Signal Processing
A Generic Multi-dimensional Symbol Construction for Digital Over-the-Air Computation and Practical Aspects
Alphan Sahin
In this paper, we propose a general-purpose multi-dimensional symbol construction for computing an arbitrary symmetric function with digital over-the-air computation (OAC) and discuss the practical aspects of coherent aggregation. For our first contribution, we discuss the categorical representation of a symmetric function. By using this representation and leveraging the sufficiency of the histogram to evaluate a symmetric function, i.e., inspired by type-based multiple access (TBMA), we introduce a general approach to design a single set of OAC symbols to compute any digital function. For our second contribution, we use a comprehensive platform based on low-cost nodes that maintain synchronization in time, frequency, phase, and amplitude via a trigger mechanism, enabling coherent OAC experiments without Global Positioning System (GPS) or cable-based synchronization. Using measurements from the platform, we characterize the phase and amplitude statistics of the composite channel to derive a realistic impairment model for coherent OAC. Through a comprehensive analysis, we demonstrate the effectiveness of the proposed scheme under impairments captured by the proposed model
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Audio and Speech Processing
One-Step Token-to-Waveform Generation with MeanFlow in Latent Space
Zheqi Dai, Guangyan Zhang, Zhen Ye et al. (+5 more)
Neural audio codecs are central to modern LLM-based Text-to-
Speech (TTS) and
multimodal systems. As low-bitrate semantic codecs gain prominence, the
Token-to-Waveform (
Token2Wav) decoder becomes a bottleneck determining both perceptual quality and system efficiency. Conventional multi-step flow-matching decoders offer superior quality but suffer from high inference latency due to iterative sampling, creating a severe quality-speed trade-off. In this paper, we propose a novel
Token2Wav architecture that overcomes this limitation by applying MeanFlow in a highly compressed latent space. By modeling the average velocity rather than the instantaneous velocity field, MeanFlow enables true one-step generation. Operating in the latent domain mitigates the
memory and stability issues of waveform-level flows, yielding up to a 17$\times$ improvement in Real-Time Factor (RTF) compared to multi-step baselines with negligible quality degradation. Furthermore, we introduce refinement strategies that mitigate latent mismatch, including decoder-only fine-tuning with the MeanFlow generator frozen and end-to-end joint fine-tuning, improving fidelity without increasing inference-time cost. Code and demo are publicly available.
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All authors Zheqi Dai, Guangyan Zhang, Zhen Ye, Jingyu Li, Haolin He, Chunyat Wu, Yiwen Guo, Qiuqiang Kong
Image and Video Processing
Multiscale reconstruction of protein conformations from cryo-EM images
David Y. W. Thong, Ozan Öktem and Joakim Andén
We present a novel multiscale
algorithm for directly recovering the atomic model structure of a
protein from single-particle cryo-EM data. Our
algorithm is able to estimate
protein structures to state-of-the-art accuracy for high-noise and low-contrast data. It is also robust to misspecifications in the TEM image formation model. These desirable properties are primarily due to the use of an explicit representation of the
protein backbone in terms of bonds, torsion angles and bond angles, which supplies rich prior information to the structure recovery process. We apply our method on three
protein cryo-EM
datasets, generated using an electron microscope digital twin, and show that using a multiscale approach yields an improvement of the root-mean-square deviation (RMSD) and template modelling (TM) scores with respect to the ground truth. Furthermore, there is evidence that larger-scale structures are being prioritised with the multiscale
algorithm, which reduces the possibility of convergence to bad local minima.
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Audio and Speech Processing
AI-based Cognitive-linguistic Features for Dementia Assessment in Picture Description
Lingfeng Xu, Prad Kadambi, Samuel Goldinger et al. (+3 more)
Picture descriptions provide valuable insights into several clinical constructs related to cognitive-linguistic abilities. However, operationalizing these constructs into quantitative measures remains challenging, limiting interpretability and clinical utility. We introduced seven constructs tailored to the Cookie Theft picture description task and prompted large
language models (LLMs) to evaluate them, generating severity scores and example-based explanations. Among the examined LLMs, Claude 3.5 Sonnet performed the best, producing severity scores that significantly distinguish cognitively impaired individuals from healthy controls. The model achieves a high accuracy of 85% on the ADReSS
dataset. Expert evaluation of Claude's scores and explanations yields a 3.99/5 average agreement. The findings demonstrate the potential of LLMs to operationalize clinical constructs and generate interpretable evaluations, offering a promising approach for accessible cognitive screening tools.
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All authors Lingfeng Xu, Prad Kadambi, Samuel Goldinger, Visar Berisha, Kimberly D. Mueller, Julie Liss
Audio and Speech Processing
Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews
Franziska Braun, Alea Rüggeberg, Thomas Ranzenberger et al. (+4 more)
Dementia and depression are the most prevalent neuropsychiatric disorders in geriatric populations, and their overlapping symptoms pose major challenges for differential diagnosis. In this study, we investigate open-weights Large
Language Models (LLMs) for predicting dementia and depression severity from
speech samples collected during standardized history taking interviews with 154 German-speaking subjects. We introduce an observer-based Global Depression Scale (GDS-D) aligned with the established Global Deterioration Scale (GDS), enabling parallel global staging of affective and cognitive symptoms. We compare three LLMs (Mistral 3.1, DeepHermes, Qwen3) in two settings: (1) zero-shot prediction and (2) LLM-based feature extraction for Support Vector Regression, using human and pause-enriched transcripts. Results show that LLMs effectively predict depression severity in zero-shot settings (best MAE of 0.60), while dementia assessment benefits substantially from structured feature extraction (best MAE of 0.78), reducing errors by up to 35% over zero-shot baselines. Pause-enriched transcripts achieve competitive performance with human transcriptions, demonstrating the viability of fully automatic screening pipelines for differential neuropsychiatric assessment.
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All authors Franziska Braun, Alea Rüggeberg, Thomas Ranzenberger, Hartmut Lehfeld, Thomas Hillemacher, Tobias Bocklet, Korbinian Riedhammer
Signal Processing
On the Optimum Energy-per-bit Launch Power in Coherent Hollow-core Fibre Transmission Systems
Ronit Sohanpal, Eric Sillekens, Mindaugas Jarmolovicius et al. (+2 more)
We investigate the optimum energy per bit in hollow-core-fibre transmission systems. We show that a 1000 km C-band link can achieve a 41.5% reduction in total power consumption when operating at the minimum energy-per-bit launch power with only 2.2% throughput penalty.
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All authors Ronit Sohanpal, Eric Sillekens, Mindaugas Jarmolovicius, Robert I. Killey, Polina Bayvel
Signal Processing · Cross-listed from physics.optics
Dual Line Coherent Detection
Nelson Castro, Yiming Li, Mohammed Patel et al. (+3 more)
We experimentally demonstrate dual-line coherent detection using an optical frequency comb local oscillator, enabling large frequency offset tolerance with minimal additional signal processing. The proposed method achieves 200 GHz offset tolerance for 400 Gbit/s signals with low penalty, supporting uncooled, low-cost coherent transceivers.
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All authors Nelson Castro, Yiming Li, Mohammed Patel, Frank Smyth, Sergei Turitsyn, Andrew Ellis
Systems and Control
Three-phase model of unbalanced distribution networks with DERs
S. Perna, C. Lillo, A.R. Di Fazio et al. (+4 more)
Classical DistFlow equations for steady-state distribution network analysis fail to capture the inherent imbalances of three-phase systems arising from asymmetrical lines, loads, and distributed energy resources (DERs). This paper extends the classical power flow (PF) equations into a rigorous, non-approximated three-phase formulation, termed Dist3Flow. The proposed branch flow model (BFM) utilizes the real and imaginary components of nodal voltages and the active and reactive power flows as state variables. Lines are modelled by nonlinear forward and backward equations, while loads and DERs are represented via ZIP models and P-Q control, respectively. By incorporating specific boundary conditions at the terminal nodes, the formulation generalizes PF analysis to both radial and closed-ring
topologies. The solution is obtained by using a backward/borward sweep (BFS)
algorithm. The approach is validated against OpenDSS across various configurations, considering open-ring and closed-ring
topologies with and without DERs.
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All authors S. Perna, C. Lillo, A.R. Di Fazio, M. Russo, G.M. Casolino, P. Varilone, P. Verde
Systems and Control
Reducing Building Heat Demand Through Intelligent Control: A Comparative Simulation Study
Ueli Schilt, Curtis Meister and Philipp Schuetz
Space heating remains the dominant energy consumer in buildings. While structural retrofitting can substantially reduce demand, it is often costly and time-intensive. As an alternative, this study investigates the potential of intelligent heating control strategies to reduce heat consumption with lower investment and faster implementation. Previous studies have shown that replacing conventional heating-curve-based controllers with model predictive controllers (MPCs) can reduce heating energy demand. Whereas most studies compare MPC to conventional control, this work evaluates two MPC strategies with different control objectives and quantifies their impact on indoor temperature tracking and heating demand. A virtual residential building model was developed in Python based on ISO 52016-1 to generate synthetic measurement data. A simplified resistance-capacitance (RC) model was parametrised using this
dataset and used as the internal model for two MPC strategies implemented in MATLAB. The strategies differ only in their optimisation objective: one minimises quadratic heating power, while the other prioritises indoor temperature tracking for thermal comfort. Simulations over six days show that both strategies satisfy comfort and system constraints, but differ in energy use and temperature variation. The comfort-oriented controller achieves lower total heat consumption than the controller minimising heating power, which is attributed to the penalisation of high heating rates in the quadratic objective function. The results demonstrate the importance of objective function formulation in MPC design and show that high comfort levels can be maintained while achieving lower heating demand without structural modifications to the building envelope.
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Signal Processing
Constellation Design for Nonlinear Unified SWIPT Receiver Channels with Memory
Triantafyllos Mavrovoltsos, Elio Faddoul, Zulqarnain Bin Ashraf et al. (+3 more)
Unified receivers (URs) have emerged as a promising architecture for simultaneous wireless information and power transfer (SWIPT), since a common rectifying front-end enables information decoding (ID) and energy harvesting (EH) from the same rectified output. However, rectification is nonlinear due to the diode, while the capacitor introduces
memory across symbols, making constellation design over the channel challenging. In this paper, we study constellation design for nonlinear UR-SWIPT channels in both memoryless and
memory regimes. First, we propose a tractable unified rectification model that captures both (i) the nonlinear steady-state mapping and (ii) the asymmetric capacitor charging/discharging dynamics under transient operation. To isolate the impact of rectification with
memory on ID, we study the information-based design. In this setting, we develop a state-adaptive policy with an algorithmic constellation design that accounts for the rectifier state and shapes the constellation in the observation domain. By approximating the rectifier state distribution, we derive a closed-form average symbol error rate (SER) expression and characterize the rate-reliability (R-R) tradeoff. We then seek constellations that minimize the SER under average transmit power and EH constraints. We address the resulting energy-constrained setting in the memoryless regime using an autoencoder-based framework that embeds the nonlinear rectification model as a differentiable channel block. Numerical results validate the proposed models, demonstrate the impact of
memory on the R-R tradeoff, and show how learned constellations adapt to EH requirements in the rate-energy tradeoff.
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All authors Triantafyllos Mavrovoltsos, Elio Faddoul, Zulqarnain Bin Ashraf, Constantinos Psomas, Besma Smida, Ioannis Krikidis
Signal Processing
Time-Slotted Multi-Cluster UAV AirComp with Energy-Awareness: A Pointer Network-Assisted Soft Actor-Critic Learning Framework
Xunqiang Lan, Xiao Tang, Ruonan Zhang et al. (+2 more)
Over-the-air computation (AirComp) has emerged as a promising approach for massive data aggregation, which is yet challenged by the channel variations, task distributions, and inherent energy limitation of the computation nodes. In this paper, we propose an unmanned aerial vehicle (
UAV)-assisted Aircomp system to serve multi-cluster computation tasks over time, where the
UAV mobility-facilitated spatial and time diversity is exploited for efficient and accurate data computation. Specifically, we aim for the minimization of AirComp aggregation error and the energy consumption by jointly optimizing the transceiver beamforming, normalizing factors, sensor scheduling, and
UAV trajectory. To solve the formulated problem, we decompose it into two layers where the inner layer addresses the optimization-based AirComp transceiver design, and the outer layer focuses on the deep reinforcement learning (DRL)-based scheduling and trajectory design. In particular, a pointer network actor-critic learning is developed to tackle the binary scheduling problem, and a soft actor-critic DRL
algorithm is employed to determine the
UAV trajectory. Simulation results validate the convergence of the proposed hierarchical learning framework and demonstrate its significant performance gains in terms of AirComp aggregation error and energy consumption as compared with baseline schemes.
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All authors Xunqiang Lan, Xiao Tang, Ruonan Zhang, Qinghe Du, Tony Q.S. Quek
Signal Processing
Condition-Wise Sinkhorn Drifting for One-Shot Learned Channel Simulation
Rick Fritschek and Rafael F. Schaefer
Learned communication systems may evaluate
stochastic channel surrogates millions of times inside differentiable training loops, making
diffusion-style reverse sampling expensive. This paper proposes condition-wise Sinkhorn drifting, a one-shot channel surrogate that preserves the transmitted symbol and transports only the conditional output laws \(p(y\mid x)\). We formulate a conditional Sinkhorn objective over repeated outputs at the same transmitted symbol and train the generator with finite-sample barycentric velocities followed by detached particle regression. Experiments on additive white Gaussian noise (AWGN), Rayleigh fading, solid-state power amplifier (SSPA) nonlinearity, and a compact tapped-delay-line (TDL) channel compare direct drifting, joint Sinkhorn drifting, condition-wise Sinkhorn drifting, conditional denoising
diffusion probabilistic modeling (DDPM), denoising
diffusion implicit modeling (DDIM), and Wasserstein generative
adversarial network (WGAN) references. Within the evaluated one-shot drifting-family variants, condition-wise Sinkhorn is strongest under conditional diagnostics and symbolic-coding checks, while
diffusion remains strongest on the hardest downstream symbol-error-rate (SER) curves. The resulting operating point is a condition-preserving one-shot simulator for settings where repeated channel calls make
diffusion-style sampling too costly.
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Audio and Speech Processing
A 399uW 114.3 dB DR Companding Readout ASIC for MEMS Microphones Employing a Multirate Time-Domain ADC
Javier Granizo, Ruben Garvi, Ricardo Carrero et al. (+5 more)
Improvements in the dynamic range and sensitivity of digital MEMS microphones are essential in applications like advanced noise canceling and voice recognition. A cost effective solution to achieve these goals is the companding ADC architecture. Companding ADCs split the dynamic range in several segments with different quantization noise levels, relaxing power constraints. A common problem of companding microphones are audible artifacts generated when the input signal crosses the boundaries between different amplitude segments. We show in this paper a companding ADC architecture that mitigates the boundary artifacts by leveraging the instantaneous and high-resolution time-domain representation of the input signal in a VCO-based ADC. The use of a multi-rate frequency-to-digital converter allows to decouple quantization noise from the VCO frequency, keeping standard audio sampling rates. Co-optimization of the driver and oscillator
circuits enables our VCO-ADC to reach \textgreater 112dBc of peak SFDR without a feedback DAC, keeping a Giga-Ohm input impedance compatible with a capacitive MEMS. We show measurements of a 0.13 $\mu$m ASIC implementing a complete readout
circuit for a digital MEMS microphone. This includes two analog channels and the digital signal processing and calibration blocks required to deliver a standard single-bit PDM output. This ADC reaches a dynamic range of 114.3dB with a power budget under 400 uW, a Schreier FoM_{SNDR} of 171.0 dB and a FoM_{DR} of 191.3 dB.
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All authors Javier Granizo, Ruben Garvi, Ricardo Carrero, Jorge de la Torre, Javier Fernandez, Dietmar Straeussnigg, Andreas Wiesbauer, Luis Hernandez
Signal Processing
Feedforward and Iterative Phase Noise Compensation for Channels with Chromatic Dispersion
Alex Jäger and Gerhard Kramer
Equalization-enhanced phase noise is avoided by applying phase noise compensation (PNC) before chromatic dispersion compensation. Feedforward and iterative PNC
algorithms based on expectation propagation are proposed. Both achieve information rates close to channels without phase noise for 100 GBaud 64-QAM and 10,000 km of fiber.
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Systems and Control
Model-Free Control for Multi-Time Scale Dynamics of Grid-Connected Power Converters
Dewan Mahnaaz Mahmud, Vinu Thomas and Bogdan Marinescu
Controller synthesis in power electronics-based systems depends predominantly on the mathematical model of the system, which is a limitation when the actual system is complex and the mathematical model cannot capture all its dynamics. Model-free control addresses this limitation by using an ad-hoc simple model which is compensated by high-rate evaluation of dynamics in terms of their derivatives. However, application of the model-free control strategy to power electronics-based multi-time scale dynamical systems is challenging because of the derivative action needed to implement such control. Grid-connected power converters are examples of such systems, yet experimental validation has not been adequately addressed in the literature. This letter presents the validation of such control including the hardware implementation level. An intelligent proportional-integral (iPI) controller is synthesized and validated on a 16 kW experimental test bench. This proves the benefits of the approach in control of grid-connected power converters, among which their participation in the secondary voltage control.
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Image and Video Processing
Perceptually-Weighted Video Quality Metric for Asymmetric Encoded Sports Videos
Anna Meyer, Jonas Janzen, Diwakara Reddy et al. (+5 more)
Objective
video quality metrics commonly assume uniform spatial
attention, an assumption that conflicts with the selective nature of human visual perception, particularly in sports
videos. Here, allocating more bits for salient regions through semantic encoding can lead to significant bitrate savings. We present a Perceptually-Weighted
Video Quality Metric (PW-VQM), a full-reference metric that accounts for the unequal perceptual importance of spatial regions and therefore targets quality evaluation for asymmetrically encoded content. SSIM maps computed in a multiscale wavelet domain are weighted by differentiating between foreground and background regions. Perceptually salient foreground regions are identified by combining open-vocabulary object detection with optical flow analysis, and are assigned higher weight during quality aggregation. Evaluated on sports
video content, PW-VQM achieves a Spearman Rank Order Correlation Coefficient of 0.9511, outperforming established metrics including SSIM, VMAF, FUNQUE, and LPIPS. An ablation study confirms the individual contributions of the components of the perceptual weighting.
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All authors Anna Meyer, Jonas Janzen, Diwakara Reddy, Alexander Kopte, Simon Deniffel, Paul Wawerek-López, Marc Windsheimer, André Kaup
Audio and Speech Processing · Cross-listed from cs.CL
Perceptual compensation for tonal context in self-supervised speech models
James Kirby, Ioana Krehan and Michele Gubian
This study examines the extent to which the wav2vec2.0 architecture exhibits evidence of compensation for phonological context. We conducted a pseudo-replication of a perceptional compensation experiment on Mandarin Chinese tones, and compared the
embedding similarities and probing classifier outputs between a purely self-supervised pre-trained model and a model fine-tuned for Mandarin ASR. No evidence of compensation was found in the
embedding similarities of the purely pre-trained model. Probing classifiers showed some evidence of compensation in addition to the expected layer-wise improvements in categorization, but failed to replicate human performance on isolated test syllables. Our findings contrast with previous reports of sensitivity to phonological structure emerging through pre-training alone, and suggest that supervised objectives may be necessary to encourage the abstraction of at least some types of phonological regularities.
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Audio and Speech Processing
PhASE-Flow: Phonetic-Conditioned Acoustic Flow Matching in SSL Representation Domain for Speech Enhancement
Jun Gao, Xiaobin Rong, Yu Sun et al. (+2 more)
Flow matching (FM) enables high-fidelity generation, while self-supervised learning (SSL)
speech models provide hierarchical representations spanning acoustic and phonetic levels. However, existing FM-based
speech enhancement (SE) methods operate primarily in the spectral domain, treating SSL features only as external conditions rather than modeling directly in the SSL latent space. To fully exploit the structural richness of SSL representations, we propose PhASE-Flow, an FM-based SE framework that operates entirely in the SSL space. It models the conditional distribution of clean acoustic representations given phonetic ones, reconstructing the waveform via a neural vocoder. Experiments show that PhASE-Flow outperforms state-of-the-art baselines in perceptual quality and intelligibility. Notably, it achieves competitive performance with only four sampling steps, enabling highly efficient inference. Audio demos are available at https://anonymous.4open.science/w/phase-flow_demo-E6E1/.
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All authors Jun Gao, Xiaobin Rong, Yu Sun, Dahan Wang, Jing Lu
Signal Processing
Joint Direction-of-Arrival and Range Estimation for Millimeter-Wave Uniform Linear Array Radar
Necati Kagan Erkek and Zeynep Gul Pehlivanli
An FFT-based direction-of-arrival (DOA) and range-estimation framework for a monostatic uniform linear array (ULA) operating at 77 GHz is presented. A narrowband sinusoidal waveform is used to derive the spatial phase model, determine an aliasing-free inter-element spacing, and select the aperture required to obtain a boresight angular resolution of 2 degree. The resulting design uses an element spacing of 0.97 mm and 58
antenna elements, corresponding to an aperture length of 56.42 mm. Numerical results show accurate angular estimation for a single target at 30 degree and for multiple simultaneous targets. The analysis is further extended to two-dimensional localization by replacing the narrowband waveform with a 1 GHz sinc-modulated signal, which provides an approximate range resolution of 0.15 m. Additional simulations quantify the effects of additive complex Gaussian noise, increased
antenna spacing, and target decorrelation on the DOA response.
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Systems and Control · Cross-listed from cs.IT
Information-Theoretic Meta Dynamic Programming for Signalling and Control of POMDPs
Charalambos D. Charalambous, Stelios Louka and Photios A. Stavrou
In this paper, we study the information-theoretic characterization of simultaneous signalling and control over channels modeled by partially observable Markov decision processes (POMDPs). The problem is formulated as an optimization over randomized control strategies that maximize the directed information from actions to observations, subject to an average-cost constraint. We derive a novel dynamic programming framework in which the state is defined on the space of conditional probability distributions, leading to a high-level “meta” dynamic program. Specifically, we show that two coupled information states, namely, the posterior distribution of the system state and a distribution over such posteriors, satisfy Markov recursions and provide sufficient statistics for optimal control. This structure enables the decomposition of optimal strategies into separated randomized policies that depend only on these information states. Our results establish necessary and sufficient conditions for optimality and unify classical
stochastic control and information-theoretic formulations. In particular, we show that in the absence of signalling, the proposed framework reduces to the standard dynamic programming equations for POMDPs. The developed approach provides a principled foundation for analyzing and designing control systems with intrinsic information constraints.
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Systems and Control
A Wearable Multimodal Ultrasound+Inertial System for Real-Time Virtual Reality Interaction
Giusy Spacone, Sebastian Frey, Enzo Baraldi et al. (+3 more)
A-mode ultrasound (US) is a promising sensing modality for Virtual Reality (VR) interaction, as it enables the mapping of muscular activity into control commands while retaining the benefits of wearable sensing. However, existing approaches still face limitations in terms of wearability and interaction complexity, often relying on external hardware such as cameras. In this work, we propose a fully wearable
multimodal interface for real-time VR-interaction, based on concurrent US and inertial (accelerometry) sensing from the forearm and upper arm. The system is built on the WULPUS platform and integrates an end-to-end software framework for real-time acquisition, visualization, and communication with a Unity-based VR environment. A
multimodal learning pipeline is introduced for concurrent hand pose and forearm position estimation in 2D space. The interface is evaluated through offline and online experiments with five subjects, during the execution of three functional tasks: cylinder grasping (gross motor) and relocation, marble pinching (fine motor) and relocation, and
liquid pouring. For offline experiments, we collect 5 acquisition sessions across multiple days, achieving an average inter-session accuracy across subjects of 80$\pm$6% for hand pose estimation and 77$\pm$7% for forearm position estimation. Online validation with minimal fine-tuning (5 min) demonstrates success rates of 92.0$\pm$16.0%, 88.0$\pm$9.8%, and 96.0$\pm$8.0% for the three tasks, respectively. With a power consumption of only 19.9~mW, our system enables more than 2.5 days of continuous use on a small 350 mAh LiPo battery without the need for recharge, enabling truly wearable,
multimodal, and functionally meaningful VR interaction.
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All authors Giusy Spacone, Sebastian Frey, Enzo Baraldi, Mattia Orlandi, Luca Benini, Andrea Cossettini
Signal Processing
Deep CSI Feedback for FDD Massive MIMO Systems: A Curvelet Learning Approach
Mengli Tao, Jiancun Fan, Huiqiang Xie et al. (+1 more)
Downlink channel state information (CSI) feedback plays a key role in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems. The growth of
antennas in ultra-massive
MIMO increases the difficulty and overhead of CSI feedback, which poses significant challenges for conventional downlink CSI feedback mechanisms. To address the limitations of existing CSI feedback approaches, this paper proposes a novel curvelet learning based framework termed SwinCANet, comprising a frequency-domain information processing module and a denoising module. The frequency-domain information processing module employs curvelet transform to decompose CSI into low-frequency and high-frequency components. Subsequently, Swin
Transformer and channel-wise
attention block are utilized for extracting the low-frequency and high-frequency representations, respectively, thereby enhancing reconstruction quality. Notably, an additional Swin
Transformer facilitates the fusion of multi-scale frequency components, enhancing capabilities across different angular resolutions and spatial directions. Furthermore, we develop a variant (De-SwinCANet), which employs a Sigmoid threshold function to effectively suppress noise coefficients, thereby mitigating various channel impairments and nonlinear distortions. Numerical simulation results demonstrate that the proposed methodology achieves superior performance compared to existing
benchmarks while maintaining robust performance under challenging propagation conditions.
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All authors Mengli Tao, Jiancun Fan, Huiqiang Xie, Kai Xie
Signal Processing
BASIIS: Bistatic Angular Sampling and Interpolation for ISAC Setups
Alexander Felix, Marcus Henninger, Lucas Giroto et al. (+3 more)
Integrated Sensing and Communications (
ISAC) is a defining feature of 6G, extending cellular networks with radar-like sensing at limited additional overhead. In bistatic deployments, sensing requires coordinating the transmitter (TX) and receiver (RX) arrays to scan the Cartesian product of angle of departure and arrival, resulting in a four-dimensional sampling problem in the angular domain. This work establishes a complete angular sampling framework for bistatic
ISAC, extending the DFT-based optimal-sampling methodology to the full azimuth and elevation domains of both arrays. We show that the bistatic geometry couples the TX and RX elevation angles, and represent this coupling through the ortho-baseline coarray, a virtual array that captures the joint elevation aperture of the array pair. From the coarray we derive a minimal sampling and interpolation scheme, near-lossless and realizable with any beamforming architecture. Monte Carlo simulations confirm the proposed minimal acquisition essentially equalizes the detection accuracy of dense oversampled imaging while acquiring 3 to 5 times fewer TX-RX direction pairs. This allows having bistatic operations with drastically reduced overhead on the
radio resource usage of
ISAC systems.
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All authors Alexander Felix, Marcus Henninger, Lucas Giroto, Maximilian Bauhofer, Stephan ten Brink, Silvio Mandelli
Signal Processing
Joint Synchronization and Radar Parameter Estimation for OFDM-based DISAC Systems
Niclas Führling, Hyeon Seok Rou, Kuranage Roche Rayan Ranasinghe et al. (+2 more)
We propose a novel approach to the synchronization paradigm in distributed
ISAC (DISAC) systems in doubly-dispersive (DD) channel environments via a joint synchronization and radar parameter estimation framework. The proposed method exploits the structure of the system model, which can be linearized in order to apply a bivariate Gaussian belief propagation (GaBP)
algorithm that jointly estimates the time offset (TO) and carrier frequency offset (CFO) of each base station (BS), as well as the delay and Doppler parameters of the DD channel in conventional orthogonal frequency division multiplexing (OFDM) systems. Simulation results demonstrate the effectiveness of the proposed
algorithm, showing that the radar parameter estimates (i.e., range and velocity) and synchronization parameter estimates (i.e., TO and CFO) approach the Cramér Rao lower bound (CRLB) even at moderate-to-high signal-to-noise ratio (SNR) regimes.
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All authors Niclas Führling, Hyeon Seok Rou, Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, Nuria González-Prelcic
Audio and Speech Processing
An Analysis of the Effectiveness of Synthetic Speech Data for ASR Fine-tuning in Selected Indic Languages
Sujith Pulikodan, Agneedh Basu, Pavan Kumar et al. (+4 more)
Synthetic data has the potential to be a valuable resource for training machine learning models, particularly Automatic
Speech Recognition (ASR) Systems; however, its effectiveness requires systematic evaluation. In this study, we investigate the impact of incorporating synthetic
speech data alongside real-world recordings for three Indic
languages: Hindi, Kannada, and Telugu. We analyze the performance gains achieved by augmenting synthetic data with real data and independently examine how ASR performance varies with the sources of scripts used to generate synthetic
speech. In addition, we evaluate the effect of synthetic
speech generated using different
speech synthesis models. Finally, we study the impact of voice cloning in synthetic
speech generation on ASR performance, including how performance varies with the number of distinct cloned voices used during data generation.
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All authors Sujith Pulikodan, Agneedh Basu, Pavan Kumar, Pranav Bhat, Visruth Sanka, Nihar Desai, Prasanta Kumar Ghosh
Signal Processing
Toward Quantum-Enhanced ISAC: Active-RIS-Aided Integrated Sensing and Communication with Rydberg Atomic Receivers
Hong-Bae Jeon, Hyung-Joo Moon and Yonghwi Kim
In this paper, we investigate an active-RIS (ARIS)-aided integrated sensing and communication (
ISAC) system with Rydberg Atomic REceiver (RARE). Leveraging the magnitude-only and real-domain observation structure of RARE, we first derive a unified
ISAC model, along with a closed-form Cramer-Rao bound (CRB) for direction-of-arrival (DoA) estimation. Based on this formulation, we propose a joint design of the {base station (BS)} beamforming and ARIS reflection coefficients to minimize the CRB under RARE-specific signal-to-interference-noise-ratio (SINR) and ARIS power constraints. To tackle the resulting highly non-convex problem, we develop an alternating optimization (AO) framework that combines semidefinite relaxation (SDR) for beamforming and a majorization-minimization (MM)-based approach for ARIS design. Numerical results demonstrate that the proposed RARE-aware framework significantly outperforms conventional RF-based designs and achieves performance close to the radar-only
benchmark, highlighting the potential of RARE for
quantum-enhanced
ISAC with ARIS.
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Systems and Control
Low-Thrust Orbital Differential Games with Speed Constraint Enforcement Using Cost Weighting
Yahli Drucker and Vitaly Shaferman
This paper considers the problem of a low-thrust spacecraft pursuit-evasion differential game with an arbitrary terminal relative speed constraint. It addresses the terminal phase of the engagement for two relatively close spacecraft near a circular orbit. The problem is formulated as a linear-quadratic zero-sum differential game, with soft constraints on the terminal relative position and velocity, and running costs on the players' control efforts. An analytical, closed-loop, minimum-fuel-consumption optimal guidance law is derived for each player, forming a saddle-point solution. It is proven that any terminal speed can be achieved by properly choosing the weighting parameters of the cost function. To verify the optimality of the solution, a conjugate point analysis is performed when the cost function velocity weighting
matrix is either positive or negative definite. The negative-definite case arises at high terminal speeds and is seldom seen in the literature. The performance of the derived guidance law is evaluated in simulations for different target maneuvers and compared to a state-of-the-art optimal-control-based guidance law. The simulations show that the derived guidance law satisfies the constraints and offers a substantial advantage over the optimal-control-based guidance law when the target is optimally evading.
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Systems and Control
Dynamic Analysis of Centralized Energy Storage Systems – A Comparison between Grid-following and Grid-forming Controls
Qiang Fu, Siqi Bu, Yang Wang et al. (+1 more)
This study investigates the small-signal stability of centralized energy storage systems (CESSs) using grid-following (GFL) and grid-forming (GFM) controls, particularly focusing on bidirectional power flow and multiple energy storage systems (ESSs). To address the issue of complex dynamics in CESSs when comprehensive GFL and GFM control loops are considered, high-order dynamics are simplified using the virtual damping method by focusing on the dominant oscillation mode. Damping analysis verifies that CESSs using a single-type control (either GFL or GFM) have dynamic superimposition characteristics. Specifically, as ESS number increases, the damping of GFM-CESSs improves but that of GFL-CESSs decreases. The damping sensitivity shows that the damping of GFM-CESSs is more sensitive to bidirectional power flow and all control loops, whereas that of GFL-CESSs is more sensitive to d-axis control loop. Consequently, GFM-CESSs are preferred for large-scale integration but are limited in scenarios with significant power reversal. If GFL and GFM controls are hybridized in CESSs, the ratio of GFM-CESSs should be constrained to avoid instability from modal resonance between GFL-CESSs and GFM-CESSs. This highlights that implementing GFM-CESSs necessitates considering scenario limitations rather than pursuing maximal integration under hybrid integration conditions. The conclusions are validated through modal analysis and time-domain simulations.
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All authors Qiang Fu, Siqi Bu, Yang Wang, Mingyu Yan
Systems and Control · Cross-listed from cs.LG
When Dynamics Models Read the Wrong Time Steps: Label-Free Event Credit Re-Anchoring for Robust Global Readouts
Yifan Wang
Learned dynamics models often answer global physical questions, such as fault severity or impact stiffness, by pooling a per-step feature sequence into one readout vector. This sequence-to-global interface creates an under-studied temporal credit problem: with only trajectory-level supervision, a model can predict accurately in training conditions while reading from abundant smooth correlates rather than the brief physical events that determine the target. We call this failure temporal credit dilution. It is not exposed by the training loss and is not removed by standard physics-informed residuals, because the error lies in where the global readout assigns functional credit. We introduce Credit-in-Event, an interface-level probe for measuring how much pooled credit lands on event steps, and prove in closed form that a pooled linear reader routes credit to a spurious background channel as the event fraction shrinks. We then propose CREST, a training-free and label-free readout that estimates a transient event core from learned features and re-anchors the pooled representation through event-versus-rest contrast. Across simulated gear and impact systems, recurrent and
attention encoders, and public bearing vibration data, CREST reduces out-of-distribution error while restoring event credit. Ablations show that stable-step selection and receptive-field shrinking fail, confirming that the gain comes from event-core credit re-anchoring rather than a generic locality or stability prior.
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Image and Video Processing
Fine-UNETR for PSMA PET/CT Lesion Segmentation: Automated Tumor Quantification and Overall Survival Stratification in Prostate Cancer
Mansour Abtahi, Chae Moon Hong, Nikhil Deveshwar et al. (+4 more)
Introduction: To develop and evaluate Fine-UNETR, a
Vision Transformer-based architecture for automated
segmentation of PSMA-avid lesions on whole-body PET/CT, and to assess clinical utility of AI-derived tumor burden biomarkers for overall survival stratification in radioligand therapy. Methods: In this retrospective study, 373 PSMA PET/CT scans (mean age, 71+-8 years) from patients with prostate cancer were analyzed. Fine-UNETR, a modified UNETR with 8x8x8 voxel patch
embedding and axial sliding window training, was trained on 299 scans and validated on 74 scans. Overall survival stratification was assessed in an independent cohort of 67 pre-radioligand therapy patients using Kaplan-Meier analysis and log-rank testing. External validation was performed on 192 cases from the AutoPET IV PSMA PET/CT
dataset. Results: Fine-UNETR achieved a Dice similarity coefficient (DSC) of 66.63%, sensitivity of 70.27%, precision of 67.77%, and a lesion detection rate of 79.53% (96.05% for lesions with SUVmax >= 5). On the external validation
dataset, the model achieved a DSC of 44.11% and a lesion detection rate of 87.18%, indicating that lesion detection performance was preserved despite reduced voxel-level overlap. AI-derived biomarkers showed excellent agreement with ground truth (total tumor volume: r=0.984; total lesion uptake: r=0.989; lesion count: r=0.960). In the clinical cohort, total tumor volume (p=0.0019), SUVmax (p=0.014), and SUVmean (p=0.016) significantly stratified overall survival. Conclusion: Fine-UNETR enables accurate automated whole-body PSMA lesion
segmentation and tumor burden quantification. Performance on an external
dataset demonstrates robustness despite evidence of domain shift. AI-derived biomarkers significantly stratified overall survival in a pre-radioligand therapy cohort, supporting the clinical utility of automated PSMA PET/CT quantification for prognostication.
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All authors Mansour Abtahi, Chae Moon Hong, Nikhil Deveshwar, Stellamaris Nwihim, Minkyung Lee, Peder E.Z. Larson, Thomas A. Hope