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In this article, the non-negative edge consensus problem is addressed for positive networked systems with undirected graphs using state-feedback protocols. In contrast to existing results, the major contributions of this work included: 1) significantly improved criteria of consequentiality and non-negativity, therefore leading to a linear programming approach and 2) necessary and sufficient criteria giving rise to a semidefinite programming approach. Specifically, an improved upper bound is given for the maximum eigenvalue of the Laplacian matrix and the (out-) in-degree of the degree matrix, and an improved consensuability and non-negativevity condition is obtained. The sufficient condition presented only requires the number of edges of a nodal network without the connection topology. Also, with the introduction of slack matrix variables, two equivalent conditions of consensuability and non-negativevity are obtained. In the conditions, the system matrices, controller gain, as well as Lyapunov matrices are separated, which is helpful for parameterization. Based on the results, a semidefinite programming algorithm for the controller is readily developed. Finally, a comprehensive analytical and numerical comparison of three illustrative examples is conducted to show that the proposed results are less conservative than the existing work.

This paper investigates the positive consensus problem of multi-agent systems with directed communication topologies where all the agents have identical continuous-time positive linear dynamics. Existing works of such problem mainly focus on the case where the networked communication topologies are of either undirected and incomplete graphs, or strongly connected directed graphs. In stark contrast to them, we study the problem in which the communication topologies of the networked system are described by directed graphs each containing a spanning tree which is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains. Based on the existing results in spectral graph theory and positive linear systems theory, several necessary and sufficient conditions on the positive consensus of the directed multi-agent system are derived through using linear matrix inequality techniques. Then a primal-dual iterative algorithm is developed for computation and finally several numerical simulations are provided to illustrate the effectiveness of the proposed theoretical results.

This article is focused on the design of an output tracking control scheme for a class of continuous-time periodic piecewise time-varying systems (PPTVSs) with actuator saturation and nonlinear perturbations. The energy-to-peak tracking performance is studied based on an equivalent condition on the definiteness property of matrix polynomials. Considering the actuator saturation and nonlinear perturbation, matrix polynomial-based sufficient conditions are derived through the Lyapunov method using periodic matrix functions. From a perspective of subinterval segmentation aimed at PPTVSs, the proposed conditions can achieve less conservatism for tracking the output of a periodic time-varying reference system, while the controller gains can be computed using convex optimization. Moreover, a heuristic algorithm is constructed to simultaneously guarantee the closed-loop state convergence and the output tracking performance. The reduction in conservatism and the effectiveness of algorithm are demonstrated by illustrative case studies.

This article aims to design proportional-derivative (PD) controllers for interval positive linear systems in the continuous-time domain, which still remains a widely-discussed open problem in positive systems theory. The specific objective is to design a PD controller for the system with interval uncertain parameters and time-varying delay, which simultaneously ensures closed-loop system stability and preserves positivity. The work proposes a systematic framework, with the aim of finding PD controller gains for positive robust stabilization. The methodology and algorithm are presented first in the study, and the performance of such methods is instantiated by numerical examples.

This paper investigates the decay rates of continuous-time singular systems with unbounded delays. By introducing an auxiliary system for the original system, the positivity and asymptotic stability conditions of the system are investigated first. Then, µ-stability criteria, which are applied to characterize the decay rates of the systems, are proposed, and the relation between the system matrices and µ-stability is studied. Those results include the stability of positive singular systems with bounded time-varying delays and time-varying delays with linear growth rate as special cases. Finally, a numerical example is given to illustrate the obtained theoretical results.

With fast recovery time and effective in situ tumor tissue killing ability, thermal ablation has become a popular treatment for tumors compared with chemotherapy and radiation. The thermal dose measurement of current technology is usually accompanied by monitoring a large area impedance across two ablation catheters and the localized impedance measurement is difficult to achieve. In this work, thermal‐resistive sensor and impedance sensor are fabricated on the curved surface of a capillary tube with 1 mm outer diameter. The device is applied for real‐time in situ tissue impedance monitoring during thermal ablation. The calibrated thermal‐resistive sensors have an average temperature coefficient of resistance (TCR) of 0.00161 ± 5.9% °C‐1 with an accuracy of ±0.7 °C. By adding electro‐polymerized PEDOT:PSS (poly(3,4‐ethylenedioxythiophene)‐poly(styrenesulfonate)) on the 300 µm diameter gold electrodes, the interface impedance reduces two orders from 408 to 3.7 kΩ at 100 Hz. The Randles equivalent circuit model fittings show a two‐order improvement in the electrode capacitance from 7.29 to 753 nF. In the ex vivo porcine liver laser ablation test, the temperature of the porcine liver tissue can reach 70°C and the impedance would drop by 50% in less than 5 minutes. The integration of laser ablation fiber with the impedance and temperature sensors can further expand the laser ablation technique to smaller scale and for precise therapeutics.

In this article, a Bernstein polynomial approach is first applied to the estimation of reachable set for a class of periodic piecewise polynomial systems, whose subsystems are time-varying and can be expanded to Bernstein polynomial forms. A lemma on the negativity/positivity for a class of Bernstein polynomial matrix functions is presented, which can provide a feasible set larger than that by the existing method. Based on the integration of the presented lemma and the theory of matrix polynomials, two tractable sufficient conditions are developed. For comparison of conservatism, the reachable set estimation is achieved through optimizing the ellipsoidal bounding region. Four sets of constraints with different conservatism are derived and compared. The effectiveness and superiority of the Bernstein polynomial approach in reachable set estimation are demonstrated via an illustrative example. The results show that the proposed approach enables lower conservatism in reachable set estimation, providing an intuitive route to tackle time-varying parameter products with high powers.

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a valuable diagnostic tool in prostate lesion assessment, for which many reports conclude the advantageous use of convolutional neural networks (CNNs) in prostate lesion detection and classification (PLDC). However, the network training inevitably involves prostate magnetic resonance (MR) images from multiple sites/cohorts. There always exists variation in scanning protocol among the cohorts, inducing significant changes in data distribution between source and target domains. This challenge has greatly limited clinical adoption on a large scale. Recent domain adaptation (DA) models can alleviate the inherent cross-site heterogeneity. Some could leverage cross-domain knowledge transfer using whole-slide images, without prior knowledge of lesion regions. In this paper, we propose a coarse mask-guided deep domain adaptation network (CMD²A-Net) in order to develop a fully automated framework for PLDC using multi-cohort images. No category or mask label is required from the target domain. A coarse segmentation module is trained to cover the possible lesion-related regions, so that attention maps can be generated to dedicate the local feature extraction of lesions within those regions. As a result, the features of both prostate lesion and region can be fused to align the robust features between the source and target domains. Experiments have been performed on 512 mpMRI sets from datasets of PROSTATEx (with 330 sets) and two cohorts, A (with 74 sets) and B (with 108 sets). Using the ensemble learning, our CMD²A-Net accomplishes an AUC of 0.921 in cohort A and 0.913 in cohort B, demonstrating its transferability from a large-scale public dataset PROSTATEx to our small-scale target domains. Our results and ablation study also support the CMD²A-Net’s effectiveness in lesion classification between benign or malignant, compared to the state-of-the-art models Corresponding author(s) Email: kwokkw@hku.hk (K.W. Kwok), varv@hku.hk (V. Vardhanabhuti)

Focused ultrasound (FUS) technology attracts increasing interests accrediting to its non-invasive and painless treatment of tumors. Magnetic resonance imaging (MRI) guidance has been introduced to monitor this procedure, thus allowing the ultrasound foci to be precisely controlled. However, manual positioning of the FUS transducers is challenging, especially for the intra-operative (intra-op) adjustment in the MRI room. Currently, there are very few devices capable to provide robotic transducer positioning for the treatment of abdominopelvic organ diseases under MRI. The high intensity focused ultrasound (HIFU) spot would have to be “steered” to ablate large (>O 3.5 cm) or multiple tumors (e.g., in liver). To this end, we proposed a hydraulic-driven tele-operated robot platform that enables 5-DoF manipulation of the FUS transducer. Even operated close to the MRI iso-center, the robot can guarantee zero electromagnetic (EM) artifact to the MR image. Our proof-of-concept robot prototype can offer a large workspace (100 mm × 100 mm × 35 mm) for FUS foci steering. Accurate manipulation (0.2 mm in translation, 0.4° in rotation) of the FUS transducer holder is achieved using rolling diaphragm-sealed hydraulic actuators. The robot control responsiveness (from 0.1 to 4 Hz) is also evaluated to show the potential to compensate for the spot tracking error induced by respiratory motion. We also demonstrate the use of wireless radiofrequency (RF) markers to continuously register the robot task space in the MRI coordinates.