Image and Video Processing
BAT-RM: A Boundary-Aware Transformer with Region-Aware Multi-Directional Mamba for Clinically Deployed Cervical Cancer Radiotherapy Auto-Contouring
Istiak Ahmed, Kazi Shahriar Sanjid, Galib Ahmed et al. (+16 more)
We present a clinically deployed end-to-end auto-contouring system for cervical cancer radiotherapy planning, anchored by the Boundary-Aware
Transformer with Region-Aware Mamba (BAT-RM), a hybrid architecture that integrates Sobel-gated boundary
attention, a linear-time, multi-directional Mamba module for long-range context, and a boundary-skeleton-guided fusion gate. This design achieves linear-time complexity for long-range context modeling, avoiding the quadratic cost of full spatial self-
attention. The full pipeline spans multi-institutional data collection, rigorous inter-rater quality assurance, external validation in an independent cohort, and a web-based clinical interface natively compatible with Varian, RayStation, and Monaco. Against four baselines, BAT-RM achieves superior performance across seven anatomical classes, with statistically significant improvements in target volumes, including GTV and CTV, and in organs at risk such as the rectum and bladder. A prospective multi-center reader study involving 13 radiation oncologists demonstrated that AI assistance elevates junior oncologists' IoU from 0.899 to 0.965, approaching senior-level accuracy, while reducing contouring time by more than 80%. The system also reduced expert consultation rates and improved inter-reader consistency, reflecting gains in both efficiency and quality assurance. Following clinical deployment at a partner hospital, the system reduced patient wait times from days to hours without additional staffing, enabling same-day or next-day initiation of treatment for routine cases. BAT-RM demonstrates that a rigorous research pipeline, from data curation to clinical deployment, can translate directly into measurable patient benefit in resource-constrained settings where the demand for radiotherapy far exceeds specialist capacity.
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All authors Istiak Ahmed, Kazi Shahriar Sanjid, Galib Ahmed, Md. Tanzim Hossain, Md. Anwarul Islam, Shahrukh Khan, Md. Ashrif Rahman Arian, Md. Nishan Khan, Md. Misbah Khan, S M Hasibul Hoque, Rahnuma Shahrin Rista, Md. Jobairul Islam, Sheikh Anisul Haque, Md Arifur Rahman, Syed Md. Akram Hussain, Syeda Nashra, Sayeed Shafayet Chowdhury, Md. Mostafa Kamal Sarker, M. Monir Uddin
Audio and Speech Processing · Cross-listed from cs.CL
Hybrid Continual Learning for Low-Resource Australian Aboriginal Language Identification
Pravina Mylvaganam, Ting Dang, Eliathamby Ambikairajah et al. (+2 more)
Language identification is an important step toward integrating endangered Australian Aboriginal
languages (AALs) into
speech technologies supporting
language revitalisation and digital inclusion. However, extreme data scarcity limits model performance. Transfer learning from high-resource
languages shows promise but often suffers from catastrophic forgetting when adapting to new
languages. Continual learning (CL) can mitigate this issue, though it remains challenging with very limited data. To address this, we propose two hybrid continual learning methods: Replay Augmented Elastic Weight Consolidation and Constraint Guided Knowledge
Distillation to adapt pretrained
speech models for AAL identification while preserving previously learned knowledge. Experiments on Warlpiri, Dalabon and Dharawal show that the proposed methods outperform fine-tuning and existing CL baselines, improving adaptation to multiple AALs while maintaining performance on previously learnt high-resource
languages.
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All authors Pravina Mylvaganam, Ting Dang, Eliathamby Ambikairajah, Vidhyasaharan Sethu, Jingyao Wu
Signal Processing · Cross-listed from cs.CR
Firewall3D: A Hardware Firewall for Defending 3D Printers Against Firmware Attacks
Seyed Ali Ghazi Asgar and Narasimha Reddy
As the 3D printing
market continues to grow rapidly, with an estimated value exceeding $30 billion, cybersecurity risks and attacks targeting additive manufacturing systems are also increasing. These attacks aim to sabotage printed components, steal intellectual property, or even physically damage the 3D printer itself. One major cybersecurity threat in this domain is firmware level attacks, which can be introduced through supply chain compromises, malicious firmware updates, or insider threats that deploy modified firmware to manipulate printer behavior. To defend against such threats, we propose a dedicated hardware based security solution,Firewall3D, that acts as a hardware firewall for 3D printers. Firewall3D continuously monitors physical layer signals, including stepper motor currents, end stop switches, nozzle and bed temperatures and cooling fans, to verify that the printer's physical behavior matches the intended G-code execution. Our experimental results demonstrate that Firewall3D can effectively detect a wide range of firmware attacks that could compromise print integrity, damage printer components, or leak intellectual property. Upon detecting abnormal behavior, the system can immediately trigger an alarm and halt the printing process, thereby preventing further damage and risks.
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Systems and Control
Tulip-Shaped Orbits for Lunar South-Pole PNT and Direct-to-Earth Relay Missions
Darin C. Koblick and Michael Casey
This
geometric study evaluates a compact seven-petal, 6:5-resonant tulip-shaped orbit constellation for lunar south-pole positioning,
navigation, and timing (PNT) and direct-to-Earth relay services. The tulip-shaped orbits are compared against elliptical lunar frozen orbit (ELFO) constellations over the NASA LunaNet Service Volume 2 (SV2), covering lunar latitudes south of -75 degrees. We compare a six-satellite tulip baseline with a minimum-cost five-satellite variant; both use the same shared three-body orbit and differ only in satellite count and along-track phasing. Performance is scored against three Initial Operating Capability C (IOC-C) metrics: line-of-sight (LOS) link availability, Lunar Augmented
Navigation System (LANS)
geometric dilution of precision (GDOP) below 6, and daily extravehicular activity (EVA) usable-PNT windows. Both tulip constellations satisfy all three IOC-C metrics across SV2. The six-satellite configuration meets requirements with wide margin: 75% worst-point daily GDOP availability and 18 hours of daily EVA support. The five-satellite variant also passes, but with thinner margin: 44% availability and 10 hours of EVA support. Unlike the ELFO configurations, each spacecraft in the tulip-shaped constellation maintains continuous Earth line of sight, providing persistent
geometric opportunity for direct single-hop Earth relay. Because all spacecraft share a single three-body orbit, initial phasing and post-failure reconstitution reduce to along-track drift maneuvers between neighboring orbits, with screening-level estimates indicating low maneuver cost.
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Image and Video Processing
Differentiable Proxy Learning for Adaptive Quantization Control in H.264 Video Coding
Qihan Xu and Ivan V. Bajić
H.264 has been the most widely used
video coding format for the past two decades due to its relative simplicity, efficiency, and wide availability of software and hardware implementations. However, optimizing codec parameters such as the quantization parameter (QP) for specific objectives (e.g., perceptual quality or machine
vision tasks) is challenging due to the non-differentiable nature of standard
video codecs. While differentiable proxies have recently been used to enable gradient-based optimization around standard codecs, their fidelity to the target codec is rarely explicitly characterized. In this paper, we propose a differentiable proxy learning method for H.264 intra codec to enable adaptive quantization control. Built upon a variable-rate learned compression model, the proposed proxy is made differentiable with respect to codec QP through a soft-indexing mechanism. It is then trained to approximate the rate-distortion behavior of H.264 under two quantization settings: global-QP, which uses one QP per image, and spatial-QP, which assigns QPs at the macroblock level. Using the frozen trained proxy, we develop a proxy-based adaptive quantization (AQ) framework for both perceptual optimization and machine
vision tasks. Experimental results demonstrate that the proposed proxies closely approximate the rate-distortion behavior of H.264 intra codec. The resulting proxy-based AQ framework consistently improves rate-task trade-offs over fixed-QP H.264 baselines, achieving BD-rate reduction of up to 17.12% for semantic
segmentation and 15.30% for MS-SSIM.
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Systems and Control
Fast Data-Driven Modeling of Hydraulic Clutch Control Pressure with Latch-State Classification and Gaussian Process Regression
Yash Bagla and Jason Schneider
This paper presents a data-driven method for modeling the pressure response of a hydraulic clutch control
circuit. The system consists of a variable-force solenoid, accumulator, pressure regulator valve, and latch valve, and exhibits nonlinear behavior caused by hysteresis, latch transitions, and actuator dynamics. A baseline model using commanded current variables captured the general pressure response but failed to represent hysteresis and latch behavior accurately. The input vector was therefore extended with current derivative information, and several classifiers were tested to separate latch-related operating regimes before fitting Gaussian Process regression models to the resulting partitions. Nonlinear SVC and gradient boosting produced the highest latch-classification accuracy, and nonlinear SVC was selected for the final local-regression pipeline. The proposed approach was evaluated on unseen ramp-rate data and compared against a physics-based Amesim model. The machine-learning model reproduced the measured pressure response and hysteresis behavior more accurately than the physics-based simulation for the tested operating conditions. These results suggest that machine-learning plant models can complement physics-based hydraulic models during hardware development and controller calibration when representative test-stand data are available.
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Image and Video Processing · Cross-listed from cs.CV
On the Real-World Generalisability of Optical Flow Models
Petter Reijalt, Sander Gielisse, Rickard Karlsson et al. (+1 more)
Real-world deployment of
vision models to broadly benefit society is arguably a main research objective. In optical flow, however, the difficulty to obtain the ground truth has focused research mainly on synthetic data and domain-specific
benchmarks. Here, we investigate the severity of this mismatch. We study how well modern optical flow estimation models generalise to real-world
video and question if accuracy on synthetic
benchmark proxies actually predicts accuracy on real-world optical flow. To address this, we build a real-world evaluation
benchmark and evaluate the real-world generalisability of a broad set of recent optical flow models using standard checkpoints. Our
benchmark contains 8,204 frame pairs across TAP-Flow, Slow Flow, and our own
dataset FlowFactor. FlowFactor is a manually annotated real-world
benchmark of 1,000 HD frame pairs organised into four confounding factors: large displacements, repetitive textures, occlusions, and lighting variation. Each setting mainly varies only one factor, enabling diagnostic, confounder-specific analysis. Using FlowFactor, we reveal that performance on varying lighting and large displacements correlates most strongly with real-world accuracy, and that improvements on large-motion regimes can trade off against robustness in small-motion, stationary scenes. Our experiments show that progress on Sintel, KITTI and Spring only weakly predicts accuracy on real-world data, highlighting the need for a broad real-world optical flow
benchmark. Interestingly, scaling up the amount of training data does not necessarily resolve the gap, calling for new innovative research instead of simply scaling data and compute.
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All authors Petter Reijalt, Sander Gielisse, Rickard Karlsson, Jan van Gemert
Systems and Control
Tracking Through Decoupling Singularities: A Singularity-Robust Homotopy-Continuation Extension of Feedback Linearization
Alex Borisevich
Input–output feedback linearization fails at decoupling singularities, where the decoupling
matrix loses rank, the relative degree is lost, and the linearizing control becomes unbounded. This paper develops a singularity-robust trajectory-tracking controller for square nonlinear control-affine systems that tracks through isolated decoupling singularities with bounded control. The method recasts tracking as real-time arc-length homotopy continuation, equivalently a continuous-time Newton/Davidenko flow, and replaces the inverse decoupling
matrix by the least-norm Moore–Penrose solution of an augmented
matrix $A=[\Lambda\mid b]$, where $b$ is the homotopy direction. A transversality condition $w^T b \ne 0$, with $w$ in the left null space of the decoupling
matrix, keeps the augmented
matrix full row rank through a generic rank-one loss. The resulting flow agrees with feedback linearization away from the singular set, tracks with $O(1/k)$ error, and re-locks after each crossing. The theory also characterizes the reflection-versus-branch-crossing dichotomy at Whitney folds and relates the reflection case to a Filippov sliding mode. Extensions cover dynamic relative-degree-one minimum-phase systems and arbitrary relative degree via filtered-error reduction. The core results are formalized and machine-checked in the Lean proof assistant, including the multi-parameter (higher-corank) repair criterion, the uniqueness of the sliding motion at the fold, and the machine-checked treatment of Filippov solutions. Simulations include a redundant 2-DOF manipulator, relative-degree-one and relative-degree-two plants, and a dual-active-bridge series-resonant DC/DC converter, where the method performs bounded inversion across buck/boost and resonance singularities while preserving zero-voltage soft switching.
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Audio and Speech Processing
FdAudio: MeanFlow-Anchored Fréchet-Distance Post-Training for One-Step Text-to-Audio Generation
Kuan-Po Huang, Bo-Ru Lu, Ho-Lam Chung et al. (+2 more)
While recent few-step sampling text-to-audio generation models like MeanAudio substantially accelerate generation by modeling average velocities, their strict one-step generation quality still lags significantly behind multi-step counterparts. We propose FdAudio to bridge this gap. Unlike MeanAudio, which relies solely on regression against target velocity fields, our post-training approach optimizes the final one-step distribution directly across pre-trained
embedding spaces via a multi-representation Fréchet-distance (FD) loss. Crucially, to prevent the multi-step degradation that naive post-training with FD-loss causes, we introduce a MeanFlow consistency objective as a structural anchor. Results demonstrate that FdAudio establishes state-of-the-art one-step T2A generation quality among few-step systems, yielding an 11.4% reduction in FD score and a 28.8% improvement in FAD score relative to the baseline MeanAudio framework. Notably, we solve FD post-training's naive multi-step degradation issue by proposing the MeanFlow anchor, enabling a 25-step sampling path to maintain high-fidelity audio synthesis that matches or surpasses strong multi-step models at a fraction of their computational latency.
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All authors Kuan-Po Huang, Bo-Ru Lu, Ho-Lam Chung, Shih-Hsin Wang, Hung-yi Lee
Image and Video Processing · Cross-listed from cs.CV
Vertical Fusion: Condensing Internal Representations for Robust ViT Classification
Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi et al. (+4 more)
Despite exposing rich intermediate representations,
Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks. We challenge this convention by introducing the notion of recoverability: the capacity of intermediate representations to correct last-layer failures. By evaluating independent classification probes at every model depth across 16
datasets, we observe that intermediate probes correctly classify 18% to 76% of samples that the last-layer probe misclassifies. We show that these gains are not primarily driven by predictive diversity, but by a redundancy-correctness correspondence, where the internal hierarchy acts as a series of stable, redundant probes of a shared discriminative signal. While established horizontal ensemble strategies (i.e., across multiple models) can improve performance, they incur high computational cost and ignore this vertical signal within a single model. To bridge this gap, we propose VFusion, a principled vertical aggregation strategy employing a learnable mapping into a low-dimensional latent space that synthesizes features across the internal ViT hierarchy. VFusion substantially outperforms established aggregation baselines in both in-distribution and out-of-distribution settings, notably closing 45% of the accuracy gap between the best individual layer and a theoretical oracle performance. Our gains consistently generalize across model sizes and pre-training regimes, confirming that VFusion offers a robust and efficient alternative to horizontal ensemble methods. The code is available at https://github.com/francescodisalvo05/vit-vertical-fusion.
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All authors Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi, Ignacio Meza De la Jara, Sebastian Doerrich, Marco Lents, Christian Ledig
Audio and Speech Processing
GigaChat Audio: Time-aware Large Audio Language Model
Aleksandr Kutsakov, Mariia Sadovina, Georgii Gospodinov et al. (+4 more)
Temporal grounding in long recordings remains challenging for audio-conditioned LLMs. We present a time-aware audio LLM that answers questions with explicit timestamps over up to 120 minutes of input. Our approach interleaves periodic time markers with continuous audio
tokens using large-scale synthetic supervision from a cascaded pipeline. Our model achieves strong temporal-grounding accuracy on short and long
benchmarks and supports time-anchored fragment descriptions and summaries. Extensive ablations examine how time representation, marker frequency, tokenization, and duration-mixture design affect accuracy and computational cost. We release model weights and
datasets to support further research on time-aware audio understanding, available at https://huggingface.co/ai-sage/GigaChat3.1-Audio-10B-A1.8B.
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All authors Aleksandr Kutsakov, Mariia Sadovina, Georgii Gospodinov, Alexandr Maximenko, Oleg Kutuzov, Pavel Bogomolov, Fyodor Minkin
Audio and Speech Processing
GigaAM Multilingual: Foundation Model for Underrepresented Languages
Andrei Kuzmenko, Alexandr Maximenko, Aleksandr Kutsakov et al. (+5 more)
Despite recent scaling successes,
multilingual ASR performance remains highly uneven, with long-tail
languages suffering from severe data scarcity. This work addresses the challenge of building robust foundation models for underrepresented Central Asian
languages (Kazakh, Kyrgyz, Uzbek). We present GigaAM
Multilingual, a Conformer encoder pre-trained on 2M hours of audio using a HuBERT-style objective. Crucially, we introduce a cluster-level data balancing strategy during pre-training and a domain-aware sampling method during fine-tuning to mitigate head-
language dominance. In controlled comparisons, our approach outperforms strong open pretrained encoders (Whisper Large v3, Omnilingual-1B) on target
languages, achieving significant gains on spontaneous
speech while maintaining efficiency. We release the foundation encoder and ASR model, offering a proven recipe for effective
multilingual adaptation under realistic data imbalance.
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All authors Andrei Kuzmenko, Alexandr Maximenko, Aleksandr Kutsakov, Georgii Gospodinov, Dmitrii Bolotov, Oleg Kutuzov, Pavel Bogomolov, Fyodor Minkin
Audio and Speech Processing
Perceived Annoyance in Multi-source Electric Vehicle AVAS Environments
Berkay Kullukcu, Jonas Krautwurm, Serkan Atamer et al. (+1 more)
The increasing usage of electric vehicles in urban environments has resulted in a widespread presence of AVAS sounds. While individual vehicle sound design and testing is a common approach, real-world traffic scenarios often involve the simultaneous presence of multiple vehicles. Their combined presence may lead to changes in perception, compared to when they are presented individually, specifically regarding annoyance. The work addresses annoyance perception in scenarios involving multiple electric vehicle AVAS sounds. It changes the traditional isolated source-based view into a scene-based one by investigating the combined presence of multiple vehicle sounds as they are experienced in realistic traffic environments. Binaural listening tests were conducted using recorded electric vehicle pass-by sounds. The stimuli presented different traffic scenarios, including single and multiple-vehicles. Selected stimuli were spatially arranged to simulate vehicles approaching from opposite directions. After each stimulus, participants rated their perceived annoyance, enabling a comparison of annoyance responses between isolated and multiple AVAS sound scenarios. The test investigated how different levels of overlapping AVAS sounds affect perceived annoyance when multiple electric vehicles are passing by.
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All authors Berkay Kullukcu, Jonas Krautwurm, Serkan Atamer, Ercan Altinsoy
Signal Processing
Non-Reciprocal Dynamic Metasurface Antenna: Practical Multiport-Network Modeling and Optimization for Multi-User Interference Resilience
Shuai S. A. Yuan, Jean Tapie, Bahman Amrahi et al. (+2 more)
Channel reciprocity fundamentally limits full-duplex (FD) base stations due to multi-user co-channel interference. We examine the potential of deploying a non-reciprocal dynamic metasurface
antenna (NR-DMA) at the base station to overcome this limitation. Our NR-DMA architecture connects a single circulator to three feed ports of a multi-feed DMA with strong mutual coupling (MC) between its seven feeds and 96 1-bit-programmable meta-elements. We model our system with multiport network theory, using experimentally estimated proxy parameters of a fabricated 19-GHz DMA and the measured circulator response. Our NR-DMA's reconfigurability is captured by a diagonal tunable scattering
matrix, showing that non-reciprocal DMAs and RISs need not require a "beyond-diagonal" tunable scattering
matrix. We jointly optimize the DMA state, analog feed weights, circulator-port assignment, and circulation direction. Our optimized NR-DMA realizes distinct forward and reverse channel responses. In our interference-limited high-SNR case study, the NR-DMA improves the FD sum rate by about 60% over a reciprocal DMA
benchmark. Comparisons with proxy objectives and MC-unaware optimization show that end-to-end FD optimization and MC-aware modeling are both essential.
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All authors Shuai S. A. Yuan, Jean Tapie, Bahman Amrahi, Viktar Asadchy, Philipp del Hougne
Systems and Control
Geometric Decentralized Stability Certificate of Power Electronics-Dominated Power Systems Covering Variable Operating Points
Ruohan Leng, Linbin Huang, Liangxiao Luo et al. (+3 more)
The integration of power converters is profoundly changing the power system dynamics and poses significant challenges for stability analysis. The dynamic interactions between the power grid and the heterogeneous converters are highly complex and difficult to analyze due to the curse of dimensionality. Moreover, system stability varies with the operating points, which are determined by the voltage magnitude, active power, and reactive power of each converter. This further complicates the analysis as it is difficult to enumerate and examine all the possible operating points. To tackle these challenges, this paper proposes a
geometric decentralized stability certificate for power electronics (PE)-dominated power systems, which can simultaneously handle heterogeneous power converters and their variable operating points. The certificate can be checked in a decentralized and modular manner, and it is scalable for large-scale power systems. Our approach is developed based on the concept of Davis-Wielandt (DW) shell and its projections, which can effectively visualize the characteristics of high-dimensional complex
matrices. We investigate how the projections of the DW shell vary with operating points and how this variation can guide the search for worst-case operating conditions. We further propose an efficient
algorithm to compute the stability margin and construct the certified operating regions. The effectiveness of the proposed method is validated through case studies on single-converter and 54-converter wind power systems.
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All authors Ruohan Leng, Linbin Huang, Liangxiao Luo, Huanhai Xin, Xiongfei Wang, Florian Dörfler
Signal Processing
Networked ISAC Enabled Target Recognition Towards Low-Altitude Economy
Hongliang Luo, Chuanbin Zhao, Boxuan Sun et al. (+4 more)
In this paper, we propose a low-altitude target (LAT) recognition scheme based on multi-base station (BS) collaboration and multi-scale feature fusion for integrated sensing and communications (
ISAC) network. Firstly, we formulate the motion equations, echo channels, and echo signals for unmanned aerial vehicle (
UAV), bird, vehicle, and pedestrian under multi-BS collaborative monitoring scenario. Then we extract the velocityresolution-preferred time-frequency spectrum, time-resolutionpreferred time-frequency spectrum, and the velocity-transfer time-frequency spectrum observed by each BS from echo signals. We collectively refer to these three types of time-frequency spectrum as the multi-scale feature of the LAT. Next, we design a multi-BS and multi-scale feature fusion enabled LAT recognition network with Swin
Transformer, which employs the visualized images of multi-scale feature to jointly recognize the target through deep feature extraction, intra-BS feature interaction, inter-BS feature interaction, and target recognition output. We generate a massive echo signal
dataset comprising 1,440,000 samples for LAT recognition within
ISAC network. This
dataset can serve as a public
benchmark to evaluate our proposed scheme and facilitate future research. Simulation results demonstrate that the proposed scheme realizes high recognition accuracy and robust unseen-subtype generalization, confirming the effectiveness of multi-scale feature fusion and the additional gains brought by multi-BS collaboration. The project page is available at: https://alivn999.github.io/COSMOS-Networked-
ISAC-Enabl ed-Target-Recognition-Towards-Low-Altitude-Economy/.
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All authors Hongliang Luo, Chuanbin Zhao, Boxuan Sun, Zhonghua Chu, Shengjie Quan, Guangyi Liu, Feifei Gao
Signal Processing
Rotating ULA-Enabled Computed Tomography for Efficient 3D Spatial Power Spectrum Synthesis: Architecture and Principled Orientation Design
Haocheng Hua, Weidong Mei, Jie Xu et al. (+1 more)
This paper proposes an efficient three-dimensional (3D) spatial power spectrum synthesis method by rotating a uniform linear array (ULA) about its center in 3D space. Inspired by classical computed
tomography (CT), the ULA performs analog receive combining at each rotation angle to produce a partial coherent sum. By collecting such sums over multiple rotations, the full 3D spectrum can be synthesized online via a single
radio-frequency (RF) chain, without explicitly acquiring per-
antenna signals. Depending on whether the overall coherent sum is accessible, the synthesis is obtained through either a minimum operation over partial spectrum images or joint synthesis after accumulating all coherent sums. Compared with fixed uniform planar array (UPA)-based combining and dense single movable-
antenna (MA) sampling for 3D cubic virtual arrays, the proposed scheme achieves full-space 3D coverage with substantially reduced sampling and movement overhead while maintaining uniformly high angular resolution. Its sampling geometry and sequential orientation design also support pipelined analog beamforming, reducing practical hardware cost. To design rotation orientations in a principled manner without prior environmental information, we aim to maximize the expected worst-case projected separation between multi-path component (MPC) pairs. A secondary criterion then minimizes the worst-case projective correlation among orientation axes to reduce orientation redundancy. Accordingly, we optimize orientation sets for different numbers of orientations under isotropic-
matrix and unit-norm constraints, using a multistart smooth minimax
algorithm. Numerical results show that the optimized orientations uniformly span 3D space and reconstruct full-space 3D spectra close to the dense 3D cubic reference using only a fraction of spatial samples.
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All authors Haocheng Hua, Weidong Mei, Jie Xu, Rui Zhang
Signal Processing
Tremerity-Fi: Non-Contact Daily-Life Tremor Severity Assessment by Commercial mmWave Radar
Xiao Li, Jingyang Hu, Shang Gao et al. (+4 more)
Tremor is a common symptom of neurological diseases. The regular assessment of daily tremors facilitates the evaluation of disease progression and assists clinicians in optimizing treatment strategies. However, current home monitoring solutions have difficulty in dealing with user cooperation,
privacy concerns, environmental interference, and system generalization, leading to feasibility concerns in activities of daily living (ADL). To this end, we propose Tremerity-Fi, a non-contact and
privacy-friendly tremor severity assessment system based on mmWave radar. To realize Tremerity-Fi, we first design an adaptive beamforming
algorithm to accurately identify useful but weak signals from numerous reflections captured in the environment. Second, unlike primary reflections commonly used in mmWave sensing, we leverage multipath reflections that carry useful information about the target's motion, even though they are generally considered harmful, to help reconstruct hand signals and improve sensing performance. Furthermore, we propose an unsupervised domain adaptation
algorithm to improve the ability to adapt to unseen environments and users. We collect a diverse
dataset of 5 patients and 25 healthy subjects in 3 scenarios, such as offices, homes, and hospitals. Extensive experiments show that our system achieves 94.51% accuracy in tremor detection, about 5 higher than the SOTA mmWave radar method, and 89.13% in tremor severity assessment, demonstrating its sufficient potential as a tremor monitoring assistant for patients with neurological diseases.
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All authors Xiao Li, Jingyang Hu, Shang Gao, Yichao Gao, Yiyu Xin, Hongbo Jiang, Xiang-Yang Li
Audio and Speech Processing · Cross-listed from cs.CL
Which Languages Transfer Best to Warlpiri? A Similarity-Based Study for Low-Resource ASR
Pravina Mylvaganam, Eliathamby Ambikairajah, Ting Dang et al. (+2 more)
This paper investigates how
language similarity can improve cross-lingual transfer for automatic
speech recognition (ASR) in extremely low-resource settings. Warlpiri, an Australian Aboriginal
language, has very limited transcribed
speech data, making transfer learning essential. We propose a framework combining acoustic similarity from pre-trained
speech models with linguistic similarity based on typology, phoneme inventories, grammatical, and syntactic features to rank high-resource source
languages and evaluate their effectiveness for ASR transfer to Warlpiri. Experiments with Whisper show that acoustically and typologically similar
languages outperform monolingual and
multilingual baselines. Assamese and Hindi achieve substantial reductions in word and character error rates. Correlation analysis further indicates that acoustic similarity is the strongest predictor of fine-tuning performance, while phoneme inventory and typological similarity better explain zero-shot transfer.
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All authors Pravina Mylvaganam, Eliathamby Ambikairajah, Ting Dang, Vidhyasaharan Sethu, Tuende Szalay
Systems and Control · Cross-listed from cs.RO
Diffusion-Residual Model Predictive Steering Control for Vehicle Stabilization at the Limit of Handling under Model Uncertainty
Bongsob Song
At the limit of handling, a stabilizing MPC depends on the yaw-rate reference it tracks and the stable-handling envelope it enforces, both operating-point-dependent and unknown a priori, so fixed or worst-case settings are either too conservative or unsafe. We learn this uncertainty with a conditional
diffusion residual model and apply it to the controller's reference and constraints rather than its control law. Conditioned on the steering command, the model returns the residual's mean and a predictive spread: the mean re-sizes the tracked yaw reference, while the spread, propagated over the prediction horizon, tightens the stable-handling envelope through a one-sided chance back-off. Together these form the proposed
diffusion-residual MPC (D-res), so caution is anticipated ahead of the tracking error rather than corrected after it by a high-gain loop. Because only two moments per command are needed, the generator is tabulated offline and the online controller adds a single table lookup to the baseline MPC, with no in-loop
diffusion; it runs within the 100 Hz budget on an NVIDIA Jetson AGX Xavier (worst-case 4.08 ms per step). Across a 7-DOF model and high-fidelity CarMaker co-simulation spanning vehicle, tire, road, and maneuver diversity, D-res reduces peak side-slip where the fixed bicycle model is least accurate and restores directional stability on low-friction maneuvers, where the fixed reference over-commands the available grip.
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Signal Processing
Dual-Satellite Doppler Accuracy Prediction and Geometry Selection for Sparse LEO Signals of Opportunity
Qi Liu, Marc Fernández-Temprado, Antoni Reus-Bergas et al. (+4 more)
Low Earth Orbit (LEO) satellites have emerged as a promising complement to GNSS for positioning in signal challenged environments. In sparse LEO signals of opportunity scenarios, Doppler positioning often relies on only one or two satellite passes, making positioning accuracy highly dependent on pass geometry. This paper investigates dual satellite LEO Doppler accuracy prediction and geometry selection. A single pass Doppler accuracy model based on the Doppler Dilution of Precision (DDOP) framework is first validated using real Iridium measurements. An information domain fusion model is then developed to combine the effective position information from two satellite passes while accounting for pass specific clock parameters. Based on this model, an analytical relationship between the intersection angle of the two predicted error ellipses and the fused positioning accuracy is derived and verified through simulations. Long term ORBCOMM observations are further used to evaluate the practical availability of favorable satellite pairs. Results show that an intersection angle of about 20{\deg} is sufficient to achieve approximately 50 m theoretical positioning accuracy, and that such complementary satellite pairs are typically available within about 30 min. These results provide practical guidance for geometry aware satellite selection and observation scheduling in sparse LEO Doppler positioning.
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All authors Qi Liu, Marc Fernández-Temprado, Antoni Reus-Bergas, Shuguo Pan, Wang Gao, Gonzalo Seco-Granados, José A. López-Salcedo
Signal Processing
Low-Altitude ISAC With Spherical Directly-Connected Antenna Array: Performance Analysis and Beamforming Optimization
Zhiqiang Xiao, Tao Zhang, Zhenjun Dong et al. (+3 more)
The safety development requirements of low-altitude economy (LAE) renders the robust low-altitude airspace monitoring critical important than ever before. Integrated sensing and communication (
ISAC) as one of the key development trends of 6G provides potential solutions for the LAE. However, conventional
antenna arrays suffer from limited three-dimensional (3D) sensing coverage and degraded angular resolution at high elevation angles. To address these challenges, this paper investigates low-altitude
ISAC systems enabled by the recently proposed spherical directly-connected
antenna array (DCAA). By carefully deploying multiple simple uniform planar arrays (sUPAs) over a spherical surface, without relying on any phase shifter, spherical DCAA enjoys advantages of full 3D coverage, superior and uniform angular resolution, enhanced energy-focusing and low hardware cost. In this paper, we first characterizes the sensing performance of the spherical DCAA, in terms of the sensing signal-to-noise ratio (SNR), area average probability of detection, and Cramér-Rao lower bounds (CRLBs) for both elevation and azimuth angle estimation. Then, a low-altitude
ISAC optimization problem is formulated to maximize the worst-case sensing SNR over a prescribed aerial region while satisfying the communication quality-of-service requirements of ground users. To effective solve this mixed-integer non-convex problem, we develop a novel greed-based joint array selection and beamforming optimization framework. Simulation results demonstrate that spherical DCAA significantly outperforms conventional UPA in terms of sensing coverage, angle estimation accuracy, and communication-sensing SNR tradeoff, highlighting its potential for future low-altitude
ISAC systems.
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All authors Zhiqiang Xiao, Tao Zhang, Zhenjun Dong, Hao Wu, Xiaoqiang Qiao, Jianhua Zhang
Systems and Control
Comparing Socially-Equitable Renewable Energy Budget Allocation MDP Policies in Mature and Emerging Economies
Riya Kinnarkar, Mansur M. Arief, Yan Pratama Akhra et al. (+1 more)
Equitable renewable-energy planning is a sequential decision problem, but the decision variables available to a public planner differ sharply between mature and emerging economies. In the former the government largely builds generation, while in the latter it steers private investment through incentives and quotas. We formulate socially-equitable renewable-energy budget allocation as a Markov Decision Process (MDP) and, using a single problem-agnostic solver interface, compare the same policies across the two settings: eight U.S. cities (a mature economy) and West Java, Indonesia (an emerging economy). The results show that across both settings, a receding-horizon value-iteration policy dominates. In the U.S., it reaches 66% renewable penetration while cutting the underserved low-income population by 96% versus a random baseline. In West Java it closes the low-access gap while crowding in about seven times the private capital of a naive
market-chasing heuristic. More interestingly, this heuristic, which is mildly sub-optimal in the U.S., could yield catastrophic outcomes in Indonesia, by underserving every low-access region, because chasing attractive
markets and serving the underserved diverge once the planner acts through private developers.
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All authors Riya Kinnarkar, Mansur M. Arief, Yan Pratama Akhra, Dino Arla
Systems and Control · Cross-listed from cs.RO
From Non-Rigid to Rigid: Safe Acquisition of Rigid Communication Graphs under Limited Sensing
S. Saharsh, Vedhas Talnikar and Pushpak Jagtap
Communication graph
rigidity is a fundamental requirement in many multi
robot formation control approaches. However, ensuring and maintaining a rigid communication
topology becomes challenging in practice due to limited sensing ranges and dynamic operating conditions. This paper provides a method for achieving an inter
robot collision free, rigid time varying communication graph, where communication links are established or broken according to limited sensing ranges, without assuming an initial rigid graph. In addition, the proposed approach guarantees the realization of a rigid graph for heterogeneous nonlinear multi
robot systems. A computationally lean, distributed quadratic optimization-based controller is developed for a leader follower architecture, acquiring
rigidity based on hierarchical second-order consensus among
robots. Follower
agents do not require global absolute positions of any
agent, including their own. The proposed method is validated through both simulations and hardware experiments in a motion-capture environment, demonstrating reliable performance under the limited sensing capabilities of individual
robots.
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Audio and Speech Processing
Hearing Like Humans? Sound Symbolism and Perceptual Alignment in Speech Language Models
Yun-Shao Tsai, Chun-Wei Chen, Chee-En Yu et al. (+2 more)
Sound symbolism, the human tendency to map
speech sounds to perceptual qualities such as roundness or sharpness, arises primarily from the acoustics of
speech rather than spelling. Whether
Speech Language Models (SLMs) share this tendency remains open, as prior evaluations rely on text or images rather than real
speech. We study it using genuine human
speech recordings, comparing model judgments against human data across the auditory, crossmodal, and visual components of the effect. We find that SLMs' auditory judgments align poorly with human perception and miss the acoustic cues, such as spectral tilt, that drive human intuitions, and open-weight models cannot reliably link a heard sound to its corresponding shape. With a visual-only control ruling out shape perception, the weakness localizes to how
speech is represented, suggesting that perceptual
alignment depends not on stronger
vision but on
speech representations that capture the cues humans hear.
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All authors Yun-Shao Tsai, Chun-Wei Chen, Chee-En Yu, Yi-Cheng Lin, Hung-yi Lee
Audio and Speech Processing
Evaluating SSL and ViViT Architectures for Cross-Corpus Audio MOS Prediction via LODO Validation
Mustafa Ozan Duman and Ahmet Emir Dirik
Automatic Mean Opinion Score (MOS) prediction is essential for evaluating large-scale synthetic
speech and audio enhancement systems, yet models frequently struggle with domain shift. This study presents a comprehensive benchmarking of three architectural frameworks: Frozen Self-Supervised Learning (SSL-FRZ), Fine-Tuned SSL (SSL-FT), and a
Video Vision Transformer (ViViT). Evaluation is conducted in two phases: Part I utilizes a consolidated corpus of 130,000 samples across 19 diverse
datasets, while Part II focuses on a purified 17-
dataset English-only corpus. To assess robustness, a systematic Leave-One-
Dataset-Out (LODO) protocol is employed to quantify the generalization gap between seen and unseen distributions. Finally, the top-performing model is benchmarked against 18 state-of-the-art (SOTA) metrics using the ARECHO framework. Results demonstrate that an English-only purified corpus consistently yields higher predictive precision across all architectures. While SSL-FT achieves the highest performance on seen validation data, the SSL-FRZ model provides superior robustness on unseen distributions, achieving a competitive Mean Squared Error (MSE) of 0.36 on the URGENT 2024
benchmark-closely matching domain-optimized SOTA metrics (MSE 0.30). Although the ViViT architecture remains below SSL-based models in total capacity, it delivers stable results in English-only trials. LODO results confirm that while models perform significantly better on seen samples, frozen SSL
embeddings combined with deep
Transformer encoders offer the most stable and scalable solution for universal
speech quality assessment. To support further research, the top-performing English-only SSL-
Transformer model and weights are made publicly available via Hugging Face.
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Audio and Speech Processing
CoFi-Lite: Pushing the Limits of Ultra-Lightweight Speech Enhancement
Leyan Yang, Dahan Wang, Xiaobin Rong et al. (+2 more)
Ultra-lightweight models are essential for the deployment of deep learning-based
speech enhancement
algorithms on edge devices. Although recent approaches have achieved a certain balance between computational complexity and performance, pushing the complexity limits further demands more sophisticated designs. In this letter, we propose CoFi-Lite, a highly efficient model that decouples spectral modeling into coarse- and fine-grained streams. By leveraging two parallel and symmetric encoder-decoder paths, it simultaneously extracts full-band envelopes and low-frequency details for complementary enhancement. In addition, a novel Cross-Path Fusion (CPF) module is introduced to bridge the distinct paths, facilitating efficient feature interaction. Remarkably, CoFi-Lite requires extremely low computational resources, featuring only 12.87M MACs/s and 83.12k parameters. Experimental results demonstrate that our proposed model outperforms the ultra-lightweight baseline GTCRN while requiring only 40.26% of its computational complexity. Its scaled-up variant also delivers performance on par with that of the SOTA ultra-lightweight model AdaptCRN alongside a 19.34% reduction in computational cost. Audio examples are available at https://acceleration123.github.io/CoFiLite-demo/.
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All authors Leyan Yang, Dahan Wang, Xiaobin Rong, Jiadong Zhao, Jing Lu
Signal Processing
A Closed-Form Noise-Sensitivity Asymmetry for Causal Branch Selection in Minimal-Array TDoA Localization
Abeer Nasir Chaudhry, Salman Liaquat and Hasan Saeed Mir
Minimal-array time-difference-of-arrival (TDoA) localization with three planar receivers reduces to a scalar quadratic whose two roots can both be feasible target positions, leaving a branch-selection ambiguity intrinsic to the minimal geometry. Because a minimal array is the cheapest, most deployable passive configuration, this ambiguity is conventionally broken only by adding a fourth receiver or an angle sensor, sacrificing the very minimality that motivates the array. This article resolves it from the measurements alone. Implicit differentiation shows that the two roots share an identical sensitivity denominator, so classical root conditioning cannot separate them and the entire asymmetry resides in the numerator. A closed-form analysis then yields an exact sign condition for the asymmetry, and over the feasible interior the more sensitive root is the outer, physical one, away from two algebraic degeneracy loci. A causal, constant-
memory selector smooths a per-root variability statistic, whose expectation is shown to be proportional to the per-root sensitivity in the noise-dominated regime, and selects the larger one. Across a dimensionless receiver atlas the physical root is the more sensitive at a median of 85% of two-feasible-root operating points (median sensitivity ratio 16), and, because the more sensitive root is the outer one, a simple outer-root rule attains near-0.99 branch-selection accuracy on this interior, matched online by a causal, constant-
memory realization. The decisive empirical finding is that smoothness- and continuity-based disambiguation, the natural alternatives, invert under timing noise and fall below chance.
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Signal Processing
Data-Aided Target Localization in Multistatic ISAC Systems With Communication Constraints
Na Zhao, Xiao Shen, Chao Ge et al. (+2 more)
Integrated sensing and communication (
ISAC) enables future wireless networks to perform sensing and communication (S&C) over a shared waveform. In multistatic
ISAC systems, however, the sensing receivers do not know the realizations of transmitted data symbols, making it challenging to exploit communication signals for sensing. In this paper, we propose a data-aided framework for target localization with two receiver strategies, namely statistical data-aided sensing and joint data-aided sensing and decoding, where the former marginalizes the random unknown data symbols and the latter reuses the reliably decoded data symbols as known virtual pilots. Under orthogonal frequency division multiplexing (OFDM) signaling, we derive the performance limits for target localization in both strategies and adopt the achievable ergodic data rate as the communication metric. Then, we formulate a joint time-allocation and transmit data-covariance design problem for target localization under communication constraints, which characterizes the joint S&C bound and quantifies the sensing gain provided by data symbols. In addition, we develop two target localization
algorithms that implement the proposed data-aided receiver processing, and extend the framework to finite-alphabet signaling. Simulation results validate theoretical analysis and the effectiveness of the proposed data-aided schemes.
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All authors Na Zhao, Xiao Shen, Chao Ge, Ziping Lu, Yuan Shen
Systems and Control
Analytical Confidence Boundaries for Non-Gaussian Uncertainty in Perturbed Spacecraft Dynamics
Niccolò Michelotti, Ethan R. Burnett and Francesco Topputo
This work investigates nonlinear uncertainty propagation in perturbed astrodynamics, focusing on the rapid characterization of non-Gaussian distributions and the construction of three-dimensional "banana-shaped" confidence boundaries. To bridge the gap between computationally intensive high-fidelity methods and inaccurate linear approximations, this paper introduces a fully analytical, sample-free framework for higher-order moments extraction. Leveraging Differential
Algebra to bypass repeated numerical integration, statistical moments are extracted analytically via Isserlis'
theorem and a monomial-to-Hermite basis transformation. A pair-product projection strategy is exploited to overcome the severe computational bottleneck of full fourth-order tensor contractions and compute only relevant terms via efficient
polynomial algebra. The extracted skewness and kurtosis components directly parameterize non-elliptical confidence geometries that capture spatial bending and out-of-plane coupling of typical non-Gaussian distributions in astrodynamics. The approach is validated in high-fidelity environments including a cislunar Near-Rectilinear
Halo Orbit and close-proximity trajectories around Apophis during Earth's flyby, where the analytical approach achieves
geometric accuracy comparable to expensive Monte Carlo simulations while reducing computational runtime by orders of magnitude.
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