Molecular dynamics (MD) is a cornerstone of scientific computing, but strong scaling often collapses at high parallelism because communication is bursty and highly sensitive to tail latency. MD advances by repeating a fixed timestep loop (one iteration of force computation and state update), and performance is largely determined by how quickly timesteps complete. A key reason is that each timestep contains short, synchronized communication phases, followed by a global dependency before the next timestep. Wafer-scale chips (WSCs) offer cycle-level latency and high on-chip bandwidth, yet their 2D mesh fabrics can still suffer burst-induced queue buildup; existing wavelet scheduling relies on a static stride that either over-injects (triggering credit backpressure) or over-throttles (wasting bandwidth) as conditions evolve.

Service restoration implemented by multiple distributed energy resources (DERs) is a resilience-enhancing paradigm for modern distribution systems. To address the challenges of complex system modeling and the problem of cooperative control over heterogeneous multiple agents, this article proposes a graph reinforcement learning (G-RL) method based on heterogeneous multiagent systems (MASs). The method leverages graph-structured data to enhance the representation of distribution system states and employs graph attention networks (GATs) to deeply explore the power flow features and spatial characteristics of nodes in the restoration process. Additionally, a multihead self-attention (MHSA) is incorporated to strengthen collaboration among heterogeneous agents, enabling them to focus on relevant information from multiple perspectives during training. Finally, a joint simulation test platform is developed using Python and OpenDSS, and case studies on a 123-bus distribution system are conducted. Experimental results demonstrate that the proposed approach achieves efficient and autonomous service restoration by enhancing spatial feature extraction and improving collaborative decision-making among agents.

Accurate Six Degrees of Freedom (6-DoF) pose estimation of Ship-To-Shore (STS) quay crane spreaders is crucial for ensuring safe and efficient container handling in port automation. However, existing pose estimation techniques face significant challenges, as camera-based systems either rely on markers, which are prone to damage, or struggle with depth estimation inaccuracies. Additionally, 3D sensor-based approaches, particularly point cloud registration (PCR), face challenges such as initial pose errors, high-latency inference, and difficulties in object identification based purely on geometric features. To address these limitations, we propose LCSPose, a LiDAR-camera fusion-based 6-DoF pose estimation method that is marker-free, accurate, efficient, and scalable. Our approach integrates three key modules: (1) a semantic-geometric segmentation module for spreader segmentation and outlier removal, (2) a spatial consistency template sampling module based on Spatial Consistency Score (SC-Score) for reliable template selection across varying distances, and (3) a multi-view coarse-to-fine pose refinement module which incorporates multi-view PCA alignment for robust initial posture prior estimation and iterative pose refinement strategy for long-range registration. Our method demonstrates a 60% improvement in registration recall over state-of-the-art (SOTA) PCR methods, achieving up to 6 cm in translation error and 0.19 degrees in rotation error, while maintaining real-time processing at 20Hz.

Video streaming based teleoperation often faces a trade-off between bandwidth consumption and the need for high-fidelity telepresence. Higher image resolution or a wider field of view (FOV) substantially increases bandwidth requirements. In this paper, we propose a novel telepresence model for teleoperated vehicles operating in bandwidth-constrained environments. Our approach employs a LiDAR-fused 3D Gaussian Splatting (3DGS) as a compact scene representation to efficiently generate remote views. Initially, a static point cloud map is constructed using LiDAR-based semantic mapping, which serves as the initial Gaussians for optimizing the 3DGS model. During teleoperation, the prebuilt 3DGS is then rendered on the teleoperation platform, while only safety-critical information, such as vehicle pose and dynamic objects, is transmitted from the vehicle to the teleoperator in real-time. The proposed telepresence model significantly reduces data transmission requirements while maintaining photorealistic telepresence, enabling reliable and effective teleoperation even under stringent bandwidth constraints. This capability ensures safe and efficient vehicle teleoperation under challenging environments without relying on traditional high-bandwidth communication, thereby broadening the applicability of teleoperation technology to more demanding and diverse operational scenarios. Real-world experimental results show that the developed system can provide immersive teleoperation experiences at Kbps-level bandwidth consumption.

The core of multi-robot collision avoidance is to guide robots to avoid collisions with other robots and obstacles in a dynamic multi-robot environment, which has recently gained increasing interest among the main challenges of robotics. However, the current multi-robot navigation policy neural network exhibits weak position encoding capabilities for spatial environmental features in mapping environment states and robot actions, as well as an inability to recurrently infer information on dynamic environmental features in the temporal dimension, leading to insufficient safety and effectiveness in guiding robot motion. In this paper, we propose a novel spatial-temporal RetNet (STR) that encodes reciprocal collision avoidance states between robots in both spatial and temporal dimensions, aiming to enhance the safety and effectiveness of the policy neural network in guiding robots to accomplish specified tasks. The spatial state encoder module is developed based on parallel RetNet structure, which enhances the ability of the neural network in multi-robot navigation policies to extract reciprocal collision avoidance states between robots in spatial dimensions and overcomes the weak position encoding capability of advanced transformer-based multi-robot navigation policy neural networks. A temporal state encoder is designed by introducing the recurrent RetNet structure. This enhances the multi-robot navigation policy neural network’s ability to encode features in the temporal dimension of multi-robot movements and overcomes the transformer-based multi-robot navigation policy neural network’s inability to recurrently infer information in the time dimension. Simulation experiments were designed to demonstrate that the safety and effectiveness of our proposed method outperform the previous state-of-the-art approaches in guiding the robot to complete the task. Physical experiments illustrate that our policy can be effectively applied to real-world systemsNote to Practitioners—Multi-robot navigation has a wide range of real-world applications, such as multi-robot formation flying for search and rescue, autonomous warehouse operations, and robots navigating through human crowds. This paper introduces a novel Spatial-Temporal RetNet (STR) framework aimed at enhancing safety and effectiveness in multi-robot collision avoidance. STR addresses the limitations of existing methods by improving the neural network’s ability to extract reciprocal collision avoidance states in both spatial and temporal dimensions. The spatial state encoder strengthens the extraction of spatial features, while the temporal state encoder improves the handling of time-dependent information. Simulation and physical experiments demonstrate that STR enhances robot navigation in dynamic environments, making it suitable for real-world applications such as multi-robot coordination.

With the rapid evolution of autonomous vehicles (AVs), ensuring reliable navigation has become paramount, especially against threats like Global Navigation Satellite Systems (GNSS) spoofing. This paper presents an attention-based Autoencoder approach for real-time GNSS spoofing detection in AVs. The proposed method leverages data from multiple sensors, including IMU, GNSS, and LiDAR, fully utilizing the redundancy and correlations among them. By integrating a multi-head attention mechanism into the Autoencoder, the model can thoroughly capture and analyze the complex relationships within sensor data, enhancing its capability to promptly and accurately identify spoofing attacks. Field experiments demonstrate the effectiveness of the proposed method in achieving a high detection rate and short detection time, highlighting its potential for practical deployment in AV applications.

This paper addresses the critical challenge of reliable object detection in autonomous vehicles operating in dynamic urban environments, particularly when facing adversarial attacks on perception systems. It presents a novel dualvalidation methodology leveraging the synergy of image based and point cloud based object detection systems. The approach comprises sensor calibration to align data from both sources, an attack detection algorithm utilizing cross-validation technique to identify inconsistencies, and a self-restoration strategy to ensure correct detection despite malicious manipulation. The methodology has been tested in a complex urban environment with adversarial scenarios including patch and random removal attacks. Experimental results demonstrate the robustness and accuracy of the proposed method in maintaining reliable object detection under adversarial conditions.

Navigating robots through dynamic multi-robot environments, avoiding collisions with both other robots and obstacles, has emerged as a central challenge in robotics. The existing approaches fall short in allowing the policy network to effectively capture spatial-temporal reciprocal collision avoidance in multi-robot environments, comprising both static and dynamic obstacles, resulting in inadequate safety and efficiency in directing robot movement. In this study, we introduce a novel policy neural network called Spatial-Temporal RetNet (STR), designed to encode reciprocal collision avoidance states between robots in spatial and temporal dimensions. The goal is to improve the safety and efficacy of the policy neural network in directing robots to complete assigned tasks. The spatial state encoder module is built upon a parallel RetNet structure, which strengthens the neural network's capacity in extracting reciprocal collision avoidance states between robots in spatial dimensions. This module addresses the limitations of position encoding in transformer-based multi-robot navigation policy neural networks. We design a temporal state encoder utilizing a recurrent RetNet structure. This innovation bolsters the multi-robot navigation policy neural network's capability to capture features in the temporal dimension of multi-robot movements. It addresses the limitations of transformer-based multi-robot navigation policy neural networks, particularly in recurrently inferring information across time dimensions. Simulation experiments were conducted to showcase the superior safety and effectiveness of our proposed method compared to previous state-of-the-art approaches in guiding robots to accomplish tasks.

Curb following is a key technology for autonomous road sweeping vehicles. Currently, existing implementations primarily involve pre-recording waypoints during human driving and subsequently retracing them autonomously. Moreover, existing research related to this topic predominately focuses on curb detection for driver assistance, yet the resultant curb detection outcomes remain underutilized in the development of autonomous curb following systems. To fill this gap, this paper proposes a real-time path generation and alignment control approach to facilitate autonomous curb following. Firstly, a segmented path generation algorithm is introduced that progressively generates reference path segments while ensuring the overall continuity of the reference path. Secondly, a parameterized alignment control algorithm is developed to accurately navigate the vehicle along the planned reference path with proved stability. Real public road experiments have been conducted to validate the proposed approach. The experimental results demonstrate the efficacy of the proposed methodologies across various curb following scenarios, including common concave, convex, and straight-concave curbs, thereby showcasing the practical viability of our methods in real-world applications.

Vision-assisted container loading of Rubber Tyred Gantry (RTG) cranes are facing two primary challenges. Firstly, the uncertainty inherent in Covolutional Neural Network (CNN) based detection hinders its direct application in the safety-critical operation of such heavy-duty machinery. Secondly, sensor calibration introduces additional complexities and errors into the system. However, existing studies have not adequately addressed these challenges. Motivated by this gap, this paper proposes an integrated approach for target detection and alignment control in container loading of RTG cranes. To ensure reliable target marker identification, a heuristic post-processing algorithm is developed as a complement to CNN-based foreground segmentation, thereby ensuring safety during the container handling process. On this basis, a pixel-based control scheme is designed to align the container with the target markers, which eliminates the need for offline or online sensor calibrations. The proposed approach has been successfully implemented on a real RTG crane manufactured by Shanghai Zhenhua Heavy Industries Co., Ltd. (ZPMC) and validated at the Port of Ningbo, China. Experimental results demonstrate the superiority of the proposed approach over current manual operations in port industries, highlighting its potential for crane automation.

Traditional teleoperation technologies based on video streaming are facing several challenges in practical applications, including limited bandwidth, constrained spatial awareness, and sensitivity to illumination. Existing studies have not adequately addressed these issues. This paper presents a novel non-video based teleoperation framework for autonomous vehicles operating in bandwidth-limited environments. To reduce the amount of data being transmitted, a persistent-transient environment model is proposed for telepresence. Initially, a digital twin of the environment is preconstructed, containing only persistent environmental information. Subsequently, transient information captured by onboard sensors, such as vehicle state and dynamic objects, necessitate real-time transmission. Based on this model, a 3D virtual scene is rendered in front of the teleoperator, offering any desired virtual viewpoint to enhance spatial awareness. This telepresence model only requires real-time transmission of minimal data, i.e., vehicle state and detected objects, and remains unaffected by illumination conditions, enabling teleoperation even in applications with Kbps-level bandwidth constraints. Experimental results showcase the substantial potential of the proposed framework in bandwidth-limited settings.

This paper presents a novel non-fragile output sliding mode load frequency control (OSMLFC) strategy designed for multi-area interconnected power systems that incorporate electric vehicles (EVs), particularly in the presence of frequency-triggered denial-of-service (DoS) attacks. We delve into the realm of network communication security concerning load frequency control (LFC) power systems combined with EVs, investigating a real-time frequency-triggered DoS attack by combining real-time frequency dynamics with event-triggering mechanisms. A non-fragile output sliding mode control (SMC) method is proposed, strategically devised to balance the load and frequency aspects of the power systems. Then, a sufficient stability criterion is derived to ensure the non-fragile$H_\infty$stability of the power system integrated with EVs, even when subjected to the perturbations caused by real-time frequency-triggered DoS attacks. The efficacy of our proposed approach and the characteristics of the real-time frequency-triggered DoS attacks are validated through extensive simulations.

This paper investigates the cruise control problem of unmanned ground vehicle (UGV) platoons from the implementation perspective. Unlike most existing works related to platoon cruise control which rely on positioning infrastructures such as lane markings, roadside units, and global navigation satellite systems (GNSS), this paper explores a new problem: platoon cruise control in environments without positioning infrastructures. The introduction of this constraint disables most existing cruise control approaches. To address this problem, an integrated localization and planning framework is proposed, which is composed of three modular algorithms. Firstly, to localize multiple vehicles in a common coordinate system, a collaborative localization algorithm is developed through matching local perceptions of different vehicles. Secondly, to maintain the desired platoon configuration, the historical trajectory of the preceding vehicle is reconstructed, based on which the target state is planned for the following vehicle. Finally, a virtual controller based algorithm is designed to generate feasible trajectories for the following vehicle in real time. The proposed framework has two salient features. Firstly, it does not depend on positioning infrastructures and does not introduce additional positioning sensors, such as GNSS/INS modules, ultra-wideband (UWB) devices, magnetic meters and so on, as long as each vehicle is equipped with a perception sensor (Lidar, radar or camera), which however is essential equipment for nowaday autonomous systems. Secondly, the proposed framework does not depend on direct observations between vehicles to achieve relative localization, making it applicable in non-line-of-sight (non-LOS) situations. Real-world experiments have been conducted to validate the effectiveness, robustness and practicality of the proposed framework.

Collaborative heterogeneous robots are expected to perform comprehensive perception, mapping and coordination in search and rescue scenarios. The challenge of collaboration between heterogeneous robots lies in their huge differences in perception, mobility and processing capabilities. In this paper, a novel collaborative UAV-UGV mapping framework is proposed in GNSS-denied and unknown environments. The key novelty of this work is the proposing of a unified framework to formulate the UAV-UGV collaborative mapping problem with a continuous-discrete model, as well as its realization in real robotic systems. In order to project continuous space into discrete space, a novel information gain trigger scheme is pro-posed. The continuous space allows each robot to perform high frequency local map estimation, while discrete space describes the problem of multi-resolution hybrid map fusion. Considering the nature of data heterogeneity, a flexible probabilistic fusion algorithm is proposed that addresses the multi-resolution hybrid map fusion problem, where the local maps generated by UAV and UGV are fused based on Bayesian rule. The proposed UAV-UGV hybrid system is validated in various challenging scenarios, demonstrating its accuracy and utility in practical tasks.

Terahertz (THz) communication and the application of massive multiple-input multiple-output (MIMO) technology are significant for the sixth generation (6G) communication systems. In this paper, we employ the shooting and bouncing ray (SBR) method integrated with GPU acceleration technology to model THz and massive MIMO channel. The results of ray tracing (RT) simulation in this paper, i.e., angle of departure (AoD), angle of arrival (AoA), and power delay profile (PDP) under the frequency band supported by the commercial RT software Wireless Insite (WI) are in agreement with those produced by WI. Based on the Kirchhoff scattering effect on material surfaces and atmospheric absorption loss showing at THz frequency band, the modified propagation models of Fresnel reflection coefficients and free-space attenuation are consistent with the measured results. For massive MIMO, the channel capacity and the stochastic power distribution are analyzed. The results indicate the applicability of SBR method for building deterministic models of THz and massive MIMO channels with extensive functions and acceptable accuracy.

This paper investigates the cyber-security problem for autonomous vehicles under sensor attacks. In particular, a model-based framework is proposed which can detect sensor attacks and identify their sources in order to achieve the secure localization of self-driving vehicles. To ensure robustness of the vehicle against cyber-attacks, sensor redundancy is introduced, that is to deploy multiple sensors, each of which provides real-time pose observations of the vehicle. A bank of attack detectors is developed to capture anomalies in each sensor measurement, which is a combination of an extended Kalman filter (EKF) and a cumulative sum (CUSUM) discriminator. EKFs are employed to estimate the vehicle position and orientation recursively, while each CUSUM discriminator is designed to analyze the residual generated by its combined EKF to detect the possible deviation of the sensor measurement from the expected pose derived according to the mathematical model of the vehicle. To monitor the inconsistency amongst multiple sensor measurements, an auxiliary detector is introduced which fuses observations from multiple sensors. Based on the results of all the detectors, a rule-based isolation scheme is developed to identify the source anomalous sensor. The effectiveness of our proposed framework has been demonstrated on real vehicle data.

The discovery of unique or suspicious people is essential for active surveillance of security or patrol robots, and multi-robot collaboration and dynamic reasoning can further enhance their adaptability in large-scale environments. This paper proposes a hierarchical probabilistic reasoning framework for a multi-robot system to actively identify the unique person with distinct motion patterns in large-scale and dynamic environments. Linear and angular velocities are considered typical motion patterns, which are extracted by using heterogeneous sensors to detect and track people. First, single robot reasoning is performed, each robot judges the uniqueness of people by comparing their motion patterns based on local observations. Meanwhile, multi-robot reasoning is also performed, by fusing the perceptual information from each individual robot to form a global observation and then make another judgment based on it. Finally, each robot can decide which result should be adopted by comparing the beliefs of local and global judgments. Experimental results show that the method is feasible in various environments.

As robots are sharing work spaces with human, human-robot teamwork is becoming increasingly important. It is foreseeable that the daily work team will be composed of human and robots. The integration of the appropriate decision-making process is an essential part to design and develop the team. If robots can understand the activities and intents of human, it is convenient for a person to cooperate with robots in a natural manner. This paper proposes a system that enables robots to understand human pose and execute given command. The system provides two options for different hardware systems: the first one is suitable for powerful computational units; the second model is compact and efficient on a normal robot platform. In order to enrich application scenarios, we propose a method to extract human pose from thermal images so that our system can be used in all-weather scenario. In addition, we collected extensive training data and trained a MLP neural network to classify several human poses. The experimental results show the accuracy and efficiency of the proposed MLP neural network in day and night environments.

Performing collaborative semantic mapping is a critical challenge for cooperative robots to maintain a comprehensive contextual understanding of the surroundings. Most of the existing work either focus on single robot semantic mapping or collaborative geometry mapping. In this paper, a novel hierarchical collaborative probabilistic semantic mapping framework is proposed, where the problem is formulated in a distributed setting. The key novelty of this work is the mathematical modeling of the overall collaborative semantic mapping problem and the derivation of its probability decomposition. In the single robot level, the semantic point cloud is obtained based on heterogeneous sensor fusion model and is used to generate local semantic maps. Since the voxel correspondence is unknown in collaborative robots level, an Expectation-Maximization approach is proposed to estimate the hidden data association, where Bayesian rule is applied to perform semantic and occupancy probability update. The experimental results show the high quality global semantic map, demonstrating the accuracy and utility of 3D semantic map fusion algorithm in real missions.

This paper studies navigation for multiple mobile robots while avoiding collisions and ensuring global network connectivity in unknown environments. It is found that when the traditional navigation function is employed, control input is extremely small if robots work in a large environment, which implies that robots will almost stop at their initial positions. To solve this problem, a new decentralized navigation function is proposed with a novel goal function which is then applied in a multi-robot navigation scenario. Based on the properties of navigation function and dual Lyapunov theorem, a sufficient condition is derived for robots converging to regions surrounding their corresponding goal positions in a collision-free and connectivity-keeping manner. Simulation results demonstrate the efficacy of the proposed method.

Transient analysis of large-scale power delivery network (PDN) is a critical task to ensure the functional correctness and desired performance of today's integrated circuits (ICs), especially if significant transient noises are induced by clock and/or power gating due to the utilization of extensive power management. In this paper, we propose an efficient algorithm for PDN transient analysis based on sparse approximation. The key idea is to exploit the fact that the transient response caused by clock/power gating is often localized and the voltages at many other “inactive” nodes are almost unchanged, thereby rendering a unique sparse structure. By taking advantage of the underlying sparsity of the solution structure, a modified conjugate gradient algorithm is developed and tuned to efficiently solve the PDN analysis problem with low computational cost. Our numerical experiments based on standard benchmarks demonstrate that the proposed transient analysis with sparse approximation offers up to 2.2× runtime speedup over other traditional methods, while simultaneously achieving similar accuracy.

There have been continuing thrusts in developing efficient modeling techniques for circuit simulation. However, most circuit simulation methods are time-domain solvers. In this paper we propose a frequency-domain simulation method based on Laguerre function expansion. The proposed method handles both linear and nonlinear circuits. The Laguerre method can invert multidimensional Laplace transform efficiently with a high accuracy, which is a key step of the proposed method. Besides, an adaptive mesh refinement (AMR) technique is developed and its parallel implementation is introduced to speed up the computation. Numerical examples show that our proposed method can accurately simulate large circuits while enjoying low computation complexity.

We present a new time-domain simulation algorithm (named OPM) based on operational matrices, which naturally handles system models cast in ordinary differential equations (ODEs), differential algebraic equations (DAEs), high-order differential equations and fractional differential equations (FDEs). When applied to simulating linear systems (represented by ODEs or DAEs), OPM has similar performance to advanced transient analysis methods such as trapezoidal or Gear's method in terms of complexity and accuracy. On the other hand, OPM naturally handles FDEs without much extra effort, which can not be efficiently solved using existing time-domain methods. High-order differential systems, being special cases of FDEs, can also be simulated using OPM. Moreover, adaptive time step can be utilized in OPM to provide a more flexible simulation with low CPU time. Numerical results then validate OPM's wide applicability and superiority.

Power grid verification has become an indispensable step to guarantee a functional and robust chip design. Vectorless power grid verification methods, by solving linear programming (LP) problems under current constraints, enable worst-case voltage drop predictions at an early stage of design when the specific waveforms of current drains are unknown. In this paper, a novel power grid verification algorithm based on hierarchical constraints is proposed. By introducing novel power constraints, the proposed algorithm generates more realistic current patterns and provides less pessimistic voltage drop predictions. The model order reduction-based coefficient computation algorithm reduces the complexity of formulating the LP problems from being proportional to steps to being independent of steps. Utilizing the special hierarchical constraint structure, the submodular polyhedron greedy algorithm dramatically reduces the complexity of solving the LP problems from overO(km3) to roughlyO(kmlogkm), wherekmis the number of variables. Numerical results have shown that the proposed algorithm provides less pessimistic voltage drop prediction while at the same time achieves dramatic speedup.

The authors for the above titled paper were incorrectly listed. The correct list of authors is presented here.

Passivity is an important property of circuits and systems to guarantee stable global simulation. Nonetheless, nonpassive models may result from passive underlying structures due to numerical or measurement error/inaccuracy. A postprocessing passivity enforcement algorithm is therefore desirable to perturb the model to be passive under a controlled error. However, previous literature only reports such passivity enforcement algorithms for pole-residue models and regular systems (RSs). In this paper, passivity enforcement algorithms for descriptor systems (DSs, a superset of RSs) with possibly singular direct term (specifically,D+DTorI-DDT) are proposed. The proposed algorithms cover all kinds of state-space models (RSs or DSs, with direct terms being singular or nonsingular, in the immittance or scattering representation) and thus have a much wider application scope than existing algorithms. The passivity enforcement is reduced to two standard optimization problems that can be solved efficiently. The objective functions in both optimization problems are the error functions, hence perturbed models with adequate accuracy can be obtained. Numerical examples then verify the efficiency and robustness of the proposed algorithms.

Vectorless power grid verification algorithms, by solving linear programming (LP) problems under current constraints, enable worst-case voltage drop predictions at an early design stage. However, worst-case current patterns obtained by many existing vectorless algorithms are time-invariant (i.e., are constant throughout the simulation time), which may result in an overly pessimistic voltage drop prediction. In this paper, a more realistic power grid verification algorithm based on hierarchical current and power constraints is proposed. The proposed algorithm naturally handles general RCL power grid models. Currents at different time steps are treated as independent variables and additional power constraints are introduced; this results in more realistic time-varying worst-case current patterns and less pessimistic worst-case voltage drop predictions. Moreover, a sorting-deletion algorithm is proposed to speed up solving LP problems by utilizing the hierarchical constraint structure. Experimental results confirm that worst-case current patterns and voltage drops obtained by the proposed algorithm are more realistic, and that the sorting-deletion algorithm reduces runtime needed to solve LP problems by 85%.

Multitime partial differential equations (MPDEs) provide an efficient method to simulate circuits with widely separated rates of inputs. This paper proposes a fast and accurate frequency-domain multitime transient analysis method for MPDE systems, which fills in the gap for the lack of general frequency-domain solver for MPDE systems. A block-pulse function-based multidimensional inverse Laplace transform strategy is adopted. The method can be applied to discrete input systems. Numerical examples then confirm its superior accuracy, under similar efficiency, over time-domain solvers.

Numerous algorithms to macromodel a linear time-invariant (LTI) system from its frequency-domain sampling data have been proposed in recent years [1, 2, 3, 4, 5, 6, 7, 8], among which Loewner matrix-based tangential interpolation proves to be especially suitable for modeling massive-port systems [6, 7, 8]. However, the existing Loewner matrix-based method follows vector-format tangential interpolation (VFTI), which fails to explore all the information contained in the frequency samples. In this paper, a novel matrix-format tangential interpolation (MFTI) is proposed, which requires much fewer samples to recover the system and yields better accuracy when handling under-sampled, noisy and/or ill-conditioned data. A recursive version of MFTI is proposed to further reduce the computational complexity. Numerical examples then confirm the superiority of MFTI over VFTI.

Passivity is a crucial property of macromodels to guarantee stable global (interconnected) simulation. However, weakly nonpassive models may be generated for passive circuits and systems in various contexts, such as data fitting, model order reduction (MOR) and electromagnetic (EM) macromodeling. Therefore, a post-processing passivity enforcement algorithm is desired. Most existing algorithms are designed to handle pole-residue models. The few algorithms for state space models only handle regular systems (RSs) with a nonsingular D+DTterm. To the authors' best knowledge, no algorithm has been proposed to enforce passivity for more general descriptor systems (DSs) and state space models with singular D+DTterms. In this paper, a new post-processing passivity enforcement algorithm based on perturbation of Hamiltonian-symplectic matrix pencil, PEDS, is proposed. PEDS, for the first time, can enforce passivity for DSs. It can also handle all kinds of state space models (both RSs and DSs) with singular D+DTterms. Moreover, a criterion to control the error of perturbation is devised, with which the optimal passive models with the best accuracy can be obtained. Numerical examples then verify that PEDS is efficient, robust and relatively cheap for passivity enforcement of DSs with mild passivity violations.