Signal Processing
Multifunctional Wideband Digital Metasurface for Secure Electromagnetic Manipulation in S-Band
Longpan Wang, Zhuoran Zhang, Zhenyuan Li et al. (+4 more)
Digital metasurfaces have attracted significant
attention in recent years due to their ability to manipulate electromagnetic (EM) waves for secure sensing and communication. However, most reported metasurfaces operate at relatively high frequencies, primarily due to the constraints imposed by the physical scale of the dielectric substrate, thus limiting their full-wave system applications. In this work, a wideband digital reflective metasurface is presented for capable of dynamically controlling EM waves, with multifunctional applications in the lower-frequency S-band. The metasurface is composed of electronically reconfigurable meta-atoms with wideband characteristics, and designed by using trapezoidal and M-shaped patches connected by a pin diode. Simulation results show that the proposed digital metasurface could achieve wideband 1-bit phase quantization with a stable phase difference within 180 degree +/- 25 degree and small reflection loss below 0.6 dB from 2.72 to 3.25 GHz. To validate the proposed design, a 20x20-unit metasurface array was designed, simulated and fabricated. By dynamically adjusting the coding sequence, the metasurface could enable multi-mode orbital angular momentum (OAM) beam generation, dynamic beam scanning, and precise direction finding. These capabilities support secure sensing and secure communications through high-resolution target detection and anti-jamming beam steering, as well as physical-layer security. The proposed wideband metasurface may serve as an effective candidate for enhancing spectral efficiency and security performance in radar and wireless systems.
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All authors Longpan Wang, Zhuoran Zhang, Zhenyuan Li, Xuetao Gan, Xudong Bai, Wen Chen, Qingqing Wu
Signal Processing
Advanced Closed-Loop Method with Limited Feedback for ISAC
Ashkan Jafari Fesharaki, Yasser Mestrah, Yi Ma et al. (+6 more)
6G wireless networks are poised to seamlessly integrate communication, computing, localization, and sensing functionalities, ensuring high reliability and trustworthiness. This paper introduces Smart Sensing Feedback (SSF), a limited-feedback framework designed to enhance sensing capabilities while maintaining communication performance. SSF adapts the concept of retransmission from communication to sensing. Specifically, we focus on downlink (DL) bistatic sensing, where the User Equipment (UE) performs measurements from reflected sensing signals and provides feedback to the network (NW). In sensing services, UE reporting can vary significantly due to dynamic factors such as target characteristics, environmental conditions, and UE status. Our results demonstrate that SSF significantly improves sensing quality while preserving communication efficiency. Additionally, it enhances key performance metrics such as probability of detection, latency, and power consumption. These improvements underscore SSF's ability to deliver robust, low-overhead feedback and adaptability to support a wide range of
ISAC applications.
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All authors Ashkan Jafari Fesharaki, Yasser Mestrah, Yi Ma, Rahim Tafazolli, Ibrahim Hemadeh, Mohammad Heggo, Arman Shojaeifard, Javier Lorca Hernando, Alain Mourad
Audio and Speech Processing · Cross-listed from cs.SD
Audio Signal Processing Using Time Domain Mel-Frequency Wavelet Coefficient
Rinku Sebastian, Simon O'Keefe and Martin Trefzer
Extracting features from the
speech is the most critical process in
speech signal processing. Mel Frequency Cepstral Coefficients (MFCC) are the most widely used features in the majority of the speaker and
speech recognition applications, as the filtering in this feature is similar to the filtering taking place in the human ear. But the main drawback of this feature is that it provides only the frequency information of the signal but does not provide the information about at what time which frequency is present. The wavelet transform, with its flexible time-frequency window, provides time and frequency information of the signal and is an appropriate tool for the analysis of non-stationary signals like
speech. On the other hand, because of its uniform frequency scaling, a typical wavelet transform may be less effective in analysing
speech signals, have poorer frequency resolution in low frequencies, and be less in line with human auditory perception. Hence, it is necessary to develop a feature that incorporates the merits of both MFCC and wavelet transform. A great deal of studies are trying to combine both these features. The present Wavelet Transform based Mel-scaled feature extraction methods require more computation when a wavelet transform is applied on top of Mel-scale filtering, since it adds extra processing steps. Here we are proposing a method to extract Mel scale features in time domain combining the concept of wavelet transform, thus reducing the computational burden of time-frequency conversion and the complexity of wavelet extraction. Combining our proposed Time domain Mel frequency Wavelet Coefficient(TMFWC) technique with the reservoir computing methodology has significantly improved the efficiency of audio signal processing.
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Signal Processing
Heuristic Quality Coefficients for Interferometric Phase Linking
Magnus Heimpel, Irena Hajnsek and Othmar Frey
In multitemporal InSAR, phase linking (PL) refers to the estimation of a single-reference interferometric phase history for distributed scatterers (DS) from the information contained in the sample coherence
matrix. Because the phase information in this
matrix is typically inconsistent, DS processing needs practical reliability indicators to decide whether a pixel's PL estimate is sufficiently supported by the data for subsequent deformation analysis. For maximum-likelihood estimation, uncertainty can be quantified via Fisher-information-based covariance estimates, but no analogous, generally applicable uncertainty quantification is available for the broad range of non-ML methods. We propose three heuristic quality coefficients within a unified mathematical framework that covers common PL methods: (1) a method-specific goodness-of-fit coefficient that normalizes the achieved PL objective between a method-consistent upper bound and an empirically modeled noise floor level; (2) a closure phase coefficient computed from the sample coherence
matrix in advance; and (3) an ambiguity coefficient that compares the obtained PL estimate with the best alternative in its orthogonal complement in the solution space. All coefficients are normalized to the interval $[0,1]$, where 1 indicates maximum reliability and 0 matches the behavior expected under pure noise. Simulations under exponential and seasonal decorrelation models show that the goodness-of-fit coefficient tracks the normalized absolute phase error most consistently, whereas the closure phase coefficient provides an a priori indicator for pre-screening. Experiments on a TerraSAR-X stack over Visp, Switzerland, reveal plausible spatial patterns across urban and vegetated areas and show that the ambiguity coefficient provides complementary information, especially in regions with temporally varying scattering mechanisms.
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Audio and Speech Processing · Cross-listed from cs.SD
Online neural fusion of distortionless differential beamformers for robust speech enhancement
Yuanhang Qian, Kunlong Zhao, Jilu Jin et al. (+4 more)
Fixed beamforming is widely used in practice since it does not depend on the estimation of noise statistics and provides relatively stable performance. However, a single beamformer cannot adapt to varying acoustic conditions, which limits its interference suppression capability. To address this, adaptive convex combination (ACC)
algorithms have been introduced, where the outputs of multiple fixed beamformers are linearly combined to improve robustness. Nevertheless, ACC often fails in highly non-stationary scenarios, such as rapidly moving interference, since its adaptive updates cannot reliably track rapid changes. To overcome this limitation, we propose a frame-online neural fusion framework for multiple distortionless differential beamformers, which estimates the combination weights through a neural network. Compared with conventional ACC, the proposed method adapts more effectively to dynamic acoustic environments, achieving stronger interference suppression while maintaining the distortionless constraint.
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All authors Yuanhang Qian, Kunlong Zhao, Jilu Jin, Xueqin Luo, Gongping Huang, Jingdong Chen, Jacob Benesty
Signal Processing
Diffusion Models for Wireless Transceivers: From Pilot-Efficient Channel Estimation to AI-Native 6G Receivers
Yuzhi Yang, Sen Yan, Weijie Zhou et al. (+4 more)
With the development of artificial intelligence (AI) techniques, implementing AI-based techniques to improve wireless transceivers becomes an emerging research topic. Within this context, AI-based channel characterization and estimation become the focus since these methods have not been solved by traditional methods very well and have become the bottleneck of transceiver efficiency in large-scale orthogonal frequency division multiplexing (OFDM) systems. Specifically, by formulating channel estimation as a generative AI problem, generative AI methods such as
diffusion models (DMs) can efficiently deal with rough initial estimations and have great potential to cooperate with traditional signal processing methods. This paper focuses on the transceiver design of OFDM systems based on DMs, provides an illustration of the potential of DMs in wireless transceivers, and points out the related research directions brought by DMs. We also provide a proof-of-concept case study of further adapting DMs for better wireless receiver performance.
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All authors Yuzhi Yang, Sen Yan, Weijie Zhou, Brahim Mefgouda, Ridong Li, Zhaoyang Zhang, Mérouane Debbah
Audio and Speech Processing
Forward Convolutive Prediction for Frame Online Monaural Speech Dereverberation Based on Kronecker Product Decomposition
Yujie Zhu, Jilu Jin, Xueqin Luo et al. (+5 more)
Dereverberation has long been a crucial research topic in
speech processing, aiming to alleviate the adverse effects of reverberation in voice communication and
speech interaction systems. Among existing approaches, forward convolutional prediction (FCP) has recently attracted
attention. It typically employs a deep neural network to predict the direct-path signal and subsequently estimates a linear prediction filter to suppress residual reverberation. However, a major drawback of this approach is that the required linear prediction filter is often excessively long, leading to considerable computational complexity. To address this, our work proposes a novel FCP method based on Kronecker product (KP) decomposition, in which the long prediction filter is modeled as the KP of two much shorter filters. This decomposition significantly reduces the computational cost. An adaptive
algorithm is then provided to iteratively update these shorter filters online. Experimental results show that, compared to conventional methods, our approach achieves competitive dereverberation performance while substantially reducing computational cost.
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All authors Yujie Zhu, Jilu Jin, Xueqin Luo, Wenxing Yang, Zhong-Qiu Wang, Gongping Huang, Jingdong Chen, Jacob Benesty
Systems and Control · Cross-listed from math.OC
Efficient Network Reconfiguration by Randomized Switching
Samuel Talkington, Dmitrii M. Ostrovskii and Daniel K. Molzahn
We present an
algorithm that efficiently computes nearly-optimal solutions to a class of combinatorial reconfiguration problems on weighted, undirected graphs. Inspired by societally relevant applications in networked infrastructure systems, these problems consist of simultaneously finding an unreweighted sparsified graph and nodal potentials that satisfy fixed demands, where the objective is to minimize some congestion criterion, e.g., a
Laplacian quadratic form. These are mixed-integer nonlinear programming problems that are NP-hard in general. To circumvent these challenges, instead of solving for a single best configuration, the proposed randomized switching
algorithm seeks to design a distribution of configurations that, when sampled, ensures that congestion concentrates around its optimum. We show that the proposed congestion metric is a generalized self-concordant function in the space of switching probabilities, which enables the use of efficient and simple conditional gradient methods. We implement our
algorithm and show that it outperforms a state-of-the-art commercial mixed-integer second-order cone programming (MISOCP) solver by orders of magnitude over a large range of problem sizes.
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Systems and Control · Cross-listed from cs.RO
Flatness-based trajectory planning for 3D overhead cranes with friction compensation and collision avoidance
Jorge Vicente-Martinez and Edgar Ramirez-Laboreo
This paper presents an optimal trajectory generation method for 3D overhead cranes by leveraging differential flatness. This framework enables the direct inclusion of complex physical and dynamic constraints, such as nonlinear friction and collision avoidance for both payload and rope. Our approach allows for aggressive movements by constraining payload swing only at the final point. A comparative simulation study validates our approach, demonstrating that neglecting dry friction leads to actuator saturation and collisions. The results show that friction modeling is a fundamental requirement for fast and safe crane trajectories.
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Systems and Control
Analyzing Parametric Oscillator Ising Machines through the Kuramoto Lens
Nikhat Khan, E.M.H.E.B. Ekanayake, Nicolas Casilli et al. (+3 more)
Networks of coupled nonlinear oscillators are emerging as powerful physical platforms for implementing Ising machines. Yet the relationship between parametric-oscillator implementations and traditional oscillator-based Ising machines remains underexplored. In this work, we develop a Kuramoto-style, canonical phase description of parametric oscillator Ising machines by starting from the Stuart-Landau oscillator model – the canonical normal form near a
Hopf bifurcation, and a natural reduced description for many parametric oscillator implementations such as the degenerate optical parametric oscillator (DOPO) among others. The resulting phase dynamics combine the usual phase-difference coupling observed in the standard Kuramoto model along with an intrinsic phase sum term that is generated when conjugate coupling is considered. Moreover, our formulation helps explain why explicit second-harmonic driving is unnecessary in parametric oscillators and also reveals how quasi-steady amplitude heterogeneity scales the original strength of the
spin interaction with potentially adverse impacts on the solution quality. Our work helps develop a unifying view of the oscillator-based approach to designing Ising machines.
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All authors Nikhat Khan, E.M.H.E.B. Ekanayake, Nicolas Casilli, Cristian Cassella, Luke Theogarajan, Nikhil Shukla
Signal Processing
Deep Learning-Based CSI Prediction Framework for Channel Aging Mitigation in TDD 5G Systems
Francisco Díaz-Ruiz, Francisco J. Martín-Vega, José Antonio Cortés et al. (+2 more)
Time division duplexing (TDD) has become the dominant duplexing mode in 5G and beyond due to its ability to exploit channel reciprocity for efficient downlink channel state information (CSI) acquisition. However, channel aging caused by user mobility and processing delays degrades the accuracy of CSI, leading to suboptimal link adaptation and loss of performance. To address this issue, we propose a learning-based CSI prediction framework that leverages temporal correlations in wireless channels to forecast future signal to interference plus noise ratio (SINR) values. The prediction operates in the effective SINR domain, obtained via exponential effective SINR mapping (EESM), ensuring full compatibility with existing 5G standards without requiring continuous pilot signaling. Two models are considered: a fully connected deep neural network (DNN) and an long short-term
memory (LSTM)-based network. The simulation results show that the LSTM predictor achieves an improvement of up to 2 dB in normalized mean squared error (NMSE) and a gain of up to 1.2 Mbps throughput over a baseline without prediction under moderate Doppler conditions. These results confirm the potential of lightweight AI-based CSI prediction to effectively mitigate channel aging and enhance link adaptation in TDD 5G systems.
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All authors Francisco Díaz-Ruiz, Francisco J. Martín-Vega, José Antonio Cortés, Gerardo Gómez, Mari Carmen Aguayo-Torres
Audio and Speech Processing · Cross-listed from cs.CR
Your Microphone Array Retains Your Identity: A Robust Voice Liveness Detection System for Smart Speakers
Yan Meng, Jiachun Li, Matthew Pillari et al. (+5 more)
Though playing an essential role in smart home systems, smart speakers are vulnerable to voice spoofing attacks. Passive liveness detection, which utilizes only the collected audio rather than the deployed sensors to distinguish between live-human and replayed voices, has drawn increasing
attention. However, it faces the challenge of performance degradation under the different environmental factors as well as the strict requirement of the fixed user gestures. In this study, we propose a novel liveness feature, array fingerprint, which utilizes the microphone array inherently adopted by the smart speaker to determine the identity of collected audios. Our theoretical analysis demonstrates that by leveraging the circular layout of microphones, compared with existing schemes, array fingerprint achieves a more robust performance under the environmental change and user's movement. Then, to leverage such a fingerprint, we propose ARRAYID, a lightweight passive detection scheme, and elaborate a series of features working together with array fingerprint. Our evaluation on the
dataset containing 32,780 audio samples and 14 spoofing devices shows that ARRAYID achieves an accuracy of 99.84%, which is superior to existing passive liveness detection schemes.
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All authors Yan Meng, Jiachun Li, Matthew Pillari, Arjun Deopujari, Liam Brennan, Hafsah Shamsie, Haojin Zhu, Yuan Tian
Systems and Control
Contributions to Semialgebraic-Set-Based Stability Verification of Dynamical Systems with Neural-Network-Based Controllers
Alvaro Detailleur, Dalim Wahby, Guillaume Ducard et al. (+1 more)
Neural-network-based controllers (NNCs) can represent complex, highly nonlinear control laws, but verifying the closed-loop stability of dynamical systems using them remains challenging. This work presents contributions to a state-of-the-art stability verification procedure for NNC-controlled systems which relies on semialgebraic-set-based input-output modeling to pose the search for a Lyapunov function as an optimization problem. Specifically, this procedure's conservatism when analyzing NNCs using transcendental activation functions and the restriction to feedforward NNCs are addressed by a) introducing novel semialgebraic activation functions that preserve key properties of common transcendental activations and b) proving compatibility of NNCs from the broader class of recurrent
equilibrium networks (RENs) with this procedure. Furthermore, the indirect optimization of a local region of attraction (RoA) estimate using a restricted set of candidate Lyapunov functions is greatly improved via c) the introduction of a richer parameterization of candidate Lyapunov functions than previously reported and d) the formulation of novel semidefinite programs (SDPs) that directly optimize the resulting RoA estimate. The value of these contributions is highlighted in two numerical examples.
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All authors Alvaro Detailleur, Dalim Wahby, Guillaume Ducard, Christopher Onder
Systems and Control
Development of a Digital Twin for an Electric Vehicle Emulator Modeling, Control, and Experimental Validation
Lamine Chalal and Ahmed Rachid
This paper presents the development and validation of a digital twin for a scaled-down electric vehicle (EV) emulator, designed to replicate longitudinal vehicle dynamics under diverse operating conditions. The emulator integrates a separately excited DC motor (SEDCM), a four-quadrant DC-DC converter, a battery emulator, and a mechanical load emulator. The system models tractive effort, aerodynamic drag, and gradient resistance using Newton's second law. In contrast to conventional graphical modeling tools (e.g., block diagrams and bond graphs), the adopted Energetic Macroscopic Representation (EMR) framework offers clear advantages by explicitly representing energy interactions and facilitating the systematic derivation of control structures. A control strategy developed within this framework governs energy flow across the powertrain, enabling accurate speed control via armature voltage regulation. Experimental tests conducted on a Lucas-Nulle test bench show strong correlation with simulation results. The study also introduces a methodology to compute the maximum admissible vehicle mass - determined to be 13.5 kg for a 180 W motor operating at 1900 rpm - based on acceleration and slope constraints. Furthermore, a switching
algorithm for the bidirectional converter ensures reliable four quadrant operation. Overall, the proposed framework provides a scalable and effective approach for EV emulation, control design, and energy management validation.
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Audio and Speech Processing · Cross-listed from cs.SD
Bayesian Speech Synthesizers Can Learn from Multiple Teachers
Ziyang Zhang, Yifan Gao, Xuenan Xu et al. (+3 more)
Text-to-
speech (TTS) is a "one-to-many" problem: one text can be spoken in many valid ways, so its realizations form a distribution whose spread depends on the input. Most TTS models do not model this spread explicitly: they leave it implicit in the sampling process or fix it in advance. To model this distribution explicitly, we propose BELLE (
Bayesian evidential learning with
language modelling), which places a
Bayesian prior over the mean and variance of each mel-spectrogram frame and predicts this prior from the input, while keeping the architecture and single-pass sampling of common continuous-valued autoregressive TTS models such as MELLE. Estimating this variance requires several recordings of each text, whereas standard TTS corpora provide one. We therefore construct two parallel-
speech corpora: ParallelVox from real human recordings and LS+6TTS, which adds
speech from six pre-trained TTS teachers to LibriSpeech (LS). Trained on these corpora, BELLE outperforms MELLE under the same training conditions, is competitive with larger TTS systems on the objective metrics of LibriSpeech test-clean, and supports streaming generation. Analyses of repeated human readings show that real
speech has the properties the
Bayesian prior assumes and that the variance predicted by BELLE follows the variation across these readings. Parallel
speech improves BELLE, and synthetic parallel
speech performs comparably to real parallel
speech at the same volume and is far easier to scale.
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All authors Ziyang Zhang, Yifan Gao, Xuenan Xu, Baoxiang Li, Wen Wu, Chao Zhang
Systems and Control
Mechanism-Guided Residual Lifting and Control Consistent Modeling for Pneumatic Drying Processes
Yue Wu
Pneumatic drying processes in industries such as agriculture, chemicals,and pharmaceuticals are notoriously difficult to model and control due to multi-source disturbances,coupled stage dynamics, and significant measurement delays. Traditional modeling paradigms often fail to simultaneously deliver accuracy, interpretability, and closed-loop applicability. To address this challenge, this paper introduces a unified hybrid modeling framework, termed Physics-Guided Residual Lifting with Control-Consistent Correction,which integrates a transient mechanistic model with a stability-constrained data-driven component. The framework covers the complete process chain of drying, transport, and winnowing. On the mechanistic level, the model unifies mass transfer dynamics using the partial pressure difference of water vapor, incorporates water activity clamping and latent heat corrections for bound water, and ensures energy closure with moisture-dependent specific heat. On the data-driven level,we propose an orthogonal residual learning scheme. It leverages intermediate states from the mechanistic model as proxy variables to construct a physics-inspired dictionary, preventing parameter compensation and overfitting during ridge regression. Furthermore, to ensure suitability for predictive control, a Control-Consistent Extended Dynamic Mode Decomposition with stability constraints is employed to learn the residual dynamics, for which we provide boundedness proofs and stability guarantees. The framework was validated on 10 industrial batches, comprising 63,000 samples. On unseen test data, the hybrid model achieved a Mean Absolute Error of 0.016% for outlet moisture and 0.015 {\deg}C for outlet temperature, with values improving to 0.986 and 0.995, respectively. The resulting prediction residuals exhibit white-noise characteristics, with significantly reduced spectral energy at low frequencies.
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Systems and Control · Cross-listed from cs.AI
An N-of-1 Artificial Intelligence Ecosystem for Precision Medicine
Pedram Fard, Alaleh Azhir, Neguine Rezaii et al. (+2 more)
Artificial intelligence in medicine is built to serve the average patient. By minimizing error across large
datasets, most systems deliver strong aggregate accuracy yet falter at the margins: patients with rare variants, multimorbidity, or underrepresented demographics. This average patient fallacy erodes both equity and trust. We propose a different design: a multi-
agent ecosystem for N-of-1 decision support. In this environment,
agents clustered by organ systems, patient populations, and analytic modalities draw on a shared library of models and evidence synthesis tools. Their results converge in a coordination layer that weighs reliability, uncertainty, and data density before presenting the clinician with a decision-support packet: risk estimates bounded by confidence ranges, outlier flags, and linked evidence. Validation shifts from population averages to individual reliability, measured by error in low-density regions, calibration in the small, and risk–coverage trade-offs. Anticipated challenges include computational demands, automation bias, and regulatory fit, addressed through caching strategies, consensus checks, and adaptive trial frameworks. By moving from monolithic models to orchestrated intelligence, this approach seeks to align medical AI with the first principle of medicine: care that is transparent, equitable, and centered on the individual.
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All authors Pedram Fard, Alaleh Azhir, Neguine Rezaii, Jiazi Tian, Hossein Estiri
Signal Processing
Achieving Constant-Envelope Waveform in CP-OFDMA Framework
Yiming Zhu, Zhuhong Zhu, Xiaodong Xu et al. (+3 more)
OFDM is widely adopted in modern wireless communication systems, but its power efficiency is limited by high envelope fluctuations. Although various high power-efficiency waveforms have been proposed, most are incompatible with the CP-OFDMA framework and remain ineffective in multi-user downlink transmissions. To address this issue, we propose a constant-envelope (CE) waveform design, which enables low-complexity transceiver architectures while maintaining full compatibility with the prevailing CP-OFDMA framework. Specifically, we start from a general CE FDMA signal model and develop a CP-OFDMA-compatible waveform implementation structure, followed by the design of an optimized CE-constrained pulse-shaping filter to suppress out-of-band emissions. To tackle channel estimation challenge under non-flat frequency-domain pilots induced by CE modulation, we optimize the time-domain binary pilot sequence to achieve frequency-domain CE properties, and then propose a multi-stage method combining delay-domain denoising with power delay profile estimation to facilitate reduced-dimension LMMSE estimation. Subsequently, we design a low-complexity maximum ratio combining-aided LMMSE equalizer by exploiting the periodicity and conjugate
symmetry of the CE received signals. To mitigate the downlink peak-to-average power ratio increase caused by FDMA, we further develop a multi-user downlink CE transmission scheme including multiple access mechanism, downlink control information design, and corresponding system-level implementation, which ensures compatibility with the New
Radio standard. Numerical results demonstrate that the proposed scheme achieves bit error rate performance close to the ideal case while significantly reducing transceiver complexity compared to existing CE waveform solutions.
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All authors Yiming Zhu, Zhuhong Zhu, Xiaodong Xu, Hongwei Hou, Wenjin Wang, Rui Ding
Image and Video Processing
High-Quality and Large-Scale Image Downscaling for Modern Display Devices
Suvrojit Mitra, G B Kevin Arjun and Sanjay Ghosh
In modern display technology and visualization tools, downscaling images is one of the most important activities. This procedure aims to maintain both visual authenticity and structural integrity while reducing the dimensions of an image at a large scale to fit the dimension of the display devices. In this study, we proposed a new technique for downscaling images that uses co-occurrence learning to maintain structural and perceptual information while reducing resolution. The technique uses the input image to create a data-driven co-occurrence profile that captures the frequency of intensity correlations in nearby neighborhoods. A refined filtering process is guided by this profile, which acts as a content-adaptive range kernel. The contribution of each input pixel is based on how closely it resembles pair-wise intensity values with it's neighbors. We validate our proposed technique on four
datasets: DIV2K, BSD100, Urban100, and RealSR to show its effective downscaling capacity. Our technique could obtain up to 39.22 dB PSNR on the DIV2K
dataset and PIQE up to 26.35 on the same
dataset when downscaling by 8x and 16x, respectively. Numerous experimental findings attest to the ability of the suggested picture downscaling method to outperform more contemporary approaches in terms of both visual quality and performance measures. Unlike most existing methods, which did not focus on the large-scale image resizing scenario, we achieve high-quality downscaled images without texture loss or edge blurring. Our method, LSID (large scale image downscaling), successfully preserves high-frequency structures like edges, textures, and repeating patterns by focusing on statistically consistent pixels while reducing aliasing and blurring artifacts that are typical of traditional downscaling techniques.
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Audio and Speech Processing · Cross-listed from cs.SD
Sound Source Localization for Spatial Mapping of Surgical Actions in Dynamic Scenes
Jonas Hein, Lazaros Vlachopoulos, Maurits Geert Laurent Olthof et al. (+3 more)
Purpose:
Surgical scene understanding is key to advancing computer-aided and intelligent
surgical systems. Current approaches predominantly rely on visual data or end-to-end learning, which limits fine-grained contextual modeling. This work aims to enhance
surgical scene representations by integrating 3D acoustic information, enabling temporally and spatially aware
multimodal understanding of
surgical environments. Methods: We propose a novel framework for generating 4D audio-visual representations of
surgical scenes by projecting acoustic localization information from a phased microphone array onto dynamic point clouds from an RGB-D camera. A
transformer-based acoustic event detection module identifies relevant temporal segments containing tool-tissue interactions which are spatially localized in the audio-visual scene representation. The system was experimentally evaluated in a realistic operating room setup during simulated
surgical procedures performed by experts. Results: The proposed method successfully localizes
surgical acoustic events in 3D space and associates them with visual scene elements. Experimental evaluation demonstrates accurate spatial sound localization and robust fusion of
multimodal data, providing a comprehensive, dynamic representation of
surgical activity. Conclusion: This work introduces the first approach for spatial sound localization in dynamic
surgical scenes, marking a significant advancement toward
multimodal surgical scene representations. By integrating acoustic and visual data, the proposed framework enables richer contextual understanding and provides a foundation for future intelligent and autonomous
surgical systems.
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All authors Jonas Hein, Lazaros Vlachopoulos, Maurits Geert Laurent Olthof, Bastian Sigrist, Philipp Fürnstahl, Matthias Seibold
Signal Processing
Towards actionable hypotension prediction – predicting catecholamine therapy initiation in the intensive care unit
Richard Koebe, Noah Saibel, Juan Miguel Lopez Alcaraz et al. (+2 more)
Hypotension in critically ill ICU patients is common and life-threatening. Escalation to catecholamine therapy marks a key management step, with both undertreatment and overtreatment posing risks. Most machine learning (ML) models predict hypotension using fixed MAP thresholds or MAP
forecasting, overlooking the clinical decision behind treatment escalation. Predicting catecholamine initiation, the start of vasoactive or inotropic
agent administration offers a more clinically actionable target reflecting real decision-making. Using the MIMIC-III database, we modeled catecholamine initiation as a binary event within a 15-minute prediction window. Input features included statistical descriptors from a two-hour sliding MAP context window, along with demographics, biometrics, comorbidities, and ongoing treatments. An Extreme Gradient Boosting (XGBoost) model was trained and interpreted via SHapley Additive exPlanations (SHAP). The model achieved an AUROC of 0.822 (0.813-0.830), outperforming the hypotension baseline (MAP < 65, AUROC 0.686 [0.675-0.699]). SHAP analysis highlighted recent MAP values, MAP trends, and ongoing treatments (e.g., sedatives, electrolytes) as dominant predictors. Subgroup analysis showed higher performance in males, younger patients (<53 years), those with higher BMI (>32), and patients without comorbidities or concurrent medications. Predicting catecholamine initiation based on MAP dynamics, treatment context, and patient characteristics supports the critical decision of when to escalate therapy, shifting focus from threshold-based alarms to actionable decision support. This approach is feasible across a broad ICU cohort under natural event imbalance. Future work should enrich temporal and physiological context, extend label definitions to include therapy escalation, and
benchmark against existing hypotension prediction systems.
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All authors Richard Koebe, Noah Saibel, Juan Miguel Lopez Alcaraz, Simon Schäfer, Nils Strodthoff
Audio and Speech Processing · Cross-listed from cs.SD
TsetlinKWS: A 65nm 16.58uW, 0.63mm2 State-Driven Convolutional Tsetlin Machine-Based Accelerator For Keyword Spotting
Baizhou Lin, Yuetong Fang, Renjing Xu et al. (+2 more)
The Tsetlin Machine (TM) has recently attracted
attention as a low-power alternative to neural networks due to its simple and interpretable inference mechanisms. However, its performance on
speech-related tasks remains limited. This paper proposes TsetlinKWS, the first
algorithm-hardware co-design framework for the Convolutional Tsetlin Machine (CTM) on the 12-keyword spotting task. Firstly, we introduce a novel Mel-Frequency Spectral Coefficient and Spectral Flux (MFSC-SF) feature extraction scheme together with spectral convolution, enabling the CTM to reach its first-ever competitive accuracy of 87.35% on the 12-keyword spotting task. Secondly, we develop an Optimized Grouped Block-Compressed Sparse Row (OG-BCSR)
algorithm that achieves a remarkable 9.84$\times$ reduction in model size, significantly improving the storage efficiency on CTMs. Finally, we propose a state-driven architecture tailored for the CTM, which simultaneously exploits data reuse and sparsity to achieve high energy efficiency. The full system is evaluated in 65 nm process technology, consuming 16.58 $\mu$W at 0.7 V with a compact 0.63 mm$^2$ core area. TsetlinKWS requires only 907k logic operations per inference, representing a 10$\times$ reduction compared to the state-of-the-art KWS accelerators, positioning the CTM as a highly-efficient candidate for ultra-low-power
speech applications.
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All authors Baizhou Lin, Yuetong Fang, Renjing Xu, Rishad Shafik, Jagmohan Chauhan
Audio and Speech Processing · Cross-listed from cs.SD
HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves
Matteo Calafà, Yuanxin Xia and Cheol-Ho Jeong
We present a novel neural network architecture for the efficient prediction of sound fields in two and three dimensions. The network is designed to automatically satisfy the Helmholtz equation, ensuring that the outputs are physically valid. Therefore, the method can effectively learn solutions to boundary-value problems in various wave phenomena, such as acoustics, optics, and electromagnetism. Numerical experiments show that the proposed strategy can potentially outperform state-of-the-art methods in room acoustics simulation, in particular in the range of mid to high frequencies.
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Systems and Control
Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering
Maximilian Bloor, Max Mowbray, Ehecatl Antonio Del Rio Chanona et al. (+1 more)
Sequential decision making under uncertainty is central to many Process Systems Engineering (PSE) challenges, where traditional methods often face limitations related to controlling and optimizing complex and
stochastic systems. Reinforcement Learning (RL) offers a data-driven approach to derive control policies for such challenges. This paper presents a survey and tutorial on RL methods, tailored for the PSE community. We deliver a tutorial on RL, covering fundamental concepts and key algorithmic families including value-based, policy-based and actor-critic methods. Subsequently, we survey existing applications of these RL techniques across various PSE domains, such as in fed-batch and continuous process control, process optimization, and supply chains. We conclude with PSE focused discussion of specialized techniques and emerging directions. By synthesizing the current state of RL
algorithm development and implications for PSE this work identifies successes, challenges, trends, and outlines avenues for future research at the interface of these fields.
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All authors Maximilian Bloor, Max Mowbray, Ehecatl Antonio Del Rio Chanona, Calvin Tsay
Signal Processing
Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach
Jihao Luo, Zesong Fei, Xinyi Wang et al. (+4 more)
Unmanned aerial vehicles (
UAVs) are emerging as key enablers for low-altitude wireless network (LAWN), particularly when terrestrial networks are unavailable. In such scenarios, the environmental
topology is typically unknown; hence, designing efficient and safe
UAV trajectories is essential yet challenging. To address this, we propose a digital twin (DT)-assisted training and deployment framework. In this framework, the
UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs). These VEs accelerate model training and are continuously updated with real-time
UAV sensing data during deployment, supporting decision-making and enhancing flight safety. Based on this framework, we further develop a trajectory design scheme that integrates simulated annealing for efficient user scheduling with the twin-delayed deep deterministic policy gradient
algorithm for continuous trajectory design, aiming to minimize mission completion time while ensuring obstacle avoidance. Simulation results demonstrate that the proposed approach achieves faster convergence, higher flight safety, and shorter mission completion time compared with baseline methods, providing a robust and efficient solution for LAWN deployment in unknown environments.
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All authors Jihao Luo, Zesong Fei, Xinyi Wang, Le Zhao, Yuanhao Cui, Guangxu Zhu, Dusit Niyato
Signal Processing
Joint Beamforming for Multi-user Multi-target FD ISAC System: A Hybrid GRQ-GA Approach
Duc Nguyen Dao, Haibin Zhang, Andre B. J. Kokkeler et al. (+1 more)
In this paper, we consider a full-duplex (FD) Integrated Sensing and Communication (
ISAC) system, in which the base station (BS) performs downlink and uplink communications with multiple users while simultaneously sensing multiple targets. In the scope of this work, we assume a narrowband and static scenario, aiming to focus on the beamforming and power allocation strategies. We propose a joint beamforming strategy for designing transmit and receive beamformer vectors at the BS. The optimization problem aims to maximize the communication sum-rate, which is critical for ensuring high-quality service to users, while also maintaining accurate sensing performance for detection tasks and adhering to maximum power constraints for efficient resource usage. The optimal receive beamformers are first derived using a closed-form Generalized Rayleigh Quotient (GRQ) solution, reducing the variables to be optimized. Then, the remaining problem is solved using floating-point Genetic
Algorithms (GA). The numerical results show that the proposed GA-based solution demonstrates up to a 98% enhancement in sum-rate compared to a baseline half-duplex
ISAC system and provides better performance than a
benchmark algorithm from the literature. Additionally, it offers insights into sensing performance effects on beam patterns as well as communicationsensing trade-offs in multi-target scenarios.
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All authors Duc Nguyen Dao, Haibin Zhang, Andre B. J. Kokkeler, Yang Miao
Systems and Control
A comparison between joint and dual UKF implementations for state estimation and leak localization in water distribution networks
Luis Romero-Ben, Paul Irofti, Florin Stoican et al. (+1 more)
The sustainability of modern cities highly depends on efficient water distribution management, including effective pressure control and leak detection and localization. Accurate information about the network hydraulic state is therefore essential. This article presents a comparison between two data-driven state estimation methods based on the Unscented Kalman Filter (UKF), fusing pressure, demand and flow data for head and flow estimation. One approach uses a joint state vector with a single
estimator, while the other uses a dual-
estimator scheme. We analyse their main characteristics, discussing differences, advantages and limitations, and compare them theoretically in terms of accuracy and complexity. Finally, we show several estimation results for the L-TOWN
benchmark, allowing to discuss their properties in a real implementation.
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All authors Luis Romero-Ben, Paul Irofti, Florin Stoican, Vicenç Puig
Signal Processing
Pilot Distortion Design for ToA Obfuscation in Uplink OFDM Communication
Mahmut Kemal Ercan, Alireza Pourafzal, Musa Furkan Keskin et al. (+2 more)
We study uplink orthogonal frequency-division multiplexing (OFDM) pilot distortion to deliberately obfuscate time-of-arrival (ToA) estimation at a single base station while preserving communication performance. We design a complex per-subcarrier distortion vector that increases sidelobes of the mismatched ambiguity function (MAF) relative to its mainlobe, using two objectives: the sidelobe-to-peak level ratio and the integrated sidelobe level. The design is subject to a transmit-power budget and a proximity (dissimilarity) constraint around the communication-optimal pilot. Communication impact is quantied by a capacity-motivated lower bound obtained from the linear minimum mean-squared error error covariance with a mismatched channel estimate. The resulting generalized fractional program is solved with Dinkelbach's transform and a difference-of-convex update that yields a closed-form Karush-Kuhn-Tucker step. Simulations on a single-input single-output OFDM link show that the optimized distortions raise MAF sidelobes and degrade delay estimation, as validated by a mismatched maximum-likelihood ToA
estimator, while incurring only marginal capacity loss over a broad signal-to-noise ratio range. The method requires no protocol changes or artificial path injection and provides a signal-level mechanism to control ToA observability under communication constraints.
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All authors Mahmut Kemal Ercan, Alireza Pourafzal, Musa Furkan Keskin, Sinan Gezici, Henk Wymeersch
Signal Processing · Cross-listed from cs.IT
Robustness to Sparse Adversarial Corruption in Arbitrary Linear Measurements: Beyond Exact Recovery
Vishal Halder, Alexandre Reiffers-Masson, Abdeldjalil Aïssa-El-Bey et al. (+1 more)
Recovery from linear measurements under sparse
adversarial corruption is typically formulated as an exact-recovery problem: one seeks structural conditions on $\mathbf{A}$ (e.g., restricted isometry property) guaranteeing unique recovery of $\mathbf{x}^\star$ from $\mathbf{y} = \mathbf{A}\mathbf{x}^\star + \mathbf{e}$ with $\|\mathbf{e}\|_0 \leq q$. However, these guarantees provide no guidance once exact recovery fails. This limitation obscures simple robustness phenomena – for instance, repeated rows in $\mathbf{A}$ can preserve nontrivial information about $\mathbf{x}^\star$ under sparse corruption. In this paper, we study what information about $\mathbf{x}^\star$ can be uniformly recovered from $\mathbf{y} = \mathbf{A}\mathbf{x}^\star + \mathbf{e}$ for arbitrary $\mathbf{A}\in\mathbb{R}^{m\times n}$ and any $q$-sparse $\mathbf{e}$. We show that the robust information is precisely $\mathbf{x}^\star + \ker(\mathbf{U})$, where $\mathbf{U}$ is the orthogonal projection onto the intersection of rowspaces of all submatrices of $\mathbf{A}$ obtained by deleting $2q$ rows. This clarifies how the row structure of $\mathbf{A}$ governs whether a $q$-sparse corruption allows exact, partial, or only trivial recovery. We further prove every $\mathbf{x}$ minimizing $\|\mathbf{y} - \mathbf{A} \mathbf{x}\|_0$ belongs to $\mathbf{x}^\star + \ker(\mathbf{U})$, yielding a constructive approach to recover this set. For i.i.d. Gaussian
matrices, we establish a sharp phase transition between exact and trivial recovery. We sketch two applications: robust network
tomography and signal reconstruction from oversampled DCT.
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All authors Vishal Halder, Alexandre Reiffers-Masson, Abdeldjalil Aïssa-El-Bey, Gugan Thoppe
Signal Processing
Dual-Domain Constraints: Designing Covert and Efficient Adversarial Examples for Secure Communication
Tailai Wen, Da Ke, Xiang Wang et al. (+1 more)
The advancements in Automatic Modulation Classification (AMC) have propelled the development of signal sensing and identification technologies in non-cooperative communication scenarios but also enable eavesdroppers to effectively intercept user signals in wireless communication environments. To protect user
privacy in communication links, we have optimized the
adversarial example generation model and introduced a novel framework for generating
adversarial perturbations for transmitted signals. This framework implements dual-domain constraints in both the time and frequency domains, ensuring that the
adversarial perturbation cannot be filtered out. Comparative experiments confirm the superiority of the proposed method and the concealment of the
adversarial examples it generates.
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All authors Tailai Wen, Da Ke, Xiang Wang, Zhitao Huang