Signal Processing
EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review
Mohammad Hossein Koohi Ghamsari, Seyede Fatemeh Ghamkhari, Siavash Bayat et al. (+1 more)
Brain-computer interfaces (BCIs) have emerged as transformative technologies that enable direct communication between the
brain and external devices. Among various BCI paradigms,
EEG-based motor imagery (MI) has gained prominence due to its simplicity, non-invasiveness, and potential to restore motor function and facilitate rehabilitation for patients with motor impairments. This paper presents a comprehensive review of the most practical processing
algorithms developed over the past decade for decoding
brain sensorimotor cortex signals. Specifically, this paper discusses the integration of artificial intelligence (AI)-based
algorithms, particularly machine learning and deep learning techniques, and their contributions to improving the performance and efficiency of MI-BCI systems in detail. Furthermore, the paper reviews state-of-the-art hardware platforms and emerging converging technologies, including system-on-chip (SoC) architectures, application-specific integrated
circuits (ASICs), field-programmable gate arrays (FPGAs), wearable devices, the Internet of Things (IoT), and augmented/virtual reality (AR/VR), and discusses their integration with advanced signal processing
algorithms to enable next-generation MI-BCI systems. By highlighting current achievements of
EEG-based MI-BCI technology and predicting future research directions that could further enhance real-time capabilities, this paper aims to provide valuable insights for researchers and practitioners, fostering innovation in high-performance
EEG-based MI-BCI systems.
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All authors Mohammad Hossein Koohi Ghamsari, Seyede Fatemeh Ghamkhari, Siavash Bayat, Ahmed Hemani
Signal Processing · Cross-listed from cs.AI
TRACE: Learning to Self-Calibrate Wireless Digital Twins from ISAC Measurements
Saad Masrur, Saeed R. Khosravirad and Ismail Guvenc
Wireless digital twins (DTs) rely on 3D environment models to predict
radio propagation and support wireless-network decisions, yet these models are often initialized from imperfect 3D maps. Errors in building position, height, footprint, and orientation can therefore cause a high-fidelity propagation engine to simulate the wrong physical environment. In this paper, we study how a deployed wireless network can repair an existing DT using its own
radio frequency (RF) measurements. In particular, we introduce Twin Residual
Alignment and Calibration Engine (TRACE), a physics-grounded learning-based self-calibration framework that treats twin maintenance as residual
alignment between the physical world and the current DT. Using the same sensing configuration as the physical measurements, TRACE ray-traces the current DT, coherently backprojects the measured and simulated RF onto a common world grid, and extracts the same local region around each building's current DT position. A multi-view corrector then fuses evidence across sensing nodes and neighboring buildings to predict a gated six-parameter correction per building, without relying on absolute layout or sensor ordering, and supports iterative correction through re-rendering. On 5,400 held-out samples from unseen simulated scenes at 28 GHz, TRACE reduces 3D position RMSE from 2.202 m to 0.302 m and yaw RMSE from 4.978{\deg} to 0.894{\deg}, outperforming ViT and U-Net baselines under changes in layout, building count, sensing-node count, and SNR. On measured 28 GHz RF data from the NIST outdoor courtyard, a model trained only on synthetic RF reduces mean planar wall-position error from 1.00 m to 7.8 cm, without measured-data fine-tuning or
geometric labels. These results show that the discrepancy between measured and twin-rendered RF can serve as a learning signal for repairing a wireless DT.
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Systems and Control · Cross-listed from math.OC
No Spurious Local Minima in Full-Order Linear Quadratic Gaussian Control
Yang Zheng and Yujie Tang
This paper studies the nonconvex optimization landscape of Linear Quadratic Gaussian (LQG) control under direct state-space parameterization. Although the LQG cost may possess suboptimal stationary points, whether it admits suboptimal local minima has remained open. We answer this question negatively: Every local minimum of the full-order LQG cost is globally optimal. More generally, at any controller order, every local minimum corresponding to an uncontrollable or unobservable realization attains the globally optimal LQG cost over all stabilizing policies of arbitrary orders. We further show that any suboptimal policy can be augmented with decoupled stable controller states, so that the augmented policy admits a direction of negative
curvature with probability one. Consequently, any suboptimal stationary point in full-order LQG cost can be converted into a strict saddle with probability one. One key proof idea connects the state-space Hessian to a frequency-domain global-optimality condition derived from the Youla parameterization. These results reveal a benign local-minimum landscape despite the presence of suboptimal stationary points.
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Audio and Speech Processing · Cross-listed from cs.SD
Whisper-Flash: Acoustically Conditioned Parallel Drafting for Faster Whisper Decoding
Huapeng Zhou, Huayu Wang, Junkai Wu et al. (+2 more)
Whisper is a widely used encoder-decoder model for
speech recognition. Its encoder reads an utterance in one parallel pass, but its decoder writes the transcript one
token at a time, which dominates inference time. Speculative decoding shortens such loops without changing their output: a small drafter guesses several upcoming
tokens, and the original model verifies them all in one forward pass. We present Whisper-Flash, a two-layer drafter built on a property of
speech recognition: the words still to be written have already been spoken. It reads Whisper's encoded audio and accepted decoder states and proposes eight
tokens in a single forward pass. On the complete LibriSpeech test sets, Whisper-Flash processes $3.16\times/2.85\times$ as much audio per second as greedy decoding with identical outputs, and it remains faster at batch sizes up to 96 and under temperature sampling. Ablations show that direct access to the audio matters most.
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All authors Huapeng Zhou, Huayu Wang, Junkai Wu, Kangqi Wang, Xinyu Wang
Image and Video Processing
Mask2Restore: Self-Supervised Ultrasound Despeckling via Inpainting
Xuesong Li, Yingtai Xu, Zhongliang Jiang et al. (+2 more)
Medical ultrasound (US) is inherently degraded by speckle, a granular interference pattern that is often treated as a complex form of noise in image restoration. However, unlike random noise, US speckle originates from coherent scattering within tissue and is therefore highly spatially dependent and deterministic under fixed acquisition conditions, making US speckle suppression fundamentally different from natural image denoising. Because speckle-free US targets are unavailable in practice, self-supervised denoising is necessary. Blind-spot networks (BSN) are the dominant self-supervised paradigm for natural images, but their pixel-wise masking strategy assumes spatially independent noise, an assumption poorly matched to US speckle, which is spatially correlated over multiple pixels rather than pixel-wise independent. To address this mismatch, we propose Mask2Restore, a self-supervised US despeckling framework that reformulates despeckling as contextual inpainting with block-wise masking on single noisy images. Unlike pixel-wise BSN masking, block-wise masking addresses this multi-pixel speckle correlation by removing locally correlated speckle neighborhoods and shifting the reconstruction cues used by the network from adjacent speckle correlations to broader anatomical context. We further introduce cross-resolution context regularization (CRCR), which suppresses residual speckle bias by enforcing consistency across multi-resolution predictions. Experiments on simulated and in vivo carotid US, unseen fine-structure cases, and downstream cardiac segmentation demonstrate improved speckle-detail trade-offs, better preservation of fine anatomical structures, and practical value for subsequent image analysis.
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All authors Xuesong Li, Yingtai Xu, Zhongliang Jiang, Nassir Navab, Yuan Bi
Audio and Speech Processing
WhisperVC-AV: Audio-Visual Content Restoration for Noise-Robust Whisper-to-Normal Voice Conversion
Ziyue Yin, Dong Liu and Ming Li
Environmental noise obscures the content cues needed for whisper-to-normal conversion. We propose WhisperVC-AV, which restores acoustic content features while leaving the original WhisperVC conversion module unchanged. Its context-guided restoration module combines temporal acoustic context with synchronized lip features through
attention and gated residual correction. Experiments on AISHELL6-Whisper show lower character error rates (CERs) than WhisperVC across three ASR systems, on clean
speech and under all six signal-to-noise ratio (SNR) conditions with MUSAN noise. The largest gains occur at 0 dB SNR, where Qwen3-ASR CER falls from 36.29% to 28.88%. WhisperVC-AV also improves predicted
speech quality while maintaining speaker similarity. The CER gains extend to unseen background noise without retraining, while visual controls support the use of utterance-specific lip cues. Audio examples are available on our demo page.
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Signal Processing
Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning
Xiaojie Li, Yu Han, Han Fang et al. (+4 more)
Radiomap blind prediction aims to infer radiomaps from observable representations of the propagation environment and base station configuration without field measurements. In practice, the observable representations are inherently incomplete. Thus, the target radiomap is not fully determined by the inputs when generalizing to unseen configurations or environments. Under incomplete observation, we establish a population-level theory of deterministic radiomap blind prediction that identifies the conditional mean as its optimal target and separates prediction error into reducible predictor approximation and irreducible uncertainty caused by missing physical information. The framework further characterizes the train-test risk gap and the uncertainty reduction enabled by observation enrichment. Building on it, we reveal the dual role of propagation priors: they provide physically grounded guidance, yet their implementable forms may bias the attainable predictor. This motivates RadioDecomp, which treats a prior-guided predictor as a correctable base and learns its remaining predictable discrepancy through residual refinement. To evaluate RadioDecomp across distinct propagation-prior designs, we instantiate it with a feature-guided monolithic base and a LoS-Shadow structured base, yielding RadioFR and RadioLSR, respectively. Across random, cross-configuration, and cross-environment settings, experiments confirm the benefit of propagation-related representations and show that both instantiations improve upon their respective bases. Further controlled studies on base capacity, training-support coverage, and observation coarsening corroborate the proposed analysis.
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All authors Xiaojie Li, Yu Han, Han Fang, Shangqing Liu, Guangxu Zhu, Shi Jin, Chao-Kai Wen
Image and Video Processing · Cross-listed from cs.CV
Unlocking Geodesic Gromov-Wasserstein Distances for 3D Modeling
Krzysztof Marcin Choromanski, Derek Long, Ananya Parashar et al. (+1 more)
Gromov-Wasserstein Distances (GWDs) provide quantitative ways of comparing probabilistic distributions defined on different metric spaces by applying techniques from the optimal transport theory. As such, GWD can be potentially useful in a large variety of applications ranging from graph matching problems to 3D object detection. However its practical use at scale is significantly limited by cubic time complexity computations involving dense intra-space distance
matrices. Even though in the Euclidean metric spaces several techniques (e.g. involving scalable kernel methods) were proposed to address it, to the best of our knowledge, analogous techniques for general geodesic distances on
manifolds, or shortest-path distance on graphs in their discretized variants, were not developed. In this paper, we present Efficient Geodesic Gromov-Wasserstein methods (EGGroW), a new class of efficient
algorithms designed to calculate geodesic Gromov-Wasserstein distances with entropic Sinkhorn-like approaches, leveraging recently introduced GenusSink methods \citep{genussink} and the theory of random features. We provide important downstream applications, namely: 3D pose estimation and 3D template detection. In the latter setting, we formulate a partial 3D template recovery as a staged problem: capacity-constrained scene selection is followed by semi-relaxed recovery of template visibility and correspondence. Our empirical findings show that EGGroW provides accurate solutions when standard Euclidean-based techniques fail and is characterized by light computational footprint, as our theoretical analysis predicts.
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All authors Krzysztof Marcin Choromanski, Derek Long, Ananya Parashar, Dwaipayan Saha
Signal Processing
Characterizing the Effects of Mixed Division Waveform Schemes in MIMO Radar
Oliver P.R. Kirkpatrick, Santiago Ozafrain, Christopher Gilliam et al. (+1 more)
Direction of departure estimation in Multiple-Input-Multiple-Output (
MIMO) radar is based on transmitting a set of orthogonal (or at least separable) waveforms, one per transmit element. Assuming orthogonality is preserved, these waveforms can then be separated into different channels at the receiver before beamforming. In practice, however,
MIMO waveforms are not perfectly orthogonal, and at different points in the processing chain, this can manifest as phase errors in the steering
manifolds used in beamforming. Furthermore, due to resource constraints, either by the channel or the sensing scenario, a mixture of orthogonality techniques, mixed division strategies, are often employed, resulting in further phase errors. Naturally, such errors will interfere in the beamforming process, spoiling the beam, introducing direction finding errors, or increasing sidelobe levels, and consequently reducing SNR. In this paper, we demonstrate how mixing division strategies significantly degrades orthogonality of otherwise orthogonal waveforms resulting in degraded beamforming performance. Specifically, we show that waveforms which exhibit favourable sidelobe levels may still induce large direction of departure errors and vice versa. Additionally, through multiple exhaustive searches of potential mixed division strategies with combinations of time-, Doppler- and code-based division techniques we show that large numbers of the possible waveforms exhibit poor beamforming qualities.
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All authors Oliver P.R. Kirkpatrick, Santiago Ozafrain, Christopher Gilliam, Beth Jelfs
Audio and Speech Processing · Cross-listed from cs.SD
Finding Emotions Where They Belong: Rethinking Audio Emotion Recognition through Masked Temporal Affective Grounding
Abdelrahman Mohamed, Lars Kai Hansen and Zheng-Hua Tan
Audio emotion recognition (AER) typically assigns a single label to an entire recording, leaving the temporal scope of that label ambiguous when multiple speakers and affective events are present. We address this limitation by reformulating AER as a Temporal Affective Grounding (TAG) task that associates emotions with temporally bounded
speech spans and vocal tone descriptions. To support this formulation, we curate temporally annotated versions of existing emotion recognition
datasets and construct recordings containing two to four affective
speech spans, including overlapping
speech. Training in this longer format with a standard
language-modeling objective can degrade both emotion recognition and temporal grounding performance, while tone descriptions can provide shortcuts for emotion prediction. To address these challenges, we introduce Masked Temporal Affective Grounding (M-TAG), a supervised training objective that combines full-sequence
language modeling with emotion and timestamp cross-
entropy losses under
attention masking. The masking varies the context visible to emotion-label
tokens to reduce reliance on shortcuts and improve generalization, while the timestamp loss incorporates a distance-aware weight to penalize larger temporal errors. We evaluate EMO-TAG, a model fine-tuned using our
dataset and objective, on emotion recognition and affective temporal-grounding against three AER and audio-
language baselines: Flamingo-Next, Audio-Reasoner, and AffectGPT. Our results show that existing models achieve limited affective temporal-grounding despite competitive emotion recognition performance.
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Audio and Speech Processing · Cross-listed from cs.LG
Getting Motif-ated: Controllable AI Compositions from Injected Motif Prompts
Chao Peter Yang, Cynthia Rudin, Yue Jiang et al. (+2 more)
Deep learning has transformed symbolic music generation by borrowing the training paradigms of large
language models, with systems such as NotaGen now producing complete, stylistically convincing classical scores from a short
prompt. These systems could become powerful creative partners, helping musicians generate endless possibilities. However, current systems expose almost no control handles on the music itself. In principle, control handles could be built into a foundation model trained from scratch, but this is rarely practical without massive amounts of quality annotated data and compute resources. We therefore present MotiGen, a recipe for retrofitting pretrained symbolic music models to use new instruction
prompts. MotiGen injects a musical motif as a structured
prompt line, reinforces it with a scalar
attention bias toward the motif
tokens, and learns the association with a two-phase curriculum. First, it learns from focused excerpts cropped around motif occurrences in the training data, then full scores including the motifs. Our experiments show that our model composes with the prompted motif in over 92.3% of generated pieces. Generated pieces using a variety of motifs are included in our sample site: https://motigen-site.github.io/.
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All authors Chao Peter Yang, Cynthia Rudin, Yue Jiang, Simon Mak, Stephen Ni-Hahn
Audio and Speech Processing · Cross-listed from cs.SD
DEFINE: Exemplar-Guided Accent Control for Zero-Shot TTS
Ambuj Mehrish, Abhinaba Roy, Alex Ivanov et al. (+2 more)
Zero-shot text-to-
speech (TTS) can reproduce an unseen speaker from a short reference recording, but typically entangles speaker identity and accent within the same reference. We introduce DEFINE, an end-to-end framework that decouples these factors by conditioning speaker identity and target accent on separate audio exemplars. A single inference-time guidance weight continuously controls accent strength without retraining. Built on F5-TTS with parameter-efficient LoRA adaptation, DEFINE maps short accent exemplars into a conditioning space using an exemplar encoder supervised through learned accent prototypes, requiring neither accent labels at inference time nor post-synthesis waveform conversion. On seen accents, increasing accent guidance improves accent-probe accuracy from 6.5% to 19.6%. More importantly, a single DEFINE model generalizes accent control beyond its training accent set: on seen and out-of-domain accents, though not on held-out accents, it matches the accent transfer performance of a two-model TTS-voice-conversion cascade while achieving higher speaker similarity and comparable predicted
speech quality. These results demonstrate that speaker identity and accent can be independently controlled from audio exemplars within a single zero-shot TTS model, including for accents unseen during training.
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All authors Ambuj Mehrish, Abhinaba Roy, Alex Ivanov, Tawsif Ahmed, Dorien Herremans
Audio and Speech Processing · Cross-listed from cs.SD
SAGE: Semantic Audio Generative Encoder
Francesco Brigante, Luca Cerovaz, Davide Marincione et al. (+4 more)
Audio autoencoders compress waveforms into compact latent representations that serve as the interface between raw audio and downstream models. Current systems navigate a three-way trade-off between reconstruction quality, semantic structure of the latent space, and inference speed, typically favoring one or two of these at the expense of the others. This paper introduces SAGE, Semantic Audio Generative Encoder: a compact variational autoencoder, trained solely on publicly available music, that shapes its latent by distilling
embeddings from a pretrained audio-text model. This 105M-parameter model runs at the inference cost of Stable Audio Open and reaches the listening-test quality of SAME-L, an autoencoder 8x larger and 4x slower, while surpassing both on objective perceptual and distributional metrics of reconstruction. Furthermore, it sets the state of the art on all nineteen probing tasks of latent semantics, in domain and out of domain. These results establish SAGE as a lightweight audio autoencoder that strikes the best balance of the three-way trade-off among those we evaluate, combining high reconstruction fidelity, state-of-the-art semantic structure, and fast inference.
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All authors Francesco Brigante, Luca Cerovaz, Davide Marincione, Giorgio Strano, Luca Zhou, Emanuele Rodolà, Michele Mancusi
Systems and Control
Path Invariance of a Quadrotor System under Cyber Attacks with Theoretical Guarantees
Hamza Mahmood, Usman Ali and Adeel Akhtar
This paper presents a path-following controller for a quadrotor system to guarantee safe maneuvers, in terms of forward path invariance, in the presence of cyber-physical attacks. We assume that an
adversarial agent can control any one of the rotors through a false data injection (FDI) type of attack. A feedback controller is designed using transverse feedback linearization which guarantees that the system follows a class of smooth curves under FDI attacks. Our proposed controller is computationally efficient, with a closed-form analytical expression, that not only mitigates the effect of bounded malicious signal but also ensures mission success. We provide theoretical guarantees of forward path invariance under FDI attacks with realistic assumptions and demonstrate the effectiveness of our approach through simulation.
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Signal Processing · Cross-listed from cs.LG
Benchmarking EEG Foundation Models at Scale: Lessons from 20,000 Evaluations
Zhige Chen, Shu Peng, Chengxuan Qin et al. (+4 more)
Electroencephalography (
EEG) foundation models (FMs) promise transferable neural representations, yet their advantages over strong supervised baselines and their prospects for further scaling remain unclear. To address these questions, we introduce
EEG-Arena, an open-source
benchmark covering 30
EEG FMs and 25 supervised baselines evaluated on 57 downstream tasks from 23 public
datasets. Through more than 20,000 evaluations across five experimental protocols, we assess downstream performance, pretraining benefits, model size scaling, pretraining data scaling, and robustness to channel configuration. We find that (1)
EEG FMs outperform strong task-specific supervised baselines on most evaluated tasks, particularly under non-bipolar settings; (2) compared with architecture-matched supervised training from scratch, pretraining improves both early optimization and final downstream performance, with larger and more consistent gains as more labeled downstream data become available; (3) existing
EEG FMs do not exhibit a consistent positive relationship between parameter count and downstream performance; (4) under a fixed architecture, increasing the pretraining data scale yields sustained downstream gains; and (5) channel-flexible FMs achieve higher absolute performance than channel-constrained models across most evaluated channel configurations. Together, these findings demonstrate the downstream value of
EEG FMs and identify pretraining data expansion as a promising direction for further progress. To support continued research, we release
EEG-Arena as an open-source evaluation framework that provides shared infrastructure for reproducible benchmarking, model comparison, and community-driven development.
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All authors Zhige Chen, Shu Peng, Chengxuan Qin, Rui Liu, Rui Yang, Kay Chen Tan, Jibin Wu
Image and Video Processing · Cross-listed from cs.CV
LoCoVSR: Local Context Diffusion Posterior Sampling for Video Super-Resolution
Matan Ben Chorin and Michael Elad
Video super-resolution (VSR) is an ill-posed inverse problem that aims to reconstruct a high-resolution (HR)
video from a noisy, low-resolution (LR) version of it. We present LoCoVSR, a
diffusion-based VSR framework that leverages pixel-space denoising
diffusion probabilistic models. LoCoVSR integrates the
Diffusion Posterior Sampling technique with spatio-temporal context learning, operating in a moving-average form. A localized window of adjacent LR frames is used for recovering each center frame, while applying a shared noise trajectory across all frames. The localized windowing enables processing of long
videos without length limitations, supports parallel inference, and prevents error accumulation that may occur in recursive processing. Unlike prior methods, LoCoVSR offers a simple yet very effective VSR solution, avoiding explicit optical flow estimation, or information loss caused by latent space processing. Trained on the VFHQ face
dataset, LoCoVSR achieves accurate, temporally consistent and high-quality upscaling with competitive results against recent
diffusion-based VSR approaches.
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Systems and Control
Characterization and Monitoring of Nonlinear Dynamics and Chaos in Complex Physiological Systems
Hui Yang, Yun Chen and Fabio Leonelli
Nonlinear dynamics arise whenever multifarious entities of a system cooperate, compete, or interfere. For example, cardiovascular system involves a great level of complexity. Multi-lead ECG signals are generated through orchestrated depolarization and repolarization of
cells and manifest significant nonlinear dynamics. Nonlinear dynamical systems defy understanding based on the traditional reductionist's approach, in which one attempts to understand a system's behavior by combining all constituent parts that have been analyzed separately. In order to cope with system complexity, modern healthcare systems are investing in advanced physiological sensing and patient monitoring, thereby giving rise to big data. Realizing the full potential of big data for healthcare intelligence requires fundamentally new methodologies to harness and exploit complexity. However, available nonlinear dynamics techniques are either not concerned with healthcare analytical objectives or fail to effectively analyze big data to extract useful information for improving healthcare services. There is an urgent need to develop analytical methodologies that fully exploit the underlying nonlinear dynamics in physiological systems for advancing healthcare services with exceptional features such as personalization, responsiveness, and superior quality. This chapter presents some theoretical developments and tools to advance the applications of nonlinear dynamics principles in health care. Specifically, we focus on sensor-based characterization and modeling of nonlinear dynamics (i.e., multifractal analysis and multiscale recurrence quantification). Then, current developments and applications of these methodologies are examined for characterizing and exploiting heart rate variability and space-time ECG signals.
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Signal Processing · Cross-listed from cs.CV
Region-Local Copula Evidence Fusion for Heterogeneous Remote Sensing Change Detection
Zhiyuan Ji, Junjun Yin and Jian Yang
Superpixel copula models provide stable regional evidence for heterogeneous remote sensing change detection, but a single label per region limits localization within mixed superpixels. This letter develops a region-local copula evidence fusion method that retains the regional decision structure while introducing spatially varying local dependence anomalies. Independently fitted local models characterize departures from unchanged cross-image relationships. Reference ranking and an upper-tail gate transform these anomalies for fusion with continuous regional confidence. We derive the resulting regiondependent local decision threshold and identify a condition under which gating is equivalent to reparameterizing ungated fusion. On Lake and UK, whole-image optimized configurations achieve kappa coefficients of 0.78136 and 0.90817 and improve mixedregion and boundary decisions. Four-fold retrospective spatial validation over ten training subsets confirms complementary local information, with ungated reference fusion increasing mean kappa by 0.00693 and 0.01793. Fixed gating yields a larger UK gain of 0.03353 but only 0.00041 on Lake. These results support regional-local dependence interaction, while showing that calibration and gating have scene-dependent benefits.
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Signal Processing
Secure Near-Field ISAC with Pinching-Antenna Systems: Exploiting Constructive-Destructive Interference
Dimitrios Bozanis, Vasilis K. Papanikolaou, Sotiris A. Tegos et al. (+3 more)
This paper investigates secure near-field integrated sensing and communication (
ISAC) with pinching-
antenna systems (PASS), where a base station serves legitimate users while localizing a target that may also eavesdrop on the transmitted data. We jointly design symbol-level precoding (SLP) and the transmit and receive pinching-
antenna positions to minimize the Cartesian position error bound (PEB) averaged over the data symbols. Constructive interference ensures reliable reception at the legitimate users, whereas destructive interference drives the target's observation toward incorrect symbol regions without sacrificing the illumination required for sensing. We derive the PEB for unknown complex target reflectivity and establish an exact factorization into transmit-illumination and receive-geometry terms. This decomposition reveals the distinct sensing roles of the two apertures, enables separate receive-placement and joint transmit-placement/SLP optimization, and provides analytical insights into local position identifiability, the range-information limitations of compact arrays, sensing-oriented receive placement, and in-waveguide attenuation. Building on these results, we develop an efficient
algorithm by combining geometry-informed receive initialization, one-dimensional transmit-position searches, and convex per-symbol precoding. Numerical results demonstrate sub-millimeter localization, erroneous symbol decisions at the target, an expanded feasible operating region, and substantial gains over fixed-placement PASS and fully digital extremely large-scale
MIMO (XL-
MIMO)
benchmarks.
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All authors Dimitrios Bozanis, Vasilis K. Papanikolaou, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, Christos Masouros, George K. Karagiannidis
Signal Processing
Realistic modeling of Photonic Terahertz Communication based on a real-world 30 km 30 Gbps experimental demonstration
Mingxu Wang, Jianjun Yu, Xianming Zhao et al. (+9 more)
Experimental studies have demonstrated that THz links can support multi-kilometer, gigabit-per-second wireless transmission. However, few studies have effectively validated channel models through high-capacity, long-distance experiments. In this work, we experimentally demonstrate a 30 km ultra-long-haul photonic THz wireless communication system. 16 Gbaud quadrature phase shift keying (QPSK) signals are transmitted and tested, achieving a maximum data rate of 30 Gbps. For the first time, we validate two widely used THz channel models using a fully implemented 30 km experimental setup, providing a systematic and thorough analysis that integrates theoretical modeling with experimental results. We also perform an analysis of power consumption in photonic THz communication systems and provide a detailed comparison between simulated and experimental power consumption results. Finally, a detailed performance comparison between the simulation and experimental results, including signal-to-noise ratio (SNR), bit error rate (BER) and data rate, are provided. Our work provides essential insights for the design and energy-efficient deployment of future high-capacity THz networks, establishing a
benchmark for subsequent research in channel modeling, experimental verification, and system-level energy efficiency.
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All authors Mingxu Wang, Jianjun Yu, Xianming Zhao, Jiali Chen, Ye Zhou, Xin Lu, Hansong Ma, Chengzhen Bian, Wen Zhou, Kaihui Wang, Weiping Li, Iman Tavakkolnia
Systems and Control
Schur-Neural KF: Learned Schur-Consistent Corrections to the Extended Kalman Filter
Min Kim, Lianghao Cao, Soon-Jo Chung et al. (+1 more)
We present Schur-Neural KF (SN-KF), a learning-based correction to the extended Kalman filter (EKF) that preserves the probabilistic conditioning interpretation of the EKF. The method perturbs the predictive state-measurement cross-covariance and the Cholesky factor of the measurement noise covariance so that the resulting joint predictive covariance is always positive semidefinite. The positive semidefiniteness is ensured by a Schur complement-based parametrization. We instantiate the parametrization with a recurrent neural architecture whose
matrix outputs are modulated by amplitude gates. We prove that incorporating a measurement does not increase the filter's state uncertainty, and show that no measurement can induce an arbitrarily large state correction relative to its statistical surprise. We also present a perturbative analysis suggesting SN-KF's structural strength in the data-scarce regime. We provide two numerical experiments to illustrate the practical benefits of SN-KF. In a two-radar experiment, enforcing Schur-consistency provides a much broader failure-free hyperparameter region and reduces RMSE for small training subsets, consistent with our theoretical analysis in the data-scarce regime. In the unicycle experiment, SN-KF achieves the best precision, recall, false alarm rate, and gated RMSE under innovation-based sensor-fault rejection.
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All authors Min Kim, Lianghao Cao, Soon-Jo Chung, Andrew M. Stuart
Signal Processing
A Novel Dynamic Ray-Tracing Channel Model for 6G LEO Satellite-to-Ground Communication Systems
Songjiang Yang, Cheng-Xiang Wang, Yinghua Wang et al. (+4 more)
The sixth generation (6G) communication systems envision a space-air-ground integrated network, making accurate satellite-to-ground (S2G) channel modeling crucial for communication systems design. Currently, the majority of S2G channel models rely on
stochastic methods, which cannot provide multipath information based on realistic satellite orbits and environments. In this paper, we propose a novel low Earth orbit (LEO) S2G channel model based on dynamic ray-tracing to overcome the challenges of tracking rays for long-distance propagation by setting a virtual transmission plane (VTP). Moreover, since the line-of-sight propagation mechanism contributes the most power to satellite communications, the VTP is constructed based on the first Fresnel zone to generate parallel rays close to the ground. The realistic satellite orbit and environmental information are used in the proposed LEO S2G ray-tracing channel model to compute channel characteristics. The results show that the proposed LEO S2G ray-tracing channel model can accurately generate the channel characteristics based on the satellite orbits. The channel characteristics of LEO S2G communications computed by the proposed ray-tracing method can well match the channel measurements.
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All authors Songjiang Yang, Cheng-Xiang Wang, Yinghua Wang, Jie Huang, Yuyang Zhou, Wei Feng, el-Hadi M. Aggoune
Signal Processing
From Electromagnetic Physics to Intelligent Optimization: A Deterministic Modeling for Indoor Pinching-Antenna Systems
Qiushi Zhao, Zihan Feng, Ximing Xie et al. (+1 more)
Pinching-
antenna systems (PASS) enable flexible wireless transmission by activating pinching
antennas (PAs) along dielectric waveguides. However, existing PASS analyses commonly rely on tractable analytical or statistical channel abstractions that do not jointly capture in-waveguide field
evolution, sequential PA power extraction, and site-specific indoor multipath propagation. This paper develops a physics-driven deterministic framework for end-to-end modeling of indoor PASS. Specifically, the parabolic wave equation (PWE) resolves the position-dependent electromagnetic field inside the dielectric waveguide, and coupled-mode theory (CMT) maps the local guided field to the radiation power and phase of each PA. Ray tracing (RT) subsequently models the site-specific indoor electromagnetic propagation from each PA to the users, including LoS, reflected, and diffracted components. To enable efficient repeated evaluations, we combine a reusable PWE-CMT field representation with a physics-assisted neural-network-based RT surrogate that preserves propagation geometry, visibility, and phase information. The proposed framework achieves a complex-channel NMSE of -19.14 dB while reducing the end-to-end single-link evaluation time from approximately 2.5 s to 77 ms. Its application to single- and multi-user deployment demonstrates that the physical model can be efficiently reused for PA-position and power-allocation optimization while preserving the underlying position-dependent propagation physics.
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All authors Qiushi Zhao, Zihan Feng, Ximing Xie, Hao Qin
Audio and Speech Processing
Yang Xiao, Vidhyasaharan Sethu, Eun-Jung Holden et al. (+1 more)
Spoken conversational systems must recover information from prior interactions (i.e.,
memory), yet relevant information in
speech extends beyond what was said to who said it, how it was spoken, and what was audible, information that exists only in the audio signal and cannot be recovered from a transcript. Beyond what to remember,
memory also demands diverse operations: retrieving a single fact, integrating evidence across turns, tracking an evolving state. Real interactions further unfold across sessions, meaning information accumulates across distinct episodes rather than a single continuous recording. Existing
benchmarks fall short on all three dimensions: they focus primarily on lexical content, adopt limited and ad hoc
memory operations, and treat
memory as a single-session problem. We argue that principled
memory evaluation requires jointly characterizing the acoustic evidence to be retained and the operations applied to it, and introduce a taxonomy along these two axes. Building on this taxonomy, we present VoxMem: 3,196 evaluation instances over 34,743 spoken sessions (177 hours) crossing four acoustic evidence types (
speech semantics, speaker identity, paralinguistic cues, environmental sound) with four
memory operations (information extraction, multi-session
reasoning, temporal tracking, and answer refusal), grounded in multi-session histories and stratified across context budgets from 8K to 64K
tokens. Evaluating 15 LALMs, no model exceeds 40% at 32K. Models retain what was said far better than who said it, how, or what was audible, a gap that widens for complex operations, grows with history length, and manifests as qualitatively distinct failure modes across evidence types. VoxMem aims to provide a foundation to measure and drive progress on the full scope of spoken conversational
memory.
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All authors Yang Xiao, Vidhyasaharan Sethu, Eun-Jung Holden, Ting Dang
Signal Processing
Integrated Communication and Computing with Index Modulation
Shreesal Shrestha, Kuranage Roche Rayan Ranasinghe, Hyeon Seok Rou et al. (+1 more)
We propose an integrated communication and computing (ICC) architecture that repurposes the inactive
antennas of a spatial index modulation (IM) transmitter to perform over-the-air computation (AirComp). While IM delivers excellent spectral efficiency by selectively activating a subset of transmit
antennas, the unused
antennas are conventionally completely deactivated, forgoing spatial degrees of freedom (DoF) that can be exploited without additional bandwidth. By instead transmitting a pre-equalized, low-power computing stream over these idle
antennas, the proposed architecture achieves simultaneous data transmission and computation without requiring orthogonal frequency or time resources.
Alignment is performed entirely at the transmitters, such that the pre-equalization occurs leveraging local channel state information (CSI), while at the receiver, a vector Gaussian belief propagation (VGaBP) detector recovers the data payload under the discrete IM codebook constraint before the target function is estimated from the residual. Numerical results against exact maximum likelihood (ML) baselines confirm near-optimal detection under the adopted statistical model, and reveal that the number of
antennas assigned to data transmission governs a direct trade-off between modulation robustness and AirComp accuracy.
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All authors Shreesal Shrestha, Kuranage Roche Rayan Ranasinghe, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu
Audio and Speech Processing · Cross-listed from cs.SD
Do Audio LLMs Listen Before They Act? Diagnosing Acoustic-Context Gating in Voice Agents
Yanjie Zhang, Nanchen Hu and Yushi Sun
Audio
language models can recognize spoken commands and invoke tools, but an
agent must first decide whether the acoustic and conversational context warrants action. We introduce VGBench, a 1,018-item diagnostic
benchmark for action-level addressedness across side-talk, self-talk, and speaker-switch scenarios. Each item uses a shared action space comprising silence, a tool call, and a natural-
language answer. Speaker-switch pairs hold the specified words fixed while source, distance rendering, and a temporal boundary define a controlled wearer-to-bystander shift. Six raw Audio LLMs and three training-free adaptations often identify the target tool yet rarely withhold action under this shift; the highest raw switch mute rate is 14%. We then use VoxGate as a post-training case study. Supervised training mutes 91.3% of switched commands while choosing the correct tool for all nearby wearer commands and text-only controls. An exploratory GRPO stage has similar switch performance; side-talk accuracy rises from 68.4% to 70.9%, and self-talk muting from 52.0% to 60.0%. Factorized controls identify an independent source-change effect, while sensitivity to the far-field manipulation varies across acoustic renderings. The
benchmark therefore measures multi-cue acoustic-context gating rather than isolated speaker identity.
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Audio and Speech Processing · Cross-listed from cs.AI
Beyond Dyadic Memory: Interaction-Aware Multimodal Memory with Adaptive Agentic Retrieval for Multi-Party Spoken Conversations
Wenxu Jia, Xize Cheng, Zihan Zhang et al. (+5 more)
Long-term
memory enables
agents to accumulate information and reason across sessions, yet existing research primarily focuses on dyadic text or image-text conversations, leaving long-term
memory for multi-party spoken conversations underexplored. This setting requires preserving conversational content, identifying participants across sessions, and retaining who speaks to whom. To this end, we propose VoxPolyMem, an interaction-aware
multimodal memory framework combining incremental speaker identification with a
memory hierarchy comprising interaction
memory, fact
memory, and participant profiles. We formulate
retrieval as sequential decision-making, where an
agent rewrites queries and selects
retrieval tools and
memory layers based on accumulated evidence to address information gaps. We further introduce Evidence-Gain GRPO (EG-GRPO), which uses round-wise credit assignment to encourage complementary evidence acquisition. We also construct VoxPolyBench to evaluate
memory evolution, personalized answering,
memory retrieval and
reasoning, and interaction
reasoning and attribution in multi-party spoken conversations. VoxPolyMem achieves an overall score of 85.0 on VoxPolyBench, surpassing the strongest evaluated baseline by 23.6 points. On Mem-Gallery and H2HMem-Multi, it scores 89.6 and 74.4, respectively, exceeding the strongest evaluated public
memory baselines by over 8 points each. These results highlight its potential for persistent, personalized assistance in multi-party
multimodal interactions. Code and
datasets are available at https://voxpolymem.github.io/VoxPolyBench/demo/
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All authors Wenxu Jia, Xize Cheng, Zihan Zhang, Dongjie Fu, Linjun Li, Wenshi Chen, Yangyang Wu, Tao Jin
Systems and Control · Cross-listed from cs.LG
What Do Latent Predictive Vehicle Representations Retain? Measuring State, Geometry, and Local Response
Enzo Nicolás Spotorno, Josafat Leal Filho and Antônio Augusto Fröhlich
Models of vehicle dynamics learned from logged states and commands complement physics-based models, and latent world models, which predict in a learned representation, are used to plan and train controllers in other domains. Vehicle controllers are usually specified in physical terms: costs, limits, and references depend on position, yaw angle, speed, and yaw rate, and the optimizer compares or differentiates predicted outcomes across nearby commands. A latent model placed in such a controller must therefore let these quantities be recovered and must change its predictions with commands as the vehicle does, and prediction error on its own latent targets measures neither. We contribute a measurement protocol for action-conditioned latent predictors with a physical readout that separately tests retention, physical-neighborhood organization, forecasting, and local response to command perturbations, using an untrained-encoder reference and three matched response paths that locate errors in the representation or the predictor. In a case study of a temporal joint-
embedding predictive model trained on signals logged in IPG CarMaker, the representations retain the measured planar outputs, though an untrained encoder of the same architecture retains them slightly better; future-command input improves one-second forecasts with retention nearly unchanged; and responses to small command pulses diverge from the simulator already in latent coordinates, raising regret when choosing among nearby commands in all comparisons. Updating the predictor on responses corrects them locally at a cost in forecast accuracy. Measuring retention, forecasting, and local response separately is thus what qualifies a predictive latent as a candidate model for control, and the protocol provides the basis for its closed-loop evaluation.
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Audio and Speech Processing
Toward Human-Aligned Judgement of Speech Emotion Similarity
Yun-Shao Tsai, Yi-Cheng Lin, Chih-Kai Yang et al. (+7 more)
Evaluating emotion preservation in expressive
speech generation involves assessing how closely generated
speech matches a reference in emotion. Human listening tests assess this similarity, but their cost motivates automatic measures aligned with human judgments. To support the development and evaluation of such measures, we introduce SES-Bench, a
speech emotion similarity
benchmark built from human comparisons of two candidate utterances against a shared reference. These comparisons record which candidate listeners find emotionally closer to the reference and the strength of their preference. Using these annotations, we train SES-Judge to score emotion similarity between two utterances. SES-Judge significantly outperforms
embedding cosine similarity and prompted large audio-
language models in preference accuracy and correlation with human ratings that capture both preference direction and strength.
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All authors Yun-Shao Tsai, Yi-Cheng Lin, Chih-Kai Yang, Ho-Jung Cheng, Tsun-Yi Chang, Sheng-Wei Wu, Yi-Shan Chen, Hsiang-Chun Chang, Liang-Chieh Lee, Hung-yi Lee
Image and Video Processing · Cross-listed from cs.CV
RefCompose: Multi-Reference Image Generation via LoRA-Conditioned Diffusion
Sai Sri Teja Kuppa, Parth Shinde, Priyadharsan Balaji S et al. (+2 more)
Filmmakers and visual artists routinely need to compose multiple references, actors, locations, props, cultural elements, into a single coherent shot, but existing tools either fail to scale past a handful of references or destroy fine grained subject identity in the process, since per reference tokenization scales
memory linearly with reference count $N$ and generated content often departs from the given references rather than reproducing them. We propose RefCompose, a pixel space compositional conditioning framework that decouples where things go from what they look like, via a single fixed resolution reference canvas that keeps conditioning size constant regardless of reference count. Spatial layout is induced at inference time from a frozen
diffusion transformer and extracted via Grounding DINO, requiring no LLM or dedicated layout model, while dual stream LoRA adapters inject a layout derived depth map and the encoded canvas through separate low rank streams, disentangling
geometric scaffolding from localized appearance. On the Dense Layout protocol, RefCompose consistently outperforms layout based and state of the art multi reference baselines on color, texture, shape, spatial accuracy, and identity/content preservation at higher reference counts, all with constant inference
memory, making it a practical building block for multi subject cinematic composition at production scale.
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All authors Sai Sri Teja Kuppa, Parth Shinde, Priyadharsan Balaji S, Jinka Harshavardhan, Sriprabha Ramanarayanan