Systems and Control · Cross-listed from cs.CR
Lifecycle-Integrated Security for AI-Cloud Convergence in Cyber-Physical Infrastructure
S M Zia Ur Rashid, Deepa Gurung, Sonam Raj Gupta et al. (+1 more)
The convergence of Artificial Intelligence (AI) inference pipelines with cloud infrastructure creates a dual attack surface where cloud security standards and AI governance frameworks intersect without unified enforcement mechanisms. AI governance, cloud security, and industrial control system standards intersect without unified enforcement, leaving hybrid deployments exposed to cross-layer attacks that threaten safety-critical operations. This paper makes three primary contributions: (i) we synthesize these frameworks into a lifecycle-staged threat taxonomy structured around explicit attacker capability tiers, (ii) we propose a Unified Reference Architecture spanning a Secure Data Factory, a hardened model supply chain, and a runtime governance layer, (iii) we present a case study through Grid-Guard, a hybrid Transmission System Operator scenario in which coordinated defenses drawn from NIST AI RMF, MITRE ATLAS, OWASP AI Exchange and GenAI, CSA MAESTRO, and NERC CIP defeat a multi-tier physical-financial manipulation campaign without human intervention. Controls are mapped against all five frameworks and current NERC CIP standards to demonstrate that a single cloud-native architecture can simultaneously satisfy AI governance,
adversarial robustness, agentic safety, and industrial regulatory compliance obligations.
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All authors S M Zia Ur Rashid, Deepa Gurung, Sonam Raj Gupta, Suman Rath
Systems and Control
Millimeter-Wave RIS: Hardware Design and System-Level Considerations
Ruiqi Wang, Pinjun Zheng, Yiming Yang et al. (+6 more)
Reconfigurable intelligent surfaces have emerged as a promising hardware platform for shaping wireless propagation environments at millimeter-wave (mm-Wave) frequencies and beyond. While many existing studies emphasize channel modeling and signal processing, practical RIS deployment is fundamentally governed by hardware design choices and their system-level implications. This paper presents a hardware-centric overview of recent mm-Wave RIS developments, covering wideband realizations, high-resolution phase-quantized designs, fully printed low-cost implementations, optically transparent surfaces, RIS-on-chip solutions, and emerging three-dimensional architectures. Key challenges including mutual coupling, calibration, multi-RIS interaction, and frequency-dependent phase control are discussed to bridge hardware realization with system-level optimization. This overview provides practical design insights and aims to guide future RIS research toward scalable, efficient, and practically deployable intelligent surface architectures.
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All authors Ruiqi Wang, Pinjun Zheng, Yiming Yang, Xiarui Su, Mohammad Vaseem, Anas Chaaban, Md. Jahangir Hossain, Tareq Y. Al-Naffouri, Atif Shamim
Signal Processing
CubeSounder: Low SWaP-C 180 GHz Radiometer for Atmospheric Sensing Tested on High Altitude Balloons
Kyle D. Massingill, Tyler M. Karasinski, Sean Bryan et al. (+13 more)
Microwave sounding is the leading driver of global numerical weather
forecasting, but is limited by the scalability of such instruments. With modern machining and commercial microwave components, it is now possible to design low size, weight, power, and cost (SWaP-C) microwave spectrometers while maintaining wide bandwidth performance. Here we report on the status of CubeSounder, a spectrometer tailored for water vapor radiometry that utilizes passive wave guide filter banks. After developing a prototype and high altitude balloon payload, we demonstrated CubeSounder on commercial stratospheric balloon flights. We report on our design process, especially the simulation and fabrication of the custom millimeter-wave filter banks. We also report the initial results of the data collected from the balloon flights.
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All authors Kyle D. Massingill, Tyler M. Karasinski, Sean Bryan, Michael Baricuatro, Daniel Bliss, Delondrae Carter, Walter Goodwin, Jonathan Greenfield, Christopher Groppi, Philip Mauskopf, Philip Rybak, Scott Smas, Roshni Suresh, Sage Tinlin, Bianca Wullen, Peter Wullen
Systems and Control
Signal Temporal Logic Evaluation and Synthesis Using Deep Reachability Analysis and Layered Control Architecture
Joonwon Choi, Kartik Anand Pant, Youngim Nam et al. (+3 more)
We propose a signal temporal logic (STL)-based framework that checks the feasibility of a mission described in STL and synthesizes control to safely execute it. The proposed framework evaluates the safety and reliability of operation through two phases. First, the proposed framework assesses the feasibility of STL by computing a backward reachable tube (BRT), which captures all states that can satisfy the given STL. The proposed framework accommodates the multiple reach-avoid (MRA) problem to address more general STL specifications and leverages a deep neural network to alleviate the computation burden for reachability analysis, significantly reducing the inference time compared to a baseline method. We further propose a layered planning and control architecture that combines mixed-integer linear programming (MILP) for global planning with model predictive control (MPC) as a local controller for the given STL. Consequently, the proposed framework can robustly handle unexpected behavior of obstacles that are not described in the environment information or STL, thereby providing reliable mission performance. Our numerical simulations demonstrate that the proposed framework can successfully compute BRT for a given STL and perform the mission.
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All authors Joonwon Choi, Kartik Anand Pant, Youngim Nam, Henry Hellmann, Karthik Nune, Inseok Hwang
Systems and Control · Cross-listed from cs.HC
Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction
Rafael R. Baptista, André de Lima Salgado, Ricardo V. Godoy et al. (+3 more)
Leader-follower interaction is an important paradigm in human-
robot interaction (HRI). Yet, assigning roles in real time remains challenging for resource-constrained mobile and assistive
robots. While large
language models (LLMs) have shown promise for natural communication, their size and latency limit on-device deployment. Small
language models (SLMs) offer a potential alternative, but their effectiveness for role classification in HRI has not been systematically evaluated. In this paper, we present a
benchmark of SLMs for leader-follower communication, introducing a novel
dataset derived from a published database and augmented with synthetic samples to capture interaction-specific dynamics. We investigate two adaptation strategies:
prompt engineering and fine-tuning, studied under zero-shot and one-shot interaction modes, compared with an untrained baseline. Experiments with Qwen2.5-0.5B reveal that zero-shot fine-tuning achieves robust classification performance (86.66% accuracy) while maintaining low latency (22.2 ms per sample), significantly outperforming baseline and
prompt-engineered approaches. However, results also indicate a performance degradation in one-shot modes, where increased context length challenges the model's architectural capacity. These findings demonstrate that fine-tuned SLMs provide an effective solution for direct role assignment, while highlighting critical trade-offs between dialogue complexity and classification reliability on the edge.
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All authors Rafael R. Baptista, André de Lima Salgado, Ricardo V. Godoy, Marcelo Becker, Thiago Boaventura, Gustavo J. G. Lahr
Audio and Speech Processing · Cross-listed from cs.CL
A Mixture-of-Experts Model for Multimodal Emotion Recognition in Conversations
Soumya Dutta, Smruthi Balaji and Sriram Ganapathy
Emotion Recognition in Conversations (ERC) presents unique challenges, requiring models to capture the temporal flow of multi-turn dialogues and to effectively integrate cues from multiple modalities. We propose Mixture of
Speech-Text Experts for Recognition of
Emotions (MiSTER-E), a modular Mixture-of-Experts (MoE) framework designed to decouple two core challenges in ERC: modality-specific context modeling and
multimodal information fusion. MiSTER-E leverages large
language models (LLMs) fine-tuned for both
speech and text to provide rich utterance-level
embeddings, which are then enhanced through a convolutional-recurrent context modeling layer. The system integrates predictions from three experts-
speech-only, text-only, and cross-modal-using a learned gating mechanism that dynamically weighs their outputs. To further encourage consistency and
alignment across modalities, we introduce a supervised contrastive loss between paired
speech-text representations and a KL-divergence-based regulariza-tion across expert predictions. Importantly, MiSTER-E does not rely on speaker identity at any stage. Experiments on three
benchmark datasets-IEMOCAP, MELD, and MOSI-show that our proposal achieves 70.9%, 69.5%, and 87.9% weighted F1-scores respectively, outperforming several baseline
speech-text ERC systems. We also provide various ablations to highlight the contributions made in the proposed approach.
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Signal Processing
Analog Time Multiplexing for Digital-to-Analog Conversion
Juana M. Martínez-Heredia and Alfredo P. Vega-Leal
The signal bandwidth of Digital to Analog Converters based on Sigma Delta Modulation is limited by speed constrains. Time-Interleaving allows coping with complexity vs. speed by replacing the original architecture by M parallel paths. These path are clocked at a frequency M times smaller and their digital outputs time multiplexed. This is then converted to analog by means of a Digital to Analog Converter clocked at the high rate. This preprint proposes that time multiplexing be performed in the analog domain. As a result robustness against dynamic effects is achieved.
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Signal Processing
A Scaling Law for Bandwidth Under Quantization
Maximilian Kalcher and Tena Dubcek
We derive a scaling law relating ADC bit depth to effective bandwidth for signals with $1/f^\alpha$ power spectra. Quantization introduces a flat noise floor whose intersection with the declining signal spectrum defines an effective cutoff frequency $f_c$. We show that each additional bit extends this cutoff by a factor of $2^{2/\alpha}$, approximately doubling bandwidth per bit for $\alpha = 2$. The law requires that quantization noise be approximately white, a condition whose minimum bit depth $N_{\min}$ we show to be $\alpha$-dependent. Validation on synthetic $1/f^\alpha$ signals for $\alpha \in \{1.5, 2.0, 2.5\}$ yields prediction errors below 3% using the theoretical noise floor $\Delta^2/(6f_s)$, and approximately 14% when the noise floor is estimated empirically from the quantized signal's spectrum. We illustrate practical implications on real
EEG data.
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Image and Video Processing · Cross-listed from cs.CV
Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
Chenhe Du, Xuanyu Tian, Qing Wu et al. (+4 more)
Plug-and-Play
diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generative models as modular priors. However, we identify a critical flaw in prevailing PnP solvers (e.g., based on HQS or Proximal Gradient): they function as memoryless operators, updating estimates solely based on instantaneous gradients. This lack of historical tracking inevitably leads to non-vanishing steady-state bias, where the reconstruction fails to strictly satisfy physical measurements under heavy corruption. To resolve this, we propose Dual-Coupled PnP
Diffusion (DC-PnPDP), which restores the classical dual variable to provide integral feedback, progressively enforce agreement between the data-consistency and prior. However, this rigorous
geometric coupling introduces a secondary challenge: the accumulated dual residuals exhibit spectrally colored, structured artifacts that violate the Additive White Gaussian Noise (AWGN) assumption of
diffusion priors, causing severe
hallucinations. To bridge this gap, we introduce Spectral Homogenization (SH), a frequency-domain adaptation mechanism that modulates these structured residuals into statistically compliant pseudo-AWGN inputs. This effectively aligns the solver's rigorous optimization trajectory with the denoiser's valid statistical
manifold. Extensive experiments on CT and MRI reconstruction demonstrate that our approach resolves the bias-
hallucination trade-off, achieving state-of-the-art fidelity with significantly accelerated convergence. The code is available at https://github.com/duchenhe/DC-PnPDP
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All authors Chenhe Du, Xuanyu Tian, Qing Wu, Muyu Liu, Jingyi Yu, Hongjiang Wei, Yuyao Zhang
Audio and Speech Processing
Align-Consistency: Improving Non-autoregressive and Semi-supervised ASR with Consistency Regularization
Wanting Huang and Weiran Wang
Consistency regularization (CR) improves the robustness and accuracy of Connectionist Temporal Classification (CTC) by ensuring predictions remain stable across input perturbations. In this work, we propose Align-Consistency, an extension of CR designed for Align-Refine – a non-autoregressive (non-AR) model that performs iterative refinement of frame-level hypotheses. This method leverages the speed of parallel inference while significantly boosting recognition performance. The effectiveness of Align-Consistency is demonstrated in two settings. First, in the fully supervised setting, our results indicate that applying CR to both the base CTC model and the subsequent refinement steps is critical, and the accuracy improvements from non-AR decoding and CR are mutually additive. Second, for semi-supervised ASR, we employ fast non-AR decoding to generate online pseudo-labels on unlabeled data, which are used to further refine the supervised model and lead to substantial gains.
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Audio and Speech Processing
A Directional-Derivative-Constrained Method for Continuously Steerable Differential Beamformers with Uniform Circular Arrays
Tiantian Xiong, Yongyi Deng, Kunlong Zhao et al. (+5 more)
Differential microphone arrays offer a promising solution for far-field acoustic signal acquisition due to their high spatial directivity and compact array structure. A key challenge lies in designing differential beamformers that are continuously steerable and capable of enhancing target signals arriving from arbitrary directions. This paper studies the design of differential beamformers for circular arrays and proposes a novel framework that incorporates directional derivative constraints. By constraining the first-order derivatives of the beampattern at the desired steering direction to zero and assigning suitable values to higher-order derivatives, the beamformer is ensured to achieve its maximum response in the target direction and provide sufficient beam steering. This approach not only improves steering flexibility but also enables a more intuitive and robust beampattern design. Simulation results demonstrate that the proposed method produces continuously steerable beampatterns.
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All authors Tiantian Xiong, Yongyi Deng, Kunlong Zhao, Jilu Jin, Xueqin Luo, Gongping Huang, Jingdong Chen, Jacob Benesty
Audio and Speech Processing · Cross-listed from cs.SD
Make It Hard to Hear, Easy to Learn: Long-Form Bengali ASR and Speaker Diarization via Extreme Augmentation and Perfect Alignment
Sanjid Hasan, Risalat Labib, A H M Fuad et al. (+1 more)
Although Automatic
Speech Recognition (ASR) in Bengali has seen significant progress, processing long-duration audio and performing robust speaker diarization remain critical research gaps. To address the severe scarcity of joint ASR and diarization resources for this
language, we introduce Lipi-Ghor-882, a comprehensive 882-hour multi-speaker Bengali
dataset. In this paper, detailing our submission to the DL Sprint 4.0 competition, we systematically evaluate various architectures and approaches for long-form Bengali
speech. For ASR, we demonstrate that raw data scaling is ineffective; instead, targeted fine-tuning utilizing perfectly aligned annotations paired with synthetic acoustic degradation (noise and reverberation) emerges as the singular most effective approach. Conversely, for speaker diarization, we observed that global open-source state-of-the-art models (such as Diarizen) performed surprisingly poorly on this complex
dataset. Extensive model retraining yielded negligible improvements; instead, strategic, heuristic post-processing of baseline model outputs proved to be the primary driver for increasing accuracy. Ultimately, this work outlines a highly optimized dual pipeline achieving a $\sim$0.019 Real-Time Factor (RTF), establishing a practical, empirically backed
benchmark for low-resource, long-form
speech processing.
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All authors Sanjid Hasan, Risalat Labib, A H M Fuad, Bayazid Hasan
Systems and Control · Cross-listed from cs.AI
Learning-based Multi-agent Race Strategies in Formula 1
Giona Fieni, Joschua Wüthrich, Marc-Philippe Neumann et al. (+1 more)
In Formula 1, race strategies are adapted according to evolving race conditions and competitors' actions. This paper proposes a reinforcement learning approach for multi-
agent race strategy optimization.
Agents learn to balance energy management, tire degradation, aerodynamic interaction, and pit-stop decisions. Building on a pre-trained single-
agent policy, we introduce an interaction module that accounts for the behavior of competitors. The combination of the interaction module and a self-play training scheme generates competitive policies, and
agents are ranked based on their relative performance. Results show that the
agents adapt pit timing, tire selection, and energy allocation in response to opponents, achieving robust and consistent race performance. Because the framework relies only on information available during real races, it can support race strategists' decisions before and during races.
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All authors Giona Fieni, Joschua Wüthrich, Marc-Philippe Neumann, Christopher H. Onder
Systems and Control
Integrated Flight and Propulsion Control for Fixed-Wing UAVs via Thrust and Disturbance Compensation
Chong-Yi Sun, Heling Yuan, Xu Fang et al. (+2 more)
This paper investigates the position-tracking control problem for fixed-wing unmanned aerial vehicles (
UAVs) equipped with a turbojet engine via an integrated flight and propulsion control scheme. To this end, a hierarchical control framework with thrust and disturbance compensation is proposed. In particular, we first propose a perturbed fixed-wing
UAV model with turbojet engine dynamics, accounting for both unmodeled dynamics and external disturbances. Second, a versatile extended observer is designed to handle both unmeasurable thrust dynamics and external disturbances. Third, a hierarchical control framework is implemented using three observer-based controllers to guarantee position-tracking performance. With the proposed control strategy, we prove that the closed-loop system asymptotically converges to the desired trajectory. Finally, a comparative simulation is performed to illustrate the proposed control strategy.
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All authors Chong-Yi Sun, Heling Yuan, Xu Fang, Yan He, Xi-Ming Sun
Signal Processing
Scattering Transform for Auditory Attention Decoding
René Pallenberg, Fabrice Katzberg, Alfred Mertins et al. (+1 more)
The use of hearing aids will increase in the coming years due to demographic change. One open problem that remains to be solved by a new generation of hearing aids is the cocktail party problem. A possible solution is electroencephalography-based auditory
attention decoding. This has been the subject of several studies in recent years, which have in common that they use the same preprocessing methods in most cases. In this work, in order to achieve an advantage, the use of a scattering transform is proposed as an alternative to these preprocessing methods. The two-layer scattering transform is compared with a regular filterbank, the synchrosqueezing short-time
Fourier transform and the common preprocessing. To demonstrate the performance, the known and the proposed preprocessing methods are compared for different classification tasks on two widely used
datasets, provided by the KU Leuven (KUL) and the Technical University of Denmark (DTU). Both established and new neural-network-based models, CNNs, LSTMs, and recent
Transformer/graph-based models are used for classification. Various evaluation strategies were compared, with a focus on the task of classifying speakers who are unknown from the training. We show that the two-layer scattering transform can significantly improve the performance for subject-related conditions, especially on the KUL
dataset. However, on the DTU
dataset, this only applies to some of the models, or when larger amounts of training data are provided, as in 10-fold cross-validation. This suggests that the scattering transform is capable of extracting additional relevant information.
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All authors René Pallenberg, Fabrice Katzberg, Alfred Mertins, Marco Maass
Signal Processing
Digital Twin-Based Beamforming for Interference Mitigation in AF Relay MIMO Systems
Alexander Bonora, Anna V. Guglielmi, Davide Scazzoli et al. (+4 more)
Beamforming in multiple-input multiple-output (
MIMO) systems should take interference mitigation into account. However, for beamform design, accurate channel state information (CSI) is needed, which is often difficult to obtain due to channel variability, feedback overhead, or hardware constraints. For example, amplify-and-forward (AF) relays passively forward signals without measurement, precluding full CSI acquisition to and from the relay. To address these issues, this paper introduces a novel prediction-assisted optimization (PAO) framework for beamform design in AF relay-assisted multiuser
MIMO systems. The proposed solution in the AF relay aims at maximizing the signal-plus-interference-to-noise ratio (SINR). Unlike other methods, PAO relies solely on received power measurements, making it suitable for scenarios where CSI is unreliable or unavailable. PAO consists of two stages: a supervised-learning-based neural network (NN) that predicts the positions of transmitters using signal observations, and an optimization
algorithm, guided by a digital twin (DT), that iteratively refines the beam direction of the relay in a simulated
radio environment. As a key contribution, we validate the proposed framework using realistic measurements collected on a custom-built experimental millimeter wave (mmWave) platform, which enables training of the NN model under practical wireless conditions. The estimated information is then used to update the digital twin with knowledge of the surrounding environment, enabling online optimization. Numerical results show the trade-off between localization accuracy and beamforming performance and confirm that PAO maintains robustness even in the presence of localization errors while reducing the need for real-world measurements.
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All authors Alexander Bonora, Anna V. Guglielmi, Davide Scazzoli, Marco Giordani, Maurizio Magarini, Vineeth Teeda, Stefano Tomasin
Image and Video Processing · Cross-listed from cs.CE
An automatic counting algorithm for the quantification and uncertainty analysis of the number of microglial cells trainable in small and heterogeneous datasets
L. Martino, M. M. Garcia, P. S. Paradas et al. (+1 more)
Counting immunopositive
cells on biological tissues generally requires either manual annotation or (when available) automatic rough systems, for scanning signal surface and intensity in whole slide imaging. In this work, we tackle the problem of counting microglial
cells in lumbar spinal cord cross-sections of rats by omitting
cell detection and focusing only on the counting task. Manual
cell counting is, however, a time-consuming task and additionally entails extensive personnel training. The classic automatic color-based methods roughly inform about the total labeled area and intensity (
protein quantification) but do not specifically provide information on
cell number. Since the images to be analyzed have a high resolution but a huge amount of pixels contain just noise or artifacts, we first perform a pre-processing generating several filtered images {(providing a tailored, efficient feature extraction)}. Then, we design an automatic kernel counter that is a non-parametric and non-linear method. The proposed scheme can be easily trained in small
datasets since, in its basic version, it relies only on one hyper-parameter. However, being non-parametric and non-linear, the proposed
algorithm is flexible enough to express all the information contained in rich and heterogeneous
datasets as well (providing the maximum overfit if required). Furthermore, the proposed kernel counter also provides uncertainty estimation of the given prediction, and can directly tackle the case of receiving several expert opinions over the same image. Different numerical experiments with artificial and real
datasets show very promising results. Related Matlab code is also provided.
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All authors L. Martino, M. M. Garcia, P. S. Paradas, E. Curbelo
Signal Processing · Cross-listed from stat.ME
A note on the area under the likelihood and the fake evidence for model selection
L. Martino and F. Llorente
Improper priors are not allowed for the computation of the
Bayesian evidence $Z=p({\bf y})$ (a.k.a., marginal likelihood), since in this case $Z$ is not completely specified due to an arbitrary constant involved in the computation. However, in this work, we remark that they can be employed in a specific type of model selection problem: when we have several (possibly infinite) models belonging to the same parametric family (i.e., for tuning parameters of a parametric model). However, the quantities involved in this type of selection cannot be considered as
Bayesian evidences: we suggest to use the name “fake evidences” (or “areas under the likelihood” in the case of uniform improper priors). We also show that, in this model selection scenario, using a diffuse prior and increasing its scale parameter asymptotically to infinity, we cannot recover the value of the area under the likelihood, obtained with a uniform improper prior. We first discuss it from a general point of view. Then we provide, as an applicative example, all the details for
Bayesian regression models with nonlinear bases, considering two cases: the use of a uniform improper prior and the use of a Gaussian prior, respectively. A numerical experiment is also provided confirming and checking all the previous statements.
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Signal Processing
A guided residual search for nonlinear state-space identification
Merijn Floren and Jan Swevers
Identifying the parameters of nonlinear state-space models from input-output data typically requires solving a highly non-convex optimization problem, which is prone to slow convergence and suboptimal local solutions. This work improves the reliability and efficiency of the estimation process by decomposing the overall optimization problem into a sequence of tractable subproblems. Starting from a linear baseline model, nonlinear residual dynamics are first estimated using a guided residual search (GRS) and subsequently refined through multiple-shooting optimization. Experiments on two
benchmarks show competitive performance with state-of-the-art black-box methods and improved convergence over naive initialization.
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Signal Processing · Cross-listed from math.ST
Effective sample size approximations as entropy measures
L. Martino and V. Elvira
In this work, we analyze alternative effective sample size (ESS) metrics for importance sampling
algorithms, and discuss a possible extended range of applications. We show the relationship between the ESS expressions used in the literature and two
entropy families, the Rényi and Tsallis
entropy. The Rényi
entropy is connected to the Huggins-Roy's ESS family introduced in \cite{Huggins15}. We prove that that all the ESS functions included in the Huggins-Roy's family fulfill all the desirable theoretical conditions. We analyzed and remark the connections with several other fields, such as the Hill numbers introduced in ecology, the Gini inequality coefficient employed in economics, and the Gini
impurity index used mainly in machine learning, to name a few. Finally, by numerical simulations, we study the performance of different ESS expressions contained in the previous ESS families in terms of approximation of the theoretical ESS definition, and show the application of ESS formulas in a variable selection problem.
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Systems and Control
Steady State Covariance Steering via Sparse Intervention
Yosuke Inoue and Masaki Inoue
This paper addresses the steady state covariance steering for linear dynamical systems via structural intervention on the system
matrix. We formulate the covariance steering problem as the minimization of the Kullback-Leibler (KL) divergence between the steady state and target Gaussian distributions. To solve the problem, we develop a solution method, hereafter referred to as the proximal gradient-based
algorithm, of promoting sparsity in the structural intervention by integrating the objective into a proximal gradient framework with L1 regularization. The main contribution of this paper lies in the analytical expression of the KL divergence gradient with respect to the intervention
matrix: the gradient is characterized by the solutions to two Lyapunov equations related to the state covariance equation and its adjoint. Numerical simulations demonstrate that the proximal gradient-based
algorithm effectively identifies sparse, structurally-constrained interventions to achieve precise covariance steering.
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Signal Processing · Cross-listed from physics.med-ph
Continuous Blood Monitoring with Particle-based Integrated Sensing and Communication (ISAC)
Fatih E. Bilgen and Ozgur B. Akan
Although the circulatory system functions as a continuous source of physiological data, contemporary diagnostics remain bound to intermittent, time-delayed assessments. To resolve this, we present a framework for ubiquitous hematological profiling driven by Integrated Sensing and Communication (
ISAC). We demonstrate how electromagnetic signals can be exploited to monitor blood in real-time, effectively converting them into diagnostic tools. We analyze the biological foundations of blood, review existing Complete Blood Count (CBC) and sensing technologies, and detail a novel pipeline for continuous blood monitoring. Furthermore, we discuss the potential applications of deploying these devices to enable real-time CBC and biomarker detection, ultimately revolutionizing how we predict, detect, and manage individual and public health.
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Signal Processing · Cross-listed from cs.CV
Reflectance Multispectral Imaging for Soil Composition Estimation and USDA Texture Classification
G.A.S.L Ranasinghe, J.A.S.T. Jayakody, M.C.L. De Silva et al. (+5 more)
Soil texture is a foundational attribute that governs water availability and erosion in agriculture, as well as load bearing capacity, deformation response, and shrink-swell risk in geotechnical engineering. Yet texture is still typically determined by slow and labour intensive laboratory particle size tests, while many sensing alternatives are either costly or too coarse to support routine field scale deployment. This paper proposes a robust and field deployable multispectral imaging (MSI) system and machine learning framework for predicting soil composition and the United States Department of Agriculture (USDA) texture classes. The proposed system uses a cost effective in-house MSI device operating from 365 nm to 940 nm to capture thirteen spectral bands, which effectively capture the spectral properties of soil texture. Regression models use the captured spectral properties to estimate clay, silt, and sand percentages, while a direct classifier predicts one of the twelve USDA textural classes. Indirect classification is obtained by mapping the regressed compositions to texture classes via the USDA soil texture triangle. The framework is evaluated on mixture data by mixing clay, silt, and sand in varying proportions, using the USDA classification triangle as a basis. Experimental results show that the proposed approach achieves a coefficient of determination R^2 up to 0.99 for composition prediction and over 99% accuracy for texture classification. These findings indicate that MSI combined with data-driven modeling can provide accurate, non-destructive, and field deployable soil texture characterization suitable for geotechnical screening and precision agriculture.
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All authors G.A.S.L Ranasinghe, J.A.S.T. Jayakody, M.C.L. De Silva, G. Thilakarathne, G.M.R.I. Godaliyadda, H.M.V.R. Herath, M.P.B. Ekanayake, S.K. Navaratnarajah
Signal Processing · Cross-listed from cs.ET
Molecule Mixture Detection and Design for MC Systems with Non-linear, Cross-reactive Receiver Arrays
Bastian Heinlein, Kaikai Zhu, Sümeyye Carkit-Yilmaz et al. (+6 more)
Air-based molecular communication (MC) has the potential to be one of the first MC systems to be deployed in real-world applications, enabled by commercially available sensors. However, these sensors usually exhibit non-linear and cross-reactive behavior, contrary to the idealizing assumption of linear and perfectly molecule type-specific sensing often made in the MC literature. To address this mismatch, we propose several detectors and transmission schemes for a molecule mixture communication system where the receiver (RX) employs non-linear, cross-reactive sensors. All proposed schemes are based on the first- and second-order moments of the symbol likelihoods that are fed through the non-linear RX using the Unscented Transform. In particular, we propose an approximate maximum likelihood (AML) symbol-by-symbol detector for inter-symbol-interference (ISI)-free transmission scenarios and a complementary mixture alphabet design
algorithm which accounts for the RX characteristics. When significant ISI is present at high data rates, the AML detector can be adapted to exploit statistical ISI knowledge. Additionally, we propose a sequence detector which combines information from multiple symbol intervals. For settings where sequence detection is not possible due to extremely limited computational power at the RX, we propose an adaptive transmission scheme which can be combined with symbol-by-symbol detection. Using computer simulations, we validate all proposed detectors and
algorithms based on the responses of commercially available sensors as well as artificially generated sensor data incorporating the characteristics of metal-
oxide semiconductor sensors. By employing a general system model that accounts for transmitter noise, ISI, and general non-linear, cross-reactive RX arrays, this work enables reliable communication for a large class of MC systems.
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All authors Bastian Heinlein, Kaikai Zhu, Sümeyye Carkit-Yilmaz, Sebastian Lotter, Helene M. Loos, Andrea Buettner, Yansha Deng, Robert Schober, Vahid Jamali
Systems and Control
Transformer Actor-Critic for Efficient Freshness-Aware Resource Allocation
Maryam Ansarifard, Mohit K. Sharma, Kishor C. Joshi et al. (+1 more)
Emerging applications such as autonomous driving and industrial automation demand ultra-reliable and low-latency communication (URLLC), where maintaining fresh and timely information is critical. A key performance metric in such systems is the age of information (AoI). This paper addresses AoI minimization in a multi-user uplink wireless network using non-orthogonal multiple access (NOMA), where users offload tasks to a base station. The system must handle user heterogeneity in task sizes, AoI thresholds, and penalty sensitivities, while adhering to NOMA constraints on user scheduling. We propose a deep reinforcement learning (DRL) framework based on proximal policy optimization (PPO), enhanced with a
Transformer encoder. The
attention mechanism allows the
agent to focus on critical user states and capture inter-user dependencies, improving policy performance and scalability. Extensive simulations show that our method reduces average AoI compared to baselines. We also analyze the
evolution of
attention weights during training and observe that the model progressively learns to prioritize high-importance users.
Attention maps reveal meaningful structure: early-stage policies exhibit uniform
attention, while later stages show focused patterns aligned with user priority and NOMA constraints. These results highlight the promise of
attention-driven DRL for intelligent, priority-aware resource allocation in next-generation wireless systems.
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All authors Maryam Ansarifard, Mohit K. Sharma, Kishor C. Joshi, George Exarchakos
Signal Processing
Constructing Knowledge Map for MIMO-OFDM Clustered Channel Estimation
Heling Zhang, Xiujun Zhang, Xiaofeng Zhong et al. (+1 more)
Channel knowledge map (CKM) exploits environ-ment information to assist channel estimation during communi-cation. For clustered channels, which represent a typical type ofwireless propagation environment, there has been no researchdevoted to designing an appropriate CKM to enhance theirestimation. To exploit environment information for clusteredchannel, improve channel estimation accuracy and reduce pilotoverhead, we propose ClusterCKM, a CKM providing the rangeof clustered multipath parameters for any pair of transmitter-receiver links in the region of interest. Firstly, we construct Clus-terCKM through estimating the spatial range of scatterer clustersfrom historical channel information. From these spatial range ofscatterer clusters, ClusterCKM infers the range of multipathparameters for the target link. Furthermore, a ClusterCKM-based channel estimation
algorithm is developed to utilize theparameter range provided by ClusterCKM. Simulation resultsshow that, more accurate channel estimation can be achievedand pilot overhead can also be reduced by ClusterCKM and theClusterCKM-based estimation
algorithm.
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All authors Heling Zhang, Xiujun Zhang, Xiaofeng Zhong, Shidong Zhou
Signal Processing
CSI-RFF: Leveraging Micro-Signals on CSI for RF Fingerprinting of Commodity WiFi
Ruiqi Kong and He Chen
This paper introduces CSI-RFF, a new framework that leverages micro-signals embedded within Channel State Information (CSI) curves to realize
Radio-Frequency Fingerprinting of commodity off-the-shelf (COTS) WiFi devices for open-set authentication. The micro-signals that serve as RF fingerprints are termed “micro-CSI”. Through experimentation, we have found that the presence of micro-CSI can primarily be attributed to imperfections in the RF circuitry. Furthermore, this characteristic signal is detectable in WiFi 4/5/6 network interface cards (NICs). We have conducted further experiments to determine the most effective CSI collection configurations to stabilize micro-CSI. Yet, extracting micro-CSI for authentication purposes poses a significant challenge. This complexity arises from the fact that CSI measurements inherently include both micro-CSI and the distortions introduced by wireless channels. These two elements are intricately intertwined, making their separation non-trivial. To tackle this challenge, we have developed a signal space-based extraction technique for line-of-sight (LoS) scenarios, which can effectively separate the distortions caused by wireless channels and micro-CSI. Over the course of our comprehensive CSI data collection period extending beyond one year, we found that the extracted micro-CSI displays unique characteristics specific to each WiFi device and remains invariant over time. This establishes micro-CSI as a suitable candidate for device fingerprinting. Finally, we conduct a case study focusing on area access control for mobile
robots. Our experimental results demonstrate that the micro-CSI-based authentication
algorithm can achieve an average attack detection rate close to 99% with a false alarm rate of 0% in both static and mobile conditions when using 20 CSI measurements to construct one fingerprint.
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Systems and Control · Cross-listed from cs.RO
Robust Helicopter Ship Deck Landing With Guaranteed Timing Using Shrinking-Horizon Model Predictive Control
Philipp Schitz, Paolo Mercorelli and Johann C. Dauer
We present a runtime efficient
algorithm for autonomous helicopter landings on moving ship decks based on Shrinking-Horizon Model Predictive Control (SHMPC). First, a suitable planning model capturing the relevant aspects of the full nonlinear helicopter dynamics is derived. Next, we use the SHMPC together with a touchdown controller stage to ensure a pre-specified maneuver time and an associated landing time window despite the presence of disturbances. A high disturbance rejection performance is achieved by designing an ancillary controller with disturbance feedback. Thus, given a target position and time, a safe landing with suitable terminal conditions is be guaranteed if the initial optimization problem is feasible. The efficacy of our approach is shown in simulation where all maneuvers achieve a high landing precision in strong winds while satisfying timing and operational constraints with maximum computation times in the millisecond range.
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Systems and Control
Opacity in Discrete Event Systems: A Perspective and Overview
Xiang Yin
Opacity has emerged as a central confidentiality notion for information-flow security in discrete event systems (DES), capturing the requirement that an external observer (intruder) should never be able to determine with certainty whether the system is, was, or will be in a secret state. This article provides a concise, newcomer-friendly overview of opacity in DES, emphasizing core definitions and the unifying estimation viewpoint behind major opacity notions,. We summarize representative verification techniques and highlight how different observation models reshape both the problem formulation and algorithmic structure. We then review principal enforcement paradigms, ranging from opacity-enforcing supervisory control to sensor activation/information release optimization and obfuscation/editing mechanisms. Beyond finite automata, we outline how opacity has been studied in richer models such as
stochastic systems, timed systems, Petri nets, and continuous/hybrid dynamics, and we briefly survey applications in robotics, location
privacy, and information services. Finally, we discuss selected open challenges, including solvability under incomparable information, scalable methods beyond worst-case complexity, and opacity under intelligent or data-driven adversaries.
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Image and Video Processing
U-Net-Based Generative Joint Source-Channel Coding for Wireless Image Transmission
Ming Ye, Kui Cai, Cunhua Pan et al. (+4 more)
Deep learning (DL)-based joint source-channel coding (JSCC) methods have achieved remarkable success in wireless image transmission. However, these methods either focus on conventional distortion metrics that do not necessarily yield high perceptual quality or incur high computational complexity. In this paper, we propose two DL-based JSCC (DeepJSCC) methods that leverage deep generative architectures for wireless image transmission. Specifically, we propose G-UNet-JSCC, a scheme comprising an encoder and a U-Net-based generator serving as the decoder. Its skip connections enable multi-scale feature fusion to improve both pixel-level fidelity and perceptual quality of reconstructed images by integrating low- and high-level features. To further enhance pixel-level fidelity, the encoder and the U-Net-based decoder are jointly optimized using a weighted sum of structural similarity and mean-squared error (MSE) losses. Building upon G-UNet-JSCC, we further develop a DeepJSCC method called cGAN-JSCC, where the decoder is enhanced through
adversarial training. In this scheme, we retain the encoder of G-UNet-JSCC and adversarially train the decoder's generator against a patch-based discriminator. cGAN-JSCC employs a two-stage training procedure. The outer stage trains the encoder and the decoder end-to-end using an MSE loss, while the inner stage adversarially trains the decoder's generator and the discriminator by minimizing a joint loss combining
adversarial and distortion losses. Simulation results demonstrate that the proposed methods achieve superior pixel-level fidelity and perceptual quality on both high- and low-resolution images. For low-resolution images, cGAN-JSCC achieves better reconstruction performance and greater robustness to channel variations than G-UNet-JSCC.
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All authors Ming Ye, Kui Cai, Cunhua Pan, Zhen Mei, Wanting Yang, Chunguo Li, Bo Wang