Image and Video Processing · Cross-listed from cs.CV
An Efficient and Generalizable Transfer Learning Method for Weather Condition Detection on Ground Terminals
Wenxuan Zhang and Peng Hu
The increasing adoption of satellite Internet with low-Earth-orbit (LEO) satellites in mega-constellations allows ubiquitous connectivity to rural and remote areas. However, weather events have a significant impact on the performance and reliability of satellite Internet. Adverse weather events such as snow and rain can disturb the performance and operations of satellite Internet's essential ground terminal components, such as satellite
antennas, significantly disrupting the space-ground link conditions between LEO satellites and ground stations. This challenge calls for not only region-based weather forecasts but also fine-grained detection capability on ground terminal components of fine-grained weather conditions. Such a capability can assist in fault diagnostics and mitigation for reliable satellite Internet, but its solutions are lacking, not to mention the effectiveness and generalization that are essential in real-world deployments. This paper discusses an efficient transfer learning (TL) method that can enable a ground component to locally detect representative weather-related conditions. The proposed method can detect snow, wet, and other conditions resulting from adverse and typical weather events and shows superior performance compared to the typical deep learning methods, such as YOLOv7, YOLOv9, Faster R-CNN, and R-YOLO. Our TL method also shows the advantage of being generalizable to various scenarios.
Read the full abstract ▾
Systems and Control · Cross-listed from math.OC
Multivariable Gradient-Based Extremum Seeking Control with Saturation Constraints
Enzo Ferreira Tomaz Silva, Pedro Henrique Silva Coutinho, Tiago Roux Oliveira et al. (+2 more)
This paper addresses the multivariable gradient-based extremum seeking control (ESC) subject to saturation. Two distinct saturation scenarios are investigated here: saturation acting on the input of the function to be optimized, which is addressed using an anti-windup compensation strategy, and saturation affecting the gradient estimate. In both cases, the unknown Hessian
matrix is represented using a polytopic uncertainty description, and sufficient conditions in the form of linear
matrix inequalities (LMIs) are derived to design a stabilizing control gain. The proposed conditions guarantee exponential stability of the origin for the average closed-loop system under saturation constraints. With the proposed design conditions, non-diagonal control gain
matrices can be obtained, generalizing conventional ESC designs that typically rely on diagonal structures. Stability and convergence are rigorously proven using the Averaging Theory for dynamical systems with Lipschitz continuous right-hand sides. Numerical simulations illustrate the effectiveness of the proposed ESC
algorithms, confirming the convergence even in the presence of saturation.
Read the full abstract ▾
All authors Enzo Ferreira Tomaz Silva, Pedro Henrique Silva Coutinho, Tiago Roux Oliveira, Miroslav Krstić, Sophie Tarbouriech
Systems and Control · Cross-listed from cs.CR
Supply Chain Exploitation of Secure ROS 2 Systems: A Proof-of-Concept on Autonomous Platform Compromise via Keystore Exfiltration
Tahmid Hasan Sakib, Yago Romano Martinez, Carter Brady et al. (+2 more)
This paper presents a proof-of-concept supply chain attack against the Secure ROS 2 (SROS 2) framework, demonstrated on a Quanser QCar2 autonomous vehicle platform. A Trojan-infected Debian package modifies core ROS 2 security commands to exfiltrate newly generated keystore credentials via DNS in base64-encoded chunks to an attacker-controlled nameserver. Possession of these credentials enables the attacker to rejoin the SROS 2 network as an authenticated participant and publish spoofed control or perception messages without triggering authentication failures. We evaluate this capability on a secure ROS 2 Humble testbed configured for a four-stop-sign
navigation routine using an Intel RealSense camera for perception. Experimental results show that control-topic injections can cause forced braking, sustained high-speed acceleration, and continuous turning loops, while perception-topic spoofing can induce phantom stop signs or suppress real detections. The attack generalizes to any data distribution service (DDS)-based robotic system using SROS 2, highlighting the need for both supply chain integrity controls and runtime semantic validation to safeguard autonomous systems against insider and impersonation threats.
Read the full abstract ▾
All authors Tahmid Hasan Sakib, Yago Romano Martinez, Carter Brady, Syed Rafay Hasan, Terry N. Guo
Signal Processing · Cross-listed from cs.LG
Data-Augmented Deep Learning for Downhole Depth Sensing and Validation
Si-Yu Xiao, Xin-Di Zhao, Tian-Hao Mao et al. (+8 more)
Accurate downhole depth measurement is essential for oil and gas well operations, directly influencing reservoir contact, production efficiency, and operational safety. Collar correlation using a casing collar locator (CCL) is fundamental for precise depth calibration. While neural network has achieved significant progress in collar recognition, preprocessing methods for such applications remain underdeveloped. Moreover, the limited availability of real well data poses substantial challenges for training neural network models that require extensive
datasets. This paper presents a system integrated into a downhole toolstring for CCL log acquisition to facilitate
dataset construction. Comprehensive preprocessing methods for data augmentation are proposed, and their effectiveness is evaluated using baseline neural network models. Through systematic experimentation across diverse configurations, the contribution of each augmentation method is analyzed. Results demonstrate that standardization, label distribution smoothing, and random cropping are fundamental prerequisites for model training, while label smoothing regularization, time scaling, and multiple sampling significantly enhance model generalization capabilities. Incorporating the proposed augmentation methods into the two baseline models results in maximum F1 score improvements of 0.027 and 0.024 for the TAN and MAN models, respectively. Furthermore, applying these techniques yields F1 score gains of up to 0.045 for the TAN model and 0.057 for the MAN model compared to prior studies. Performance evaluation on real CCL waveforms confirms the effectiveness and practical applicability of our approach. This work addresses the existing gaps in data augmentation methodologies for training casing collar recognition models under CCL data-limited conditions, and provides a technical foundation for the future automation of downhole operations.
Read the full abstract ▾
All authors Si-Yu Xiao, Xin-Di Zhao, Tian-Hao Mao, Yi-Wei Wang, Yu-Qiao Chen, Hong-Yun Zhang, Jian Wang, Jun-Jie Wang, Shuang Liu, Tu-Pei Chen, Yang Liu
Systems and Control · Cross-listed from cs.LG
DCcluster-Opt: Benchmarking Dynamic Multi-Objective Optimization for Geo-Distributed Data Center Workloads
Antonio Guillen-Perez, Avisek Naug, Vineet Gundecha et al. (+8 more)
The increasing energy demands and carbon footprint of large-scale AI require intelligent workload management in globally distributed data centers. Yet progress is limited by the absence of
benchmarks that realistically capture the interplay of time-varying environmental factors (grid carbon intensity, electricity prices, weather), detailed data center physics (CPUs, GPUs,
memory, HVAC energy), and geo-distributed network dynamics (latency and transmission costs). To bridge this gap, we present DCcluster-Opt: an open-source, high-fidelity simulation
benchmark for sustainable, geo-temporal task scheduling. DCcluster-Opt combines curated real-world
datasets, including AI workload traces, grid carbon intensity, electricity
markets, weather across 20 global regions, cloud transmission costs, and empirical network delay parameters with physics-informed models of data center operations, enabling rigorous and reproducible research in sustainable computing. It presents a challenging scheduling problem where a top-level coordinating
agent must dynamically reassign or defer tasks that arrive with resource and service-level agreement requirements across a configurable cluster of data centers to optimize multiple objectives. The environment also models advanced components such as heat recovery. A modular reward system enables an explicit study of trade-offs among carbon emissions, energy costs, service level agreements, and water use. It provides a Gymnasium API with baseline controllers, including reinforcement learning and rule-based strategies, to support reproducible ML research and a fair comparison of diverse
algorithms. By offering a realistic, configurable, and accessible testbed, DCcluster-Opt accelerates the development and validation of next-generation sustainable computing solutions for geo-distributed data centers.
Read the full abstract ▾
All authors Antonio Guillen-Perez, Avisek Naug, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna Gutierrez, Ashwin Ramesh Babu, Munther Salim, Shubhanker Banerjee, Eoin H. Oude Essink, Damien Fay, Soumyendu Sarkar
Systems and Control · Cross-listed from cs.LG
LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers
Avisek Naug, Antonio Guillen, Vineet Kumar et al. (+7 more)
Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable
Liquid Cooling (LC)
benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient
liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL
agents optimize critical thermal controls like
liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We
benchmark centralized and decentralized multi-
agent RL approaches, demonstrate policy
distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural
language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable
liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center
liquid cooling control solutions.
Read the full abstract ▾
All authors Avisek Naug, Antonio Guillen, Vineet Kumar, Scott Greenwood, Wesley Brewer, Sahand Ghorbanpour, Ashwin Ramesh Babu, Vineet Gundecha, Ricardo Luna Gutierrez, Soumyendu Sarkar
Image and Video Processing · Cross-listed from physics.med-ph
Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models
Joyoni Dey, Hunter C. Meyer and Murtuza S. Taqi
Low-dose computed
tomography (LDCT) is the current standard for lung cancer screening, yet its adoption and accessibility remain limited. Many regions lack LDCT infrastructure, and even among those screened, early-stage cancer detection often yield false positives, as shown in the National Lung Screening Trial (NLST) with a sensitivity of 93.8 percent and a false-positive rate of 26.6 percent. We aim to investigate whether X-ray dark-field imaging (DFI) radiograph, a technique sensitive to small-angle scatter from alveolar microstructure and less susceptible to organ shadowing, can significantly improve early-stage lung tumor detection when coupled with deep-learning
segmentation. Using paired attenuation (ATTN) and DFI radiograph images of euthanized mouse lungs, we generated realistic synthetic tumors with irregular boundaries and intensity profiles consistent with physical lung contrast. A U-Net
segmentation network was trained on small patches using either ATTN, DFI, or a combination of ATTN and DFI channels. Results show that the DFI-only model achieved a true-positive detection rate of 83.7 percent, compared with 51 percent for ATTN-only, while maintaining comparable specificity (90.5 versus 92.9 percent). The combined ATTN and DFI input achieved 79.6 percent sensitivity and 97.6 percent specificity. In conclusion, DFI substantially improves early-tumor detectability in comparison to standard attenuation radiography and shows potential as an accessible, low-cost, low-dose alternative for pre-clinical or limited-resource screening where LDCT is unavailable.
Read the full abstract ▾
Systems and Control
Technical Report for Dissipativity Learning in Reproducing Kernel Hilbert Space
Xiuzhen Ye and Wentao Tang
This work presents a nonparametric framework for dissipativity learning in reproducing kernel
Hilbert spaces, which enables data-driven certification of stability and performance properties for unknown nonlinear systems without requiring an explicit dynamic model. Dissipativity is a fundamental system property that generalizes Lyapunov stability, passivity, and finite L2 gain conditions through an energy balance inequality between a storage function and a supply rate. Unlike prior parametric formulations that approximate these functions using quadratic forms with fixed
matrices, the proposed method represents them as
Hilbert Schmidt operators acting on canonical kernel features, thereby capturing nonlinearities implicitly while preserving convexity and analytic tractability. The resulting operator optimization problem is formulated in the form of a one-class support vector machine and reduced, via the representer
theorem, to a finite dimensional convex program expressed through kernel Gram
matrices. Furthermore, statistical learning theory is applied to establish generalization guarantees, including confidence bounds on the dissipation rate and the L2 gain. Numerical results demonstrate that the proposed RKHS based dissipativity learning method effectively identifies nonlinear dissipative behavior directly from input output data, providing a powerful and interpretable framework for model free control analysis and synthesis.
Read the full abstract ▾
Image and Video Processing
Bayesian model selection and misspecification testing in imaging inverse problems only from noisy and partial measurements
Tom Sprunck, Marcelo Pereyra and Tobias Liaudat
Modern imaging techniques heavily rely on
Bayesian statistical models to address difficult image reconstruction and restoration tasks. This paper addresses the objective evaluation of such models in settings where ground truth is unavailable, with a focus on model selection and misspecification diagnosis. Existing unsupervised model evaluation methods are often unsuitable for computational imaging due to their high computational cost and incompatibility with modern image priors defined implicitly via machine learning models. We herein propose a general methodology for unsupervised model selection and misspecification detection in
Bayesian imaging sciences, based on a novel combination of
Bayesian cross-validation and data fission, a randomized measurement splitting technique. The approach is compatible with any
Bayesian imaging sampler, including
diffusion and plug-and-play samplers. We demonstrate the methodology through experiments involving various scoring rules and types of model misspecification, where we achieve excellent selection and detection accuracy with a low computational cost.
Read the full abstract ▾
Systems and Control · Cross-listed from cs.CL
SpecAttn: Speculating Sparse Attention
Harsh Shah
Large
Language Models (LLMs) face significant computational bottlenecks during inference due to the quadratic complexity of self-
attention mechanisms, particularly as context lengths increase. We introduce SpecAttn, a novel training-free approach that seamlessly integrates with existing speculative decoding techniques to enable efficient sparse
attention in pre-trained
transformers. Our key insight is to exploit the
attention weights already computed by the draft model during speculative decoding to identify important
tokens for the target model, eliminating redundant computation while maintaining output quality. SpecAttn employs three core techniques: KL divergence-based layer
alignment between draft and target models, a GPU-optimized sorting-free
algorithm for top-p
token selection from draft
attention patterns, and dynamic key-value cache
pruning guided by these predictions. By leveraging the computational work already performed in standard speculative decoding pipelines, SpecAttn achieves over 75% reduction in key-value cache accesses with a mere 15.29% increase in perplexity on the PG-19
dataset, significantly outperforming existing sparse
attention methods. Our approach demonstrates that speculative execution can be enhanced to provide approximate verification without significant performance degradation.
Read the full abstract ▾
Image and Video Processing
Navigated hepatic tumor resection using intraoperative ultrasound imaging
Karin Olthof, Theo Ruers, Tiziano Natali et al. (+6 more)
Purpose: This proof-of-concept study evaluates feasibility and accuracy of an ultrasound-based
navigation system for open liver surgery. Unlike most conventional systems that rely on registration to preoperative imaging, the proposed system provides
navigation-guided resection using 3D models generated from intraoperative ultrasound. Methods: A pilot study was conducted in 25 patients undergoing resection of liver metastases. The first five cases served to optimize the workflow. Intraoperatively, an electromagnetic sensor compensated for organ motion, after which an ultrasound volume was acquired. Vasculature was segmented automatically and tumors semi-automatically using region-growing (n=15) or a deep learning
algorithm (n=5). The resulting 3D model was visualized alongside tracked
surgical instruments. Accuracy was assessed by comparing the distance between
surgical clips and tumors in the
navigation software with the same distance on a postoperative CT of the resected specimen. Results:
Navigation was successfully established in all 20 patients. However, four cases were excluded from accuracy assessment due to intraoperative sensor detachment (n=3) or incorrect data recording (n=1). The complete
navigation workflow was operational within 5-10 minutes. In 16 evaluable patients, 78 clip-to-tumor distances were analyzed. The median
navigation accuracy was 3.2 mm [IQR: 2.8-4.8 mm], and an R0 resection was achieved in 15/16 (93.8%) patients and one patient had an R1 vascular resection. Conclusion:
Navigation based solely on intra-operative ultrasound is feasible and accurate for liver surgery. This registration-free approach paves the way for simpler and more accurate image guidance systems.
Read the full abstract ▾
All authors Karin Olthof, Theo Ruers, Tiziano Natali, Lisanne Venix, Jasper Smit, Anne den Hartor, Niels Kok, Matteo Fusaglia, Koert Kuhlmann
Image and Video Processing
Combined fluorescence and photoacoustic imaging of tozuleristide in muscle tissue in vitro – toward optically-guided solid tumor surgery: feasibility studies
Ruibo Shang, Matthew Thompson, Matthew D. Carson et al. (+3 more)
Near-infrared fluorescence (NIRF) can deliver high-contrast,
video-rate, non-contact imaging of tumor-targeted contrast
agents with the potential to guide surgeries excising solid tumors. However, it has been met with skepticism for wide-margin excision due to sensitivity and resolution limitations at depths larger than ~5 mm in tissue. To address this limitation, fast-sweep photoacoustic-ultrasound (PAUS) imaging is proposed to complement NIRF. In an exploratory in vitro feasibility study using dark-red bovine muscle tissue, we observed that PAUS scanning can identify tozuleristide, a clinical stage investigational imaging
agent, at a concentration of 20 uM from the background at depths of up to ~34 mm, highly extending the capabilities of NIRF alone. The capability of spectroscopic PAUS imaging was tested by direct injection of 20 uM tozuleristide into bovine muscle tissue at a depth of ~ 8 mm. It is shown that
laser-fluence compensation and strong clutter suppression enabled by the unique capabilities of the fast-sweep approach greatly improve spectroscopic accuracy and the PA detection limit, and strongly reduce image artifacts. Thus, the combined NIRF-PAUS approach can be promising for comprehensive pre- (with PA) and intra- (with NIRF) operative solid tumor detection and wide-margin excision in optically guided solid tumor surgery.
Read the full abstract ▾
All authors Ruibo Shang, Matthew Thompson, Matthew D. Carson, Eric J. Seibel, Matthew O'Donnell, Ivan Pelivanov
Signal Processing
Trends and Challenges in Next-Generation GNSS Interference Management
Leatile Marata, Mariona Jaramillo-Civill, Tales Imbiriba et al. (+4 more)
The global
navigation satellite system (GNSS) continues to evolve in order to meet the demands of emerging applications such as autonomous driving and smart environmental monitoring. However, these advancements are accompanied by a rise in interference threats, which can significantly compromise the reliability and safety of GNSS. Such interference problems are typically addressed through signal-processing techniques that rely on physics-based mathematical models. Unfortunately, solutions of this nature can often fail to fully capture the complex forms of interference. To address this, artificial intelligence (AI)-inspired solutions are expected to play a key role in future interference management solutions, thanks to their ability to exploit data in addition to physics-based models. This magazine paper discusses the main challenges and tasks required to secure GNSS and present a research
vision on how AI can be leveraged towards achieving more robust GNSS-based positioning.
Read the full abstract ▾
All authors Leatile Marata, Mariona Jaramillo-Civill, Tales Imbiriba, Petri Välisuo, Heidi Kuusniemi, Elena Simona Lohan, Pau Closas
Systems and Control · Cross-listed from math.OC
Risk-aware stochastic scheduling of multi-market energy storage systems
Gabriel D. Patrón, Di Zhang, Lavinia M.P. Ghilardi et al. (+9 more)
Energy storage promotes the integration of renewables by operating with charge and discharge policies that balance an intermittent power supply. A key challenge in this emerging sector is how to optimize the operation of storage assets given future price uncertainties and the need to recover the costs of project finance while ensuring an attractive return on equity and hedging against downside risk. This study investigates the scheduling of energy storage assets under price uncertainty, with a focus on electricity
markets. A two-stage
stochastic risk-constrained approach is employed, whereby electricity price trajectories or specific power
markets are observed, allowing for recourse in the schedule. Conditional value-at-risk is used to quantify risk in the optimization problems; this allows for explicit specification of a probabilistic risk limit. The proposed approach is tested in an integrated hydrogen system (IHS) and a battery energy storage system (BESS). In the joint design and operation context for the IHS, the risk constraint results in large installed unit capacities, increasing capital cost but enabling more inventory to buffer price uncertainty. In both case studies, there is an operational trade-off between risk and expected reward; this is reflected in higher expected costs (or lower expected profits) with increasing risk aversion. Despite the decrease in expected reward (up to 500\$k), both systems exhibit substantial benefits of increasing risk aversion (up to 1.5\$mn) with respect to risk-neutral settings. This work provides a general method to address uncertainties in energy storage scheduling, allowing operators to input their level of risk tolerance on asset decisions.
Read the full abstract ▾
All authors Gabriel D. Patrón, Di Zhang, Lavinia M.P. Ghilardi, Evelin Blom, Maldon Goodridge, Erik Solis, Hamidreza Jahangir, Jorge Angarita, Nandhini Ganesan, Kevin West, Nilay Shah, Calvin Tsay
Signal Processing
pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements
Anubhab Ghosh, Yonina C. Eldar and Saikat Chatterjee
We consider the problem of designing a data-driven nonlinear state estimation (DANSE) method that uses (noisy) nonlinear measurements of a process whose underlying state transition model (STM) is unknown. Such a process is referred to as a model-free process. A recurrent neural network (RNN) provides parameters of a Gaussian prior that characterize the state of the model-free process, using all previous measurements at a given time point. In the case of DANSE, the measurement system was linear, leading to a closed-form solution for the state posterior. However, the presence of a nonlinear measurement system renders a closed-form solution infeasible. Instead, the secondorder statistics of the state posterior are computed using the nonlinear measurements observed at the time point. We address the nonlinear measurements using a reparameterization trickbased particle sampling approach, and estimate the second-order statistics of the state posterior. The proposed method is referred to as particle-based DANSE (pDANSE). The RNN of pDANSE uses sequential measurements efficiently and avoids the use of computationally intensive sequential Monte-Carlo (SMC) and/or ancestral sampling. We describe the semi-supervised learning method for pDANSE, which transitions to unsupervised learning in the absence of labeled data. Using a
stochastic Lorenz-63 system as a
benchmark process, we experimentally demonstrate the state estimation performance for four nonlinear measurement systems. We explore cubic nonlinearity and a cameramodel nonlinearity where unsupervised learning is used; then we explore half-wave rectification nonlinearity and Cartesian-tospherical nonlinearity where semi-supervised learning is used. Additionally, we also show the performance of pDANSE for the
stochastic Lorenz-96 system with a half-wave, rectified measurement system. The performance of state estimation is shown to be competitive vis-a-vis model-driven methods that have complete knowledge of the STM of the dynamical system.
Read the full abstract ▾
Image and Video Processing
Towards robust quantitative photoacoustic tomography via learned iterative methods
Anssi Manninen, Janek Gröhl, Felix Lucka et al. (+1 more)
Photoacoustic
tomography (PAT) is a medical imaging modality that can provide high-resolution tissue images based on the optical absorption. Classical reconstruction methods for quantifying the absorption coefficients rely on sufficient prior information to overcome noisy and imperfect measurements. As these methods utilize computationally expensive forward models, the computation becomes slow, limiting their potential for time-critical applications. As an alternative approach, deep learning-based reconstruction methods have been established for faster and more accurate reconstructions. However, most of these methods rely on having a large amount of training data, which is not the case in practice. In this work, we adopt the model-based learned iterative approach for the use in Quantitative PAT (QPAT), in which additional information from the model is iteratively provided to the updating networks, allowing better generalizability with scarce training data. We compare the performance of different learned updates based on gradient descent, Gauss-Newton, and Quasi-Newton methods. The learning tasks are formulated as greedy, requiring iterate-wise optimality, as well as end-to-end, where all networks are trained jointly. The implemented methods are tested with ideal simulated data as well as against a digital twin
dataset that emulates scarce training data and high modeling error.
Read the full abstract ▾
All authors Anssi Manninen, Janek Gröhl, Felix Lucka, Andreas Hauptmann
Systems and Control
Context-Aware Stochastic Modeling of Consumer Energy Resource Aggregators in Electricity Markets
Chatum Sankalpa, Ghulam Mohy-ud-din, Erik Weyer et al. (+1 more)
Aggregators of consumer energy resources (CERs) like rooftop
solar and battery energy storage (BES) face challenges due to their inherent uncertainties. A sensible approach is to use
stochastic optimization to handle such uncertainties, which can lead to infeasible problems or loss in revenues if not chosen appropriately. This paper presents three efficient two-stage
stochastic optimization methods: risk-neutral, robust, and chance-constrained, to address the impact of CER uncertainties for aggregators who participate in energy and regulation services
markets in the Australian National Electricity
Market. Furthermore, these methods utilize the flexibility of BES, considering precise state-of-charge dynamics and complementarity constraints, aiming for scalable performance while managing uncertainty. The problems are formed as two-stage
stochastic mixed-integer linear programs, with relaxations adopted for large scenario sets. The solution approach employs scenario-based methodologies and affine recourse policies to obtain tractable reformulations. These methods are evaluated across use cases reflecting diverse operational and
market settings, uncertainty characteristics, and decision-making preferences, demonstrating their ability to mitigate uncertainty, enhance profitability, and provide context-aware guidance for aggregators in choosing the most appropriate
stochastic optimization method.
Read the full abstract ▾
All authors Chatum Sankalpa, Ghulam Mohy-ud-din, Erik Weyer, Maria Vrakopoulou
Systems and Control
A Switching Strategy for Event-Trigger Control of Spacecraft Rendezvous
Tommaso Del Carro, Gerson Portilla, Alexandre Seuret et al. (+1 more)
This paper presents the design of a state-feedback control law for spacecraft rendezvous, formulated using the Hill-Clohessy-Wiltshire equations. The proposed method introduces an impulsive control strategy to regulate thruster operations. Specifically, a state-dependent switching framework is developed to determine both the control input magnitudes and the precise state conditions that trigger thruster activation. The nonlinear control law is derived using principles from automatic control theory, particularly Lyapunov stability analysis and the Linear
Matrix Inequality framework. The resulting closed-loop system is proven to be stable, while simultaneously minimizing the total number of actuation events. The effectiveness of the proposed method is demonstrated through a numerical case study, which includes a comparative analysis with a standard Model Predictive Control scheme, highlighting the advantages and trade-offs of the developed control structure.
Read the full abstract ▾
All authors Tommaso Del Carro, Gerson Portilla, Alexandre Seuret, Rafael Vazquez
Signal Processing
Estimation of aboveground biomass in a tropical dry forest: An intercomparison of airborne, unmanned, and space laser scanning
Nelson Mattié, Arturo Sanchez-Azofeifa, Pablo Crespo-Peremarch et al. (+1 more)
According to the Paris
Climate Change Agreement, all nations are required to submit reports on their greenhouse gas emissions and absorption every two years by 2024. Consequently, forests play a crucial role in reducing carbon emissions, which is essential for meeting these obligations. Recognizing the significance of forest conservation in the global battle against
climate change, Article 5 of the Paris Agreement emphasizes the need for high-quality forest data. This study focuses on enhancing methods for mapping aboveground biomass in tropical dry forests. Tropical dry forests are considered one of the least understood tropical forest environments; therefore, there is a need for accurate approaches to estimate carbon pools. We employ a comparative analysis of AGB estimates, utilizing different discrete and full-waveform
laser scanning
datasets in conjunction with Ordinary Least Squares and
Bayesian approaches SVM. Airborne
Laser Scanning, Unmanned
Laser Scanning, and Space
Laser Scanning were used as independent variables for extracting forest metrics. Variable selection, SVM regression tuning, and cross-validation via a machine-learning approach were applied to account for overfitting and underfitting. The results indicate that six key variables primarily related to tree height: Elevṁinimum, ElevL̇3, levṀADṁode, Elevṁode, ElevṀADṁedian, and Elevṡkewness, are important for AGB estimation using ALSD and ULSD, while Leaf Area Index, canopy coverage and height, terrain elevation, and full-waveform signal energy emerged as the most vital variables. AGB values estimated from ten permanent tropical dry forest plots in Costa Rica Guanacaste province ranged from 26.02 Mg/ha to 175.43 Mg/ha. The SVM regressions demonstrated a 17.89 error across all
laser scanning systems, with SLSF W exhibiting the lowest error 17.07 in estimating total biomass per plot.
Read the full abstract ▾
All authors Nelson Mattié, Arturo Sanchez-Azofeifa, Pablo Crespo-Peremarch, Juan-Ygnacio López-Hernández
Signal Processing
UNILocPro: Unified Localization Integrating Model-Based Geometry and Channel Charting
Yuhao Zhang, Guangjin Pan, Musa Furkan Keskin et al. (+3 more)
In this paper, we propose a unified localization framework (called UNILocPro) that integrates model-based localization and channel charting (CC) for mixed line-of-sight (LoS)/non-line-of-sight (NLoS) scenarios. Specifically, based on LoS/NLoS identification, an adaptive activation between the model-based and CC-based methods is conducted. Aiming for unsupervised learning, information obtained from the model-based method is utilized to train the CC model, where a pairwise distance loss (involving a new dissimilarity metric design), a triplet loss (if timestamps are available), a LoS-based loss, and an optimal transport (OT)-based loss are jointly employed such that the global geometry can be well preserved. To reduce the training complexity of UNILocPro, we propose a low-complexity implementation (called UNILoc), where the CC model is trained with self-generated labels produced by a single pre-training OT transformation, which avoids iterative Sinkhorn updates involved in the OT-based loss computation. Extensive numerical experiments demonstrate that the proposed unified frameworks achieve significantly improved positioning accuracy compared to both model-based and CC-based methods. Notably, UNILocPro with timestamps attains performance on par with fully-supervised fingerprinting despite operating without labelled training data. It is also shown that the low-complexity UNILoc can substantially reduce training complexity with only marginal performance degradation.
Read the full abstract ▾
All authors Yuhao Zhang, Guangjin Pan, Musa Furkan Keskin, Ossi Kaltiokallio, Mikko Valkama, Henk Wymeersch
Signal Processing
Classification of Induction Motor Fault and Imbalance Based on Vibration Signal Using Single Antenna's Reactive Near Field
Sagar Dutta, Banani Basu and Fazal Ahmed Talukdar
Early fault diagnosis is imperative for the proper functioning of rotating machines. It can reduce economic losses in the industry due to unexpected failures. Existing fault analysis methods are either expensive or demand expertise for the installation of the sensors. This article proposes a novel method for the detection of bearing faults and imbalance in induction motors using an
antenna as the sensor, which is noninvasive and cost-efficient. Time-varying S11 is measured using an omnidirectional
antenna, and it is seen that the spectrogram of S11 shows unique characteristics for different fault conditions. The experimental setup has analytically evaluated the vibration frequencies due to fault and validated the characteristic fault frequency by applying FFT analysis on the captured S11 data. This article has evaluated the average power content of the detected signals at normal and different fault conditions. A deep learning model is used to classify the faults based on the reflection coefficient ( S11). It is found that classification accuracy of 98.2% is achieved using both magnitude and phase of S11, 96% using the magnitude of S11 and 92.1% using the phase of S11. The classification accuracy for different operating frequencies,
antenna location, and time windows are also investigated.
Read the full abstract ▾
Signal Processing
Classification of Lower Limb Activities Based on Discrete Wavelet Transform Using On-Body Creeping Wave Propagation
Sagar Dutta, Banani Basu and Fazal Ahmed Talukdar
This article investigates how the creeping wave propagation around the human thigh could be used to monitor the leg movements. The propagation path around the human thigh gives information regarding leg motions that can be used for the classification of activities. The variation of the transmission coefficient is measured between two on-body polyethylene terephthalate (PET) flexible
antennas for six different leg-based activities that exhibit unique time-varying signatures. A discrete wavelet transform (DWT) along with different classifiers, such as support vector machine (SVM), decision trees, naive Bayes, and K-nearest neighbors (KNN), is applied for feature extraction and classification to evaluate the efficiency for classifying different activity signals. Additional
algorithms, such as dynamic time warping (DTW) and deep convolutional neural network (DCNN), have also been implemented, and in each case, SVM with DWT outperforms the others. Simulation to evaluate a specific absorption rate (SAR) is carried out as the
antenna is positioned on the human thigh leaving no gap. The results show that the SAR is within the threshold as per the Federal Communications Commission (FCC) standard.
Read the full abstract ▾
Signal Processing
Variational Bayesian Estimation of Low Earth Orbits for Satellite Communication
Anders Malthe Westerkam, Amélia Struyf, Dimitri Lederer et al. (+2 more)
Low-earth-orbit (LEO) satellite communication systems that use millimeter-wave frequencies rely on large
antenna arrays with hybrid analog-digital architectures for rapid beam steering. LEO satellites are only visible from the ground for short periods of times (a few tens of minutes) due to their high orbital speeds. This paper presents a variational message passing
algorithm for joint localization and beam tracking of a LEO satellite from a ground station equipped with a hybrid transceiver architecture. The
algorithm relies on estimating the parameters of the orbit, which is modelled as circular. Angles are then obtained from the orbit in a straightforward manner. Simulation results show that the proposed method is highly resilient to missed detections, enables reliable satellite tracking even near the horizon, and effectively alleviates the ambiguities inherent in hybrid architectures.
Read the full abstract ▾
All authors Anders Malthe Westerkam, Amélia Struyf, Dimitri Lederer, Troels Pedersen, François Quitin
Signal Processing · Cross-listed from cs.SI
Meritocracy versus Matthew-effect: Two underlying network formation mechanisms of online social platforms
Yuchen Xu, Wenjun Mei, Ge Chen et al. (+1 more)
With the rapid development of the internet industry, online social networks have come to play an increasingly significant role in everyday life. In recent years, content-based emerging platforms such as TikTok, Instagram, and Bilibili have diverged fundamentally in their underlying logic from traditional connection-based social platforms like Facebook and LinkedIn. Empirical data on follower counts and follower-count-based rankings reveal that the distribution of social power varies significantly across different types of platforms, with content-based platforms exhibiting notably greater inequality. Here we propose two fundamental network formation mechanisms: a meritocracy-based model and a Matthew-effect-based model, designed to capture the formation logic underlying traditional and emerging social networks, respectively. Through theoretical and numerical analysis, we demonstrate that both models replicate salient statistical features of social networks including scale-free and small-world property, while also closely match empirical patterns on the relationship between in-degrees and in-degree rankings, thereby capturing the distinctive distributions of social power in respective platforms. Moreover, networks such as academic collaboration networks, where the distribution of social power usually lies between that of traditional and emerging platorms, can be interpreted through a hybrid of the two proposed mechanisms. Deconstructing the formation mechanisms of online social networks offers valuable insights into the
evolution of the content ecosystems and the behavioral patterns of content creators on online social platforms.
Read the full abstract ▾
All authors Yuchen Xu, Wenjun Mei, Ge Chen, Linyuan Lü
Image and Video Processing
A fragile zero-watermarking method based on dual quaternion matrix decomposition
Mingcui Zhang and Zhigang Jia
Medical images play a crucial role in assisting diagnosis, remote consultation, and academic research. However, during the transmission and sharing process, they face serious risks of copyright ownership and content tampering. Therefore, protecting medical images is of great importance. As an effective means of image copyright protection, zero-watermarking technology focuses on constructing watermarks without modifying the original carrier by extracting its stable features, which provides an ideal approach for protecting medical images. This paper aims to propose a fragile zero-watermarking model based on dual quaternion
matrix decomposition, which utilizes the operational relationship between the standard part and the dual part of dual quaternions to correlate the original carrier image with the watermark image, and generates zero-watermarking information based on the characteristics of dual quaternion
matrix decomposition, ultimately achieving copyright protection and content tampering detection for medical images.
Read the full abstract ▾
Systems and Control
Simplifying Preference Elicitation in Local Energy Markets: Combinatorial Clock Exchange
Shobhit Singhal and Lesia Mitridati
As distributed energy resources (DERs) proliferate, future power system will need new
market platforms enabling prosumers to trade various electricity and grid-support products. However, prosumers often exhibit complex, product interdependent preferences and face limited cognitive and computational resources, hindering engagement with complex
market structures and bid formats. We address this challenge by introducing a multi-product
market that allows prosumers to express complex preferences through an intuitive format, by fusing combinatorial clock exchange and machine learning (ML) techniques. The iterative mechanism only requires prosumers to report their preferred package of products at posted prices, eliminating the need for
forecasting product prices or adhering to complex bid formats, while the ML-aided price discovery speeds up convergence. The linear pricing rule further enhances transparency and interpretability. Finally, numerical simulations demonstrate convergence to clearing prices in approximately 15 clock iterations.
Read the full abstract ▾
Audio and Speech Processing · Cross-listed from cs.HC
Inferring trust in recommendation systems from brain, behavioural, and physiological data
Vincent K.M. Cheung, Pei-Cheng Shih, Masato Hirano et al. (+2 more)
As people nowadays increasingly rely on artificial intelligence (AI) to curate information and make decisions, assigning the appropriate amount of trust in automated intelligent systems has become ever more important. However, current measurements of trust in automation still largely rely on self-reports that are subjective and disruptive to the user. Here, we take music recommendation as a model to investigate the neural and cognitive processes underlying trust in automation. We observed that system accuracy was directly related to users' trust and modulated the influence of recommendation cues on music preference. Modelling users' reward encoding process with a reinforcement learning model further revealed that system accuracy, expected reward, and prediction error were related to oscillatory neural activity recorded via
EEG and changes in pupil diameter. Our results provide a neurally grounded account of calibrating trust in automation and highlight the promises of a
multimodal approach towards developing trustable AI systems.
Read the full abstract ▾
All authors Vincent K.M. Cheung, Pei-Cheng Shih, Masato Hirano, Masataka Goto, Shinichi Furuya
Systems and Control · Cross-listed from math.OC
Value of Multi-pursuer Single-evader Pursuit-evasion Game with Terminal Cost of Evader's Position: Relaxation of Convexity Condition
Weiwen Huang, Li Liang, Ningsheng Xu et al. (+1 more)
In this study, we consider a multi-pursuer single-evader quantitative pursuit-evasion game with payoff function that includes only the terminal cost. The terminal cost is a function related only to the terminal position of the evader. This problem has been extensively studied in target defense games. Here, we prove that a candidate for the value function generated by
geometric method is the viscosity solution of the corresponding Hamilton-Jacobi-Isaacs partial differential equation (HJI PDE) Dirichlet problem. Therefore, the value function of the game at each point can be computed by a mathematical program. In our work, the convexity of the terminal cost or the target is not required. The terminal cost only needs to be locally Lipschitz continuous. The cases in which the terminal costs or the targets are not convex are covered. Therefore, our result is more universal than those of previous studies, and the complexity of the proof is improved. We also discuss the optimal strategies in this game and present an intuitive explanation of this value function.
Read the full abstract ▾
All authors Weiwen Huang, Li Liang, Ningsheng Xu, Fang Deng
Signal Processing
SIM-Assisted End-to-End Co-Frequency Co-Time Full-Duplex System
Yida Zhang, Qiuyan Liu, Yuqi Xia et al. (+2 more)
To further suppress the inherent self-interference (SI) in co-frequency and co-time full-duplex (CCFD) systems, we propose integrating a stacked intelligent metasurface (SIM) into the RF front-end to enhance signal processing in the wave domain. Furthermore, an end-to-end (E2E) learning-based signal processing method is adopted to control the metasurface. Specifically, the real metasurface is abstracted as hidden layers of a network, thereby constructing an electromagnetic neural network (EMNN) to enable driving control of the real communication system. Traditional communication tasks, such as channel coding, modulation, precoding, combining, demodulation, and channel decoding, are synchronously carried out during the electromagnetic (EM) forward propagation through the metasurface. Simulation results show that, benefiting from the additional wave-domain processing capability of the SIM, the SIM-assisted CCFD system achieves significantly reduced bit error rate (BER) compared with conventional CCFD systems. Our study fully demonstrates the potential applications of EMNN and SIM-assisted E2E CCFD systems in next-generation transceiver design.
Read the full abstract ▾
All authors Yida Zhang, Qiuyan Liu, Yuqi Xia, Guoxu Xia, Qiang Wang
Signal Processing
Joint Visible Light and Backscatter Communications for Proximity-Based Indoor Asset Tracking Enabled by Energy-Neutral Devices
Boxuan Xie, Lauri Mela, Alexis A. Dowhuszko et al. (+4 more)
In next-generation wireless systems, providing location-based mobile computing services for energy-neutral devices has become a crucial objective for the provision of sustainable Internet of Things (IoT). Visible light positioning (VLP) has gained great research
attention as a complementary method to
radio frequency (RF) solutions since it can leverage ubiquitous lighting infrastructure. However, conventional VLP receivers often rely on photodetectors or cameras that are power-hungry, complex, and expensive. To address this challenge, we propose a hybrid indoor asset tracking system that integrates visible light communication (VLC) and backscatter communication (BC) within a simultaneous lightwave information and power transfer (SLIPT) framework. We design a low-complexity and energy-neutral IoT node, namely backscatter device (BD) which harvests energy from light-emitting diode (LED) access points, and then modulates and reflects ambient RF carriers to indicate its location within particular VLC
cells. We present a multi-
cell VLC deployment with frequency division multiplexing (FDM) method that mitigates interference among LED access points by assigning them distinct frequency pairs based on a four-color map scheduling principle. We develop a lightweight particle filter (PF) tracking
algorithm at an edge RF reader, where the fusion of proximity reports and the received backscatter signal strength are employed to track the BD. Experimental results show that this approach achieves the positioning error of 0.318 m at 50th percentile and 0.634 m at 90th percentile, while avoiding the use of complex photodetectors and active RF synthesizing components at the energy-neutral IoT node. By demonstrating robust performance in multiple indoor trajectories, the proposed solution enables scalable, cost-effective, and energy-neutral indoor tracking for pervasive and edge-assisted IoT applications.
Read the full abstract ▾
All authors Boxuan Xie, Lauri Mela, Alexis A. Dowhuszko, Yu Bai, Zehui Xiong, Zhu Han, Riku Jäntti