Systems and Control
Understanding Regional Inertia Dynamics in CAISO from Real Grid Disturbances
Saurav Dulal, Mohammed M. Olama, Ali R. Ekti et al. (+2 more)
The shift from synchronous generators to inverter-based resources has caused power system inertia to be unevenly distributed across power grids. As a result, certain grid regions are more vulnerable to high rate-of-change of frequency (RoCoF) during disturbances. This paper presents a measurement-based framework for estimating grid inertia in CAISO (California Independent System Operator) region using real disturbance-driven frequency data from the Frequency Monitoring Network (FNET/GridEye). By analyzing confirmed disturbances from 2013 to 2024, we identify trends in regional inertia and frequency dynamics, highlighting their relationship with renewable generation and the evolving duck curve. Regional RoCoF values were up to six times higher than interconnection-wide values, coinciding with declining inertia. Recent recovery in inertia is attributed to the increased deployment of battery energy storage systems with synthetic inertia capabilities. These findings underscore the importance of regional inertia monitoring, strategic resource planning, and adaptive operational practices to ensure grid reliability amid growing renewable integration.
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All authors Saurav Dulal, Mohammed M. Olama, Ali R. Ekti, Nils M. Stenvig, Yilu Liu
Systems and Control
Influence of converter current limiting and prioritization on protection of highly IBR-penetrated networks
Andrés E. Quintero, Vinícius A. Lacerda, Oriol Gomis-Bellmunt et al. (+2 more)
This paper investigates how grid-forming (GFM) and grid-following (GFL) control strategies in inverter-based resources (IBRs) influence line distance and differential protection in converter-dominated transmission systems. A modified IEEE 39-bus system is evaluated with GFM and GFL units equipped with low-voltage ride-through logic, current limiting, and positive- or negative-sequence prioritization. Distance protection is implemented with a mho characteristic, while line differential protection uses an alpha-plane approach. Results show that phase-to-ground loops in distance protection can substantially overestimate the fault location near the Zone-1 reach. For line differential protection, external faults may cause the operating point to briefly enter the trip region of the alpha-plane, even for the healthy-phase in ABG faults under GFL control and during the initial moments of the fault, demanding strong external security measures. These findings highlight that modern converter controls, together with current limitation and sequence-current prioritization, can compromise the reliability and security of traditional protection schemes.
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All authors Andrés E. Quintero, Vinícius A. Lacerda, Oriol Gomis-Bellmunt, Moisés J. B. B. Davi, Mario Oleskovicz
Systems and Control
Evaluation of Large Language Models for Numeric Anomaly Detection in Power Systems
Yichen Liu, Hongyu Wu and Bo Liu
Large
language models (LLMs) have gained increasing
attention in power grids for their general-purpose capabilities. Meanwhile, anomaly detection (AD) remains critical for grid resilience, requiring accurate and interpretable decisions based on multivariate telemetry. Yet the performance of LLMs on large-scale numeric data for AD remains largely unexplored. This paper presents a comprehensive evaluation of LLMs for numeric AD in power systems. We use GPT-OSS-20B as a representative model and evaluate it on the IEEE 14-bus system. A standardized
prompt framework is applied across zero-shot, few-shot, in-context learning, low rank adaptation (LoRA), fine-tuning, and a hybrid LLM-traditional approach. We adopt a rule-aware design based on the three-sigma criterion, and report detection performance and rationale quality. This study lays the groundwork for further investigation into the limitations and capabilities of LLM-based AD and its integration with classical detectors in cyber-physical power grid applications.
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Signal Processing
Blind Turbo Demodulation for Differentially Encoded OFDM with 2D Trellis Decomposition
Chin-Hung Chen, Yan Wu, Wim van Houtum et al. (+1 more)
Digital Audio Broadcasting (DAB)-like systems employ differentially encoded (DE) phase-shift keying (PSK) for transmission. While turbo-DE-PSK receivers offer substantial performance gains through iterative decoding by making the DE-PSK an inner code, they rely on accurate channel estimation without pilots, which is a key challenge in DAB-like scenarios. This paper develops a fully blind turbo-DE-PSK scheme that jointly estimates channel phase, channel gain, and noise variance directly from the received signal. The design leverages a two-dimensional (2D) trellis decomposition for blind phase estimation, complemented by power-based
estimators for channel gain and noise variance. We provide a comprehensive system assessment across practical system parameters, including inner code length, phase quantization, and 2D block size. Simulation results show that the blind 2D turbo demodulator approaches the performance of receivers with perfect channel knowledge and remains robust under realistic transmission conditions.
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All authors Chin-Hung Chen, Yan Wu, Wim van Houtum, Alex Alvarado
Systems and Control
Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models
Laura Boca de Giuli, Samuel Mallick, Alessio La Bella et al. (+3 more)
This paper presents a model predictive control (MPC) framework leveraging an ensemble of data-based models to optimally control complex systems under multiple operating conditions. A novel combination rule for ensemble models is proposed, based on the statistical Mahalanobis distance, enabling the ensemble weights to suitably vary across the prediction window based on the system input. In addition, a novel state observer for ensemble models is developed using moving horizon estimation (MHE). The effectiveness of the proposed methodology is demonstrated on a
benchmark energy system operating under multiple conditions.
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All authors Laura Boca de Giuli, Samuel Mallick, Alessio La Bella, Azita Dabiri, Bart De Schutter, Riccardo Scattolini
Signal Processing
Phase-Aware Code-Aided EM Algorithm for Blind Channel Estimation in PSK-Modulated OFDM
Chin-Hung Chen, Ivana Nikoloska, Wim van Houtum et al. (+2 more)
This paper presents a fully blind phase-aware expectation-maximization (EM)
algorithm for OFDM systems with the phase-shift keying (PSK) modulation. We address the well-known local maximum problem of the EM
algorithm for blind channel estimation. This is primarily caused by the unknown phase ambiguity in the channel estimates, which conventional blind EM
estimators cannot resolve. To overcome this limitation, we propose to exploit the extrinsic information from the decoder as model evidence metrics. A finite set of candidate models is generated based on the inherent
symmetries of PSK modulation, and the decoder selects the most likely candidate model. Simulation results demonstrate that, when combined with a simple convolutional code, the phase-aware EM
algorithm reliably resolves phase ambiguity during the initialization stage and reduces the local convergence rate from 80% to nearly 0% in frequency-selective channels with a constant phase ambiguity. The
algorithm is invoked only once after the EM initialization stage, resulting in negligible additional complexity during subsequent turbo iterations.
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All authors Chin-Hung Chen, Ivana Nikoloska, Wim van Houtum, Yan Wu, Alex Alvarado
Systems and Control · Cross-listed from econ.TH
The Theory of Storage in a Power System with Stochastic Demand
Darryl Biggar and Mohammad Reza Hesamzadeh
Electric power systems are increasingly turning to energy storage systems to balance supply and demand. But how much storage is required? What is the optimal volume of storage in a power system and on what does it depend? In addition, what form of hedge contracts do storage facilities require? We answer these questions in the special case in which the uncertainty in the power system involves successive draws of an independent, identically-distributed random variable. We characterize the conditions for the optimal operation of, and investment in, storage and show how these conditions can be understood graphically using price-duration curves. We also characterize the optimal hedge contracts for storage units.
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Systems and Control
Analytical Phasor-Based Fault Location Enhancement for Wind Farm Collector Networks
Alailton J. Alves Junior, Daniel Barbosa, Ricardo A. S. Fernandes et al. (+1 more)
The increasing integration of Inverter-Based Resources (IBRs) is reshaping fault current characteristics, presenting significant challenges to traditional protection and fault location methods. This paper addresses a key limitation in fault location within wind farm collector networks, i.e., one-terminal phasor-based methods become inaccurate when IBRs are electrically located downstream from the fault. In such cases, the voltage drop caused by IBR fault current injections is not captured by the Intelligent Electronic Device, resulting in a systematic overestimation of fault distance. To mitigate this issue, a general compensation framework was proposed by augmenting classical loop formulations with a distance-dependent voltage correction term. The methodology was derived analytically using a sequence-domain representation and generalized to multiple fault types through a unified notation. It maintains the simplicity and interpretability of conventional approaches and can be implemented using only local measurements. The method was evaluated through EMT simulations in PSCAD using a realistic wind farm model. Results show significant improvements in location accuracy, with average and maximum errors notably reduced, especially for ground-involved faults where reductions exceed 90%. Furthermore, the compensation eliminates sensitivity to wind penetration levels and ensures uniform performance across feeders, positioning the method as a practical solution for modern renewable-dominated grids.
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All authors Alailton J. Alves Junior, Daniel Barbosa, Ricardo A. S. Fernandes, Denis V. Coury
Systems and Control
Scalable Multisubject Vital Sign Monitoring With mmWave FMCW Radar and FPGA Prototyping
Jewel Benny, Narahari N. Moudhgalya, Mujeev Khan et al. (+3 more)
In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a non-contact way using a Frequency Modulated Continuous Wave (FMCW) radar-based system. Traditional vital sign monitoring methods often face significant limitations, including subject discomfort with wearable devices, challenges in calibration, and the risk of infection transmission through contact measurement devices. To address these issues, this research is motivated by the need for versatile, non-contact vital monitoring solutions applicable in various critical scenarios. This work also explores the challenges of extending this capability to an arbitrary number of subjects, including hardware and theoretical limitations. Supported by rigorous experimental results and discussions, the paper illustrates the system's potential to redefine vital sign monitoring. An FPGA-based implementation is also presented as proof of concept for a hardware-based and portable solution, improving upon previous works by offering 2.7x faster execution and 18.4% less Look-Up Table (LUT) utilization, as well as providing over 7400x acceleration compared to its software counterpart.
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All authors Jewel Benny, Narahari N. Moudhgalya, Mujeev Khan, Hemant Kumar Meena, Mohd Wajid, Abhishek Srivastava
Audio and Speech Processing · Cross-listed from cs.SD
Acoustic neural networks: Identifying design principles and exploring physical feasibility
Ivan Kalthoff, Marcel Rey and Raphael Wittkowski
Wave-guide-based physical systems provide a promising route toward energy-efficient analog computing beyond traditional electronics. Within this landscape, acoustic neural networks represent a promising approach for achieving low-power computation in environments where electronics are inefficient or limited, yet their systematic design has remained largely unexplored. Here we introduce a framework for designing and simulating acoustic neural networks, which perform computation through the propagation of sound waves. Using a digital-twin approach, we train conventional neural network architectures under physically motivated constraints including non-negative signals and weights, the absence of bias terms, and nonlinearities compatible with intensity-based, non-negative acoustic signals. Our work provides a general framework for acoustic neural networks that connects learnable network components directly to physically measurable acoustic properties, enabling the systematic design of realizable acoustic computing systems. We demonstrate that constrained recurrent and hierarchical architectures can perform accurate
speech classification, and we propose the SincHSRNN, a hybrid model that combines learnable acoustic bandpass filters with hierarchical temporal processing. The SincHSRNN achieves up to 95% accuracy on the AudioMNIST
dataset while remaining compatible with passive acoustic components. Beyond computational performance, the learned parameters correspond to measurable material and
geometric properties such as attenuation and transmission. Our results establish general design principles for physically realizable acoustic neural networks and outline a pathway toward low-power, wave-based neural computing.
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Systems and Control
Design and Performance Assessment of a Virtualized IED for Digital Substations
Alailton J. Alves Junior, Denis V. Coury and Ricardo A. S. Fernandes
Digital substations have significantly enhanced power grid protection by replacing traditional copper wiring with fiber-optic communication and integrating IEC 61850-compliant Intelligent Electronic Devices (IEDs), resulting in greater efficiency, reliability, and interoperability. While these advancements provide improved interoperability, challenges such as high costs, complex networks, and limited upgradeability persist. To mitigate these issues, the virtualization of IEDs has emerged as a cost-effective solution, offering scalability, simplified maintenance, and reduced hardware costs by replacing traditional hardware-based IEDs with software-based counterparts. However, the performance and reliability of virtual IEDs (vIED) must be rigorously evaluated to ensure their robustness in real-time applications. This paper develops, implements, and evaluates a vIED designed to match the performance of its hardware-based counterparts. The vIED was deployed on a server using virtual machines, with its core logic implemented in low-level programming
languages to ensure high-speed, deterministic behavior. The performance was evaluated using real-time simulations, focusing on the response times of the protection functions. The results demonstrated that vIEDs achieved acceptable response times, validating their suitability for deployment in critical time-sensitive environments within digital substations.
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Systems and Control
Sparse shepherding control of large-scale multi-agent systems via Reinforcement Learning
Luigi Catello, Italo Napolitano, Davide Salzano et al. (+1 more)
We propose a Reinforcement Learning framework for sparse indirect control of large-scale multi-
agent systems, where few controlled
agents shape the collective behavior of many uncontrolled
agents. The approach addresses this multi-scale challenge by coupling ODEs (modeling controlled
agents) with a PDE (describing the uncontrolled population density), capturing how microscopic control achieves macroscopic objectives. Our method combines model-free Reinforcement Learning with adaptive interaction strength compensation to overcome sparse actuation limitations. Numerical validation demonstrates effective density control, with the system achieving target distributions while maintaining robustness to disturbances and measurement noise, confirming that learning-based sparse control can replace computationally expensive online optimization.
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All authors Luigi Catello, Italo Napolitano, Davide Salzano, Mario di Bernardo
Systems and Control
Data-Driven Reduction of Fault Location Errors in Onshore Wind Farm Collectors
A. J. Alves Junior, M. J. B. B. Davi, R. A. S. Fernandes et al. (+2 more)
Accurate fault location is essential for operational reliability and fast restoration in wind farm collector networks. However, the growing integration of inverter-based resources changes the current and voltage behavior during faults, challenging the effectiveness of traditional phasor-based diagnostic methods. In this context, the present paper introduces an advanced machine-learning solution that enhances a deterministic fault distance
estimator by incorporating a correction model driven by a Gated Residual Network, specifically designed to minimize residual fault location errors. Through comprehensive feature engineering and selection processes, an improved predictor was developed and trained on a diverse set of fault scenarios simulated in a PSCAD-based real-world wind farm model, including variations in fault type, resistance, location, inception angle, and generation penetration. Hyperparameter optimization was performed using the Optuna framework, and the robustness of the method was statistically validated. Results show a significant improvement in accuracy, with a 76% overall decrease in fault location error compared to state-of-the-art approaches. The proposed method demonstrates strong scalability and adaptability to topological and operational changes. This approach advances the deployment of data-driven fault location frameworks for modern power systems.
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All authors A. J. Alves Junior, M. J. B. B. Davi, R. A. S. Fernandes, M. Oleskovicz, D. V. Coury
Signal Processing
Multiport Analytical Pixel Electromagnetic Simulator (MAPES) for AI-assisted RFIC and Microwave Circuit Design
Junhui Rao, Yi Liu, Jichen Zhang et al. (+6 more)
This paper proposes a novel analytical framework, denoted the Multiport Analytical Pixel Electromagnetic Simulator (MAPES). MAPES enables efficient and accurate prediction of the electromagnetic (EM) performance of arbitrary pixel-based microwave (MW) and RFIC structures. Unlike the Internal Multiport Method (IMPM), which optimizes only connecting elements within a fixed, gap-separated pixel skeleton, MAPES operates directly on the all-pixel presence/absence formulation used in recent MW/RFIC design. This is enabled by diagonal virtual pixels, an occupancy-to-load mapping, and a multi-layer/via port-level formulation that have no counterpart in IMPM. By introducing virtual pixels and diagonal virtual pixels and inserting virtual ports at critical positions, MAPES captures all horizontal, vertical, and diagonal electromagnetic couplings within a single multiport impedance
matrix. Only a small set of full-wave simulations (typically about 1% of the
datasets required by AI-assisted EM emulators) is needed to construct this
matrix. Subsequently, any arbitrary pixel configuration can be evaluated analytically using a closed-form multiport relation without additional full-wave calculations. The proposed approach eliminates data-driven overfitting and ensures accurate results across all design variations. Using MAPES, comprehensive examples for single- and double-layer PCBs and CMOS processes (180 nm and 65 nm) confirm that high prediction accuracy with 600-2000$\times$ speed improvement is achieved compared to CST simulations. Owing to its efficiency, scalability, and reliability, MAPES provides a practical and versatile tool for AI-assisted MW
circuit and RFIC design across diverse fabrication technologies.
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All authors Junhui Rao, Yi Liu, Jichen Zhang, Zhaoyang Ming, Tianrui Qiao, Yujie Zhang, Chi Yuk Chiu, Hua Wang, Ross Murch
Systems and Control
Respiratory Motion Compensation and Haptic Feedback for X-ray-Guided Teleoperated Robotic Needle Insertion
Ana Cordon-Avila, Mostafa Selim and Momen Abayazid
Respiratory motion limits the accuracy and precision of abdominal percutaneous procedures. In this paper, respiratory motion is compensated robotically using motion estimation models. Additionally, a teleoperated insertion is performed using proximity-based haptic feedback to guide physicians during insertion, enabling a radiation-free remote insertion for the end-user. The study has been validated using a robotic liver phantom, and five insertions were performed. The resulting motion estimation errors were below 3 mm for all directions of motion, and the overall resulting 3D insertion errors were 2.60, 7.75, and 2.86 mm for the superior-inferior, lateral, and anterior-posterior directions of motion, respectively. The proposed approach is expected to minimize the chances of inaccurate treatment or diagnosis due to respiratory-induced motion and reduce radiation exposure.
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Systems and Control
Adaptive Lighting Control in Visible Light Systems: An Integrated Sensing, Communication, and Illumination Framework
Xinyan Xie, Xuesong Wang, Xin Lai et al. (+5 more)
Indoor visible light communication (VLC) is a promising sixth-generation (6G) technology, as its directional and sensitive optical signals are naturally suited for integrated sensing and communication (
ISAC). However, current research mainly focuses on maximizing data rates and sensing accuracy, creating a conflict between high performance, high energy consumption, and user visual comfort. This paper proposes an adaptive integrated sensing, communication, and illumination (ISCI) framework that resolves this conflict by treating energy savings as a primary objective. The framework's mechanism first partitions the receiving plane using a
geometric methodology, defining an activity area and a surrounding non-activity area to match distinct user requirements. User location, determined using non-line-of-sight (NLOS) sensing, then acts as a dynamic switch for the system's optimization objective. The system adaptively shifts between minimizing total transmit power while guaranteeing communication and illumination performance in the activity area and maximizing signal-to-noise ratio (SNR) uniformity in the non-activity area. Numerical results confirm that this adaptive ISCI approach achieves 53.59% energy savings over a non-adaptive system and improves SNR uniformity by 57.79%, while satisfying all illumination constraints and maintaining a mean localization error of 0.071 m.
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All authors Xinyan Xie, Xuesong Wang, Xin Lai, Yongheng Wen, Fengrui Yang, Haoyang He, Lai Zhang, Dong Zhao
Systems and Control
Response-Based Frequency Stability Assessment under Multi-Scale Disturbances in High-Renewable Power Systems
Jinhui Chen, Huadong Sun, Ping Wu et al. (+2 more)
In high-renewable power systems, active-power disturbances are becoming larger and exhibit increasingly diverse time scales, which complicates frequency stability assessment under unanticipated events. This paper presents a response-based frequency stability assessment method that uses disturbance power, inferred from generator electrical responses, to provide a unified treatment of multi-scale disturbances. Unanticipated disturbances are first classified into short-term and permanent events; permanent disturbances are further divided into step, second-level slope and minute-level slope disturbances. Based on the measured power responses of generator groups, a unified disturbance-power model is constructed to identify the disturbance type online and to quantify disturbance intensity through the disturbance power and its rate of change. Analytical frequency-response models are then derived for each disturbance class. For step disturbances, the maximum tolerable disturbance power is obtained under steady-state and transient frequency deviation constraints, and a safety-margin index is defined. For slope-type disturbances, an improved system frequency response (SFR) model and the rotor motion equation after exhaustion of primary frequency regulation are used to compute the over-limit time of frequency deviation. The proposed response-based assessment method is validated on the CSEE-FS frequency-stability
benchmark system, demonstrating its effectiveness and accuracy for quantitative frequency stability assessment in high-renewable power systems.
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All authors Jinhui Chen, Huadong Sun, Ping Wu, Baocai Wang, Bing Zhao
Systems and Control
Design and Measurements of mmWave FMCW Radar Based Non-Contact Multi-Patient Heart Rate and Breath Rate Monitoring System
Jewel Benny, Pranjal Mahajan, Srayan Sankar Chatterjee et al. (+2 more)
Recent developments in mmWave radar technologies have enabled the truly non-contact heart-rate (HR) and breath-rate (BR) measurement approaches, which provides a great ease in patient monitoring. Additionally, these technologies also provide opportunities to simultaneously detect HR and BR of multiple patients, which has become increasingly important for efficient mass monitoring scenarios. In this work, a frequency modulated continuous wave (FMCW) mmWave radar based truly non-contact multiple patient HR and BR monitoring system has been presented. Furthermore, a novel approach is also proposed, which combines multiple processing methods using a least squares solution to improve measurement accuracy, generalization, and handle measurement error. The proposed system has been developed using Texas Instruments' FMCW radar and experimental results with multiple subjects are also presented, which show >97% and >93% accuracy in the measured BR and HR values, respectively.
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All authors Jewel Benny, Pranjal Mahajan, Srayan Sankar Chatterjee, Mohd Wajid, Abhishek Srivastava
Systems and Control
Stability of data-driven Koopman MPC with terminal conditions
Irene Schimperna, Lea Bold, Johannes Köhler et al. (+2 more)
This paper derives conditions under which Model Predictive Control (MPC) with terminal conditions, using a data-driven surrogate model as a prediction model, asymptotically stabilizes the plant despite approximation errors. In particular, we prove recursive feasibility and asymptotic stability if a proportional error bound holds, where proportional means that the bound is linear in the norm of the state and the input. For a broad class of nonlinear systems, this condition can be satisfied using data-driven surrogate models generated by kernel Extended Dynamic Mode Decomposition (kEDMD) using the Koopman operator. Last, the applicability of the proposed framework is demonstrated in a numerical case study.
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All authors Irene Schimperna, Lea Bold, Johannes Köhler, Karl Worthmann, Lalo Magni
Audio and Speech Processing
The Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval
Jaime Garcia-Martinez, David Diaz-Guerra, John Anderson et al. (+5 more)
This paper introduces The Spheres
dataset, multitrack orchestral recordings designed to advance machine learning research in music source separation and related MIR tasks within the classical music domain. The
dataset is composed of over one hour recordings of musical pieces performed by the Colibrì Ensemble at The Spheres recording studio, capturing two canonical works - Tchaikovsky's Romeo and Juliet and Mozart's Symphony No. 40 - along with chromatic scales and solo excerpts for each instrument. The recording setup employed 23 microphones, including close spot, main, and ambient microphones, enabling the creation of realistic stereo mixes with controlled bleeding and providing isolated stems for supervised training of source separation models. In addition, room impulse responses were estimated for each instrument position, offering valuable acoustic characterization of the recording space. We present the
dataset structure, acoustic analysis, and baseline evaluations using X-UMX based models for orchestral family separation and microphone debleeding. Results highlight both the potential and the challenges of source separation in complex orchestral scenarios, underscoring the
dataset's value for benchmarking and for exploring new approaches to separation, localization, dereverberation, and immersive rendering of classical music.
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All authors Jaime Garcia-Martinez, David Diaz-Guerra, John Anderson, Ricardo Falcon-Perez, Pablo Cabañas-Molero, Tuomas Virtanen, Julio J. Carabias-Orti, Pedro Vera-Candeas
Systems and Control · Cross-listed from cs.OS
DynamicAdaptiveClimb: Adaptive Cache Replacement with Dynamic Resizing
Daniel Berend, Shlomi Dolev, Sweta Kumari et al. (+3 more)
Efficient cache management is critical for optimizing the system performance, and numerous caching mechanisms have been proposed, each exploring various insertion and eviction strategies. In this paper, we present AdaptiveClimb and its extension, DynamicAdaptiveClimb, two novel cache replacement policies that leverage lightweight, cache adaptation to outperform traditional approaches. Unlike classic Least Recently Used (LRU) and Incremental Rank Progress (CLIMB) policies, AdaptiveClimb dynamically adjusts the promotion distance (jump) of the cached objects based on recent hit and miss patterns, requiring only a single tunable parameter and no per-item statistics. This enables rapid adaptation to changing access distributions while maintaining low overhead. Building on this foundation, DynamicAdaptiveClimb further enhances adaptability by automatically tuning the cache size in response to workload demands. Our comprehensive evaluation across a diverse set of real-world traces, including 1067 traces from 6 different
datasets, demonstrates that DynamicAdaptiveClimb consistently achieves substantial speedups and higher hit ratios compared to other state-of-the-art
algorithms. In particular, our approach achieves up to a 29% improvement in hit ratio and a substantial reduction in miss penalties compared to the FIFO baseline. Furthermore, it outperforms the next-best contenders, AdaptiveClimb and SIEVE [43], by approximately 10% to 15%, especially in environments characterized by fluctuating working set sizes. These results highlight the effectiveness of our approach in delivering efficient performance, making it well-suited for modern, dynamic caching environments.
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All authors Daniel Berend, Shlomi Dolev, Sweta Kumari, Dhruv Mishra, Marina Kogan-Sadetsky, Archit Somani
Systems and Control
From Consensus to Robust Clustering: Multi-Agent Systems with Nonlinear Interactions
Anthony Couthures, Gustave Bainier, Vineeth Satheeskumar Varma et al. (+2 more)
This paper establishes a theoretical framework to describe the transition from consensus to stable clustering in multi-
agent systems with nonlinear, cooperative interactions. We first establish a sharp threshold for consensus. For a broad class of non-decreasing, Lipschitz-continuous interactions, an explicit inequality linking the interaction's Lipschitz constant to the second-largest
eigenvalue of the normalized adjacency
matrix of the interaction graph confines all system
equilibria to the synchronization
manifold. This condition is shown to be a sharp threshold, as its violation permits the emergence of non-synchronized
equilibria. We also demonstrate that such clustered states can only arise if the interaction law itself possesses specific structural properties, such as unstable fixed points. For the clustered states that emerge, we introduce a formal framework using Input-to-State Stability (ISS) theory to quantify their robustness. This approach allows us to prove that the internal cohesion of a cluster is robust to perturbations from the rest of the network. The analysis reveals a fundamental principle: cluster coherence is limited not by the magnitude of external influence, but by its heterogeneity across internal nodes. This unified framework, explaining both the sharp breakdown of consensus and the quantifiable robustness of the resulting modular structures, is validated on Zachary's Karate Club network, used as a classic
benchmark for community structure.
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All authors Anthony Couthures, Gustave Bainier, Vineeth Satheeskumar Varma, Samson Lasaulce, Irinel-Constantin Morarescu
Audio and Speech Processing
Evaluation of an ITD-to-ILD Transformation as a Method to Restore the Spatial Benefit in Speech Intelligibility in Hearing Impaired Listeners
Timm-Jonas Bäumer, Johannes W. de Vries, Stephan Töpken et al. (+3 more)
To improve
speech intelligibility in complex everyday situations, the human auditory system partially relies on Interaural Time Differences (ITDs) and Interaural Level Differences (ILDs). However, hearing impaired (HI) listeners often exhibit limited sensitivity to ITDs, resulting in decreased
speech intelligibility performance. This study aimed to investigate whether transforming low-frequency ITDs into ILDs could reintroduce a binaural benefit for HI listeners. We conducted two experiments with HI listeners. The first experiment used binaurally phase-shifted sinusoids at different frequencies to evaluate the HI listeners ITD sensitivity threshold. All subjects had an increased ITD threshold at higher frequencies, with different ITD sensitivities between the subjects in the lower frequencies. In the second experiment,
Speech Reception Thresholds (SRTs) were measured in different binaural configurations by manipulating Head-Related Transfer Functions (HRTFs). The results showed that, despite the decreased ITD sensitivity, removing ITDs decreased SRTs by approximately 1 dB compared to the unprocessed baseline, where ITDs and ILDs are available. Furthermore, substituting low-frequency ITDs with ILDs yielded an improvement for a lateral target speaker. Adding the low-frequency ILDs while preserving the ITDs caused a significant improvement for speakers in all directions. These findings suggest that the proposed transformation method could be effective in restoring binaural benefits in HI listeners. The results of this study suggest the use of such transformation techniques to be implemented in hearing aids and cochlear implants, directly benefiting HI listeners.
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All authors Timm-Jonas Bäumer, Johannes W. de Vries, Stephan Töpken, Richard C. Hendriks, Peyman Goli, Steven van de Par
Systems and Control · Cross-listed from math.OC
Accelerated ADMM: Automated Parameter Tuning and Improved Linear Convergence
Meisam Tavakoli, Fabian Jakob, Guido Carnevale et al. (+2 more)
This work studies the linear convergence of an accelerated scheme of the Alternating Direction Method of Multipliers (ADMM) for strongly convex and Lipschitz-smooth problems. We use the methodology of expressing the accelerated ADMM as a Lur'e system, i.e., an interconnection of a linear dynamical system in feedback with a slope-restricted operator, and we use Integral Quadratic Constraints to establish linear convergence. In addition, we propose several parameter tuning heuristics and their impact on the convergence rate through numerical analyses. Our new bounds show improved linear convergence rates compared to the vanilla
algorithm and previous proposed accelerated variants, which is also empirically validated on a LASSO regression
benchmark.
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All authors Meisam Tavakoli, Fabian Jakob, Guido Carnevale, Giuseppe Notarstefano, Andrea Iannelli
Signal Processing
2D Sparse Array Design via Reweighted L1 Second Order Cone Programming for 3D Ultrasound Imaging
Xi Zhang, Miguel Bernal and Wei-Ning Lee
Two-dimensional (2D) fully-addressed arrays can conveniently realize three-dimensional (3D) ultrasound imaging while fully controlled such arrays usually demands thousands of independent channels, which is costly. Sparse array technique using
stochastic optimization methods is one of promising techniques to reduce channel counts while due to the
stochastic nature of these methods, the optimized results are usually unstable. In this work, we introduce a sparse array design approach that formulates the synthesis problem of sparse arrays as second-order cone programming (SOCP) and a re-weighted L1 technique is implemented to sequentially optimize the SOCP. Based on this method, an on-grid quasi-flatten side-lobe (Q-Flats) 2D sparse array with side-lobe level (SLL) no more than -21.26 dB and 252 activated elements is designed, which aims to achieve as high contrast performance as possible under the limits of resolution and maximum number of independent channels (i.e., 256). The imaging performance of the Q-Flats array was compared with those of a corresponding dense array (Dense), a Fermat spiral array (Spiral) and a spatially 50%-Tukey tapered spiral array (Spiral-Taper) using Field II simulations in a multi-angle steered diverging wave transmission scheme. It was demonstrated that the Dense achieved the best resolution and contrast and the Spiral-Taper the worst. The Q-Flats showed better resolution (about 3%) but slightly worse contrast than the Spiral. All the results indicate the re-weighted L1 SOCP method is a promising and flexible method for seeking trade-offs among resolution, contrast, and number of activated elements.
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Signal Processing
Data-Driven Assessment of Concrete Slab Integrity via Impact-Echo Signals and Neural Networks
Yeswanth Ravichandran, Duoduo Liao and Charan Teja Kurakula
Subsurface defects such as delamination, voids, and honeycombing critically affect the durability of concrete bridge decks but are difficult to detect reliably using visual inspection or manual sounding. This paper presents a machine learning based Impact Echo (IE) framework that automates both defect localization and multi-class classification of common concrete defects. Raw IE signals from Federal Highway Administration (FHWA) laboratory slabs and in-service bridge decks are transformed via Fast
Fourier Transform (FFT) into dominant peak-frequency features and interpolated into spatial maps for defect zone visualization. Unsupervised k-means clustering highlights low-frequency, defect-prone regions, while Ground Truth Masks (GTMs) derived from seeded lab defects are used to validate spatial accuracy and generate high-confidence training labels. From these validated regions, spatially ordered peak-frequency sequences are constructed and fed into a stacked Long Short-Term
Memory (LSTM) network that classifies four defect types shallow delamination, deep delamination, voids, and honeycombing with 73% overall accuracy. Field validation on the bridge deck demonstrates that models trained on laboratory data generalize under realistic coupling, noise, and environmental variability. The proposed framework enhances the objectivity, scalability, and repeatability of Non-Destructive Evaluation (NDE), supporting intelligent, data-driven bridge health monitoring at a network scale.
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Signal Processing
Evaluating the Performance of a Modified Skin Temperature Sensor for Lower Limb Prostheses: An Experimental Comparison
Anirshu Devroy, Gregor Fritz and Mathias Brandstoetter
Current rehabilitation of lower limb prostheses has significant challenges, especially with skin conditions, irritation and discomfort. Understanding the skin temperature and having comfortable wearable sensors that would monitor skin temperature in a real-time outdoor environment would be useful. The system would help the user and orthopedic technician to provide feedback and changes that might be required in the prosthesis. Hence in this paper, a series of experiments are conducted in order to understand and characterize the system behavior and compare a general thermistor and a modified thermistor as a potential method of temperature measurement for outdoor usage of prostheses. The paper goes on to compare the different modified thermistors behavior with their regular counterpart and highlights the challenges and improvement areas needed for such a modified thermistor for outdoor temperature monitoring in a prosthetic system. Initial results show that some of the modified thermistors showed better temperature recording compared to the rest. Finally, such modified thermistors can be a potential alternative for comfortable temperature measurement embedded in the prosthesis system. Such a system can provide valuable insights into temperature distribution and an early warning system for skin problems
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Systems and Control · Cross-listed from math.OC
Data-driven control of continuous-time systems: A synthesis-operator approach
Masashi Wakaiki
This paper addresses data-driven control of continuous-time systems. We develop a framework based on synthesis operators associated with state and input trajectories. A key advantage of the proposed method is that it does not require the state derivative and uses continuous-time data directly without sampling or filtering. First, systems consistent with the data are represented in terms of synthesis operators, into which the data trajectories are embedded. Next, we characterize data informativity properties for system identification and for stabilization in the noise-free case. Finally, we establish a necessary and sufficient condition for noisy data to be informative for quadratic stabilization. All these informativity characterizations are formulated in terms of finite-dimensional
matrices, by leveraging the finite-rank structure of the synthesis operators.
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Image and Video Processing
Deep Parameter Interpolation for Scalar Conditioning
Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona et al. (+2 more)
We propose deep parameter interpolation (DPI), a general-purpose method for transforming an existing deep neural network architecture into one that accepts an additional scalar input. Recent deep generative models, including
diffusion models and flow matching, employ a single neural network to learn a time- or noise level-dependent vector field. Designing a network architecture to accurately represent this vector field is challenging because the network must integrate information from two different sources: a high-dimensional vector (usually an image) and a scalar. Common approaches either encode the scalar as an additional image input or combine scalar and vector information in specific network components, which restricts architecture choices. Instead, we propose to maintain two learnable parameter sets within a single network and to introduce the scalar dependency by dynamically interpolating between the parameter sets based on the scalar value during training and sampling. DPI is a simple, architecture-agnostic method for adding scalar dependence to a neural network. We demonstrate that our method improves denoising performance and enhances sample quality for both
diffusion and flow matching models, while achieving computational efficiency comparable to standard scalar conditioning techniques. Code is available at https://github.com/wustl-cig/parameter_interpolation.
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All authors Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona, Brendt Wohlberg, Ulugbek S. Kamilov
Systems and Control
An Exact, Finite Dimensional Representation for Full-Block, Circle Criterion Multipliers
Felix Biertümpfel, Bin Hu, Geir Dullerud et al. (+1 more)
This paper provides the first finite-dimensional characterization for the complete set of full-block, circle criterion multipliers. We consider the interconnection of a discrete-time, linear time-invariant system in feedback with a non-repeated, sector-bounded nonlinearity. Sufficient conditions for stability and performance can be derived using: (i) dissipation inequalities, and (ii) Quadratic Constraints (QCs) that bound the input/output pairs of the nonlinearity. Larger classes of QCs (or multipliers) reduce the conservatism of the conditions. Full-block, circle criterion multipliers define the complete set of all possible QCs for non-repeated, sector-bounded nonlinearities. These provide the least conservative conditions. However, full-block multipliers are defined by an uncountably infinite number of constraints and hence do not lead to computationally tractable solutions if left in this raw form. This paper provides a new finite-dimensional characterization for the set of full-block, circle criterion multipliers. The key theoretical insight is: the set of all input/output pairs of non-repeated sector-bounded nonlinearities is equal to the set of all incremental pairs for an appropriately constructed piecewise linear function. Our new description for the complete set of multipliers only requires a finite number of
matrix copositivity constraints. These conditions have an exact, computationally tractable implementation for problems where the nonlinearity has small input/output dimensions $(\le 4)$. We illustrate the use of our new characterization via a simple example.
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All authors Felix Biertümpfel, Bin Hu, Geir Dullerud, Peter Seiler