Audio and Speech Processing
ASK: Adaptive Self-improving Knowledge Framework for Audio Text Retrieval
Siyuan Fu, Xuchen Guo, Mingjun Liu et al. (+7 more)
The dominant paradigm for Audio-Text
Retrieval (ATR) relies on dual-encoder architectures optimized via mini-batch contrastive learning. However, restricting optimization to local in-batch samples creates a fundamental limitation we term the Gradient Locality Bottleneck (GLB), which prevents the resolution of acoustic ambiguities and hinders the learning of rare long-tail concepts. While external knowledge injection can break this bottleneck, it often triggers a problem called Representation-Drift Mismatch (RDM), where a static knowledge base becomes misaligned with evolving encoders, degrading guidance into noise. To address these intertwined challenges, we propose the Adaptive Self-improving Knowledge (ASK) framework. ASK breaks the GLB via multi-grained knowledge injection and mitigates RDM through a dynamic refinement strategy that synchronizes the knowledge base with the model. Additionally, an adaptive reliability weighting scheme is employed to filter
retrieval noise based on cross-modal consistency. Extensive experiments across multiple
benchmarks demonstrate that ASK consistently achieves new state-of-the-art performance across various backbones.
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All authors Siyuan Fu, Xuchen Guo, Mingjun Liu, Hongxiang Li, Boyin Tan, Gongxi Zhu, Xianwei Zhuang, Jinghan Ru, Yuxin Xie, Yuguo Yin
Signal Processing
Enhanced Information Security via Wave-Field Selectivity and Structured Wavefront Manipulation
Yufei Zhao, Deyu Lin, Qian Zhang et al. (+5 more)
In this paper, we propose a novel secure wireless transmission architecture that enables the co-existence of spatial field modulation (SFM) and digital bandpass modulation (DBM), utilizing multi-mode
vortex waves and programmable meta-surfaces (PMS). Distinct from conventional joint modulation schemes, our approach establishes two logically independent transmission channels–SFM and DBM–thereby eliminating the need for joint signal design or time synchronization. Specifically, the orthogonality of
vortex wave modes is exploited to construct a high-capacity multi-mode DBM channel, in which each mode carries modulated symbols independently. As the composite waveform passes through the PMS, energy from different
vortex modes is spatially focused onto distinct positions, dynamically determined by the PMS configuration. This spatial mapping forms a unique lookup table that encodes additional information in the electro-
magnetic (EM) field distribution, effectively enabling a second, concurrent SFM channel. To enhance physical-layer security, the DBM channel transmits encrypted symbols transformed via dynamic symbol-domain mapping, while the corresponding mapping relations–or key information–are carried by the SFM channel. This lightweight dual-channel encryption strategy provides strong confidentiality without requiring complex joint decoding. To validate the feasibility of the proposed architecture, we design and implement a proof-of-concept prototype system, and conduct experimental demonstrations under real-world wireless communication conditions. The experimental results confirm the effectiveness of the co-existent DBM-SFM design in achieving reliable and secure transmission. The proposed architecture offers a scalable, low-complexity, and secure transmission solution for future IoT networks, especially in scenarios demanding both spectral efficiency and physical-layer confidentiality.
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All authors Yufei Zhao, Deyu Lin, Qian Zhang, Haoyang Shi, Hong Niu, Afkar Mohamed Ismail, Yong Liang Guan, Chau Yuen
Audio and Speech Processing
LIWhiz: A Non-Intrusive Lyric Intelligibility Prediction System for the Cadenza Challenge
Ram C. M. C. Shekar and Iván López-Espejo
We present LIWhiz, a non-intrusive lyric intelligibility prediction system submitted to the ICASSP 2026 Cadenza Challenge. LIWhiz leverages Whisper for robust feature extraction and a trainable back-end for score prediction. Tested on the Cadenza Lyric Intelligibility Prediction (CLIP) evaluation set, LIWhiz achieves a root mean square error (RMSE) of 27.07%, a 22.4% relative RMSE reduction over the STOI-based baseline, yielding a substantial improvement in normalized cross-correlation.
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Audio and Speech Processing · Cross-listed from cs.SD
chatter: a Python library for applying information theory and AI/ML models to animal communication
Mason Youngblood
The study of animal communication often involves categorizing units into types (e.g. syllables in songbirds, or notes in humpback whales). While this approach is useful in many cases, it necessarily flattens the complexity and nuance present in real communication systems. chatter is a new Python library for analyzing animal communication in continuous latent space using information theory and modern machine learning techniques. It is taxonomically agnostic, and has been tested with the vocalizations of birds, bats, whales, and primates. By leveraging a variety of different architectures, including variational autoencoders and
vision transformers, chatter represents vocal sequences as trajectories in high-dimensional latent space, bypassing the need for manual or automatic categorization of units. The library provides an end-to-end workflow – from preprocessing and
segmentation to model training and feature extraction – that enables researchers to quantify the complexity, predictability, similarity, and novelty of vocal sequences.
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Signal Processing
A Unified Analysis for Dynamic Programming Track-Before-Detect Algorithms: Error Convergence and Spatial Uncertainty
Nicholas Bampton, Tian J. Ma and Minh N. Do
The Dynamic Programming Track-Before-Detect (DP-TBD) class of
algorithms is a core approach to the small low signal-to-noise ratio (SNR) target detection problem. These methods detect targets by recursively accumulating data through a sequence of iterative maximizations, a process that has traditionally limited their theoretical analysis. We propose a novel spatial analysis for the general DP-TBD class of
algorithms where we derive a fundamental inverse relationship between detection uncertainty and location uncertainty using specific threshold constructions. Our analysis explicitly incorporates spatial distance from the target state into the probability bounds and allow this distance to vary as a function of iteration count, i.e. the number of processed frames. Integrating additional observations increases confidence in target existence while reducing certainty about the target's location. Our framework precisely details how each parameter affects performance and establishes the necessary conditions under which this analysis holds. Within this framework, we propose Normalized Path Integration (NPI), a DP-TBD
algorithm that achieves broad applicability by tracking targets based on the similarity between observations as opposed to directly integrating the observations themselves. We experimentally validate this theory and compare different DP-TBD constructions on the Sequential Infrared Small Target Detection (SIRSTD)
dataset: a real
dataset consisting of small aerial infrared targets.
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Systems and Control · Cross-listed from cs.AI
CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
Akhil S Anand, Elias Aarekol, Martin Mziray Dalseg et al. (+2 more)
Combinatorial sequential decision making problems are typically modeled as mixed integer linear programs (MILPs) and solved via branch and bound (B&B)
algorithms. The inherent difficulty of modeling MILPs that accurately represent
stochastic real world problems leads to suboptimal performance in the real world. Recently, machine learning methods have been applied to build MILP models for decision quality rather than how accurately they model the real world problem. However, these approaches typically rely on supervised learning, assume access to true optimal decisions, and use surrogates for the MILP gradients. In this work, we introduce a proof of concept CORL framework that end to end fine tunes an MILP scheme using reinforcement learning (RL) on real world data to maximize its operational performance. We enable this by casting an MILP solved by B&B as a differentiable
stochastic policy compatible with RL. We validate the CORL method in a simple illustrative combinatorial sequential decision making example.
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All authors Akhil S Anand, Elias Aarekol, Martin Mziray Dalseg, Magnus Stalhane, Sebastien Gros
Image and Video Processing
Feature Compression for Machines with Range-Based Channel Truncation and Frame Packing
Juan Merlos, Fabien Racapé, Hyomin Choi et al. (+2 more)
This paper proposes a method that enhances the compression performance of the current model under development for the upcoming MPEG standard on Feature Coding for Machines (FCM). This standard aims at providing inter-operable compressed bitstreams of features in the context of split computing, i.e., when the inference of a large computer
vision neural-network (NN)-based model is split between two devices. Intermediate features can consist of multiple 3D tensors that can be reduced and
entropy coded to limit the required bandwidth of such transmission. In the envisioned design for the MPEG-FCM standard, intermediate feature tensors may be reduced using Neural layers before being converted into 2D
video frames that can be coded using existing
video compression standards. This paper introduces an additional channel truncation and packing method which enables the system to preserve the relevant channels, depending on the statistics of the features at inference time, while preserving the computer
vision task performance at the receiver. Implemented within the MPEG-FCM test model, the proposed method yields an average reduction in rate by 10.59% for a given accuracy on multiple computer
vision tasks and
datasets.
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All authors Juan Merlos, Fabien Racapé, Hyomin Choi, Mateen Ulhaq, Hari Kalva
Systems and Control · Cross-listed from cs.RO
Design and Experimental Validation of Closed-Form CBF-Based Safe Control for Stewart Platform Under Multiple Constraints
Benedictus C. G. Cinun, Tua A. Tamba, Immanuel R. Santjoko et al. (+3 more)
This letter presents a closed-form solution of Control Barrier Function (CBF) framework for enforcing safety constraints on a Stewart robotic platform. The proposed method simultaneously handles multiple position and velocity constraints through an explicit closed-form control law, eliminating the need to solve a Quadratic Program (QP) at every control step and enabling efficient real-time implementation. This letter derives necessary and sufficient conditions under which the closed-form expression remains non-singular, thereby ensuring well-posedness of the CBF solution to multi-constraint problem. The controller is validated in both simulation and hardware experiments on a custom-built Stewart platform prototype, demonstrating safetyguaranteed performance that is comparable to the QP-based formulation, while reducing computation time by more than an order of magnitude. The results confirm that the proposed approach provides a reliable and computationally lightweight framework for real-time safe control of parallel robotic systems. The experimental
videos are available on the project website. (https://nail-uh.github.io/StewartPlatformSafeControl.github.io/)
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All authors Benedictus C. G. Cinun, Tua A. Tamba, Immanuel R. Santjoko, Xiaofeng Wang, Michael A. Gunarso, Bin Hu
Image and Video Processing · Cross-listed from cs.CV
Generative Manifold Distillation: Aligning Restoration Trajectories with Natural Image Prior
Yuyang Hu, Mojtaba Sahraee-Ardakan, Arpit Bansal et al. (+4 more)
Pre-trained image restoration models often fail on out-of-distribution (OOD) real-world degradations. Adapting to these domains is challenging as real-world data lacks paired ground truth, and unsupervised methods often require unstable architectural changes. We propose Generative
Manifold Distillation (GMD), which reframes domain adaptation as
geometric manifold alignment. GMD operates in a strictly unpaired setting, requiring only low-quality (LQ) target observations. By leveraging the flow-matching dynamics of a frozen text-to-image foundation model, GMD projects off-
manifold restorations onto the natural image
manifold to generate high-quality pseudo-targets. To ensure stability, a quality-gated
manifold filter rejects off-
manifold samples, while source-anchored trajectory regularization prevents error accumulation. Ultimately, GMD distills a powerful generative prior into an efficient restoration network. Experiments demonstrate that GMD seamlessly adapts to new distributions using only LQ inputs, drastically improving perceptual quality with zero architectural modifications or added inference latency.
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All authors Yuyang Hu, Mojtaba Sahraee-Ardakan, Arpit Bansal, Kangfu Mei, Chenyang Qi, Peyman Milanfar, Mauricio Delbracio
Systems and Control · Cross-listed from math.OC
Linear quadratic control for discrete-time systems with stochastic and bounded noises
Xuehui Ma, Shiliang Zhang, Xiaohui Zhang et al. (+2 more)
This paper focuses on the linear quadratic control (LQC) design of systems corrupted by both
stochastic noise and bounded noise simultaneously. When only of these noises are considered, the LQC strategy leads to
stochastic or robust controllers, respectively. However, there is no LQC strategy that can simultaneously handle
stochastic and bounded noises efficiently. This limits the scope where existing LQC strategies can be applied. In this work, we look into the LQC problem for discrete-time systems that have both
stochastic and bounded noises in its dynamics. We develop a state estimation for such systems by efficiently combining a Kalman filter and an ellipsoid set-membership filter. The developed state estimation can recover the estimation optimality when the system is subject to both kinds of noise, the
stochastic and the bounded. Upon the estimated state, we derive a robust state-feedback optimal control law for the LQC problem. The control law derivation takes into account both
stochastic and bounded-state estimation errors, so as to avoid over-conservativeness while sustaining stability in the control. In this way, the developed LQC strategy extends the range of scenarios where LQC can be applied, especially those of real-world control systems with diverse sensing which are subject to different kinds of noise. We present numerical simulations, and the results demonstrate the enhanced control performance with the proposed strategy.
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All authors Xuehui Ma, Shiliang Zhang, Xiaohui Zhang, Jing Xin, Hector Garcia de Marina
Image and Video Processing
An Open Source Realtime GPU Beamformer for Row-Column and Top Orthogonal to Bottom Electrode (TOBE) Arrays
Randy Palamar, Darren Dahunsi, Tyler Henry et al. (+2 more)
Research ultrasound platforms have enabled many next-generation imaging sequences but have lacked realtime
navigation capabilities for emerging 2D arrays such as row-column arrays (RCAs). We present an open-source, GPU-accelerated reconstruction and rendering software suite integrated with a programmable ultrasound platform and novel electrostrictive Top-Orthogonal-to-Bottom-Electrode (TOBE) arrays. The system supports advanced real-time modes, including cross-plane aperture-encoded synthetic-aperture imaging and aperture-encoded volumetric scanning. TOBE-enabled methods demonstrate improved image quality and expanded field of view compared with conventional RCA techniques. The software implements beamforming and rendering kernels using OpenGL compute shaders and is designed for maximum data throughput helping to minimize stalls and latency. Accompanying sample
datasets and example scripts for offline reconstruction are provided to facilitate external testing.
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All authors Randy Palamar, Darren Dahunsi, Tyler Henry, Mohammad Rahim Sobhani, Roger Zemp
Image and Video Processing · Cross-listed from cs.CV
E-CHUM: Event-based Cameras for Human Detection and Urban Monitoring
Jack Brady, Andrew Dailey, Kristen Schang et al. (+1 more)
Understanding human movement and city dynamics has always been challenging. From traditional methods of manually observing the city's inhabitant, to using cameras, to now using sensors and more complex technology, the field of urban monitoring has evolved greatly. Still, there are more that can be done to unlock better practices for understanding city dynamics. This paper surveys how the landscape of urban dynamics studying has evolved with a particular focus on event-based cameras. Event-based cameras capture changes in light intensity instead of the RGB values that traditional cameras do. They offer unique abilities, like the ability to work in low-light, that can make them advantageous compared to other sensors. Through an analysis of event-based cameras, their applications, their advantages and challenges, and machine learning applications, we propose event-based cameras as a medium for capturing information to study urban dynamics. They offer the ability to capture important information while maintaining
privacy. We also suggest multi-sensor fusion of event-based cameras and other sensors in the study of urban dynamics. Combining event-based cameras and infrared, event-
LiDAR, or vibration has to potential to enhance the ability of event-based cameras and overcome the challenges that event-based cameras have.
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All authors Jack Brady, Andrew Dailey, Kristen Schang, Zo Vic Shong
Systems and Control · Cross-listed from physics.flu-dyn
Physics-Informed Dynamical Modeling of Extrusion-Based 3D Printing Processes
Mandana Mohammadi Looey, Marissa Loraine Scalise, Amrita Basak et al. (+1 more)
The trade-off between model fidelity and computational cost remains a central challenge in the computational modeling of extrusion-based 3D printing, particularly for real time optimization and control. Although high fidelity simulations have advanced considerably for offline analysis, dynamical modeling tailored for online, control-oriented applications is still significantly underdeveloped. In this study, we propose a reduced order dynamical flow model that captures the transient behavior of extrusion-based 3D printing. The model is grounded in physics-based principles derived from the Navier Stokes equations and further simplified through spatial averaging and input dependent parameterization. To assess its performance, the model is identified via a nonlinear least squares approach using Computational Fluid Dynamics (CFD) simulation data spanning a range of printing conditions and subsequently validated across multiple combinations of training and testing scenarios. The results demonstrate strong agreement with the CFD data within the nozzle, the nozzle substrate gap, and the deposited layer regions. Overall, the proposed reduced order model successfully captures the dominant flow dynamics of the process while maintaining a level of simplicity compatible with real time control and optimization.
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All authors Mandana Mohammadi Looey, Marissa Loraine Scalise, Amrita Basak, Satadru Dey
Systems and Control · Cross-listed from math.OC
Distributionally Robust Regret Optimal Control Under Moment-Based Ambiguity Sets
Feras Al Taha and Eilyan Bitar
We consider a class of finite-horizon, linear-quadratic
stochastic control problems, where the probability distribution governing the noise process is unknown but assumed to belong to an ambiguity set consisting of all distributions whose mean and covariance lie within norm balls centered at given nominal values. To cope with this ambiguity, we design causal affine control policies to minimize the worst-case expected regret over all distributions in the ambiguity set. The resulting minimax optimal control problem is shown to admit an equivalent reformulation as a tractable convex program, which can be interpreted as a regularized version of the nominal linear-quadratic
stochastic control problem. Based on the dual of this convex reformulation, we develop a scalable projected subgradient method for computing optimal controllers to arbitrary accuracy. Numerical experiments are provided to compare the proposed method with state-of-the-art data-driven control design methods.
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Systems and Control · Cross-listed from cs.NI
A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networking
Zhongyuan Zhao, Yujun Ming, Kevin Chan et al. (+2 more)
Many routing and flow optimization problems in wired networks can be solved efficiently using minimum cost flow formulations. However, this approach does not extend to wireless multi-hop networks, where the assumptions of fixed link capacity and linear cost structure collapse due to contention for shared spectrum resources. The key challenge is that the long-term capacity of a wireless link becomes a non-linear function of its network context, including network
topology, link quality, and the traffic assigned to neighboring links. In this work, we pursue a new direction of modeling wireless network under randomized medium access control by developing an analytical network digital twin (NDT) that predicts link duty cycles from network context. We generalize randomized contention as finding a Maximal Independent Set (MIS) on the conflict graph using weighted Luby's
algorithm, derive an analytical model of link duty cycles, and introduce an iterative procedure that resolves the circular dependency among duty cycle, link capacity, and contention probability. Our numerical experiments show that the proposed NDT accurately predicts link duty cycles and congestion patterns with up to a 5000x speedup over packet-level simulation, and enables us to optimize link scheduling using gradient descent for reduced congestion and
radio footprint.
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All authors Zhongyuan Zhao, Yujun Ming, Kevin Chan, Ananthram Swami, Santiago Segarra
Systems and Control · Cross-listed from math.OC
Spectral Decompositions of Controllability Gramian and Its Inverse based on System Eigenvalues in Companion Form
Alexey Iskakov and Igor Yadykin
Controllability and observability Gramians, along with their inverses, are widely used to solve various problems in control theory. This paper proposes spectral decompositions of the controllability Gramian and its inverse based on system
eigenvalues for a continuous LTI dynamical system in the controllability canonical (companion) form. The Gramian and its inverse are represented as sums of Hermitian
matrices, each corresponding to individual system
eigenvalues or their pairwise combinations. These decompositions are obtained for the solutions of both algebraic and differential Lyapunov and Riccati equations with arbitrary initial conditions, allowing for the estimation of system spectral properties over an arbitrary time interval and their prediction at future moments. The derived decompositions are also generalized to the case of multiple
eigenvalues in the dynamics
matrix spectrum, enabling a closed-form estimation of the effects of resonant interactions with the system's eigenmodes. The spectral components are interpreted as measurable quantities in the minimum energy control problem. Therefore, they are unambiguously defined and can quantitatively characterize the influence of individual eigenmodes and associated system devices on controllability, observability, and the asymptotic dynamics of perturbation energy. The additional information obtained from these decompositions can improve the accuracy of
algorithms in solving various practical problems, such as stability analysis, minimum energy control, structural design, tuning regulators, optimal placement of actuators and sensors, network analysis, and model order reduction.
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Systems and Control
Low-Order $\mathcal{H}_2 / \mathcal{H}_\infty$ Controller Design for Aeroelastic Vibration Suppression
Mohammad Mirtaba, Juan Augusto Paredes Salazar, Daning Huang et al. (+1 more)
This paper presents an $\mathcal{H}_2 / \mathcal{H}_\infty$ minimization-based output-feedback controller for active aeroelastic vibration suppression in a cantilevered beam. First, a nonlinear structural model incorporating moderate deflection and aerodynamic loading is derived and discretized using the finite element method (FEM). Then, a low-order linear model is identified from random gaussian input response data from the FEM model to synthesize an output-feedback controller using the $\mathcal{H}_2 / \mathcal{H}_\infty$ framework. A frequency-weighted dynamic filter is introduced to emphasize disturbance frequencies of interest, enabling the controller to target dominant vibration modes. Simulation results demonstrate the effectiveness of the proposed technique for vibration suppression and study its robustness to system parameter variations, including actuator placement.
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All authors Mohammad Mirtaba, Juan Augusto Paredes Salazar, Daning Huang, Ankit Goel
Signal Processing
Decision Feedback-Aided Known-Interference Cancellation
Karel Pärlin, Aaron Byman, Tommi Meriläinen et al. (+1 more)
Known-interference cancellation (KIC) in combination with cooperative jamming can be used to provide covertness and security to wireless communications at the physical layer. However, since the signal of interest (SI) of a wireless communication system acts as estimation noise, i.e., interference, to KIC, the SI limits the extent to which the known interference (KI) can be canceled and that in turn limits the throughput of the wireless communication system that is being hidden or secured. In this letter, we analyze a decision feedback-aided known-interference cancellation (DF-KIC) structure in which both the KI and SI are canceled iteratively and successively. Measurement results demonstrate that introducing decision feedback to KIC improves its KI cancellation capability and hence increases the wireless communication system's useful throughput, albeit at the expense of a higher computational load.
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All authors Karel Pärlin, Aaron Byman, Tommi Meriläinen, Taneli Riihonen
Signal Processing
A Variable Step Sizes Frequency Offsets-Compensated Least Mean Squares Algorithm
Karel Pärlin, Aaron Byman, Tommi Meriläinen et al. (+1 more)
Frequency offsets-compensated least mean squares (FO-LMS)
algorithm is a generic method for estimating a wireless channel under carrier and sampling frequency offsets when the transmitted signal is beforehand known to the receiver. The
algorithm iteratively and explicitly adjusts its estimates of the channel and frequency offsets using
stochastic gradient descent-based rules and the step sizes of these rules determine the learning rate and stability of the
algorithm. Within the stability conditions, the choice of step sizes reflects a trade-off between the
algorithm's ability to react to changes in the channel and the ability to minimize misadjustments caused by noise. This paper provides theoretical expressions to predict and optimize the tracking and misadjusment errors of FO-LMS when estimating channels and frequency offsets with known time-varying characteristics. This work also proposes a method to adjust the FO-LMS's step sizes based on the
algorithm's performance when the time-varying characteristics are not known, which is more often the case in practice. Accuracy of the expressions and performance of the proposed variable step sizes
algorithm are studied through simulations.
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All authors Karel Pärlin, Aaron Byman, Tommi Meriläinen, Taneli Riihonen
Signal Processing
Comparative analysis of WNG-DF compromising beamformers
Vitor G. P. Curtarelli
This work studies beamformers designed to achieve multiple characteristics simultaneously, specifically those compromising white-noise gain and directivity factor. We compare methods explicitly designed for these joint features against those obtained by combining specific single-task beamformers. Through simulations, we demonstrate that the robust superdirective and the tunable beamformers yield the best results among those studied. Notably, these two methods produced nearly identical outputs across all evaluated metrics. These two are also more practical, continuously compromising between the two objectives.
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Signal Processing
CSI-Based User Positioning, Channel Charting, and Device Classification with an NVIDIA 5G Testbed
Reinhard Wiesmayr, Frederik Zumegen, Sueda Taner et al. (+2 more)
Channel-state information (CSI)-based sensing will play a key role in future cellular systems. However, no CSI
dataset has been published from a real-world 5G NR system that facilitates the development and validation of suitable sensing
algorithms. To close this gap, we publish three real-world wideband multi-
antenna multi-open RAN
radio unit (O-RU) CSI
datasets from the 5G NR uplink channel: an indoor lab/office room
dataset, an outdoor campus courtyard
dataset, and a device classification
dataset with six commercial-off-the-shelf (COTS) user equipments (UEs). These
datasets have been recorded using a software-defined 5G NR testbed based on NVIDIA Aerial RAN CoLab Over-the-Air (ARC-OTA) with COTS hardware, which we have deployed at ETH Zurich. We demonstrate the utility of these
datasets for three CSI-based sensing tasks: neural UE positioning, channel charting in real-world coordinates, and closed-set device classification. For all these tasks, our results show high accuracy: neural UE positioning achieves 0.6cm (indoor) and 5.7cm (outdoor) mean absolute error, channel charting in real-world coordinates achieves 73cm mean absolute error (outdoor), and device classification achieves 99% (same day) and 95% (next day) accuracy. The CSI
datasets, ground-truth UE position labels, CSI features, and simulation code are publicly available at https://caez.ethz.ch
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All authors Reinhard Wiesmayr, Frederik Zumegen, Sueda Taner, Chris Dick, Christoph Studer
Systems and Control · Cross-listed from physics.flu-dyn
Data-driven Pressure Recovery in Diffusers
Juan Augusto Paredes Salazar, Ankit Goel, Rowen Costich et al. (+3 more)
This paper investigates the application of a data-driven technique based on retrospective cost optimization to optimize the frequency of mass injection into an S-shaped diffuser, with the objective of maximizing the pressure recovery. Experimental data indicated that there is an optimal injection frequency between 100 Hz and 300 Hz with a mass flow rate of 1 percent of the free stream. High-fidelity numerical simulations using compressible unsteady Reynolds-Averaged Navier-Stokes (URANS) are conducted to investigate the mean and temporal features resulting from mass injection into an S-shaped diffuser with differing injection speeds and pulse frequencies. The results are compared with experiments to confirm the accuracy of the numerical solution. Overall, 2-D simulations are relatively in good agreement with the experiment, with 3-D simulations currently under investigation to
benchmark the effect of spanwise instabilities. Simulation results with the proposed data-driven technique show improvements upon a baseline case by increasing pressure recovery and reducing the region of flow recirculation within the diffuser.
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All authors Juan Augusto Paredes Salazar, Ankit Goel, Rowen Costich, Meliksah Koca, Ozgur Tumuklu, Michael Amitay
Audio and Speech Processing · Cross-listed from cs.SD
Building Audio-Visual Digital Twins with Smartphones
Zitong Lan, Yiwei Tang, Yuhan Wang et al. (+3 more)
Digital twins today are almost entirely visual, overlooking acoustics-a core component of spatial realism and interaction. We introduce AV-Twin, the first practical system that constructs editable audio-visual digital twins using only commodity smartphones. AV-Twin combines mobile RIR capture and a visual-assisted acoustic field model to efficiently reconstruct room acoustics. It further recovers per-surface material properties through differentiable acoustic rendering, enabling users to modify materials, geometry, and layout while automatically updating both audio and visuals. Together, these capabilities establish a practical path toward fully modifiable audio-visual digital twins for real-world environments.
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All authors Zitong Lan, Yiwei Tang, Yuhan Wang, Haowen Lai, Yiduo Hao, Mingmin Zhao
Signal Processing
Symbol-Level Precoding for Integrated Sensing and Covert Communication
Yufei Wang, Qiang Li, Hongli Liu et al. (+2 more)
Integrated sensing and communication (
ISAC) systems have emerged as a promising solution to improve spectrum efficiency and enable functional convergence. However, ensuring secure information transmission while maintaining high-quality sensing performance remains a significant challenge. In this paper, we investigate an integrated sensing and covert communication (ISCC) system, in which a base station (BS) simultaneously serves multiple downlink users and senses malicious targets that may act as both potential eavesdroppers (Eves) and wardens. We propose a novel symbol-level precoding (SLP)-based waveform design for ISCC that achieves covert communication intrinsically, without requiring additional transmission resources such as artificial noise. The proposed design integrates symbol shaping to enhance reliability for legitimate users and noise shaping to obscure transmission activities from the targets. For imperfect channel state information (CSI), the framework incorporates bounded uncertainty models for user channels and target angles, yielding a more robust design. The resulting ISCC waveform optimization problem is non-convex; to address this, we develop a low-complexity proximal distance
algorithm (PDA) with closed-form updates under both PSK and QAM modulations. Simulation results demonstrate that the proposed method achieves superior covertness and sensing-communication performance with negligible degradation compared to traditional beamforming and conventional SLP approaches without noise-shaping mechanisms.
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All authors Yufei Wang, Qiang Li, Hongli Liu, Ying Zhang, Jingran Lin
Image and Video Processing
Fast and Robust LRSD-based SAR/ISAR Imaging and Decomposition
Hamid Reza Hashempour, Majid Moradikia, Hamed Bastami et al. (+2 more)
The earlier works in the context of low-rank-sparse-decomposition (LRSD)-driven stationary synthetic aperture radar (SAR) imaging have shown significant improvement in the reconstruction-decomposition process. Neither of the proposed frameworks, however, can achieve satisfactory performance when facing a platform residual phase error (PRPE) arising from the instability of airborne platforms. More importantly, in spite of the significance of real-time processing requirements in remote sensing applications, these prior works have only focused on enhancing the quality of the formed image, not reducing the computational burden. To address these two concerns, this article presents a fast and unified joint SAR imaging framework where the dominant sparse objects and low-rank features of the image background are decomposed and enhanced through a robust LRSD. In particular, our unified
algorithm circumvents the tedious task of computing the inverse of large
matrices for image formation and takes advantage of the recent advances in constrained quadratic programming to handle the unimodular constraint imposed due to the PRPE. Furthermore, we extend our approach to ISAR autofocusing and imaging. Specifically, due to the intrinsic sparsity of ISAR images, the LRSD framework is essentially tasked with the recovery of a sparse image. Several experiments based on synthetic and real data are presented to validate the superiority of the proposed method in terms of imaging quality and computational cost compared to the state-of-the-art methods.
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All authors Hamid Reza Hashempour, Majid Moradikia, Hamed Bastami, Ahmed Abdelhadi, Mojtaba Soltanalian