Signal Processing
Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features
Ali Al Housseini, Carlos Natalino, Paolo Monti et al. (+1 more)
We propose a
retrieval-based framework for crossdomain quality-of-transmission (QoT) estimation that leverages transferable feature representations while avoiding reliance on source-domain-specific decision boundaries. The proposed approach supports both zero-shot and few-shot adaptation without requiring model retraining. Experimental results on cross-domain QoT
datasets demonstrate improved generalization performance compared with conventional machine learning baselines and recent contrastive learning approaches, highlighting the potential of
retrieval-based inference for robust optical network automation.
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All authors Ali Al Housseini, Carlos Natalino, Paolo Monti, Omran Ayoub
Signal Processing
Multimodal Wearable-Based Olfactory-Induced Emotion Recognition in Arousal-Valence Dimensions
Chen-Yang Xu, Lan Zhang, Fei-Yi Fan et al. (+2 more)
Olfaction is important for
emotion regulation because it acts as a non-intrusive and cognitively lightweight pathway that directly engages the
brain s affective circuitry and achieves unobtrusive emotional modulation. This trait is essential for advancing practical affective computing in daily and
attention-critical scenarios. However, current olfactory
emotion research has two key limitations. First, it overemphasises the valence dimension while neglecting arousal. Second, it lacks
multimodal datasets that synchronously capture central and peripheral physiological responses to olfactory stimuli. To address these issues, we construct a large-scale
multimodal olfactory
emotion dataset based on 111 subjects, in which odors are labeled in the 2D arousal-valence space and electroencephalogram (
EEG), electrocardiogram (ECG), and photoplethysmography (PPG) signals synchronously recorded. Nevertheless,
multimodal signals present challenges such as non-stationarity, differences in latency, and cross-modal heterogeneity. Thus, we propose a spatiotemporal-frequency hybrid fusion network (STF-HFNet), which integrates three core modules. Frequency aggregation processing learns adaptive frequency aggregation in order to model non-stationary dynamics. Reciprocal guided
attention enables reciprocal bidirectional calibration for cross-modal temporal
alignment without synchronisation priors. Hybrid collaborative fusion combines spatial and channel
attention mechanisms to enhance cross-modal complementarity while suppressing redundant information. Extensive experiments show that STF-HFNet achieves state-of-the-art (SOTA) recognition accuracies of 88.34% on the AMIGOS
dataset and 92.40% on our self-constructed
dataset, and outperform the SOTA methods by 8.27% and 5.07%, respectively.
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All authors Chen-Yang Xu, Lan Zhang, Fei-Yi Fan, Bin Hu, Qing-Hao Meng
Signal Processing · Cross-listed from cs.CL
Neuromorphic Diffusion Language Models: Addressing Compute and Memory Bottlenecks via Sparsity and Block Denoising
Dengyu Wu, Clement Ruah, Jiechen Chen et al. (+2 more)
Autoregressive (AR) large
language models (LLMs) are inherently inefficient at inference time because each generated
token requires accessing the full set of model parameters, leading to low operational intensity and high energy consumption. Masked
diffusion language models (MDLMs) partially address this limitation for
memory-bound settings by allowing multiple
tokens to be generated per parameter access. In order to further enhance inference efficiency on modern platforms with extensive in-chip
memory, this work proposes neuromorphic MDLMs (N-MDLMs), which integrate block
diffusion with spike-based neuromorphic computation to jointly improve throughput and energy efficiency. While block
diffusion increases
token throughput by producing multiple
tokens per parameter access, spike-induced sparsity reduces effective parameter traffic and computations by skipping inactive channels. To analyze the synergistic effect of sparsity and
diffusion, we develop a
token-level roofline-inspired model that captures the combined impact of block-parallel generation and spike sparsity on decoding efficiency. Experimental results on translation tasks show that, thanks to spike-induced sparsity, N-MDLMs achieve substantial improvements in energy efficiency and throughput even in compute-bound platforms for which MDLMs would fail to improve over AR-LLMs.
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All authors Dengyu Wu, Clement Ruah, Jiechen Chen, Bipin Rajendran, Osvaldo Simeone
Audio and Speech Processing
Disentangling the Interpretive and Predictive Roles of LIWC: Controlled Substitution in Depression-Related Classification
Hsiang-Chen Yeh, Xiutian Zhao, Aurosweta Mahapatra et al. (+3 more)
Linguistic Inquiry and Word Count (LIWC) provides auditable psycholinguistic categories that are widely used to interpret depression-related
language, but its incremental predictive role in modern
multimodal systems remains unclear. We evaluate LIWC across five English and Chinese depression-related corpora under matched participant-level cross-validation. We ask whether LIWC improves classification and what any performance change reflects. Intact LIWC is compared with three fold-local substitutes: a PCA-rotated version that removes direct access to named category coordinates, a participant-shuffled version that preserves real LIWC profiles while breaking participant
alignment, and a random-marginal version that preserves feature-wise distributions. Across multiple fixed representation contexts, the results provide limited evidence for stable LIWC gains under frozen, participant-level early fusion. None of the prespecified
dataset-blocked contrasts survives multiple-comparison correction. A separate SBERT calibration produces larger observed intact-versus-shuffled and intact-versus-random separations, indicating that larger participant-aligned signals can produce correspondingly larger separations under the same procedure, while not resolving the five-corpus power limitation. LIWC remains useful as an auditable, corpus-conditioned interpretive layer. These conclusions should not be generalized to fine-tuned, sequence-aware, or end-to-end architectures.
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All authors Hsiang-Chen Yeh, Xiutian Zhao, Aurosweta Mahapatra, Shreeram Suresh Chandra, Ryan L. Boyd, Berrak Sisman
Audio and Speech Processing
Speech Entrainment in Multi-Party Conversations with a Digital Agent
Nicholas Mehlman, Kaitlin Zareno, Kleanthis Avramidis et al. (+2 more)
It has been widely observed that individuals engaged in conversation tend to adapt their speaking style to more closely match the other interlocutors. However, most prior work has focused on dyadic interactions among humans. In this paper, we investigate entrainment effects in a novel setting: a multi-party interaction between groups of humans and a digital
agent. This scenario offers important insights into how the participation of non-human actors modulates both short-time and temporally resolved conversational dynamics. To address these questions, we collect and analyze a unique
dataset that consists of both adult and family (parent/children) sessions, enabling us to examine how entrainment manifests differently across these cohorts. We consider a range of knowledge-driven and model-based entrainment features and find that, while individuals locally entrain with other humans, global entrainment, and entrainment with the
agent remains limited and cohort-dependent.
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All authors Nicholas Mehlman, Kaitlin Zareno, Kleanthis Avramidis, Anfeng Xu, Shrikanth Narayanan
Systems and Control
Census Tract-Level Power Outage Prediction and Sensitivity Analysis During Extreme Events
Antar Kumar Biswas and Masoud H. Nazari
This paper develops a two-stage hurdle model for predicting power outage occurrence and severity at the census-tract level. The proposed framework is then used to assess the sensitivity of power outage to socioeconomic, demographic, and environmental factors during extreme events. Five heterogeneous data streams are integrated at the census tract level: 15-minute customer outage data, OpenMeteo hourly weather records, American Community Survey (ACS) socioeconomic indicators, Centers for Disease Control (CDC) social vulnerability indices (SVI), and Geographic Information System (GIS) derived vegetation coverage. The proposed framework is validated using a high-resolution power outage
dataset covering 290 census tracts in the Detroit area over a period exceeding 14 months, with a temporal resolution of 15 minutes.
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Image and Video Processing · Cross-listed from cs.CV
Hybrid Semantic and Spectral Ensemble for Robust Synthetic Image Source Attribution
Md. Ajwad Hossain
The rapid advancement of text-to-image (T2I) models has necessitated robust Synthetic Image Source Attribution (SIA) methodologies. A critical challenge in SIA is the distribution shift between pristine training images and real-world deployed images, which undergo unknown post-processing operations such as JPEG compression and blurring. In this work, proposed for the DLMMDD Challenge at ICANN 2026, we introduce a dual-branch ensemble framework fusing Semantic Deep Learning with Mathematical Forensic Feature Extraction. The semantic branch employs EfficientNet-B0 regularized with Exponential Moving Averaging (EMA) and Label Smoothing. The forensic branch extracts 126 mathematical features – including SVD spectral profiles and Local Binary Patterns – from high-pass noise residuals, compressed via Truncated SVD and classified with XGBoost. Evaluated on a
dataset of 10 generators where 55% of the test set is degraded, our approach achieves a private leaderboard accuracy of 95.60%. Furthermore, the entire pipeline is highly computationally efficient, requiring no GPU acceleration and executing end-to-end on a standard CPU in under 6.5 hours, highlighting the practicality and scalability of mathematical forensics for real-world deployment.
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Image and Video Processing
Learning Dense 2D-3D Correspondence for X-ray-to-CT Registration of Knee Bones
Rembert Daems, Jonas Grammens, Caro Roten et al. (+3 more)
Recovering the 6-DoF pose of the knee bones from a plain radiograph, given the patient's segmented pre-operative CT, turns a routine low-dose image into a quantitative measurement of joint geometry, without the added dose of a repeat CT or a fixed biplanar rig. Classic solutions align a rendered bone silhouette to image edges; recent alternatives refine pose by backpropagating an image-similarity loss through a differentiable X-ray renderer. Both operate one patient at a time and are fragile under a single view. Silhouettes are depth-ambiguous, and differentiable-rendering refinement has a narrow capture range at substantial per-iteration cost. We instead learn an amortized, subject-agnostic dense 2D-3D correspondence, supervised solely by projection geometry. One shared-weight model per bone, trained across 758 patients, registers patients unseen during training. The pose then follows in closed form from a global, initialization-free, render-free PnP+RANSAC solve. Because X-ray formation is transmissive, our correspondence target is transmission-aware rather than tied to a single surface. Though trained only to register, the representation is anatomically semantic: a simple classifier reads a landmark's anatomical region from its
embedding across held-out patients, and the same features separate the knee's bones into a 2D-3D-consistent identity learned without any bone label. On a large single-institution cohort the model generalizes well to held-out patients.
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All authors Rembert Daems, Jonas Grammens, Caro Roten, Andrew Meyer, Thomas Luyckx, Matthias Verstraete
Image and Video Processing
DY-LUT: Depth-Aware YCbCr Lookup Tables for Real-Time Underwater Image Enhancement
Cunhao Zhu, Xiangtao Kong, Dongliang Xu et al. (+3 more)
Underwater image enhancement is challenged by spatially non-uniform, wavelength-dependent attenuation. Propagation distance and wavelength govern this degradation, while YCbCr separates luminance from chrominance for restoration. We propose DY-LUT, a depth-aware YCbCr lookup-table framework for real-time enhancement. A dual-branch encoder predicts image-level fusion weights and a joint pair of pixel-wise degradation indices from image and depth features. These quantities condition learnable 4D LUTs, followed by lightweight local refinement. DY-LUT preserves traditional LUT efficiency while enabling depth-conditioned, spatially adaptive restoration. With externally supplied depth, its 3.56M-parameter enhancement network achieves competitive quality on UIEB-90 and LSUI and runs $9$–$304\times$ faster than representative high-capacity baselines. Adaptive inference further maintains real-time performance ($\sim7$ ms) for 4K UIQAD images. DY-LUT also benefits downstream detection and feature matching. Ablations show that YCbCr is a more effective basis than RGB for depth-conditioned lookup, while the jointly learned indices further improve adaptive querying. These results provide a physically grounded route to efficient UIE on practical platforms.
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All authors Cunhao Zhu, Xiangtao Kong, Dongliang Xu, Zhiheng Zhang, Tianyu Wang, Yue Yao
Image and Video Processing
Small, Bias-Free, Blind and Convolutional Denoiser: A compact ConvNeXt U-Net for blind Gaussian color-image denoising
Nikolas Markou
We describe and evaluate BF-ConvUNeXt, a compact bias-free ConvNeXt U-Net for blind additive-white-Gaussian-noise color image denoising, combining four existing ingredients so a single property survives end to end: a frozen depthwise Gabor stem (oriented band-pass, zero trainable parameters), a
Laplacian-pyramid encoder routing the high-frequency residual into each skip connection, a ConvNeXt-V1 U-Net body, and bias-free construction throughout (no additive bias, linear head, LeakyReLU, variance-only batch norm). Together these make the 0.82M-parameter network exactly degree-1 homogeneous at inference, D(alpha y) = alpha D(y), licensing a Miyasawa/Tweedie score reading of the residual and blind generalization across noise levels from one model. We train a single blind model on a noise-sigma curriculum (sigma approximately 6.4 to 64, 0-255 scale); it extrapolates past that ceiling without a cliff, degrading smoothly to 22.8 dB at sigma=150 and 20.0 dB at sigma=200. Evaluated unchanged on four standard color sets (CBSD68, Kodak24, McMaster, Urban100) at sigma in {15,25,50}, it matches or beats DnCNN and FFDNet on every set and level, averaging about +0.7 dB over DnCNN. Against heavyweight CNN/
transformer state of the art it trails by a small margin (roughly 0.3-1.7 dB depending on set) at 1/15 to 1/39 of their parameters. The homogeneity is inference-only and checkpoint-specific, and the learned residual is a local, not global, score (non-conservative Jacobian), so plug-and-play/RED guarantees do not transfer; it still drives
stochastic sampling and linear inverse problems (inpainting, super-resolution, deblurring, compressive sensing).
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Systems and Control · Cross-listed from cs.AR
Reducing Instruction-Fetch Energy in RISC-V for Embedded AI Processing via Dynamic and Static Loop Caching
Wiebren Wijnstra, Sameed Sohail, Berend-Jan van der Zwaag et al. (+2 more)
Embedded RISC-V processors are increasingly deployed for on-device AI inference at the edge, where energy efficiency is a primary design constraint. Instruction fetching from SRAM-based
memory is a dominant source of energy consumption in these cores, accounting for over 40% of total energy in our baseline measurements. This paper presents two loop cache architectures integrated into the datapath of a RISC-V processor: a dynamic loop cache that automatically detects and caches short backward-branch loops at runtime, and a static loop cache that functions as a software-managed hot-code instruction buffer, allowing preloading of arbitrary instruction blocks during the boot sequence. Both designs are implemented in the open-source NEORV32 RISC-V processor and evaluated on a LeNet-5 convolutional neural network inference workload, synthesized on GlobalFoundries 22nm FDX+ technology at 0.5\,V and 250\,MHz. The dynamic cache reduces instruction fetches by 48.3% and total energy by 21.5%, while the static cache achieves an 83.3% fetch reduction and 35.5% total energy savings. The area overhead of both designs remains below 0.2% of the full SoC area. The complete implementation is open source.
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All authors Wiebren Wijnstra, Sameed Sohail, Berend-Jan van der Zwaag, Sabih Gerez, Amirreza Yousefzadeh
Image and Video Processing
Learning-based Hierarchical Tracheal Anatomy Understanding from Sparse Surgical Demonstration Annotations for Ultrasound Robots
Hiu Ching Cheung, Wenchao Yue, Zhengran Han et al. (+4 more)
Tracheostomy requires precise localization of the tracheal incision site; however, conventional manual palpation is subjective and often unreliable, while ultrasound utility remains operator-dependent. This work presents a learning-based framework for hierarchical tracheal anatomy understanding, designed specifically for ultrasound-guided robotic systems. We propose a two-stage perception pipeline integrating a YOLOv8n localization backbone with a sparse,
prompt-optimized SAM2 decoder to achieve high-fidelity
segmentation from sparse
surgical annotations. Our hybrid training strategy, bridging curated laboratory data with unconstrained sequences, ensures clinical robustness. Experimental
benchmarks demonstrate that this decoupled architecture effectively balances generalization, precision, and efficiency. The YOLOv8n and SAM2 framework achieves a consistent Mean Dice Similarity Coefficient (DSC) of 0.777 across both controlled and generalized domains. This significantly outperforms U-Net baselines, which often suffer from anatomical fragmentation and performance degradation (Generalization DSC $\le$ 0.494). By constraining mask decoding to targeted, sparse regions of interest, our model achieves a throughput of 6.92 FPS, which is vital for closed-loop robotic teleoperation. This study confirms that a robust hierarchical understanding of tracheal anatomy can be derived by coupling lightweight localization with foundation-scale visual models. Our framework establishes a scalable foundation for standardized, autonomous
surgical assistance, effectively navigating the variability of real-world ultrasound to enhance the safety and precision of robotic-assisted tracheostomy.
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All authors Hiu Ching Cheung, Wenchao Yue, Zhengran Han, Mingcong Chen, Guanglin Cao, Hongbin Liu, Hongliang Ren
Image and Video Processing
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding
Mohsen Jenadeleh, Jon Sneyers, João Ascenso et al. (+8 more)
Recent advances in conventional and learning-based image coding have increased the demand for
benchmark datasets that support fine-grained assessment of compressed image quality, particularly for learning-based image compression methods. This paper introduces Assessment of Image Coding 2026 (AIC2026), a large-scale
dataset for high-fidelity image compression containing 70 source images selected from 2,787 candidates using semantic clustering, inter-metric disagreement among objective image quality assessment (IQA) methods, and manual inspection and refinement. The
dataset covers a wide range of compression artifacts produced by eight conventional and four learning-based codecs across 17 coding configurations. Each source image is encoded using seven codecs. For each source-codec pair, decoded images are provided at 20 perceptually spaced distortion levels, corresponding approximately to 0.2-4.0 just-noticeable difference (JND) units using the ColorVideoVDP (CVVDP) metric for distortion estimation, yielding 9,618 distorted images. This fine-grained sampling enables analysis of rate-distortion behavior and objective metric evaluation for subtle quality differences across a wide range of compression artifacts. We report an extensive objective analysis using 24 conventional and 12 learning-based IQA methods. The results show substantial disagreement among current IQA methods for fine-grained quality differences, particularly for artifacts introduced by learning-based codecs. The complete
dataset is publicly available at https://doi.org/10.18419/DARUS-6156.
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All authors Mohsen Jenadeleh, Jon Sneyers, João Ascenso, Thomas Richter, Alexander Karabutov, Panqi Jia, Elena Alshina, Osamu Watanabe, António Pinheiro, Touradj Ebrahimi, Dietmar Saupe
Image and Video Processing
Metric Surface Reconstruction of Neurosurgical Scenes from Monocular Operating Microscope Images and Microscope Pose
Thomas Bucher, Didier Neuenschwander, Thomas Petutschnigg et al. (+4 more)
Objective: We evaluated whether metric 3D geometry of neurosurgical operative exposure can be recovered from standard
monocular operating-microscope images combined with microscope pose data. Methods: In a phantom-based laboratory study, two aneurysm training phantoms were imaged with a ZEISS Pentero 800 microscope integrated with Brainlab Cranial
Navigation. Microscope images from the standard composite
video output were stored with synchronous microscope poses. After intrinsic and extrinsic calibration, depth was estimated with the pretrained Depth Anything 3 model without task-specific fine-tuning. Fused point clouds were converted to meshes using Poisson surface reconstruction. Reconstructions were compared with reference surfaces from structured-light scanning and fine-slice CT. Results: For phantom A, representing a deeper
surgical corridor, reconstruction accuracy ranged from 1.95 $\pm$ 1.70 mm to 2.33 $\pm$ 2.15 mm. For phantom B, representing a directly exposed surface, accuracy ranged from 1.02 $\pm$ 0.93 mm to 1.52 $\pm$ 1.21 mm. Larger image sets mainly improved completeness, while accuracy remained within a narrower range. Corridor analysis showed preservation of overall geometry with local deviations in incompletely reconstructed regions. Conclusions: Standard
monocular microscope images combined with
navigation-derived pose data can reconstruct millimeter-range 3D surfaces using a foundation-model-based pipeline. These results show technical feasibility in a controlled phantom setting and support further development toward objective quantification of operative exposure, image fusion, and characterization of working spaces for future
surgical instrumentation.
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All authors Thomas Bucher, Didier Neuenschwander, Thomas Petutschnigg, Michael Murek, David Bervini, Andreas Raabe, Manuela Eugster
Image and Video Processing
Generative Video Compression with Adaptive Score Distillation
Naifu Xue, Zhaoyang Jia, Haosen Li et al. (+7 more)
Diffusion models provide strong generative capabilities for
video compression at ultra-low bitrates. Existing
diffusion-based
video codecs adapt base models originally developed for text-conditioned generation, whereas
diffusion models designed and trained specifically for compression remain unexplored. To fill this gap, we introduce our Generative
Video Codec (GenVC), built on a
video diffusion model trained from scratch for compression. To our knowledge, this is the first compression-oriented
video diffusion model. We realize this model directly in pixel space with a global-to-local hierarchy that recovers fine spatio-temporal details, enabling high-quality generative reconstruction from compressed representations. To accelerate inference, we distill the multi-step model into one step using distribution matching
distillation (DMD). Applying DMD directly, however, drives the student toward motion-stalled reconstructions. We trace this to a teacher-side guidance failure: once student-induced perturbations leave the frozen teacher's training region, its guidance can become misleading, causing DMD updates to reinforce rather than correct the student drift. To break the resulting feedback loop, we propose Adaptive Score
Distillation, which gates DMD updates according to their
alignment with the ground-truth direction, enabling high-quality reconstruction with coherent motion. Experimental results show that GenVC achieves state-of-the-art perceptual quality at ultra-low bitrates, with average bitrate savings of 62.5% at matched LPIPS and 71.3% at matched FID over GLVC. Unlike prior codecs that inherit billion-scale pretrained backbones, our
diffusion model has only 478.0M parameters and decodes 1080p
video in a single step at 15.1 fps on an A100 GPU.
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All authors Naifu Xue, Zhaoyang Jia, Haosen Li, Zihan Zheng, Jiahao Li, Bin Li, Xiaoyi Zhang, Qi Meng, Yuan Zhang, Yan Lu
Systems and Control · Cross-listed from cs.CE
Multi-Domain Physics-Based MDO of Multirotor UAVs: A Deterministic Framework for Discrete COTS Sizing
Akshay Gupta Burela and P. B. Sujit
Multirotor Unmanned Aerial Vehicle (
UAV) design is governed by a tightly coupled system of non-linear equations spanning structural mechanics, electrochemistry, aerodynamics, and kinematics. Solved sequentially, a miscalibrated sub-model coefficient triggers a mass-compounding cascade failure–the Mass Snowball effect. This paper presents AeroEval, a physics-based, Multidisciplinary Design Optimization (MDO) engine that simultaneously resolves all subsystem couplings and maps continuous sizing optima to physically purchasable, commercial off-the-shelf (COTS) components. The MDO engine is validated under a strict calibrate/test protocol that eliminates circularity: three structural/packaging coefficients are fitted on a held-out cohort of 20 do-it-yourself (DIY) and legacy platforms, then frozen and evaluated blind on 19 modern commercial drone platforms spanning 377 g to 76 kg. On the commercial test cohort the engine predicts Maximum Takeoff Weight (MTOW) within 7.2% Mean Absolute Percentage Error (MAPE), with a mean complexity factor $k \approx 1.05 \pm 0.08$ and $\text{RMSE}_{\text{MTOW}} = 1.59$ kg; the DIY calibration cohort, characterized by high build variability, yields 26.1% MAPE. Battery mass is predicted within 7.9% on single-pack platforms, with a disclosed systematic underprediction on redundant multi-battery enterprise platforms. A 14-parameter sensitivity suite of >300 simulations quantifies partial derivatives of takeoff mass and flight time; forward velocity diverges beyond 25.5 m/s due to cubic parasite power growth. The path-dependent mass-shedding model for agricultural and delivery roles reduces structural frame mass by up to 40.6% and energy capacity by 33.7% relative to static baselines. Typical solver convergence requires 25-50 iterations, completing in under 50 ms on a standard desktop CPU.
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Image and Video Processing
Frequency-Aware Dual-Stream Learning for Balanced Realism and Fidelity in Electron Microscopy Imaging
Longmi Gao, Zhengkai Zhao, Pan Gao et al. (+1 more)
Electron microscopy enables nanoscale cellular visualization but faces a trade-off between imaging resolution and acquisition speed. Existing learning-based methods rely on single-stream architectures that struggle to balance perceptual realism and quantitative fidelity, either over-smoothing details or generating unrealistic
hallucinations. This work introduces a frequency-adaptive dual-stream architecture to resolve this conflict. Using discrete wavelet transform, we decompose images into low-frequency structures and high-frequency details, then employ a conditional
diffusion model for realistic global synthesis and a
transformer network for precise detail recovery. Experiments on the EMDiffuse
dataset show the method achieves superior LPIPS and resolution ratio, substantially outperforming existing approaches. The method also shows strong generalization across diverse biological samples, supporting fast and reliable electron microscopy imaging for structural biology and nanotechnology applications. The source code and associated
dataset are publicly available to facilitate further research.
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All authors Longmi Gao, Zhengkai Zhao, Pan Gao, Manoranjan Paul
Signal Processing · Cross-listed from cs.IT
Microwave Linear Analog Computers (MiLACs) for Communications: Opportunities and Challenges
Matteo Nerini and Bruno Clerckx
Future wireless systems will require ever larger
antenna arrays and heavier signal processing, making conventional digital multiple-input multiple-output (
MIMO) architectures difficult to scale. In this paper, we show that a possible solution is to offload part of the processing from the digital to the analog domain. This can be done through linear microwave networks designed to compute directly using the communication signals at
radio frequency (RF). These networks, denoted as microwave linear analog computers (MiLACs), can perform useful
matrix operations instantly through wave propagation. Remarkably, although MiLACs are linear, the output signals can depend nonlinearly on the tunable parameters of the network, enabling the computation of operations beyond simple linear transforms. In particular, MiLACs can realize
matrix inversion and pseudo-inversion with complexity scaling quadratically with
matrix size, rather than cubically, which is essential in zero-forcing beamforming. We then review how MiLAC-aided
MIMO architectures can reduce the number of RF chains, relax the resolution requirements on digital-to-analog converters (DACs) and analog-to-digital converters (ADCs), and decrease the beamforming complexity. We finally discuss the main challenges related to MiLAC and promising directions for future research.
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Signal Processing · Cross-listed from cs.LG
Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes
Athanasios Papastathopoulos-Katsaros, Steven T. Lee, Lin Yao et al. (+4 more)
Electroencephalography (
EEG) is widely used to diagnose neurological conditions, but its analysis usually relies on either predefined spectral features or deep neural networks. Predefined features carry a strong bias, since they fix in advance what counts as informative, while deep neural networks and foundation models are hard to interpret and need large amounts of data and compute. We present bag-of-waves, an interpretable framework that learns a small dictionary of recurring
EEG waveform templates, called atoms, using shift-invariant k-means without labels. The continuous
EEG is then turned into a sequence of atom
tokens, whose counts feed a simple downstream classifier or clustering step. We extend this representation in two ways: we add atom-to-atom transitions, which we call n- grams, to capture temporal structure, and we move from single-channel atoms to regional and cross-channel spatial atoms for the multichannel case. We test the method on three complementary
datasets, each probing a different aspect: single-channel mouse genotype clustering with only sixteen animals (the low-data and temporal case), resting-state dementia classification (the spatial case), and the TUEV
benchmark, a six-way classification of clinical
EEG events (a high-data comparison against strong deep and foundation baselines). Across all three
datasets, bag-of-waves achieves performance competitive with state-of-the-art deep and foundation models. Yet, it operates with a fraction of the parameter count and provides full interpretability: because every atom corresponds to an inspectable waveform, the method explicitly recovers known clinical morphologies that a neurophysiologist can directly validate. Its main advantage is that it works in the low-data regime where heavier models are a poor fit.
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All authors Athanasios Papastathopoulos-Katsaros, Steven T. Lee, Lin Yao, Ajay Thomas, Junseok Park, Matthew J. McGinley, Zhandong Liu
Systems and Control
StateFormer: A Multivariate Transformer for Learning History-Dependent Battery State Dynamics and Long-Horizon Health Forecasting
Zhe Bai and Stephen Harris
This paper introduces a novel multivariate
Transformer StateFormer that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of battery states including state of charge (SOC), state of health (SOH), or battery temperature. By capturing long-range dependencies and identifying the operating conditions that drive future degradation, the model simultaneously represents fast electrochemical and thermal processes and slow aging mechanisms within a unified framework. StateFormer achieves robust and accurate predictions of battery state estimation across both synthetic and real-world
datasets. It maintains high predictive performance under additive current/voltage noise levels ranging from $1\%$ to $10\%$ and a wide range of ambient temperatures in a synthetic battery fleet accommodating different types of electrode chemistry and manufacturing, as well as five years of field data collected from residential utility battery systems. The resulting model bridges the laboratory-to-field gap and provides predictive intelligence for maintenance planning, operational optimization, and economic decision-making.
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Systems and Control · Cross-listed from cs.RO
Conformal Constraint Tightening for Chance-Constrained Motion Planning with Unknown Dynamics
Shubham Natraj, Bruno Sinopoli and Yiannis Kantaros
Motion planning
algorithms compute control sequences that drive autonomous
robots to goal regions while avoiding unsafe states. Existing methods, from sampling-based planning to deep reinforcement learning, typically provide task-completion guarantees only with respect to a nominal model or simulator, which may be invalidated when the true dynamics are unknown or difficult to model accurately. This letter addresses this limitation for systems with unknown dynamics and an available approximate nominal model, contributing a planner-agnostic constraint-tightening procedure that equips existing planners with a probabilistic task-completion guarantee on the true system. We leverage conformal prediction to provide a probabilistic bound on the nominal-to-true trajectory deviation over a distribution of planning problems. We tighten the planning constraints using that bound, and show that solving the tightened problem under the nominal model is a sufficient condition for solving the original problem on the true system with a prescribed probability. We validate the theoretical guarantees empirically and demonstrate substantially improved task completion relative to nominal-model planning.
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Signal Processing
Uncertainty-Aware Fusion for Resilient Distributed Radar Sensing
Christian Eckrich, Maik Pfefferkorn, Rolf Findeisen et al. (+2 more)
Distributed radar sensing enables safe and resilient operation in mobile robotic and vehicular systems by combining multiple local views of the scene. In this paper, we consider a moving
agent equipped with a frequency modulated continuous wave (FMCW) radar that fuses its local measurements with those of surrounding static radar sensors to reduce the uncertainty in target state estimation. The benefit of sharing measurements over capacity-limited wireless links come at the expense of two competing degradation mechanisms: quantization distortion, which increases the effective noise floor, and latency, which causes the received information to age. A unified framework is thus needed to quantify how individual radars contribute to uncertainty reduction under limited communication resources. To this end, we derive the Cramer-Rao lower bound (CRLB) for the fused target state by combining local Fisher information
matrices (FIMs) through coordinate transformations, incorporating distortion bounds from rate-distortion theory and information aging via a state transition model. We show that the resulting expression reveals a fundamental tradeoff between update rate and quantization fidelity governed by the available channel capacity. Our numerical simulations illustrate this tradeoff and demonstrate that a careful choice of system parameters (e.g., selected radar systems, quantization, update rate) is necessary for maximum uncertainty reduction.
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All authors Christian Eckrich, Maik Pfefferkorn, Rolf Findeisen, Abdelhak M. Zoubir, Vahid Jamali