Systems and Control
Safe Trajectory Gradient Flow Control of a Grid-Interfacing Inverter
Trager Joswig-Jones and Baosen Zhang
Grid-interfacing inverters serve as the interface between renewable energy resources and the electric power grid, offering fast, programmable control capabilities. However, their operation is constrained by hardware limitations, such as bounds on the current magnitude. Existing control methods for these systems often neglect these constraints during controller design and instead rely on ad hoc limiters, which can introduce instability or degrade performance. In this work, we present a control framework that directly incorporates constraints into the control of a voltage-source inverter. We propose a safe trajectory gradient flow controller, which applies the safe gradient flow method to a rolling horizon trajectory optimization problem to ensure that the states remain within a safe set defined by the constraints while directing the trajectory towards an optimal
equilibrium point of a nonlinear program. Simulation results demonstrate that our approach can drive the outputs of a simulated inverter system to optimal values and maintain state constraints, even when using a limited number of optimization steps per control cycle.
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Audio and Speech Processing
VoiceSculptor: Your Voice, Designed By You
Jingbin Hu, Huakang Chen, Linhan Ma et al. (+19 more)
Despite rapid progress in text-to-
speech (TTS), open-source systems still lack truly instruction-following, fine-grained control over core
speech attributes (e.g., pitch, speaking rate, age,
emotion, and style). We present VoiceSculptor, an open-source unified system that bridges this gap by integrating instruction-based voice design and high-fidelity voice cloning in a single framework. It generates controllable speaker timbre directly from natural-
language descriptions, supports iterative refinement via
Retrieval-Augmented Generation (RAG), and provides attribute-level edits across multiple dimensions. The designed voice is then rendered into a
prompt waveform and fed into a cloning model to enable high-fidelity timbre transfer for downstream
speech synthesis. VoiceSculptor achieves open-source state-of-the-art (SOTA) on InstructTTSEval-Zh, and is fully open-sourced, including code and pretrained models, to advance reproducible instruction-controlled TTS research.
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All authors Jingbin Hu, Huakang Chen, Linhan Ma, Dake Guo, Qirui Zhan, Wenhao Li, Haoyu Zhang, Kangxiang Xia, Ziyu Zhang, Wenjie Tian, Chengyou Wang, Jinrui Liang, Shuhan Guo, Zihang Yang, Bengu Wu, Binbin Zhang, Pengcheng Zhu, Pengyuan Xie, Chuan Xie, Qiang Zhang, Jie Liu, Lei Xie
Image and Video Processing
Multi-Objective Pareto-Front Optimization for Efficient Adaptive VVC Streaming
Angeliki Katsenou, Vignesh V. Menon, Guoda Laurinaviciute et al. (+2 more)
Adaptive
video streaming has facilitated improved
video streaming over the past years. A balance among coding performance objectives such as bitrate,
video quality, and decoding complexity is required to achieve efficient, content- and codec-dependent, adaptive
video streaming. This paper proposes a multi-objective Pareto-front (PF) optimization framework to construct quality-monotonic, content-adaptive bitrate ladders Versatile
Video Coding (VVC) streaming that jointly optimize
video quality, bitrate, and decoding time, which is used as a practical proxy for decoding energy. Two strategies are introduced: the Joint Rate-Quality-Time Pareto Front (JRQT-PF) and the Joint Quality-Time Pareto Front (JQT-PF), each exploring different tradeoff formulations and objective prioritizations. The ladders are constructed under quality monotonicity constraints during adaptive streaming to ensure a consistent Quality of Experience (QoE). Experiments are conducted on a large-scale UHD
dataset (Inter-4K), with quality assessed using PSNR, VMAF, and XPSNR, and complexity measured via decoding time and energy consumption. The JQT-PF method achieves 11.76% average bitrate savings while reducing average decoding time by 0.29% to maintain the same XPSNR, compared to a widely-used fixed ladder. More aggressive configurations yield up to 27.88% bitrate savings at the cost of increased complexity. The JRQT-PF strategy, on the other hand, offers more controlled tradeoffs, achieving 6.38 % bitrate savings and 6.17 % decoding time reduction. This framework outperforms existing methods, including fixed ladders, VMAF- and XPSNR-based dynamic resolution selection, and complexity-aware
benchmarks. The results confirm that PF optimization with decoding time constraints enables sustainable, high-quality streaming tailored to network and device capabilities.
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All authors Angeliki Katsenou, Vignesh V. Menon, Guoda Laurinaviciute, Benjamin Bross, Detlev Marpe
Systems and Control · Cross-listed from cs.NI
A user subscription model in mobile radio access networks with network slicing
José-Ramón Vidal, Luis Guijarro and Vicent Pla
Network slicing is an architectural enabling technology that logically decouples the current cellular networks into infrastructure providers (InPs) and Network Slice Tenants (NSTs). The network resources (e.g.,
radio access resources at each
cell) are owned by the InP, and are shared by the NSTs to provide a service to their mobile users. In this context, we proposed a business model that includes resource allocation and user subscription to NSTs in a competitive setting, and provides, among other things, closed-form expressions for the subscription indicators in
equilibrium of each NST at each
cell. This model relies on the widely adopted logit model to characterize user subscriptions. However, as a consequence of user mobility and
radio propagation, some of the underlying assumptions in the logit model do not hold. Therefore, further research is needed to assess the accuracy of the results provided by the logit model in a mobile
radio scenario. We carry out a thorough evaluation of the validity of the model by comparing its results against those obtained through computer simulation. Our simulation model includes complete and realistic characterizations of user mobility and
radio propagation. From the results, we conclude in most cases the logit model provides valid results in a mobile
radio scenario.
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Signal Processing
Achievable Degrees of Freedom Analysis and Optimization in Massive MIMO via Characteristic Mode Analysis
Shaohua Yue, Siyu Miao, Shuhao Zeng et al. (+2 more)
Massive multiple-input multiple-output (
MIMO) is esteemed as a critical technology in 6G communications, providing large degrees of freedom (DoF) to improve multiplexing gain. This paper introduces characteristic mode analysis (CMA) to derive the achievable DoF. Unlike existing works primarily focusing on the DoF of the wireless channel,the excitation and radiation properties of
antennas are also involved in our DoF analysis, which influences the number of independent data streams for communication of a
MIMO system. Specifically, we model the excitation and radiation properties of transceiver
antennas using CMA to analyze the excitation and radiation properties of
antennas. The CMA-based DoF analysis framework is established and the achievable DoF is derived. A characteristic mode optimization problem of
antennas is then formulated to maximize the achievable DoF. A case study where the reconfigurable
holographic surface (RHS)
antennas are deployed at the transceiver is investigated, and a CMA-based genetic
algorithm is later proposed to solve the above problem. By changing the characteristic modes electric field and surface current distribution of RHS, the achievable DoF is enhanced. Full-wave simulation verifies the theoretical analysis on the the achievable DoF and shows that, via the reconfiguration of RHS based on the proposed
algorithm, the achievable DoF is improved.
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All authors Shaohua Yue, Siyu Miao, Shuhao Zeng, Fenghan Lin, Boya Di
Audio and Speech Processing · Cross-listed from cs.SD
Stable Differentiable Modal Synthesis for Learning Nonlinear Dynamics
Victor Zheleznov, Stefan Bilbao, Alec Wright et al. (+1 more)
Modal methods are a long-standing approach to physical modelling synthesis. Extensions to nonlinear problems are possible, leading to coupled nonlinear systems of ordinary differential equations. Recent work in scalar auxiliary variable techniques has enabled construction of explicit and stable numerical solvers for such systems. On the other hand, neural ordinary differential equations have been successful in modelling nonlinear systems from data. In this work, we examine how scalar auxiliary variable techniques can be combined with neural ordinary differential equations to yield a stable differentiable model capable of learning nonlinear dynamics. The proposed approach leverages the analytical solution for linear vibration of the system's modes so that physical parameters of a system remain easily accessible after the training without the need for a parameter encoder in the model architecture. Compared to our previous work that used multilayer perceptrons to parametrise nonlinear dynamics, we employ gradient networks that allow an interpretation in terms of a closed-form and non-negative potential required by scalar auxiliary variable techniques. As a proof of concept, we generate synthetic data for the nonlinear transverse vibration of a string and show that the model can be trained to reproduce the nonlinear dynamics of the system. Sound examples are presented.
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All authors Victor Zheleznov, Stefan Bilbao, Alec Wright, Simon King
Image and Video Processing
An effective interactive brain cytoarchitectonic parcellation framework using pretrained foundation model
Shiqi Zhang, Fang Xu and Pengcheng Zhou
Cytoarchitectonic mapping provides anatomically grounded parcellations of
brain structure and forms a foundation for integrative, multi-modal neuroscience analyses. These parcellations are defined based on the shape, density, and spatial arrangement of neuronal
cell bodies observed in histological imaging. Recent works have demonstrated the potential of using deep learning models toward fully automatic
segmentation of cytoarchitectonic areas in large-scale
datasets, but performance is mainly constrained by the scarcity of training labels and the variability of staining and imaging conditions. To address these challenges, we propose an interactive cytoarchitectonic parcellation framework that leverages the strong transferability of the DINOv3
vision transformer. Our framework combines (i) multi-layer DINOv3 feature fusion, (ii) a lightweight
segmentation decoder, and (iii) real-time user-guided training from sparse scribbles. This design enables rapid human-in-the-loop refinement while maintaining high
segmentation accuracy. Compared with training an nnU-Net from scratch, transfer learning with DINOv3 yields markedly improved performance. We also show that features extracted by DINOv3 exhibit clear anatomical correspondence and demonstrate the method's practical utility for
brain region
segmentation using sparse labels. These results highlight the potential of foundation-model-driven interactive
segmentation for scalable and efficient cytoarchitectonic mapping.
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Signal Processing · Cross-listed from cs.IT
Codebook Design for Limited Feedback in Near-Field XL-MIMO Systems
Liujia Yao, Changsheng You, Zixuan Huang et al. (+3 more)
In this paper, we study efficient codebook design for limited feedback in extremely large-scale multiple-input-multiple-output (XL-
MIMO) frequency division duplexing (FDD) systems. It is worth noting that existing codebook designs for XL-
MIMO, such as polar-domain codebook, have not well taken into account user (location) distribution in practice, thereby incurring excessive feedback overhead. To address this issue, we propose in this paper a novel and efficient feedback codebook tailored to user distribution. To this end, we first consider a typical scenario where users are uniformly distributed within a specific polar-region, based on which a sum-rate maximization problem is formulated to jointly optimize angle-range samples and bit allocation among angle/range feedback. This problem is challenging to solve due to the lack of a closed-form expression for the received power in terms of angle and range samples. By leveraging a Voronoi partitioning approach, we show that uniform angle sampling is optimal for received power maximization. For more challenging range sampling design, we obtain a tight lower-bound on the received power and show that
geometric sampling, where the ratio between adjacent samples is constant, can maximize the lower bound and thus serves as a high-quality suboptimal solution. We then extend the proposed framework to accommodate more general non-uniform user distribution via an alternating sampling method. Furthermore, theoretical analysis reveals that as the array size increases, the optimal allocation of feedback bits increasingly favors range samples at the expense of angle samples. Finally, numerical results validate the superior rate performance and robustness of the proposed codebook design under various system setups, achieving significant gains over
benchmark schemes, including the widely used polar-domain codebook.
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All authors Liujia Yao, Changsheng You, Zixuan Huang, Chao Zhou, Zhaohui Yang, Xiaoyang Li
Systems and Control · Cross-listed from cs.RO
Online sparse Bayesian identification of nonlinear time-varying systems
He Ren, Gaowei Yan, Hang Liu et al. (+3 more)
Sparse regression provides a compact and interpretable route for nonlinear system modeling by selecting a small number of active terms from a candidate dictionary. Most sparse regressors, however, are constructed offline and then used as static predictors. In online operation, changing load, material properties, ambient conditions, or equipment states may alter both the coefficient values and the effective active support within the dictionary. Moreover, a direct recursive update over a rich dictionary may spread the adaptation over many weakly relevant terms, causing an initially sparse model to become increasingly dense. The key problem is therefore to maintain a sparse regressor online, so that it can absorb streaming data while keeping a compact but revisable active structure. This paper develops a
Bayesian recursive sparse learning (BRSL) method for online sparse identification over candidate dictionary terms. The coefficient distribution is updated through a
Bayesian posterior recursion, where sliding-window likelihood-ratio information recursion incorporates new samples, removes expired samples, and discounts historical information in a unified update. To preserve sparsity during recursion, posterior-guided shrinkage is introduced to suppress weakly supported dictionary terms and revise the active structure according to posterior evidence. The posterior update is performed in a candidate subspace with an adaptive information floor to keep the recursive solve well posed, and a bounded-error relation is given to clarify the influence of shrinkage, residual information, coefficient drift, and information conditioning. The proposed method is evaluated on sparse coefficient tracking and a power-plant-oriented multi-input multi-output (
MIMO) nonlinear time-varying identification
benchmark.
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All authors He Ren, Gaowei Yan, Hang Liu, Lifeng Cao, Zhijun Zhao, Gang Dang
Signal Processing
Low-Complexity Blind Estimator of SNR and MSE for mmWave Multi-Antenna Communications
Hanyoung Park and Ji-Woong Choi
To enhance the robustness and resilience of wireless communication and meet performance requirements, various environment-reflecting metrics, such as the signal-to-noise ratio (SNR), are utilized as the system parameter. To obtain these metrics, training signals such as pilot sequences are generally employed. However, the rapid fluctuations of the millimeter-wave (mmWave) propagation channel often degrade the accuracy of such estimations. To address this challenge, various blind
estimators that operate without pilot have been considered as potential solutions. However, these
algorithms often involve a training phase for machine learning or a large number of iterations, which implies prohibitive computational complexity, making them difficult to employ for real-time services and the system less resilient to dynamic environment variation. In this paper, we propose blind
estimators for average noise power, signal power, SNR, and mean-square error (MSE) that do not require knowledge of the ground-truth signal or involve high computational complexity. The proposed
algorithm leverages the inherent sparsity of mmWave channel in beamspace domain, which makes the signal and noise power components more distinguishable.
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Image and Video Processing · Cross-listed from cs.CV
SRAW-Attack: Space-Reweighted Adversarial Warping Attack for SAR Target Recognition
Yiming Zhang, Weibo Qin, Yuntian Liu et al. (+1 more)
Synthetic aperture radar (SAR) imagery exhibits intrinsic information sparsity due to its unique electromagnetic scattering mechanism. Despite the widespread adoption of deep neural network (DNN)-based SAR automatic target recognition (SAR-ATR) systems, they remain vulnerable to
adversarial examples and tend to over-rely on background regions, leading to degraded
adversarial robustness. Existing
adversarial attacks for SAR-ATR often require visually perceptible distortions to achieve effective performance, thereby necessitating an attack method that balances effectiveness and stealthiness. In this paper, a novel attack method termed Space-Reweighted
Adversarial Warping (SRAW) is proposed, which generates
adversarial examples through optimized spatial deformation with reweighted budgets across foreground and background regions. Extensive experiments demonstrate that SRAW significantly degrades the performance of state-of-the-art SAR-ATR models and consistently outperforms existing methods in terms of imperceptibility and
adversarial transferability. Code is made available at https://github.com/boremycin/SAR-ATR-TransAttack.
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All authors Yiming Zhang, Weibo Qin, Yuntian Liu, Feng Wang
Systems and Control
Single-Feed Circularly Polarized Super Realized Gain Antenna
Georgia Psychogiou, Donal P. Lynch, Spyridon N. Daskalakis et al. (+3 more)
This paper presents a super realized gain, circularly polarized strip-crossed dipole
antenna operating at 3.5 GHz. Superdirective behavior is achieved by leveraging strong inter-element mutual coupling through careful adjustment of the strip dimensions. The
antenna features a single driven element, with the other element passively loaded with a reactive impedance. The structure is optimized to maximize left-hand circularly polarized (LHCP) realized gain, ensuring high polarization purity and good impedance matching. The optimized design exhibits a 50 $\Omega$ impedance bandwidth of 3.29 - 4.17 GHz (23.75%) and an axial-ratio bandwidth of 3.43 - 3.57 GHz (4%). At 3.5 GHz, the
antenna achieves a peak realized gain of 6.1 dB ($ka \approx 1.65$), with an axial ratio of 1.4 dB. These results demonstrate that circular polarization and superdirectivity can be simultaneously realized in a geometrically simple, low-profile ($0.15\lambda$)
antenna, rendering it suitable for integration into compact sub-6~GHz wireless and sensing platforms.
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All authors Georgia Psychogiou, Donal P. Lynch, Spyridon N. Daskalakis, Manos M. Tentzeris, George Goussetis, Stylianos D. Asimonis
Signal Processing
Sim2Real Deep Transfer for Per-Device CFO Calibration
Jingze Zheng, Zhiguo Shi, Shibo He et al. (+1 more)
Carrier Frequency Offset (CFO) estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems faces significant performance degradation across heterogeneous software-defined
radio (SDR) platforms due to uncalibrated hardware impairments. Existing deep neural network (DNN)-based approaches lack device-level adaptation, limiting their practical deployment. This paper proposes a Sim2Real transfer learning framework for per-device CFO calibration, combining simulation-driven pretraining with lightweight receiver adaptation. A backbone DNN is pre-trained on synthetic OFDM signals incorporating parametric hardware distortions (e.g., phase noise, IQ imbalance), enabling generalized feature learning without costly cross-device data collection. Subsequently, only the regression layers are fine-tuned using $1,000$ real frames per target device, preserving hardware-agnostic knowledge while adapting to device-specific impairments. Experiments across three SDR families (USRP B210, USRP N210, HackRF One) achieve $30\times$ BER reduction compared to conventional CP-based methods under indoor multipath conditions. The framework bridges the simulation-to-reality gap for robust CFO estimation, enabling cost-effective deployment in heterogeneous wireless systems.
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All authors Jingze Zheng, Zhiguo Shi, Shibo He, Chaojie Gu
Image and Video Processing
Cell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopy
Raffaella Fiamma Cabini, Deborah Barkauskas, Guangyu Chen et al. (+19 more)
The classification of microscopy
videos capturing complex cellular behaviors is crucial for understanding and quantifying the dynamics of biological processes over time. However, it remains a frontier in computer
vision, requiring approaches that effectively model the shape and motion of objects without rigid boundaries, extract hierarchical spatiotemporal features from entire image sequences rather than static frames, and account for multiple objects within the field of view. To this end, we organized the
Cell Behavior
Video Classification Challenge (CBVCC), benchmarking 35 methods based on three approaches: classification of tracking-derived features, end-to-end deep learning architectures to directly learn spatiotemporal features from the entire
video sequence without explicit
cell tracking, or ensembling tracking-derived with image-derived features. We discuss the results achieved by the participants and compare the potential and limitations of each approach, serving as a basis to foster the development of computer
vision methods for studying cellular dynamics.
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All authors Raffaella Fiamma Cabini, Deborah Barkauskas, Guangyu Chen, Zhi-Qi Cheng, David E Cicchetti, Judith Drazba, Rodrigo Fernandez-Gonzalez, Raymond Hawkins, Yujia Hu, Jyoti Kini, Charles LeWarne, Xufeng Lin, Sai Preethi Nakkina, John W Peterson, Koert Schreurs, Ayushi Singh, Kumaran Bala Kandan Viswanathan, Inge MN Wortel, Sanjian Zhang, Rolf Krause, Santiago Fernandez Gonzalez, Diego Ulisse Pizzagalli
Image and Video Processing · Cross-listed from cs.CV
Optimizing Multimodal LLMs for Egocentric Video Understanding: A Solution for the HD-EPIC VQA Challenge
Sicheng Yang, Yukai Huang, Shitong Sun et al. (+4 more)
Multimodal Large
Language Models (MLLMs) struggle with complex
video QA
benchmarks like HD-EPIC VQA due to ambiguous queries/options, poor long-range temporal
reasoning, and non-standardized outputs. We propose a framework integrating query/choice pre-processing, domain-specific Qwen2.5-VL fine-tuning, a novel Temporal Chain-of-Thought (T-CoT) prompting for multi-step
reasoning, and robust post-processing. This system achieves 41.6% accuracy on HD-EPIC VQA, highlighting the need for holistic pipeline optimization in demanding
video understanding. Our code, fine-tuned models are available at https://github.com/YoungSeng/
Egocentric-Co-Pilot.
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All authors Sicheng Yang, Yukai Huang, Shitong Sun, Weitong Cai, Jiankang Deng, Jifei Song, Zhensong Zhang
Signal Processing
BeamCKMDiff: Beam-Aware Channel Knowledge Map Construction via Diffusion Transformer
Le Zhao, Yining Wang, Xinyi Wang et al. (+2 more)
Channel knowledge map (CKM) is emerging as a critical enabler for environment-aware 6G networks, offering a site-specific database to significantly reduce pilot overhead. However, existing CKM construction methods typically rely on sparse sampling measurements and are restricted to either omnidirectional maps or discrete codebooks, hindering the exploitation of beamforming gain. To address these limitations, we propose BeamCKMDiff, a generative framework for constructing high-fidelity CKMs conditioned on arbitrary continuous beamforming vectors without site-specific sampling. Specifically, we incorporate a novel adaptive layer normalization (adaLN) mechanism into the noise prediction network of the
Diffusion Transformer (DiT). This mechanism injects continuous beam
embeddings as {global control parameters}, effectively steering the generative process to capture the complex coupling between beam patterns and environmental geometries. Simulation results demonstrate that BeamCKMDiff significantly outperforms state-of-the-art baselines, achieving superior reconstruction accuracy in capturing main lobes and side lobes.
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All authors Le Zhao, Yining Wang, Xinyi Wang, Zesong Fei, Yong Zeng
Systems and Control
Model Predictive Control of Thermo-Hydraulic Systems Using Primal Decomposition
Jonathan Vieth, Annika Eichler and Arne Speerforck
Decarbonizing the global energy supply requires more efficient heating and cooling systems. Model predictive control enhances the operation of cooling and heating systems but depends on accurate system models, often based on control volumes. We present an automated framework including time discretization to generate model predictive controllers for such models. To ensure scalability, a primal decomposition exploiting the model structure is applied. The approach is validated on an underground heating system with varying numbers of states, demonstrating the primal decomposition's advantage regarding scalability.
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Signal Processing
Service Provisioning and Path Planning with Obstacle Avoidance for Low-Altitude Wireless Networks
Senning Wan, Bin Li, Hongbin Chen et al. (+1 more)
This paper investigates the three-dimensional (3D) deployment of uncrewed aerial vehicles (
UAVs) as aerial base stations in heterogeneous communication networks under constraints imposed by diverse ground obstacles. Given the diverse data demands of user equipments (UEs), a user satisfaction model is developed to provide personalized services. In particular, when a UE is located within a ground obstacle, the
UAV must approach the obstacle boundary to ensure reliable service quality. Considering constraints such as
UAV failures due to battery depletion, heterogeneous UEs, and obstacles, we aim to maximize overall user satisfaction by jointly optimizing the 3D trajectories of
UAVs, transmit beamforming vectors, and binary association indicators between
UAVs and UEs. To address the complexity and dynamics of the problem, a block coordinate descent method is adopted to decompose it into two subproblems. The beamforming subproblem is efficiently addressed via a bisection-based water-filling
algorithm. For the trajectory and association subproblem, we design a deep reinforcement learning
algorithm based on proximal policy optimization to learn an adaptive control policy. Simulation results demonstrate that the proposed scheme outperforms baseline schemes in terms of convergence speed and overall system performance. Moreover, it achieves efficient association and accurate obstacle avoidance.
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All authors Senning Wan, Bin Li, Hongbin Chen, Lei Liu
Systems and Control
HyMGP: A Customized MILP-Based Tool for Techno-Economic Planning of Islanded Microgrids
Andres Intriago, Rongxing Hu, Nabil Mohammed et al. (+9 more)
This paper presents a customized microgrid planning
algorithm and tool, HyMGP, for remote sites in arid regions, which is formulated as a Mixed Integer Linear Programming (MILP) problem. HyMGP is compared with HOMER Pro to evaluate its performance in optimizing the sizing of microgrid components, including photovoltaic panels (PVs), vertical axis wind turbines (VAWTs), and battery energy storage systems (BESS), for remote and off-grid applications. The study focuses on a standalone microgrid in the Saudi Arabia, considering high
solar irradiance, limited wind availability, and a constant load profile composed of continuous cathodic protection and daytime cooling. In the simulation environment, comparisons with HOMER solutions demonstrate the advantages of HyMGP, which provides optimal and more flexible solutions by allowing user-defined component specifications and strictly enforcing all constraints. Further analysis shows that incorporating wind turbines reduces the Net Present Cost (NPC) by decreasing the required PV and battery capacities. Increasing battery autonomy leads to a higher NPC in both PV-only and hybrid systems due to the need for larger storage. Finally, lithium iron phosphate (Li-ion LFP) batteries are found to be more cost effective than lead acid, offering lower NPCs due to their longer lifespan, deeper discharge capability, and fewer replacement cycles.
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All authors Andres Intriago, Rongxing Hu, Nabil Mohammed, S. Gokul Krishnan, Konstantinos Kotsovos, Issam Gereige, Nesren Attiah, Ali Basaheeh, Sarah Aqeel, Hamad A. Saiari, Shehab Ahmed, Charalambos Konstantinou
Systems and Control
Leveraging Digital Twin Technologies: All-Photonics Networks-as-a-Service for Data Center Xchange in the Era of AI [Invited Tutorial]
Hideki Nishizawa, Kazuya Anazawa, Tetsuro Inui et al. (+8 more)
This paper presents a data center exchange (Data Center Xchange, DCX) architecture for all-photonics networks-as-a-service in distributed data center infrastructures, enabling the creation of a virtual large-scale data center by directly interconnecting distributed data centers in metropolitan areas. Key requirements for such an architecture are identified: support for low-latency operations, scalability, reliability, and flexibility within a single network architecture; the ability to add new operator-driven automation functionalities based on an open networking approach; and the ability to control and manage remotely deployed transponders connected via access links with unknown physical parameters. We propose a set of technologies that enable digital twin operations for optical networks, including a cloud-native architecture for coherent transceivers, remote transponder control, fast end-to-end optical path provisioning, transceiver-based physical-parameter estimation incorporating digital longitudinal monitoring, and optical line system calibration, demonstrating their feasibility through field validations.
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All authors Hideki Nishizawa, Kazuya Anazawa, Tetsuro Inui, Toru Mano, Takeo Sasai, Giacomo Borraccini, Tatsuya Matsumura, Hiroyuki Ishihara, Sae Kojima, Yoshiaki Sone, Koichi Takasugi
Systems and Control
On the Computation and Approximation of Backward Reachable Sets for Max-Plus Linear Systems using Polyhedras
Yuda Li, Shaoyuan Li and Xiang Yin
This paper investigates reachability analysis for max-plus linear systems (MPLS), an important class of dynamical systems that model synchronization and delay phenomena in timed discrete-event systems. We specifically focus on backward reachability analysis, i.e., determining the set of states that can reach a given target set within a certain number of steps. Computing backward reachable sets presents significant challenges due to the non-convexity of max-plus dynamics and the complexity of set complement operations. To address these challenges, we propose a novel approximation framework that efficiently computes backward reachable sets by exploiting the structure of tropical polyhedra. Our approach reformulates the problem as a sequence of symbolic operations and approximates non-convex target sets through closure operations on unions of tropical polyhedra. We develop a systematic
algorithm that constructs both outer (M-form) and inner (V-form) representations of the resulting sets, incorporating extremal filtering to reduce computational complexity. The proposed method offers a scalable alternative to traditional DBM-based approaches, enabling reliable approximate backward reachability analysis for general target regions in MPLS.
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Audio and Speech Processing
Nearest Kronecker Product Decomposition Based Subband Adaptive Filter: Algorithms and Applications
Jianhong Ye and Haiquan Zhao
Recently, the nearest Kronecker product (NKP) decomposition-based normalized least mean square (NLMS-NKP)
algorithm has demonstrated superior convergence performance compared to the conventional NLMS
algorithm. However, its convergence rate exhibits significant degradation when processing highly correlated input signals. To address this problem, we propose a type-I NKP-based normalized subband adaptive filter (NSAF)
algorithm, namely NSAF-NKP-I. Nevertheless, this
algorithm incurs substantially higher computational overhead than the NLMS-NKP
algorithm. Remarkably, our enhanced type-II NKP-based NSAF (NSAF-NKP-II)
algorithm achieves equivalent convergence performance while substantially reducing computational complexity. Furthermore, to enhance robustness against impulsive noise interference, we develop two robust variants: the maximum correntropy criterion-based robust NSAF-NKP (RNSAF-NKP-MCC) and logarithmic criterion-based robust NSAF-NKP (RNSAF-NKP-LC)
algorithms. Additionally, detailed analyses of computational complexity, step-size range, and theoretical steady-state performance are provided for theproposed
algorithms. To enhance the practicability of the NSAF-NKP-II
algorithm in complex nonlinear environments, we further devise two nonlinear implementations: the trigonometric functional link network-based NKP-NSAF (TFLN-NSAF-NKP) and Volterra series expansion-based NKP-NSAF (Volterra-NKP-NSAF)
algorithms. In active noise control (ANC) systems, we further propose the filtered-x NSAF-NKP-II (NKP-FxNSAF)
algorithm. Simulation experiments in echo cancellation, sparse system identification, nonlinear processing, and ANC scenarios are conducted to validate the superiority of the proposed
algorithms over existing state-of-the-art counterparts.
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Signal Processing
P-norm based Fractional-Order Robust Subband Adaptive Filtering Algorithm for Impulsive Noise and Noisy Input
Jianhong Ye, Haiquan Zhao and Yi Peng
Building upon the mean p-power error (MPE) criterion, the normalized subband p-norm (NSPN)
algorithm demonstrates superior robustness in $\alpha$-stable noise environments ($1 < \alpha \leq 2$) through effective utilization of low-order moment hidden in robust loss functions. Nevertheless, its performance degrades significantly when processing noise input or additive noise characterized by $\alpha$-stable processes ($0 < \alpha \leq 1$). To overcome these limitations, we propose a novel fractional-order NSPN (FoNSPN)
algorithm that incorporates the fractional-order
stochastic gradient descent (FoSGD) method into the MPE framework. Additionally, this paper also analyzes the convergence range of its step-size, the theoretical domain of values for the fractional-order $\beta$, and establishes the theoretical steady-state mean square deviation (MSD) model. Simulations conducted in diverse impulsive noise environments confirm the superiority of the proposed FoNSPN
algorithm against existing state-of-the-art
algorithms.
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Image and Video Processing · Cross-listed from cs.LG
Comparative Evaluation of Deep Learning-Based and WHO-Informed Approaches for Sperm Morphology Assessment
Mohammad Abbadi
Assessment of sperm morphological quality remains a critical yet subjective component of male fertility evaluation, often limited by inter-observer variability and resource constraints. This study presents a comparative biomedical artificial intelligence framework evaluating an image-based deep learning model (HuSHeM) alongside a clinically grounded baseline derived from World Health Organization criteria augmented with the Systemic Inflammation Response Index (WHO(+SIRI)). The HuSHeM model was trained on high-resolution sperm morphology images and evaluated using an independent clinical cohort. Model performance was assessed using discrimination, calibration, and clinical utility analyses. The HuSHeM model demonstrated higher discriminative performance, as reflected by an increased area under the receiver operating characteristic curve with relatively narrow confidence intervals compared to WHO(+SIRI). Precision-recall analysis further indicated improved performance under class imbalance, with higher precision-recall area values across evaluated thresholds. Calibration analysis indicated closer agreement between predicted probabilities and observed outcomes for HuSHeM, while decision curve analysis suggested greater net clinical benefit across clinically relevant threshold probabilities. These findings suggest that image-based deep learning may offer improved predictive reliability and clinical utility compared with traditional rule-based and inflammation-augmented criteria. The proposed framework supports objective and reproducible assessment of sperm morphology and may serve as a decision-support tool within fertility screening and referral workflows. The proposed models are intended as decision-support or referral tools and are not designed to replace clinical judgment or laboratory assessment.
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Signal Processing
Microwave Linear Analog Computer (MiLAC)-aided Multiuser MISO: Fundamental Limits and Beamforming Design
Zheyu Wu, Matteo Nerini and Bruno Clerckx
As wireless communication systems evolve toward the 6G era, ultra-massive/gigantic
MIMO is envisioned as a key enabling technology. Recently, microwave linear analog computer (MiLAC) has emerged as a promising approach to realize beamforming entirely in the analog domain, thereby alleviating the scalability challenges associated with gigantic
MIMO. In this paper, we investigate the fundamental beamforming flexibility and design of lossless and reciprocal MiLAC-aided beamforming for MU-MISO systems. We first provide a rigorous characterization of the set of beamforming
matrices achievable by MiLAC. Based on this characterization, we prove that MiLAC-aided beamforming does not generally achieve the full flexibility of digital beamforming, while offering greater flexibility than conventional phase-shifter-based analog beamforming. Furthermore, we propose a hybrid digital-MiLAC architecture and show that it achieves digital beamforming flexibility when the number of
radio frequency (RF) chains equals the number of data streams, halving that required by conventional hybrid beamforming. We then formulate the MiLAC-aided sum-rate maximization problem for MU-MISO systems. To solve the problem efficiently, we reformulate the MiLAC-related constraints as a convex linear
matrix inequality and establish a low-dimensional subspace property that significantly reduces the problem dimension. Leveraging these results, we propose WMMSE-based
algorithms for solving the resulting problem. Simulation results demonstrate that MiLAC-aided beamforming achieves performance close to that of digital beamforming in gigantic
MIMO systems. Compared with hybrid beamforming, it achieves comparable or superior performance with lower hardware and computational complexity by avoiding symbol-level digital processing and enabling low-resolution digital-to-analog converters (DACs).
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Systems and Control
Event-Driven Deep RL Dispatcher for Post-Storm Distribution System Restoration
Farshad Amani, Faezeh Ardali and Amin Kargarian
Natural hazards such as hurricanes and floods damage power grid equipment, forcing operators to replan restoration repeatedly as new information becomes available. This paper develops a deep reinforcement learning (DRL) dispatcher that serves as a real-time decision engine for crew-to-repair assignments. We model restoration as a sequential, information-revealing process and learn an actor-critic policy over compact features such as component status, travel/repair times, crew availability, and marginal restoration value. A feasibility mask blocks unsafe or inoperable actions, such as power flow limits, switching rules, and crew-time constraints, before they are applied. To provide realistic runtime inputs without relying on heavy solvers, we use lightweight surrogates for wind and flood intensities, fragility-based failure, spatial clustering of damage, access impairments, and progressive ticket arrivals. In simulated hurricane and flood events, the learned policy updates crew decisions in real time as new field reports arrive. Because the runtime logic is lightweight, it improves online performance (energy-not-supplied, critical-load restoration time, and travel distance) compared with mixed-integer programs and standard heuristics. The proposed approach is tested on the IEEE 13- and 123-bus feeders with mixed hurricane/flood scenarios.
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Systems and Control · Cross-listed from cs.PF
Emergency Department Patient Flow Optimization with an Alternative Care Threshold Policy
Sahba Baniasadi, Paul M. Griffin and Prakash Chakraborty
Emergency department (ED) overcrowding and patient boarding represent critical systemic challenges that compromise care quality. We propose a threshold-based admission policy that redirects non-urgent patients to alternative care pathways, such as telemedicine, during peak congestion. The ED is modeled as a two-class $M/M/c$ preemptive-priority queuing system, where high-acuity patients are prioritized and low-acuity patients are subject to state-dependent redirection. Analyzed via a level-dependent Quasi-Birth-Death (QBD) process, the model determines the optimal threshold by maximizing a long-run time-averaged objective function comprising redirection-affected revenue and costs associated with patient balking and system occupancy. Structural analysis establishes monotone comparative statics relating the optimal threshold to all model parameters analytically. Numerical analysis using national healthcare data reveals that optimal policies are highly context-dependent. While rural EDs generally optimize at lower redirection thresholds, urban EDs exhibit performance peaks at moderate thresholds. Results indicate that our optimal policy yields significant performance gains of up to $4.84\%$ in rural settings and $5.90\%$ in urban environments. This research provides a mathematically rigorous framework for balancing clinical priority with operational efficiency across diverse ED settings.
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Signal Processing
Clustering-Based User Selection in Federated Learning: Metadata Exploitation for 3GPP Networks
Ce Zheng, Shiyao Ma, Ke Zhang et al. (+2 more)
Federated learning (FL) enables collaborative model training without sharing raw user data, but conventional simulations often rely on unrealistic data partitioning and current user selection methods ignore data correlation among users. To address these challenges, this paper proposes a metadatadriven FL framework. We first introduce a novel data partition model based on a homogeneous Poisson point process (HPPP), capturing both heterogeneity in data quantity and natural overlap among user
datasets. Building on this model, we develop a clustering-based user selection strategy that leverages metadata, such as user location, to reduce data correlation and enhance label diversity across training rounds. Extensive experiments on FMNIST and CIFAR-10 demonstrate that the proposed framework improves model performance, stability, and convergence in non-IID scenarios, while maintaining comparable performance under IID settings. Furthermore, the method shows pronounced advantages when the number of selected users per round is small. These findings highlight the framework's potential for enhancing FL performance in realistic deployments and guiding future standardization.
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All authors Ce Zheng, Shiyao Ma, Ke Zhang, Chen Sun, Wenqi Zhang
Systems and Control
Extremum Seeking Nonovershooting Control of Strict-Feedback Systems Under Unknown Control Direction
Kaixin Lu, Ziliang Lyu, Yanfang Mo et al. (+2 more)
This paper addresses the nonovershooting control problem for strict-feedback nonlinear systems with unknown control direction. We propose a method that integrates extremum seeking with Lie bracket-based design to achieve approximately nonovershooting tracking. The approach ensures that arbitrary reference trajectories can be tracked from below for any initial condition, with the overshoot reducible to arbitrarily small levels through parameter tuning. The method further provides a mechanism for enforcing high-relative-degree nonovershooting constraints in safety-critical scenarios involving unknown control directions.
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All authors Kaixin Lu, Ziliang Lyu, Yanfang Mo, Yiguang Hong, Haoyong Yu
Signal Processing
Towards Native Intelligence: 6G-LLM Trained with Reinforcement Learning from NDT Feedback
Zhuoran Xiao, Tao Tao, Chenhui Ye et al. (+4 more)
Owing to its comprehensive understanding of upper-layer application requirements and the capabilities of practical communication systems, the 6G-LLM (6G domain large
language model) offers a promising pathway toward realizing network native intelligence. Serving as the system orchestrator, the 6G-LLM drives a paradigm shift that fundamentally departs from existing rule-based approaches, which primarily rely on modular, experience-driven optimization. By contrast, the 6G-LLM substantially enhances network flexibility and adaptability. Nevertheless, current efforts to construct 6G-LLMs are constrained by their reliance on large-scale, meticulously curated, human-authored corpora, which are impractical to obtain in real-world scenarios. Moreover, purely offline-trained models lack the capacity for continual self-improvement, limiting their ability to adapt to the highly dynamic requirements of wireless communication environments. To overcome these limitations, we propose a novel training paradigm termed RLDTF (Reinforcement Learning from Digital Twin Feedback) for 6G-LLMs. This framework leverages network digital twins to generate reward signals based on orchestration outcomes, while employing reinforcement learning to guide the model toward optimal decision-making dynamically. Furthermore, we introduce a weighted
token mechanism to improve output accuracy. Comprehensive experimental results demonstrate that our proposed framework significantly outperforms state-of-the-art baselines in orchestration accuracy and solution optimality.
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All authors Zhuoran Xiao, Tao Tao, Chenhui Ye, Yunbo Hu, Yijia Feng, Tianyu Jiao, Liyu Cai