Signal Processing
Development of a Transit-Time Ultrasonic Flow Measurement System for Partially Filled Pipes: Incorporating Flow Profile Correction Factor and Real-Time Clogging Detection
Mohammadhadi Mesmarian, Mohammad Mahdi Kharidar and Hossein Nejat Pishkenari
Flow measurement in partially filled pipes presents greater complexity compared to fully filled systems, primarily due to the complex velocity distribution within the cross-section, which is a key source of measurement inaccuracy. To address this challenge, an ultrasonic flow meter was designed and developed, capable of simultaneously measuring both flow velocity and fluid level. To improve measurement accuracy, a flow profile correction factor (FPCF) was derived based on the velocity distribution characteristics and applied to the raw flow meter output. A dedicated open-channel flow loop incorporating a 250 mm diameter pipe was constructed to test and calibrate the system under controlled conditions. Flow rates in the loop varied from 2 to 6 liters per second. The accuracy of the flow meter was evaluated using the Flow-Weighted Mean Error (FWME) metric. Experimental results showed that applying the FPCF significantly improved accuracy, reducing the maximum flow measurement error from 8.51% to 2.44%. Furthermore, calibration led to a substantial decrease in FWME from 1.78% to 0.08%, confirming the effectiveness of the proposed methodology. The flow meter was also subjected to clogging scenarios by artificially obstructing the flow. Under these conditions, the device was able to reliably measure the flow and successfully detected the clogging, triggering an alarm to the operator to take necessary action.
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Systems and Control
Innovative Modular Design and Kinematic Approach based on Screw Theory for Triple Scissors Links Deployable Space Antenna Mechanism
Mamoon Aamir, Mariyam Sattar, Naveed Ur Rehman Junejo et al. (+1 more)
This paper presents the geometry design and analysis of a novel triple scissors links deployable
antenna mechanism (TSDAM) to deal with the problems of large aperture and high precision space
antennas for deep space communication and Earth observation. This mechanism has only one degree of freedom (DoF) and thus makes for efficient and reliable deployment without loss of structural integrity. It employed a systematic design approach starting from a triple scissors links modular unit to a 25m aperture assembly. Different configurations constituting variable numbers of modular units were analyzed in SolidWorks to identify the deployable mechanism with lowest deformation. While the 24 units configuration offered superior stowage compactness, it exhibited higher deformation (0.01437mm), confirming the 12 units configuration as the optimal balance between structural stability and deployment efficiency. Screw theory was employed to analyze the kinematic properties, and numerical simulations were performed in MATLAB and SolidWorks. The deployable space
antenna showed transition from stowed to fully deployed state in just 53 seconds with high stability throughout the deployment process. The TSDAM attained a storage ratio of up to 15.3 for height and volume with 0.01048mm of deformation for a 12 units configuration. Mesh convergence analysis proved the consistency of the simulation results for 415314 tetrahedral shaped elements. The virtual experiments in SolidWorks verified the analytical Screw theory based model and ensured that the design was smooth and flexible for deployment in operational conditions. The research establishes a robust design framework for future deployable
antennas, offering enhanced performance, simplified structure, and improved reliability
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All authors Mamoon Aamir, Mariyam Sattar, Naveed Ur Rehman Junejo, Aqsa Zafar Abbasi
Audio and Speech Processing · Cross-listed from cs.SD
Dynamic Multi-Species Bird Soundscape Generation with Acoustic Patterning and 3D Spatialization
Ellie L. Zhang, Duoduo Liao and Callie C. Liao
Generation of dynamic, scalable multi-species bird soundscapes remains a significant challenge in computer music and algorithmic sound design. Birdsongs involve rapid frequency-modulated chirps, complex amplitude envelopes, distinctive acoustic patterns, overlapping calls, and dynamic inter-bird interactions, all of which require precise temporal and spatial control in 3D environments. Existing approaches, whether Digital Signal Processing (DSP)-based or data-driven, typically focus only on single species modeling, static call structures, or synthesis directly from recordings, and often suffer from noise, limited flexibility, or large data needs. To address these challenges, we present a novel, fully
algorithm-driven framework that generates dynamic multi-species bird soundscapes using DSP-based chirp generation and 3D spatialization, without relying on recordings or training data. Our approach simulates multiple independently-moving birds per species along different moving 3D trajectories, supporting controllable chirp sequences, overlapping choruses, and realistic 3D motion in scalable soundscapes while preserving species-specific acoustic patterns. A visualization interface provides bird trajectories, spectrograms, activity timelines, and sound waves for analytical and creative purposes. Both visual and audio evaluations demonstrate the ability of the system to generate dense, immersive, and ecologically inspired soundscapes, highlighting its potential for computer music, interactive virtual environments, and computational bioacoustics research.
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Systems and Control
Data-driven certificates of constraint enforcement and stability for unmodeled, discrete dynamical systems using tree data structures
Amy K. Strong, Ali Kashani, Claus Danielson et al. (+1 more)
This paper addresses the critical challenge of developing data-driven certificates for the stability and safety of unmodeled dynamical systems by leveraging a tree data structure and an upper bound of the system's Lipschitz constant. Previously, an invariant set was synthesized by iteratively expanding an initial invariant set. In contrast, this work iteratively prunes the constraint set to synthesize an invariant set – eliminating the need for a known, initial invariant set. Furthermore, we provide stability assurances by characterizing the asymptotic stability of the system relative to an invariant approximation of the minimal positive invariant set through synthesis of a discontinuous piecewise affine Lyapunov function over the computed invariant set. The proposed method takes inspiration from subdivision techniques and requires no prior system knowledge beyond Lipschitz continuity.
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All authors Amy K. Strong, Ali Kashani, Claus Danielson, Leila J. Bridgeman
Systems and Control · Cross-listed from cs.RO
Reference-Free Sampling-Based Model Predictive Control
Fabian Schramm, Pierre Fabre, Nicolas Perrin-Gilbert et al. (+1 more)
We present a sampling-based model predictive control (MPC) framework that enables emergent locomotion without relying on handcrafted gait patterns or predefined contact sequences. Our method discovers diverse motion patterns, ranging from trotting to galloping, robust standing policies, jumping, and handstand balancing, purely through the optimization of high-level objectives. Building on model predictive path integral (MPPI), we propose a cubic Hermite spline parameterization that operates on position and velocity control points. Our approach enables contact-making and contact-breaking strategies that adapt automatically to task requirements, requiring only a limited number of sampled trajectories. This sample efficiency enables real-time control on standard CPU hardware, eliminating the GPU acceleration typically required by other state-of-the-art MPPI methods. We validate our approach on the Go2 quadrupedal
robot, demonstrating a range of emergent gaits and basic jumping capabilities. In simulation, we further showcase more complex behaviors, such as backflips, dynamic handstand balancing and locomotion on a
Humanoid, all without requiring reference tracking or offline pre-training.
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All authors Fabian Schramm, Pierre Fabre, Nicolas Perrin-Gilbert, Justin Carpentier
Systems and Control · Cross-listed from physics.soc-ph
Decarbonization pathways for liquid fuels: A multi-sector energy system perspective
Jun Wen Law, Bryan K. Mignone and Dharik S. Mallapragada
Low-carbon
liquid fuels play a key role in energy system decarbonization scenarios. This study uses a multi-sector capacity expansion model of the contiguous United States to examine fuels production in deeply decarbonized energy systems. Our analysis evaluates how the shares of biofuels, synthetic fuels, and fossil
liquid fuels change under varying assumptions about resource constraints (biomass and CO2 sequestration availability), fuel demand distributions, and supply flexibility to produce different fuel products. Across all scenarios examined, biofuels provide a substantial share of
liquid fuel supply, while synthetic fuels deploy only when biomass or CO2 sequestration is assumed to be more limited. Fossil
liquid fuels remain in all scenarios examined, primarily driven by the extent to which their emissions can be offset with removals. Limiting biomass increases biogenic CO2 capture within biofuel pathways, while limiting sequestration availability increases the share of captured atmospheric (including biogenic) carbon directed toward utilization for synthetic fuel production. While varying assumptions about
liquid fuel demand distributions and fuel product supply flexibility alter competition among individual fuel production technologies, broader energy system outcomes are robust to these assumptions. Biomass and CO2 sequestration availability are key drivers of energy system outcomes in deeply decarbonized energy systems.
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Systems and Control
Optimal policy design for innovation diffusion: shaping today's incentives for transforming the future
Lisa Piccinin, Valentina Breschi, Chiara Ravazzi et al. (+2 more)
In this paper, we propose a new framework for the design of incentives aimed at promoting innovation
diffusion in social influence networks. In particular, our framework relies on an extension of the Friedkin and Johnsen opinion dynamics model characterizing the effects of (i) short-
memory incentives, which have an immediate yet transient impact, and (ii) long-term structural incentives, whose impact persists via an exponentially decaying
memory. We propose to design these incentives via a model-predictive control (MPC) scheme over an augmented state that captures the
memory in our opinion dynamics model, yielding a convex quadratic program with linear constraints. Our numerical simulations based on data on sustainable mobility habits show the effectiveness of the proposed approach, which balances large-scale adoption and resource allocation
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All authors Lisa Piccinin, Valentina Breschi, Chiara Ravazzi, Fabrizio Dabbene, Mara Tanelli
Systems and Control · Cross-listed from cs.IT
Directional Pinching-Antenna Systems
Runxin Zhang, Yulin Shao and Yuanwei Liu
We propose a directional pinching-
antenna system (DiPASS), a comprehensive framework that transitions PASS modeling from idealized abstraction to physical consistency. DiPASS introduces the first channel model that accurately captures the directional, pencil-like radiation of pinching
antennas, incorporates a practical waveguide attenuation of 1.3 dB/m, and accounts for
stochastic line-of-sight blockage. A key enabler of DiPASS is our new "equal quota division" power allocation strategy, which guarantees predetermined coupling lengths independent of
antenna positions, thereby overcoming a critical barrier to practical deployment. Our analysis yields foundational insights: we derive closed-form solutions for optimal
antenna placement and orientation in single-PA scenarios, quantifying the core trade-off between waveguide and free-space losses. For multi-PA systems, we develop a scalable optimization framework that leverages directional sparsity, revealing that waveguide diversity surpasses
antenna density in enhancing system capacity. Extensive simulations validate our analysis and demonstrate that DiPASS provides a realistic performance
benchmark, fundamentally reshaping the understanding and design principles for future PASS-enabled 6G networks.
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Systems and Control
PolyOCP.jl – A Julia Package for Stochastic OCPs and MPC
Ruchuan Ou, Learta Januzi, Jonas Schießl et al. (+3 more)
The consideration of
stochastic uncertainty in optimal and predictive control is a well-explored topic. Recently
Polynomial Chaos Expansions (PCE) have received considerable
attention for problems involving stochastically uncertain system parameters and also for problems with additive
stochastic i.i.d. disturbances. While there exist a number of open-source PCE toolboxes, tailored open-source codes for the solution of OCPs involving additive
stochastic i.i.d. disturbances in julia are not available. Hence, this paper introduces the toolbox PolyOCP$.$jl which enables to efficiently solve
stochastic OCPs for linear systems subject to a large class of disturbance distributions. We explain the main mathematical concepts between the PCE transcription of
stochastic OCPs and how they are provided in the toolbox. We draw upon two examples to illustrate the functionalities of PolyOCP$.$jl.
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All authors Ruchuan Ou, Learta Januzi, Jonas Schießl, Michael Heinrich Baumann, Lars Grüne, Timm Faulwasser
Signal Processing · Cross-listed from cs.IT
On the Tail Transition of First Arrival Position Channels: From Cauchy to Exponential Decay
Yen-Chi Lee
While the zero-drift first arrival position (FAP) channel exhibits a Cauchy-distributed lateral displacement, nonzero drift in practical systems introduces advective transport that regularizes this singular limit. This letter characterizes the drift-induced transition of FAP distribution from heavy-tailed algebraic regime to exponential regularization. By asymptotically examining the exact FAP density, we identify a characteristic propagation distance (CPD) that serves as the fundamental boundary separating
diffusion-dominated and drift-dominated regimes. Numerical experiments demonstrate that in low-drift environments, variance-matched Gaussian approximations severely underestimate the true communication potential, whereas the zero-drift Cauchy law provides a robust, physically grounded performance baseline.
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Systems and Control
Impact Analysis of COVID-19 in Bangladesh Power Sector and Recommendations based on Practical Data and Machine Learning Approach
Anis Ahmed, Arefin Ahamed Shuvo, Naruttam Kumar Roy et al. (+2 more)
This paper investigates the impact of COVID-19 on the power sector in Bangladesh, how the country has dealt with it, and explores the path to stability. The study employs data visualisation and complex statistics to examine critical data about power systems in Bangladesh. This includes load patterns on a daily, monthly, annual, weekend, and weekday basis. Significant alterations in these patterns have been observed during our study e.g., in April and May of 2020, the power demand decreased by approximately 15.4% and 17.2%, respectively, compared to the corresponding period in 2019. We have used a Long-Short-Term
Memory (LSTM) framework to predict the load profile of 2020 excluding COVID-19 effects. This model is compared with the actual load profile to determine the degree to which COVID-19 has impacted. The comparison indicates that the average power demand decreased by approximately 19.5% in April 2020 and 18.3% in May 2020, relative to its projected value. The study also investigates system stability by analyzing transmission loss and load factor, and the environmental effect by analyzing the Carbon Dioxide emission rate. Finally, the study provides recommendations for overcoming future disasters, such as developing more resilient power systems, investing in renewable energy, and improving energy efficiency.
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All authors Anis Ahmed, Arefin Ahamed Shuvo, Naruttam Kumar Roy, Neloy Prosad Bishnu, Ali Nasir
Systems and Control
Large Language Model-Assisted Planning of Electric Vehicle Charging Infrastructure with Real-World Case Study
Xinda Zheng, Canchen Jiang and Hao Wang
The growing demand for electric vehicle (EV) charging infrastructure presents significant planning challenges, requiring efficient strategies for investment and operation to deliver cost-effective charging services. However, the potential benefits of EV charging assignment, particularly in response to varying spatial-temporal patterns of charging demand, remain under-explored in infrastructure planning. This paper proposes an integrated approach that jointly optimizes investment decisions and charging assignments while accounting for spatial-temporal demand dynamics and their interdependencies. To support efficient model development, we leverage a large
language model (LLM) to assist in generating and refining the mathematical formulation from structured natural-
language descriptions, significantly reducing the modeling burden. The resulting optimization model enables optimal joint decision-making for investment and operation. Additionally, we propose a distributed optimization
algorithm based on the Alternating Direction Method of Multipliers (ADMM) to address computational complexity in high-dimensional scenarios, which can be executed on standard computing platforms. We validate our approach through a case study using 1.5 million real-world travel records from Chengdu, China, demonstrating a 30% reduction in total cost compared to a baseline without EV assignment.
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Signal Processing
Adaptive Probabilistic Constellation Shaping based on Enumerative Sphere Shaping for FSO Channel with Turbulence and Pointing Errors
Jingtian Liu, Bin Chen, Xiongwei Yang et al. (+3 more)
Free-space optical (FSO) transmission enables fast, secure, and efficient next-generation communications with abundant spectrum resources. However, atmospheric
turbulence, pointing errors, path loss, and atmospheric loss induce random attenuation, challenging link reliability. Adaptive coded modulation technology enhances spectrum utilization and reliability. We propose an adaptive probabilistic constellation shaping (A-PCS) coherent system utilizing enumerative sphere shaping (ESS) for distribution matcher (DM). With PCS-64QAM, the system achieves continuous rate control from conventional QPSK-equivalent to 64QAM spectral efficiency, providing quasi-continuous control with granularities of approximately $0.05$~bits/4D for spectral efficiency and $0.1$~dB for the post-FEC SNR threshold, and a maximum control depth of $12.5$~dB. Leveraging ESS for efficient sequence utilization, it offers higher spectral efficiency and finer control granularity than constant composition distribution matcher (CCDM)-based A-PCS systems. We further model and analyze the FSO channel, presenting calculations and comparisons of outage probability and ergodic capacity under varying
turbulence intensities and pointing errors. Results demonstrate 99.999~% reliability at maximum $\sigma_\mathrm{R}^2 = 1.02$ and $\sigma_\mathrm{s} = 0.51~\mathrm{m}$, meeting requirements under severe
turbulence and large pointing errors. {Furthermore, under non-ideal delayed channel state information (CSI) feedback conditions, the system adapts to varying
turbulence coherence times and feedback delays, with results showing that finer modulation granularity (provided by A-PCS-ESS) enhances immunity to feedback delay, maintaining a performance advantage over conventional adaptive schemes across a range of channel environments and delay values.
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All authors Jingtian Liu, Bin Chen, Xiongwei Yang, Yi Wei, Jianjun Yu, Feng Zhao
Signal Processing
Movable-Antenna Array Enhanced Multi-Target Sensing: CRB Characterization and Optimization
Haobin Mao, Lipeng Zhu, Wenyan Ma et al. (+3 more)
Movable
antennas (MAs) have emerged as a promising technology to improve wireless communication and sensing performance towards sixth-generation (6G) networks through flexible
antenna movement. In this paper, we propose a novel wireless sensing system based on MA arrays to enhance multi-target spatial angle estimation performance. We begin by characterizing the Cramér-Rao bound (CRB)
matrix for multi-target angle of arrival (AoA) estimation as a function of the
antenna's positions in MA arrays, thereby establishing a theoretical foundation for
antenna position optimization. Then, aiming at improving the sensing coverage performance, we formulate an optimization problem to minimize the expectation of the trace of the CRB
matrix over random target angles subject to a given distribution by optimizing the
antennas' positions. To tackle the formulated challenging optimization problem, the Monte Carlo method is employed to approximate the intractable objective function, and a swarm-based gradient descent
algorithm is subsequently proposed to address the approximated problem. In addition, a lower-bound on the sum of CRBs for multi-target AoA estimation is derived. Numerical results demonstrate that the proposed MA-based design achieves superior sensing performance compared to conventional systems using fixed-position
antenna (FPA) arrays and single-target-oriented MA arrays, in terms of decreasing both CRB and the actual AoA estimation mean square error (MSE). Fundamentally, the designed MA array geometry exhibits low correlation and high effective power of sensitivity vectors for multi-target sensing in the angular domain, leading to significant CRB performance improvement. The resultant low correlation of steering vectors over multiple targets' directions further helps mitigate angle estimation ambiguity and thus enhances MSE performance.
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All authors Haobin Mao, Lipeng Zhu, Wenyan Ma, Zhenyu Xiao, Xiang-Gen Xia, Rui Zhang
Signal Processing
Semi-Passive IRS Enabled Sensing with Group Movable Sensors
Qiaoyan Peng, Qingqing Wu, Wen Chen et al. (+4 more)
The performance of the sensing system is limited by the signal attenuation and the number of receiving components. In this letter, we investigate the sensor position selection in a semi-passive intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) sensing system. The IRS consists of passive elements and active sensors, where the sensors can receive and process the echo signal for direction-of-arrival (DoA) estimation. Motivated by the movable
antenna array and fluid
antenna system, we consider the case where the sensors are integrated into a group for movement and derive the corresponding Cramer-Rao bound (CRB). Then, the optimal solution for the positions of the movable sensors (MSs) to the CRB minimization problem is derived in closed form. Moreover, we characterize the relationship between the CRB and system parameters. Theoretical analysis and numerical results are provided to demonstrate the superiority of the proposed MS scheme over the fixed-position (FP) scheme.
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All authors Qiaoyan Peng, Qingqing Wu, Wen Chen, Guangji Chen, Ying Gao, Lexi Xu, Shaodan Ma
Signal Processing
Robust Nonlinear Transform Coding: A Framework for Generalizable Joint Source-Channel Coding
Jihun Park, Junyong Shin, Jinsung Park et al. (+1 more)
This paper proposes robust nonlinear transform coding (Robust-NTC), a generalizable digital joint source-channel coding (JSCC) framework that couples variational latent modeling with channel-adaptive transmission. Unlike learning-based JSCC methods that implicitly absorb channel variations, Robust-NTC explicitly models element-wise latent distributions via a variational objective with a Gaussian proxy for quantization and channel noise, allowing encoder-decoder to capture latent uncertainty without channel-specific training. Using the learned statistics, Robust-NTC also facilitates rate-distortion optimization to adaptively select element-wise quantizers and bit depths according to online channel conditions. To support practical deployment, Robust-NTC is integrated into an orthogonal frequency-division multiplexing (OFDM) system, where a unified resource allocation framework jointly optimizes latent quantization, bit allocation, modulation order, and power allocation to minimize transmission latency while guaranteeing learned distortion targets. Simulation results demonstrate that for practical OFDM systems, Robust-NTC achieves superior rate-distortion efficiency and stable reconstruction fidelity compared to both a conventional separated coding scheme and digital JSCC baselines across various channel conditions.
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All authors Jihun Park, Junyong Shin, Jinsung Park, Yo-Seb Jeon
Audio and Speech Processing · Cross-listed from cs.SD
Hear: Hierarchically Enhanced Aesthetic Representations For Multidimensional Music Evaluation
Shuyang Liu, Yuan Jin, Rui Lin et al. (+3 more)
Evaluating song aesthetics is challenging due to the multidimensional nature of musical perception and the scarcity of labeled data. We propose HEAR, a robust music aesthetic evaluation framework that combines: (1) a multi-source multi-scale representations module to obtain complementary segment- and track-level features, (2) a hierarchical augmentation strategy to mitigate overfitting, and (3) a hybrid training objective that integrates regression and ranking losses for accurate scoring and reliable top-tier song identification. Experiments demonstrate that HEAR consistently outperforms the baseline across all metrics on both tracks of the ICASSP 2026 SongEval
benchmark. The code and trained model weights are available at https://github.com/Eps-Acoustic-Revolution-Lab/EAR_HEAR.
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All authors Shuyang Liu, Yuan Jin, Rui Lin, Shizhe Chen, Junyu Dai, Tao Jiang
Audio and Speech Processing · Cross-listed from cs.SD
PrismAudio: Decomposed Chain-of-Thoughts and Multi-dimensional Rewards for Video-to-Audio Generation
Huadai Liu, Kaicheng Luo, Wen Wang et al. (+6 more)
Video-to-Audio (V2A) generation requires balancing four critical perceptual dimensions: semantic consistency, audio-visual temporal synchrony, aesthetic quality, and spatial accuracy; yet existing methods suffer from objective
entanglement that conflates competing goals in single loss functions and lack human preference
alignment. We introduce PrismAudio, the first framework to integrate Reinforcement Learning into V2A generation with specialized Chain-of-Thought (CoT) planning. Our approach decomposes monolithic
reasoning into four specialized CoT modules (Semantic, Temporal, Aesthetic, and Spatial CoT), each paired with targeted reward functions. This CoT-reward correspondence enables multidimensional RL optimization that guides the model to jointly generate better
reasoning across all perspectives, solving the objective
entanglement problem while preserving interpretability. To make this optimization computationally practical, we propose Fast-GRPO, which employs hybrid ODE-SDE sampling that dramatically reduces the training overhead compared to existing GRPO implementations. We also introduce AudioCanvas, a rigorous
benchmark that is more distributionally balanced and covers more realistically diverse and challenging scenarios than existing
datasets, with 300 single-event classes and 501 multi-event samples. Experimental results demonstrate that PrismAudio achieves state-of-the-art performance across all four perceptual dimensions on both the in-domain VGGSound test set and out-of-domain AudioCanvas
benchmark. The project page is available at https://PrismAudio.github.io.
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All authors Huadai Liu, Kaicheng Luo, Wen Wang, Qian Chen, Peiwen Sun, Rongjie Huang, Xiangang Li, Jieping Ye, Wei Xue
Systems and Control
Equivariant Tracking Control for Fully Actuated Mechanical Systems on Matrix Lie Groups
Matthew Hampsey, Pieter van Goor, Ravi Banavar et al. (+1 more)
Mechanical control systems such as aerial, marine, space, and terrestrial
robots often naturally admit a state-space that has the structure of a Lie group. The kinetic energy of such systems is commonly invariant to the induced action by the Lie group, and the system dynamics can be written as a coupled ordinary differential equation on the group and the dual space of its Lie
algebra, termed a Lie-Poisson system. In this paper, we show that Lie-Poisson systems can also be written as a left-invariant system on a semi-direct Lie group structure placed on the trivialised cotangent bundle of the
symmetry group. The authors do not know of a prior reference for this observation and we are confident the insight has never been exploited in the context of tracking control. We use this representation to build a right-invariant tracking error for the full state of a Lie-Poisson mechanical system and show that the error dynamics for this error are themselves of Lie-Poisson structure, albeit with time-varying inertia. This allows us to tackle the general trajectory tracking problem using an energy shaping design metholodology. To demonstrate the approach, we apply the proposed design methodology to a simple attitude tracking control.
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All authors Matthew Hampsey, Pieter van Goor, Ravi Banavar, Robert Mahony
Systems and Control
Accelerated Transformer Energization Sequence for Inverter Based Resources in Black-Start Procedures with Active Flux Trajectory Manipulation in the Stationary Reference Frame
Jiyu Lee and Shenghui Cui
This paper proposes advanced soft-magnetization techniques to enable ultra-fast and reliable black-start of grid-forming (GFM) converters. Conventional hard-magnetization with well-established three-phase voltages during
transformer energization induces severe inrush currents due to flux offset, which can damage power
semiconductor devices. To overcome this drawback, an ultra-fast soft-magnetization method is firstly introduced, leveraging the voltage programmability of the inverter to actively reshape the initial voltage profile and thereby eliminate flux offset of the
transformer core. By suppressing the formation of flux offset itself, the proposed approach prevents
magnetic saturation and achieves nominal terminal voltage within a few milliseconds while effectively suppressing inrush current. However, this method can still trigger surge currents to power
semiconductor devices in the presence of an LC filter due to abrupt voltage magnitude and phase transitions. To address this issue, an enhanced Archimedean spiral soft-magnetization method is developed, where both voltage magnitude and phase evolve smoothly to simultaneously suppress inrush and surge currents. Furthermore, residual flux in the
transformer core is considered, and a demagnetization sequence using the inverter is validated to ensure reliable start-up. Experimental results confirm that the proposed methods achieve rapid black-start performance within one fundamental cycle while ensuring safe and stable operation of GFM converters.
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Signal Processing
Near-Field Sparse Bayesian Channel Estimation and Tracking for XL-IRS-Aided Wideband mmWave Systems
Xiaokun Tuo, Zijian Chen, Ming-Min Zhao et al. (+2 more)
The rapid development of 6G systems demands advanced technologies to boost network capacity and spectral efficiency, particularly in the context of intelligent reflecting surfaces (IRS)-aided millimeter-wave (mmWave) communications. A key challenge here is obtaining accurate channel state information (CSI), especially with extremely large IRS (XL-IRS), due to near-field propagation, high-dimensional wideband cascaded channels, and the passive nature of the XL-IRS. In addition, most existing CSI acquisition methods fail to leverage the spatio-temporal sparsity inherent in the channel, resulting in suboptimal estimation performance. To address these challenges, we consider an XL-IRS-aided wideband multiple-input multiple-output orthogonal frequency division multiplexing (
MIMO-OFDM) system and propose an efficient channel estimation and tracking (CET)
algorithm. Specifically, a unified near-field cascaded channel representation model is presented first, and a hierarchical spatio-temporal sparse prior is then constructed to capture two-dimensional (2D) block sparsity in the polar domain, one-dimensional (1D) clustered sparsity in the angle-delay domain, and temporal correlations across different channel estimation frames. Based on these priors, a tensor-based sparse CET (TS-CET)
algorithm is proposed that integrates tensor-based orthogonal matching pursuit (OMP) with particle-based variational
Bayesian inference (VBI) and message passing. Simulation results demonstrate that the TS-CET framework significantly improves the estimation accuracy and reduces the pilot overhead as compared to existing
benchmark methods.
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All authors Xiaokun Tuo, Zijian Chen, Ming-Min Zhao, Changsheng You, Min-Jian Zhao
Systems and Control · Cross-listed from cs.CR
Evaluation of Real-Time Mitigation Techniques for Cyber Security in IEC 61850 / IEC 62351 Substations
Akila Herath, Chen-Ching Liu, Junho Hong et al. (+1 more)
The digitalization of substations enlarges the cyber-attack surface, necessitating effective detection and mitigation of cyber attacks in digital substations. While machine learning-based intrusion detection has been widely explored, such methods have not demonstrated detection and mitigation within the required real-time budget. In contrast, cryptographic authentication has emerged as a practical candidate for real-time cyber defense, as specified in IEC 62351. In addition, lightweight rule-based intrusion detection that validates IEC 61850 semantics can provide specification-based detection of anomalous or malicious traffic with minimal processing delay. This paper presents the design logic and implementation aspects of three potential real-time mitigation techniques capable of countering GOOSE-based attacks: (i) IEC 62351-compliant message authentication code (MAC) scheme, (ii) a semantics-enforced rule-based intrusion detection system (IDS), and (iii) a hybrid approach integrating both MAC verification and Intrusion Detection System (IDS). A comparative evaluation of these real-time mitigation approaches is conducted using a cyber-physical system (CPS) security testbed. The results show that the hybrid integration significantly enhances mitigation capability. Furthermore, the processing delays of all three methods remain within the strict delivery requirements of GOOSE communication. The study also identifies limitations that none of the techniques can fully address, highlighting areas for future work.
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All authors Akila Herath, Chen-Ching Liu, Junho Hong, Kuchan Park
Audio and Speech Processing
First Deep Learning Approach to Hammering Acoustics for Stem Stability Assessment in Total Hip Arthroplasty
Dongqi Zhu, Zhuwen Xu, Youyuan Chen et al. (+7 more)
Audio event classification has recently emerged as a promising approach in medical applications. In total hip arthroplasty (THA), intra-operative hammering acoustics provide critical cues for assessing the initial stability of the femoral stem, yet variability due to femoral morphology, implant size, and
surgical technique constrains conventional assessment methods. We propose the first deep learning framework for this task, employing a TimeMIL model trained on Log-Mel Spectrogram features and enhanced with pseudo-labeling. On intra-operative recordings, the method achieved 91.17 % +/- 2.79 % accuracy, demonstrating reliable estimation of stem stability. Comparative experiments further show that reducing the diversity of femoral stem brands improves model performance, although limited
dataset size remains a bottleneck. These results establish deep learning-based audio event classification as a feasible approach for intra-operative stability assessment in THA.
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All authors Dongqi Zhu, Zhuwen Xu, Youyuan Chen, Minghao Jin, Wan Zheng, Yi Zhou, Huiwu Li, Yongyun Chang, Feng Hong, Zanjing Zhai
Image and Video Processing
Neural B-Frame Coding: Tackling Domain Shift Issues with Lightweight Online Motion Resolution Adaptation
Sang NguyenQuang, Xiem HoangVan and Wen-Hsiao Peng
Learned B-frame codecs with hierarchical temporal prediction often encounter the domain-shift issue due to mismatches between the Group-of-Pictures (GOP) sizes for training and testing, leading to inaccurate motion estimates, particularly for large motion. A common solution is to turn large motion into small motion by downsampling
video frames during motion estimation. However, determining the optimal downsampling factor typically requires costly rate-distortion optimization. This work introduces lightweight classifiers to predict downsampling factors. These classifiers leverage simple state signals from current and reference frames to balance rate-distortion performance with computational cost. Three variants are proposed: (1) a binary classifier (Bi-Class) trained with Focal Loss to choose between high and low resolutions, (2) a multi-class classifier (Mu-Class) trained with novel soft labels based on rate-distortion costs, and (3) a co-class approach (Co-Class) that combines the predictive capability of the multi-class classifier with the selective search of the binary classifier. All classifier methods can work seamlessly with existing B-frame codecs without requiring codec retraining. Experimental results show that they achieve coding performance comparable to exhaustive search methods while significantly reducing computational complexity. The code is available at: https://github.com/NYCU-MAPL/Fast-OMRA.git.
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Signal Processing
LLM4AMC: Adapting Large Language Models for Adaptive Modulation and Coding
Xinyu Pan, Boxun Liu, Xiang Cheng et al. (+1 more)
Adaptive modulation and coding (AMC) is a key technology in 5G new
radio (NR), enabling dynamic link adaptation by balancing transmission efficiency and reliability based on channel conditions. However, traditional methods often suffer from performance degradation due to the aging issues of channel quality indicator (CQI). Recently, the emerging capabilities of large
language models (LLMs) in contextual understanding and temporal modeling naturally align with the dynamic channel adaptation requirements of AMC technology. Leveraging pretrained LLMs, we propose a channel quality prediction method empowered by LLMs to optimize AMC, termed LLM4AMC. We freeze most parameters of the LLM and fine-tune it to fully utilize the knowledge acquired during pretraining while better adapting it to the AMC task. We design a network architecture composed of four modules, a preprocessing layer, an
embedding layer, a backbone network, and an output layer, effectively capturing the time-varying characteristics of channel quality to achieve accurate predictions of future channel conditions. Simulation experiments demonstrate that our proposed method significantly improves link performance and exhibits potential for practical deployment.
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All authors Xinyu Pan, Boxun Liu, Xiang Cheng, Chen Chen
Image and Video Processing
Evaluation of Hardware-based Video Encoders on Modern GPUs for UHD Live-Streaming
Kasidis Arunruangsirilert and Jiro Katto
Many GPUs have incorporated hardware-accelerated
video encoders, which allow
video encoding tasks to be offloaded from the main CPU and provide higher power efficiency. Over the years, many new
video codecs such as H.265/HEVC, VP9, and AV1 were added to the latest GPU boards. Recently, the rise of live
video content such as VTuber, game live-streaming, and live event broadcasts, drives the demand for high-efficiency hardware encoders in the GPUs to tackle these real-time
video encoding tasks, especially at higher resolutions such as 4K/8K UHD. In this paper, RD performance, encoding speed, as well as power consumption of hardware encoders in several generations of NVIDIA, Intel GPUs as well as Qualcomm Snapdragon Mobile SoCs were evaluated and compared to the software counterparts, including the latest H.266/VVC codec, using several metrics including PSNR, SSIM, and machine-learning based VMAF. The results show that modern GPU hardware encoders can match the RD performance of software encoders in real-time encoding scenarios, and while encoding speed increased in newer hardware, there is mostly negligible RD performance improvement between hardware generations. Finally, the bitrate required for each hardware encoder to match YouTube transcoding quality was also calculated.
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Image and Video Processing · Cross-listed from cs.CV
Data Augmentation Strategies for Robust Lane Marking Detection
Flora Lian, Dinh Quang Huynh, Hector Penades et al. (+3 more)
Robust lane detection is essential for advanced driver assistance and autonomous driving, yet models trained on public
datasets such as CULane often fail to generalise across different camera viewpoints. This paper addresses the challenge of domain shift for side-mounted cameras used in lane-wheel monitoring by introducing a generative AI-based data enhancement pipeline. The approach combines
geometric perspective transformation, AI-driven inpainting, and vehicle body overlays to simulate deployment-specific viewpoints while preserving lane continuity. We evaluated the effectiveness of the proposed augmentation in two state-of-the-art models, SCNN and UFLDv2. With the augmented data trained, both models show improved robustness to different conditions, including shadows. The experimental results demonstrate gains in precision, recall, and F1 score compared to the pre-trained model. By bridging the gap between widely available
datasets and deployment-specific scenarios, our method provides a scalable and practical framework to improve the reliability of lane detection in a pilot deployment scenario.
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All authors Flora Lian, Dinh Quang Huynh, Hector Penades, J. Stephany Berrio Perez, Mao Shan, Stewart Worrall
Image and Video Processing
Equivariant Deep Equilibrium Models for Imaging Inverse Problems
Alexander Mehta, Ruangrawee Kitichotkul, Vivek K Goyal et al. (+1 more)
Equivariant imaging (EI) enables training signal reconstruction models without requiring ground truth data by leveraging signal
symmetries. Deep
equilibrium models (DEQs) are a powerful class of neural networks where the output is a fixed point of a learned operator. However, training DEQs with complex EI losses requires implicit differentiation through fixed-point computations, whose implementation can be challenging. We show that backpropagation can be implemented modularly, simplifying training. Experiments demonstrate that DEQs trained with implicit differentiation outperform those trained with Jacobian-free backpropagation and other baseline methods. Additionally, we find evidence that EI-trained DEQs approximate the proximal map of an invariant prior.
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All authors Alexander Mehta, Ruangrawee Kitichotkul, Vivek K Goyal, Julián Tachella