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Electrical Engineering and Systems Science

eess Watch Electrical Engineering and Systems Science

Wednesday 3 December

Signal Processing

Pixel-based Reconfigurable Beamforming Networks Emulating Physical Movement in FAS

Jichen Zhang, Junhui Rao, Tianqu Kang et al. (+5 more)

The concept of Fluid Antenna Systems (FAS) has emerged as an attractive new system technology for use in sixth-generation (6G) wireless systems. However, most FAS implementations rely on mechanical antenna movement and thus are too slow to be useful. In this paper, a novel pixel-based reconfigurable beamforming network (PRBFN) is used to emulate movement in Fluid Antenna Systems (FASs). Using the insight that changing an antenna's physical position is equivalent to changing radiation patterns that satisfy the desired pattern correlation, the PRBFN is used to control the excitation current vectors of a multi-port antenna, thereby governing the pattern correlation. Key novelties of our work involve the selection of current vectors, and the methodology for scaling the PRBFN to realize large-aperture FAS. Results are provided for our PRBFN combined with an FAS (denoted as a PRBFN-FAS) when the equivalent physical movement is set to 1.5 wavelengths. Measurements demonstrate that the PRBFN-FAS provides the desired spatial correlation, including the Bessel function relation from Clarke's model across a 5% bandwidth, satisfying FAS requirements. System-level experiments confirm the viability of the PRBFN-FAS in communication scenarios.
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All authors Jichen Zhang, Junhui Rao, Tianqu Kang, Zhaoyang Ming, Yijun Chen, Alikhan Umirbayev, Chi-Yuk Chiu, Ross Murch

Systems and Control

Output-Constrained Controller with Fuzzy-Tuned Parameters for Overhead Cranes

Dawei Zhao, Kai Wang, Xianglong Zhou et al. (+2 more)

This study proposes a fuzzy-adjusted nonlinear control method based on torque jitter output limit constraints for overhead crane systems with double pendulum effects. The proposed control method can effectively suppress swing and achieve precise positioning. Firstly, by enhancing the coupling relationship between the trolley displacement and swing angle, a composite signal with an error term was designed. Then, an energy-based Lyapunov function was constructed using the composite error signal, which incorporated a new formulation of the inertia matrix and potential energy function. Subsequently, using the backstepping method in conjunction with the hyperbolic tangent function, a controller with partial performance constraints was designed. In addition, to further enhance the system's dynamic performance, a fuzzy control scheme with online adjustable system parameters was designed. Finally, the stability of the system is proven using Lyapunov theory combined with LaSalle's invariance principle. Simulation results demonstrate that the proposed controller exhibits superior performance and robustness.
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All authors Dawei Zhao, Kai Wang, Xianglong Zhou, Xin Ma, Lei Jia

Audio and Speech Processing · Cross-listed from cs.HC

Head, posture, and full-body gestures in unscripted dyadic conversations in noise

Ľuboš Hládek and Bernhard U. Seeber

Visual prosody may be critical for communication success in face-to-face conversations in noisy settings. Here, we explore the involvement of hand, head, and whole-body movements, as well as gesturing quality, in dyadic conversations in noisy settings. We hypothesize that increasing background noise would alter the frequency of conversation-related movements to support the roles of the speaker and the listener. Specifically, talkers may increase gesticulation and thus the use of hand, head, trunk, or leg movements more often, while listeners may increase backchanneling or head and trunk movements to improve the signal-to-noise ratio. Additionally, we test whether the synchrony between speech and hand gestures is affected by background noise. Here, pairs of normal hearing participants (n=8) stood in an audiovisual virtual environment while talking freely. The conversational movements were described using a newly developed labeling system with categories that respect their communicative function. The results showed higher gesturing rate during speaking than during listening. Increased levels of background noise led to increased hand-gesture complexity, modulation of head movements, and a change in trunk movements. People spoke 0.7 dB - 1.4 dB louder during hand gesturing in comparison to times with static drop posture but this was unrelated to presence of background noise. The analysis of hand-speech synchrony showed a modest decrease in synchrony for moderate noise level. People adapt their communicative behavior to increased background noise levels by increases in speech production levels and gesturing which may drive additional increase in speech production due to biomechanical coupling; listeners may increase backchanneling to support the exchange and their own signal-to-noise ratio. The synchrony analysis may reflect motivational factors of communication in noisy environments.
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Systems and Control

Covariance Stabilization for a class of Stochastic Discrete-time Linear Systems using the S-Variable Approach

Kaouther Moussa and Dimitri Peaucelle

This paper deals with the problem of covariance stabilization for a class of linear stochastic discrete-time systems in the Stochastic Model Predictive Control (SMPC) framework. The considered systems are affected by independent and identically distributed (i.i.d.) additive and parametric stochastic uncertainties (potentially unbounded), in addition to polytopic deterministic uncertainties bounding the mean of the state and input parameters. The design conditions presented in this paper are formulated as Linear Matrix Inequalities (LMIs), using the S-variable approach in order to reduce the potential conservatism. These conditions are derived using a deterministic exact characterization of the covariance dynamics, the latter involves bilinear terms in the control gain. A technique to linearize such dynamics is presented, it results in a descriptor representation allowing to derive sufficient conditions for the design of a covariance-stabilizing controller. The derived condition is first compared with a known necessary and sufficient stability condition for systems without deterministic uncertainties and additive stochastic noise. Although more conservative, the proposed condition is more numerically tractable, with an LMI size scaling as O(n^2) instead of O(n^3). Then, the same condition is used to design controllers that are robust to both deterministic and stochastic uncertainties. Several numerical examples are presented for comparison and illustration.
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Systems and Control

A Perception-feedback position-tracking control for quadrotors

Eduardo Espindola and Yu Tang

In this paper a position-tracking controller for quadrotors based on perception feedback is developed, which directly uses measurements from onboard sensors such as low cost IMUs and GPS to generate the control commands without state estimation. Bias in gyros sensors are corrected to enhance the tracking performance. Practical stability of the origin of the tracking error system in the presence of external disturbances is proved using the Lyapunov analysis, which turns out to exponential stability in the absence of external disturbances. Numerical simulations are included to illustrate the proposed control scheme and to verify the robustness of the proposed controller under noisy measurements and parameter uncertainties.
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Systems and Control

Physics-Based Communication Compression via Lyapunov-Weighted Event-Triggered Control

Abbas Tariverdi

Event-Triggered Control (ETC) reduces communication overhead in networked systems by transmitting only when stability requires it. Conventional mechanisms use isotropic error thresholds ($\|e\| \le \sigma \|x\|$), treating all directions equally. This ignores stability geometry and triggers conservatively. We propose a static directional triggering mechanism that exploits this asymmetry. By weighting errors via the Lyapunov matrix $P$, we define an anisotropic half-space scaling with instantaneous energy margins: larger deviations tolerated along stable modes, strict bounds where instability threatens. We prove global asymptotic stability and exclusion of Zeno behavior. Monte Carlo simulations ($N=100$) show 43.6% fewer events than optimally tuned isotropic methods while achieving $2.1\times$ better control performance than time-varying alternatives. The mechanism functions as a runtime safety gate for learning-based controllers operating under communication constraints.
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Systems and Control

Resilient AFE Drive Control using Neural Networks with Tracking Guarantees

Nicolas Kirsch, Catalin Arghir, Silvia Mastellone et al. (+1 more)

Industrial installations across several sectors have seen a dramatic increase in productivity, accuracy and efficiency over the last decade due to expanded utilization of medium voltage, variable speed power electronic converters to drive their processes. Specifically, active front-end (AFE) drives have become popular due to their ability to deliver power while maintaining safe electrical setpoints. However, under abnormal grid conditions such as phase loss, conventional AFE control may fail to enforce safety constraints, potentially leading to drive shutdown and significant financial losses. In this work, we propose using reference-tracking Performance Boosting (rPB) to improve the resilience of standard AFE control to faults. This neural-network control framework provides a principled way to optimize transient performance while preserving the steady-state tracking properties of AFE-based drives. By carefully shaping the input signals to the rPB controller, we ensure that it activates only during grid faults, leaving nominal operation unaffected. Simulation results show that the proposed approach successfully maintains the DC bus voltage and the grid current within safe limits during single-phase loss events.
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All authors Nicolas Kirsch, Catalin Arghir, Silvia Mastellone, Giancarlo Ferrari-Trecate

Systems and Control · Cross-listed from physics.optics

Real-Time Control and Automation Framework for Acousto-Holographic Microscopy

Hasan Berkay Abdioğlu, Yağmur Iş{\i}k, Mustafa İsmail İnal et al. (+5 more)

Manual operation of microscopes for repetitive tasks in cell biology is a significant bottleneck, consuming invaluable expert time, and introducing human error. Automation is essential, and while Digital Holographic Microscopy (DHM) offers powerful, label-free quantitative phase imaging (QPI), its inherently noisy and low-contrast holograms make robust autofocus and object detection challenging. We present the design, integration, and validation of a fully automated closed-loop DHM system engineered for high-throughput mechanical characterization of biological cells. The system integrates automated serpentine scanning, real-time YOLO-based object detection, and a high-performance, multi-threaded software architecture using pinned memory and SPSC queues. This design enables the GPU-accelerated reconstruction pipeline to run fully in parallel with the 50 fps data acquisition, adding no sequential overhead. A key contribution is the validation of a robust, multi-stage holographic autofocus strategy; we demonstrate that a selected metric (based on a low-pass filter and standard deviation) provides reliable focusing for noisy holograms where conventional methods (e.g., Tenengrad, Laplacian) fail entirely. Performance analysis of the complete system identifies the 2.23-second autofocus operation-not reconstruction-as the primary throughput bottleneck, resulting in a 9.62-second analysis time per object. This work delivers a complete functional platform for autonomous DHM screening and provides a clear, data-driven path for future optimization, proposing a hybrid brightfield imaging modality to address current bottlenecks.
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All authors Hasan Berkay Abdioğlu, Yağmur Iş{\i}k, Mustafa İsmail İnal, Nehir Serin, Kerem Bayer, Muhammed Furkan Koşar, Taha Ünal, Hüseyin Üvet

Systems and Control · Cross-listed from math.OC

Market share maximizing strategies of CAV fleet operators may cause chaos in our cities

Grzegorz Jamróz, Rafał Kucharski and David Watling

We study the dynamics and equilibria of a new kind of routing games, where players - drivers of future autonomous vehicles - may switch between individual (HDV) and collective (CAV) routing. In individual routing, just like today, drivers select routes minimizing expected travel costs, whereas in collective routing an operator centrally assigns vehicles to routes. The utility is then the average experienced travel time discounted with individually perceived attractiveness of automated driving. The market share maximising strategy amounts to offering utility greater than for individual routing to as many drivers as possible. Our theoretical contribution consists in developing a rigorous mathematical framework of individualized collective routing and studying algorithms which fleets of CAVs may use for their market-share optimization. We also define bi-level CAV - HDV equilibria and derive conditions which link the potential marketing behaviour of CAVs to the behavioural profile of the human population. Practically, we find that the fleet operator may often be able to equilibrate at full market share by simply mimicking the choices HDVs would make. In more realistic heterogenous human population settings, however, we discover that the market-share maximizing fleet controller should use highly variable mixed strategies as a means to attract or retain customers. The reason is that in mixed routing the powerful group player can control which vehicles are routed via congested and uncongested alternatives. The congestion pattern generated by CAVs is, however, not known to HDVs before departure and so HDVs cannot select faster routes and face huge uncertainty whichever alternative they choose. Consequently, mixed market-share maximising fleet strategies resulting in unpredictable day-to-day driving conditions may, alarmingly, become pervasive in our future cities.
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Systems and Control · Cross-listed from cs.HC

Left shifting analysis of Human-Autonomous Team interactions to analyse risks of autonomy in high-stakes AI systems

Ben Larwood, Oliver J. Sutton and Callum Cockburn

Developing high-stakes autonomous systems that include Artificial Intelligence (AI) components is complex; the consequences of errors can be catastrophic, yet it is challenging to plan for all operational cases. In stressful scenarios for the human operator, such as short decision-making timescales, the risk of failures is exacerbated. A lack of understanding of AI failure modes obstructs this and so blocks the robust implementation of applications of AI in smart systems. This prevents early risk identification, leading to increased time, risk and cost of projects. A key tenet of Systems Engineering and acquisition engineering is centred around a "left-shift" in test and evaluation activities to earlier in the system lifecycle, to allow for "accelerated delivery of [systems] that work". We argue it is therefore essential that this shift includes the analysis of AI failure cases as part of the design stages of the system life cycle. Our proposed framework enables the early characterisation of risks emerging from human-autonomy teaming (HAT) in operational contexts. The cornerstone of this is a new analysis of AI failure modes, built on the seminal modelling of human-autonomy teams laid out by LaMonica et al., 2022. Using the analysis of the interactions between human and autonomous systems and exploring the failure modes within each aspect, our approach provides a way to systematically identify human-AI interactions risks across the operational domain of the system of interest. The understanding of the emergent behaviour enables increased robustness of the system, for which the analysis should be undertaken over the whole scope of its operational design domain. This approach is illustrated through an example use case for an AI assistant supporting a Command & Control (C2) System.
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Signal Processing

A Comprehensive Survey of 3GPP Release 19 ISAC Channel Modeling: From Empirical Features to Unified Methodology and Standardized Simulator

Yameng Liu, Yuxiang Zhang, Jianhua Zhang et al. (+17 more)

Integrated Sensing and Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP. Channel measurement and modeling is a prerequisite for ISAC system design and has attracted widespread attention from both academia and industry. 3GPP Release 19 initiated the ISAC channel study item in December 2023 and finalized its modeling specification in May 2025 after extensive technical discussions. However, a comprehensive survey that provides a systematic overview,from empirical channel features to modeling methodologies and standardized simulators,remains unavailable. In this paper, the key requirements and challenges in ISAC channel research are first analyzed, followed by a structured overview of the standardization workflow throughout the 3GPP Release 19 process. Then, critical aspects of ISAC channels, including physical objects, target channels, and background channels, are examined in depth, together with additional features such as spatial consistency, environment objects, Doppler characteristics, and shared clusters, supported by measurement-based analysis. To establish a unified ISAC channel modeling framework, an Extended Geometry-based Stochastic Model (E-GBSM) is proposed, incorporating all the aforementioned ISAC channel characteristics. Finally, a standardized simulator is developed based on E-GBSM, and a two-phase calibration procedure aligned with 3GPP Release 19 is conducted to validate both the model and the simulator, demonstrating close agreement with industrial reference results. Overall, this paper provides a systematic survey of 3GPP Release 19 ISAC channel standardization and offers insights into best practices for new feature characterization, unified modeling methodology, and standardized simulator implementation, which can effectively supporting ISAC technology evaluation and future 6G standardization.
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All authors Yameng Liu, Yuxiang Zhang, Jianhua Zhang, Yuanpeng Pei, Changsheng Zhao, Shilin Luo, Lei Tian, Yingyang Li, Wei Hong, Jianming Wu, Guangyi Liu, Yan Li, Tao Jiang, Chuangxin Jiang, Junchen Liu, Yongqiang Fei, Woo-Suk Ko, Jing Xu, Bin Liang, Takahiro Tomie

Signal Processing

Resource Allocation for Pinching-Antenna Systems (PASS)-enabled NOMA Communications

Songtao Xue, Jingjing Zhao, Kaiquan Cai et al. (+3 more)

Pinching-antenna systems (PASS) have emerged as a promising technology due to their ability to dynamically reconfigure wireless propagation environments. A novel PASS-based multi-user non-orthogonal multiple access (NOMA) framework is proposed by exploiting the waveguide-division (WD) transmission characteristic. Specifically, each NOMA user cluster is served by one dedicated waveguide, and the corresponding pinching beamforming is exploited to enhance the intra-cluster performance while mitigating the inter-cluster interference. Based on this framework, a sum-rate maximization problem is formulated for jointly optimizing power allocation, pinching beamforming, and user scheduling. To solve this problem, a two-step algorithm is developed, which decomposes the original problem into two subproblems. For the joint power allocation and pinching beamforming design, a penalty dual decomposition (PDD) algorithm is proposed to obtain the locally optimal solutions. Specifically, the coupling constraints are alleviated through augmented Lagrangian relaxation, and the resulting augmented Lagrangian (AL) problem is decomposed into four subproblems, which are solved by the block coordinate descent (BCD) method. For the user scheduling, a low-complexity matching algorithm is developed to solve the user-to-waveguide assignment problem. Simulation results demonstrate that 1) the proposed PASS-based NOMA framework under the WD transmission structure achieves significant sum-rate gain over conventional fixed-position antenna systems and orthogonal multiple access (OMA) scheme; and 2) the proposed matching-based user scheduling algorithm achieves near-optimal user-waveguide association with low computational complexity.
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All authors Songtao Xue, Jingjing Zhao, Kaiquan Cai, Xidong Mu, Zhenyu Xiao, Yuanwei Liu

Audio and Speech Processing

A Universal Harmonic Discriminator for High-quality GAN-based Vocoder

Nan Xu, Zhaolong Huang and Xiao Zeng

With the emergence of GAN-based vocoders, the discriminator, as a crucial component, has been developed recently. In our work, we focus on improving the time-frequency based discriminator. Particularly, Short-Time Fourier Transform (STFT) representation is usually used as input of time-frequency based discriminator. However, the STFT spectrogram has the same frequency resolution at different frequency bins, which results in an inferior performance, especially for singing voices. Motivated by this, we propose a universal harmonic discriminator for dynamic frequency resolution modeling and harmonic tracking. Specifically, we design a harmonic filter with learnable triangular band-pass filter banks, where each frequency bin has a flexible bandwidth. Additionally, we add a half-harmonic to capture fine-grained harmonic relationships at low-frequency band. Experiments on speech and singing datasets validate the effectiveness of the proposed discriminator on both subjective and objective metrics.
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Systems and Control

Variable-Impedance Muscle Coordination under Slow-Rate Control Frequencies and Limited Observation Conditions Evaluated through Legged Locomotion

Hidaka Asai, Tomoyuki Noda and Jun Morimoto

Human motor control remains agile and robust despite limited sensory information for feedback, a property attributed to the body's ability to perform morphological computation through muscle coordination with variable impedance. However, it remains unclear how such low-level mechanical computation reduces the control requirements of the high-level controller. In this study, we implement a hierarchical controller consisting of a high-level neural network trained by reinforcement learning and a low-level variable-impedance muscle coor dination model with mono- and biarticular muscles in monoped locomotion task. We systematically restrict the high-level controller by varying the control frequency and by introducing biologically inspired observation conditions: delayed, partial, and substituted observation. Under these conditions, we evaluate how the low-level variable-impedance muscle coordination contributes to learning process of high-level neural network. The results show that variable-impedance muscle coordination enables stable locomotion even under slow-rate control frequency and limited observation conditions. These findings demonstrate that the morphological computation of muscle coordination effectively offloads high-frequency feedback of the high-level controller and provide a design principle for the controller in motor control.
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Signal Processing

A Convolutional Framework for Mapping Imagined Auditory MEG into Listened Brain Responses

Maryam Maghsoudi, Mohsen Rezaeizadeh and Shihab Shamma

Decoding imagined speech engages complex neural processes that are difficult to interpret due to uncertainty in timing and the limited availability of imagined-response datasets. In this study, we present a Magnetoencephalography (MEG) dataset collected from trained musicians as they imagined and listened to musical and poetic stimuli. We show that both imagined and perceived brain responses contain consistent, condition-specific information. Using a sliding-window ridge regression model, we first mapped imagined responses to listened responses at the single-subject level, but found limited generalization across subjects. At the group level, we developed an encoder-decoder convolutional neural network with a subject-specific calibration layer that produced stable and generalizable mappings. The CNN consistently outperformed the null model, yielding significantly higher correlations between predicted and true listened responses for nearly all held-out subjects. Our findings demonstrate that imagined neural activity can be transformed into perception-like responses, providing a foundation for future brain-computer interface applications involving imagined speech and music.
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Systems and Control · Cross-listed from cs.RO

PerFACT: Motion Policy with LLM-Powered Dataset Synthesis and Fusion Action-Chunking Transformers

Davood Soleymanzadeh, Xiao Liang and Minghui Zheng

Deep learning methods have significantly enhanced motion planning for robotic manipulators by leveraging prior experiences within planning datasets. However, state-of-the-art neural motion planners are primarily trained on small datasets collected in manually generated workspaces, limiting their deployment in various everyday scenarios. Additionally, these planners often rely on monolithic network architectures that struggle to encode critical planning information. To address these challenges, we introduce Motion Policy with Dataset Synthesis powered by large language models (LLMs) and Fusion Action-Chunking Transformers (PerFACT), which incorporates two key components. Firstly, a novel workspace generation method, PerFACT, enables large-scale planning data collection by leveraging procedural primitive generation, and LLM-powered primitive suggestion and placement. Secondly, we introduce Fusion Motion Policy Networks (M$\pi$NetsFusion), an end-to-end, open-loop neural motion planner that uses a fusion action-chunking transformer to better encode planning signals and attend to multiple feature modalities. Leveraging PerFACT, we collect a dataset of 3.5M trajectories to train and evaluate M$\pi$NetsFusion against state-of-the-art planners. Results show that M$\pi$NetsFusion achieves consistently low planning time with sub-second inference, while maintaining competitive performance compared to both sampling-based and end-to-end neural benchmark planners. Project website: \href{https://davoodsz.github.io/perfact.github.io/}{https://davoodsz.github.io/perfact.github.io/}
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Systems and Control · Cross-listed from quant-ph

Quantum Encrypted Control of Networked Systems

Zihao Ren, Daniel Quevedo, Salah Sukkarieh et al. (+1 more)

Encrypted control has been extensively studied to ensure the confidentiality of system states and control inputs for networked control systems. This paper presents a computationally efficient encrypted control framework for networked systems enabled by quantum communication. A quantum channel between sensors and actuators is used to generate identical secret keys, whose security is further enhanced through quantum key distribution. These keys enable lightweight encryption and decryption while preserving confidentiality and control accuracy. We develop a novel encryption-decryption architecture for state-feedback control of linear systems based on quantum keys, and characterize the impact of quantum state errors on closed-loop stability. In particular, we establish the existence of a critical threshold on intrinsic quantum noise below which stability is guaranteed. In contrast to classical encrypted control schemes, which may collapse under a single key-bit error, the proposed quantum encrypted control exhibits strong robustness to key imperfections. We further adopt quantization techniques to address the scenarios with limited communication bits in practical situations, and implement privacy protection for quantum keys based on a stochastic quantizer. These results demonstrate that integrating quantum technologies into control systems in a nontrivial and principled manner, even at their current level of maturity, can yield substantial performance gains in reducing computational complexity and improving resilience to key errors while ensuring security against multiple eavesdropping sources.
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All authors Zihao Ren, Daniel Quevedo, Salah Sukkarieh, Guodong Shi

Signal Processing

Optimizing ISAC MIMO Systems with Reconfigurable Pixel Antennas

Ataher Sams, Yu-Cheng Hsiao, Muhammad Talha et al. (+2 more)

The integration of sensing and communication demands architectures that can flexibly exploit spatial and electromagnetic (EM) degrees of freedom (DoF). This paper proposes an Integrated Sensing and Communication (ISAC) MIMO framework that uses Reconfigurable Pixel Antenna (RPixA), which introduces additional EM-domain DoF that are electronically controlled through binary antenna coder switch networks. We introduce a beamforming architecture combining this EM and digital precoding to jointly optimize Sensing and Communication. Based on full-wave simulation of pixel antenna, we formulate a non-convex joint optimization problem to maximize sensing rate under user-specific constraints on communication rate. We utilize an Alternating Optimization framework incorporating genetic algorithm for port states of Pixel antennas, and semi-definite relaxation (SDR) for digital beamforming. Numerical results demonstrate that the proposed EM-aware design achieves considerably higher sensing rate compared to conventional arrays and enables considerable antenna reduction for equivalent ISAC performance. These findings highlight the potential of reconfigurable pixel antennas to realize efficient and scalable EM-aware ISAC systems for future 6G networks.
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All authors Ataher Sams, Yu-Cheng Hsiao, Muhammad Talha, Besma Smida, Ashutosh Sabharwal

Tuesday 2 December

Signal Processing

An extensive analysis and calibration of the Modular Aggregation Algorithm across three categories of for GNSS trajectories data sources

Marie-Dominique van Damme, Yann Méneroux and Ana-Maria Olteanu-Raimond

This technical report aims to complement the conference paper (https://doi.org/10.1145/3678717.3691325) by providing additional experiments or further details that could not be included in the paper.

Image and Video Processing · Cross-listed from cs.GR

A Fast Volumetric Capture and Reconstruction Pipeline for Dynamic Point Clouds and Gaussian Splats

Athanasios Charisoudis, Simone Croci, Lam Kit Yung et al. (+2 more)

We present a fast and efficient volumetric capture and reconstruction system that processes either RGB-D or RGB-only input to generate 3D representations in the form of point clouds and Gaussian splats. For Gaussian splat reconstructions, we took the GPS-Gaussian regressor and improved it, enabling high-quality reconstructions with minimal overhead. The system is designed for easy setup and deployment, supporting in-the-wild operation under uncontrolled illumination and arbitrary backgrounds, as well as flexible camera configurations, including sparse setups, arbitrary camera numbers and baselines. Captured data can be exported in standard formats such as PLY, MPEG V-PCC, and SPLAT, and visualized through a web-based viewer or Unity/Unreal plugins. A live on-location preview of both input and reconstruction is available at 5-10 FPS. We present qualitative findings focused on deployability and targeted ablations. The complete framework is open-source, facilitating reproducibility and further research.
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All authors Athanasios Charisoudis, Simone Croci, Lam Kit Yung, Pascal Frossard, Aljosa Smolic

Image and Video Processing · Cross-listed from cs.AR

FSL-HDnn: A 40 nm Few-shot On-Device Learning Accelerator with Integrated Feature Extraction and Hyperdimensional Computing

Weihong Xu, Chang Eun Song, Haichao Yang et al. (+5 more)

This paper introduces FSL-HDnn, an energy-efficient accelerator that implements the end-to-end pipeline of feature extraction and on-device few-shot learning (FSL). The accelerator addresses fundamental challenges of on-device learning (ODL) for resource-constrained edge applications through two synergistic modules: a parameter-efficient feature extractor employing weight clustering and an FSL classifier based on hyperdimensional computing (HDC). The feature extractor exploits the weight clustering mechanism to reduce computational complexity, while the HDC-based FSL classifier eliminates gradient-based back propagation operations, enabling single-pass training with substantially reduced latency. Additionally, FSL-HDnn enables low-latency ODL and inference via two proposed optimization strategies, including an early-exit mechanism with branch feature extraction and batched single-pass training that improves hardware utilization. Measurement results demonstrate that our chip fabricated in a 40 nm CMOS process delivers superior training energy efficiency of 6 mJ/image and end-to-end training throughput of 28 images/s on a 10-way 5-shot FSL task. The end-to-end training latency is also reduced by 2x to 20.9x compared to state-of-the-art ODL chips.
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All authors Weihong Xu, Chang Eun Song, Haichao Yang, Leo Liu, Meng-Fan Chang, Carlos H. Diaz, Tajana Rosing, Mingu Kang

Audio and Speech Processing · Cross-listed from cs.SD

Enhancing Automatic Speech Recognition Through Integrated Noise Detection Architecture

Karamvir Singh

This research presents a novel approach to enhancing automatic speech recognition systems by integrating noise detection capabilities directly into the recognition architecture. Building upon the wav2vec2 framework, the proposed method incorporates a dedicated noise identification module that operates concurrently with speech transcription. Experimental validation using publicly available speech and environmental audio datasets demonstrates substantial improvements in transcription quality and noise discrimination. The enhanced system achieves superior performance in word error rate, character error rate, and noise detection accuracy compared to conventional architectures. Results indicate that joint optimization of transcription and noise classification objectives yields more reliable speech recognition in challenging acoustic conditions.
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Audio and Speech Processing

Comparing Unsupervised and Supervised Semantic Speech Tokens: A Case Study of Child ASR

Mohan Shi, Natarajan Balaji Shankar, Kaiyuan Zhang et al. (+2 more)

Discrete speech tokens have gained attention for their storage efficiency and integration with Large Language Models (LLMs). They are commonly categorized into acoustic and semantic tokens, with the latter being more advantageous for Automatic Speech Recognition (ASR). Traditionally, unsupervised K-means clustering has been used to extract semantic speech tokens from Speech Foundation Models (SFMs). Recently, supervised methods, such as finite scalar quantization (FSQ) trained with ASR loss, have emerged for speech generation. Both approaches leverage pre-trained SFMs, benefiting low-resource tasks such as child ASR. This paper systematically compares supervised and unsupervised semantic speech tokens for child ASR. Results show that supervised methods not only outperform unsupervised ones but even unexpectedly surpass continuous representations, and they perform well even in ultra-low bitrate settings. These findings highlight the advantages of supervised semantic tokens and offer insights for improving discrete speech tokenization.
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All authors Mohan Shi, Natarajan Balaji Shankar, Kaiyuan Zhang, Zilai Wang, Abeer Alwan

Signal Processing · Cross-listed from cs.MA

Learning Network Sheaves for AI-native Semantic Communication

Enrico Grimaldi, Mario Edoardo Pandolfo, Gabriele D'Acunto et al. (+2 more)

Recent advances in AI call for a paradigm shift from bit-centric communication to goal- and semantics-oriented architectures, paving the way for AI-native 6G networks. In this context, we address a key open challenge: enabling heterogeneous AI agents to exchange compressed latent-space representations while mitigating semantic noise and preserving task-relevant meaning. We cast this challenge as learning both the communication topology and the alignment maps that govern information exchange among agents, yielding a learned network sheaf equipped with orthogonal maps. This learning process is further supported by a semantic denoising end compression module that constructs a shared global semantic space and derives sparse, structured representations of each agent's latent space. This corresponds to a nonconvex dictionary learning problem solved iteratively with closed-form updates. Experiments with mutiple AI agents pre-trained on real image data show that the semantic denoising and compression facilitates AI agents alignment and the extraction of semantic clusters, while preserving high accuracy in downstream task. The resulting communication network provides new insights about semantic heterogeneity across agents, highlighting the interpretability of our methodology.
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All authors Enrico Grimaldi, Mario Edoardo Pandolfo, Gabriele D'Acunto, Sergio Barbarossa, Paolo Di Lorenzo

Image and Video Processing · Cross-listed from physics.ins-det

Kaleidoscopic Scintillation Event Imaging

Alex Bocchieri, John Mamish, David Appleyard et al. (+1 more)

Scintillators are transparent materials that interact with high-energy particles and emit visible light as a result. They are used in state of the art methods of measuring high-energy particles and radiation sources. Most existing methods use fast single-pixel detectors to detect and time scintillation events. Cameras provide spatial resolution but can only capture an average over many events, making it difficult to image the events associated with an individual particle. Emerging single-photon avalanche diode cameras combine speed and spatial resolution to enable capturing images of individual events. This allows us to use machine vision techniques to analyze events, enabling new types of detectors. The main challenge is the very low brightness of the events. Techniques have to work with a very limited number of photons. We propose a kaleidoscopic scintillator to increase light collection in a single-photon camera while preserving the event's spatial information. The kaleidoscopic geometry creates mirror reflections of the event in known locations for a given event location that are captured by the camera. We introduce theory for imaging an event in a kaleidoscopic scintillator and an algorithm to estimate the event's 3D position. We find that the kaleidoscopic scintillator design provides sufficient light collection to perform high-resolution event measurements for advanced radiation imaging techniques using a commercial CMOS single-photon camera.
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All authors Alex Bocchieri, John Mamish, David Appleyard, Andreas Velten

Image and Video Processing

Quality assurance of the Federal Interagency Traumatic Brain Injury Research (FITBIR) database for multi-site MRI analysis

Adam M. Saunders, Michael E. Kim, Gaurav Rudravaram et al. (+8 more)

The Federal Interagency Traumatic Brain Injury Research (FITBIR) database is a centralized data repository for traumatic brain injury (TBI) research. It includes over 45,529 magnetic resonance images (MRI) from 6,211 subjects (9,229 imaging sessions) across 26 studies with heterogeneous organization formats, contrasts, acquisition parameters, and demographics. In this work, we organized and harmonized all available structural and diffusion MRI from FITBIR along with relevant demographic information into the Brain Imaging Data Structure. We analyzed whole-brain mean fractional anisotropy, mean diffusivity, total intracranial volume, and the volumes of 132 regions of interest using UNesT segmentations. There were 4,868 subjects (7,035 sessions) with structural MRI and 2,666 subjects (3,763 sessions) with diffusion MRI following quality assurance and harmonization. We modeled profiles for these metrics across ages with generalized additive models for location, scale, and shape (GAMLSS) and found significant differences in subjects with TBI compared to controls in volumes of 15 regions of the brain (q < 0.05, likelihood ratio test with false discovery rate correction).
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All authors Adam M. Saunders, Michael E. Kim, Gaurav Rudravaram, Elyssa M. McMaster, Chloe Scholten, Sequoia Wade, Marselle Rasdall, Simon Vandekar, Tonia S. Rex, François Rheault, Bennett A. Landman

Image and Video Processing

Ultra-Strong Gradient Diffusion MRI with Self-Supervised Learning for Prostate Cancer Characterization

Tanishq Patil, Snigdha Sen, Kieran G. Foley et al. (+7 more)

Diffusion MRI (dMRI) enables non-invasive assessment of prostate microstructure but conventional dMRI metrics such as the Apparent Diffusion Coefficient in multiparametric MRI and reflect a mixture of underlying tissues features rather than distinct histologic characteristics. Integrating dMRI with the compartment-based biophysical VERDICT (Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumours) framework offers richer microstructural insights, though clinical gradient systems (40-80 mT/m) often suffer from poor signal-to-noise ratio at stronger diffusion weightings due to prolonged echo times. Ultra-strong gradients (e.g., 300 mT/m) can mitigate these limitations by improving SNR and contrast-to-noise ratios. This study investigates whether physics-informed self-supervised VERDICT (ssVERDICT) fitting when combined with ultra-strong gradient data, enhances prostate microstructural characterization relative to current fitting approaches and clinical gradient systems. We developed enhanced ssVERDICT fitting approaches using dense multilayer perceptron and convolutional U-Net architectures, comparing them against non-linear least-squares (NLLS) VERDICT fitting, original ssVERDICT implementation, and Diffusion Kurtosis Imaging across clinical- to ultra-strong gradient systems. For the same ultra-strong gradient data, Dense ssVERDICT outperformed NLLS VERDICT, boosting median CNR by 47%, cutting inter-patient Coefficient of Variation by 52%, and reducing pooled $f_{ic}$ variation by 50%. Overall, Dense ssVERDICT delivered the highest CNR, the most stable parameter estimates, and the clearest tumour-normal contrast compared with conventional fitting methods and clinical gradient systems. These findings underscore that meaningful gains in non-invasive prostate cancer characterization arise from the combination of advanced gradient systems and deep learning-based modelling.
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All authors Tanishq Patil, Snigdha Sen, Kieran G. Foley, Fabrizio Fasano, Chantal M. W. Tax, Derek K. Jones, Mara Cercignani, Marco Palombo, Paddy J. Slator, Eleftheria Panagiotaki

Systems and Control

Time-Invariant Polytopic and Interval Observers for Uncertain Linear Systems via Non-Square Transformation

Feiya Zhu, Tarun Pati and Sze Zheng Yong

This paper presents novel polytopic and interval observer designs for uncertain linear continuous-time (CT) and discrete-time (DT) systems subjected to bounded disturbances and noise. Our approach guarantees enclosure of the true state and input-to-state stability (ISS) of the polytopic and interval set estimates. Notably, our approach applies to all detectable systems that are stabilized by any optimal observer design, utilizing a potentially non-square (lifted) time-invariant coordinate transformation based on polyhedral Lyapunov functions and mixed-monotone embedding systems that do not impose any positivity constraints, enabling feasible and optimal observer designs, even in cases where previous methods fail. The effectiveness of our approach is demonstrated through several examples of uncertain linear CT and DT systems.
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Systems and Control

Learning Physically Consistent Lagrangian Control Models Without Acceleration Measurements

Ibrahim Laiche, Mokrane Boudaoud, Patrick Gallinari et al. (+1 more)

This article investigates the modeling and control of Lagrangian systems involving non-conservative forces using a hybrid method that does not require acceleration calculations. It focuses in particular on the derivation and identification of physically consistent models, which are essential for model-based control synthesis. Lagrangian or Hamiltonian neural networks provide useful structural guarantees but the learning of such models often leads to inconsistent models, especially on real physical systems where training data are limited, partial and noisy. Motivated by this observation and the objective to exploit these models for model-based nonlinear control, a learning algorithm relying on an original loss function is proposed to improve the physical consistency of Lagrangian systems. A comparative analysis of different learning-based modeling approaches with the proposed solution shows significant improvements in terms of physical consistency of the learned models, on both simulated and experimental systems. The model's consistency is then exploited to demonstrate, on an experimental benchmark, the practical relevance of the proposed methodology for feedback linearization and energy-based control techniques.
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All authors Ibrahim Laiche, Mokrane Boudaoud, Patrick Gallinari, Pascal Morin

Systems and Control

GNSS Array-Based Multipath Detection Employing UKF on Manifolds

Abdelgabar Ahmed, Tarig Ballal, Xing Liu et al. (+2 more)

Global Navigation Satellite Systems (GNSS) applications are often hindered by various sources of error, with multipath interference being one of the most challenging, particularly in urban environments. In this work, we build on previous research by implementing a GNSS array-based multipath detection algorithm, incorporating real-time attitude estimation for dynamic scenarios. The method fuses GNSS and IMU data using an Unscented Kalman Filter (UKF) on a manifold, enabling continuous attitude tracking. The proposed approach utilizes attitude information from satellite combinations to identify and exclude multipath-affected satellites, improving the accuracy of both positioning and attitude determination. To address computational challenges associated with evaluating large numbers of satellite combinations, we propose the use of the Random Sample Consensus (RANSAC) algorithm, which reduces the number of combinations assessed while maintaining high detection performance. Performance evaluations are conducted using trajectories and IMU readings from the KITTI dataset. GNSS observations are simulated based on ground truth positions and satellite ephemeris. The results demonstrate the effectiveness of the proposed approach in detecting satellites affected by multipath interference. Significant improvements in positioning accuracy are observed, particularly in scenarios where a large portion of the visible satellites are contaminated by severe multipath.
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All authors Abdelgabar Ahmed, Tarig Ballal, Xing Liu, Mohanad Ahmed, Tareq Y. Al-Naffouri