Signal Processing
Inverse Design of Multi-Layer Sub-Pixel-Resolution RF Passives Through Grayscale Diffusion with Flexible S-Parameter Conditioning
Tommaso Dreossi, Christopher M. Bryant, Hao Liu et al. (+4 more)
Inverse design of RF passive components from S-parameters is a high-dimensional, ill-posed problem, and prior generative approaches are limited to single-layer binary-metallization structures. This paper presents an inverse design approach that generates passive components from partial S-parameter inputs on an $8\times8$ mm board discretized at $64\times64$ pixels with sub-pixel grayscale metallization across 1-20 GHz. The framework generates two-layer copper layouts with vias, with hard physical constraints on feed locations enforced through annealed Langevin projection, flexible multi-modal conditioning on partial S-parameter specifications, port locations, dielectric properties, reference
topology, and variable port placement. Candidate designs are generated in seconds, with surrogate-predicted S-parameters matching targets to within $0.77 \pm 1.28$ dB weighted mean absolute error. We validate the approach with two fabricated designs on RO4003C: a manufacturable alternative to a hairpin filter whose coupling gaps violate fabrication rules, and a combline bandpass filter designed from scratch given only target S-parameters.
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All authors Tommaso Dreossi, Christopher M. Bryant, Hao Liu, Nathan Mirman, Noah Kessler, Michael Frei, Harish Krishnaswamy
Audio and Speech Processing · Cross-listed from cs.SD
Bangla-WhisperDiar: Fine-Tuning Whisper and PyAnnote for Bangla Long-Form Speech Recognition and Speaker Diarization
Mohammed Aman Bhuiyan, Md Sazzad Hossain Adib, Samiul Basir Bhuiyan et al. (+4 more)
Automatic
Speech Recognition (ASR) and speaker diarization in Bangla remain challenging due to long form recordings, diverse acoustic conditions, and significant speaker variability. This work addresses these two core tasks in Bangla spoken
language understanding by developing robust systems for long form ASR and speaker diarization. For ASR (Problem 1), we fine tune the tugstugi bengaliai regional asr whisper medium model on a custom-curated
dataset of approximately 15,000 chunked and aligned Bangla audio segments, employing full weight training with extensive data augmentation including noise injection, reverb simulation, echo, clipping distortion, and pitch/time perturbation. For speaker diarization (Problem 2), we fine-tune the pyannote/
segmentation-3.0 model using PyTorch Lightning on the competition annotated diarization
dataset, swapping the fine-tuned
segmentation backbone into the pyannote/speaker-diarization-community-1 pipeline while retaining the pretrained speaker
embedding and clustering components. Our ASR system achieves a Word Error Rate (WER) of 0.2441, while our diarization system achieves a Diarization Error Rate (DER) of 0.2392, both evaluated on the test set, demonstrating notable improvements over the respective pretrained baselines. We describe our complete pipeline, including data preprocessing, text normalization, audio augmentation, training strategies, inference optimization, and post-processing for both tasks.
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All authors Mohammed Aman Bhuiyan, Md Sazzad Hossain Adib, Samiul Basir Bhuiyan, Amit Chakraborty, Aritra Islam Saswato, Ahmed Faizul Haque Dhrubo, Mohammad Ashrafuzzaman Khan
Signal Processing
Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators
Prasenjit Dhara and Daniel Romero
Channel-gain maps provide the channel gain between any two locations in a geographical region. They find numerous applications, from resource allocation and interference control to path planning for autonomous vehicles. Channel-gain map estimation (CGME) is considerably more challenging than conventional
radio map estimation (RME) because channel-gain maps are functions over a 6-dimensional input space. This calls for specialized methods, which currently rely on the (inaccurate)
radio tomographic model or require a prohibitively large number of measurements since they do not exploit any spatial structure. This paper overcomes this issue by leveraging spatial patterns that channel-gain maps exhibit across environments, as dictated by the laws of physics and typical environmental characteristics (e.g. building materials and layouts). Adopting a metalearning perspective, a
transformer-based
estimator is proposed to implicitly learn this common structure from measurements collected in multiple environments. This enables CGME in new environments from significantly fewer measurements (five times less in our experiments). To maximize learning efficiency, the
transformer is composed with a feature map that enforces the invariances of CGME, such as those following from reciprocity. Numerical experiments corroborate the merits of the proposed
estimator relative to existing methods.
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Image and Video Processing
Tumor-aware augmentation with task-guided attention analysis improves rectal cancer segmentation from magnetic resonance images
Aneesh Rangnekar, Joao Miranda, Natally Horvat et al. (+13 more)
Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities substantially different from the pretraining domain, and on complex tumor-
segmentation tasks, remains understudied. Evaluating CT-pretrained
transformers on MRI rectal cancer
segmentation, we identified two interacting failure modes in CT-to-MRI transfer: (a) inefficient
token usage caused by zero-padding to match pretrained input dimensions, and (b) ineffective feature adaptation. We investigated these vulnerabilities using two primary CT-pretrained hierarchical shifted-window
transformer backbones, SMIT and Swin UNETR, together with VoCo as a large-scale-pretrained supporting
benchmark; these models differ in pretraining objectives and
datasets. Mechanistic analysis leveraged an
attention dilution index (ADI), an
entropy-based metric quantifying
attention diverted toward uninformative padding
tokens, and centered kernel
alignment (CKA) to measure feature reuse during MRI adaptation. ADI increased with zero-padding, while high feature reuse did not necessarily translate to improved downstream accuracy. To mitigate these issues, we introduced two interventions: a tumor-aware augmentation strategy to expand tumor appearance heterogeneity coverage, and an anisotropic cropping strategy to restore
token efficiency. Fine-tuning with these strategies on identical rectal MRI
datasets yielded detection rates of 91.1% (225/247) and 88.7% (219/247) for the primary SMIT and Swin UNETR backbones, with the supporting VoCo
benchmark reaching 90.3% (223/247), demonstrating significantly improved robustness under CT-to-MRI transfer. This study is among the first to examine when pretrained
transformers fail to transfer across imaging modalities and demonstrates how targeted mitigation strategies can systematically overcome cross-modality transfer limitations.
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All authors Aneesh Rangnekar, Joao Miranda, Natally Horvat, Stephanie Chahwan, Samir Alrayess, Aditya Apte, Aditi Iyer, Eve LoCastro, Revathi Ravella, Marc J Gollub, Iva Petkovska, Jesse Joshua Smith, Paul Romesser, Julio Garcia-Aguilar, Harini Veeraraghavan, Joseph O Deasy
Signal Processing · Cross-listed from cs.IT
When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization
Yalin E. Sagduyu and Tugba Erpek
Semantic communication (SemCom) with learned encoder-decoder architectures enables end-to-end learning of compact task-oriented representations optimized for the wireless channel, reducing channel resources needed to convey task-relevant information and improving spectrum efficiency. This paper studies semantic image transmission over block Rayleigh fading with AWGN using a multi-task semantic autoencoder that jointly reconstructs images and predicts labels from the received waveform. The latent dimension (complex channel uses per source sample) serves as a cross-layer control variable governing semantic fidelity and channel resource usage. We characterize the resulting latency-task fidelity tradeoff: larger latent representations improve inference accuracy but increase service time, channel uses, and queueing delay. Building on this insight, we develop online semantic-rate controllers that adapt the latent dimension per update under a long-term semantic error constraint. A queue-aware drift-plus-penalty policy minimizes delay subject to an average semantic error cap, while a complementary age-aware policy minimizes time-average Age of Information (AoI). By adapting the semantic rate to congestion and fidelity requirements, the proposed framework improves spectrum utilization and enables timely semantic updates with significantly lower delay and AoI than fixed-rate baselines.
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Systems and Control · Cross-listed from math.OC
A Computationally Tractable Path-Planning Method for Airborne Wind Energy Systems
Manuel C.R.M. Fernandes and Fernando A.C.C. Fontes
Airborne Wind Energy Systems (AWES) have emerged as a promising renewable energy technology that exploits stronger, more consistent high-altitude winds via tethered airborne devices. Among the various concepts, crosswind systems, where efficient flight control is essential to maximise energy output, offer significant potential. This paper addresses the problem of reference selection for crosswind flight control, focusing on the design of power-maximising
geometric flight paths for the reel-out phase of Groundgen systems. To overcome the computational challenges associated with optimal control approaches, a computationally tractable framework is proposed in which a path-planning problem is formulated as a nonlinear program. The method optimises the parameters of a Lissajous curve to maximise the average power production over the reel-out phase, while incorporating
curvature constraints. The proposed approach provides an efficient alternative to existing optimal control and learning-based methods.
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Image and Video Processing · Cross-listed from cs.AI
Intelligent CCTV for Urban Design: AI-Based Analysis of Soft Infrastructure at Intersections
Vinit Katariya, Seungjin Kim, Curtis Craig et al. (+2 more)
Artificial intelligence (AI) and computer
vision are transforming transportation data collection. This study introduces an AI-enabled analytics framework leveraging existing CCTV infrastructure to evaluate the impact of soft interventions, such as temporary pedestrian refuges and curb extensions, on vehicle speed and safety. Using deep learning and perspective-based speed estimation, we evaluated driver behavior before and after interventions, with repeated post-installation monitoring in Week 1 and Week 2, in Minneapolis. Findings reveal that at unsignalized intersections, mean and 85th-percentile speeds fell by up to 18.75% and 16.56%, respectively, while pass-through traffic decreased by as much as 12.2%. Signalized intersections showed comparable reductions except one location, with mean and 85th-percentile speeds dropping by up to 20.0% and 17.19%. These results demonstrate the traffic-calming effectiveness of soft infrastructure and underscore the utility of AI-powered methods for rapid, low-cost, and evidence-based transport policy evaluation.
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All authors Vinit Katariya, Seungjin Kim, Curtis Craig, Nichole Morris, Hamed Tabkhi
Systems and Control · Cross-listed from cs.RO
A Closed-Form Dual-Barrier CBF Safety Filter for Holonomic Robots on Incrementally Built Occupancy Grid Maps
Himanshu Paudel, Basanta Joshi, Dhirendra Raj Madai et al. (+3 more)
We present a dual-barrier control barrier function (CBF) safety filter for real-time, safety-critical velocity control of holonomic
robots operating in incrementally built occupancy grid maps. As a
robot explores an unknown environment, unmapped regions introduce irreducible uncertainty, since obstacle geometry beyond the explored frontier is unknown, making entry into such regions a source of collision risk, especially with front-facing sensors. To address this, we enforce two constraints: avoidance of mapped obstacles and restriction from unexplored regions. Both constraints are derived analytically from the occupancy grid's signed distance field, yielding a closed-form safety filter that requires only a small linear system solve per cycle. On resource-constrained platforms such as the Raspberry Pi, where SLAM and planning already consume significant compute, the low overhead of the proposed filter preserves resources. An adaptive gain schedule relaxes the frontier constraint in information-rich regions and tightens it in well-mapped areas, improving exploration efficiency while maintaining safety. The filter operates in velocity space as a minimally invasive correction and composes with arbitrary nominal controllers, including learning-based methods. Hardware flight experiments on a PX4-controlled quadrotor demonstrate zero collisions across multiple indoor runs.
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All authors Himanshu Paudel, Basanta Joshi, Dhirendra Raj Madai, Alina Bartaula, Biman Rimal, Sanjay Neupane
Image and Video Processing
MRI-Eval: A Tiered Benchmark for Evaluating LLM Performance on MRI Physics and GE Scanner Operations Knowledge
Perry E. Radau
Background: Existing MRI LLM
benchmarks rely mainly on review-book multiple-choice questions, where top proprietary models already score highly, limiting discrimination. No systematic
benchmark has evaluated vendor-specific scanner operational knowledge central to research MRI practice. Purpose: We developed MRI-Eval, a tiered
benchmark for relative model comparison on MRI physics and GE scanner operations knowledge using primary multiple-choice questions (MCQ), with stem-only and primed diagnostic conditions as complementary analyses. Methods: MRI-Eval includes 1365 scored items across nine categories and three difficulty tiers from textbooks, GE scanner manuals, programming course materials, and expert-generated questions. Five model families were evaluated (GPT-5.4, Claude Opus 4.6, Claude Sonnet 4.6, Gemini 2.5 Pro, Llama 3.3 70B). MCQ was primary; stem-only removed options and used an independent LLM judge; primed stem-only tested responses to incorrect user claims. Results: Overall MCQ accuracy was 93.2% to 97.1%. GE scanner operations was the lowest category for every model (88.2% to 94.6%). In stem-only, frontier-model accuracy fell to 58.4% to 61.1%, and Llama 3.3 70B fell to 37.1%; GE scanner operations stem-only accuracy was 13.8% to 29.8%. Conclusion: High MCQ performance can mask weak free-text recall, especially for vendor-specific operational knowledge. MRI-Eval is most informative as a relative comparison
benchmark rather than an absolute competency measure and supports caution in using raw LLM outputs for GE-specific protocol guidance.
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Image and Video Processing
CTseg: A Tool for Brain CT Segmentation, Spatial Normalisation, and Volumetrics
Mikael Brudfors
This paper presents and validates CTseg, a freely available software for
brain CT
segmentation, spatial normalisation, and volumetrics. CTseg builds on the Multi-
Brain generative modelling framework, providing a CT-specific pipeline that produces tissue maps, deformation fields, and
brain volume estimates in the same format as SPM's unified
segmentation, thereby extending SPM's established analysis chain from MRI to CT. CTseg is designed for routine hospital CT scans without requiring preprocessing or resampling in deployment. Although CTseg has been adopted in clinical research spanning, among other things, stroke, dementia, and
brain morphometry, a systematic validation against an independent reference standard has been lacking. Using paired MR/CT head scans, we evaluate CTseg across four dimensions:
segmentation accuracy against an MRI-derived silver standard; spatial normalisation consistency through group-average sharpness and voxelwise coefficient of variation;
brain volume agreement via intraclass correlation and Bland-Altman analysis; and downstream sex classification performance from normalised tissue maps. As a baseline, we apply SPM's MRI-based unified
segmentation directly to the CT images. CTseg significantly outperformed this baseline for
segmentation and normalisation, showed stronger TBV agreement, and achieved comparable TIV agreement. CTseg is freely available at https://github.com/WCHN/CTseg, and all experiment code is included in the repository for full reproducibility.
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Signal Processing · Cross-listed from cs.IT
Age of Gossip in Ring Networks With Non-Poisson Updates
Arunabh Srivastava and Sennur Ulukus
We consider a network consisting of $n$ nodes connected in a ring formation and a source that generates updates according to a renewal process and disseminates them to the ring network according to a Poisson process. The nodes in the network gossip with each other according to a push-based gossiping protocol, and disseminate version updates. Gossip between two neighbors happens at the arrivals of renewal processes with finite mean and variance. All renewal processes and Poisson processes in the network are independent but not identically distributed. We consider both uni-directional ring networks and bi-directional ring networks. We use version age of information to quantify the freshness of information at each node. Prior work has used the
stochastic hybrid systems (SHS) approach or a first passage percolation (FPP) approach to analyze ring networks with edges following identical Poisson processes. In this work, we use a sample-path backtracking approach to characterize the probabilistic scaling of the version age of information of an arbitrary node in the gossip network, where each edge follows an independent but not identically distributed renewal process. We show that the version age of information of any node in the network is stochastically equivalent to $\sqrt{n}$ at any time instant after the node has received its first update from the source.
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Signal Processing · Cross-listed from cs.LG
Physiologically Grounded Driver Behavior Classification: SHAP-Driven Elite Feature Selection and Hybrid Gradient Boosting for Multimodal Physiological Signals
Sahar Askari, Mohammad Mahdi Mirza Ali Mohammadi, Fatemeh Ensafdoust et al. (+2 more)
An interpretable and scalable framework for decoding driving behaviors from
multimodal physiological signals is proposed in this study. We utilize
multimodal physiological driving behavior large-scale
dataset comprising synchronized electroencephalogram (
EEG), electromyography (EMG), and galvanic skin response (GSR) signals. Our approach involves rigorous preprocessing followed by a domain-specific feature extraction pipeline targeting time-domain, frequency-domain, and derived physiological indices. To address high dimensionality, we employ SHAP-based elite feature selection, retaining the top 250 features to reduce computational overhead while preserving predictive power. Hyperparameter optimization for extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) models is conducted using
Bayesian optimization via Optuna. Finally, a weighted soft-voting ensemble is constructed to leverage the complementary strengths of both gradient boosting frameworks. The results demonstrate that the proposed ensemble achieves a test accuracy of 80.91% and a macro-F1 score of 0.79, significantly outperforming single-modality baselines and traditional machine learning models. Ablation studies confirm an 8% performance gain over the best single modality (
EEG), validating the necessity of
multimodal fusion. SHAP analysis further validates the physiological plausibility of the model, revealing that the
EEG contributes the majority of predictive weight, GSR and EMG features provide critical discriminatory signals for high-arousal and motor-intensive maneuvers.
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All authors Sahar Askari, Mohammad Mahdi Mirza Ali Mohammadi, Fatemeh Ensafdoust, Amin Golnari, Saeid Sanei
Systems and Control
Input-Output Specifications and Dynamic Droop Coefficients: Stability and Performance Conditions for Grid-Forming IBRs
Jennifer T. Bui and Dominic Groß
This paper proposes dynamic stability and performance conditions for grid-connected inverter-based resources (IBRs). To this end, we extend the notion of steady-state droop coefficients to dynamic droop coefficients to capture the small-signal dynamics of IBRs and synchronous generators (SGs). Notably, the dynamic droop coefficients can be obtained from input-output data collected at the unit's (e.g., IBR or SG) point of interconnection without requiring prior knowledge of IBR internals or controls structure. To obtain frequency stability conditions, this IBR model is combined with a lightweight dynamic transmission network model that accounts for uncertainty of line dynamics. The resulting stability conditions are highly scalable and, given a few key network parameters, can be verified at the unit level. To make the conditions practical and offer intuitive and illustrative interpretations, we map the frequency stability conditions to bounds on the Bode plot of the dynamic droop coefficient for two broad types of IBR responses. Moreover, our specifications on the dynamic droop coefficient (i) translate basic frequency control ancillary services into verifiable requirements, and (ii) provide insights into the much-debated question of how to certify an IBR as grid-forming (GFM). The results are illustrated using dynamic droop coefficients obtained using detailed simulations of GFM and GFL IBRs as well as SGs.
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Systems and Control
Minimizing the Expected Cost of Synchronization in Lossless Power Networks
Gerald Ogbonna, David Bindel and Lindsay C. Anderson
The reliable operation of large-scale electric power networks is increasingly challenging, particularly with the integration of
stochastic renewable generation. In this work, we address the problem of minimizing network transients by optimally modifying the underlying network. We formulate the problem in terms of graph
Laplacian matrices and show that, under certain assumptions, the problem is convex. We derive a linear
matrix inequality whose feasibility guarantees the existence and uniqueness of phase cohesive steady-state angles; this condition can be directly incorporated as a convex constraint in the optimization framework and we provide several
geometric interpretations of the optimization problem. The proposed method is validated on the IEEE 30-bus test system, where results demonstrate that our approach effectively identifies critical links on the network. Dynamic simulations show a significant reduction in network transients and overall improvements across several performance metrics. We explore the sparsity-optimality trade-off using a reweighted $\ell_1$ heuristic.
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Image and Video Processing
External Validation of Deep Learning Models for BI-RADS Breast Density Prediction from Ultrasound Images
Yuxuan Chen, Arianna Bunnell, Yanqi Xu et al. (+4 more)
We externally validated three deep learning models (DenseNet121, ViT-B/32, and ResNet50) for predicting mammographic breast density from breast ultrasound exams on an independent cohort. The external validation set comprised 2,000 ultrasound exams, including 500 cancer cases defined by an initial negative exam (BI-RADS 1 or 2) followed by a cancer diagnosis within 6 months to 10 years, and 1,500 negative controls matched by manufacturer and study year. Performance was measured using patient-level AUROC across four density categories: A (fatty), B (scattered), C (heterogeneous), and D (extremely dense). As a downstream assessment, we also evaluated 10-year risk prediction by incorporating age and AI-derived density into the Tyrer-Cuzick model and comparing performance against a reference model using age and mammography-reported density. All three models performed best in extremely dense breasts (AUROC 0.868-0.899), with strong performance in fatty (0.814-0.838) and scattered density (0.764-0.799), and lower performance in heterogeneously dense breasts (0.699-0.729). DenseNet121 achieved the highest overall performance (micro-averaged AUROC 0.885), and performance across categories was comparable between internal and external testing. For risk modeling, age combined with AI-derived density yielded a lower AUROC than age combined with mammography-reported density (0.541 vs. 0.570; p = 0.23), with no statistically significant difference. These findings indicate that deep learning models generalize well to external data with different racial composition for breast density assessment. While performance is strongest in extremely dense breasts, heterogeneously dense remains more challenging, highlighting the need for targeted optimization.
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All authors Yuxuan Chen, Arianna Bunnell, Yanqi Xu, Haoyan Yang, Thomas K. Wolfgruber, John A. Shepherd, Yiqiu Shen