Systems and Control
Power Reserve Capacity from Virtual Power Plants with Reliability and Cost Guarantees
Lorenzo Zapparoli, Blazhe Gjorgiev and Giovanni Sansavini
The growing penetration of renewable energy sources is expected to drive higher demand for power reserve ancillary services (AS). One solution is to increase the supply by integrating distributed energy resources (DERs) into the AS
market through virtual power plants (VPPs). Several methods have been developed to assess the potential of VPPs to provide services. However, the existing approaches fail to account for AS products' requirements (reliability and technical specifications) and to provide accurate cost estimations. Here, we propose a new method to assess VPPs' potential to deliver power reserve capacity products under
forecasting uncertainty. First, the maximum feasible reserve quantity is determined using a novel formulation of subset simulation for efficient uncertainty quantification. Second, the supply curve is characterized by considering explicit and opportunity costs. The method is applied to a VPP based on a representative Swiss low-voltage network with a diversified DER portfolio. We find that VPPs can reliably offer reserve products and that opportunity costs drive product pricing. Additionally, we show that the product's requirements strongly impact the reserve capacity provision capability. This approach aims to support VPP managers in developing
market strategies and policymakers in designing DER-focused AS products.
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Systems and Control
Robust stability of event-triggered nonlinear moving horizon estimation
Isabelle Krauss, Victor G. Lopez and Matthias A. Müller
In this work, we propose an event-triggered moving horizon estimation (ET-MHE) scheme for the remote state estimation of general nonlinear systems. In the presented method, whenever an event is triggered, a single measurement is transmitted and the nonlinear MHE optimization problem is subsequently solved. If no event is triggered, the current state estimate is updated using an open-loop prediction based on the system dynamics. Moreover, we introduce a novel event-triggering rule under which we demonstrate robust global exponential stability of the ET-MHE scheme, assuming a suitable detectability condition is met. In addition, we show that with the adoption of a varying horizon length, a tighter bound on the estimation error can be achieved. Finally, we validate the effectiveness of the proposed method through two illustrative examples.
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Systems and Control
Efficient Probabilistic Planning with Maximum-Coverage Distributionally Robust Backward Reachable Trees
Alex Rose, Naman Aggarwal, Christopher Jewison et al. (+1 more)
This paper presents a new multi-query motion planning
algorithm for linear Gaussian systems with the goal of reaching a Euclidean ball with high probability. We develop a new formulation for ball-shaped ambiguity sets of Gaussian distributions and leverage it to develop a distributionally robust belief roadmap construction
algorithm. This
algorithm synthe- sizes robust controllers which are certified to be safe for maximal size ball-shaped ambiguity sets of Gaussian distributions. Our
algorithm achieves better coverage than the maximal coverage
algorithm for planning over Gaussian distributions [1], and we identify mild conditions under which our
algorithm achieves strictly better coverage. For the special case of no process noise or state constraints, we formally prove that our
algorithm achieves maximal coverage. In addition, we present a second multi-query motion planning
algorithm for linear Gaussian systems with the goal of reaching a region parameterized by the Minkowski sum of an ellipsoid and a Euclidean ball with high probability. This
algorithm plans over ellipsoidal sets of maximal size ball-shaped ambiguity sets of Gaussian distributions, and provably achieves equal or better coverage than the best-known
algorithm for planning over ellipsoidal ambiguity sets of Gaussian distributions [2]. We demonstrate the efficacy of both methods in a wide range of conditions via extensive simulation experiments.
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All authors Alex Rose, Naman Aggarwal, Christopher Jewison, Jonathan P. How
Systems and Control
MPC strategies for density profile control with pellet fueling in nuclear fusion tokamaks under uncertainty
Christopher A. Orrico, Hari Prasad Varadarajan, Matthijs van Berkel et al. (+4 more)
Control of the density profile based on pellet fueling for the ITER
nuclear fusion tokamak involves a multi-rate nonlinear system with safety-critical constraints, input delays, and discrete actuators with parametric uncertainty. To address this challenging problem, we propose a multi-stage MPC (msMPC) approach to handle uncertainty in the presence of mixed-integer inputs. While the scenario tree of msMPC accounts for uncertainty, it also adds complexity to an already computationally intensive mixed-integer MPC (MI-MPC) problem. To achieve real-time density profile controller with discrete pellets and uncertainty handling, we systematically reduce the problem complexity by (1) reducing the identified prediction model size through dynamic mode decomposition with control, (2) applying principal component analysis to reduce the number of scenarios needed to capture the parametric uncertainty in msMPC, and (3) utilizing the penalty term homotopy for MPC (PTH-MPC)
algorithm to reduce the computational burden caused by the presence of mixed-integer inputs. We compare the performance and safety of the msMPC strategy against a nominal MI-MPC in plant simulations, demonstrating the first predictive density control strategy with uncertainty handling, viable for real-time pellet fueling in ITER.
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All authors Christopher A. Orrico, Hari Prasad Varadarajan, Matthijs van Berkel, Lennard Ceelen, Thomas O. S. J. Bosman, W. P. M. H. Heemels, Dinesh Krishnamoorthy
Signal Processing
Interference Alignment for Multi-cluster Over-the-Air Computation
Lucas Sempéré, Yue Bi, Yue Wu et al. (+2 more)
One of the main challenges facing Internet of Things (IoT) networks is managing interference caused by the large number of devices communicating simultaneously, particularly in multi-cluster networks where multiple devices simultaneously transmit to their respective receiver. Over-the-Air Computation (AirComp) has emerged as a promising solution for efficient real-time data aggregation, yet its performance suffers in dense, interference-limited environments. To address this, we propose a novel Interference
Alignment (IA) scheme tailored for up-link AirComp systems. Unlike previous approaches, the proposed method scales to an arbitrary number $\sf K$ of clusters and enables each cluster to exploit half of the available channels, instead of only $\tfrac{1}{\sf K}$ as in time-sharing. In addition, we develop schemes tailored to scenarios where users are shared between adjacent clusters.
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All authors Lucas Sempéré, Yue Bi, Yue Wu, Pengwenlong Gu, Selma Boumerdassi
Signal Processing
Multilayer Non-Terrestrial Networks with Spectrum Access aided by Beyond-Diagonal RIS
Wali Ullah Khan, Chandan Kumar Sheemar, Eva Lagunas et al. (+4 more)
In this work, we study a multi-user NTN in which a satellite serves as the primary network and a high-altitude platform station (HAPS) operates as the secondary network, acting as a cognitive
radio. To reduce the cost, complexity, and power consumption of conventional
antenna arrays, we equip the HAPS with a transmissive BD-RIS
antenna front end. We then formulate a joint optimization problem for the BD-RIS phase response and the HAPS transmit power allocation under strict per-user interference temperature constraints. To tackle the resulting highly nonconvex problem, we propose an alternating-optimization framework: the power-allocation subproblem admits a closed-form, water-filling-type solution derived from the Karush-Kuhn-Tucker (KKT) conditions, while the BD-RIS configuration is refined via Riemannian
manifold optimization. Simulation results show significant gains in data rate and interference suppression over diagonal RIS-assisted
benchmarks, establishing BD-RIS as a promising enabler for future multilayer NTNs.
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All authors Wali Ullah Khan, Chandan Kumar Sheemar, Eva Lagunas, Xingwang Li, Symeon Chatzinotas, Petar Popovski, Zhu Han
Audio and Speech Processing · Cross-listed from cs.SD
Speak, Edit, Repeat: High-Fidelity Voice Editing and Zero-Shot TTS with Cross-Attentive Mamba
Baher Mohammad, Magauiya Zhussip and Stamatios Lefkimmiatis
We introduce MAVE (Mamba with Cross-
Attention for Voice Editing and Synthesis), a novel autoregressive architecture for text-conditioned voice editing and high-fidelity text-to-
speech (TTS) synthesis, built on a cross-attentive Mamba backbone. MAVE achieves state-of-the-art performance in
speech editing and very competitive results in zero-shot TTS, while not being explicitly trained on the latter task, outperforming leading autoregressive and
diffusion models on diverse, real-world audio. By integrating Mamba for efficient audio sequence modeling with cross-
attention for precise text-acoustic
alignment, MAVE enables context-aware voice editing with exceptional naturalness and speaker consistency. In pairwise human evaluations on a random 40-sample subset of the RealEdit
benchmark (400 judgments), 57.2% of listeners rated MAVE - edited
speech as perceptually equal to the original, while 24.8% prefered the original and 18.0% MAVE - demonstrating that in the majority of cases edits are indistinguishable from the source. MAVE compares favorably with VoiceCraft and FluentSpeech both on pairwise comparisons and standalone mean opinion score (MOS) evaluations. For zero-shot TTS, MAVE exceeds VoiceCraft in both speaker similarity and naturalness, without requiring multiple inference runs or post-processing. Remarkably, these quality gains come with a significantly lower
memory cost and approximately the same latency: MAVE requires ~6x less
memory than VoiceCraft during inference on utterances from the RealEdit database (mean duration: 6.21s, A100, FP16, batch size 1). Our results demonstrate that MAVE establishes a new standard for flexible, high-fidelity voice editing and synthesis through the synergistic integration of structured state-space modeling and cross-modal
attention.
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Signal Processing
Dimensionally-Efficient Transmission and Storage of Unitary Matrices
Juan Vidal Alegría
Unitary matrices are the basis of a large number of signal processing applications. In many of these applications, finding ways to efficiently store, and even transmit these
matrices, can significantly reduce
memory and throughput requirements. In this work, we study the problem of efficient transmission and storage of
unitary matrices. Specifically, we explicitly derive a dimensionally-efficient parametrization (DEP) for
unitary matrices that allows identifying them with sequences of real numbers, where the dimension coincides with the dimension of the
unitary group where they lie. We also characterize its inverse map that allows retrieving the original
unitary matrices from their DEP. The proposed approach effectively allows halving the dimension with respect to naively considering all the entries of each
unitary matrix, thus reducing the resources required to store and transmit these
matrices. Furthermore, we show that the sequence of real numbers associated to the proposed DEP is bounded, and we delimit the interval where these numbers are contained, facilitating the implementation of quantization approaches with limited distortion. On the other hand, we outline ways to further reduce the dimension of the DEP when considering more restrictive constraints for
matrices that show up in certain applications. The numerical results showcase the potential of the proposed approach in general settings, as well as in three specific applications of current interest for wireless communications research.
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Systems and Control
Learning a Shape-adaptive Assist-as-needed Rehabilitation Policy from Therapist-informed Input
Zhimin Hou, Jiacheng Hou, Xiao Chen et al. (+3 more)
Therapist-in-the-loop robotic rehabilitation has shown great promise in enhancing rehabilitation outcomes by integrating the strengths of therapists and robotic systems. However, its broader adoption remains limited due to insufficient safe interaction and limited adaptation capability. This article proposes a novel telerobotics-mediated framework that enables therapists to intuitively and safely deliver assist-as-needed~(AAN) therapy based on two primary contributions. First, our framework encodes the therapist-informed corrective force into via-points in a latent space, allowing the therapist to provide only minimal assistance while encouraging patient maintaining own motion preferences. Second, a shape-adaptive ANN rehabilitation policy is learned to partially and progressively deform the reference trajectory for movement therapy based on encoded patient motion preferences and therapist-informed via-points. The effectiveness of the proposed shape-adaptive AAN strategy was validated on a telerobotic rehabilitation system using two representative tasks. The results demonstrate its practicality for remote AAN therapy and its superiority over two state-of-the-art methods in reducing corrective force and improving movement smoothness.
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All authors Zhimin Hou, Jiacheng Hou, Xiao Chen, Hamid Sadeghian, Tianyu Ren, Sami Haddadin
Systems and Control · Cross-listed from cs.CR
Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
Ines Akaichi, Giorgos Flouris, Irini Fundulaki et al. (+1 more)
In the digital age, data frequently crosses organizational and jurisdictional boundaries, making effective governance essential. Usage control policies have emerged as a key paradigm for regulating data usage, safeguarding
privacy, protecting intellectual property, and ensuring compliance with regulations. A central mechanism for usage control is the handling of obligations, which arise as a side effect of using and sharing data. Effective monitoring of obligations requires capturing usage traces and accounting for temporal aspects such as start times and deadlines, as obligations may evolve over times into different states, such as fulfilled, violated, or expired. While several solutions have been proposed for obligation monitoring, they often lack formal semantics or provide limited support for
reasoning over obligation states. To address these limitations, we extend GUCON, a policy framework grounded in the formal semantics of SPAQRL graph patterns, to explicitly model the temporal aspects of an obligation. This extension enables the expressing of temporal obligations and supports continuous monitoring of their evolving states based on usage traces stored in temporal knowledge graphs. We demonstrate how this extended model can be represented using RDF-
star and SPARQL-
star and propose an Obligation State Manager that monitors obligation states and assess their compliance with respect to usage traces. Finally, we evaluate both the extended model and its prototype implementation.
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All authors Ines Akaichi, Giorgos Flouris, Irini Fundulaki, Sabrina Kirrane
Signal Processing · Cross-listed from cs.LG
Forecasting-based Biomedical Time-series Data Synthesis for Open Data and Robust AI
Youngjoon Lee, Seongmin Cho, Yehhyun Jo et al. (+3 more)
The limited data availability due to strict
privacy regulations and significant resource demands severely constrains biomedical time-series AI development, which creates a critical gap between data requirements and accessibility. Synthetic data generation presents a promising solution by producing artificial
datasets that maintain the statistical properties of real biomedical time-series data without compromising patient confidentiality. While GANs, VAEs, and
diffusion models capture global data distributions,
forecasting models offer inductive biases tailored for sequential dynamics. We propose a framework for synthetic biomedical time-series data generation based on recent
forecasting models that accurately replicates complex electrophysiological signals such as
EEG and EMG with high fidelity. These synthetic
datasets can be freely shared for open AI development and consistently improve downstream model performance. Numerical results on sleep-stage classification show up to a 3.71% performance gain with augmentation and a 91.00% synthetic-only accuracy that surpasses the real-data-only baseline.
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All authors Youngjoon Lee, Seongmin Cho, Yehhyun Jo, Jinu Gong, Hyunjoo Jenny Lee, Joonhyuk Kang
Systems and Control
On Prediction-Based Properties of Discrete-Event Systems: Notions, Applications and Supervisor Synthesis
Bohan Cui, Yu Chen, Alessandro Giua et al. (+1 more)
In this work, we investigate the problem of synthesizing property-enforcing supervisors for partially-observed discrete-event systems (DES). Unlike most existing approaches, where the enforced property depends solely on the executed behavior of the system, here we consider a more challenging scenario in which the property relies on predicted future behaviors that have not yet occurred. This problem arises naturally in applications involving future information, such as active prediction or intention protection. To formalize the problem, we introduce the notion of prediction-based properties, a new class of observational properties tied to the system's future information. We demonstrate that this notion is very generic and can model various practical properties, including predictability in fault prognosis and pre-opacity in intention security. We then present an effective approach for synthesizing supervisors that enforce prediction-based properties. Our method relies on a novel information structure that addresses the fundamental challenge arising from the dependency between current predictions and the control policy. The key idea is to first borrow information from future instants and then ensure information consistency. This reduces the supervisor synthesis problem to a safety game in the information space. We prove that the proposed
algorithm is both sound and complete, and the resulting supervisor is maximally permissive.
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All authors Bohan Cui, Yu Chen, Alessandro Giua, Xiang Yin
Systems and Control
Design Process of a Self Adaptive Smart Serious Games Ecosystem
X. Tao, P. Chen, M. Tsami et al. (+2 more)
This paper outlines the design
vision and planned
evolution of Blexer v3, a modular and AI-driven rehabilitation ecosystem based on serious games. Building on insights from previous versions of the system, we propose a new architecture that aims to integrate
multimodal sensing, real-time
reasoning, and intelligent control. The envisioned system will include distinct modules for data collection, user state inference, and gameplay adaptation. Key features such as dynamic difficulty adjustment (DDA) and procedural content generation (PCG) are also considered to support personalized interventions. We present the complete conceptual framework of Blexer v3, which defines the modular structure and data flow of the system. This serves as the foundation for the next phase: the development of a functional prototype and its integration into clinical rehabilitation scenarios.
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All authors X. Tao, P. Chen, M. Tsami, F. Khayati, M. Eckert
Signal Processing
Coordinated Beamforming for Networked Integrated Communication and Multi-TMT Localization
Meidong Xia, Zhenyao He, Wei Xu et al. (+3 more)
Networked integrated sensing and communication (
ISAC) has emerged as a pivotal paradigm for next-generation wireless networks, where dedicated target monitoring terminals (TMTs) can be extensively leveraged for their low-cost flexible deployment and capability to facilitate bistatic and multistatic sensing. Nevertheless, the coordinated beamforming design for networked
ISAC tailored for time-of-arrival (ToA)-based multi-TMT localization remains largely unexplored. To address this gap, we present a comprehensive study in this paper. Specifically, we first establish signal models for both communication and localization, and, for the first time, derive a closed-form Cramer-Rao lower bound (CRLB) to quantify the localization performance. Leveraging this CRLB, we formulate two optimization problems focusing on sensing-centric and communication-centric criteria, respectively, to thoroughly investigate the fundamental communication-localization trade-offs. For the sensing-centric problem, we develop a globally optimal
algorithm based on semidefinite relaxation (SDR), applicable to scenarios where the number of BS
antennas exceeds the total number of communication users. In parallel, for the communication-centric problem, we design a globally optimal
algorithm for the single-BS case utilizing bisection search. To address the general cases of both problems, we propose a unified and efficient successive convex approximation (SCA)-based
algorithm, which is further extended to multi-target scenarios. Finally, simulation results demonstrate the effectiveness of our proposed
algorithms, reveal the intrinsic trade-offs between communication and localization, and further show that deploying more TMTs is more beneficial than deploying more BSs in networked
ISAC systems.
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All authors Meidong Xia, Zhenyao He, Wei Xu, Yongming Huang, Derrick Wing Kwan Ng, Naofal Al-Dhahir
Audio and Speech Processing
UniVoice: Unifying Autoregressive ASR and Flow-Matching based TTS with Large Language Models
Wenhao Guan, Zhikang Niu, Ziyue Jiang et al. (+5 more)
Large
language models (LLMs) have demonstrated promising performance in both automatic
speech recognition (ASR) and text-to-
speech (TTS) systems, gradually becoming the mainstream approach. However, most current approaches address these tasks separately rather than through a unified framework. This work aims to integrate these two tasks into one unified model. Although discrete
speech tokenization enables joint modeling, its inherent information loss limits performance in both recognition and generation. In this work, we present UniVoice, a unified LLM framework through continuous representations that seamlessly integrates
speech recognition and synthesis within a single model. Our approach combines the strengths of autoregressive modeling for
speech recognition with flow matching for high-quality generation. To mitigate the inherent divergence between autoregressive and flow-matching models, we further design a dual
attention mechanism, which switches between a causal mask for recognition and a bidirectional
attention mask for synthesis. Furthermore, the proposed text-prefix-conditioned
speech infilling method enables high-fidelity zero-shot voice cloning. Experimental results demonstrate that our method can achieve or exceed current single-task modeling methods in both ASR and zero-shot TTS tasks. This work explores new possibilities for end-to-end
speech understanding and generation. Code is available at https://github.com/gwh22/UniVoice.
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All authors Wenhao Guan, Zhikang Niu, Ziyue Jiang, Kaidi Wang, Peijie Chen, Qingyang Hong, Lin Li, Xie Chen
Systems and Control
Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
Junsei Ito and Yasuaki Wasa
This article proposes a data-driven PID controller design based on the principle of adaptive gain optimization, leveraging Physics-Informed Neural Networks (PINNs) generated for predictive modeling purposes. The proposed control design method utilizes gradients of the PID gain optimization, achieved through the automatic differentiation of PINNs, to apply model predictive control using a cost function based on tracking error and control inputs. By optimizing PINNs-based PID gains, the method achieves adaptive gain tuning that ensures stability while accounting for system nonlinearities. The proposed method features a systematic framework for integrating PINNs-based models of dynamical control systems into closed-loop control systems, enabling direct application to PID control design. A series of numerical experiments is conducted to demonstrate the effectiveness of the proposed method from the control perspectives based on both time and frequency domains.
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Audio and Speech Processing · Cross-listed from cs.CL
Robustness assessment of large audio language models in multiple-choice evaluation
Fernando López, Santosh Kesiraju and Jordi Luque
Recent advances in large audio
language models (LALMs) have primarily been assessed using a multiple-choice question answering (MCQA) framework. However, subtle changes, such as shifting the order of choices, result in substantially different results. Existing MCQA frameworks do not account for this variability and report a single accuracy number per
benchmark or category. We dive into the MCQA evaluation framework and conduct a systematic study spanning three
benchmarks (MMAU, MMAR and MMSU) and four models: Audio Flamingo 2, Audio Flamingo 3, Qwen2.5-Omni-7B-Instruct, and Kimi-Audio-7B-Instruct. Our findings indicate that models are sensitive not only to the ordering of choices, but also to the paraphrasing of the question and the choices. Finally, we propose a simpler evaluation protocol and metric that account for subtle variations and provide a more detailed evaluation report of LALMs within the MCQA framework.
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Audio and Speech Processing · Cross-listed from cs.SD
Language Model Based Text-to-Audio Generation: Anti-Causally Aligned Collaborative Residual Transformers
Juncheng Wang, Chao Xu, Cheng Yu et al. (+5 more)
While
language models (LMs) paired with residual vector quantization (RVQ) tokenizers have shown promise in text-to-audio (T2A) generation, they still lag behind
diffusion-based models by a non-trivial margin. We identify a critical dilemma underpinning this gap: incorporating more RVQ layers improves audio reconstruction fidelity but exceeds the generation capacity of conventional LMs. To address this, we first analyze RVQ dynamics and uncover two key limitations: 1) orthogonality of features across RVQ layers hinders effective LMs training, and 2) descending semantic richness in
tokens from deeper RVQ layers exacerbates exposure bias during autoregressive decoding. Based on these insights, we propose Siren, a novel LM-based framework that employs multiple isolated
transformers with causal conditioning and anti-causal
alignment via reinforcement learning. Extensive experiments demonstrate that Siren outperforms both existing LM-based and
diffusion-based T2A systems, achieving state-of-the-art results. By bridging the representational strengths of LMs with the fidelity demands of audio synthesis, our approach repositions LMs as competitive contenders against
diffusion models in T2A tasks. Moreover, by aligning audio representations with linguistic structures, Siren facilitates a promising pathway toward unified multi-modal generation frameworks.
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All authors Juncheng Wang, Chao Xu, Cheng Yu, Zhe Hu, Haoyu Xie, Guoqi Yu, Lei Shang, Shujun Wang
Signal Processing
Performance Analysis for Multi-User Holographic MIMO Downlink with Matched Filter Precoding
Gayathri Shekar, Saman Atapattu, Prathapasinghe Dharmawansa et al. (+1 more)
Holographic MIMO (HMIMO) has emerged as a promising solution for future wireless systems by enabling ultra-dense, spatially continuous
antenna deployments. While prior studies have primarily focused on electromagnetic (EM) modeling or simulation-based performance analysis, a rigorous communication-theoretic framework remains largely unexplored. This paper presents the first analytical performance study of a multi-user HMIMO downlink system with matched filter (MF) precoding - a low-complexity baseline scheme. By incorporating multipath propagation, mutual coupling, and element excitation, we derive a novel closed-form expression for the MF signal-to-interference-plus-noise ratio (SINR) using an equivalent random variable model. Leveraging bivariate gamma distributions, we then develop tractable throughput approximations under full, partial, and no channel state information (CSI) scenarios. Additionally, we formulate a max-min beamforming problem to
benchmark optimal user
fairness performance. Numerical results validate the accuracy of the proposed framework and reveal that MF precoding achieves competitive performance with strong robustness to low SINR and CSI uncertainty.
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All authors Gayathri Shekar, Saman Atapattu, Prathapasinghe Dharmawansa, Kandeepan Sithamparanathan
Systems and Control
On properties of hydraulic equilibria in district heating networks
Ask Hällström, Felix Agner and Richard Pates
District heating networks are an integral part of the energy system in many countries. In future smart energy systems, they are expected to enhance energy flexibility and support the integration of renewable and waste energy sources. An important aspect of these networks is the control of flow rates, which dictates the heat delivered to consumers. This paper concerns the properties of flow rates in tree-structured district heating networks. We show that under mild assumptions of monotonicity in the hydraulic network components, statements regarding the stationary flow rate distribution can be made. In particular, when all consumers in a network incrementally open their valves, an increase in total flow rate throughput is guaranteed, while if one consumer does not open their valve when others do, they will receive a reduced flow rate. These properties are illustrated numerically on a small 2-consumer network as well as on a larger 22-consumer network. Previous works have shown that these properties allow the design and use of efficient control strategies for optimal heat distribution.
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Systems and Control · Cross-listed from cs.RO
Velocity-Form Data-Enabled Predictive Control of Soft Robots under Unknown External Payloads
Huanqing Wang, Kaixiang Zhang, Kyungjoon Lee et al. (+5 more)
Data-driven control methods such as data-enabled predictive control (DeePC) have shown strong potential in efficient control of soft
robots without explicit parametric models. However, in object manipulation tasks, unknown external payloads and disturbances can significantly alter the system dynamics and behavior, leading to offset error and degraded control performance. In this paper, we present a novel velocity-form DeePC framework that achieves robust and optimal control of soft
robots under unknown payloads. The proposed framework leverages input-output data in an incremental representation to mitigate performance degradation induced by unknown payloads, eliminating the need for weighted
datasets or disturbance
estimators. We validate the method experimentally on a planar soft
robot and demonstrate its superior performance compared to standard DeePC in scenarios involving unknown payloads.
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All authors Huanqing Wang, Kaixiang Zhang, Kyungjoon Lee, Yu Mei, Vaibhav Srivastava, Jun Sheng, Ziyou Song, Zhaojian Li
Signal Processing
Joint Probing and Scheduling for Cache-Aided Hybrid Satellite-Terrestrial Networks
Zhou Zhang, Yizhu Wang, Saman Atapattu et al. (+1 more)
Caching is crucial in hybrid satellite-terrestrial networks to reduce latency, optimize throughput, and improve data availability by storing frequently accessed content closer to users, especially in bandwidth-limited satellite systems, requiring strategic Medium Access Control (MAC) layer. This paper addresses throughput optimization in satellite-terrestrial integrated networks through opportunistic cooperative caching. We propose a joint probing and scheduling strategy to enhance content
retrieval efficiency. The strategy leverages the LEO satellite to probe satellite-to-ground links and cache states of multiple cooperative terrestrial stations, enabling dynamic user scheduling for content delivery. Using an optimal stopping theoretic approach with two levels of incomplete information, we make real-time decisions on satellite-terrestrial hybrid links and caching probing. Our threshold-based strategy optimizes probing and scheduling, significantly improving average system throughput by exploiting cooperative caching, satellite-terrestrial link transmission, and time diversity from dynamic user requests. Simulation results validate the effectiveness and practicality of the proposed strategies.
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All authors Zhou Zhang, Yizhu Wang, Saman Atapattu, Sumei Sun
Image and Video Processing · Cross-listed from cs.CV
SPEGNet: Synergistic Perception-Guided Network for Camouflaged Object Detection
Baber Jan, Saeed Anwar, Aiman H. El-Maleh et al. (+2 more)
Camouflaged object detection segments objects with intrinsic similarity and edge disruption. Current detection methods rely on accumulated complex components. Each approach adds components such as boundary modules,
attention mechanisms, and multi-scale processors independently. This accumulation creates a computational burden without proportional gains. To manage this complexity, they process at reduced resolutions, eliminating fine details essential for camouflage. We present SPEGNet, addressing fragmentation through a unified design. The architecture integrates multi-scale features via channel calibration and spatial enhancement. Boundaries emerge directly from context-rich representations, maintaining semantic-spatial
alignment. Progressive refinement implements scale-adaptive edge modulation with peak influence at intermediate resolutions. This design strikes a balance between boundary precision and regional consistency. SPEGNet achieves 0.887 $S_\alpha$ on CAMO, 0.890 on COD10K, and 0.895 on NC4K, with real-time inference speed. Our approach excels across scales, from tiny, intricate objects to large, pattern-similar ones, while handling occlusion and ambiguous boundaries. Code, model weights, and results are available on \href{https://github.com/Baber-Jan/SPEGNet}{https://github.com/Baber-Jan/SPEGNet}.
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All authors Baber Jan, Saeed Anwar, Aiman H. El-Maleh, Abdul Jabbar Siddiqui, Abdul Bais
Systems and Control
A Diffusion-based Generative Machine Learning Paradigm for Dynamic Contingency Screening
Quan Tran, Suresh S. Muknahallipatna, Dongliang Duan et al. (+1 more)
Dynamic contingency screening is a challenging task in dynamic security assessment, when traditional numerical approaches are computationally intensive and often not able to repeatedly solve full AC power flow for all possible contingencies in real time, especially for large-scale power grids. Moreover, the severity caused by a contingency is not identical for all operating points, which does not necessitate solving all possible contingencies computationally inefficient and time-consuming. This paper introduces a novel,
diffusion-based generative machine learning paradigm that transforms contingency analysis from conventional scenario selection to a proactive, likely-unsupervised scenario generation. The margin to the steady-state voltage stability limit determines the ranking of contingencies corresponding to each operating point. By leveraging physical information from each operating point, the proposed approach anticipates the contingencies most likely to be critical, without relying on static assumptions or exhaustive simulations. This data-prompted generative approach enables the identification of high-risk scenarios under varying load and generator conditions, providing dynamic security assessment in real time. The correctness, effectiveness, and scalability of the methodology are demonstrated through methodological derivations and comprehensive experiments on multiple IEEE
benchmark systems, including IEEE-6, IEEE-14, IEEE-30, and IEEE-118, highlighting its potential to incorporate contingency screening in complex, evolving smart grids.
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All authors Quan Tran, Suresh S. Muknahallipatna, Dongliang Duan, Nga Nguyen
Audio and Speech Processing · Cross-listed from cs.SD
Evaluating Self-Supervised Speech Models via Text-Based LLMS
Takashi Maekaku, Keita Goto, Jinchuan Tian et al. (+2 more)
Self-Supervised Learning (SSL) has gained traction for its ability to learn rich representations with low labeling costs, applicable across diverse downstream tasks. However, assessing the downstream-task performance remains challenging due to the cost of extra training and evaluation. Existing methods for task-agnostic evaluation also require extra training or hyperparameter tuning. We propose a novel evaluation metric using large
language models (LLMs). By inputting discrete
token sequences and minimal domain cues derived from SSL models into LLMs, we obtain the mean log-likelihood; these cues guide in-context learning, rendering the score more reliable without extra training or hyperparameter tuning. Experimental results show a correlation between LLM-based scores and automatic
speech recognition task. Additionally, our findings reveal that LLMs not only functions as an SSL evaluation tools but also provides inference-time
embeddings that are useful for speaker verification task.
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All authors Takashi Maekaku, Keita Goto, Jinchuan Tian, Yusuke Shinohara, Shinji Watanabe
Audio and Speech Processing
Differentiable physics for sound field reconstruction
Samuel A. Verburg, Efren Fernandez-Grande and Peter Gerstoft
Sound field reconstruction involves estimating sound fields from a limited number of spatially distributed observations. This work introduces a differentiable physics approach for sound field reconstruction, where the initial conditions of the wave equation are approximated with a neural network, and the differential operator is computed with a differentiable numerical solver. The use of a numerical solver enables a stable network training while enforcing the physics as a strong constraint, in contrast to conventional physics-informed neural networks, which include the physics as a constraint in the loss function. We introduce an additional sparsity-promoting constraint to achieve meaningful solutions even under severe undersampling conditions. Experiments demonstrate that the proposed approach can reconstruct sound fields under extreme data scarcity, achieving higher accuracy and better convergence compared to physics-informed neural networks.
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Systems and Control · Cross-listed from cs.RO
PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Jushan Chen and Santiago Paternain
Recently,
diffusion models have gained popularity and
attention in trajectory optimization due to their capability of modeling multi-modal probability distributions. However, addressing nonlinear equality constraints, i.e, dynamic feasibility, remains a great challenge in
diffusion-based trajectory optimization. Recent
diffusion-based trajectory optimization frameworks rely on a single-shooting style approach where the denoised control sequence is applied to forward propagate the dynamical system, which cannot explicitly enforce constraints on the states and frequently leads to sub-optimal solutions. In this work, we propose a novel direct trajectory optimization approach via model-based
diffusion, which directly generates a sequence of states. To ensure dynamic feasibility, we propose a gradient-free projection mechanism that is incorporated into the reverse
diffusion process. Our results show that, compared to a recent state-of-the-art baseline, our approach leads to zero dynamic feasibility error and approximately 4x higher success rate in a quadrotor waypoint
navigation scenario involving dense static obstacles.
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Signal Processing
The Role of ISAC in 6G Networks: Enabling Next-Generation Wireless Systems
Muhammad Umar Farooq Qaisar, Weijie Yuan, Onur Günlü et al. (+6 more)
The commencement of the sixth-generation (6G) wireless networks represents a fundamental shift in the integration of communication and sensing technologies to support next-generation applications. Integrated sensing and communication (
ISAC) is a key concept in this
evolution, enabling end-to-end support for both communication and sensing within a unified framework. It enhances spectrum efficiency, reduces latency, and supports diverse use cases, including smart cities, autonomous systems, and perceptive environments. This tutorial provides a comprehensive overview of
ISAC's role in 6G networks, beginning with its
evolution since 5G and the technical drivers behind its adoption. Core principles and system variations of
ISAC are introduced, followed by an in-depth discussion of the enabling technologies that facilitate its practical deployment. The paper further analyzes current research directions to highlight key challenges, open issues, and emerging trends. Design insights and recommendations are also presented to support future development and implementation. This work ultimately tries to address three central questions: Why is
ISAC essential for 6G? What innovations does it bring? How will it shape the future of wireless communication?
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All authors Muhammad Umar Farooq Qaisar, Weijie Yuan, Onur Günlü, Taneli Riihonen, Yuanhao Cui, Lin Zhang, Nuria Gonzalez-Prelcic, Marco Di Renzo, Zhu Han
Signal Processing
Effect of nearby Metals on Electro-Quasistatic Human Body Communication
Samyadip Sarkar, Arunashish Datta, David Yang et al. (+3 more)
In recent decades Human Body Communication has emerged as a promising alternative to traditional
radio wave communication, utilizing the body's conductive properties for low-power connectivity among wearables. This method harnesses the human body as an energy-efficient channel for data transmission within the electro-quasistatic frequency range, enabling advancements in human-machine interaction. While prior work has noted the role of parasitic return paths in such capacitively coupled systems, the influence of surrounding metallic objects on these paths, which are critical for EQS wireless signaling, has not been fully explored. This paper fills that gap with a structured study of how various conducting objects, from non-grounded (floating) metals and grounded metals to enclosed metallic environments such as elevators and cars, affect the body-communication channel. We present a theoretical framework supported by finite element method simulations and experiments with wearable devices. Results show that metallic objects within 20 cm of devices can reduce transmission loss by about 10 dB. When a device ground connects to a grounded metallic object, channel gain can increase by at least 20 dB. Contact area during touch-based interactions with grounded metals produces contact-impedance dependent high-pass channel characteristics. Proximity to metallic objects introduces variability within a critical distance, with grounded metals producing a larger overall effect than floating metals. These findings improve understanding of body-centric communication links and inform design for healthcare, consumer electronics, defense, and industrial applications.
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All authors Samyadip Sarkar, Arunashish Datta, David Yang, Mayukh Nath, Shovan Maity, Shreyas Sen
Signal Processing
Low-Rank-Based Approximate Computation with Memristors
Binyu Lu, Matthias Frey, Stark Draper et al. (+1 more)
Memristor crossbars enable vector-
matrix multiplication (VMM), and are promising for low-power applications. However, it can be difficult to write the memristor conductance values exactly. To improve the accuracy of VMM, we propose a scheme based on low-rank
matrix approximation. Specifically, singular value decomposition (SVD) is first applied to obtain a low-rank approximation of the target
matrix, which is then factored into a pair of smaller
matrices. Subsequently, a two-step serial VMM is executed, where the
stochastic write errors are mitigated through step-wise averaging. To evaluate the performance of the proposed scheme, we derive a general expression for the resulting computation error and provide an asymptotic analysis under a prescribed singular-value profile, which reveals how the error scales with
matrix size and rank. Both analytical and numerical results confirm the superiority of the proposed scheme compared with the
benchmark scheme.
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All authors Binyu Lu, Matthias Frey, Stark Draper, Jingge Zhu