Image and Video Processing
Anisotropic Tensor Deconvolution of Hyperspectral Images
Xinjue Wang, Xiuheng Wang, Esa Ollila et al. (+1 more)
Hyperspectral image (HSI) deconvolution is a challenging ill-posed inverse problem, made difficult by the data's high dimensionality.We propose a parameter-parsimonious framework based on a low-rank Canonical Polyadic Decomposition (CPD) of the entire latent HSI $\mathbf{\mathcal{X}} \in \mathbb{R}^{P\times Q \times N}$.This approach recasts the problem from recovering a large-scale image with $PQN$ variables to estimating the CPD factors with $(P+Q+N)R$ variables.This model also enables a structure-aware, anisotropic Total Variation (TV) regularization applied only to the spatial factors, preserving the smooth spectral signatures.An efficient
algorithm based on the Proximal Alternating Linearized Minimization (PALM) framework is developed to solve the resulting non-convex optimization problem.Experiments confirm the model's efficiency, showing a numerous parameter reduction of over two orders of magnitude and a compelling trade-off between model compactness and reconstruction accuracy.
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All authors Xinjue Wang, Xiuheng Wang, Esa Ollila, Sergiy A. Vorobyov
Image and Video Processing
Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype
Jan-Philipp Redlich, Friedrich Feuerhake, Stefan Nikolin et al. (+12 more)
Glioblastoma, IDH-wildtype (GBM-IDHwt) is the most common malignant
brain tumor. While histomorphology is a crucial component of GBM-IDHwt diagnosis, it is not further considered for prognosis. Here, we present an explainable artificial intelligence (AI) framework to identify and interpret histomorphological features associated with patient survival. The framework combines an explainable multiple instance learning (MIL) architecture that directly identifies prognostically relevant image tiles with a sparse autoencoder (SAE) that maps these tiles to interpretable visual patterns. The MIL model was trained and evaluated on a new real-world
dataset of 720 GBM-IDHwt cases from three hospitals and four cancer registries across Germany. The SAE was trained on 1,878 whole-slide images from five independent public glioblastoma collections. Despite the many factors influencing survival time, our method showed some ability to discriminate between patients living less than 180 days or more than 360 days solely based on histomorphology (AUC: 0.67; 95% CI: 0.63-0.72). Cox proportional hazards regression confirmed a significant survival difference between predicted groups after adjustment for established prognostic factors (hazard ratio: 1.47; 95% CI: 1.26-1.72). Three neuropathologists categorized the identified visual patterns into seven distinct histomorphological groups, revealing both established prognostic features and unexpected associations, the latter being potentially attributable to surgery-related confounders. The presented explainable AI framework facilitates prognostic biomarker discovery in GBM-IDHwt and beyond, highlighting promising histomorphological features for further analysis and exposing potential confounders that would be hidden in black-box models.
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All authors Jan-Philipp Redlich, Friedrich Feuerhake, Stefan Nikolin, Nadine Sarah Schaadt, Sarah Teuber-Hanselmann, Joachim Weis, Sabine Luttmann, Andrea Eberle, Christoph Buck, Timm Intemann, Pascal Birnstill, Klaus Kraywinkel, Jonas Ort, Peter Boor, André Homeyer
Image and Video Processing
Bridging Modalities: Joint Synthesis and Registration Framework for Aligning Diffusion MRI with T1-Weighted Images
Xiaofan Wang, Junyi Wang, Yuqian Chen et al. (+2 more)
Multimodal image registration between
diffusion MRI (dMRI) and T1-weighted (T1w) MRI images is a critical step for aligning
diffusion-weighted imaging (DWI) data with structural anatomical space. Traditional registration methods often struggle to ensure accuracy due to the large intensity differences between
diffusion data and high-resolution anatomical structures. This paper proposes an unsupervised registration framework based on a generative registration network, which transforms the original
multimodal registration problem between b0 and T1w images into a unimodal registration task between a generated image and the real T1w image. This effectively reduces the complexity of cross-modal registration. The framework first employs an image synthesis model to generate images with T1w-like contrast, and then learns a deformation field from the generated image to the fixed T1w image. The registration network jointly optimizes local structural similarity and cross-modal statistical dependency to improve deformation estimation accuracy. Experiments conducted on two independent
datasets demonstrate that the proposed method outperforms several state-of-the-art approaches in
multimodal registration tasks.
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All authors Xiaofan Wang, Junyi Wang, Yuqian Chen, Lauren J. O' Donnell, Fan Zhang
Image and Video Processing
Towards Efficient Image Deblurring for Edge Deployment
Srinivas Miriyala, Sowmya Vajrala and Sravanth Kodavanti
Image deblurring is a critical stage in mobile image signal processing pipelines, where the ability to restore fine structures and textures must be balanced with real-time constraints on edge devices. While recent deep networks such as
transformers and activation-free architectures achieve state-of-the-art (SOTA) accuracy, their efficiency is typically measured in FLOPs or parameters, which do not correlate with latency on embedded hardware. We propose a hardware-aware adaptation framework that restructures existing models through sensitivity-guided block substitution, surrogate
distillation, and training-free multi-objective search driven by device profiling. Applied to the 36-block NAFNet baseline, the optimized variants achieve up to 55% reduction in GMACs compared to the recent
transformer-based SOTA while maintaining competitive accuracy. Most importantly, on-device deployment yields a 1.25X latency improvement over the baseline. Experiments on motion deblurring (GoPro), defocus deblurring (DPDD), and auxiliary
benchmarks (RealBlur-J/R, HIDE) demonstrate the generality of the approach, while comparisons with prior efficient baselines confirm its accuracy-efficiency trade-off. These results establish feedback-driven adaptation as a principled strategy for bridging the gap between algorithmic design and deployment-ready deblurring models.
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Image and Video Processing
Mobile-friendly Image de-noising: Hardware Conscious Optimization for Edge Application
Srinivas Miriyala, Sowmya Vajrala, Hitesh Kumar et al. (+2 more)
Image enhancement is a critical task in computer
vision and photography that is often entangled with noise. This renders the traditional Image Signal Processing (ISP) ineffective compared to the advances in deep learning. However, the success of such methods is increasingly associated with the ease of their deployment on edge devices, such as smartphones. This work presents a novel mobile-friendly network for image de-noising obtained with
Entropy-Regularized differentiable Neural Architecture Search (NAS) on a hardware-aware search space for a U-Net architecture, which is first-of-its-kind. The designed model has 12% less parameters, with ~2-fold improvement in ondevice latency and 1.5-fold improvement in the
memory footprint for a 0.7% drop in PSNR, when deployed and profiled on Samsung
Galaxy S24 Ultra. Compared to the SOTA Swin-
Transformer for Image Restoration, the proposed network had competitive accuracy with ~18-fold reduction in GMACs. Further, the network was tested successfully for Gaussian de-noising with 3 intensities on 4
benchmarks and real-world de-noising on 1
benchmark demonstrating its generalization ability.
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All authors Srinivas Miriyala, Sowmya Vajrala, Hitesh Kumar, Sravanth Kodavanti, Vikram Rajendiran
Image and Video Processing
FourierPET: Deep Fourier-based Unrolled Network for Low-count PET Reconstruction
Zheng Zhang, Hao Tang, Yingying Hu et al. (+2 more)
Low-count positron emission
tomography (PET) reconstruction is a challenging inverse problem due to severe degradations arising from Poisson noise,
photon scarcity, and attenuation correction errors. Existing deep learning methods typically address these in the spatial domain with an undifferentiated optimization objective, making it difficult to disentangle overlapping artifacts and limiting correction effectiveness. In this work, we perform a
Fourier-domain analysis and reveal that these degradations are spectrally separable: Poisson noise and
photon scarcity cause high-frequency phase perturbations, while attenuation errors suppress low-frequency amplitude components. Leveraging this insight, we propose FourierPET, a
Fourier-based unrolled reconstruction framework grounded in the Alternating Direction Method of Multipliers. It consists of three tailored modules: a spectral consistency module that enforces global frequency
alignment to maintain data fidelity, an amplitude-phase correction module that decouples and compensates for high-frequency phase distortions and low-frequency amplitude suppression, and a dual adjustment module that accelerates convergence during iterative reconstruction. Extensive experiments demonstrate that FourierPET achieves state-of-the-art performance with significantly fewer parameters, while offering enhanced interpretability through frequency-aware correction.
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All authors Zheng Zhang, Hao Tang, Yingying Hu, Zhanli Hu, Jing Qin
Image and Video Processing
Pigment Network Detection and Classification in Dermoscopic Images Using Directional Imaging Algorithms and Convolutional Neural Networks
M. A. Rasel, Sameem Abdul Kareem and Unaizah Obaidellah
Early diagnosis of melanoma, which can save thousands of lives, relies heavily on the analysis of dermoscopic images. One crucial diagnostic criterion is the identification of unusual pigment network (PN). However, distinguishing between regular (typical) and irregular (atypical) PN is challenging. This study aims to automate the PN detection process using a directional imaging
algorithm and classify PN types using machine learning classifiers. The directional imaging
algorithm incorporates Principal Component Analysis (PCA), contrast enhancement, filtering, and noise reduction. Applied to the PH2
dataset, this
algorithm achieved a 96% success rate, which increased to 100% after pixel intensity adjustments. We created a new
dataset containing only PN images from these results. We then employed two classifiers, Convolutional Neural Network (CNN) and Bag of Features (BoF), to categorize PN into atypical and typical classes. Given the limited
dataset of 200 images, a simple and effective CNN was designed, featuring two convolutional layers and two batch normalization layers. The proposed CNN achieved 90% accuracy, 90% sensitivity, and 89% specificity. When compared to state-of-the-art methods, our CNN demonstrated superior performance. Our study highlights the potential of the proposed CNN model for effective PN classification, suggesting future research should focus on expanding
datasets and incorporating additional dermatological features to further enhance melanoma diagnosis.
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Systems and Control · Cross-listed from cs.SE
From Everything-is-a-File to Files-Are-All-You-Need: How Unix Philosophy Informs the Design of Agentic AI Systems
Deepak Babu Piskala
A core abstraction in early Unix systems was the principle that 'everything is a file', enabling heterogeneous devices and kernel resources to be manipulated via uniform read/write interfaces. This paper explores how an analogous unification is emerging in contemporary agentic AI. We trace the
evolution from Unix to DevOps, Infrastructure-as-Code, and finally autonomous software
agents, highlighting how file-like abstractions and code-based specifications collapse diverse resources into consistent, composable interfaces. The resulting perspective suggests that adopting file- and code-centric interaction models may enable agentic systems that are more maintainable, auditable, and operationally robust.
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Systems and Control · Cross-listed from cs.LG
MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management
Miriam K. Wolff, Peter Calhoun, Eleonora Maria Aiello et al. (+2 more)
Progress in Type 1 Diabetes (T1D)
algorithm development is limited by the fragmentation and lack of standardization across existing T1D management
datasets. Current
datasets differ substantially in structure and are time-consuming to access and process, which impedes data integration and reduces the comparability and generalizability of algorithmic developments. This work aims to establish a unified and accessible data resource for T1D
algorithm development. Multiple publicly available T1D
datasets were consolidated into a unified resource, termed the MetaboNet
dataset. Inclusion required the availability of both continuous glucose monitoring (CGM) data and corresponding insulin pump dosing records. Additionally, auxiliary information such as reported carbohydrate intake and physical activity was retained when present. The MetaboNet
dataset comprises 3135 subjects and 1228 patient-years of overlapping CGM and insulin data, making it substantially larger than existing standalone
benchmark datasets. The resource is distributed as a fully public subset available for immediate download at https://metabo-net.org/ , and with a Data Use Agreement (DUA)-restricted subset accessible through their respective application processes. For the
datasets in the latter subset, processing pipelines are provided to automatically convert the data into the standardized MetaboNet format. A consolidated public
dataset for T1D research is presented, and the access pathways for both its unrestricted and DUA-governed components are described. The resulting
dataset covers a broad range of glycemic profiles and demographics and thus can yield more generalizable algorithmic performance than individual
datasets.
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All authors Miriam K. Wolff, Peter Calhoun, Eleonora Maria Aiello, Yao Qin, Sam F. Royston
Signal Processing · Cross-listed from cs.IT
Convergence Properties of Good Quantum Codes for Classical Communication
Alptug Aytekin, Mohamed Nomeir, Lei Hu et al. (+1 more)
An important part of the information theory folklore had been about the output statistics of codes that achieve the capacity and how the empirical distributions compare to the output distributions induced by the optimal input in the channel capacity problem. Results for a variety of such empirical output distributions of good codes have been known in the literature, such as the comparison of the output distribution of the code to the optimal output distribution in vanishing and non-vanishing error probability cases. Motivated by these, we aim to achieve similar results for the
quantum codes that are used for classical communication, that is the setting in which the classical messages are communicated through
quantum codewords that pass through a noisy
quantum channel. We first show the uniqueness of the optimal output distribution, to be able to talk more concretely about the optimal output distribution. Then, we extend the vanishing error probability results to the
quantum case, by using techniques that are close in spirit to the classical case. We also extend non-vanishing error probability results to the
quantum case on block codes, by using the second-order converses for such codes based on hypercontractivity results for the
quantum generalized depolarizing semi-groups.
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All authors Alptug Aytekin, Mohamed Nomeir, Lei Hu, Sennur Ulukus
Systems and Control
Implications of Grid-Forming Inverter Parameters on Disturbance Localization and Controllability
Matt Baughman, Marena Trujillo, Bri-Mathias Hodge et al. (+1 more)
The shift from traditional synchronous generator (SG) based power generation to generation driven by power electronic devices introduces new dynamic phenomena and considerations for the control of large-scale power systems. In this paper, two aspects of all-inverter power systems are investigated: greater localization of system disturbance response and greater system controllability. The prevalence of both of these aspects are shown to be related to the lower effective inertia of inverters and have implications for future widearea control system design. Greater disturbance localization implies the need for feedback measurement placement close to generator nodes to properly reject disturbances in the system while increased system controllability implies that widearea control systems should preferentially actuate inverters to most efficiently control the system. This investigation utilizes reduced-order linear time-invariant models of both SGs and inverters that are shown to capture the frequency dynamics of interest in both all-SG and all-inverter systems, allowing for the efficient use of both frequency and time domain analysis methods.
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All authors Matt Baughman, Marena Trujillo, Bri-Mathias Hodge, Emily Jensen
Systems and Control · Cross-listed from math.OC
Projection-based discrete-time consensus on the unit sphere
Johan Thunberg and Galina Sidorenko
We address discrete-time consensus on the Euclidean unit sphere. For this purpose we consider a distributed
algorithm comprising the iterative projection of a conical combination of neighboring states. Neighborhoods are represented by a strongly connected directed graph, and the conical combinations are represented by a (non-negative) weight
matrix with a zero structure corresponding to the graph. A first result mirrors earlier results for gradient flows. Under the assumptions that each diagonal element of the weight
matrix is more than $\sqrt{2}$ larger than the sum of the other elements in the corresponding row, the sphere dimension is greater or equal to 2, and the graph, as well as the weight
matrix, is symmetric, we show that the
algorithm comprises gradient ascent, stable fixed points are consensus points, and the set of initial points for which the
algorithm converges to a non-consensus fixed point has measure zero. The second result is that for the unit circle and a strongly connected graph or for any unit sphere with dimension greater than or equal to $1$ and the complete graph, only for a measure zero set of weight
matrices there are fixed points for the
algorithm which do not have consensus or antipodal configurations.
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Signal Processing
Channel Estimation in MIMO Systems Aided by Microwave Linear Analog Computers (MiLACs)
Qiaosen Zhang, Matteo Nerini and Bruno Clerckx
Microwave linear analog computers (MiLACs) have recently emerged as a promising solution for future gigantic multiple-input multiple-output (
MIMO) systems, enabling beamforming with greatly reduced hardware and computational cost. However, channel estimation for MiLAC-aided systems remains an open problem. Conventional least squares (LS) and minimum mean square error (MMSE) estimation rely on intensive digital computation, which undermines the computational advantage offered by MiLACs. In this letter, we propose efficient LS and MMSE channel estimation schemes for MiLAC-aided
MIMO systems. By designing the training precoder and combiner implemented by lossless and reciprocal MiLACs, the proposed schemes perform LS and MMSE estimation in the analog domain, leaving only simple digital scaling. They achieve identical estimation performance to their digital counterparts while significantly reducing computational complexity. Numerical results verify the effectiveness of the proposed schemes.
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Systems and Control
Learning-Based Shrinking Disturbance-Invariant Tubes for State- and Input-Dependent Uncertainty
Abdelrahman Ramadan and Sidney Givigi
We develop a learning-based framework for constructing shrinking disturbance-invariant tubes under state- and input-dependent uncertainty, intended as a building block for tube Model Predictive Control (MPC), and certify safety via a lifted, isotone (order-preserving) fixed-point map. Gaussian Process (GP) posteriors become $(1-\alpha)$ credible ellipsoids, then polytopic outer sets for deterministic set operations. A two-time-scale scheme separates learning epochs, where these polytopes are frozen, from an inner, outside-in iteration that converges to a compact fixed point $Z^\star\!\subseteq\!\mathcal G$; its state projection is RPI for the plant. As data accumulate, disturbance polytopes tighten, and the associated tubes nest monotonically, resolving the circular dependence between the set to be verified and the disturbance model while preserving hard constraints. A double-integrator study illustrates shrinking tube cross-sections in data-rich regions while maintaining invariance.
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Signal Processing · Cross-listed from cs.IT
Efficient Channel Autoencoders for Wideband Communications leveraging Walsh-Hadamard interleaving
Cel Thys, Rodney Martinez Alonso and Sofie Pollin
This paper investigates how end-to-end (E2E) channel autoencoders (AEs) can achieve energy-efficient wideband communications by leveraging Walsh-Hadamard (WH) interleaved converters. WH interleaving enables high sampling rate analog-digital conversion with reduced power consumption using an analog WH transformation. We demonstrate that E2E-trained neural coded modulation can transparently adapt to the WH-transceiver hardware without requiring algorithmic redesign. Focusing on the short block length regime, we train WH-domain AEs and
benchmark them against standard neural and conventional baselines, including 5G Polar codes. We quantify the system-level energy tradeoffs among baseband compute, channel signal-to-noise ratio (SNR), and analog converter power. Our analysis shows that the proposed WH-AE system can approach conventional Polar code SNR performance within 0.14dB while consuming comparable or lower system power. Compared to the best neural baseline, WH-AE achieves, on average, 29% higher energy efficiency (in bit/J) for the same reliability. These findings establish WH-domain learning as a viable path to energy-efficient, high-throughput wideband communications by explicitly balancing compute complexity, SNR, and analog power consumption.
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Systems and Control · Cross-listed from cs.RO
The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning
Henrik Hose, Paul Brunzema, Devdutt Subhasish et al. (+1 more)
The development of robust learning-based control
algorithms for unstable systems requires high-quality, real-world data, yet access to specialized robotic hardware remains a significant barrier for many researchers. This paper introduces a comprehensive dynamics
dataset for the Mini Wheelbot, an open-source, quasi-symmetric balancing reaction wheel unicycle. The
dataset provides 1 kHz synchronized data encompassing all onboard sensor readings, state estimates, ground-truth poses from a motion capture system, and third-person
video logs. To ensure data diversity, we include experiments across multiple hardware instances and surfaces using various control paradigms, including pseudo-random binary excitation, nonlinear model predictive control, and reinforcement learning
agents. We include several example applications in dynamics model learning, state estimation, and time-series classification to illustrate common robotics
algorithms that can be benchmarked on our
dataset.
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All authors Henrik Hose, Paul Brunzema, Devdutt Subhasish, Sebastian Trimpe
Systems and Control · Cross-listed from cs.LG
Offline Reinforcement-Learning-Based Power Control for Application-Agnostic Energy Efficiency
Akhilesh Raj, Swann Perarnau, Aniruddha Gokhale et al. (+1 more)
Energy efficiency has become an integral aspect of modern computing infrastructure design, impacting the performance, cost, scalability, and durability of production systems. The incorporation of power actuation and sensing capabilities in CPU designs is indicative of this, enabling the deployment of system software that can actively monitor and adjust energy consumption and performance at runtime. While reinforcement learning (RL) would seem ideal for the design of such energy efficiency control systems, online training presents challenges ranging from the lack of proper models for setting up an adequate simulated environment, to perturbation (noise) and reliability issues, if training is deployed on a live system. In this paper we discuss the use of offline reinforcement learning as an alternative approach for the design of an autonomous CPU power controller, with the goal of improving the energy efficiency of parallel applications at runtime without unduly impacting their performance. Offline RL sidesteps the issues incurred by online RL training by leveraging a
dataset of state transitions collected from arbitrary policies prior to training. Our methodology applies offline RL to a gray-box approach to energy efficiency, combining online application-agnostic performance data (e.g., heartbeats) and hardware performance counters to ensure that the scientific objectives are met with limited performance degradation. Evaluating our method on a variety of compute-bound and
memory-bound
benchmarks and controlling power on a live system through Intel's Running Average Power Limit, we demonstrate that such an offline-trained
agent can substantially reduce energy consumption at a tolerable performance degradation cost.
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All authors Akhilesh Raj, Swann Perarnau, Aniruddha Gokhale, Solomon Bekele Abera
Signal Processing
Modulation, ISI, and Detection for Langmuir Adsorption-Based Microfluidic Molecular Communication
Ruifeng Zheng, Pengjie Zhou, Pit Hofmann et al. (+4 more)
This paper studies microfluidic molecular communication receivers with finite-capacity Langmuir adsorption driven by an effective surface concentration. In the reaction-limited regime, we derive a closed-form single-pulse response kernel and a symbol-rate recursion for on-off keying that explicitly exposes channel
memory and inter-symbol interference. We further develop short-pulse and long-pulse approximations, revealing an interference asymmetry in the long-pulse regime due to saturation. To account for stochasticity, we adopt a finite-receptor binomial counting model, employ pulse-end sampling, and propose a low-complexity midpoint-threshold detector that reduces to a fixed threshold when interference is negligible. Numerical results corroborate the proposed characterization and quantify detection performance versus pulse and symbol durations.
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All authors Ruifeng Zheng, Pengjie Zhou, Pit Hofmann, Martín Schottlender, Fatima Rani, Juan A. Cabrera, Frank H. P. Fitzek
Systems and Control · Cross-listed from cs.RO
Distributed Control Barrier Functions for Safe Multi-Vehicle Navigation in Heterogeneous USV Fleets
Tyler Paine, Brendan Long, Jeremy Wenger et al. (+3 more)
Collision avoidance in heterogeneous fleets of uncrewed vessels is challenging because the decision-making processes and controllers often differ between platforms, and it is further complicated by the limitations on sharing trajectories and control values in real-time. This paper presents a pragmatic approach that addresses these issues by adding a control filter on each autonomous vehicle that assumes worst-case behavior from other contacts, including crewed vessels. This distributed safety control filter is developed using control barrier function (CBF) theory and the application is clearly described to ensure explainability of these safety-critical methods. This work compares the worst-case CBF approach with a Collision Regulations (COLREGS) behavior-based approach in simulated encounters. Real-world experiments with three different uncrewed vessels and a human operated vessel were performed to confirm the approach is effective across a range of platforms and is robust to uncooperative behavior from human operators. Results show that combining both CBF methods and COLREGS behaviors achieves the best safety and efficiency.
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All authors Tyler Paine, Brendan Long, Jeremy Wenger, Michael DeFilippo, James Usevitch, Michael Benjamin
Systems and Control
Machine Learning on the Edge for Sustainable IoT Networks: A Systematic Literature Review
Luisa Schuhmacher, Jimmy Fernandez Landivar, Ihsane Gryech et al. (+3 more)
The Internet of Things (IoT) has become integral to modern technology, enhancing daily life and industrial processes through seamless connectivity. However, the rapid expansion of IoT systems presents significant sustainability challenges, such as high energy consumption and inefficient resource management. Addressing these issues is critical for the long-term viability of IoT networks. Machine learning (ML), with its proven success across various domains, offers promising solutions for optimizing IoT operations. ML
algorithms can learn directly from raw data, uncovering hidden patterns and optimizing processes in dynamic environments. Executing ML at the edge of IoT networks can further enhance sustainability by reducing bandwidth usage, enabling real-time decision-making, and improving data
privacy. Additionally, testing ML models on actual hardware is essential to ensure satisfactory performance under real-world conditions, as it captures the complexities and constraints of real-world IoT deployments. Combining ML at the edge and actual hardware testing, therefore, increases the reliability of ML models to effectively improve the sustainability of IoT systems. The present systematic literature review explores how ML can be utilized to enhance the sustainability of IoT networks, examining current methodologies, benefits, challenges, and future opportunities. Through our analysis, we aim to provide insights that will drive future innovations in making IoT networks more sustainable.
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All authors Luisa Schuhmacher, Jimmy Fernandez Landivar, Ihsane Gryech, Hazem Sallouha, Michele Rossi, Sofie Pollin
Systems and Control
Composite and Staged Trust Evaluation for Multi-Hop Collaborator Selection
Botao Zhu and Xianbin Wang
Multi-hop collaboration offers new perspectives for enhancing task execution efficiency by increasing available distributed collaborators for resource sharing. Consequently, selecting trustworthy collaborators becomes critical for realizing effective multi-hop collaboration. However, evaluating device trust requires the consideration of multiple factors, including relatively stable factors, such as historical interaction data, and dynamic factors, such as varying resources and network conditions. This differentiation makes it challenging to achieve the accurate evaluation of composite trust factors using one identical evaluation approach. To address this challenge, this paper proposes a composite and staged trust evaluation (CSTE) mechanism, where stable and dynamic factors are separately evaluated at different stages and then integrated for a final trust decision. First, a device interaction graph is constructed from stable historical interaction data to represent direct trust relationships between devices. A graph neural network framework is then used to propagate and aggregate these trust relationships to produce the historical trustworthiness of devices. In addition, a task-specific trust evaluation method is developed to assess the dynamic resources of devices based on task requirements, which generates the task-specific resource trustworthiness of devices. After these evaluations, CSTE integrates their results to identify devices within the network
topology that satisfy the minimum trust thresholds of tasks. These identified devices then establish a trusted
topology. Finally, within this trusted
topology, an A* search
algorithm is employed to construct a multi-hop collaboration path that satisfies the task requirements. Experimental results demonstrate that CSTE outperforms the comparison
algorithms in identifying paths with the highest average trust values.
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Systems and Control
On Data-based Nash Equilibria in LQ Nonzero-sum Differential Games
Victor G. Lopez and Matthias A. Müller
This paper considers data-based solutions of linear-quadratic nonzero-sum differential games. Two cases are considered. First, the deterministic game is solved and Nash
equilibrium strategies are obtained by using persistently excited data from the multiagent system. Then, a
stochastic formulation of the game is considered, where each
agent measures a different noisy output signal and state observers must be designed for each player. It is shown that the proposed data-based solutions of these games are equivalent to known model-based procedures. The resulting data-based solutions are validated in a numerical experiment.
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Image and Video Processing · Cross-listed from physics.med-ph
Building Digital Twins of Different Human Organs for Personalized Healthcare
Yilin Lyu, Zhen Li, Vu Tran et al. (+9 more)
Digital twins are virtual replicas of physical entities and are poised to transform personalized medicine through the real-time simulation and prediction of human physiology. Translating this paradigm from engineering to biomedicine requires overcoming profound challenges, including anatomical variability, multi-scale biological processes, and the integration of multi-physics phenomena. This survey systematically reviews methodologies for building digital twins of human organs, structured around a pipeline decoupled into anatomical twinning (capturing patient-specific geometry and structure) and functional twinning (simulating multi-scale physiology from cellular to organ-level function). We categorize approaches both by organ-specific properties and by technical paradigm, with particular emphasis on multi-scale and multi-physics integration. A key focus is the role of artificial intelligence (AI), especially physics-informed AI, in enhancing model fidelity, scalability, and personalization. Furthermore, we discuss the critical challenges of clinical validation and translational pathways. This study not only charts a roadmap for overcoming current bottlenecks in single-organ twins but also outlines the promising, albeit ambitious, future of interconnected multi-organ digital twins for whole-body precision healthcare.
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All authors Yilin Lyu, Zhen Li, Vu Tran, Xuan Yang, Hao Li, Meng Wang, Ching-Yu Cheng, Mamatha Bhat, Viktor Jirsa, Roger Foo, Chwee Teck Lim, Lei Li
Signal Processing
Scalable mm-Wave Liquid Crystal Reconfigurable Intelligent Surfaces based on the Delay Line Architecture
Julia Schwarzbeck, Robin Neuder, Marc Späth et al. (+1 more)
This paper presents the design, fabrication, and characterization of broadband
liquid crystal (LC) reconfigurable intelligent surfaces (RIS) operating around 60 GHz and scaling up to 750 radiating elements. The RISs employ a delay line architecture (DLA) that decouples the phase shifting and radiating layer, enabling wide bandwidth, continuous phase control exceeding 360{\deg}, and fast response times with a micrometer-thin LC layer of 4.6 micrometer. Two prototypes with 120 and 750 elements are realized using identical unit
cells and column-wise biasing. Measurements demonstrate beam steering over +-60{\deg} and -3 dB bandwidths exceeding 9% for both apertures, confirming the scalability of the proposed architecture. On top of a measured nanowatt power consumption per unit
cell, aperture efficiencies above 20% are predicted by simulations. While the measured efficiencies are reduced to 9.2% and 2.6%, a detailed analysis verifies that this reduction can be attributed to technological challenges in a laboratory environment. Finally, a comprehensive comparison between the applied DLA-based LC-RIS and a conventional approach highlights the superior potential of applied architecture.
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All authors Julia Schwarzbeck, Robin Neuder, Marc Späth, Alejandro Jiménez-Sáez
Systems and Control
Analysis of Full Order Observer Based Control for Spacecraft Orbit Maneuver Trajectory Under Solar Radiation Pressure
Haoyang Zhang
This study investigates the application of modern control theory to improve the precision of spacecraft orbit maneuvers in low Earth orbit under the influence of
solar radiation pressure. A full order observer based feedback control framework is developed to estimate system states and compensate for external disturbances during the trajectory correction phase following main engine cut off. The maneuver trajectory is generated using Lambert guidance, while the observer based controller ensures accurate tracking of the target orbit despite SRP perturbations. The effectiveness of the proposed design is assessed through stability, observability, and controllability analyses. Stability is validated by step-response simulations and
eigenvalue distributions of the system dynamics. Observability is demonstrated through state
matrix rank analysis, confirming complete state estimation. Controllability is verified using state feedback rank conditions and corresponding control performance plots. Comparative simulations highlight that, in contrast to uncontrolled or conventional control cases, the observer based controller achieves improved trajectory accuracy and robust disturbance rejection with moderate control effort. These findings indicate that observer-based feedback control offers a reliable and scalable solution for precision orbital maneuvering in LEO missions subject to environmental disturbances.
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Systems and Control · Cross-listed from cs.RO
Adaptive Monitoring of Stochastic Fire Front Processes via Information-seeking Predictive Control
Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou et al. (+1 more)
We consider the problem of adaptively monitoring a wildfire front using a mobile
agent (e.g., a drone), whose trajectory determines where sensor data is collected and thus influences the accuracy of fire propagation estimation. This is a challenging problem, as the
stochastic nature of wildfire
evolution requires the seamless integration of sensing, estimation, and control, often treated separately in existing methods. State-of-the-art methods either impose linear-Gaussian assumptions to establish optimality or rely on approximations and heuristics, often without providing explicit performance guarantees. To address these limitations, we formulate the fire front monitoring task as a
stochastic optimal control problem that integrates sensing, estimation, and control. We derive an optimal recursive
Bayesian estimator for a class of
stochastic nonlinear elliptical-growth fire front models. Subsequently, we transform the resulting nonlinear
stochastic control problem into a finite-horizon Markov decision process and design an information-seeking predictive control law obtained via a lower confidence bound-based adaptive search
algorithm with asymptotic convergence to the optimal policy.
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All authors Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou
Systems and Control
Solution Concepts and Existence Results for Hybrid Systems with Continuous-time Inputs
W.P.M.H. Heemels, R. Postoyan, P. Bernard et al. (+2 more)
In many scenarios, it is natural to model a plant's dynamical behavior using a hybrid dynamical system influenced by exogenous continuous-time inputs. While solution concepts and analytical tools for existence and completeness are well established for autonomous hybrid systems, corresponding results for hybrid dynamical systems involving continuous-time inputs are generally lacking. This work aims to address this gap. We first formalize notions of a solution for such systems. We then provide conditions that guarantee the existence and forward completeness of solutions. Moreover, we leverage results and ideas from viability theory to present more explicit conditions in terms of various tangent cone formulations. Variants are provided that depend on the regularity of the exogenous input signals.
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All authors W.P.M.H. Heemels, R. Postoyan, P. Bernard, K.J.A. Scheres, R.G. Sanfelice
Signal Processing · Cross-listed from cs.IT
Performance Analysis of Cell-Free Massive MIMO under Imperfect LoS Phase Tracking
Noor Ul Ain, Lorenzo Miretti, Renato L. G. Cavalcante et al. (+1 more)
We study the impact of imperfect line-of-sight (LoS) phase tracking on the uplink performance of
cell-free massive
MIMO networks. Unlike prior works that assume perfectly known or completely unknown phases, we consider a realistic regime where LoS phases are estimated with residual uncertainty due to hardware impairments, mobility, and synchronization errors. To this end, we propose a Rician fading model where LoS components are rotated by imperfect phase estimates and attenuated by a deterministic phase-error penalty factor. We derive a linear MMSE channel
estimator that accounts for statistical phase errors and unifies prior results, reducing to the
Bayesian MMSE
estimator when phase is perfectly known and to a zero-mean model when no phase information is available. To address the non-Gaussian setting, we introduce a virtual uplink model that preserves second-order statistics of channel estimation, enabling the derivation of tractable virtual centralized and distributed MMSE beamformers. To ensure fair assessment of network performance, we apply these virtual beamformers to the operational uplink model that reflects the actual physical channel and compute the spectral efficiency bounds available in the literature. Numerical results show that our framework bridges idealized assumptions and practical tracking limitations, providing rigorous performance
benchmarks and design insights for 6G
cell-free networks.
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All authors Noor Ul Ain, Lorenzo Miretti, Renato L. G. Cavalcante, Slawomir Stanczak
Audio and Speech Processing · Cross-listed from cs.SD
FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning
Tanyu Chen, Tairan Chen, Kai Shen et al. (+4 more)
Recent end-to-end spoken dialogue systems leverage
speech tokenizers and neural audio codecs to enable LLMs to operate directly on discrete
speech representations. However, these models often exhibit limited speaker identity preservation, hindering personalized voice interaction. In this work, we present Chroma 1.0, the first open-source, real-time, end-to-end spoken dialogue model that achieves both low-latency interaction and high-fidelity personalized voice cloning. Chroma achieves sub-second end-to-end latency through an interleaved text-audio
token schedule (1:2) that supports streaming generation, while maintaining high-quality personalized voice synthesis across multi-turn conversations. Our experimental results demonstrate that Chroma achieves a 10.96% relative improvement in speaker similarity over the human baseline, with a Real-Time Factor (RTF) of 0.43, while maintaining strong
reasoning and dialogue capabilities. Our code and models are publicly available at https://github.com/FlashLabs-AI-Corp/FlashLabs-Chroma and https://huggingface.co/FlashLabs/Chroma-4B .
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All authors Tanyu Chen, Tairan Chen, Kai Shen, Zhenghua Bao, Zhihui Zhang, Man Yuan, Yi Shi
Signal Processing
Comprehensive Robust Dynamic Mode Decomposition from Mode Extraction to Dimensional Reduction
Yuki Nakamura, Shingo Takemoto and Shunsuke Ono
We propose Comprehensive Robust Dynamic Mode Decomposition (CR-DMD), a novel framework that robustifies the entire DMD process - from mode extraction to dimensional reduction - against mixed noise. Although standard DMD widely used for uncovering spatio-temporal patterns and constructing low-dimensional models of dynamical systems, it suffers from significant performance degradation under noise due to its reliance on least-squares estimation for computing the linear time
evolution operator. Existing robust variants typically modify the least-squares formulation, but they remain unstable and fail to ensure faithful low-dimensional representations. First, we introduce a convex optimization-based preprocessing method designed to effectively remove mixed noise, achieving accurate and stable mode extraction. Second, we propose a new convex formulation for dimensional reduction that explicitly links the robustly extracted modes to the original noisy observations, constructing a faithful representation of the original data via a sparse weighted sum of the modes. Both stages are efficiently solved by a preconditioned primal-dual splitting method. Experiments on fluid dynamics
datasets demonstrate that CR-DMD consistently outperforms state-of-the-art robust DMD methods in terms of mode accuracy and fidelity of low-dimensional representations under noisy conditions.
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