Signal Processing · Cross-listed from q-bio.NC
Agonist-Antagonist Neural Coordination without Mechanical Coupling after Targeted Muscle Reinnervation
Laura Ferrante, Anna Boesendorfer, Benedikt Baumgartner et al. (+4 more)
Following limb amputation and targeted muscle reinnervation (TMR), nerves that originally innervated agonist and antagonist muscles are rerouted into one or more residual target muscles. This rerouting profoundly alters the natural mechanical coupling and afferent signalling that normally link muscle groups in intact limbs. Despite this disruption, in this study we demonstrate, using high-density intramuscular microelectrode arrays implanted in reinnervated muscles of three TMR participants, that motor units (MUs) associated with agonist and antagonist tasks remain functionally coupled. Specifically, over 40% of motor units active during agonist tasks were also recruited during the corresponding antagonist tasks, even though no visual feedback on antagonist neural activity was provided. These motor units exhibited significantly different firing rates depending on their functional role. These results provide the first motor-unit-level evidence that the central nervous system preserves coordinated agonist-antagonist control after TMR and inform restorative
surgical strategies and prosthetic systems capable of regulating both limb kinematics and dynamics based on agonist-antagonist commands interplay.
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All authors Laura Ferrante, Anna Boesendorfer, Benedikt Baumgartner, Manuel Catalano, Antonio Bicchi, Oskar Aszmann, Dario Farina
Systems and Control
Computation-Accuracy Trade-Off in Service-Oriented Model-Based Control
Hazem Ibrahim, Julius Beerwerth, Lorenz Dörschel et al. (+1 more)
Representing a control system as a Service-Oriented Architecture (SOA)-referred to as Service-Oriented Model-Based Control (SOMC)-enables runtime-flexible composition of control loop elements. This paper presents a framework that optimizes the computation-accuracy trade-off by formulating service orchestration as an A$^\star$search problem, complemented by Contextual
Bayesian Optimization (BO) to tune the multi-objective cost weights. A vehicle longitudinal-velocity control case study demonstrates online, performancedriven reconfiguration of the control architecture. We show that our framework not only combines control and software structure but also considers the real-time requirements of the control system during performance optimization.
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All authors Hazem Ibrahim, Julius Beerwerth, Lorenz Dörschel, Bassam Alrifaee
Audio and Speech Processing · Cross-listed from cs.SD
I Guess That's Why They Call it the Blues: Causal Analysis for Audio Classifiers
David A. Kelly and Hana Chockler
It is well-known that audio classifiers often rely on non-musically relevant features and spurious correlations to classify audio. Hence audio classifiers are easy to manipulate or confuse, resulting in wrong classifications. While inducing a misclassification is not hard, until now the set of features that the classifiers rely on was not well understood. In this paper we introduce a new method that uses causal
reasoning to discover features of the frequency space that are sufficient and necessary for a given classification. We describe an implementation of this
algorithm in the tool FreqReX and provide experimental results on a number of standard
benchmark datasets. Our experiments show that causally sufficient and necessary subsets allow us to manipulate the outputs of the models in a variety of ways by changing the input very slightly. Namely, a change to one out of 240,000 frequencies results in a change in classification 58% of the time, and the change can be so small that it is practically inaudible. These results show that causal analysis is useful for understanding the
reasoning process of audio classifiers and can be used to successfully manipulate their outputs.
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Signal Processing
OFDM-Based ISAC Imaging of Extended Targets via Inverse Virtual Aperture Processing
Michael Negosanti, Lorenzo Pucci and Andrea Giorgetti
This work investigates the performance of an integrated sensing and communication (
ISAC) system exploiting inverse virtual aperture (IVA) for imaging moving extended targets in vehicular scenarios. A base station (BS) operates as a monostatic sensor using
MIMO-OFDM waveforms. Echoes reflected by the target are processed through motion-compensation techniques to form an IVA range-Doppler (cross-range) image. A case study considers a 5G NR waveform in the upper mid-band, with the target model defined in 3GPP Release 19, representing a vehicle as a set of spatially distributed scatterers. Performance is evaluated in terms of image contrast (IC) and the root mean squared error (RMSE) of the estimated target-centroid range. Finally, the trade-off between sensing accuracy and communication efficiency is examined by varying the subcarrier allocation for IVA imaging. The results provide insights for designing effective sensing strategies in next-generation
radio networks.
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Systems and Control · Cross-listed from cs.DB
A Categorical Approach to Semantic Interoperability across Building Lifecycle
Zoltan Nagy, Ryan Wisnesky, Kevin Carlson et al. (+2 more)
Buildings generate heterogeneous data across their lifecycle, yet integrating these data remains a critical unsolved challenge. Despite three decades of standardization efforts, over 40 metadata schemas now span the building lifecycle, with fragmentation accelerating rather than resolving. Current approaches rely on point-to-point mappings that scale quadratically with the number of schemas, or universal ontologies that become unwieldy monoliths. The fundamental gap is the absence of mathematical foundations for structure-preserving transformations across heterogeneous building data. Here we show that category theory provides these foundations, enabling systematic data integration with $O(n)$ specification complexity for $n$ ontologies. We formalize building ontologies as first-order theories and demonstrate two proof-of-concept implementations in Categorical Query
Language (CQL): 1) generating BRICK models from IFC design data at commissioning, and 2) three-way integration of IFC, BRICK, and RealEstateCore where only two explicit mappings yield the third automatically through categorical composition. Our correct-by-construction approach treats property sets as first-class schema entities and provides automated bidirectional migrations, and enables cross-ontology queries. These results establish feasibility of categorical methods for building data integration and suggest a path toward an app ecosystem for buildings, where mathematical foundations enable reliable component integration analogous to smartphone platforms.
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All authors Zoltan Nagy, Ryan Wisnesky, Kevin Carlson, Eswaran Subrahmanian, Gioele Zardini
Signal Processing
Low-Power On-Device Gesture Recognition with Einsum Networks
Sahar Golipoor, Lingyun Yao, Martin Andraud et al. (+1 more)
We design a gesture-recognition pipeline for networks of distributed, resource constrained devices utilising Einsum Networks. Einsum Networks are probabilistic
circuits that feature a tractable inference, explainability, and energy efficiency. The system is validated in a scenario of low-power, body-worn, passive
Radio Frequency Identification-based gesture recognition. Each constrained device includes task-specific processing units responsible for Received Signal Strength (RSS) and phase processing or Angle of Arrival (AoA) estimation, along with feature extraction, as well as dedicated Einsum hardware that processes the extracted features. The output of all constrained devices is then fused in a decision aggregation module to predict gestures. Experimental results demonstrate that the method outperforms the
benchmark models.
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All authors Sahar Golipoor, Lingyun Yao, Martin Andraud, Stephan Sigg
Image and Video Processing
Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models
Maxence Noble, Gonzalo Iñaki Quintana, Benjamin Aubin et al. (+1 more)
Diffusion-based image super-resolution (SR) has recently attracted significant
attention by leveraging the expressive power of large pre-trained text-to-image
diffusion models (DMs). A central practical challenge is resolving the trade-off between reconstruction faithfulness and photorealism. To address inference efficiency, many recent works have explored knowledge
distillation strategies specifically tailored to SR, enabling one-step
diffusion-based approaches. However, these teacher-student formulations are inherently constrained by information compression, which can degrade perceptual cues such as lifelike textures and depth of field, even with high overall perceptual quality. In parallel, self-
distillation DMs, known as Flow Map models, have emerged as a promising alternative for image generation tasks, enabling fast inference while preserving the expressivity and training stability of standard DMs. Building on these developments, we propose FlowMapSR, a novel
diffusion-based framework for image super-resolution explicitly designed for efficient inference. Beyond adapting Flow Map models to SR, we introduce two complementary enhancements: (i) positive-negative prompting guidance, based on a generalization of classifier free-guidance paradigm to Flow Map models, and (ii)
adversarial fine-tuning using Low-Rank Adaptation (LoRA). Among the considered Flow Map formulations (Eulerian, Lagrangian, and Shortcut), we find that the Shortcut variant consistently achieves the best performance when combined with these enhancements. Extensive experiments show that FlowMapSR achieves a better balance between reconstruction faithfulness and photorealism than recent state-of-the-art methods for both x4 and x8 upscaling, while maintaining competitive inference time. Notably, a single model is used for both upscaling factors, without any scale-specific conditioning or degradation-guided mechanisms.
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All authors Maxence Noble, Gonzalo Iñaki Quintana, Benjamin Aubin, Clément Chadebec
Systems and Control
A Cognitive Framework for Autonomous Agents: Toward Human-Inspired Design
Francesco Guidi, Jingfeng Shan, Mehrdad Saeidi et al. (+8 more)
This work introduces a human-inspired reinforcement learning (RL) architecture that integrates Pavlovian and instrumental processes to enhance decision-making in autonomous systems. While existing engineering solutions rely almost exclusively on instrumental learning, neuroscience shows that humans use Pavlovian associations to leverage predictive cues to bias behavior before outcomes occur. We translate this dual-system mechanism into a cue-guided RL framework in which
radio-frequency (RF) stimuli act as conditioned (Pavlovian) cues that modulate action selection. The proposed architecture combines Pavlovian values with instrumental policy optimization, improving
navigation efficiency and cooperative behavior in unknown, partially observable environments. Simulation results demonstrate that cue-driven
agents adapt faster, achieving superior performance compared to traditional instrumental-solo
agents. This work highlights the potential of human learning principles to reshape digital
agents intelligence.
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All authors Francesco Guidi, Jingfeng Shan, Mehrdad Saeidi, Enrico Testi, Elia Favarelli, Andrea Giorgetti, Davide Dardari, Alberto Zanella, Giorgio Li Pira, Francesca Starita, Anna Guerra
Image and Video Processing
Ming Kang, Fung Fung Ting, Raphaël C.-W. Phan et al. (+2 more)
Nuclei panoptic
segmentation supports cancer diagnostics by integrating both semantic and instance
segmentation of different
cell types to analyze overall tissue structure and individual
nuclei in histopathology images. Major challenges include detecting small objects, handling ambiguous boundaries, and addressing class imbalance. To address these issues, we propose PanopMamba, a novel hybrid encoder-decoder architecture that integrates Mamba and
Transformer with additional feature-enhanced fusion via state space modeling. We design a multiscale Mamba backbone and a State Space Model (SSM)-based fusion network to enable efficient long-range perception in pyramid features, thereby extending the pure encoder-decoder framework while facilitating information sharing across multiscale features of
nuclei. The proposed SSM-based feature-enhanced fusion integrates pyramid feature networks and dynamic feature enhancement across different spatial scales, enhancing the feature representation of densely overlapping
nuclei in both semantic and spatial dimensions. To the best of our knowledge, this is the first Mamba-based approach for panoptic
segmentation. Additionally, we introduce alternative evaluation metrics, including image-level Panoptic Quality ($i$PQ), boundary-weighted PQ ($w$PQ), and frequency-weighted PQ ($fw$PQ), which are specifically designed to address the unique challenges of
nuclei segmentation and thereby mitigate the potential bias inherent in vanilla PQ. Experimental evaluations on two multiclass
nuclei segmentation benchmark datasets, MoNuSAC2020 and NuInsSeg, demonstrate the superiority of PanopMamba for
nuclei panoptic
segmentation over state-of-the-art methods. Consequently, the robustness of PanopMamba is validated across various metrics, while the distinctiveness of PQ variants is also demonstrated. Code is available at https://github.com/mkang315/PanopMamba.
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All authors Ming Kang, Fung Fung Ting, Raphaël C.-W. Phan, Zongyuan Ge, Chee-Ming Ting
Systems and Control
Challenges in the Proper Metrological Verification of Smart Energy Meters
Antonio Bracale, Jakub Janowicz, Piotr Kuwałek et al. (+1 more)
The most common instruments currently used to measure active/reactive energy and power quality indicators are smart energy meters (EM). Unfortunately, the verification of such meters is currently performed under ideal conditions or with simple signal models, which do not recreate actual states occurring in the power grid and do not ensure the verification of the properties of their signal chains. This paper presents challenges in proper metrological verification of smart EM. It presents existing legal and normative requirements and scientific research directions regarding these meters. Although the meters tested comply with the normative and legal requirements, the results reveal numerous imperfections in the signal and measurement chains for the selected test signal. Based on the results of the research results, further directions have been determined in the field of smart EM.
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All authors Antonio Bracale, Jakub Janowicz, Piotr Kuwałek, Grzegorz Wiczyński
Signal Processing
Assessment of Errors of Fundamental Frequency Estimation Methods in the Presence of Voltage Fluctuations and Distortions
Antonio Bracale, Pasquale De Falco, Piotr Kuwałek et al. (+1 more)
The fundamental frequency is one of the parameters that define power quality. Correctly determining this parameter under the conditions that prevail in modern power grids is crucial. Diagnostic purposes often require an efficient estimation of this parameter within short time windows. Therefore, this article presents the results of numerical simulation studies that allow the assessment of errors in various fundamental frequency estimation methods, including the standard IEC 61000-4-30 method, when the analyzed signal has a form similar to that found in modern power grids. For the purposes of this study, a test signal was adopted recreating the states of the power grid, including the simultaneous occurrence of voltage fluctuations and distortions. Conclusions are presented based on conducted research.
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All authors Antonio Bracale, Pasquale De Falco, Piotr Kuwałek, Grzegorz Wiczyński
Audio and Speech Processing · Cross-listed from cs.SD
Omni-directional attention mechanism based on Mamba for speech separation
Ke Xue, Chang Sun, Rongfei Fan et al. (+2 more)
Mamba, a selective state-space model (SSM), has emerged as an efficient alternative to
Transformers for
speech modeling, enabling long-sequence processing with linear complexity. While effective in
speech separation, existing approaches, whether in the time or time-frequency domain, typically decompose the input along a single dimension into short one-dimensional sequences before processing them with Mamba, which restricts it to local 1D modeling and limits its ability to capture global dependencies across the 2D spectrogram. In this work, we propose an efficient omni-directional
attention (OA) mechanism built upon unidirectional Mamba, which models global dependencies from ten different directions on the spectrogram. We expand the proposed mechanism into two baseline separation models and evaluate on three public
datasets. Experimental results show that our approach consistently achieves significant performance gains over the baselines while preserving linear complexity, outperforming existing state-of-the-art (SOTA) systems.
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All authors Ke Xue, Chang Sun, Rongfei Fan, Jing Wang, Han Hu
Image and Video Processing
Unsupervised Super-Resolution of Hyperspectral Remote Sensing Images Using Fully Synthetic Training
Xinxin Xu, Yann Gousseau, Christophe Kervazo et al. (+1 more)
Considerable work has been dedicated to hyperspectral single image super-resolution to improve the spatial resolution of hyperspectral images and fully exploit their potential. However, most of these methods are supervised and require some data with ground truth for training, which is often non-available. To overcome this problem, we propose a new unsupervised training strategy for the super-resolution of hyperspectral remote sensing images, based on the use of synthetic abundance data. Its first step decomposes the hyperspectral image into abundances and endmembers by unmixing. Then, an abundance super-resolution neural network is trained using synthetic abundances, which are generated using the dead leaves model in such a way as to faithfully mimic real abundance statistics. Next, the spatial resolution of the considered hyperspectral image abundances is increased using this trained network, and the high resolution hyperspectral image is finally obtained by recombination with the endmembers. Experimental results show the training potential of the synthetic images, and demonstrate the method effectiveness.
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All authors Xinxin Xu, Yann Gousseau, Christophe Kervazo, Saïd Ladjal
Signal Processing
Learning Successive Interference Cancellation for Low-Complexity Soft-Output MIMO Detection
Benedikt Fesl and Fatih Capar
Low-complexity multiple-input multiple-output (
MIMO) detection remains a key challenge in modern wireless systems, particularly for 5G reduced capability (RedCap) and internet-of-things (IoT) devices. In this context, the growing interest in deploying machine learning on edge devices must be balanced against stringent constraints on computational complexity and
memory while supporting high-order modulation. Beyond accurate hard detection, reliable soft information is equally critical, as modern receivers rely on soft-input channel decoding, imposing additional requirements on the detector design. In this work, we propose recurSIC, a lightweight learning-based
MIMO detection framework that is structurally inspired by successive interference cancellation (SIC) and incorporates learned processing stages. It generates reliable soft information via multi-path hypothesis tracking with a tunable complexity parameter while requiring only a single forward pass and a minimal parameter count. Numerical results in realistic wireless scenarios show that recurSIC achieves strong hard- and soft-detection performance at very low complexity, making it well suited for edge-constrained
MIMO receivers.
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Systems and Control · Cross-listed from cs.RO
Zero-Shot MARL Benchmark in the Cyber-Physical Mobility Lab
Julius Beerwerth, Jianye Xu, Simon Schäfer et al. (+2 more)
We present a reproducible
benchmark for evaluating sim-to-real transfer of Multi-
Agent Reinforcement Learning (MARL) policies for Connected and Automated Vehicles (CAVs). The platform, based on the Cyber-Physical Mobility Lab (CPM Lab) [1], integrates simulation, a high-fidelity digital twin, and a physical testbed, enabling structured zero-shot evaluation of MARL motion-planning policies. We demonstrate its use by deploying a SigmaRL-trained policy [2] across all three domains, revealing two complementary sources of performance degradation: architectural differences between simulation and hardware control stacks, and the sim-to-real gap induced by increasing environmental realism. The open-source setup enables systematic analysis of sim-to-real challenges in MARL under realistic, reproducible conditions.
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All authors Julius Beerwerth, Jianye Xu, Simon Schäfer, Fynn Belderink, Bassam Alrifaee
Signal Processing
Real-Time Evaluation of an Ultra-Tight GNSS/INS Integration Based on Adaptive PLL Bandwidth
Gaël Pages, Priot Benoît and Guillaume Beaugendre
In this contribution, we propose a GNSS/INS ultra-tight coupling in which the GNSS receiver architecture is based on a vector tracking loop type architecture. In the proposed approach, the phase lock loop bandwidth is adapted according to the inertial
navigation system information. The latter has the advantage to be easily implementable on a System-on-Chip component such as an FPGA (Field-Programmable Gate Arrays), and can be implemented with minor modifications on an existing GNSS receiver platform. Moreover, compared to classical vector-based solutions, the proposed architecture decodes the
navigation message in the loop, without the need to run scalar loops in parallel or having to store pre-downloaded ephemeris data. This architecture therefore does not increase the area occupied on the FPGA and does not use additional resources for storage. The proposed GNSS receiver architecture uses GPS L1/C and Galileo E1 signals and is composed of one acquisition module and 16 tracking channels (8 GPS and 8 Galileo) which are implemented within a FPGA (Zynq-Ultrascale).
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Systems and Control
Agentic AI-RAN Empowering Synergetic Sensing, Communication, Computing, and Control
Lingxiao Sun, Zhaoyang Zhang, Zihan Lin et al. (+4 more)
Future sixth-generation (6G) networks are expected to support low-altitude wireless networks (LAWNs), where unmanned aerial vehicles (
UAVs) and aerial
robots operate in highly dynamic three-dimensional environments under stringent latency, reliability, and autonomy requirements. In such scenarios, autonomous task execution at the network edge demands holistic coordination among sensing, communication, computing, and control (SC3) processes. Agentic Artificially Intelligent
Radio Access Networks (Agentic AI-RAN) offer a promising paradigm by enabling the edge network to function as an autonomous decision-making entity for low-altitude
agents with limited onboard resources. In this article, we propose a task-oriented Agentic AI-RAN architecture that enables SC3 task execution within a single edge node. The proposed architecture addresses the challenge of coordinating heterogeneous workloads in resource-constrained edge environments. To validate this framework, we prototype a representative low-altitude
UAV system on a general-purpose Graphics Processing Unit (GPU) platform and evaluate it through an autonomous drone-
navigation case study. The current prototype instantiates the platform-agnostic design through Multi-Instance GPU (MIG) partitioning and containerized deployment, providing physical resource isolation and coordinated execution between real-time communication and
multimodal inference. Experimental results demonstrate low closed-loop latency, robust bidirectional communication, and stable performance under dynamic runtime conditions, highlighting the feasibility of the proposed framework for mission-critical low-altitude wireless networks in 6G.
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All authors Lingxiao Sun, Zhaoyang Zhang, Zihan Lin, Zirui Chen, Weijie Zhou, Zhaohui Yang, Tony Q. S. Quek
Signal Processing
Spiking Neural Networks for Communication Systems: Encoding Schemes, Learning Algorithms, and Equalization~Techniques
Eike-Manuel Edelmann
Machine learning with artificial neural networks (ANNs), provides solutions for the growing complexity of modern communication systems. This complexity, however, increases power consumption, making the systems energy-intensive. Spiking neural networks (SNNs) represent a novel generation of neural networks inspired by the highly efficient human
brain. By emulating its event-driven and energy-efficient mechanisms, SNNs enable low-power, real-time signal processing. They differ from ANNs in two key ways: they exhibit inherent temporal dynamics and process and transmit information as short binary signals called spikes. Despite their promise, major challenges remain, e.g., identifying optimal learning rules and effective neural encoding. This thesis investigates the design of SNN-based receivers for nonlinear time-invariant frequency-selective channels. Backpropagation through time with surrogate gradients is identified as a promising update rule and the novel quantization encoding (QE) as promising neural encoding. Given the model of the intensity modulation with direct detection link, we compare two different receiver architectures based on equalization performance and spike count. Using decision feedback and QE achieves both strong performance and low spike counts. Notably, SNN-based receivers significantly outperform ANN-based counterparts. We furthermore introduce policy gradient-based update (PGU), an reinforcement learning-based update
algorithm that requires no backpropagation. Using PGU, encoding parameters are optimized, drastically reducing runtime, complexity, and spikes per inference while maintaining performance. This thesis contributes a successful design and optimization framework for SNN-based receivers. By addressing key challenges in SNN optimization, it facilitates future advances in the design and deployment of energy-efficient SNN receivers.
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Audio and Speech Processing · Cross-listed from cs.SD
CORD: Bridging the Audio-Text Reasoning Gap via Weighted On-policy Cross-modal Distillation
Jing Hu, Danxiang Zhu, Xianlong Luo et al. (+9 more)
Large Audio
Language Models (LALMs) have garnered significant research interest. Despite being built upon text-based large
language models (LLMs), LALMs frequently exhibit a degradation in knowledge and
reasoning capabilities. We hypothesize that this limitation stems from the failure of current training paradigms to effectively bridge the acoustic-semantic gap within the feature representation space. To address this challenge, we propose CORD, a unified
alignment framework that performs online cross-modal self-
distillation. Specifically, it aligns audio-conditioned
reasoning with its text-conditioned counterpart within a unified model. Leveraging the text modality as an internal teacher, CORD performs multi-granularity
alignment throughout the audio rollout process. At the
token level, it employs on-policy reverse KL divergence with importance-aware weighting to prioritize early and semantically critical
tokens. At the sequence level, CORD introduces a judge-based global reward to optimize complete
reasoning trajectories via Group Relative Policy Optimization (GRPO). Empirical results across multiple
benchmarks demonstrate that CORD consistently enhances audio-conditioned
reasoning and substantially bridges the audio-text performance gap with only 80k synthetic training samples, validating the efficacy and data efficiency of our on-policy, multi-level cross-modal
alignment approach.
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All authors Jing Hu, Danxiang Zhu, Xianlong Luo, Dan Zhang, Shuwei He, Yishu Lei, Haitao Zheng, Shikun Feng, Jingzhou He, Yu Sun, Hua Wu, Haifeng Wang
Signal Processing
Cell-Free MIMO with Rotatable Antennas: When Macro-Diversity Meets Antenna Directivity
Xingxiang Peng, Qingqing Wu, Ziyuan Zheng et al. (+5 more)
Cell-free networks leverage distributed access points (APs) to achieve macro-diversity, yet their performance is often constrained by large disparities in channel quality arising from user geometry and blockages. To address this, rotatable
antennas (RAs) add a lightweight hardware degree of freedom by steering the
antenna boresight toward dominant propagation directions to strengthen unfavorable links, thereby enabling the network to better exploit macro-diversity for higher and more uniform performance. This paper investigates an RA-enabled
cell-free downlink network and formulates a max-min rate problem that jointly optimizes transmit beamforming and
antenna orientations. To tackle this challenging problem, we develop an alternating-optimization-based
algorithm that iteratively updates the beamformers via a second-order cone program (SOCP) and optimizes the
antenna orientations using successive convex approximation. To reduce complexity, we further propose an efficient two-stage scheme that first designs orientations by maximizing a proportional-fair log-utility using
manifold-aware Frank-Wolfe updates, and then computes the beamformers using an SOCP-based design. Simulation results demonstrate that the proposed orientation-aware designs achieve a substantially higher worst-user rate than conventional beamforming-only
benchmarks. Furthermore, larger
antenna directivity enhances
fairness with proper orientation but can degrade the worst-user performance otherwise.
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All authors Xingxiang Peng, Qingqing Wu, Ziyuan Zheng, Yanze Zhu, Wen Chen, Penghui Huang, Ying Gao, Honghao Wang
Audio and Speech Processing · Cross-listed from cs.SD
Do Models Hear Like Us? Probing the Representational Alignment of Audio LLMs and Naturalistic EEG
Haoyun Yang, Xin Xiao, Jiang Zhong et al. (+5 more)
Audio Large
Language Models (Audio LLMs) have demonstrated strong capabilities in integrating
speech perception with
language understanding. However, whether their internal representations align with human neural dynamics during naturalistic listening remains largely unexplored. In this work, we systematically examine layer-wise representational
alignment between 12 open-source Audio LLMs and Electroencephalogram (
EEG) signals across 2
datasets. Specifically, we employ 8 similarity metrics, such as Spearman-based Representational Similarity Analysis (RSA), to characterize within-sentence representational geometry. Our analysis reveals 3 key findings: (1) we observe a rank-dependence split, in which model rankings vary substantially across different similarity metrics; (2) we identify spatio-temporal
alignment patterns characterized by depth-dependent
alignment peaks and a pronounced increase in RSA within the 250-500 ms time window, consistent with N400-related neural dynamics; (3) we find an affective dissociation whereby negative prosody, identified using a proposed Tri-modal Neighborhood Consistency (TNC) criterion, reduces
geometric similarity while enhancing covariance-based dependence. These findings provide new neurobiological insights into the representational mechanisms of Audio LLMs.
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All authors Haoyun Yang, Xin Xiao, Jiang Zhong, Yu Tian, Dong Xiaohua, Yu Mao, Hao Wu, Kaiwen Wei
Systems and Control
Sequential Operating Simulation of Solid State Transformer-Driven Next-Generation 800 VDC Data Center
Jian Xu, Xinxiong Jiang, Yi Bao et al. (+6 more)
Artificial-intelligence (AI) workloads are driving rapid growth in data-center electricity use and rack power density, increasing demand for power-delivery systems that are efficient and robust to fast load transients. Conventional uninterruptible power supply (UPS) based AC distribution chains involve multiple conversion stages and line-frequency
transformers, which compound losses and are less compatible with dynamic AI power profiles. Although solid-state
transformers (SSTs) and 800 VDC distribution architecture are widely discussed, implementable
topology/control details, and long-horizon validation with realistic operating profiles remain limited. This paper develops an SST-driven 800 VDC architecture that converts 10 kV MVAC to an 800V LVDC bus using a three-phase H-bridge AC/DC stage cascaded with a dual-active-bridge (DAB) DC/DC stage. A coordinated closed-loop control scheme, combining rectifier voltage/current regulation and DAB phase-shift control, is designed to maintain DC-bus voltage stability. The proposed system is implemented on the real-time digital simulation (RTDS) platform and evaluated via sequential simulations using real-world day- and month-scale operating profiles of data centers, benchmarked against a UPS supply chain. Numerical studies demonstrate tight 800 VDC regulation, reduced input-side energy consumption compared with the UPS baseline, and satisfactory power-quality performance. A capacitance sensitivity test quantifies tradeoffs between DC-bus ripple and low-frequency input-power oscillations, yielding a practical capacitance range for design. Overall, the work provides a reproducible evaluation workflow and actionable guidance for next-generation AI data centers.
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All authors Jian Xu, Xinxiong Jiang, Yi Bao, Yuchen Zheng, Xuhui Chen, Qiang Xu, Siyang Liao, Deping Ke, Xiaoqi Gao
Signal Processing · Cross-listed from cs.IT
Load Balanced ISAC Systems for URLLC Users
Shivani Singh, Amudheesan Nakkeeran, Prem Singh et al. (+2 more)
This paper presents an energy-efficient downlink
cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (
ISAC) network that serves ultra-reliable low-latency communication (URLLC) users while simultaneously detecting a target. We propose a load-balancing
algorithm that minimizes the total network power consumption; including transmit power, fixed static power, and traffic-dependent fronthaul power at the access points (APs) without degrading system performance. To this end, we formulate a mixed-integer non-convex optimization problem and introduce an iterative joint power allocation and AP load balancing (JPALB)
algorithm. The
algorithm aims to reduce total power usage while meeting both the communication quality-of-service (QoS) requirements of URLLC users and the sensing QoS needed for target detection. Proposed JPALB
algorithm for
ISAC systems was simulated with maximum-ratio transmission (MRT) and regularized zero-forcing (RZF) precoders. Simulation results show approximately 33% reduction in power consumption, using JPALB
algorithm compared to a baseline with no load balancing, without compromising communication and sensing QoS requirements.
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All authors Shivani Singh, Amudheesan Nakkeeran, Prem Singh, Ekant Sharma, Jyotsna Bapat
Audio and Speech Processing
FlowSE-GRPO: Training Flow Matching Speech Enhancement via Online Reinforcement Learning
Haoxu Wang, Biao Tian, Yiheng Jiang et al. (+5 more)
Generative
speech enhancement offers a promising alternative to traditional discriminative methods by modeling the distribution of clean
speech conditioned on noisy inputs. Post-training
alignment via reinforcement learning (RL) effectively aligns generative models with human preferences and downstream metrics in domains such as natural
language processing, but its use in
speech enhancement remains limited, especially for online RL. Prior work explores offline methods like Direct Preference Optimization (DPO); online methods such as Group Relative Policy Optimization (GRPO) remain largely uninvestigated. In this paper, we present the first successful integration of online GRPO into a flow-matching
speech enhancement framework, enabling efficient post-training
alignment to perceptual and task-oriented metrics with few update steps. Unlike prior GRPO work on Large
Language Models, we adapt the
algorithm to the continuous, time-series nature of
speech and to the dynamics of flow-matching generative models. We show that optimizing a single reward yields rapid metric gains but often induces reward hacking that degrades audio fidelity despite higher scores. To mitigate this, we propose a multi-metric reward optimization strategy that balances competing objectives, substantially reducing overfitting and improving overall performance. Our experiments validate online GRPO for
speech enhancement and provide practical guidance for RL-based post-training of generative audio models.
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All authors Haoxu Wang, Biao Tian, Yiheng Jiang, Zexu Pan, Shengkui Zhao, Bin Ma, Daren Chen, Xiangang Li
Signal Processing · Cross-listed from cs.CR
Secure Intellicise Wireless Network: Agentic AI for Coverless Semantic Steganography Communication
Rui Meng, Song Gao, Bingxuan Xu et al. (+6 more)
Semantic Communication (SemCom), leveraging its significant advantages in transmission efficiency and reliability, has emerged as a core technology for constructing future intellicise (intelligent and concise) wireless networks. However, intelligent attacks represented by semantic eavesdropping pose severe challenges to the security of SemCom. To address this challenge, Semantic Steganographic Communication (SemSteCom) achieves “invisible” encryption by implicitly
embedding private semantic information into cover modality carriers. The state-of-the-art study has further introduced generative
diffusion models to directly generate stega images without relying on original cover images, effectively enhancing steganographic capacity. Nevertheless, the recovery process of private images is highly dependent on the guidance of private semantic keys, which may be inferred by intelligent eavesdroppers, thereby introducing new security threats. To address this issue, we propose an Agentic AI-driven SemSteCom (AgentSemSteCom) scheme, which includes semantic extraction, digital
token controlled reference image generation, coverless steganography, semantic codec, and optional task-oriented enhancement modules. The proposed AgentSemSteCom scheme obviates the need for both cover images and private semantic keys, thereby boosting steganographic capacity while reinforcing transmission security. The simulation results on open-source
datasets verify that, AgentSemSteCom achieves better transmission quality and higher security levels than the baseline scheme.
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All authors Rui Meng, Song Gao, Bingxuan Xu, Xiaodong Xu, Jianqiao Chen, Nan Ma, Pei Xiao, Ping Zhang, Rahim Tafazolli
Signal Processing
Auditory Attention Decoding without Spatial Information: A Diotic EEG Study
Masahiro Yoshino, Haruki Yokota, Junya Hara et al. (+2 more)
Auditory
attention decoding (AAD) identifies the attended
speech stream in multi-speaker environments by decoding
brain signals such as electroencephalography (
EEG). This technology is essential for realizing smart hearing aids that address the cocktail party problem and for facilitating objective audiometry systems. Existing AAD research mainly utilizes dichotic environments where different
speech signals are presented to the left and right ears, enabling models to classify directional
attention rather than
speech content. However, this spatial reliance limits applicability to real-world scenarios, such as the "cocktail party" situation, where speakers overlap or move dynamically. To address this challenge, we propose an AAD framework for diotic environments where identical
speech mixtures are presented to both ears, eliminating spatial cues. Our approach maps
EEG and
speech signals into a shared latent space using independent encoders. We extract
speech features using wav2vec 2.0 and encode them with a 2-layer 1D convolutional neural network (CNN), while employing the BrainNetwork architecture for
EEG encoding. The model identifies the attended
speech by calculating the cosine similarity between
EEG and
speech representations. We evaluate our method on a diotic
EEG dataset and achieve 72.70% accuracy, which is 22.58% higher than the state-of-the-art direction-based AAD method.
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All authors Masahiro Yoshino, Haruki Yokota, Junya Hara, Yuichi Tanaka, Hiroshi Higashi
Signal Processing
TransfoREM: Transformer aided 3D Radio Environment Mapping
Gautham Reddy, Ismail Guvenc, Mihail L. Sichitiu et al. (+3 more)
Providing reliable cellular connectivity to Unmanned Aerial Vehicles (
UAV) is a key challenge, as existing terrestrial networks are deployed mainly for ground-level coverage. The cellular network coverage may be available for a limited range from the
antenna side lobes, with poor connectivity further exacerbated by
UAV flight dynamics. In this work, we propose TransfoREM, a 3D
Radio Environment Map (REM) generation method that combines deterministic channel models and real-world data to map terrestrial network coverage at higher altitudes. At the core of our solution is a
transformer model that translates
radio propagation mapping into a sequence prediction task to construct REMs. Our results demonstrate that TransfoREM offers improved interpolation capability on real-world data compared against conventional Kriging and other machine learning (ML) techniques. Furthermore, TransfoREM is designed for holistic integration into cellular networks at the base station (BS) level, where it can build REMs, which can then be leveraged for enhanced resource allocation, interference management, and spatial spectrum utilization.
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All authors Gautham Reddy, Ismail Guvenc, Mihail L. Sichitiu, Arupjyoti Bhuyan, Bryton Petersen, Jason Abrahamson
Systems and Control
Robust Grid-Forming Control Based on Virtual Flux Observer
Xueqing Gao, Jun Zhang, Tao Li et al. (+1 more)
This paper investigates the design and analysis of a novel grid-forming (GFM) control method for grid-connected converters (GCCs). The core novelty lies in a virtual flux observer-based synchronization and load angle control method. The terminal voltage of the converter is directly regulated to provide voltage-source behavior. The control parameters are designed for decoupling and pole placement. The proposed method exhibits strong robustness in stability and dynamical performance across varying and uncertain grid strengths. The robust control performance of the proposed method is first demonstrated by small-signal analysis, then validated by experiments on a 20 kVA power conversion system.
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All authors Xueqing Gao, Jun Zhang, Tao Li, Mingming Zhang
Image and Video Processing
On The Robustness of Foundational 3D Medical Image Segmentation Models Against Imprecise Visual Prompts
Soumitri Chattopadhyay, Basar Demir and Marc Niethammer
While 3D foundational models have shown promise for promptable
segmentation of medical volumes, their robustness to imprecise
prompts remains under-explored. In this work, we aim to address this gap by systematically studying the effect of various controlled perturbations of dense visual
prompts, that closely mimic real-world imprecision. By conducting experiments with two recent foundational models on a multi-organ abdominal
segmentation task, we reveal several facets of promptable medical
segmentation, especially pertaining to reliance on visual shape and spatial cues, and the extent of resilience of models towards certain perturbations. Codes are available at: https://github.com/ucsdbiag/
Prompt-Robustness-MedSegFMs
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