pwc-archive/papers-with-abstracts huggingface dataset:
VeRecycle: Reclaiming Guarantees from Probabilistic Certificates for Stochastic Dynamical Systems after Change
Authors: Sterre Lutz, Matthijs T. J. Spaan, Anna Lukina
Date: 2025-05-20 00:00:00

Autonomous systems operating in the real world encounter a range of uncertainties. Probabilistic neural Lyapunov certification is a powerful approach to proving safety of nonlinear stochastic dynamical systems. When faced with changes beyond the modeled uncertainties, e.g., unidentified obstacles, probabilistic certificates must be transferred to the new system dynamics. However, even when the changes are localized in a known part of the state space, state-of-the-art requires complete re-certification, which is particularly costly for neural certificates. We introduce VeRecycle, the first framework to formally reclaim guarantees for discrete-time stochastic dynamical systems. VeRecycle efficiently reuses probabilistic certificates when the system dynamics deviate only in a given subset of states. We present a general theoretical justification and algorithmic implementation. Our experimental evaluation shows scenarios where VeRecycle both saves significant computational effort and achieves competitive probabilistic guarantees in compositional neural control.
On the Input-Output Monotonicity of Voltage Dynamics of Power System with Grid-Forming Converters
Authors: Zhenyao Li, Shengwen Liao, Qian Zhang, Xuechun Zhang, Deqiang Gan
Date: 2025-05-20 00:00:00

Integration of renewable resources is profoundly reshaping the dynamics of modern power systems. This study shows that the voltage dynamics of power systems with multiple grid-forming (GFM) converters often enjoys a desirable property called input-output monotonicity. A systematic approach for computing the derivatives of the voltage subsystem is presented first, which provides insight into the structural characteristics of these models. Next, the sign pattern of the trajectory Jacobian matrix associated with the voltage subsystem is analyzed and revealed. The analysis indicates that the voltage dynamics of power systems often exhibits the so-called input-output monotonicity property. The theoretical results are then validated through several simulation examples, underscoring their practical implications.
Incremental Firmware Update Over-the-Air for Low-Power IoT Devices over LoRaWAN
Authors: Andrea De Simone, Giovanna Turvani, Fabrizio Riente
Date: 2025-05-19 00:00:00

Efficiently supporting remote firmware updates in Internet of Things (IoT) devices remains a significant challenge due to the limitations of many IoT communication protocols, which often make it impractical to transmit full firmware images. Techniques such as firmware partitioning have been introduced to mitigate this issue, but they frequently fall short, especially in battery-powered systems where time and energy constraints are critical. As a result, physical maintenance interventions are still commonly required, which is costly and inconvenient in large-scale deployments. In this work, we present a lightweight and innovative method that addresses this challenge by generating highly compact delta patches, enabling firmware reconstruction directly on the device. Our algorithm is specifically optimized for low-power devices, minimizing both memory usage and computational overhead. Compared to existing solutions, our approach significantly reduces the data volume needed for updates while maintaining performance comparable to more complex alternatives. Experimental evaluations confirm that our method yields substantial time and energy savings, making it particularly well-suited for battery-powered IoT nodes. Although our implementation targets the LoRaWAN protocol, the approach is flexible and can be adapted to other IoT communication technologies.
Reduced Muscle Fatigue Using Continuous Subthreshold Kilohertz Stimulation of Peripheral Nerves
Authors: Long Meng, Paola Terolli, Xiaogang Hu
Date: 2025-05-19 00:00:00

Functional electrical stimulation (FES) is a prevalent technique commonly used to activate muscles in individuals with neurological disorders. Traditional FES strategies predominantly utilize low-frequency (LF) stimulation, which evokes synchronous action potentials, leading to rapid muscle fatigue. To address these limitations, we introduced a subthreshold high-frequency (HF) stimulation method that employed continuous, charge-balanced subthreshold current pulses at kilohertz frequencies, designed to evoke motor unit (MU) activation similar to voluntary activation. We evaluated the effectiveness of HF stimulation on the reduction of muscle fatigue across different force levels (10 %, 25 %, and 40 % of maximum force). The HF stimulation utilized continuous charge-balanced, short pulses of 80 {\mu}s (at a 10 kHz frequency) targeted the ulnar/median nerve bundles. We compared the fatigue effects with conventional LF stimulation and voluntary muscle contractions. Our results indicated that HF stimulation maintained more sustained force outputs and muscle activation over a prolonged time compared with LF stimulation. The HF stimulation also evoked a more dispersed muscle activation pattern, similar to voluntary muscle contractions. These findings suggest that HF stimulation can significantly enhance the sustainability of muscle contractions and reduce muscle fatigue, potentially improving the efficacy and applicability of FES in clinical and home-based settings for individuals with neurological impairments.
An Empirical Bayes approach to ARX Estimation
Authors: Timofei Leahu, Giorgio Picci
Date: 2025-05-19 00:00:00

Empirical Bayes inference is based on estimation of the parameters of an a priori distribution from the observed data. The estimation technique of the parameters of the prior, called hyperparameters, is based on the marginal distribution obtained by integrating the joint density of the model with respect to the prior. This is a key step which needs to be properly adapted to the problem at hand. In this paper we study Empirical Bayes inference of linear autoregressive models with inputs (ARX models) for time series and compare the performance of the marginal parametric estimator with that a full Empirical Bayesian analysis based on the estimated prior. Such a comparison, can only make sense for a (realistic) finite data length. In this setting, we propose a new estimation technique of the hyperparameters by a sequential Bayes procedure which is essentially a backward Kalman filter. It turns out that for finite data length the marginal Bayes tends to behave slightly better than the full Empirical Bayesian parameter estimator and so also in the case of slowly varying random parameters.
Frequency-Dependent Power Consumption Modeling of CMOS Transmitters for WNoC Architectures
Authors: Mohammad Shahmoradi, Korkut Kaan Tokgöz, Eduard Alarcón, Sergi Abadal
Date: 2025-05-19 00:00:00

Wireless Network-on-Chip (WNoC) systems, which wirelessly interconnect the chips of a computing system, have been proposed as a complement to existing chip-to-chip wired links. However, their feasibility depends on the availability of custom-designed high-speed, tiny, ultra-efficient transceivers. This represents a challenge due to the tradeoffs between bandwidth, area, and energy efficiency that are found as frequency increases, which suggests that there is an optimal frequency region. To aid in the search for such an optimal design point, this paper presents a behavioral model that quantifies the expected power consumption of oscillators, mixers, and power amplifiers as a function of frequency. The model is built on extensive surveys of the respective sub-blocks, all based on experimental data. By putting together the models of the three sub-blocks, a comprehensive power model is obtained, which will aid in selecting the optimal operating frequency for WNoC systems.
Output behavior equivalence and simultaneous subspace identification of systems and faults
Authors: Gabriel de Albuquerque Gleizer
Date: 2025-05-19 00:00:00
Tasks: subspace methods

We address the problem of identifying a system subject to additive faults, while simultaneously reconstructing the fault signal via subspace methods. We do not require nominal data for the identification, neither do we impose any assumption on the class of faults, e.g., sensor or actuator faults. We show that, under mild assumptions on the fault signal, standard PI-MOESP can recover the system matrices associated to the input-output subsystem. Then we introduce the concept of output behavior equivalence, which characterizes systems with the same output behavior set, and present a method to establish this equivalence from system matrices. Finally, we show how to estimate from data the complete set of fault matrices for which there exist a fault signal with minimal dimension that explains the data.
Low-regret Strategies for Energy Systems Planning in a Highly Uncertain Future
Authors: Gabriel Wiest, Niklas Nolzen, Florian Baader, André Bardow, Stefano Moret
Date: 2025-05-19 00:00:00
Tasks: Decision Making

Large uncertainties in the energy transition urge decision-makers to develop low-regret strategies, i.e., strategies that perform well regardless of how the future unfolds. To address this challenge, we introduce a decision-support framework that identifies low-regret strategies in energy system planning under uncertainty. Our framework (i) automatically identifies strategies, (ii) evaluates their performance in terms of regret, (iii) assesses the key drivers of regret, and (iv) supports the decision process with intuitive decision trees, regret curves and decision maps. We apply the framework to evaluate the optimal use of biomass in the transition to net-zero energy systems, considering all major biomass utilization options: biofuels, biomethane, chemicals, hydrogen, biochar, electricity, and heat. Producing fuels and chemicals from biomass performs best across various decision-making criteria. In contrast, the current use of biomass, mainly for low-temperature heat supply, results in high regret, making it a must-avoid in the energy transition.
RSS-Based Localization: Ensuring Consistency and Asymptotic Efficiency
Authors: Shenghua Hu, Guangyang Zeng, Wenchao Xue, Haitao Fang, Junfeng Wu, Biqiang Mu
Date: 2025-05-19 00:00:00

We study the problem of signal source localization using received signal strength measurements. We begin by presenting verifiable geometric conditions for sensor deployment that ensure the model's asymptotic localizability. Then we establish the consistency and asymptotic efficiency of the maximum likelihood (ML) estimator. However, computing the ML estimator is challenging due to its reliance on solving a non-convex optimization problem. To overcome this, we propose a two-step estimator that retains the same asymptotic properties as the ML estimator while offering low computational complexity, linear in the number of measurements. The main challenge lies in obtaining a consistent estimator in the first step. To address this, we construct two linear least-squares estimation problems by applying algebraic transformations to the nonlinear measurement model, leading to closed-form solutions. In the second step, we perform a single Gauss-Newton iteration using the consistent estimator from the first step as the initialization, achieving the same asymptotic efficiency as the ML estimator. Finally, simulation results validate the theoretical property and practical effectiveness of the proposed two-step estimator.
Regularized Model Predictive Control
Authors: Komeil Nosrati, Juri Belikov, Aleksei Tepljakov, Eduard Petlenkov
Date: 2025-05-19 00:00:00
Tasks: model, Model Predictive Control

In model predictive control (MPC), the choice of cost-weighting matrices and designing the Hessian matrix directly affects the trade-off between rapid state regulation and minimizing the control effort. However, traditional MPC in quadratic programming relies on fixed design matrices across the entire horizon, which can lead to suboptimal performance. This letter presents a Riccati equation-based method for adjusting the design matrix within the MPC framework, which enhances real-time performance. We employ a penalized least-squares (PLS) approach to derive a quadratic cost function for a discrete-time linear system over a finite prediction horizon. Using the method of weighting and enforcing the constraint equation by introducing a large penalty parameter, we solve the constrained optimization problem and generate control inputs for forward-shifted horizons. This process yields a recursive PLS-based Riccati equation that updates the design matrix as a regularization term in each shift, forming the foundation of the regularized MPC (Re-MPC) algorithm. To accomplish this, we provide a convergence and stability analysis of the developed algorithm. Numerical analysis demonstrates its superiority over traditional methods by allowing Riccati equation-based adjustments.
6G-Enabled Smart Railways
Authors: Bo Ai, Yunlong Lu, Yuguang Fang, Dusit Niyato, Ruisi He, Wei Chen, Jiayi Zhang, Guoyu Ma, Yong Niu, Zhangdui Zhong
Date: 2025-05-19 00:00:00

Smart railways integrate advanced information technologies into railway operating systems to improve efficiency and reliability. Although the development of 5G has enhanced railway services, future smart railways require ultra-high speeds, ultra-low latency, ultra-high security, full coverage, and ultra-high positioning accuracy, which 5G cannot fully meet. Therefore, 6G is envisioned to provide green and efficient all-day operations, strong information security, fully automatic driving, and low-cost intelligent maintenance. To achieve these requirements, we propose an integrated network architecture leveraging communications, computing, edge intelligence, and caching in railway systems. We have conducted in-depth investigations on key enabling technologies for reliable transmissions and wireless coverage. For high-speed mobile scenarios, we propose an AI-enabled cross-domain channel modeling and orthogonal time-frequency space-time spread multiple access mechanism to alleviate the conflict between limited spectrum availability and massive user access. The roles of blockchain, edge intelligence, and privacy technologies in endogenously secure rail communications are also evaluated. We further explore the application of emerging paradigms such as integrated sensing and communications, AI-assisted Internet of Things, semantic communications, and digital twin networks for railway maintenance, monitoring, prediction, and accident warning. Finally, possible future research and development directions are discussed.
Scheduling of Flexible Manufacturing Systems Based on Place-Timed Petri Nets and Basis Reachability Graphs
Authors: Zhou He, Ning li, Ning Ran, Liang Li
Date: 2025-05-19 00:00:00
Tasks: Scheduling

Scheduling is a key decision-making process to improve the performance of flexible manufacturing systems. Place-timed Petri nets provide a formal method for graphically modeling and analyzing such systems. By generating reachability graphs and combining intelligent search algorithms, operation sequences from the initial state to the target state can be found for the underlying system. However, the reachability graph grows exponentially with the system size increases, which is the main challenge of existing methods for scheduling large systems. To this end, we develop an efficient improved beam search algorithm to optimize the makespan based on a compact representation of reachability graph called basis reachability graph. The key idea behind the proposed method is to form a state together with the basis markings and its corresponding transition sequences, and evaluate the cost of the state based on the resource idle time. Experimental results are conducted on several benchmark systems which show that the developed method improves the search efficiency while ensuring the quality of the solution compared with existing methods.
Connecting the Equinoctial Elements and Rodrigues Parameters: A New Set of Elements
Authors: Joseph T. A. Peterson, Vishala Arya, John L. Junkins
Date: 2025-05-19 00:00:00

A geometric interpretation of the equinoctial elements is given with a connection to orthogonal rotations and attitude dynamics in Euclidean 3-space. An identification is made between the equinoctial elements and classic Rodrigues parameters. A new set of equinoctial elements are developed using the modified Rodrigues parameters, thereby removing the coordinate singularity for retrograde equatorial orbits present in previous versions of these elements. A low-thrust trajectory optimization problem is set up using the new elements to numerically verify convergence for the two-point boundary problem, as compared to their predecessors.
Secrecy Capacity of Hybrid VLC-RF Systems with Light Energy Harvesting
Authors: Tuan A. Hoang, Thanh V. Pham, Chuyen T. Nguyen
Date: 2025-05-19 00:00:00

This paper studies the performance of physical layer security (PLS) in a multi-user hybrid heterogeneous visible light communication (VLC) and radio frequency (RF) wireless communication system with simultaneous lightwave information and power transfer (SLIPT). In the considered system, VLC is used for downlink (DL) while RF is employed for uplink (UL) transmission. In addition, to support multiple users, time division multiple access (TDMA) is assumed for both DL and UL channels. In the DL, each user receives information during its allocated time slot of the TDMA frame and harvests energy from the received signal outside the time slot. The harvested energy is then used for transmitting the signal over the UL channel, which is subject to eavesdropping by an unauthorized user. Therefore, PLS is employed to protect the confidentiality of the UL information. Then, an optimization problem is formulated to solve the optimal DL and UL time slots that maximize the PLS performance given a target sum rate of the DL. We show that the problem can be cast as a difference of convex functions (DC) program, which can be solved efficiently using the DC algorithm (DCA).
A Control Oriented Fractional-Order Model of Lithium-ion Batteries Based on Caputo Definition
Authors: Yangyang Xu, Hongyu Zhao, Chengzhong Zhang, Chenglin Liao
Date: 2025-05-19 00:00:00
Tasks: Computational Efficiency, Management

This letter proposes a fractional-order battery model based on the Caputo definition. A closed-form time-domain solution is derived, enabling a simple recursive expression for discrete-time implementation. The model requires only the current and previous time-step states in each iteration, significantly reducing memory usage compared to the conventional Gr\"{u}nwald--Letnikov (G-L) method. This recursive structure is highly compatible with filter design and online parameter identification. Experimental validation on a 40.2~Ah NCM622 cell shows that the proposed first-order model achieves voltage prediction accuracy comparable to a second-order integer-order model. The results demonstrate that the Caputo-based model offers a practical balance between accuracy and computational efficiency, making it well suited for real-time battery management systems (BMS).
Optimal Task and Motion Planning for Autonomous Systems Using Petri Nets
Authors: Zhou He, Shilong Yuan, Ning Ran, Dimitri Lefebvre
Date: 2025-05-18 00:00:00
Tasks: Computational Efficiency, Motion Planning, Task and Motion Planning

This study deals with the problem of task and motion planning of autonomous systems within the context of high-level tasks. Specifically, a task comprises logical requirements (conjunctions, disjunctions, and negations) on the trajectories and final states of agents in certain regions of interest. We propose an optimal planning approach that combines offline computation and online planning. First, a simplified Petri net system is proposed to model the autonomous system. Then, indicating places are designed to implement the logical requirements of the specifications. Building upon this, a compact representation of the state space called extended basis reachability graph is constructed and an efficient online planning algorithm is developed to obtain the optimal plan. It is shown that the most burdensome part of the planning procedure may be removed offline, thanks to the construction of the extended basis reachability graph. Finally, series of simulations are conducted to demonstrate the computational efficiency and scalability of our developed method.
Time-Continuous Frequency Allocation for Feeder Links of Mega Constellations with Multi-Antenna Gateway Stations
Authors: Zijun Liu, Yafei Wang, Tianhao Fang, Wenjin Wang, Zhili Sun
Date: 2025-05-18 00:00:00

With the recent rapid advancement of mega low earth orbit (LEO) satellite constellations, multi-antenna gateway station (MAGS) has emerged as a key enabler to support extremely high system capacity via massive feeder links. However, the densification of both space and ground segment leads to reduced spatial separation between links, posing unprecedented challenges of interference exacerbation. This paper investigates graph coloring-based frequency allocation methods for interference mitigation (IM) of mega LEO systems. We first reveal the characteristics of MAGS interference pattern and formulate the IM problem into a $K$-coloring problem using an adaptive threshold method. Then we propose two tailored graph coloring algorithms, namely Generalized Global (GG) and Clique-Based Tabu Search (CTS), to solve this problem. GG employs a low-complexity greedy conflict avoidance strategy, while CTS leverages the unique clique structure brought by MAGSs to enhance IM performance. Subsequently, we innovatively modify them to achieve time-continuous frequency allocation, which is crucial to ensure the stability of feeder links. Moreover, we further devise two mega constellation decomposition methods to alleviate the complexity burden of satellite operators. Finally, we propose a list coloring-based vacant subchannel utilization method to further improve spectrum efficiency and system capacity. Simulation results on Starlink constellation of the first and second generations with 34396 satellites demonstrate the effectiveness and superiority of the proposed methodology.
Data-Efficient Automatic Shaping of Liquid Droplets on an Air-Ferrofluid Interface with Bayesian Optimization
Authors: P. A. Diluka Harischandra, Quan Zhou
Date: 2025-05-18 00:00:00
Tasks: Bayesian Optimization, Concave shapes

Manipulating the shape of a liquid droplet is essential for a wide range of applications in medicine and industry. However, existing methods are typically limited to generating simple shapes, such as ellipses, or rely on predefined templates. Although recent approaches have demonstrated more complex geometries, they remain constrained by limited adaptability and lack of real-time control. Here, we introduce a data-efficient method that enables real-time, programmable shaping of nonmagnetic liquid droplets into diverse target forms at the air-ferrofluid interface using Bayesian optimization. The droplet can adopt either convex or concave shapes depending on the actuation of the surrounding electromagnets. Bayesian optimization determines the optimal magnetic flux density for shaping the liquid droplet into a desired target shape. Our method enables automatic shaping into various triangular and rectangular shapes with a maximum shape error of 0.81 mm, as well as into letter-like patterns. To the best of our knowledge, this is the first demonstration of real-time, automatic shaping of nonmagnetic liquid droplets into desired target shapes using magnetic fields or other external energy fields.