separation of sister chromatids. Hence, if any Cdc20 sequence can be obtained, fully controlling the mammalian cell cycle is feasible. Motivated by this fact, we study whether any Cdc20 sequence can be obtained theoretically. We formulate the foregoing problem as the invertibility of BCNs, that is, whether one can obtain any Cdc20 sequence by designing input (i.e., protein CycD) sequences. We give an algorithm to verify the invertibility of any BCN, and find that the BCN model for the core network regulating the mammalian cell cycle is not invertible, that is, one cannot obtain any Cdc20 sequence. We further present another algorithm to test whether a finite Cdc20 sequence can be generated by the BCN model, which leads to a series of periodic infinite Cdc20 sequences with alternately active and inactive Cdc20 segments. States of these sequences are alternated between the two attractors in the proposed model, which reproduces correctly how a cell exits the cell cycle to enter the quiescent state, or the opposite.

This brief investigates the controllability and observability of Boolean control networks with (not necessarily bounded) time-variant delays in states. After a brief introduction to converting a Boolean control network to an equivalent discrete-time bilinear dynamical system via the semi-tensor product of matrices, the system is split into a finite number of subsystems (constructed forest) with no time delays by using the idea of splitting time that is proposed in this brief. Then, the controllability and observability of the system are investigated by verifying any so-called controllability constructed path and any so-called observability constructed paths in the above forest, respectively, which generalize some recent relevant results. Matrix test criteria for the controllability and observability are given. The corresponding control design algorithms based on the controllability theorems are given. We also show that the computing complexity of our algorithm is much less than that of the existing algorithms.