Systems and Control
Digital Twin of Aerosol Jet Printing
Aayushya Agarwal, Jace Rozsa, Matteo Pozzi et al. (+2 more)
Aerosol Jet (AJ) printing is a versatile additive manufacturing technique capable of producing high-resolution interconnects on both 2D and 3D substrates. The AJ process is complex and dynamic with many hidden and unobservable states that influence the machine performance, including aerosol particle diameter, aerosol carrier density, vial level, and ink deposition in the tube and nozzle. Despite its promising potential, the widespread adoption of AJ printing is limited by inconsistencies in print quality that often stem from variability in these hidden states. To address these challenges, we develop a digital twin model of the AJ process that offers real-time insights into the machine's operations. The digital twin is built around a physics-based macro-model created through simulation and experimentation. The states and parameters of the digital model are continuously updated using probabilistic sequential estimation techniques to closely align with real-time measurements extracted from the AJ system's sensor and
video data. The result is a digital model of the AJ process that continuously evolves over a physical machine's lifecycle. The digital twin enables accurate monitoring of unobservable physical characteristics, detects and predicts anomalous behavior, and forecasts the effect of control adjustments. This work presents a comprehensive end-to-end digital twin framework that integrates customized computer
vision techniques, physics-based macro-modeling, and advanced probabilistic estimation methods to construct an evolving digital representation of the AJ equipment and process. While the methodologies are customized for aerosol jet printing, the process for constructing the digital twin can be applied for other advanced manufacturing techniques.
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All authors Aayushya Agarwal, Jace Rozsa, Matteo Pozzi, Rahul Panat, Gary K. Fedder
Systems and Control
Towards Quantum Algorithms for the Optimization of Spanning Trees: The Power Distribution Grids Use Case
Carsten Hartmann, Nil Rodellas-Gràcia, Christian Wallisch et al. (+5 more)
Optimizing the
topology of networks is an important challenge across engineering disciplines. In energy systems, network reconfiguration can substantially reduce losses and costs and thus support the energy transition. Unfortunately, many related optimization problems are NP hard, restricting practical applications. In this article, we address the problem of minimizing losses in radial networks, a problem that routinely arises in distribution grid operation. We show that even the computation of approximate solutions is computationally hard and propose
quantum optimization as a promising alternative. We derive two
quantum algorithmic primitives based on the
Quantum Alternating Operator Ansatz (QAOA) that differ in the sampling of network
topologies: a tailored sampling of radial
topologies and simple sampling with penalty terms to suppress non-radial
topologies. We show how to apply these algorithmic primitives to distribution grid reconfiguration and quantify the necessary
quantum resources.
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All authors Carsten Hartmann, Nil Rodellas-Gràcia, Christian Wallisch, Thiemo Pesch, Frank K. Wilhelm, Dirk Witthaut, Tobias Stollenwerk, Andrea Benigni
Signal Processing · Cross-listed from cs.NI
Advancing Fluid Antenna-Assisted Non-Terrestrial Networks in 6G and Beyond: Fundamentals, State of the Art, and Future Directions
Tianheng Xu, Runke Fan, Jie Zhu et al. (+6 more)
With the surging demand for ultra-reliable, low-latency, and ubiquitous connectivity in Sixth-Generation (6G) networks, Non-Terrestrial Networks (NTNs) emerge as a key complement to terrestrial networks by offering flexible access and global coverage. Despite the significant potential, NTNs still face critical challenges, including dynamic propagation environments, energy constraints, and dense interference. As a key 6G technology, Fluid
Antennas (FAs) can reshape wireless channels by reconfiguring radiating elements within a limited space, such as their positions and rotations, to provide higher channel diversity and multiplexing gains. Compared to fixed-position
antennas, FAs can present a promising integration path for NTNs to mitigate dynamic channel fading and optimize resource allocation. This paper provides a comprehensive review of FA-assisted NTNs. We begin with a brief overview of the classical structure and limitations of existing NTNs, the fundamentals and advantages of FAs, and the basic principles of FA-assisted NTNs. We then investigate the joint optimization solutions, detailing the adjustments of FA configurations, NTN platform motion modes, and resource allocations. We also discuss the combination with other emerging technologies and explore FA-assisted NTNs as a novel network architecture for intelligent function integrations. Furthermore, we delve into the physical layer security and covert communication in FA-assisted NTNs. Finally, we highlight the potential future directions to empower broader applications of FA-assisted NTNs.
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All authors Tianheng Xu, Runke Fan, Jie Zhu, Pei Peng, Xianfu Chen, Qingqing Wu, Ming Jiang, Celimuge Wu, Kai-Kit Wong
Systems and Control · Cross-listed from cs.LG
FTT-GRU: A Hybrid Fast Temporal Transformer with GRU for Remaining Useful Life Prediction
Varun Teja Chirukiri, Udaya Bhasker Cheerala, Sandeep Kanta et al. (+2 more)
Accurate prediction of the remaining useful life (RUL) of industrial machinery is essential for reducing downtime and optimizing maintenance schedules. Existing approaches, such as long short-term
memory (LSTM) networks and convolutional neural networks (CNNs), often struggle to model both global temporal dependencies and fine-grained degradation trends in multivariate sensor data. We propose a hybrid model, FTT-GRU, which combines a Fast Temporal
Transformer (FTT) – a lightweight
Transformer variant using linearized
attention via fast
Fourier transform (FFT) – with a gated recurrent unit (GRU) layer for sequential modeling. To the best of our knowledge, this is the first application of an FTT with a GRU for RUL prediction on NASA CMAPSS, enabling simultaneous capture of global and local degradation patterns in a compact architecture. On CMAPSS FD001, FTT-GRU attains RMSE 30.76, MAE 18.97, and $R^2=0.45$, with 1.12 ms CPU latency at batch=1. Relative to the best published deep baseline (TCN–
Attention), it improves RMSE by 1.16% and MAE by 4.00%. Training curves averaged over $k=3$ runs show smooth convergence with narrow 95% confidence bands, and ablations (GRU-only, FTT-only) support the contribution of both components. These results demonstrate that a compact
Transformer-RNN hybrid delivers accurate and efficient RUL predictions on CMAPSS, making it suitable for real-time industrial prognostics.
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All authors Varun Teja Chirukiri, Udaya Bhasker Cheerala, Sandeep Kanta, Abdul Karim, Praveen Damacharla
Systems and Control
Rotatable Antenna System Empowered Low-Altitude Economy: Opportunities and Challenges
Shuaijun Li, Jie Tang, Beixiong Zheng et al. (+5 more)
Low-altitude economy (LAE) is an emerging technological paradigm that enables continuous airspace coverage at multiple altitudes by providing highly reliable data connectivity for numerous low-altitude applications. However, existing networks cannot sufficiently support LAE development, as current base stations (BSs) are primarily designed for terrestrial users and lack the capability to provide continuous coverage at low altitudes. To overcome these challenges, rotatable
antenna system (RAS) is introduced in LAE, enabling flexible beamforming by dynamically adjusting the boresight of directional
antennas to extend low-altitude coverage and enhance the stability of data transmission. In this article, we first provide an overview of RAS-empowered LAE applications, including low-altitude communication, sensing, control, and computation. Then, we present two practical RAS deployment strategies for LAE scenarios, namely RAS-aided multi-BS and multi-unmanned aerial vehicle (
UAV) cooperative coverages, as well as provide detailed discussions on their system architectures and performance benefits. Additionally, key design issues of RAS in LAE are discussed, including channel modeling and estimation, cellular access and interference cancellation, as well as RAS configuration and boresight optimization. Finally, we demonstrate the performance gains of RAS in LAE networks through experimental and simulation results.
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All authors Shuaijun Li, Jie Tang, Beixiong Zheng, Lipeng Zhu, Cui Yang, Nan Zhao, Xiu Yin Zhang, Kai-Kit Wong
Image and Video Processing
Image-based ground distance detection for crop-residue-covered soil
Baochao Wang, Xingyu Zhang, Qingtao Zong et al. (+3 more)
Conservation agriculture features a soil surface covered with crop residues, which brings benefits of improving soil health and saving water. However, one significant challenge in conservation agriculture lies in precisely controlling the seeding depth on the soil covered with crop residues. This is constrained by the lack of ground distance information, since current distance measurement techniques, like
laser, ultrasonic, or mechanical displacement sensors, are incapable of differentiating whether the distance information comes from the residue or the soil. This paper presents an image-based method to get the ground distance information for the crop-residues-covered soil. This method is performed with 3D camera and RGB camera, obtaining depth image and color image at the same time. The color image is used to distinguish the different areas of residues and soil and finally generates a mask image. The mask image is applied to the depth image so that only the soil area depth information can be used to calculate the ground distance, and residue areas can be recognized and excluded from ground distance detection. Experimentation shows that this distance measurement method is feasible for real-time implementation, and the measurement error is within plus or minus 3mm. It can be applied in conservation agriculture machinery for precision depth seeding, as well as other depth-control-demanding applications like transplant or tillage.
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All authors Baochao Wang, Xingyu Zhang, Qingtao Zong, Alim Pulatov, Shuqi Shang, Dongwei Wang
Systems and Control · Cross-listed from cs.HC
On Improvisation and Open-Endedness: Insights for Experiential AI
Botao 'Amber' Hu
Improvisation-the art of spontaneous creation that unfolds moment-to-moment without a scripted outcome-requires practitioners to continuously sense, adapt, and create anew. It is a fundamental mode of human creativity spanning music, dance, and everyday life. The open-ended nature of improvisation produces a stream of novel, unrepeatable moments-an aspect highly valued in artistic creativity. In parallel, open-endedness (OE)-a system's capacity for unbounded novelty and endless "interestingness"-is exemplified in natural or cultural
evolution and has been considered "the last grand challenge" in artificial life (ALife). The rise of generative AI now raises the question in computational creativity (CC) research: What makes a "good" improvisation for AI? Can AI learn to improvise in a genuinely open-ended way? In this work-in-progress paper, we report insights from in-depth interviews with 6 experts in improvisation across dance, music, and contact improvisation. We draw systemic connections between human improvisational arts and the design of future experiential AI
agents that could improvise alone or alongside humans-or even with other AI
agents-embodying qualities of improvisation drawn from practice: active listening (umwelt and awareness), being in the time (mindfulness and ephemerality), embracing the unknown (source of randomness and serendipity), non-judgmental flow (acceptance and dynamical stability, balancing structure and surprise (unpredictable criticality at edge of chaos), imaginative metaphor (synaesthesia and planning), empathy, trust, boundary, and care (mutual theory of mind), and playfulness and intrinsic motivation (maintaining interestingness).
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Systems and Control · Cross-listed from cs.RO
Descriptive Model-based Learning and Control for Bipedal Locomotion
Suraj Kumar and Andy Ruina
Bipedal balance is challenging due to its multi-phase, hybrid nature and high-dimensional state space. Traditional balance control approaches for bipedal
robots rely on low-dimensional models for locomotion planning and reactive control, constraining the full
robot to behave like these simplified models. This involves tracking preset reference paths for the Center of Mass and upper body obtained through low-dimensional models, often resulting in inefficient walking patterns with bent knees. However, we observe that bipedal balance is inherently low-dimensional and can be effectively described with simple state and action descriptors in a low-dimensional state space. This allows the
robot's motion to evolve freely in its high-dimensional state space, only constraining its projection in the low-dimensional state space. In this work, we propose a novel control approach that avoids prescribing a low-dimensional model to the full model. Instead, our control framework uses a descriptive model with the minimum degrees of freedom necessary to maintain balance, allowing the remaining degrees of freedom to evolve freely in the high-dimensional space. This results in an efficient human-like walking gait and improved robustness.
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Image and Video Processing · Cross-listed from cs.CV
OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback
Kai Luo, Hao Shi, Kunyu Peng et al. (+4 more)
To address panoramic distortion, large search space, and identity ambiguity under a 360{\deg} FoV, OmniTrack++ adopts a feedback-driven framework that progressively refines perception with trajectory cues. A DynamicSSM block first stabilizes panoramic features, implicitly alleviating
geometric distortion. On top of normalized representations, FlexiTrack Instances use trajectory-informed feedback for flexible localization and reliable short-term association. To ensure long-term robustness, an ExpertTrack
Memory consolidates appearance cues via a Mixture-of-Experts design, enabling recovery from fragmented tracks and reducing identity drift. Finally, a Tracklet Management module adaptively switches between end-to-end and tracking-by-detection modes according to scene dynamics, offering a balanced and scalable solution for panoramic MOT. To support rigorous evaluation, we establish the EmboTrack
benchmark, a comprehensive
dataset for panoramic MOT that includes QuadTrack, captured with a quadruped
robot, and BipTrack, collected with a bipedal wheel-legged
robot. Together, these
datasets span wide-angle environments and diverse motion patterns, providing a challenging testbed for real-world panoramic perception. Extensive experiments on JRDB and EmboTrack demonstrate that OmniTrack++ achieves state-of-the-art performance, yielding substantial HOTA improvements of +3.94 on JRDB and +15.03 on QuadTrack over the original OmniTrack. These results highlight the effectiveness of trajectory-informed feedback, adaptive paradigm switching, and robust long-term
memory in advancing panoramic multi-object tracking.
Datasets and code will be made available at https://github.com/xifen523/OmniTrack.
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All authors Kai Luo, Hao Shi, Kunyu Peng, Fei Teng, Sheng Wu, Kaiwei Wang, Kailun Yang
Systems and Control · Cross-listed from math.OC
Optimization of continuous-flow over traffic networks with fundamental diagram constraints
Anqi Dong, Karl Henrik Johansson and Johan Karlsson
Optimal transport (OT) theory provides a principled framework for modeling mass movement in applications such as mobility, logistics, and economics. Classical formulations, however, generally ignore capacity limits that are intrinsic in applications, in particular in traffic flow problems. We address this limitation by incorporating fundamental diagrams into a dynamic continuous-flow OT model on graphs, thereby including empirical relations between local density and maximal flux. We adopt an Eulerian kinetic action on graphs that preserves displacement interpolation in direct analogy with the continuous theory. Momentum lives on edges and density on nodes, mirroring road-network semantics in which segments carry speed and intersections store mass. The resulting fundamental-diagram-constrained OT problem preserves mass conservation and admits a convex variational discretization, yielding optimal congestion-aware traffic flow over road networks. We establish the existence and uniqueness of the optimal flow with sources and sinks, and develop an efficient convex optimization method. Numerical studies begin with a single-lane line network and scale to a city-level road network.
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Signal Processing
An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI
Ljupcho Milosheski, Kuon Akiyama, Blaž Bertalanič et al. (+2 more)
The growing number of smart devices supporting bandwidth-intensive and latency-sensitive applications, such as real-time
video analytics, smart sensing, Extended Reality (XR), etc., necessitates reliable wireless connectivity in indoor environments. In such environments, accurate design of
Radio Environment Maps (REMs) enables adaptive wireless network planning and optimization of Access Point (AP) placement. However, generating realistic REMs remains difficult due to the variability of indoor environments and the limitations of existing modeling approaches, which often rely on simplified layouts or fully synthetic data. These challenges are further amplified by the adoption of next-generation Wi-Fi standards, which operate at higher frequencies and suffer from limited range and wall penetration. To support the efforts in addressing these challenges, we collected a
dataset that combines high-resolution 3D
LiDAR scans with Wi-Fi RSSI measurements collected across 20 setups in a multi-room indoor environment. The
dataset includes two measurement scenarios, the first without human presence in the environment, and the second with human presence, enabling the development and validation of REM estimation models that incorporate physical geometry and environmental dynamics. The described
dataset supports research in data-driven wireless modeling and the development of high-capacity indoor communication networks.
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All authors Ljupcho Milosheski, Kuon Akiyama, Blaž Bertalanič, Jernej Hribar, Ryoichi Shinkuma
Signal Processing
Meta-Learning Based Radio Frequency Fingerprinting for GNSS Spoofing Detection
Leatile Marata, Juhani Sankari, Eslam Eldeeb et al. (+2 more)
The rapid development of technology has led to an increase in the number of devices that rely on position, velocity, and time (PVT) information to perform their functions. As such, the Global
Navigation Satellite Systems (GNSS) have been adopted as one of the most promising solutions to provide PVT. Consequently, there are renewed efforts aimed at enhancing GNSS capabilities to meet emerging use cases and their requirements. For example, GNSS is evolving to rely on low-earth-orbit satellites, shifting the focus from traditional medium-earth-orbit satellites. Unfortunately, these developments also bring forth higher risks of interference signals such as spoofers, which pose serious security threats. To address this challenge, artificial intelligence (AI)-inspired solutions are being developed to overcome the limitations of conventional mathematics-based approaches, which have proven inflexible when dealing with diverse forms of interference. In this paper, we advance this direction by proposing a meta-learning framework that enables GNSS receivers to detect various types of spoofers. Specifically, our approach exploits the
radio frequency fingerprints present in the signal at both the pre-correlation and post-correlation stages of the receiver. The proposed solution has superior generalization properties compared to the state-of-the-art solutions. Numerical results demonstrate that our proposed solution significantly detects spoofers of different forms, with spoofing detection accuracies of more than 95% on multiple
datasets from the Texas Spoofing Test Battery (TEXBAT) and the Oak Ridge Spoofing and Interference Test Battery (OAKBAT) repositories
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All authors Leatile Marata, Juhani Sankari, Eslam Eldeeb, Mikko Valkama, Elena Simona Lohan
Signal Processing
Discrete-Periodic Ambiguity Function of Random Communication Signals
Ying Zhang, Fan Liu, Yifeng Xiong et al. (+2 more)
This paper investigates the ambiguity function (AF) of communication signals carrying random data payloads, which is a fundamental metric characterizing sensing capability in
ISAC systems. We first develop a unified analytical framework to evaluate the AF of communication-centric
ISAC signals constructed from arbitrary orthonormal bases and independent identically distributed (i.i.d.) constellation symbols. Subsequently, we derive the discrete periodic ambiguity function (DP-AF) and provide closed-form expressions for its expected integrated sidelobe level (EISL) and average sidelobe level. Notably, we prove that the normalized EISL is invariant across all constellations and modulation bases. Finally, the theoretical findings are validated through simulations.
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All authors Ying Zhang, Fan Liu, Yifeng Xiong, Tao Liu, Shi Jin
Systems and Control · Cross-listed from physics.app-ph
Symbol Detection in a MIMO Wireless Communication System Using a FeFET-coupled CMOS Ring Oscillator Array
Harsh Kumar Jadia, Abhinaba Ghosh, Md Hanif Ali et al. (+11 more)
Symbol decoding in multiple-input multiple-output (
MIMO) wireless communication systems requires the deployment of fast, energy-efficient computing hardware deployable at the edge. The brute-force, exact maximum likelihood (ML) decoder, solved on conventional classical digital hardware, has exponential time complexity. Approximate classical solvers implemented on the same hardware have
polynomial time complexity at the best. In this article, we design an alternative ring-oscillator-based coupled oscillator array to act as an oscillator Ising machine (OIM) and heuristically solve the ML-based
MIMO detection problem. Complementary metal
oxide semiconductor (CMOS) technology is used to design the ring oscillators, and ferroelectric field effect transistor (FeFET) technology is chosen as the coupling element (X) between the oscillators in this CMOS + X OIM design. For this purpose, we experimentally report high linear range of conductance variation (1 micro-S to 60 micro-S) in a FeFET device fabricated at 28 nm high-K/ metal gate (HKMG) CMOS technology node. We incorporate the conductance modulation characteristic in SPICE simulation of the ring oscillators connected in an all-to-all fashion through a crossbar array of these FeFET devices. We show that the above range of conductance variation of the FeFET device is suitable to obtain optimum OIM performance with no significant performance drop up to a
MIMO size of 100 transmitting and 100 receiving
antennas, thereby making FeFET a suitable device for this application. Our simulations and associated analysis using the Kuramoto model of oscillators also predict that this designed classical analog OIM, if implemented experimentally, will offer logarithmic scaling of computation time with
MIMO size, thereby offering a huge improvement (in terms of computation speed) over aforementioned
MIMO decoders run on conventional digital hardware.
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All authors Harsh Kumar Jadia, Abhinaba Ghosh, Md Hanif Ali, Syed Farid Uddin, Sathish N, Shirshendu Mandal, Nihal Raut, Halid Mulaosmanovic, Stefan Dunkel, Sven Beyer, Suraj Amonkar, Udayan Ganguly, Veeresh Deshpande, Debanjan Bhowmik
Image and Video Processing
Investigating Label Bias and Representational Sources of Age-Related Disparities in Medical Segmentation
Aditya Parikh, Sneha Das and Aasa Feragen
Algorithmic bias in medical imaging can perpetuate health disparities, yet its causes remain poorly understood in
segmentation tasks. While
fairness has been extensively studied in classification,
segmentation remains underexplored despite its clinical importance. In breast cancer
segmentation, models exhibit significant performance disparities against younger patients, commonly attributed to physiological differences in breast density. We audit the MAMA-MIA
dataset, establishing a quantitative baseline of age-related bias in its automated labels, and reveal a critical Biased Ruler effect where systematically flawed labels for validation misrepresent a model's actual bias. However, whether this bias originates from lower-quality annotations (label bias) or from fundamentally more challenging image characteristics remains unclear. Through controlled experiments, we systematically refute hypotheses that the bias stems from label quality sensitivity or quantitative case difficulty imbalance. Balancing training data by difficulty fails to mitigate the disparity, revealing that younger patient cases are intrinsically harder to learn. We provide direct evidence that systemic bias is learned and amplified when training on biased, machine-generated labels, a critical finding for automated annotation pipelines. This work introduces a systematic framework for diagnosing algorithmic bias in medical
segmentation and demonstrates that achieving
fairness requires addressing qualitative distributional differences rather than merely balancing case counts.
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Systems and Control
CT-ESKF: A General Framework of Covariance Transformation-Based Error-State Kalman Filter
Jiale Han, Wei Ouyang, Maoran Zhu et al. (+1 more)
Invariant extended Kalman filter (InEKF) possesses excellent trajectory-independent property and better consistency compared to conventional extended Kalman filter (EKF). However, when applied to scenarios involving both global-frame and body-frame observations, InEKF may fail to preserve its trajectory-independent property. This work introduces the concept of equivalence between error states and covariance
matrices among different error-state Kalman filters, and shows that although InEKF exhibits trajectory independence, its covariance propagation is actually equivalent to EKF. A covariance transformation-based error-state Kalman filter (CT-ESKF) framework is proposed that unifies various error-state Kalman filtering
algorithms. The framework gives birth to novel filtering
algorithms that demonstrate improved performance in integrated
navigation systems that incorporate both global and body-frame observations. Experimental results show that the EKF with covariance transformation outperforms both InEKF and original EKF in a representative INS/GNSS/Odometer integrated
navigation system.
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All authors Jiale Han, Wei Ouyang, Maoran Zhu, Yuanxin Wu
Image and Video Processing
Towards Reliable Pediatric Brain Tumor Segmentation: Task-Specific nnU-Net Enhancements
Xiaolong Li, Zhi-Qin John Xu, Yan Ren et al. (+2 more)
Accurate
segmentation of pediatric
brain tumors in multi-parametric
magnetic resonance imaging (mpMRI) is critical for diagnosis, treatment planning, and monitoring, yet faces unique challenges due to limited data, high anatomical variability, and heterogeneous imaging across institutions. In this work, we present an advanced nnU-Net framework tailored for BraTS 2025 Task-6 (PED), the largest public
dataset of pre-treatment pediatric high-grade gliomas. Our contributions include: (1) a widened residual encoder with squeeze-and-excitation (SE)
attention; (2) 3D depthwise separable convolutions; (3) a specificity-driven regularization term; and (4) small-scale Gaussian weight initialization. We further refine predictions with two postprocessing steps. Our models achieved first place on the Task-6 validation leaderboard, attaining lesion-wise Dice scores of 0.759 (CC), 0.967 (ED), 0.826 (ET), 0.910 (NET), 0.928 (TC) and 0.928 (WT).
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All authors Xiaolong Li, Zhi-Qin John Xu, Yan Ren, Tianming Qiu, Xiaowen Wang
Systems and Control
Constrained computational hybrid controller for Input Affine Hybrid Dynamical Systems
Ali Taghavian, Ali Safi and Esmaeel Khanmirza
Hybrid dynamical systems are viewed as the most complicated systems with continuous and event-based behaviors. Since traditional controllers cannot handle these systems, some newly-developed controllers have been published in recent decades to deal with them. This paper presents a novel implementable constrained final-state controller based on partitioning the system's state-space, computational simulations, and graph theory. Experimental results and a comparison with Model Predictive Controller on the three tank
benchmark and swing-up control of a pendulum show the effectiveness of the proposed Computational Hybrid Controller(CHC).
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Audio and Speech Processing · Cross-listed from cs.CR
Ultralow-power standoff acoustic leak detection
Michael P. Hasselbeck
An automated, standoff acoustic leak detection scheme has been designed, built, and tested. It merges the principles of glass breakage and smoke detection to alert for the presence of leaks emanating from pressurized plumbing. A simulated water leak flowing at 0.15 l/min has been reliably detected at a standoff distance of more than 10 m. The device is also effective at identifying the presence of leaks located behind surfaces such as walls, doors, floors, and ceilings. The anticipated application is as an autonomous, battery-powered, remote wireless node. All signal processing and analysis takes place on the edge with no need to stream audio data to the cloud. Sensor status is conveyed on-demand with only a few bytes of information, requiring minimal bandwidth. Power consumption is the range of 20–200 micro-Watts, depending on the amount of environmental noise and desired sensor latency. To attain optimum sensitivity and reliability, the hardware operates at acoustic frequencies well above the range of human conversations, making eavesdropping impossible. Development has been done with water escaping from pressurized plumbing, but the sensor concept can be used effectively to detect gas leaks.
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Systems and Control
Large Language Models for Control
Adil Rasheed, Oscar Ravik and Omer San
This paper investigates using large
language models (LLMs) to generate control actions directly, without requiring control-engineering expertise or hand-tuned
algorithms. We implement several variants: (i)
prompt-only, (ii) tool-assisted with access to historical data, and (iii) prediction-assisted using learned or simple models to score candidate actions. We compare them on tracking accuracy and actuation effort, with and without a
prompt that requests lower actuator usage. Results show
prompt-only LLMs already produce viable control, while tool-augmented versions adapt better to changing objectives but can be more sensitive to constraints, supporting LLM-in-the-loop control for evolving cyber-physical systems today and operator and human inputs.
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