Systems and Control · Cross-listed from astro-ph.EP
Higher-Order Gravitational Models: A Tutorial on Spherical Harmonics and the Newtonian Model
Felipe Arenas-Uribe
Accurate modeling of gravitational interactions is fundamental to the analysis, prediction, and control of space systems. While the Newtonian point-mass approximation suffices for many preliminary studies, real celestial bodies exhibit deviations from spherical
symmetry, including oblateness, localized mass concentrations, and higher-order shape irregularities. These features can significantly perturb spacecraft trajectories, especially in low-altitude or long-duration missions, leading to cumulative orbit prediction errors and increased control demands. This article presents a tutorial introduction to spherical harmonic
gravity models, outlining their theoretical foundations and underlying assumptions. Higher-order gravitational fields are derived as solutions to the Laplace equation, providing a systematic framework to capture the effects of non-uniform mass distributions. The impact of these higher-order terms on orbital dynamics is illustrated through examples involving Low Earth Orbit satellites and spacecraft near irregularly shaped asteroids, highlighting the practical significance of moving beyond the point-mass approximation.
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Audio and Speech Processing
ToS: A Team of Specialists ensemble framework for Stereo Sound Event Localization and Detection with distance estimation in Video
Davide Berghi and Philip J. B. Jackson
Sound event localization and detection with distance estimation (3D SELD) in
video involves identifying active sound events at each time frame while estimating their spatial coordinates. This
multimodal task requires joint
reasoning across semantic, spatial, and temporal dimensions, a challenge that single models often struggle to address effectively. To tackle this, we introduce the Team of Specialists (ToS) ensemble framework, which integrates three complementary sub-networks: a spatio-linguistic model, a spatio-temporal model, and a tempo-linguistic model. Each sub-network specializes in a unique pair of dimensions, contributing distinct insights to the final prediction, akin to a collaborative team with diverse expertise. ToS has been benchmarked against state-of-the-art audio-visual models for 3D SELD on the DCASE2025 Task 3 Stereo SELD development set, consistently outperforming existing methods across key metrics. Future work will extend this proof of concept by strengthening the specialists with appropriate tasks, training, and pre-training curricula.
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Systems and Control · Cross-listed from cs.HC
Home Health System Deployment Experience for Geriatric Care Remote Monitoring
Dong Yoon Lee, Alyssa Weakley, Hui Wei et al. (+2 more)
To support aging-in-place, adult children often provide care to their aging parents from a distance. These informal caregivers desire plug-and-play remote care solutions for
privacy-preserving continuous monitoring that enabling real-time activity monitoring and intuitive, actionable information. This short paper presents insights from three iterations of deployment experience for remote monitoring system and the iterative improvement in hardware, modeling, and user interface guided by the Geriatric 4Ms framework (matters most, mentation, mobility, and medication). An LLM-assisted solution is developed to balance user experience (
privacy-preserving, plug-and-play) and system performance.
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All authors Dong Yoon Lee, Alyssa Weakley, Hui Wei, Daniel Cardona, Shijia Pan
Image and Video Processing · Cross-listed from cs.CV
Stylizing ViT: Anatomy-Preserving Instance Style Transfer for Domain Generalization
Sebastian Doerrich, Francesco Di Salvo, Jonas Alle et al. (+1 more)
Deep learning models in medical image analysis often struggle with generalizability across domains and demographic groups due to data heterogeneity and scarcity. Traditional augmentation improves robustness, but fails under substantial domain shifts. Recent advances in stylistic augmentation enhance domain generalization by varying image styles but fall short in terms of style diversity or by introducing artifacts into the generated images. To address these limitations, we propose Stylizing ViT, a novel
Vision Transformer encoder that utilizes weight-shared
attention blocks for both self- and cross-
attention. This design allows the same
attention block to maintain anatomical consistency through self-
attention while performing style transfer via cross-
attention. We assess the effectiveness of our method for domain generalization by employing it for data augmentation on three distinct image classification tasks in the context of histopathology and dermatology. Results demonstrate an improved robustness (up to +13% accuracy) over the state of the art while generating perceptually convincing images without artifacts. Additionally, we show that Stylizing ViT is effective beyond training, achieving a 17% performance improvement during inference when used for test-time augmentation. The source code is available at https://github.com/sdoerrich97/stylizing-vit .
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All authors Sebastian Doerrich, Francesco Di Salvo, Jonas Alle, Christian Ledig
Systems and Control · Cross-listed from q-bio.QM
GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design
Maurice Filo, Nicolò Rossi, Zhou Fang et al. (+1 more)
Biomolecular networks underpin emerging technologies in synthetic biology-from robust biomanufacturing and metabolic engineering to smart therapeutics and
cell-based diagnostics-and also provide a mechanistic
language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a proposed network can be checked by simulation, the reverse problem of discovering a network from a behavioral specification is difficult, requiring substantial human insight to navigate a vast space of
topologies and kinetic parameters with nonlinear and possibly
stochastic dynamics. Here we introduce GenAI-Net, a generative AI framework that automates CRN design by coupling an
agent that proposes reactions to simulation-based evaluation defined by a user-specified objective. GenAI-Net efficiently produces novel, topologically diverse solutions across multiple design tasks, including dose responses, complex logic gates, classifiers, oscillators, and robust perfect adaptation in deterministic and
stochastic settings (including noise reduction). By turning specifications into families of
circuit candidates and reusable motifs, GenAI-Net provides a general route to programmable biomolecular
circuit design and accelerates the translation from desired function to implementable mechanisms.
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All authors Maurice Filo, Nicolò Rossi, Zhou Fang, Mustafa Khammash
Signal Processing
Development of an end-to-end hardware and software pipeline for affordable and feasible ergonomics assessment in the automotive industry
Javier González-Alonso, Cristina Simón-Martínez, Míriam Antón-Rodríguez et al. (+3 more)
An end-to-end hardware-software pipeline is introduced to automatize ergonomics assessment in industrial workplaces. The proposed modular solution can interoperate with commercial systems throughout the ergonomics assessment phases involved in the process. The pipeline includes custom-designed Inertial Measurement Unit (IMU) sensors, two real-time worker movement acquisition tools, inverse kinematics processing and Rapid Upper Limb Assessment (RULA) report generation. It is based on free tools such as Unity3D and OpenSim to avoid the problems derived from using proprietary technologies, such as security decisions being made under "black box" conditions. Experiments were conducted in an automotive factory in a workplace with WMSDs risk among workers. The proposed solution obtained comparable results to a gold standard solution, reaching measured joint angles a 0.95 cross-correlation and a Root Mean Square Error (RMSE) lower than 10 for elbows and 12 for shoulders between both systems. In addition, the global RULA score difference is lower than 5% between both systems. This work provides a low-cost solution for WMSDs risk assessment in the workplace to reduce musculoskeletal disorders and associated sick leave in industry, impacting the health of workers in the long term. Our study can ease further research and popularize the use of wearable systems for ergonomics analysis allowing these workplace prevention systems to reach different industrial environments.
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All authors Javier González-Alonso, Cristina Simón-Martínez, Míriam Antón-Rodríguez, David González-Ortega, Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela
Signal Processing
ME-WARD: A multimodal ergonomic analysis tool for musculoskeletal risk assessment from inertial and video data in working plac
Javier González-Alonso, Paula Martín-Tapia, David González-Ortega et al. (+3 more)
This study presents ME-WARD (
Multimodal Ergonomic Workplace Assessment and Risk from Data), a novel system for ergonomic assessment and musculoskeletal risk evaluation that implements the Rapid Upper Limb Assessment (RULA) method. ME-WARD is designed to process joint angle data from motion capture systems, including inertial measurement unit (IMU)-based setups, and deep learning human body pose tracking models. The tool's flexibility enables ergonomic risk assessment using any system capable of reliably measuring joint angles, extending the applicability of RULA beyond proprietary setups. To validate its performance, the tool was tested in an industrial setting during the assembly of conveyor belts, which involved high-risk tasks such as inserting rods and pushing conveyor belt components. The experiments leveraged gold standard IMU systems alongside a state-of-the-art
monocular 3D pose estimation system. The results confirmed that ME-WARD produces reliable RULA scores that closely align with IMU-derived metrics for flexion-dominated movements and comparable performance with the
monocular system, despite limitations in tracking lateral and rotational motions. This work highlights the potential of integrating multiple motion capture technologies into a unified and accessible ergonomic assessment pipeline. By supporting diverse input sources, including low-cost
video-based systems, the proposed
multimodal approach offers a scalable, cost-effective solution for ergonomic assessments, paving the way for broader adoption in resource-constrained industrial environments.
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All authors Javier González-Alonso, Paula Martín-Tapia, David González-Ortega, Míriam Antón-Rodríguez, Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela
Image and Video Processing
Fast Multirate Encoding for 360{\deg} Video in OMAF Streaming Workflows
Amritha Premkumar and Christian Herglotz
Preparing high-quality 360-degree
video for HTTP Adaptive Streaming requires encoding each sequence into multiple representations spanning different resolutions and quantization parameters (QPs). For ultra-high-resolution immersive content such as 8K 360-degree
video, this process is computationally intensive due to the large number of representations and the high complexity of modern codecs. This paper investigates fast multirate encoding strategies that reduce encoding time by reusing encoder analysis information across QPs and resolutions. We evaluate two cross-resolution information-reuse pipelines that differ in how reference encodes propagate across resolutions: (i) a strict HD -> 4K -> 8K cascade with scaled analysis reuse, and (ii) a resolution-anchored scheme that initializes each resolution with its own highest-bitrate reference before guiding dependent encodes. In addition to evaluating these pipelines on standard equirectangular projection content, we also apply the same two pipelines to cubemap-projection (CMP) tiling, where each 360-degree frame is partitioned into independently encoded tiles. CMP introduces substantial parallelism, while still benefiting from the proposed multirate analysis-reuse strategies. Experimental results using the SJTU 8K 360-degree
dataset show that hierarchical analysis reuse significantly accelerates HEVC encoding with minimal rate-distortion impact across both equirectangular and CMP-tiled content, yielding encoding-time reductions of roughly 33%-59% for ERP and about 51% on average for CMP, with Bjontegaard Delta Encoding Time (BDET) gains approaching -50% and wall-clock speedups of up to 4.2x.
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Audio and Speech Processing
Spoofing-Aware Speaker Verification via Wavelet Prompt Tuning and Multi-Model Ensembles
Aref Farhadipour, Ming Jin, Valeriia Vyshnevetska et al. (+3 more)
This paper describes the UZH-CL system submitted to the SASV section of the WildSpoof 2026 challenge. The challenge focuses on the integrated defense against generative spoofing attacks by requiring the simultaneous verification of speaker identity and audio authenticity. We proposed a cascaded Spoofing-Aware Speaker Verification framework that integrates a Wavelet
Prompt-Tuned XLSR-AASIST countermeasure with a multi-model ensemble. The ASV component utilizes the ResNet34, ResNet293, and WavLM-ECAPA-TDNN architectures, with Z-score normalization followed by score averaging. Trained on VoxCeleb2 and SpoofCeleb, the system obtained a Macro a-DCF of 0.2017 and a SASV EER of 2.08%. While the system achieved a 0.16% EER in spoof detection on the in-domain data, results on unseen
datasets, such as the ASVspoof5, highlight the critical challenge of cross-domain generalization.
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All authors Aref Farhadipour, Ming Jin, Valeriia Vyshnevetska, Xiyang Li, Elisa Pellegrino, Srikanth Madikeri
Image and Video Processing
In-situ On-demand Digital Image Correlation: A New Data-rich Characterization Paradigm for Deformation and Damage Development in Solids
Ravi Venkata Surya Sai Mogilisetti, Partha Pratim Das, Rassel Raihan et al. (+1 more)
Digital image correlation (DIC) has become one of the most popular methods for deformation characterization in experimental mechanics. DIC is based on optical images taken during experimentation and post-test image processing. Its advantages include the capability to capture full-field deformation in a non-contact manner, the robustness in characterizing excessive deformation induced by events such as yielding and cracking, and the versatility to integrate optical cameras with a variety of open-source and commercial codes. In this paper, we developed a new paradigm of DIC analysis by integrating camera control into the DIC process flow. The essential idea is to dynamically increase the camera imaging frame rate with excessive deformation or deformation rate, while maintaining a relatively low imaging frame rate with small and slow deformation. We refer to this new DIC paradigm as in-situ on-demand (ISOD) DIC. ISOD DIC enables real-time deformation analysis, visualization, and closed-loop camera control. ISOD DIC has captured approximately 178% more images than conventional DIC for samples undergoing crack growth due to its dynamically adjusted frame rate, with the potential to significantly enhance data richness for damage inspection without consuming excessive storage space and analysis time, thereby benefiting the characterization of intrinsic constitutive behaviors and damage mechanisms
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All authors Ravi Venkata Surya Sai Mogilisetti, Partha Pratim Das, Rassel Raihan, Shiyao Lin
Systems and Control
Invited: Toward Sustainable and Transparent Benchmarking for Academic Physical Design Research
Liwen Jiang, Andrew B. Kahng, Zhiang Wang et al. (+1 more)
This paper presents RosettaStone 2.0, an open
benchmark translation and evaluation framework built on OpenROAD-Research. RosettaStone 2.0 provides complete RTL-to-GDS reference flows for both conventional 2D designs and Pin-3D-style face-to-face (F2F) hybrid-bonded 3D designs, enabling rigorous apples-to-apples comparison across planar and three-dimensional implementation settings. The framework is integrated within OpenROAD-flow-scripts (ORFS)-Research; it incorporates continuous integration (CI)-based regression testing and provides a standardized evaluation pipeline based on the METRICS2.1 convention, with structured logs and reports generated by ORFS-Research. To support transparent and reproducible research, RosettaStone 2.0 further provides a community-facing leaderboard, which is governed by verified pull requests and enforced through Developer Certificate of Origin (DCO) compliance.
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All authors Liwen Jiang, Andrew B. Kahng, Zhiang Wang, Zhiyu Zheng
Audio and Speech Processing · Cross-listed from cs.SD
EuleroDec: A Complex-Valued RVQ-VAE for Efficient and Robust Audio Coding
Luca Cerovaz, Michele Mancusi and Emanuele Rodolà
Audio codecs power discrete music generative modelling, music streaming and immersive media by shrinking PCM audio to bandwidth-friendly bit-rates. Recent works have gravitated towards processing in the spectral domain; however, spectrogram-domains typically struggle with phase modeling which is naturally complex-valued. Most frequency-domain neural codecs either disregard phase information or encode it as two separate real-valued channels, limiting spatial fidelity. This entails the need to introduce
adversarial discriminators at the expense of convergence speed and training stability to compensate for the inadequate representation power of the audio signal. In this work we introduce an end-to-end complex-valued RVQ-VAE audio codec that preserves magnitude-phase coupling across the entire analysis-quantization-synthesis pipeline and removes
adversarial discriminators and
diffusion post-filters. Without GANs or
diffusion we match or surpass much longer-trained baselines in-domain and reach SOTA out-of-domain performance. Compared to standard baselines that train for hundreds of thousands of steps, our model reducing training budget by an order of magnitude is markedly more compute-efficient while preserving high perceptual quality.
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Signal Processing · Cross-listed from physics.geo-ph
Curvelet-Regularized SPDE Inversion on Piecewise-Planar Fractures with Trace-Graph Coupling
J.J. Segura
We formulate a sparse-to-dense reconstruction layer for fractured media in which sparse point measurements are mapped onto piecewise-planar fracture supports inferred from 3D trace polylines. Each plane is discretized in local coordinates and estimated via a convex objective that combines a grid SPDE/GMRF quadratic prior with an $\ell_1$ penalty on undecimated discrete curvelet coefficients, targeting anisotropic, fracture-aligned structure that is poorly represented by isotropic smoothness alone. We further define an along-fracture distance through trace-network geodesics and express connectivity-driven regularization as a quadratic form $z^\top P^\top L_G P z$, where $L_G$ is a graph
Laplacian on the trace network and $P$ maps plane grids to graph nodes; plane intersections are handled by linear consistency constraints sampled along intersection lines. The resulting optimization admits efficient splitting: sparse linear solves for the quadratic block and coefficient-wise shrinkage for the curvelet block, with standard ADMM convergence under convexity. We specify reproducible synthetic
benchmarks, baselines, ablations, and sensitivity studies that isolate directional sparsity and connectivity effects, and provide reference code to generate the figures and quantitative tables.
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Signal Processing · Cross-listed from quant-ph
Bayesian quantum sensing using graybox machine learning
Akram Youssry, Stefan Todd, Patrick Murton et al. (+4 more)
Quantum sensors offer significant advantages over classical devices in spatial resolution and sensitivity, enabling transformative applications across materials science, healthcare, and beyond. Their practical performance, however, is often constrained by unmodelled effects, including noise, imperfect state preparation, and non-ideal control fields. In this work, we report the first experimental implementation of a graybox modelling strategy for a solid-state open
quantum system. The graybox framework integrates a physics-based system model with a data-driven description of experimental imperfections, achieving higher fidelity than purely analytical (whitebox) approaches while requiring fewer training resources than fully deep-learning blackbox models. We experimentally validate the method on the task of estimating a static
magnetic field using a single-
spin quantum sensor, performing
Bayesian inference with a graybox model trained on prior experimental data. Using roughly 10,000 training datapoints, the graybox model yields several orders of magnitude improvement in mean squared error over the corresponding physics-only model, as well as significant improvement over a blackbox model with comparable size. These results are broadly applicable to a wide range of
quantum sensing platforms, not limited to single-
spin systems, and are particularly valuable for real-time adaptive protocols, where model inaccuracies can otherwise lead to suboptimal control and degraded performance.
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All authors Akram Youssry, Stefan Todd, Patrick Murton, Muhammad Junaid Arshad, Nicholas Werren, Alberto Peruzzo, Cristian Bonato
Systems and Control
Robust Output Regulation of Uncertain Linear Time-Varying Systems
Jinmeng Zha and Zhen Zhang
Robust output regulation for linear time-varying systems has remained an open problem for decades. By augmenting the classical immersion viewpoint, we propose the trajectory-matching system immersion framework. It reformulates the regulator equation as a forced system and demonstrates that finding an internal model is equivalent to reproducing the non-decaying output trajectories of this forced system by constructing an unforced one. This perspective yields an exact algebraic boundary for finite-dimensional internal models, termed finite linear parameterization. It further reveals a distinctive obstruction in time-varying systems: even highly structured, finite-dimensional affine parametric uncertainties can generate infinite-dimensional families of non-decaying error-zeroing mappings, thereby precluding exact robust regulation via linear finite-dimensional internal models in general. Hence, we develop a general finite-dimensional design for approximate robust regulation, which yields a bounded tracking error and avoids explicitly solving the regulator equation. Additionally, it recovers exact regulation for certain structured uncertainty classes. Overall, these results clarify the intrinsic limitation of exact finite-dimensional robust regulation for uncertain LTV systems and provide a general and constructive framework for designing LTV internal-model-based regulators.
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Image and Video Processing
Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound
Fangyijie Wang, Siteng Ma, Guénolé Silvestre et al. (+1 more)
Fetal ultrasound (US) data is often limited due to
privacy and regulatory restrictions, posing challenges for training deep learning (DL) models. While semi-supervised learning (SSL) is commonly used for fetal US image analysis, existing SSL methods typically rely on random limited selection, which can lead to suboptimal model performance by overfitting to homogeneous labeled data. To address this, we propose a two-stage Active Learning (AL) sampler,
Entropy-Guided Agreement-Diversity (EGAD), for fetal head
segmentation. Our method first selects the most uncertain samples using predictive
entropy, and then refines the final selection using the agreement-diversity score combining cosine similarity and mutual information. Additionally, our SSL framework employs a consistency learning strategy with feature downsampling to further enhance
segmentation performance. In experiments, SSL-EGAD achieves an average Dice score of 94.57% and 96.32% on two public
datasets for fetal head
segmentation, using 5% and 10% labeled data for training, respectively. Our method outperforms current SSL models and showcases consistent robustness across diverse pregnancy stage data. The code is available on \href{https://github.com/13204942/Semi-supervised-EGAD}{GitHub}.
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All authors Fangyijie Wang, Siteng Ma, Guénolé Silvestre, Kathleen M. Curran
Signal Processing · Cross-listed from cs.CY
Building a Bridge between the Two Schools: Realizing a Practical Path to Include Literacy-based Skills within the STEM Curricula
Jorge Torres Gómez, Erika Gericke, Anton Rassõlkin et al. (+4 more)
Developing students as well-rounded professionals is increasingly important for our modern society. Although there is a great consensus that technical and professional ("soft") skills should be developed and intertwined in the core of computer science subjects, there are still few examples of alike teaching methodologies at technical schools. This contribution investigates the integration of technical and professional skills while teaching specialized curricula in computer science. We propose a broadly applicable, step-by-step methodology that connects core technical concepts (e.g., information
entropy, network security) with fine arts practices such as music,
video production, gaming, and performing arts (e.g., Oxford-style debates). The methodology was applied in several computer science courses at technical universities, where quantitative and qualitative assessments, including student questionnaires and exam scores, showed improved learning outcomes and increased student engagement compared to traditional methods. The results indicate that this art-based integration can effectively bridge the historical divide between the two schools of thought, offering a practical direction for educators. Within this context, we also identify open issues that will guide future research on topics such as instructor engagement, female motivation in technical subjects, and scalability of these approaches.
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All authors Jorge Torres Gómez, Erika Gericke, Anton Rassõlkin, Mikołaj Leszczuk, Alexandru Iosup, Marcin Niemiec, Carmen Peláez-Moreno
Systems and Control
A new approach for combined model class selection and parameters learning for auto-regressive neural models
Corrado Sgadari, Alessio La Bella and Marcello Farina
This work introduces a novel approach for the joint selection of model structure and parameter learning for nonlinear dynamical systems identification. Focusing on a specific Recurrent Neural Networks (RNNs) family, i.e., Nonlinear Auto-Regressive with eXogenous inputs Echo State Networks (NARXESNs), the method allows to simultaneously select the optimal model class and learn model parameters from data through a new set-membership (SM) based procedure. The results show the effectiveness of the approach in identifying parsimonious yet accurate models suitable for control applications. Moreover, the proposed framework enables a robust training strategy that explicitly accounts for bounded measurement noise and enhances model robustness by allowing data-consistent evaluation of simulation performance during parameter learning, a process generally NP-hard for models with autoregressive components.
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Signal Processing · Cross-listed from cs.IT
Joint Uplink-Downlink Fronthaul Bit Allocation in Fronthaul-Limited Massive MU-MIMO Systems
Yasaman Khorsandmanesh, Emil Bjornson and Joakim Jalden
This paper optimizes the fronthaul bit allocation in massive multi-user multiple-input multiple-output (MU-
MIMO) systems operating with limited-capacity fronthaul links. We consider an advanced
antenna system (AAS) controlled by a centralized baseband unit (BBU). In the AAS, multiple
antenna elements together with their
radio units are integrated into a single unit. In this setup, a key challenge is allocating fronthaul bits between uplink channel state information (CSI) quantization and downlink precoding
matrix quantization. We formulate the problem of maximizing the sum spectral efficiency (SE) for a given fronthaul capacity. We develop an SE expression for this scenario based on the hardening bound. We compute the expression in closed form for maximum ratio transmission, which reveals the relative impact of the two types of quantization distortion. We then formulate a bit split optimization problem and propose an
algorithm that exactly solves it. Numerical results demonstrate how the relative importance of assigning bits to CSI and precoding varies depending on the signal-to-noise ratio.
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Signal Processing
Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication
Ahmad Halimi Razlighi, Pallavi Dhingra, Edgar Beck et al. (+2 more)
Task-oriented semantic communication (SemCom) prioritizes task execution over accurate symbol reconstruction and is well-suited to emerging intelligent applications. Cooperative multi-task SemCom (CMT-SemCom) further improves task execution performance. However, [1] demonstrates that cooperative multi-tasking can be either constructive or destructive. Moreover, the existing CMT-SemCom framework is not directly applicable to distributed multi-user scenarios, such as non-terrestrial satellite networks, where each satellite employs an individual semantic encoder. In this paper, we extend our earlier CMT-SemCom framework to distributed settings by proposing a
federated learning (FL) based CMT-SemCom that enables cooperative multi-tasking across distributed users. Moreover, to address performance degradation caused by negative information transfer among heterogeneous tasks, we propose a semantic-aware task clustering method integrated in the FL process to ensure constructive cooperation based on an information-theoretic approach. Unlike common clustering methods that rely on high-dimensional data or feature space similarity, our proposed approach operates in the low-dimensional semantic domain to identify meaningful task relationships. Simulation results based on a LEO satellite network setup demonstrate the effectiveness of our approach and performance gain over unclustered FL and individual single-task SemCom.
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All authors Ahmad Halimi Razlighi, Pallavi Dhingra, Edgar Beck, Bho Matthiesen, Armin Dekorsy
Signal Processing
RIS-Enabled Spoofing Against Adversary Sensing: CRB-Maximizing Design and Decoying Analysis
Ioannis Gavras, Giuseppe Thadeu Freitas de Abreu and George C. Alexandropoulos
This paper studies the capability of a Reconfigurable Intelligent Surface (RIS), when transparently covering a User Equipment (UE), to deceive an adversary monostatic radar system. A compact RIS kernel model that explicitly links the radar's angular response to the RIS phase profile is introduced, enabling an analytical investigation of the Angle of Arrival (AoA) estimation accuracy with respect to the kernel's power. This model is also leveraged to formulate an RIS-based spoofing design with the dual objective to enforce strict nulls around the UE's true reflection AoA and maximize the channel gain towards a decoy direction. The RIS's deception capability is quantified using point-wise and angle-range robust criteria, and a configuration-independent placement score guiding decoy selection is proposed. Selected numerical results confirm deep nulls at the true reflection AoA together with a pronounced decoy peak, rendering maximum-likelihood sensing at the adversary radar unreliable.
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Image and Video Processing · Cross-listed from cs.AR
SPADE: A SIMD Posit-enabled compute engine for Accelerating DNN Efficiency
Sonu Kumar, Lavanya Vinnakota, Mukul Lokhande et al. (+2 more)
The growing demand for edge-AI systems requires arithmetic units that balance numerical precision, energy efficiency, and compact hardware while supporting diverse formats. Posit arithmetic offers advantages over floating- and fixed-point representations through its tapered precision, wide dynamic range, and improved numerical robustness. This work presents SPADE, a unified multi-precision SIMD Posit-based multiplyaccumulate (MAC) architecture supporting Posit (8,0), Posit (16,1), and Posit (32,2) within a single framework. Unlike prior single-precision or floating/fixed-point SIMD MACs, SPADE introduces a regime-aware, lane-fused SIMD Posit datapath that hierarchically reuses Posit-specific submodules (LOD, complementor, shifter, and multiplier) across 8/16/32-bit precisions without datapath replication. FPGA implementation on a Xilinx Virtex-7 shows 45.13% LUT and 80% slice reduction for Posit (8,0), and up to 28.44% and 17.47% improvement for Posit (16,1) and Posit (32,2) over prior work, with only 6.9% LUT and 14.9% register overhead for multi-precision support. ASIC results across TSMC nodes achieve 1.38 GHz at 6.1 mW (28 nm). Evaluation on MNIST, CIFAR-10/100, and alphabet
datasets confirms competitive inference accuracy.
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All authors Sonu Kumar, Lavanya Vinnakota, Mukul Lokhande, Santosh Kumar Vishvakarma, Adam Teman
Audio and Speech Processing · Cross-listed from cs.SD
Window Size Versus Accuracy Experiments in Voice Activity Detectors
Max McKinnon, Samir Khaki, Chandan KA Reddy et al. (+1 more)
Voice activity detection (VAD) plays a vital role in enabling applications such as
speech recognition. We analyze the impact of window size on the accuracy of three VAD
algorithms: Silero, WebRTC, and Root Mean Square (RMS) across a set of diverse real-world digital audio streams. We additionally explore the use of hysteresis on top of each VAD output. Our results offer practical references for optimizing VAD systems. Silero significantly outperforms WebRTC and RMS, and hysteresis provides a benefit for WebRTC.
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All authors Max McKinnon, Samir Khaki, Chandan KA Reddy, William Huang
Image and Video Processing · Cross-listed from cond-mat.mtrl-sci
Unsupervised segmentation and clustering workflow for efficient processing of 4D-STEM and 5D-STEM data
Serin Lee, Stephanie M. Ribet, Arthur R. C. McCray et al. (+3 more)
Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as data dimensionality and sampling density increase, particularly for in situ scanning diffraction experiments (5D-STEM), robust
segmentation of structurally consistent behavior across sequential measurements becomes essential for efficient and physically meaningful analysis. Here, we introduce a clustering framework that identifies crystallographically distinct domains from 4D-STEM
datasets. By using local diffraction-pattern similarity as a metric, the method extracts closed contours delineating spatially contiguous regions. This approach produces cluster-averaged diffraction patterns that improve signal quality while reducing data volume by orders of magnitude, enabling rapid and accurate orientation, phase, and strain mapping. We demonstrate the applicability of this approach to in situ
liquid-
cell 4D-STEM data of gold nanoparticle growth. Our method provides a scalable and generalizable route for spatially coherent
segmentation, data compression, and quantitative structure-strain mapping across diverse 4D-STEM modalities. The full analysis code and example workflows are publicly available to support reproducibility and reuse.
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All authors Serin Lee, Stephanie M. Ribet, Arthur R. C. McCray, Andrew Barnum, Jennifer A. Dionne, Colin Ophus