Signal Processing
Semi-Blind Channel Estimation and Hybrid Receiver Beamforming in the Tera-Hertz Multi-User Massive MIMO Uplink
Abhisha Garg, Suraj Srivastava, Varsha Dubey et al. (+2 more)
We develop a pragmatic multi-user (MU) massive multiple-input multiple-output (
MIMO) channel model tailored to the THz band, encompassing factors such as molecular absorption, reflection losses and multipath diffused ray components. Next, we propose a novel semi-blind based channel state information (CSI) acquisition technique i.e. MU whitening decorrelation semi-blind (MU-WD-SB) that exploits the second order statistics corresponding to the unknown data symbols along with pilot vectors. A constrained Cramer-Rao Lower Bound (C-CRLB) is derived to bound the normalized mean square error (NMSE) performance of the proposed semi-blind learning technique. Our proposed scheme efficiently reduces the training overheads while enhancing the overall accuracy of the channel learning process. Furthermore, a novel hybrid receiver combiner framework is devised for MU THz massive
MIMO systems, leveraging multiple measurement vector based sparse
Bayesian learning (MMV-SBL) that relies on the estimated CSI acquired through our proposed semi-blind technique relying on low resolution analog-to-digital converters (ADCs). Finally, we propose an optimal hybrid combiner based on MMV-SBL, which directly reduces the MU interference. Extensive simulations are conducted to evaluate the performance gain of the proposed MU-WD-SB scheme over conventional training-based and other semi-blind learning techniques for a practical THz channel obtained from the high-resolution transmission (HITRAN) database. The metrics considered for quantifying the improvements include the NMSE, bit error rate (BER) and spectral-efficiency (SE).
Read the full abstract ▾
All authors Abhisha Garg, Suraj Srivastava, Varsha Dubey, Aditya Jagannatham, Lajos Hanzo
Audio and Speech Processing
SelfTTS: cross-speaker style transfer through explicit embedding disentanglement and self-refinement using self-augmentation
Lucas H. Ueda, João G. T. Lima, Pedro R. Corrêa et al. (+3 more)
This work presents SelfTTS, a text-to-
speech (TTS) model designed for cross-speaker style transfer that eliminates the need for external pre-trained speaker or
emotion encoders. The architecture achieves emotional expressivity in neutral speakers through an explicit disentanglement strategy utilizing Gradient Reversal Layers (GRL) combined with cosine similarity loss to decouple speaker and
emotion information. We introduce Multi Positive Contrastive Learning (MPCL) to induce clustered representations of speaker and
emotion embeddings based on their respective labels. Furthermore, SelfTTS employs a self-refinement strategy via Self-Augmentation, exploiting the model's voice conversion capabilities to enhance the naturalness of synthesized
speech. Experimental results demonstrate that SelfTTS achieves superior emotional naturalness (eMOS) and robust stability in target timbre and
emotion compared to state-of-the-art baselines.
Read the full abstract ▾
All authors Lucas H. Ueda, João G. T. Lima, Pedro R. Corrêa, Flávio O. Simões, Mário U. Neto, Paula D. P. Costa
Audio and Speech Processing · Cross-listed from cs.CL
Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease
Abner Hernandez, Eunjung Yeo, Kwanghee Choi et al. (+12 more)
The limited availability of dysarthric
speech data makes cross-lingual detection an important but challenging problem. A key difficulty is that
speech representations often encode
language-dependent structure that can confound dysarthria detection. We propose a representation-level
language shift (LS) that aligns source-
language self-supervised
speech representations with the target-
language distribution using centroid-based vector adaptation estimated from healthy-control
speech. We evaluate the approach on oral DDK recordings from Parkinson's disease
speech datasets in Czech, German, and Spanish under both cross-lingual and
multilingual settings. LS substantially improves sensitivity and F1 in cross-lingual settings, while yielding smaller but consistent gains in
multilingual settings. Representation analysis further shows that LS reduces
language identity in the
embedding space, supporting the interpretation that LS removes
language-dependent structure.
Read the full abstract ▾
All authors Abner Hernandez, Eunjung Yeo, Kwanghee Choi, Chin-Jou Li, Zhengjun Yue, Rohan Kumar Das, Jan Rusz, Mathew Magimai Doss, Juan Rafael Orozco-Arroyave, Tomás Arias-Vergara, Andreas Maier, Elmar Nöth, David R. Mortensen, David Harwath, Paula Andrea Perez-Toro
Systems and Control
A Portfolio-Level Optimization Framework for Coordinated Market Participation and Operational Scheduling of Hydrogen-Centric Companies
Seyed Amir Mansouri and Kenneth Bruninx
The
vision of electrolytic hydrogen as a clean energy vector
prompts the emergence of hydrogen-centric companies that must simultaneously engage in electricity, hydrogen, and green certificate
markets while operating complex, geographically distributed asset portfolios. This paper proposes a portfolio-level optimization framework tailored for the integrated operational scheduling and
market participation of such companies. The model co-optimizes asset scheduling and
market decisions across multiple sites, incorporating spatial distribution, technical constraints, and company-level policy requirements. It supports participation in the electricity
market, physical and virtual Power Purchase Agreements (PPAs), bundled and unbundled hydrogen
markets, and green certificate transactions. The model is applied to three operational scenarios to evaluate the economic and operational impacts of different compliance strategies. Results show that centralized, portfolio-level control unlocks the full flexibility of geographically distributed assets, enabling a 2.42-fold increase in hydrogen production and a 9.4% reduction in daily operational costs, while satisfying all company policy constraints.
Read the full abstract ▾
Systems and Control
Route-Phasing-Split-Encoded Genetic Algorithm for Multi-Satellite On-Orbit Servicing Mission Planning
Shridhar Velhal, Avijit Banerjee and George Nikolakopoulos
This article addresses multi-servicer on-orbit servicing mission planning in geosynchronous Earth orbit, where routing decisions are tightly coupled with time-dependent orbital phasing and strict propellant and mission-duration constraints. We propose a Route-Phasing-Split Genetic
Algorithm (RPS-GA) that simultaneously optimizes target sequencing, discrete phasing rotation decisions (i.e., the number of phasing revolutions/waiting cycles), and route partitioning across multiple servicing spacecrafts (SSCs). An RPS triplet chromosome encodes route order, phasing rotations, and route splits in a unified structure, enabling split-aware recombination without disrupting feasible multi-servicer route blocks. Feasibility is enforced through a constraint-aware fitness function that ranks feasible solutions based on total $\Delta V$, while penalizing propellant and mission duration violations, using aggregate and imbalance penalties. This formulation discourages the concentration of violations on a single servicing spacecraft (SSC). Once a feasible best solution is identified, it is preserved as feasible in subsequent generations, thereby enhancing convergence stability. The framework incorporates split-aware crossover, mutation and a regret-based Large Neighborhood Search for local intensification. Experiments on representative GEO servicing scenarios demonstrate that RPS-GA produces feasible multi-servicer plans with substantially improved fuel efficiency, reducing total $\Delta V$ by $24.5\%$, (from $1956.36 \ m/s$ to $ 1476.32\ m/s $) compared with a state-of-the-art LNS-AGA baseline.
Read the full abstract ▾
Systems and Control
From Singleton Obstacles to Clutter: Translation Invariant Compositional Avoid Sets
Prashant Solanki, Jasper Van Beers and Coen De Visser
This paper studies obstacle avoidance under translation invariant dynamics using an avoid-side travel cost Hamilton Jacobi formulation. For running costs that are zero outside an obstacle and strictly negative inside it, we prove that the value function is non-positive everywhere, equals zero exactly outside the avoid set, and is strictly negative exactly on it. Under translation invariance, this yields a reuse principle: the value of any translated obstacle is obtained by translating a single template value function. We show that the pointwise minimum of translated template values exactly characterizes the union of the translated single-obstacle avoid sets and provides a conservative inner certificate of unavoidable collision in clutter. To reduce conservatism, we introduce a blockwise composition framework in which subsets of obstacles are merged and solved jointly. This yields a hierarchy of conservative certificates from singleton reuse to the exact clutter value, together with monotonicity under block merging and an exactness criterion based on the existence of a common clutter avoiding control. The framework is illustrated on a Dubins car example in a repeated clutter field.
Read the full abstract ▾
Audio and Speech Processing
WiRD-Gest: Gesture Recognition In The Real World Using Range-Doppler Wi-Fi Sensing on COTS Hardware
Jessica Sanson, Rahul C. Shah, Yazhou Zhu et al. (+2 more)
Wi-Fi sensing has emerged as a promising technique for gesture recognition, yet its practical deployment is hindered by environmental sensitivity and device placement challenges. To overcome these limitations we propose Wi-Fi Range and Doppler (WiRD)-Gest, a novel system that performs gesture recognition using a single, unmodified Wi-Fi transceiver on a commercial off-the-shelf (COTS) laptop. The system leverages an monostatic full duplex sensing pipeline capable of extracting Range-Doppler (RD) information. Utilizing this, we present the first
benchmark of deep learning models for gesture recognition based on monostatic sensing. The key innovation lies in how monostatic sensing and spatial (range) information fundamentally transforms accuracy, robustness and generalization compared to prior approaches. We demonstrate excellent performance in crowded, unseen public spaces with dynamic interference and additional moving targets even when trained on data from controlled environments only. These are scenarios where prior Wi-Fi sensing approaches often fail, however, our system suffers minor degradation. The WiRD-Gest
benchmark and
dataset will also be released as open source.
Read the full abstract ▾
All authors Jessica Sanson, Rahul C. Shah, Yazhou Zhu, Rafael Rosales, Valerio Frascolla
Systems and Control
DQN Based Joint UAV Trajectory and Association Planning in NTN Assisted Networks
Afsoon Alidadi Shamsabadi, Cosmas Mwaba, Thomas Nugent et al. (+4 more)
Advanced Air Mobility (AAM) has emerged as a key pillar of next-generation transportation systems, encompassing a wide range of uncrewed aerial vehicle (
UAV) applications. To enable AAM, maintaining reliable and efficient communication links between
UAVs and control centers is essential. At the same time, the highly dynamic nature of wireless networks, combined with the limited onboard energy of
UAVs, makes efficient trajectory planning and network association crucial. Existing terrestrial networks often fail to provide ubiquitous coverage due to frequent handovers and coverage gaps. To address these challenges, geostationary Earth orbit (GEO) satellites offer a promising complementary solution for extending
UAV connectivity beyond terrestrial boundaries. This work proposes an integrated GEO terrestrial network architecture to ensure seamless
UAV connectivity. Leveraging artificial intelligence (AI), a deep Q network (DQN) based
algorithm is developed for joint
UAV trajectory and association planning (JUTAP), aiming to minimize energy consumption, handover frequency, and disconnectivity. Simulation results validate the effectiveness of the proposed
algorithm within the integrated GEO terrestrial framework.
Read the full abstract ▾
All authors Afsoon Alidadi Shamsabadi, Cosmas Mwaba, Thomas Nugent, Jie Gao, Pablo Madoery, Halim Yanikomeroglu, Subhadeep Pal
Systems and Control
Sample-based detectability and moving horizon state estimation of continuous-time systems
Isabelle Krauss, Victor G. Lopez and Matthias A. Müller
In this paper we propose a detectability condition for nonlinear continuous-time systems with irregular/infrequent output measurements, namely a sample-based version of incremental integral input/output-to-state stability (i-iIOSS). We provide a sufficient condition for an i-iIOSS system to be sample-based i-iIOSS. This condition is also exploited to analyze the relationship between sample-based i-iIOSS and sample-based observability for linear systems, such that previously established sampling strategies for linear systems can be used to guarantee sample-based i-iIOSS. Furthermore, we present a sample-based moving horizon estimation scheme, for which robust stability can be shown. Finally, we illustrate the applicability of the proposed estimation scheme through a biomedical simulation example.
Read the full abstract ▾
Systems and Control
End-to-End Differentiable Predictive Control with Probabilistic Constraint-Satisfaction Guarantees for Building Demand Response
Kaipeng Xu, Zhuo Zhi, Ruixuan Zhao et al. (+1 more)
Building Demand Response (DR) benefits from Model Predictive Control (MPC) because thermal flexibility can be optimized over a future horizon while enforcing operational constraints, but the repeated online optimization required by MPC motivates Differentiable Predictive Control (DPC) as a computationally efficient explicit-policy alternative. Conventional DPC, however, separates model identification from policy optimization and remains sensitive to plant-model mismatch. This paper proposes an End-to-End DPC (E2E-DPC) framework that jointly optimizes the learned dynamics model and control policy using prediction, constraint, and economic objectives. Stage-wise conformal prediction (CP) calibrates recursive prediction errors to construct horizon-dependent tightened state constraints, while independent Hoeffding certification provides a high-confidence lower bound on plant-level satisfaction of the original state and input constraints under receding-horizon deployment. The framework is evaluated in a high-fidelity residential building simulation using EnergyPlus through the Energym interface and compared with online MPC and conventional DPC. Constraint tightening eliminates thermal violations for both DPC variants in the stress test and reduces violations by 99.4 percent for online MPC. Compared with conventional DPC, E2E-DPC reduces the mean constraint-tightening margin at the first prediction stage by 31.0 percent and increases the median certified plant-level lower bound from 0 to 0.814. The results further reveal a trade-off between stronger certification of plant-level constraint satisfaction and the ability to maintain nonempty tightened state sets as the prediction horizon increases, with stronger multi-step identification mitigating this trade-off.
Read the full abstract ▾
All authors Kaipeng Xu, Zhuo Zhi, Ruixuan Zhao, Keyue Jiang
Systems and Control
Input Convex Encoder-Only Transformer for Computationally Efficient Model Predictive Control in Building Demand Response
Kaipeng Xu, Zhuo Zhi and Keyue Jiang
Learning-based Model Predictive Control (MPC) has emerged as a powerful strategy for building demand response (DR). However, its practical deployment is often hindered by the non-convex optimization problems induced by standard neural network models. These problems lead to long solver times and a lack of global optimality guarantees, making long-horizon real-time control challenging. Because
forecasting building thermal dynamics and energy consumption relies heavily on historical time-series data, Input Convex Neural Networks (ICNNs) tailored for temporal tasks have been developed to address this issue. Notable examples include Input Convex Recurrent Neural Networks (ICRNNs) and Input Convex Long Short-Term
Memory (IC-LSTM). Although IC-LSTM improves the modelling capability of simpler ICRNNs, its recurrent architecture remains computationally expensive and, in the experiments reported here, exhibits numerical training instability at longer historical sequence lengths. To address these limitations, this paper introduces the Input Convex Encoder-Only
Transformer (IC-EoT), which combines an encoder-only
attention architecture with input convexity guarantees. IC-EoT was evaluated in a co-simulation framework using the Energym Python library and the EnergyPlus building simulator, and compared with IC-LSTM and standard non-convex models in residential and commercial testbeds. IC-EoT maintained predictive accuracy comparable to IC-LSTM, showed no non-finite loss events across 60 multi-seed training runs, and reduced mean per-epoch training time by 26.2% and 29.8% in the two testbeds. At the eight-hour prediction horizon, IC-EoT reduced mean MPC solution time by factors of 5.8 and 5.3 relative to IC-LSTM, while providing comparable closed-loop cost and comfort performance.
Read the full abstract ▾
Systems and Control · Cross-listed from cs.LG
BOOST-RPF: Boosted Sequential Trees for Radial Power Flow
Ehimare Okoyomon and Christoph Goebel
Accurate power flow analysis is critical for modern distribution systems, yet classical solvers face scalability issues, and current machine learning models often struggle with generalization. We introduce BOOST-RPF, a novel method that reformulates voltage prediction from a global graph regression task into a sequential path-based learning problem. By decomposing radial networks into root-to-leaf paths, we leverage gradient-boosted decision trees (XGBoost) to model local voltage-drop regularities. We evaluate three architectural variants: Absolute Voltage, Parent Residual, and Physics-Informed Residual. This approach aligns the model architecture with the recursive physics of power flow, ensuring size-agnostic application and superior out-of-distribution robustness. Benchmarked against the Kerber Dorfnetz grid and the ENGAGE suite, BOOST-RPF achieves state-of-the-art results with its Parent Residual variant which consistently outperforms both analytical and neural baselines in standard accuracy and generalization tasks. While global Multi-Layer Perceptrons (MLPs) and Graph Neural Networks (GNNs) often suffer from performance degradation under topological shifts, BOOST-RPF maintains high precision across unseen feeders. Furthermore, the framework displays linear $O(N)$ computational scaling and significantly increased sample efficiency through per-edge supervision, offering a scalable and generalizable alternative for real-time distribution system operator (DSO) applications.
Read the full abstract ▾
Systems and Control
Interaction-Aware Predictive Environmental Control Barrier Function for Emergency Lane Change
Ying Shuai Quan, Paolo Falcone and Jonas Sjöberg
Safety-critical motion planning in mixed traffic remains challenging for autonomous vehicles, especially when it involves interactions between the ego vehicle (EV) and surrounding vehicles (SVs). In dense traffic, the feasibility of a lane change depends strongly on how SVs respond to the EV motion. This paper presents an interaction-aware safety framework that incorporates such interactions into a control barrier function (CBF)-based safety assessment. The proposed method predicts near-future vehicle positions over a finite horizon, thereby capturing reactive SV behavior and
embedding it into the CBF-based safety constraint. To address uncertainty in the SV response model, a robust extension is developed by treating the model mismatch as a bounded disturbance and incorporating an online uncertainty estimate into the barrier condition. Compared with classical environmental CBF methods that neglect SV reactions, the proposed approach provides a less conservative and more informative safety representation for interactive traffic scenarios, while improving robustness to uncertainty in the modeled SV behavior.
Read the full abstract ▾
Signal Processing
APEG: Adaptive Physical Layer Authentication with Channel Extrapolation and Generative AI
Xiqi Cheng, Rui Meng, Xiaodong Xu et al. (+3 more)
With the rapid advancement of 6G, identity authentication has become increasingly critical for ensuring wireless security. The lightweight and keyless Physical Layer Authentication (PLA) is regarded as an instrumental security measure in addition to traditional cryptography-based authentication methods. However, existing PLA schemes often struggle to adapt to dynamic
radio environments. To overcome this limitation, we propose the Adaptive PLA with Channel Extrapolation and Generative AI (APEG), designed to enhance authentication robustness in dynamic scenarios. Leveraging Generative AI (GAI), the framework adaptively generates Channel State Information (CSI) fingerprints, thereby improving the precision of identity verification. To refine CSI fingerprint generation, we propose the Collaborator-Cleaned Masked Denoising
Diffusion Probabilistic Model (CCMDM), which incorporates collaborator-provided fingerprints as conditional inputs for channel extrapolation. Additionally, we develop the Cross-
Attention Denoising
Diffusion Probabilistic Model (CADM), employing a cross-
attention mechanism to align multi-scale channel fingerprint features, further enhancing generation accuracy. Simulation results demonstrate the superiority of the APEG framework over existing time-sequence-based PLA schemes in authentication performance. Notably, CCMDM exhibits a significant advantage in convergence speed, while CADM, compared with model-free, time-series, and VAE-based methods, achieves superior accuracy in CSI fingerprint generation. The code is available at https://github.com/xiqicheng192-del/APEG
Read the full abstract ▾
All authors Xiqi Cheng, Rui Meng, Xiaodong Xu, Haixiao Gao, Ping Zhang, Dusit Niyato
Systems and Control
Performance Analysis of Tri-Sector Reflector Antennas for HAPS-Based Cellular Networks
German Svistunov, Matteo Bernabe and David Lopez-Perez
The increasing demand for ubiquitous, highcapacity mobile connectivity has driven cellular systems to explore beyond-terrestrial deployments. In this paper, we present a system-level performance evaluation of fifth-generation (5G) non-terrestrial network (NTN) enabled by high-altitude platform station (HAPS)-based base stations (BSs) equipped with tri-sectoral reflector
antennas against fourth-generation (4G) terrestrial network (TN) and 5G TN deployments in a multicell dense urban environment. Using the simulation results comprising the average effective downlink signal-to-interference-plus-noise ratio (SINR) and the average user throughput, along with the subsequent interference analysis, we demonstrate that the reflector-based HAPS architecture is primarily constrained by inter-
cell interference, while the combination of reflector configuration and deployment altitude represents a key design parameter.
Read the full abstract ▾
Systems and Control · Cross-listed from cs.RO
Collision-Free Velocity Scheduling for Multi-Agent Systems on Predefined Routes via Inexact-Projection ADMM
Seungyeop Lee and Jong-Han Kim
In structured multi-
agent transportation systems,
agents often must follow predefined routes, making spatial rerouting undesirable or impossible. This paper addresses route-constrained multi-
agent coordination by optimizing waypoint passage times while preserving each
agent's assigned waypoint order and nominal route assignment. A differentiable surrogate trajectory model maps waypoint timings to smooth position profiles and captures first-order tracking lag, enabling pairwise safety to be encoded through distance-based penalties evaluated on a dense temporal grid spanning the mission horizon. The resulting nonlinear and nonconvex velocity-scheduling problem is solved using an inexact-projection Alternating Direction Method of Multipliers (ADMM)
algorithm that combines structured timing updates with gradient-based collision-correction steps and avoids explicit integer sequencing variables. Numerical experiments on random-crossing, bottleneck, and graph-based network scenarios show that the proposed method computes feasible and time-efficient schedules across a range of congestion levels and yields shorter mission completion times than a representative hierarchical baseline in the tested bottleneck cases.
Read the full abstract ▾
Image and Video Processing · Cross-listed from cs.CV
A Variational Latent-Space Framework for Uncertainty-Aware Spectral Image Emulation
Chedly Ben Azizi, Claire Guilloteau, Gilles Roussel et al. (+1 more)
Synthetic spectral image generation is essential for remote sensing simulation and mission design, yet physically based radiative transfer models (RTMs) remain computationally expensive. Existing learning-based emulators reduce this cost, but are mostly deterministic parameter-to-spectrum regressors with limited spatial modeling and uncertainty information. We formulate spectral image emulation as a parameter-conditioned latent-variable problem and propose a variational autoencoder (VAE)-based framework combining nonlinear spectral-image representations, fast inference, and per-pixel uncertainty estimates. The framework is instantiated at spectrum and spatial–spectral levels through two-step VAE pretraining and latent mapping. We evaluate it on PROSAIL-simulated hyperspectral vegetation cubes (211 bands) and real Sentinel-3 OLCI multispectral ocean-colour imagery (21 bands) against classical regression emulators and a deep CNN baseline. Results show that no single architecture is optimal: pixel-to-pixel models perform best on controlled hyperspectral simulations, whereas a fully convolutional VAE is more robust on noisy real observations with missing or contaminated pixels. VAE-based emulators also achieve high throughput for large-scale generation. On Sentinel-3, the spatial–spectral VAE provides predictive intervals closer to empirical errors than pixel-wise neural and classical emulators, although absolute calibration remains incomplete. A look-up-table-based
retrieval experiment further shows that reconstruction fidelity alone does not ensure reliable leaf area index and chlorophyll
retrieval. Emulators should therefore be evaluated in representative remote-sensing end-use scenarios, not by reconstruction metrics alone.
Read the full abstract ▾
All authors Chedly Ben Azizi, Claire Guilloteau, Gilles Roussel, Matthieu Puigt
Image and Video Processing
HMS-VesselNet: Hierarchical Multi-Scale Attention Network with Topology-Preserving Loss for Retinal Vessel Segmentation
Amarnath R
Retinal vessel
segmentation methods based on standard overlap losses tend to miss thin peripheral vessels because these structures occupy very few pixels and have low contrast against the background. We propose HMS-VesselNet, a hierarchical multi-scale network that processes fundus images across four parallel branches at different resolutions and combines their outputs using learned fusion weights. The training loss combines Dice, binary cross-
entropy, and centerline Dice to jointly optimize area overlap and vessel continuity. Hard example mining is applied from epoch 20 onward to concentrate gradient updates on the most difficult training images. Tested on 68 images from DRIVE, STARE, and CHASE_DB1 using 5-fold cross-validation, the model achieves a mean Dice of 88.72 +/- 0.67%, Sensitivity of 90.78 +/- 1.42%, and AUC of 98.25 +/- 0.21%. In leave-one-
dataset-out experiments, AUC remains above 95% on each unseen
dataset. The largest improvement is in the recall of thin peripheral vessels, which are the structures most frequently missed by standard methods and most critical for early detection of diabetic retinopathy.
Read the full abstract ▾
Signal Processing
Secure Rate-Splitting and RIS Beamforming with Untrusted Energy Harvesting Receivers
Hamid Reza Hashempour, Le-Nam Tran, Duy H. N. Nguyen et al. (+1 more)
We consider a reconfigurable intelligent surface (RIS)-assisted heterogeneous network comprising legitimate information-harvesting receivers (IHRs) and untrusted energy-harvesting receivers (UEHRs). A multi-
antenna base station (BS) transmits confidential information to IHRs while ensuring sufficient energy transfer to UEHRs that may attempt eavesdropping. To enhance physical-layer security, we propose a secure rate-splitting multiple access (RSMA) scheme aided by a
UAV-mounted RIS. The objective is to maximize
fairness-based secrecy energy efficiency (SEE). Owing to the non-convexity of the formulated problem, we develop an alternating optimization framework that jointly designs the common message allocation, active precoders, and RIS phase shifts under transmit power and energy harvesting constraints, leveraging sequential convex approximation (SCA). Simulation results demonstrate the scalability of the proposed
algorithm and its superior SEE performance compared to space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA)
benchmarks.
Read the full abstract ▾
All authors Hamid Reza Hashempour, Le-Nam Tran, Duy H. N. Nguyen, Hien Quoc Ngo
Audio and Speech Processing
Adaptive Federated Fine-Tuning of Self-Supervised Speech Representations
Xin Guo, Chunrui Zhao, Hong Jia et al. (+4 more)
Integrating
Federated Learning (FL) with self-supervised learning (SSL) enables
privacy-preserving fine-tuning for
speech tasks. However,
federated environments exhibit significant heterogeneity: clients differ in computational capacity, causing straggler effects under unified fine-tuning, while diverse downstream tasks require different representation depths, making full-model updates inefficient. To address these challenges, we propose an adaptive
federated fine-tuning framework with early exits. Lightweight prediction heads are inserted at intermediate layers of the SSL backbone, allowing clients to terminate computation based on local constraints and task requirements. We further introduce a layer-wise, depth-aware partial aggregation strategy to better utilize representations from different network depths. Experiments show that the framework reduces edge overhead, supports heterogeneous hardware, and maintains competitive performance in resource-constrained
federated environments.
Read the full abstract ▾
All authors Xin Guo, Chunrui Zhao, Hong Jia, Ting Dang, Gongping Huang, Xianrui Zheng, Yan Gao
Audio and Speech Processing
Disentangling Speaker Traits for Deepfake Source Verification via Chebyshev Polynomial and Riemannian Metric Learning
Xi Xuan, Wenxin Zhang, Zhiyu Li et al. (+3 more)
Speech deepfake source verification systems aims to determine whether two synthetic
speech utterances originate from the same source generator, often assuming that the resulting source
embeddings are independent of speaker traits. However, this assumption remains unverified. In this paper, we first investigate the impact of speaker factors on source verification. We propose a speaker-disentangled metric learning (SDML) framework incorporating two novel loss functions. The first leverages Chebyshev
polynomial to mitigate gradient instability during disentanglement optimization. The second projects source and speaker
embeddings into
hyperbolic space, leveraging Riemannian metric distances to reduce speaker information and learn more discriminative source features. Experimental results on MLAAD
benchmark, evaluated under four newly proposed protocols designed for source-speaker disentanglement scenarios, demonstrate the effectiveness of SDML framework. The code, evaluation protocols and demo website are available at https://github.com/xxuan-acoustics/RiemannSD-Net.
Read the full abstract ▾
All authors Xi Xuan, Wenxin Zhang, Zhiyu Li, Jennifer Williams, Ville Hautamäki, Tomi H. Kinnunen
Signal Processing · Cross-listed from cs.LG
Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG
Mohammad Moulaeifard, Philip J. Aston, Peter H. Charlton et al. (+1 more)
Photoplethysmography (PPG) is one of the most widely captured biosignals for clinical prediction tasks, yet PPG-based
algorithms are typically trained on small-scale
datasets of uncertain quality, which hinders meaningful
algorithm comparisons. We present a comprehensive
benchmark for PPG-based clinical prediction using the \dbname~
dataset, establishing baselines across the full spectrum of clinically relevant applications: multi-class heart rhythm classification, and regression of physiological parameters including respiratory rate (RR), heart rate (HR), and blood pressure (BP). Most notably, we provide the first comprehensive assessment of PPG for general arrhythmia detection beyond atrial fibrillation (AF) and atrial flutter (AFLT), with performance stratified by BP, HR, and demographic subgroups. Using established deep learning architectures, we achieved strong performance for AF detection (AUROC = 0.96) and accurate physiological parameter estimation (RR MAE: 2.97 bpm; HR MAE: 1.13 bpm; SBP/DBP MAE: 16.13/8.70 mmHg). Cross-
dataset validation demonstrates excellent generalizability for AF detection (AUROC = 0.97), while clinical subgroup analysis reveals marked performance differences across subgroups by BP, HR, and demographic strata. These variations appear to reflect population-specific waveform differences rather than systematic bias in model behavior. This framework establishes the first integrated
benchmark for multi-task PPG-based clinical prediction, demonstrating that PPG signals can effectively support multiple simultaneous monitoring tasks and providing essential baselines for future
algorithm development.
Read the full abstract ▾
All authors Mohammad Moulaeifard, Philip J. Aston, Peter H. Charlton, Nils Strodthoff
Systems and Control · Cross-listed from cs.CV
Ctrl-A: Control-Driven Machine Learning using Adaptive Online Data Augmentation
Jesper B. Christensen, Ciaran Bench, Spencer A. Thomas et al. (+4 more)
We introduce ControlAugment (Ctrl-A), an automated data augmentation
algorithm for image-
vision tasks, which incorporates principles from control theory for online adjustment of augmentation strength distributions during model training. Ctrl-A eliminates the need for initialization of individual augmentation strengths. Instead, augmentation strength distributions are dynamically, and individually, adapted during training based on a control-loop architecture and what we define as relative operation response curves. Using an operation-dependent update procedure provides Ctrl-A with the potential to reduce the use of augmentation operations that negatively impact model performance, alleviating the need to manually engineer augmentation policies for new image classification tasks. Experiments on the CIFAR-10, CIFAR-100, SVHN-core, and Tiny ImageNet
benchmark datasets using WideResNet-28-10 and ResNet architectures demonstrate that Ctrl-A provides competitive performance across a range of image classification
benchmarks.
Read the full abstract ▾
All authors Jesper B. Christensen, Ciaran Bench, Spencer A. Thomas, Hüsnü Aslan, David Balslev-Harder, Nadia A. S. Smith, Alessandra Manzin
Systems and Control
Partial Attention in Deep Reinforcement Learning for Safe Multi-Agent Control
Turki Bin Mohaya and Peter Seiler
Attention mechanisms excel at learning sequential patterns by discriminating data based on relevance and importance. This provides state-of-the-art performance in advanced generative artificial intelligence models. This paper applies this concept of an
attention mechanism for multi-
agent safe control. We specifically consider the design of a neural network to control autonomous vehicles in a highway merging scenario. The environment is modeled as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). Within a QMIX framework, we include partial
attention for each autonomous vehicle, thus allowing each ego vehicle to focus on the most relevant neighboring vehicles. Moreover, we propose a comprehensive reward signal that considers the global objectives of the environment (e.g., safety and vehicle flow) and the individual interests of each
agent. Simulations are conducted in the Simulation of Urban Mobility (SUMO). The results show better performance compared to other driving
algorithms in terms of safety, driving speed, and reward.
Read the full abstract ▾
Image and Video Processing · Cross-listed from cs.CV
The Universal Normal Embedding
Chen Tasker, Roy Betser, Eyal Gofer et al. (+2 more)
Generative models and
vision encoders have largely advanced on separate tracks, optimized for different goals and grounded in different mathematical principles. Yet, they share a fundamental property: latent space Gaussianity. Generative models map Gaussian noise to images, while encoders map images to semantic
embeddings whose coordinates empirically behave as Gaussian. We hypothesize that both are views of a shared latent source, the Universal Normal
Embedding (UNE): an approximately Gaussian latent space from which encoder
embeddings and DDIM-inverted noise arise as noisy linear projections. To test our hypothesis, we introduce NoiseZoo, a
dataset of per-image latents comprising DDIM-inverted
diffusion noise and matching encoder representations (CLIP, DINO). On CelebA, linear probes in both spaces yield strong, aligned attribute predictions, indicating that generative noise encodes meaningful semantics along linear directions. These directions further enable faithful, controllable edits (e.g., smile, gender, age) without architectural changes, where simple orthogonalization mitigates spurious entanglements. Taken together, our results provide empirical support for the UNE hypothesis and reveal a shared Gaussian-like latent geometry that concretely links encoding and generation. Code and data are available https://rbetser.github.io/UNE/
Read the full abstract ▾
All authors Chen Tasker, Roy Betser, Eyal Gofer, Meir Yossef Levi, Guy Gilboa
Signal Processing
Cluster-Specific Predictive Modeling: A Scalable Solution for Resource-Constrained Wi-Fi Controllers
Gianluca Fontanesi, Luca Barbieri, Lorenzo Galati Giordano et al. (+2 more)
This manuscript presents a comprehensive analysis of predictive modeling optimization in managed Wi-Fi networks through the integration of clustering
algorithms and model evaluation techniques. The study addresses the challenges of deploying
forecasting algorithms in large-scale environments managed by a central controller constrained by
memory and computational resources. Feature-based clustering, supported by Principal Component Analysis (PCA) and advanced feature engineering, is employed to group time series data based on shared characteristics, enabling the development of cluster-specific predictive models. Comparative evaluations between global models (GMs) and cluster-specific models demonstrate that cluster-specific models consistently achieve superior accuracy in terms of Mean Absolute Error (MAE) values in high-activity clusters. The trade-offs between model complexity (and accuracy) and resource utilization are analyzed, highlighting the scalability of tailored modeling approaches. The findings advocate for adaptive network management strategies that optimize resource allocation through selective model deployment, enhance predictive accuracy, and ensure scalable operations in large-scale, centrally managed Wi-Fi environments.
Read the full abstract ▾
All authors Gianluca Fontanesi, Luca Barbieri, Lorenzo Galati Giordano, Alfonso Fernandez Duran, Thorsten Wild
Image and Video Processing
Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration
Jiaqi Shang, Haojin Wu, Yinyi Lai et al. (+3 more)
Deformable image registration plays a fundamental role in medical image analysis by enabling spatial
alignment of anatomical structures across subjects. While recent deep learning-based approaches have significantly improved computational efficiency, many existing methods remain limited in capturing long-range anatomical correspondence and maintaining deformation consistency. In this work, we present a cycle inverse-consistent
transformer-based framework for deformable
brain MRI registration. The model integrates a Swin-UNet architecture with bidirectional consistency constraints, enabling the joint estimation of forward and backward deformation fields. This design allows the framework to capture both local anatomical details and global spatial relationships while improving deformation stability. We conduct a comprehensive evaluation of the proposed framework on a large multi-center
dataset consisting of 2851 T1-weighted
brain MRI scans aggregated from 13 public
datasets. Experimental results demonstrate that the proposed framework achieves strong and balanced performance across multiple quantitative evaluation metrics while maintaining stable and physically plausible deformation fields. Detailed quantitative comparisons with baseline methods, including ANTs, ICNet, and VoxelMorph, are provided in the appendix. Experimental results demonstrate that CICTM achieves consistently strong performance across multiple evaluation criteria while maintaining stable and physically plausible deformation fields. These properties make the proposed framework suitable for large-scale neuroimaging
datasets where both accuracy and deformation stability are critical.
Read the full abstract ▾
All authors Jiaqi Shang, Haojin Wu, Yinyi Lai, Zongyu Li, Chenghao Zhang, Jia Guo
Systems and Control
LSAI: A Large Small AI Model Codesign Framework for Agentic Robot Scenarios
Longyu Zhou, Supeng Leng, Tianhao Liang et al. (+1 more)
The development of Artificial Intelligence (AI) has enabled agentic
robots an appealing paradigm for various applications, such as research and rescue in complex environment. In this context, the next wireless communication technology facilitates
robot cooperation for efficient environment sensing and exploration. However, traditional AI solutions cannot always provide reasonable resource utilization decisions, which makes it challenging to achieve both accurate and low-latency research and rescue. To address this issue, we propose a, LSAI, a large small AI model codesign framework to achieve highly accurate and real-time
robot cooperation with deep interaction between large AI model and small AI model. We first propose an
attention-based model aggregation for LAI construction. It can assist agentic
robots in accurately sensing physical environments. Next, we design an adaptive model splitting and update
algorithm to enable the
robots to perform accurate path planning for high-efficiency environment sensing with low energy consumption. Finally, we demonstrate the effectiveness of our proposed LSAI framework. The simulation results indicate that our solution achieves sensing accuracy of up to 20.4% while reducing sensing cooperation latency by an average of 17.9% compared to traditional AI solutions.
Read the full abstract ▾
All authors Longyu Zhou, Supeng Leng, Tianhao Liang, Jianping Yao
Signal Processing
Frequency-Guided Deformable Networks for Continuous Phase Alignment
Wangye Jiang, Haoming Yang, Jian Xu et al. (+1 more)
The core of time series analysis lies in effectively modeling the physical laws within complex signals. Existing
Transformer and Convolution Neural Network (CNN) architectures are often constrained by insufficient temporal inductive bias, restricted frequency extraction capabilities, or weak local phase
alignment. To this end, this paper proposes Adaptive Network Based on Cascaded Harmonic Offset Routing (ANCHOR), an Adaptive Network based on Cascaded Harmonic Offset Routing. The model utilizes the Real Fast
Fourier Transform (RFFT) to extract explicit dominant periods, injecting them as physical anchors into the dilation operators of multi-branch deformable convolutions. This guides the adaptive optimization of sampling locations in the time domain, achieving synergistic modeling of macroscopic periodic priors and microscopic
geometric deformations. Furthermore, to address the quantization errors and picket-fence effects introduced by the discrete RFFT, this paper imports a continuously differentiable 1D Gaussian Radial Basis Function interpolation operator to replace traditional linear interpolation. This maintains the differentiability of the interpolation process and enhances the accuracy of sub-pixel phase compensation. Additionally, ANCHOR introduces an asymmetric routing mechanism and orthogonal channel partitioning to dynamically balance the extraction weights between high-energy strong signals and low-energy weak features. Multi-task
benchmark experiments demonstrate that ANCHOR achieves the best or solid performance in short-term
forecasting, anomaly detection, and time series classification tasks. Code is available at https://github.com/Jwy-EE/Anchor_pub
Read the full abstract ▾
All authors Wangye Jiang, Haoming Yang, Jian Xu, Jingya Zhang
Systems and Control
Simple Trajectory Smoothing for UAV Reference Path Planning Based on Decoupling, Spatial Modeling and Linear Programming
Mogens Plessen
A method for trajectory smoothing for
UAV reference path planning is presented. It is derived based on the dynamics of a Dubins airplane model, and involves a decoupling step, spatial modeling and linear programming. The decoupling step enables algebraic control laws for flight-path angle and speed control. Only for roll angle control an optimization step is applied, involving the solution of a small linear program. Two variations are discussed. They differ by reference centerline tracking and the introduction of a path shaping constraint. The benefit of natural dimensionality reduction for spatial modeling is discussed. The simplicity of the overall method is highlighted. An extension to aerobatic flight is outlined, which comes at the cost of a model approximation, however at the gain of maintaining the general model structure. An extension of the method to tractor path planning along 3D terrain is discussed. The method is validated in simulations.
Read the full abstract ▾