Systems and Control
A Duality Reformulation of the Companion-Matrix Lyapunov Problem
Augusto Ferrante
We study the relation between positive semidefiniteness and entrywise nonnegativity for solutions of continuous-time Lyapunov equations. For a real Hurwitz
matrix $A$, we show that the solution of $AP+PA^\top=-Q$ is positive semidefinite for every symmetric entrywise nonnegative $Q$ if and only if the solution of $A^\top X+XA=-R$ is entrywise nonnegative for every positive semidefinite $R$. This equivalence follows from the adjointness of the two solution operators and extends to all real unmixed
matrices. We then prove that both properties hold for every real Hurwitz companion
matrix, settling a
conjecture previously established under the additional assumption of a real spectrum. The proof combines a controllability-Gramian normalization with a pairwise positivity
theorem for the coefficients of the adjugate
polynomial of a real accretive
matrix. The latter is obtained from a coefficient-sign property of bivariate
polynomials, an auxiliary determinant that does not vanish on the product of two open right half-planes, and a rank-one perturbation argument. Covariance and energy interpretations connect these results with dissipative realizations, damped second-order systems, and comparisons between input Gramians.
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Audio and Speech Processing
Building a Production Greek-English Speech Recognizer
Christos Petrocheilos, Cleopatra Papadopoulou, Chris Porikis et al. (+3 more)
We report a multi-month engineering program to build Sophea, a production bilingual Greek-English automatic
speech recognition system. We evaluate the system against nine production gates covering Greek and English word error rate,
language identification, and
hallucinations on non-
speech audio. Across twenty-three training iterations and two model architectures, no training-data composition passed all nine gates simultaneously. Meeting the Greek noisy-environment target required about 1,500 steps of dense domain exposure, while preserving English
language identification tolerated only about 250 steps, or about 1,250 with a rebalanced mix that reduced Greek accuracy. We describe a six-stage data pipeline in which calibrating an audio-quality filter against in-domain anchors reduced the discarded share of scored Greek audio from 98.7 percent to 10.6 percent. A pre-registered ablation isolated a
hallucination defect to one training-data package. A three-model ROVER ensemble increased gate coverage from 4-7 of 9 for individual models to 9 of 9 and reduced overlapping-
speech WER from 53.35 percent to 37.87 percent, a 29 percent relative improvement. A separate learned per-clip arbiter over two models is listed as sophea/asr-k1 (preview) on the public Open ASR Leaderboard, with 4.26 percent average WER across eight public English test sets, and reaches 25.88 percent WER on live Greek noisy-environment traffic. We also document five cases in which a measurement tool produced a plausible but incorrect result and seven substantial approaches that were evaluated but not shipped. No model weights or training data are released; we report methodology and quantitative results only.
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All authors Christos Petrocheilos, Cleopatra Papadopoulou, Chris Porikis, Ioakeim Perros, Ayoub Kirouane, Themistoklis Nikolis
Systems and Control
Active Filter Design for Buffering Datacenter-Scale Power Fluctuations from Training AI
Dillon Jensen, Grant Wilkins, Obi Nnorom Jr. et al. (+3 more)
Training large AI models requires thousands of processors working in parallel. This synchronized load poses a challenge for traditional power delivery architectures, because IT power ramps much faster than grid hardware can compensate. This paper presents a hardware solution to this problem, introducing a novel power delivery architecture which automatically detects changes in load power and compensates using on-rack energy storage, giving time for the grid to respond to the varying load. The architecture is validated experimentally, powering a training load while limiting the grid-side power ramp rate such that it stays within pre-specified range. The prototype presented is rated to deliver up to 10 kW of buffered power in a 400 VDC system, with a bill of materials cost of $3,500 USD. The system design is described in detail. A key advantage of this architecture versus other approaches is that it reliably buffers power fluctuations without requiring any changes to system software, making it compatible with any training workload.
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All authors Dillon Jensen, Grant Wilkins, Obi Nnorom Jr., Hugo Budd, Phil Levis, Juan Rivas
Systems and Control · Cross-listed from math.OC
Fast Relax-and-Round Unit Commitment with Topological Constraints
Evan J. R. Brody, Charles Foltz, Eve Tsybina et al. (+2 more)
Recent developments in the US knowledge economy have created a significant growth in datacenter loads, with two major consequences for power generation. First, to compensate for growth in load, datacenters are encouraged to bring their own generating units. Second, in search of the remaining pools of dispatchable generation, utilities are increasingly turning to subtransmission and distribution level generating assets. Coupled with increasing loads and the resulting tighter grid conditions, both trends are likely to create a need to commit a large number of localized generating units under grid constraints. We propose an extension of Relax-and- Round Unit Commitment (RRUC) that is capable of committing generating units for larger problems faster than conventional methods, while staying within intertemporal and spatial MVA constraints. We demonstrate the performance of RRUC using synthetic congestible test systems ranging from 100 to 20,000 buses. RRUC consistently finds low cost solutions, independent of the problem size, and its run time increases sub-quadratically in the number of buses. RRUC can solve the 100 bus system in less than a second and the 20,000 bus system 7 minutes. In contrast, a leading state of the art solver cannot find a feasible solution to the 100 bus system in 15 minutes.
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All authors Evan J. R. Brody, Charles Foltz, Eve Tsybina, Slaven Peles, Shaked Regev
Image and Video Processing · Cross-listed from physics.optics
Versatile Sleeve-and-Bulk Fabrication of Inversely Designed Nanophotonic Structures with High Pattern Transfer Fidelity
Lan Hoang Mai, Radheya Sham Sarode, William G. Eshbaugh et al. (+6 more)
Inverse design is a powerful computational tool for generating nanophotonic devices that should outperform their traditionally designed counterparts. However, translating these theoretical gains into physical devices is challenging because the irregular and non-intuitive features that make inversely designed structures so effective also make them exceptionally difficult to fabricate, typically necessitating substantial process optimization unique to each device design. We describe a versatile two-step nanofabrication process that achieves pattern transfer fidelity in excess of 90% for each of three distinct inverse device designs with no device-specific process optimization.
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All authors Lan Hoang Mai, Radheya Sham Sarode, William G. Eshbaugh, Sulaiman Al Ghadani, Tyler Manfre, Nazifa Tasnim Arony, Joshua M. O. Zide, Matthew F. Doty, Edward B. Flagg
Systems and Control
From Winding-Fault Geometry to Reliability: Estimation and Prognosis of Stator Inter-Turn Faults
Bambang L. Widjiantoro, Syahrul Munir, Katherin Indriawati et al. (+1 more)
This paper develops an integrated framework for estimation, prognosis, and reliability assessment of stator inter-turn faults in induction motors. The fault is characterized by its severity, defined as the fraction of short-circuited turns, and its spatial orientation. A
geometric fault model shows that the resulting output signature is affine in the fault severity and exhibits a fundamental spatial periodicity. Exploiting this structure, an augmented-state particle filter jointly estimates the nonlinear electromechanical state and the unknown fault severity while quantifying posterior uncertainty. The estimated degradation is then propagated using four prognostic models: linear trend, Holt exponential smoothing,
Bayesian degradation, and particle-based forecasting. Their predictions are connected to threshold-crossing remaining useful life (RUL), first-passage reliability, degradation-dependent hazard reliability, and a Weibull lifetime
benchmark, thereby providing both deterministic and probabilistic health assessments. Numerical results demonstrate accurate online fault estimation and output reconstruction, characterize the effects of degradation pattern and prediction horizon on prognosis, and show consistent reliability and threshold-crossing predictions. Moreover, estimation accuracy remains comparable across the distinct fault orientations induced by the spatial periodicity. The resulting framework provides a unified connection from physics-based inter-turn fault modeling to online diagnosis, degradation prognosis, and reliability assessment.
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All authors Bambang L. Widjiantoro, Syahrul Munir, Katherin Indriawati, Moh Kamalul Wafi
Audio and Speech Processing · Cross-listed from cs.CL
CVSS-X: A Multilingual Speech-to-Speech Translation Corpus for 28 Languages
Lucas Rafael Stefanel Gris, Alef Iury Siqueira Ferreira, Frederico Santos de Oliveira et al. (+4 more)
We introduce CVSS-X, a large-scale synthetic
speech-to-
speech translation corpus that extends CVSS by reversing the translation direction. While CVSS translates from 21
languages into English, CVSS-X enables translation from English into 28 target
languages spanning 12
language families. The corpus comprises approximately 240,000 parallel
speech pairs per
language, totaling over 16,000 hours, eight times larger than CVSS. We provide two variants: CVSS-X-C with two canonical voices per
language, and CVSS-X-T with cross-lingual voice cloning, both fully generated. Evaluation shows comparable translation quality to CVSS with consistent performance across typologically diverse
languages. Combined with CVSS, this enables research on bidirectional and multilingual
speech-to-
speech translation. The code is available at https://github.com/ErmisAI/XVSS-X and the
dataset under CC-BY-NC 4.0 license at https://huggingface.co/
datasets/lgris/XVSS-X.
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All authors Lucas Rafael Stefanel Gris, Alef Iury Siqueira Ferreira, Frederico Santos de Oliveira, Augusto Seben da Rosa, Alexandre Costa Ferro Filho, Arlindo Rodrigues Galvão Filho, Anderson da Silva Soares
Image and Video Processing
Tile-Based Phase Unwrapping with Reliability-Ordered Joining and Residual-Weighted Multipath Averaging
Antoine Moevus and Max Mignotte
Phase unwrapping recovers a continuous phase map from measurements known only modulo 2{\pi}, in the presence of noise and discontinuities. We propose a tile-based method that combines local spectral reconstruction, reliability-ordered tile joining, and residual-weighted multipath averaging. Each tile is reconstructed by a discrete cosine transform (DCT) least-squares solve, and robust measurements along tile borders guide their joining along a spanning tree. We repeat this reconstruction on shifted tile grids and on rotated and reflected copies of the image, which changes tile borders and joining paths. A residual-weighted average combines the resulting reconstructions, which are computed in parallel and averaged on the fly without being stored. We also introduce the corrected circular rewrapping error, a congruence measure: after removing a global phase offset, it measures how far the rewrapped reconstruction is from the observed phase. It applies to least-squares and path-based methods, even without an unwrapped reference. Used with reference-based errors, it separates agreement with the measurement from recovery of the correct number of cycles. Experiments on radar-interferometric,
magnetic-resonance, holographic, and constructed phase images compare our method with seven reference
algorithms. Among the methods that complete every test image, ours has the lowest mean fraction of pixels with an error above {\pi}. On the images all methods complete, it lies on several speed-accuracy Pareto fronts. Error maps also reveal isolated cycle errors that average scores or congruence alone can hide.
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Signal Processing
Krum-Inspired Central Teacher Selection and Residual Channel Bottlenecks for Efficient DeepSC
Rami Eid, Mostafa Jammoul, Omar Kaaki et al. (+3 more)
Deploying
transformer-based semantic communication models on edge devices requires compression that preserves semantic fidelity under channel variability. We study a compressed deep semantic communication (DeepSC) student trained by multi-teacher knowledge
distillation and combine two ideas: (i) a residual channel bottleneck that splits the transmitted representation into a base stream and a residual stream with unequal power allocation, and (ii) a Krum-inspired, medoid-style centrality criterion that selects a single central teacher from a five-model ensemble, applied at the logit and intermediate-feature levels and combined with a feature-dominant
distillation loss. On the EuroParl
benchmark, a two-layer student recovers about 98% of the four-layer teacher's bilingual evaluation understudy (BLEU)-1 under additive white Gaussian noise, with 93% of its BLEU-4 and 96% of its sentence-BERT (SBERT) score, and about 89%, 77%, and 87% of the teacher's BLEU-1, BLEU-4, and SBERT, respectively, under Rayleigh fading, while reducing non-
embedding parameters by 1.33x and single-teacher inference latency by 1.79x (9.0x relative to a five-teacher ensemble used here as an upper-bound reference, not a deployment baseline). Controlled ablations over three seeds indicate complementary contributions of about +8.4% BLEU-1 (+9.6% SBERT) from the residual bottleneck and +3.0% (+2.9% SBERT) from centrality-based selection over mean aggregation. A decoder-mode ablation shows that the base stream alone recovers about two-thirds of the full BLEU-1 while the residual stream alone collapses, supporting the role of the residual as a refinement on top of the base.
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All authors Rami Eid, Mostafa Jammoul, Omar Kaaki, Maria Slim, Mariette Awad, Hadi Sarieddeen
Signal Processing
Privacy-Preserving Deep Joint Source-Channel Coding with In-Loop Concept Erasure
Rami Eid, Maria Slim, Mariette Awad et al. (+1 more)
Deep joint source-channel coding (DeepJSCC) transmits learned semantic features efficiently but can leak sensitive attributes such as gender, race, or speaker identity. We propose LEAPSC (LEACE-in-the-loop
privacy for semantic communication), whose core contribution is the integration of in-loop least-squares concept erasure (LEACE) within a variational information bottleneck (VIB) encoder. By periodically refitting the projection operator during training, LEAPSC couples the encoder dynamics to the erasure mechanism, driving attribute-conditional mean differences toward zero within each task-label group on the fitting sample. Additional components, namely conditional value-at-risk (CVaR) tail-sensitive
privacy, feature-wise linear modulation (FiLM) signal-to-noise ratio conditioning, and Lagrangian dual ascent, improve robustness across channel conditions and over the high-leakage tail of samples. On CelebA, FairFace, and Google
Speech Commands, LEAPSC reaches task accuracy of 0.862, 0.755, and 0.925 respectively, with attacker accuracy at or below the label-only floor on CelebA (0.548 vs. floor 0.580) and within 2 percentage points (pp) of chance elsewhere, improving over an information-bottleneck
adversarial baseline (IBAL) at a matched 52-epoch budget by +3.6, +2.5, and +1.3 pp (Welch's t-test, p=0.019 on CelebA).
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All authors Rami Eid, Maria Slim, Mariette Awad, Hadi Sarieddeen
Image and Video Processing
PiMiX 2.02: Toward AI-Driven Data Fusion in Radiographic Imaging and Tomography
Zhehui Wang, Shanny Lin, Nicholas Amano et al. (+7 more)
PiMiX (Physics-informed Meta-instrument for eXperiments) was introduced for multi-instrument, multi-experiment, and simulation-experiment data fusion in radiographic imaging and
tomography (RadIT). Here we present PiMiX 2.02 as an evolving AI-enhanced cyber-physical meta-instrument integrating imaging sensors, near-sensor computing, data fusion, physics-informed inference, and human-supervised AI workflows across X-ray,
neutron, and other modalities. Demonstrated capabilities include
multimodal CMOS radiation imaging, simulation-assisted sub-pixel
neutron localization, and edge-deployed optical-neural-network (ONN) inference; GPU and ONN implementations achieved greater than 96% precision for
neutron-event detection with sub-micron localization. A further advance is human-in-the-loop agentic-AI co-analysis of X-ray and
neutron images from inertial-confinement-fusion experiments. Beyond conventional preprocessing, the workflow generates competing feature hypotheses, ranks contours using physics-informed evidence, estimates confidence, and presents alternatives for human review. The same architecture adapts to different physics: X-ray analysis emphasizes dark, nonuniform ring structures using deformable closed paths and multi-scale evidence, whereas
neutron analysis targets bright emission envelopes using fractional-emission levels, intensity gradients, and cross-filter persistence. We also highlight automated comparison of an as-designed stereolithography model with an X-ray CT reconstruction of an additively manufactured metal
lattice. Together, these examples show progression from AI assistance in specific processing tasks to multi-task, multi-domain scientific co-analysis. PiMiX 2.0 further provides a pathway toward PRISM, a RadIT scientific foundation model, and tighter integration of diagnostics, digital representations, inference, and experimental control.
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All authors Zhehui Wang, Shanny Lin, Nicholas Amano, Ramya Gurunathan, Katie Liu, Nathan E. Peterson, Michelle A. Espy, Adam Thompson, Amy J. Clarke, Ray T. Chen
Image and Video Processing
Recurrent Dynamic Range Extension
Sebastian Dille, Keru Fu, S. Mahdi H. Miangoleh et al. (+1 more)
We present an approach to progressively extend the highlights of an image. Instead of reconstructing the full dynamic range of a complex scene directly, we learn a simpler task first: We extend the dynamic range of an input image by a single exposure value. Once this is mastered, we retrieve the full HDR image for the scene by executing our network recurrently, progressively increasing the dynamic range of the input. Our formulation is agnostic to the input dynamic range and targets a bounded output domain. This enables us to use widely available RAW images for the reconstruction task and adapt
adversarial losses to construct realistic images. By incorporating
Memory Replay for backpropagation, we can train our network recurrently over multiple inference stages and reduce reconstruction errors. As a consequence, our system reconstructs challenging long-tailed HDR scenes robustly and shows powerful recovery of bright light sources and highlights.
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All authors Sebastian Dille, Keru Fu, S. Mahdi H. Miangoleh, Yağ{\i}z Aksoy
Signal Processing
Where to Defend? Layer-Wise Adversarial Training for Robust Transformer-Based Semantic Communications
Maria Slim, Razane Tajeddine, Mariette Awad et al. (+1 more)
Deep learning-based semantic communication (DeepSC), a
Transformer-based encoder-decoder, achieves semantic fidelity over noisy channels but remains vulnerable to
adversarial perturbations injected at multiple stages of the pipeline. We present a layer-wise robustness framework that compares fast gradient sign method (FGSM), projected gradient descent (PGD), and l2-normalized fast gradient method (FGM) defenses at the
embedding output, and then uses PGD to analyze three attack and defense points: the
embedding output, encoder output, and channel-encoder bottleneck. We evaluate reconstruction on Europarl and UK Hansard and sentiment classification on SST2 and YELP under additive white Gaussian noise (AWGN) and Rayleigh fading. A first-order damage budget, {epsilon} times the l1 norm of the clean-input loss gradient at each injection point, predicts the attack-severity ordering, and the transfer
matrix reveals asymmetric defense transfer: the
embedding defense transfers strongly to encoder attacks, whereas encoder defenses degrade robustness against upstream attacks. For reconstruction, encoder-point training yields the largest matched gain but fails severely under
embedding attacks; joint
embedding-plus-encoder training retains comparable gains under encoder attacks while mitigating this mismatch failure, and Rayleigh fading attenuates robustness gains and degradation. For classification, three of four defenses collapse to constant predictors; only the channel-encoder defense remains non-degenerate, suggesting a protective role for the 128D-to-16D bottleneck. At a signal-to-noise ratio (SNR) of 9 dB and perturbation budget {epsilon} = 0.3, the matched encoder defense recovers bilingual evaluation understudy (BLEU) from ~ 0.10 to ~ 0.64 on Europarl/AWGN, whereas the mismatched encoder defense yields a -0.444 BLEU change relative to the undefended baseline under an
embedding attack.
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All authors Maria Slim, Razane Tajeddine, Mariette Awad, Hadi Sarieddeen
Systems and Control
An Equivariant Filter for Spacecraft Nonlinear Relative Navigation with Range and Bearing Measurements
Gil Serrano, Bruno J. Guerreiro, Pedro Lourenço et al. (+1 more)
Relative navigation is a fundamental task in space proximity operations and autonomous rendezvous. The nonlinear relative orbital dynamics are equivariant and admit a semidirect product Lie group
symmetry. This property is used to design an Equivariant Filter (EqF). The filter estimates the relative position and velocity between a chaser spacecraft and a target by lifting the filter dynamics to the group, while respecting the underlying geometry of the problem. Simulations demonstrate the filter's performance and effectiveness.
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All authors Gil Serrano, Bruno J. Guerreiro, Pedro Lourenço, Rita Cunha
Audio and Speech Processing
MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant
Yi-Jen Shih, Shih-Yun Shan Kuan, Guan-Ting Lin et al. (+7 more)
Conversational voice
agents have advanced significantly, offering increasingly natural human-machine interactions through both cascaded and end-to-end architectures. However, while recent
benchmarks extensively evaluate dyadic interactions and passive audio comprehension, they largely overlook a prevalent real-world scenario: multi-party conversations. Evaluating
agents in these settings is fundamentally more challenging than in dyadic interactions due to the exponentially greater conversational complexity. For voice
agents to integrate seamlessly into human group dynamics, they must not only generate contextually appropriate responses but also demonstrate a nuanced understanding of open turn-taking. To address this gap, we introduce Multiparty Bench (MP-Bench), the first
benchmark specifically designed to objectively evaluate conversational
speech systems as active participants within multi-party contexts. MP-Bench assesses
agent behavior along two primary dimensions: turn-taking awareness and response appropriateness. Additionally, we incorporate comprehension-based question-answering tasks as a complementary evaluation. By benchmarking 12 voice
agents, we find that real-time voice
agents stay at or below 22% on multiparty comprehension and remain near chance on multiparty turn-taking, exposing an open challenge for real-time voice
agents under multiparty scenario.
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All authors Yi-Jen Shih, Shih-Yun Shan Kuan, Guan-Ting Lin, Kai-Wei Chang, Siddhant Arora, Shu-wen Yang, Abdelrahman Mohamed, Shinji Watanabe, Hung-yi Lee, David Harwath
Audio and Speech Processing
Objective Intelligibility Prediction Using Distance Metrics on Speech Foundation Model Representations
Lyonel Behringer and Andreas Brendel
High-dimensional representations of pretrained
speech foundation models have proven beneficial for objective
speech quality and intelligibility prediction. While existing work on neural intelligibility prediction usually leverages such representations for task-specific fine-tuning, in this work we evaluate the usefulness of such representations for intelligibility prediction without any further training. We conduct a layer-wise analysis of multiple
speech foundation models, correlating various
embedding distances with subjective intelligibility scores. The results show that
embeddings extracted from Whisper
speech recognition models are best suited, with the last encoder and decoder layers yielding the best correlations when using the Fréchet Audio Distance. Notably, the evaluated distances outperform classical intelligibility metrics and are more robust than Word and Character Error Rates. Further, correlations improve with increasing size of the Whisper model from which
embeddings are extracted.
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Systems and Control · Cross-listed from cs.RO
Global Path Planner with Multi-Model Switching
Pietro Gori, Francesco Iotti, Eduard Zelenay et al. (+5 more)
This work enhances global path planning via a pure-pursuit controller with multi-model kinematic switching that sustains plan fidelity across diverse terrains. The system includes a traversability graph for terrain analysis, a Heading-Aware A*
algorithm for generating feasible paths, and a multi-model Pure Pursuit controller for dynamic tracking. A core innovation is adaptive kinematic modeling, enabling real-time switching between kinematic models based on terrain features and
robot states. This adaptability optimizes path efficiency and energy use in challenging scenarios. We validate the approach in simulation on different platforms, namely the Artaban quadruped and the X3 quadrotor drone, showcasing improved performance, robustness, and adaptability over standard baselines.
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All authors Pietro Gori, Francesco Iotti, Eduard Zelenay, Rastislav Marko, Michele Pierallini, Franco Angelini, Gabriele Pannocchia, Manolo Garabini
Systems and Control
An Adaptive Multi-Parameter ADMM Algorithm for Embedded MPC
Alberto Zaupa and Mikael Johansson
We introduce an adaptive multi-parameter variant of ADMM and prove that it exhibits local superlinear convergence once the set of active constraints has been identified. In simulations, the proposed
algorithm consistently outperforms OSQP, a standard ADMM solver, in terms of iteration count. We then implement an MPC solver based on our method and compare its runtime against a broader selection of state-of-the-art
algorithms. Evaluations on challenging
benchmark problems reveal that our approach delivers competitive performance both in terms of average and worst-case solve times, without being limited to coarse tolerances, as is typically the case for standard ADMM implementations and first-order methods.
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Systems and Control
Safe Stabilising Full-Order Affine Control Barrier Functions for Linear Systems (Extended)
Faisal Lawan, Joaquin Carrasco and Lanlan Su
Control barrier function safety filters enforce constraints by modifying a nominal input, but the resulting switching can destabilise the closed loop even when the nominal and filtered modes are individually stable. This paper presents a design framework for safe and globally exponentially stabilising controllers for linear systems with a single full-relative-degree affine constraint. We show that the filtered-mode spectrum is fixed by the barrier tuning and is independent of the plant, nominal controller, and quadratic-program weighting. This structure yields an explicit nominal controller for which the safety filter remains inactive everywhere. For a prescribed nominal controller, we prove that the nominal and filtered modes admit a strong common quadratic Lyapunov function if and only if the ratio of the nominal characteristic
polynomial to the barrier
polynomial is strongly strictly positive real. This equivalence characterises the existence of a common quadratic Lyapunov and provides a scalar frequency-domain test, along with an explicit interval of admissible gains. Building on these results, the extended analysis derives an explicit common storage function and reduces an existing LMI synthesis condition to a feasibility test in a single
matrix variable. A flexible two-mass example explains a known instability mechanism and demonstrates how the proposed design restores safety and global exponential stability.
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Systems and Control · Cross-listed from cs.RO
Tuning ROS 2 for Energy-Efficient Navigation: Empirical Insights from Costmap 2D Configurations
Michel Albonico, Andreas Wortmann and Ivano Malavolta
Robots are increasingly used in diverse application areas, where autonomous navigation plays a central role. As these systems become more widespread, improving their energy efficiency is critical to extending operational time and reducing environmental impact. The
Robot Operating System (ROS) is a widely adopted middleware for robotics, offering a rich set of configurable packages. However, this flexibility can result in suboptimal software configurations in dynamic environments, negatively affecting both performance and energy consumption. This paper investigates the impact of ROS 2 package reconfigurations on the energy efficiency of mobile
robot navigation. We conduct a controlled experiment in two warehouse-like scenarios (small and large) with varying obstacle layouts and Costmap 2D configurations (essential to the Nav2 stack). Through repeated trials, we measure energy usage, power profile, CPU load,
memory consumption, and navigation performance. Results show that configurations must be carefully chosen for the specific robotic environment, and we were able to identify critical settings that lead to good and poor performance and energy consumption.
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Systems and Control
End-to-End Battery Dispatch with Exact Rainflow Degradation via Mixed-Integer Differentiable Predictive Control
Eshagh Safarzadeh Ravajiri, Jan Drgona, Mahdi Mehrtash et al. (+1 more)
Optimal dispatch of battery energy storage systems requires balancing energy arbitrage against cycle-induced degradation, which is accurately quantified through rainflow cycle counting. However, rainflow's combinatorial, nondifferentiable
algorithm is incompatible with both convex optimization and gradient-based neural network training. We present a self-supervised mixed-integer differentiable predictive control framework that trains neural policies directly on exact rainflow degradation through a novel differentiable rainflow layer combining exact gradients at state-of-charge extrema with dense proxy gradients on incremental changes, enabling stable end-to-end training while preserving true degradation physics. A mixed-integer differentiable architecture enforces power balance, dynamics, and mode exclusivity, with a safety filter guaranteeing constraint satisfaction. We evaluate the framework on 3,650 real battery-day scenarios (a 10-battery fleet over 365 days) spanning three utility regions (SDG&E California, Xcel Energy Colorado, APS Arizona) with diverse time-of-use pricing structures. A single-battery trained policy achieves a 0.35% performance gap on its training distribution with over 200x speedup, while fleet-wide training generalizes across all households and utility regions, achieving a 3.33% performance gap with 564x computational speedup (62 seconds vs. 9.7 hours) and 100% feasibility. The millisecond-scale inference reduces computation by over two orders of magnitude compared to mixed-integer solvers, enabling practical deployment at scale.
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All authors Eshagh Safarzadeh Ravajiri, Jan Drgona, Mahdi Mehrtash, Benjamin F. Hobbs
Image and Video Processing
Physics-informed denoising method for image reconstruction in quantitative low-field MRI
Catarina Redshaw Kranich, Claudia Prieto, Christoph Kolbitsch et al. (+1 more)
Low-field
magnetic resonance imaging (MRI) is becoming increasingly important for medical imaging because it can reduce healthcare costs while ensuring high diagnostic output. Nevertheless, quantitative imaging in low-field MRI faces challenges, such as low signal-to-noise ratio and long scan durations. Deep learning approaches have been proposed for image reconstruction to overcome these challenges. Still, deep learning often requires large high-quality training
datasets which are usually not available for low-field applications. Here we propose a modular unrolled end-to-end deep learning method for the denoised reconstruction of quantitative parameter maps directly from k-space data for low-field MRI. It consists of three sub-networks that are iteratively applied. They are used for the regularization of the quantitative parameter estimation, as well as for the signal estimation that is based on simulated signal curves. It generalises well and can be applied to different field strengths and even different quantitative MR sequences without the need for new training data. We applied the presented method to noisy data of knees acquired at 0.55 T for the reconstruction of $T_2$-maps and compared it to other classical and deep learning methods. We also applied the proposed approach to $T_1$-mapping of knees at 72 mT and $T_2$-mapping of
brains at 0.6 T. The presented approach outperforms the other reconstruction methods with a median difference below 4 ms to the ground truth $T_2$-map. Even though the network was trained with $T_2$-maps acquired at 0.55 T, it successfully denoised data acquired at different field strengths, sequences, and of different anatomies. As a result, the proposed network and its underlying method offer an efficient and flexible solution to denoise low-field MR data and make quantitative low-field MRI a feasible diagnostic tool for clinical applications.
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All authors Catarina Redshaw Kranich, Claudia Prieto, Christoph Kolbitsch, Felix Frederik Zimmermann
Audio and Speech Processing · Cross-listed from cs.SD
StepAudio 3 Gen Technical Report
Bin Lin, Bo Zhao, Boyang Wang et al. (+68 more)
We introduce StepAudio 3 Gen, a general-purpose audio generation model that supports zero-shot text-to-
speech (TTS), voice design, vocal generation, sound effects, music, vibe
speech, and mixtures of multiple audio types within a unified framework. At its core, StepAudio 3 Gen is a discrete autoregressive generator that models audio directly over residual vector quantization (RVQ)
tokens, departing from the
diffusion Transformer-based continuous generation paradigm prevalent in recent general audio models. Its StepAudio Tokenizer represents general audio at 12.5 Hz in a shared $16 \times 2048$ residual code space, jointly quantizing semantic and waveform-level acoustic features so that each code layer preserves both types of information. For generation, the backbone predicts the first codebook along the time axis using autoregressive modeling, while a lightweight causal
Transformer completes the remaining fifteen codebooks along the codebook axis. Our study further identifies three key design principles: (1) interference-aware progressive pretraining for acquiring audio capabilities while preserving the textual abilities of the large
language model, (2) RVQ Adaptor for effectively incorporating multi-codebook acoustic representations, and (3) discrete autoregressive modeling over a shared representation across general audio domains. With progressive pretraining, multi-task instruction training, and supervised fine-tuning, StepAudio 3 Gen achieves state-of-the-art performance on both TTS and voice design, while retaining strong generation capabilities across
speech, vocals, sound effects, and music. Audio samples are available at https://stepaudiollm.github.io/step-audio-3-gen/.
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All authors Bin Lin, Bo Zhao, Boyang Wang, Boyang Zhang, Boyong Wu, Chao Yan, Chen Geng, Chen Wu, Cheng Yi, Chengli Feng, Chenglin Zhu, DanNi Wan, Daxin Jiang, Dongqing Pang, Fei Tian, Feng Tian, Future Li, Gang Yu, Guanglong Yang, Jia Peng, Jiahao Song, Jiamin Fan, Jiangjie Zhen, Jianzheng Gao, Jun Chen, Li Xie, Lifang Zhang, Lingli Ji, Liying Shi, Lun Cai, Min Xu, Na Wang, Peilin Li, Peng Yang, Pengfei Tan, Qingjian Lin, Ruijie Xiong, Runze Li, Shenghua Hu, Shi Qiu, Siqi Tu, Siyi Zhou, Tianjiao Deng, Wanying Lu, Weiming Niu, Wen Sun, WenWen Qu, Xiangyu Zhang, Xianwei Zhang, XiaoSu Su, Xing Chen, Xinyu Liu, Xuerui Yang, Yang Li, Yang Yang, Yechang Huang, Yibo Zhu, Yifan Zhang, Yiyang Xu, Yu Fu, Yu Luo, Yu Zhou, Yumang Wang, Yunzhou Ju, Yuxiang Yang, Zekai Liu, Zengwei Yao, Zhenwei Mou, Zheqi Dai, Zhiyue Wu, Zichao Zhou
Systems and Control
Aerial Non-Stop Trajectories Preserving the Equilibrium of Loads Suspended by Variable-Length Cables
Antonio Franchi, Alberto Landi and Chiara Gabellieri
This work studies
equilibrium-preserving non-stop trajectories of aerial carriers connected to rigid loads by variable-length cables. Internal-force motions vary the cable directions without changing the load wrench, while cable-length actuation shapes the radial realization of the carrier trajectories. We derive an explicit cable-length law for realizing prescribed carrier-position profiles along a fixed direction, with constant altitude as a relevant specialization. Under this constraint, a carrier stops exactly when its cable direction stops changing. This characterization reveals a distinction between unrestricted and level motion: variable cable lengths enable bounded non-stop motion with two carriers, whereas periodic level non-stop motion is obstructed for two carriers and constructively achieved for three or more under explicit
geometric and feasibility conditions. Numerical simulations illustrate the constant-altitude construction.
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Signal Processing
Learning the Topology of a Simplicial Complex Using Noisy Simplicial Signals
Andrei Buciulea, Elvin Isufi, Geert Leus et al. (+1 more)
Graphs are a fundamental tool for modeling the irregular (non-Euclidean) structure of complex data. However, they are inherently limited to representing pairwise relationships, making them inadequate for
datasets exhibiting higher-order interactions. Simplicial complexes (SCs) have emerged as a promising framework for capturing such higher-order dependencies. This paper focuses on the problem of identifying the
topology of an SC from signals, which serves as the foundation for SC-based processing and learning schemes. We consider a setting where we observe noisy signals (features) associated with the nodes of the SC (0-simplices) and a subset of the edges (1-simplices). We assume the observed signals are smooth over the unknown SC
topology, and that the higher-order interactions are sparse. Building on these assumptions, we formulate
topology learning as a nonconvex optimization problem and propose an efficient block-coordinate descent (BCD)
algorithm to solve it. A key step in our formulation is the modeling of the
topology of the SC using binary edge and triangle selection vectors, combined with efficient greedy
algorithms for optimizing such vectors. We establish theoretical convergence guarantees to a stationary point of a relaxed (penalized) version of the problem and discuss computational complexity. Multiple numerical experiments with both synthetic and real-world
datasets validate the effectiveness of our approach, highlighting the capability of SC-learning methods to uncover and model higher-order relationships in complex
datasets.
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All authors Andrei Buciulea, Elvin Isufi, Geert Leus, Antonio G. Marques
Audio and Speech Processing
AlignDPO: Preference-Gated Alignment for Reducing Hallucination in Decoder-Only TTS
Xiao Zhou, Oisín Turbitt, Kit Bower-Morris et al. (+3 more)
Decoder-only text-to-
speech (TTS) models scale efficiently but remain prone to content
hallucinations that arise from weak text-
speech alignment during autoregressive generation. We find that robustness is governed by a non-monotone relation to the sharpness of the
alignment-bearing
attention heads: a moderate degree is best, whereas over-sharpening is no better than the unaligned backbone and even less robust. Guided by this, we present AlignDPO, a post-training method that reaches this moderate regime by folding a lightweight connectionist-temporal-classification (CTC)
alignment term into Direct Preference Optimization (DPO), applied only to the chosen samples, with no architectural or inference-time change. On the Seed-TTS-Eval English set, this significantly reduces the content-
hallucination and word error rates relative to a strong DPO baseline and lowers the severe content-
hallucination rate to ~0.6% (from 4.4%); a listening study further finds it preferred for naturalness over both the backbone and that baseline.
Alignment is thus best learned and kept moderate rather than maximized or imposed at decoding. Audio samples are available at https://align-dpo-demo.vercel.app.
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All authors Xiao Zhou, Oisín Turbitt, Kit Bower-Morris, Jonathan Carlton, Jamie Stacey, Kris Y. Hong
Systems and Control
Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models
Fabian Raisch, Felix Koch, Zack Xuereb Conti et al. (+2 more)
The widespread adoption of data-driven, energy-efficient model predictive control (MPC) in buildings remains hindered by substantial effort to collect data and train models for individual buildings. Transfer learning (TL) has consequently gained increasing
attention for target building modeling, as it reduces data requirements and modeling effort by reusing pretrained source models. However, these TL models are typically evaluated only on prediction accuracy in the target, without testing downstream control performance. To address this gap, we apply a state-of-the-art TL approach - pretraining a generalized model on multiple source buildings using standard operational data - within an MPC setup in a target building. We show that this approach is insufficient to achieve satisfactory control performance. As a solution, we introduce generalized models pretrained on excitation-based operational source data - purposefully probed inputs that explore the building's state-action space. For evaluation, we apply the generalized models via zero-shot (i.e., without fine-tuning) to 32 simulated target buildings and assess MPC performance. Our results show that excitation-based generalized models achieve the strongest control performance among all
benchmarks, outperforming an online linear model-based MPC and a PI controller by 6.4% and 36.9%, respectively. By combining strong control performance with the ability to generalize across multiple buildings, without requiring any target-specific data, our approach reduces MPC setup cost and simplifies its widespread deployment in the building sector.
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All authors Fabian Raisch, Felix Koch, Zack Xuereb Conti, Christoph Goebel, Benjamin Tischler
Audio and Speech Processing
A Device to Control and Manipulate Occlusion Effects for Own Voice Perception Studies
Rouben Rehman, Simon Kersten, Aron Schliep et al. (+1 more)
The occlusion effect (OE) refers to changes of the eardrum sound pressure through ear canal occlusion. It consists of two phenomena: an insertion loss (IL) attenuating air-conducted sounds, and an occlusion gain (OG) amplifying bone-conduction. Perceptual research on this is hindered by high variability of the OE across individuals, complicating repeatable presentation of precise OE conditions. Consequently, a method to control OE conditions reproducibly during perceptual experiments is needed. For such investigations, the system must enable separate control of IL and OG. We present an approach based on modified commercial earmuffs with integrated microphones. The design integrates dedicated impedance measurements and the derivation of digital filters, which are applied to the microphone signals to emulate arbitrary OE curves. The system is evaluated objectively through appropriate measurements. Results show that the headphones' inherent OG is guaranteed to be below 6dB above 115Hz, falling below 0dB above 155Hz. Emulation is shown to work accurately over the entire frequency range of interest. Limitations arise mainly due to a slight residual inherent OG for deep voices and the processing delay of the system. Future work will focus on the perceptual evaluation of the system and its application in perceptual studies.
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All authors Rouben Rehman, Simon Kersten, Aron Schliep, Janina Fels
Systems and Control · Cross-listed from cs.RO
VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Search
Bojan Derajić, Sebastian Bernhard and Wolfgang Hönig
As the number of autonomous
robots continues to grow, safety becomes increasingly important. Control barrier functions (CBFs) provide a theoretically grounded framework for ensuring safety, but existing design methods often face limitations in effectiveness, scalability, or interpretability, and may result in overly conservative safe sets. In this paper, we propose VertexCBF, a framework for learning neural CBFs in a scalable, systematic, and explainable way. We approximate the stationary Hamilton–Jacobi value function using a neural network trained via a combination of physics-informed and sparsely supervised learning. By exploiting control-affine dynamics and a convex polytope control set, under which the
Hamiltonian is maximized at the control vertices, we efficiently generate supervision points via GPU-parallel vertex-restricted tree search, while a residual architecture guarantees that the learned CBF is never larger than the specified constraint function. We evaluate the method on 15 systems and compare it against relevant baselines, showing that it reliably recovers large safe sets where the baselines are conservative or fail completely. In addition, we perform a hardware experiment in which a mobile
robot safely avoids pedestrians using a neural CBF trained with our method.
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Signal Processing
Multi-Label 12-Lead ECG Classification on the PTB-XL Dataset: A Comparative Evaluation of Deep Learning Architectures and Heterogeneous Ensemble Approaches
Yunus Emre Mert, Ece Akdoğan and Hüseyin Üvet
This study aimed to compare the performance of different deep learning architectures and heterogeneous ensemble learning approaches for multi-label 12-lead ECG classification on the PTB-XL
dataset. Five different models, namely 1D-ResNet18, Bidirectional Mamba, xLSTM, CWT-ViT-KAN, and the pre-trained ECGFounder, were evaluated. Utilizing the recommended split structure of the PTB-XL
dataset, folds 1-8 were allocated as the training set, fold 9 as the validation set, and fold 10 as the independent test set. In addition to the individual models, three different ensemble approaches were investigated: Equal-Weight Soft Voting, Validation-Weighted Soft Voting, and stacking. Among the individual models, the highest performance was achieved by ECGFounder, with a Macro AUROC of 0.930 and a Macro AUPRC of 0.823. For the ensemble models, the highest values in the primary macro performance metrics were obtained by the stacking approach, achieving a Macro AUROC of 0.936, a Macro AUPRC of 0.836, and a Macro F1 of 0.763. The highest subset accuracy of 0.630 was achieved using the Validation-Weighted Soft Voting method. The findings indicate that heterogeneous ensemble models, which combine different representation learning approaches, can provide additional performance improvements over individual models in multi-label ECG classification.
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