Systems and Control
Recursive Binary Identification under Data Tampering and Non-Persistent Excitation with Application to Emission Control
Jian Guo, Lihong Pei, Wenchao Xue et al. (+2 more)
This paper studies the problem of online parameter estimation for cyber-physical systems with binary outputs that may be subject to
adversarial data tampering. Existing methods are primarily offline and unsuitable for real-time learning. To address this issue, we first develop a first-order gradient-based
algorithm that updates parameter estimates recursively using incoming data. Considering that persistent excitation (PE) conditions are difficult to satisfy in feedback control scenarios, a second-order quasi-Newton
algorithm is proposed to achieve faster convergence without requiring the PE condition. For both
algorithms, corresponding versions are developed to handle known and unknown tampering strategies, and their parameter estimates are proven to converge almost surely over time. In particular, the second-order
algorithm ensures convergence under a signal condition that matches the minimal excitation required by classical least-squares estimation in
stochastic regression models. The second-order
algorithm is also extended to an adaptive control framework, providing an explicit upper bound on the tracking error for binary-output FIR systems under unknown tampering. Three numerical simulations verify the theoretical results and show that the proposed methods are robust against data tampering. Finally, the approach is validated via a vehicle emission control problem, where it effectively improves the detection accuracy of excess-emission events.
Read the full abstract ▾
All authors Jian Guo, Lihong Pei, Wenchao Xue, Yanlong Zhao, Ji-Feng Zhang
Image and Video Processing
SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images
Shuhang Chen, Hangjie Yuan, Pengwei Liu et al. (+3 more)
The Segment Anything Model (SAM) has demonstrated significant potential in medical image
segmentation. Yet, its performance is limited when only a small amount of labeled data is available, while there is abundant valuable yet often overlooked hierarchical information in medical data. To address this limitation, we draw inspiration from self-supervised learning and propose SAMora, an innovative framework that captures hierarchical medical knowledge by applying complementary self-supervised learning objectives at the image, patch, and pixel levels. To fully exploit the complementarity of hierarchical knowledge within LoRAs, we introduce HL-Attn, a hierarchical fusion module that integrates multi-scale features while maintaining their distinct characteristics. SAMora is compatible with various SAM variants, including SAM2, SAMed, and H-SAM. Experimental results on the Synapse, LA, and PROMISE12
datasets demonstrate that SAMora outperforms existing SAM variants. It achieves state-of-the-art performance in both few-shot and fully supervised settings while reducing fine-tuning epochs by 90%. The code is available at https://github.com/ShChen233/SAMora.
Read the full abstract ▾
All authors Shuhang Chen, Hangjie Yuan, Pengwei Liu, Hanxue Gu, Tao Feng, Dong Ni
Image and Video Processing · Cross-listed from cs.CV
Modulo Video Recovery via Selective Spatiotemporal Vision Transformer
Tianyu Geng, Feng Ji and Wee Peng Tay
Conventional image sensors have limited dynamic range, causing saturation in high-dynamic-range (HDR) scenes. Modulo cameras address this by folding incident irradiance into a bounded range, yet require specialized unwrapping
algorithms to reconstruct the underlying signal. Unlike HDR recovery, which extends dynamic range from conventional sampling, modulo recovery restores actual values from folded samples. Despite being introduced over a decade ago, progress in modulo image recovery has been slow, especially in the use of modern deep learning techniques. In this work, we demonstrate that standard HDR methods are unsuitable for modulo recovery.
Transformers, however, can capture global dependencies and spatial-temporal relationships crucial for resolving folded
video frames. Still, adapting existing
Transformer architectures for modulo recovery demands novel techniques. To this end, we present Selective Spatiotemporal
Vision Transformer (SSViT), the first deep learning framework for modulo
video reconstruction. SSViT employs a
token selection strategy to improve efficiency and concentrate on the most critical regions. Experiments confirm that SSViT produces high-quality reconstructions from 8-bit folded
videos and achieves state-of-the-art performance in modulo
video recovery.
Read the full abstract ▾
Image and Video Processing
Compressive Sensing Photoacoustic Imaging Receiver with Matrix-Vector-Multiplication SAR ADC
Huan-Cheng Liao, Shunyao Zhang, Yumin Su et al. (+7 more)
Wearable photoacoustic imaging devices hold great promise for continuous health monitoring and point-of-care diagnostics. However, the large data volume generated by high-density transducer arrays presents a major challenge for realizing compact and power-efficient wearable systems. This paper presents a photoacoustic imaging receiver (RX) that embeds compressive sensing directly into the hardware to address this bottleneck. The RX integrates 16 AFEs and four
matrix-vector-multiplication (MVM) SAR ADCs that perform energy- and area-efficient analog-domain compression. The architecture achieves a 4-8x reduction in output data rate while preserving low-loss full-array information. The MVM SAR ADC executes passive and accurate MVM using user-defined programmable ternary weights. Two signal reconstruction methods are implemented: (1) an optimization approach using the fast iterative shrinkage-thresholding
algorithm, and (2) a learning-based approach employing implicit neural representation. Fabricated in 65 nm CMOS, the chip achieves an ADC's SNDR of 57.5 dB at 20.41 MS/s, with an AFE input-referred noise of 3.5 nV/sqrt(Hz). MVM linearity measurements show R^2 > 0.999 across a wide range of weights and input amplitudes. The system is validated through phantom imaging experiments, demonstrating high-fidelity image reconstruction under up to 8x compression. The RX consumes 5.83 mW/channel and supports a general ternary-weighted measurement
matrix, offering a compelling solution for next-generation miniaturized, wearable PA imaging systems.
Read the full abstract ▾
All authors Huan-Cheng Liao, Shunyao Zhang, Yumin Su, Arvind Govinday, Yiwei Zou, Wei Wang, Vivek Boominathan, Ashok Veeraraghavan, Lei S. Li, Kaiyuan Yang
Systems and Control · Cross-listed from cs.RO
Underactuated Biomimetic Autonomous Underwater Vehicle for Ecosystem Monitoring
Kaustubh Singh, Shivam Kumar, Shashikant Pawar et al. (+1 more)
In this paper, we present an underactuated biomimetic
underwater robot that is suitable for ecosystem monitoring in both marine and freshwater environments. We present an updated mechanical design for a fish-like
robot and propose minimal actuation behaviors learned using reinforcement learning techniques. We present our preliminary mechanical design of the tail oscillation mechanism and illustrate the swimming behaviors on FishGym simulator, where the reinforcement learning techniques will be tested on
Read the full abstract ▾
All authors Kaustubh Singh, Shivam Kumar, Shashikant Pawar, Sandeep Manjanna
Systems and Control
Dissipativity-Based Synthesis of Distributed Control and Communication Topology Co-Design for AC Microgrids
Mohammad Javad Najafirad, Shirantha Welikala, Lei Wu et al. (+1 more)
This paper introduces a dissipativity-based framework for the joint design of distributed controllers and communication
topologies in AC microgrids (MGs), providing robust performance guarantees for voltage regulation, frequency synchronization, and proportional power sharing across distributed generators (DGs). The closed-loop AC MG is represented as a networked system in which DGs, distribution lines, and loads function as interconnected subsystems linked through cyber-physical networks. Each DG utilizes a three-layer hierarchical control structure: a steady-state controller for operating point configuration, a local feedback controller for voltage tracking, and a distributed droop-free controller implementing normalized power consensus for frequency coordination and proportional power distribution. The operating point design is formulated as an optimization problem. Leveraging dissipativity theory, we derive necessary and sufficient subsystem dissipativity conditions. The global co-design is then cast as a convex linear
matrix inequality (LMI) optimization that jointly determines distributed controller parameters and sparse communication architecture while managing the highly nonlinear, coupled dq-frame dynamics characteristic of AC systems. Simulation results from an islanded AC MG in a MATLAB/Simulink environment verify that the proposed framework achieves robust voltage regulation, frequency synchronization, and proportional power sharing through the optimized communication
topology.
Read the full abstract ▾
All authors Mohammad Javad Najafirad, Shirantha Welikala, Lei Wu, Panos J. Antsaklis
Systems and Control
Voltage-Regulated Sparse Optimization for Proactive Diagnosis of Voltage Collapses
Qinghua Ma, Seyyedali Hosseinalipour, Ming Shi et al. (+2 more)
This paper aims to proactively diagnose and manage the voltage collapse risks, i.e., the risk of bus voltages violating the safe operational bounds, which can be caused by extreme events and contingencies. We jointly answer two resilience-related research questions: (Q1) Survivability: Upon having an extreme event/contingency, will the system remain feasible with voltage staying within a (preferred) safe range? (Q2) Dominant Vulnerability: If voltage collapses, what are the dominant sources of system vulnerabilities responsible for the failure? This highlights some key locations worth paying
attention to in the planning or decision-making process. To address these questions, we propose a voltage-regulated sparse optimization that finds a minimal set of bus locations along with quantified compensations (corrective actions) that can simultaneously enforce AC network balance and voltage bounds. Results on transmission systems of varying sizes (30-bus to 2383-bus) demonstrate that the proposed method effectively mitigates voltage collapses by compensating at only a few strategically identified nodes, while scaling efficiently to large systems, taking on average less than 4 min for 2000+ bus cases. This work can further serve as a backbone for more comprehensive and actionable decision-making, such as reactive power planning to fix voltage issues.
Read the full abstract ▾
All authors Qinghua Ma, Seyyedali Hosseinalipour, Ming Shi, Jan Drgona, Shimiao Li
Systems and Control · Cross-listed from cs.LG
Efficient Approximation of Volterra Series for High-Dimensional Systems
Navin Khoshnan, Claudia K Petritsch and Bryce-Allen Bagley
The identification of high-dimensional nonlinear dynamical systems via the Volterra series has significant potential, but has been severely hindered by the curse of dimensionality. Tensor Network (TN) methods such as the Modified Alternating Linear Scheme (MVMALS) have been a breakthrough for the field, offering a tractable approach by exploiting the low-rank structure in Volterra kernels. However, these techniques still encounter prohibitive computational and
memory bottlenecks due to high-order
polynomial scaling with respect to input dimension. To overcome this barrier, we introduce the Tensor Head Averaging (THA)
algorithm, which significantly reduces complexity by constructing an ensemble of localized MVMALS models trained on small subsets of the input space. In this paper, we present a theoretical foundation for the THA
algorithm. We establish observable, finite-sample bounds on the error between the THA ensemble and a full MVMALS model, and we derive an exact decomposition of the squared error. This decomposition is used to analyze the manner in which subset models implicitly compensate for omitted dynamics. We quantify this effect, and prove that correlation between the included and omitted dynamics creates an optimization incentive which drives THA's performance toward accuracy superior to a simple truncation of a full MVMALS model. THA thus offers a scalable and theoretically grounded approach for identifying previously intractable high-dimensional systems.
Read the full abstract ▾
Systems and Control
Input-Output Data-Driven Stabilization of Continuous-Time Linear MIMO Systems
Haihui Gao, Alessandro Bosso, Lei Wang et al. (+2 more)
In this paper, we address the problem of data-driven stabilization of continuous-time multi-input multi-output (
MIMO) linear time-invariant systems using the input-output data collected from an experiment. Building on recent results for data-driven output-feedback control based on non-minimal realizations, we propose an approach that can be applied to a broad class of continuous-time
MIMO systems without requiring a uniform observability index. The key idea is to show that Kreisselmeier's adaptive filter can be interpreted as an observer of a stabilizable non-minimal realization of the plant. Then, by postprocessing the input-output data with such a filter, we derive a linear
matrix inequality that yields the feedback gain of a dynamic output-feedback stabilizer.
Read the full abstract ▾
All authors Haihui Gao, Alessandro Bosso, Lei Wang, David Saussié, Bowen Yi
Systems and Control
Investigation of lightning effects on solar power plants connected to transmission networks
Selma Grebovic, Abdulah Aksamovic, Bozidar Filipovic-Grcic et al. (+1 more)
The increasing integration of
solar power plants into transmission grids has raised concerns about their vulnerability to disturbances, particularly lightning strokes.
Solar energy, while offering significant environmental and economic benefits, faces challenges when connected to transmission lines that are prone to lightning discharges. This paper investigates the impact of lightning events on
solar power plants, focusing on overvoltage effects. Lightning stroke simulations were conducted at various distances from the
solar power plant along the transmission line, considering scenarios with and without surge arrester. Key lightning parameters such as peak current, front time, and tail time were varied to simulate different lightning strokes. The study also includes a
Fourier transform analysis of the resulting overvoltages with and without a surge arrester, along with the
Hilbert marginal spectrum of these overvoltages. The results provide insights into the effectiveness of surge arresters in mitigating lightning overvoltages and highlight the importance of proper protective measures for enhancing the reliability and safety of
solar power plants connected to transmission networks.
Read the full abstract ▾
All authors Selma Grebovic, Abdulah Aksamovic, Bozidar Filipovic-Grcic, Samim Konjicija
Systems and Control
Verification of low-frequency signal injection method for earth-fault detection
Nina Stipetic, Bozidar Filipovic-Grcic, Igor Ziger et al. (+4 more)
Unearthed neutral is commonly used in networks which require continuous power supply. This is common in MV
circuits of industrial and power plants. Unearthed networks can remain in operation during an earth-fault, but fast determination of the faulty line is key for prevention of further fault escalation. Signal injection is one of the fault location methods often used in LV unearthed networks. The possibility of applying this method in MV networks depends on how to inject the signal into unearthed phases. In such networks, it is possible to use a group of three inductive voltage
transformers (IVTs) for signal injection. After the simulations have shown promising results of signal injection and earth-fault detection in MV network, an experimental test was performed. This paper describes the experimental setup and shows the measurement results of signal injection method at MV level supported by EMT simulations.
Read the full abstract ▾
All authors Nina Stipetic, Bozidar Filipovic-Grcic, Igor Ziger, Silvio Jancin, Bruno Jurisic, Dalibor Filipovic-Grcic, Alain Xémard
Systems and Control · Cross-listed from math.OC
On Driftless Systems with m controls and 2m or 2m-1 states that are Flat by Pure Prolongation
Jean Lévine and Jaume Franch
It is widely recognized that no tractable necessary and sufficient conditions exist for determining whether a system is, in general, differentially flat. However, specific cases do provide such conditions. For instance, driftless systems with two inputs have known necessary and sufficient conditions. For driftless systems with three or more inputs, the available conditions are only sufficient. This paper presents new findings on determining whether a system with m inputs and $2m$ or $2m-1$ states is flat by pure prolongation, a specific subclass of differential flatness. While this condition is more restrictive than general differential flatness, the
algorithm for computing flat outputs remains remarkably simple, and the verification requirements are relatively lenient. Moreover, the conditions proposed in this work broaden the class of systems recognized as differentially flat, as our sufficient condition differs from existing criteria.
Read the full abstract ▾
Signal Processing · Cross-listed from cs.IT
Differential Space-Time Block Coding for Phase-Unsynchronized Cell-Free MIMO Downlink
Marx M. M. Freitas, Giovanni Interdonato and Stefano Buzzi
In the downlink of CF-mMIMO systems, spectral efficiency gains critically rely on joint coherent transmission, as all APs must align their transmitted signals in phase at the UE. Achieving such phase
alignment is challenging, as it requires tight synchronization among geographically distributed APs. In this paper, we address this issue by introducing a DSTBC approach that bypasses the need for AP phase synchronization. We first provide analytic bounds to the achievable spectral efficiency of CF-mMIMO with phase-unsynchronized APs. Then, we propose a DSTBC-based transmission scheme tailored to CF-mMIMO, which operates without CSI and phase synchronization among the APs. We derive a closed-form expression for the resulting SINR, enabling quantitative comparisons among different DSTBC schemes. Numerical simulations confirm that phase misalignments can significantly impair system performance. In contrast, the proposed DSTBC scheme can mitigate these effects, achieving performance comparable to that of fully synchronized systems. However, when more than two APs jointly serve a UE, the code rate of DSTBC schemes can limit their SE gains. Hence, we also investigate DQO-STBC schemes that achieve full code rate by relaxing the orthogonality constraints.
Read the full abstract ▾
Systems and Control
Optimal Rank-1 Directional State Transition Tensors
Grace E. Calkins, Jay W. McMahon and Jackson Kulik
An optimal rank-1 approximation of state transition tensors was developed as an efficient alternative to state transition tensors for nonlinear uncertainty quantification. While previous directional state transition tensors used the dominant right singular subspace of the state transition
matrix to construct a reduced-dimension representation of the state transition tensors, optimal directional state transition tensors are constructed to maximize the information retained in a rank-1 approximation of the state transition tensors in the Frobenius-norm sense. The optimal rank-1 directional state transition tensor is found by solving a tensor z-eigenpair problem of the "square" of the state transition tensor. This construct leads to increased approximation accuracy of the state transition tensors and improved Gaussian moment propagation for nonlinear flight scenarios like aerocapture.
Read the full abstract ▾
Signal Processing · Cross-listed from cs.LG
Learning Time-Varying Graph Signals via Koopman
Sivaram Krishnan, Jinho Choi and Jihong Park
A wide variety of real-world data, such as sea measurements, e.g., temperatures collected by distributed sensors and multiple unmanned aerial vehicles (
UAV) trajectories, can be naturally represented as graphs, often exhibiting non-Euclidean structures. These graph representations may evolve over time, forming time-varying graphs. Effectively modeling and analyzing such dynamic graph data is critical for tasks like predicting graph
evolution and reconstructing missing graph data. In this paper, we propose a framework based on the Koopman autoencoder (KAE) to handle time-varying graph data. Specifically, we assume the existence of a hidden non-linear dynamical system, where the state vector corresponds to the graph
embedding of the time-varying graph signals. To capture the evolving graph structures, the graph data is first converted into a vector time series through graph
embedding, representing the structural information in a finite-dimensional latent space. In this latent space, the KAE is applied to learn the underlying non-linear dynamics governing the temporal
evolution of graph features, enabling both prediction and reconstruction tasks.
Read the full abstract ▾
Audio and Speech Processing · Cross-listed from cs.SD
EchoMark: Perceptual Acoustic Environment Transfer with Watermark-Embedded Room Impulse Response
Chenpei Huang, Lingfeng Yao, Kyu In Lee et al. (+3 more)
Acoustic Environment Matching (AEM) is the task of transferring clean audio into a target acoustic environment, enabling engaging applications such as audio dubbing and auditory immersive virtual reality (VR). Recovering similar room impulse response (RIR) directly from reverberant
speech offers more accessible and flexible AEM solution. However, this capability also introduces vulnerabilities of arbitrary “relocation" if misused by malicious user, such as facilitating advanced voice spoofing attacks or undermining the authenticity of recorded evidence. To address this issue, we propose EchoMark, the first deep learning-based AEM framework that generates perceptually similar RIRs with embedded watermark. Our design tackle the challenges posed by variable RIR characteristics, such as different durations and energy decays, by operating in the latent domain. By jointly optimizing the model with a perceptual loss for RIR reconstruction and a loss for watermark detection, EchoMark achieves both high-quality environment transfer and reliable watermark recovery. Experiments on diverse
datasets validate that EchoMark achieves room acoustic parameter matching performance comparable to FiNS, the state-of-the-art RIR
estimator. Furthermore, a high Mean Opinion Score (MOS) of 4.22 out of 5, watermark detection accuracy exceeding 99%, and bit error rates (BER) below 0.3% collectively demonstrate the effectiveness of EchoMark in preserving perceptual quality while ensuring reliable watermark
embedding.
Read the full abstract ▾
All authors Chenpei Huang, Lingfeng Yao, Kyu In Lee, Lan Emily Zhang, Xun Chen, Miao Pan
Signal Processing
Distributed MIMO Positioning: Fundamental Limit Analysis and User Tracking Framework Design
Yingjie Xu, Xuesong Cai, Ali Al-Ameri et al. (+2 more)
This paper presents a comprehensive study on the 3D positioning capabilities in distributed multiple-input multiple-output (
MIMO) systems. Unlike previous studies that mainly rely on idealized isotropic
antenna models, we adopt a polarimetric model that takes advantage of effective aperture distribution functions to characterize realistic
antenna patterns, placements, and polarization effects. Based on this model, we analyze the fundamental limits of UE positioning using the Fisher information
matrix (FIM) and the position error bound (PEB). The FIM is shown to be expressed as a weighted sum of the information contributions from individual access point (AP)-UE pairs, with each contribution interpreted geometrically across distance, azimuth, and elevation dimensions. The impact of the UE tilt and the spatial distribution of APs on the PEBs is further analyzed. As a further advancement, we propose a complete positioning framework from a UE tracking perspective. By integrating a global probability hypothesis density filter and a PEB-aware AP management strategy, the framework enables accurate tracking while optimizing AP scheduling. Finally, we present a distributed
MIMO channel measurement campaign to validate the proposed framework. The results demonstrate a centimeter-level tracking accuracy. In addition, the PEB-aware AP management strategy is shown to maintain robust tracking performance while significantly reducing the number of concurrently active APs, thus lowering the overall system overhead.
Read the full abstract ▾
All authors Yingjie Xu, Xuesong Cai, Ali Al-Ameri, Sara Willhammar, Fredrik Tufvesson
Image and Video Processing
Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression
Amit Vaisman, Guy Ohayon, Hila Manor et al. (+2 more)
While zero-shot
diffusion-based compression methods have seen significant progress in recent years, they remain notoriously slow and computationally demanding. This paper presents an efficient zero-shot
diffusion-based compression method that runs substantially faster than existing methods, while maintaining performance that is on par with the state-of-the-art techniques. Our method builds upon the recently proposed Denoising
Diffusion Codebook Models (DDCMs) compression scheme. Specifically, DDCM compresses an image by sequentially choosing the
diffusion noise vectors from reproducible random codebooks, guiding the denoiser's output to reconstruct the target image. We modify this framework with Turbo-DDCM, which efficiently combines a large number of noise vectors at each denoising step, thereby significantly reducing the number of required denoising operations. This modification is also coupled with an improved encoding protocol. Furthermore, we introduce two flexible variants of Turbo-DDCM, a priority-aware variant that prioritizes user-specified regions and a distortion-controlled variant that compresses an image based on a target PSNR rather than a target BPP. Comprehensive experiments position Turbo-DDCM as a compelling, practical, and flexible image compression scheme.
Read the full abstract ▾
All authors Amit Vaisman, Guy Ohayon, Hila Manor, Michael Elad, Tomer Michaeli
Systems and Control
Sensor Importance towards Observability Degree via Shapley Values
Vishal Cholapadi Ravindra
Sensor selection is an often under-appreciated aspect of state
estimator or Kalman filter design. The basic minimum requirement for the choice of a sensor set while designing Kalman filters is that all states are observable. In addition, the sensors should be chosen with a view towards estimating the states with a desired accuracy. Often observability is treated as true/false check during filter design. Beyond observability – the observability degree – which measures how observable the states are, has been used as the metric of choice to for sensor selection or placement applications. The higher the degree of observability, the better the possibility of designing Kalman filters that achieve the desired state estimation accuracy and consistency requirements. When a wide variety of sensors are available, sometimes with cost and physical constraints involved, sensor selection plays a crucial role in filter design. In such situations it is important to know the expected contribution of each sensor towards observability degree. Shapley values, developed in cooperative game theory for fair allocation of the payout of a multi-player game to individual players, are widely used in machine learning to assess feature importance. This paper shows that Shapley values can indeed be leveraged to quantify the expected marginal contribution of each sensor in any given sensor set towards the observability degree. This quantification of the fair contribution of each sensor towards the observability degree can be leveraged by filter designers for sensor selection, placement and filter (state
estimator) design.
Read the full abstract ▾
Systems and Control
Dynamic Electric Vehicle Charging Pricing for Load Balancing in Power Distribution Networks based on Collaborative DDPG Agents
Leloko J. Lepolesa, Kayode E. Adetunji, Khmaies Ouahada et al. (+2 more)
The transition from the Internal Combustion Engine Vehicles (ICEVs) to the Electric Vehicles (EVs) is globally recommended to combat the unfavourable environmental conditions caused by reliance on fossil fuels. However, it has been established that the charging of EVs can destabilize the grid when they penetrate the
market in large numbers, especially in grids that were not initially built to handle the load from the charging of EVs. In this work, we present a dynamic EV charging pricing strategy that fulfills the following three objectives: distribution network-level load peak-shaving, valley-filling, and load balancing across distribution networks. Based on historical environmental variables such as temperature, humidity, wind speed, EV charging prices and distribution of vehicles in different areas in different times of the day, we first forecast the distribution network load demand, and then use deep reinforcement learning approach to set the optimal dynamic EV charging price. While most research seeks to achieve load peak-shaving and valley-filling to stabilize the grid, our work goes further into exploring the load-balancing between the distribution networks in the close vicinity to each other. We compare the performance of Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO)
algorithms for this purpose. The best
algorithm is used for dymamic EV pricing. Simulation results show an improved utilization of the grid at the distribution network level, leading to the optimal usage of the grid on a larger scale.
Read the full abstract ▾
All authors Leloko J. Lepolesa, Kayode E. Adetunji, Khmaies Ouahada, Zhenqing Liu, Ling Cheng
Signal Processing
UAV-Assisted Downlink Satellite Covert Communication
Hung D. Nguyen and Jeongseok Ha
This paper investigates the use of an unmanned aerial vehicle (
UAV) to assist covert communication between a low-Earth orbit (LEO) satellite and a ground user under the surveillance of a passive warden. The
UAV simultaneously serves its own ground network and acts as a friendly jammer to enhance the covertness of satellite transmissions. We derive a closed-form lower bound on the warden's average minimum detection error probability which is then used to define the covert constraint. Building on this, we formulate an optimization problem to jointly design the
UAV's 3D placement, its power allocation, and the satellite's transmit power to maximize the system's covert rate. To solve the resulting non-convex problem, we propose an
algorithm based on the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, and further develop a Dinkelbach's
algorithm for a special case. Numerical results validate the tightness of the derived bound and demonstrate the effectiveness of the proposed
algorithms in configuring optimal system parameters.
Read the full abstract ▾
Image and Video Processing
A Visual Perception-Based Tunable Framework and Evaluation Benchmark for H.265/HEVC ROI Encryption
Xiang Zhang, Geng Wu, Wenbin Huang et al. (+3 more)
ROI selective encryption, as an efficient
privacy protection technique, encrypts only the key regions in the
video, thereby ensuring security while minimizing the impact on coding efficiency. However, existing ROI-based
video encryption methods suffer from insufficient flexibility and lack of a unified evaluation system. To address these issues, we propose a visual perception-based tunable framework and evaluation
benchmark for H.265/HEVC ROI encryption. Our scheme introduces three key contributions: 1) A ROI region recognition module based on visual perception network is proposed to accurately identify the ROI region in
videos. 2) A three-level tunable encryption strategy is implemented while balancing security and real-time performance. 3) A unified ROI encryption evaluation
benchmark is developed to provide a standardized quantitative platform for subsequent research. This triple strategy provides new solution and significant unified performance evaluation methods for ROI selective encryption field. Experimental results indicate that the proposed
benchmark can comprehensively measure the performance of the ROI selective encryption. Compared to existing ROI encryption
algorithms, our proposed enhanced and advanced level encryption exhibit superior performance in multiple performance metrics. In general, the proposed framework effectively meets the
privacy protection requirements in H.265/HEVC and provides a reliable solution for secure and efficient processing of sensitive
video content.
Read the full abstract ▾
All authors Xiang Zhang, Geng Wu, Wenbin Huang, Daoyong Fu, Fei Peng, Zhangjie Fu
Systems and Control · Cross-listed from cs.RO
From Demonstrations to Safe Deployment: Path-Consistent Safety Filtering for Diffusion Policies
Ralf Römer, Julian Balletshofer, Jakob Thumm et al. (+3 more)
Diffusion policies (DPs) achieve state-of-the-art performance on complex manipulation tasks by learning from large-scale demonstration
datasets, often spanning multiple embodiments and environments. However, they cannot guarantee safe behavior, requiring external safety mechanisms. These, however, alter actions in ways unseen during training, causing unpredictable behavior and performance degradation. To address these problems, we propose path-consistent safety filtering (PACS) for DPs. Our approach performs path-consistent braking on a trajectory computed from the sequence of generated actions. In this way, we keep the execution consistent with the training distribution of the policy, maintaining the learned, task-completing behavior. To enable real-time deployment and handle uncertainties, we verify safety using set-based reachability analysis. Our experimental evaluation in simulation and on three challenging real-world human-
robot interaction tasks shows that PACS (a) provides formal safety guarantees in dynamic environments, (b) preserves task success rates, and (c) outperforms reactive safety approaches, such as control barrier functions, by up to 68 % in terms of task success.
Videos are available at our project website: https://tum-lsy.github.io/pacs.
Read the full abstract ▾
All authors Ralf Römer, Julian Balletshofer, Jakob Thumm, Marco Pavone, Angela P. Schoellig, Matthias Althoff
Signal Processing
Performance Bounds for Near-Field Velocity Estimation With Modular Linear Array
Khalid A. Alshumayri, Mudassir Masood and Ali. A. Nasir
Velocity estimation is a cornerstone of the recently introduced near-field predictive beamforming. This paper derives the Cramer-Rao bounds (CRBs) for joint radial and transverse velocity estimation within a predictive beamforming framework employing a modular linear array (MLA). We obtain closed-form expressions that characterize the interplay between array geometry and estimation accuracy, showing that increasing the inter-module separation enlarges the effective aperture and reduces the transverse-velocity CRB, while the radial-velocity CRB remains largely insensitive to this separation. Furthermore, we show that an MLA can achieve the same accuracy as a collocated ULA with fewer
antennas and quantify the relation between inter-module spacing and
antenna savings. The derived expressions are validated through simulations by comparing them with the mean-squared error (MSE) of the maximum likelihood
estimator (MLE) reported in the literature.
Read the full abstract ▾
Systems and Control · Cross-listed from physics.soc-ph
Public Transport Under Epidemic Conditions: Nonlinear Trade-Offs Between Risk and Accessibility
Gerhard Hiermann, Joana Ji, Ana Moreno et al. (+2 more)
Epidemics expose critical tensions between protecting public health and maintaining essential urban mobility. Public transport systems face this dilemma most acutely: they enable access to jobs, education, and services, yet also facilitate close contact among travelers. We develop an integrated modeling framework that couples
agent-based epidemic simulation (EpiSim) with an optimization-based public transport flow model under capacity constraints. Using Munich as a case study, we analyze how combinations of facility closures and transport restrictions shape epidemic outcomes and accessibility. The results reveal three key insights. First, epidemic interventions redistribute rather than simply reduce infection risks, shifting transmission to households. Second, epidemic and transport policies interact nonlinearly - moderate demand suppression can offset large capacity cuts. Third, epidemic pressures amplify temporal and spatial inequalities, disproportionately affecting peripheral and peak-hour travelers. These findings highlight that blanket restrictions are both inefficient and inequitable, calling for targeted, time- and space-differentiated measures to build epidemic-resilient and socially fair transport systems.
Read the full abstract ▾
All authors Gerhard Hiermann, Joana Ji, Ana Moreno, Rolf Moeckel, Maximilian Schiffer
Signal Processing
CSIT-Free Beamforming for Multi-Group Multicast in Overloaded mmWave Systems
Wonseok Choi, Jeongjae Lee and Songnam Hong
We study downlink multi-group multicast (MGM) transmission in overloaded millimeter-wave (mmWave) systems, where the number of users exceeds the number of transmit
antennas. We first show that, under realistic line-of-sight (LoS)-dominant user geometries, the conventional single-slot MGM scheme suffers from a fundamental collapse of the max-min
fairness degrees of freedom (MMF-DoF), regardless of beamforming optimization. Although this collapse can in principle be avoided via aggressive time-division scheduling, it requires excessive time sharing and results in severe throughput loss in overloaded regimes. To address this limitation, we propose a CSIT-free multi-group multicast framework (CF-MGM) that does not rely on instantaneous channel state information at the transmitter (CSIT) and is based on a deterministic multi-slot transmission structure. By exploiting structured precoding and receiver-side combining across multiple slots, the proposed framework eliminates inter-group interference by construction. We show that CF-MGM guarantees a strictly positive MMF-DoF in overloaded LoS mmWave systems, in sharp contrast to the DoF collapse of conventional single-slot MGM. Simulation results demonstrate that CF-MGM significantly outperforms state-of-the-art CSIT-based MGM schemes while substantially reducing signaling overhead.
Read the full abstract ▾
Systems and Control
Optical Network Digital Twin – Practical Use Cases and Architecture
Hideki Nishizawa, Toru Mano, Kazuya Anazawa et al. (+17 more)
With the widespread adoption of AI, machine-to-machine communications are rapidly increasing, reshaping the requirements for optical networks. Recent advances in Gaussian noise modeling for digital coherent transmission have raised expectations for digital-twin-based operation. However, unlike digital twins in wireless communication, which are already well established, significant barriers remain for commercialization in optical networks. This paper discusses the evolving requirements of optical networks in the AI era and proposes a practical Optical Network Digital Twin architecture enabling dynamic and Quality of Transmission aware operation beyond conventional management. Representative use cases, including operator-driven optimization, user-operator collaboration, and multi-operator interconnection, are presented, along with the architectural framework and key challenges toward practical deployment.
Read the full abstract ▾
All authors Hideki Nishizawa, Toru Mano, Kazuya Anazawa, Tatsuya Matsumura, Takeo Sasai, Masatoshi Namiki, Dmitrii Briantcev, Renato Ambrosone, Esther Le Rouzic, Stefan Melin, Oscar Gonzalez-de-Dios, Juan Pedro Fernandez-Palacios, Xiaocheng Zhang, Keigo Akahoshi, Gert Grammel, Andrea D'Amico, Giacomo Borraccini, Marco Ruffini, Daniel Kilper, Vittorio Curri
Systems and Control · Cross-listed from cs.LG
Privacy-Preserving Federated Learning for Fair and Efficient Urban Traffic Optimization
Rathin Chandra Shit and Sharmila Subudhi
The optimization of urban traffic is threatened by the complexity of achieving a balance between transport efficiency and the maintenance of
privacy, as well as the equitable distribution of traffic based on socioeconomically diverse neighborhoods. Current centralized traffic management schemes invade user location
privacy and further entrench traffic disparity by offering disadvantaged route suggestions, whereas current
federated learning frameworks do not consider
fairness constraints in multi-objective traffic settings. This study presents a
privacy-preserving
federated learning framework, termed FedFair-Traffic, that jointly and simultaneously optimizes travel efficiency, traffic
fairness, and differential
privacy protection. This is the first attempt to integrate three conflicting objectives to improve urban transportation systems. The proposed methodology enables collaborative learning between related vehicles with data locality by integrating Graph Neural Networks with differential
privacy mechanisms ($\epsilon$-
privacy guarantees) and Gini coefficient-based fair constraints using multi-objective optimization. The framework uses
federated aggregation methods of gradient clipping and noise injection to provide differential
privacy and optimize Pareto-efficient solutions for the efficiency-
fairness tradeoff. Real-world comprehensive experiments on the METR-LA traffic
dataset showed that FedFair-Traffic can reduce the average travel time by 7% (14.2 minutes) compared with their centralized baselines, promote traffic
fairness by 73% (Gini coefficient, 0.78), and offer high
privacy protection (
privacy score, 0.8) with an 89% reduction in communication overhead. These outcomes demonstrate that FedFair-Traffic is a scalable
privacy-aware smart city infrastructure with possible use-cases in metropolitan traffic flow control and
federated transportation networks.
Read the full abstract ▾
Signal Processing
Stackelberg Game-Driven Defense for ISAC Against Channel Attacks in Low-Altitude Networks
Jiacheng Wang, Changyuan Zhao, Dusit Niyato et al. (+4 more)
The increasing saturation of terrestrial resources has driven economic activities into low-altitude airspace. These activities, such as air taxis, rely on low-altitude wireless networks, and one key enabling technology is integrated sensing and communication (
ISAC). However, in low-altitude airspace,
ISAC is vulnerable to channel-access attacks, thereby degrading performance and threatening safety. To address this, we propose a defense framework based on a Stackelberg game. Specifically, we first model the system under attack, deriving metrics for the communication and the sensing to quantify performance. Then, we formulate the interaction as a three-player game where a malicious attacker acts as the leader, while the legitimate drone and ground base station act as followers. Using a backward induction
algorithm, we obtain the Stackelberg
equilibrium, allowing the defenders to dynamically adjust their strategies to mitigate the attack. Simulation results verify that the proposed
algorithm converges to a stable solution and outperforms existing baselines, ensuring reliable
ISAC performance for critical low-altitude applications.
Read the full abstract ▾
All authors Jiacheng Wang, Changyuan Zhao, Dusit Niyato, Geng Sun, Weijie Yuan, Abbas Jamalipour, Tao Xiang
Systems and Control · Cross-listed from cs.LG
Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
Nikolaus Vertovec, Frederik Baymler Mathiesen, Thom Badings et al. (+2 more)
Control barrier functions (CBFs) are a popular tool for safety certification of nonlinear dynamical control systems. Recently, CBFs represented as neural networks have shown great promise due to their expressiveness and applicability to a broad class of dynamics and safety constraints. However, verifying that a trained neural network is indeed a valid CBF is a computational bottleneck that limits the size of the networks that can be used. To overcome this limitation, we present a novel framework for verifying neural CBFs based on piecewise linear upper and lower bounds on the conditions required for a neural network to be a CBF. Our approach is rooted in linear bound propagation (LBP) for neural networks, which we extend to compute bounds on the gradients of the network. Combined with McCormick relaxation, we derive linear upper and lower bounds on the CBF conditions, thereby eliminating the need for computationally expensive verification procedures. Our approach applies to arbitrary control-affine systems and a broad range of nonlinear activation functions. To reduce conservatism, we develop a parallelizable refinement strategy that adaptively refines the regions over which these bounds are computed. Our approach scales to larger neural networks than state-of-the-art verification procedures for CBFs, as demonstrated by our numerical experiments.
Read the full abstract ▾
All authors Nikolaus Vertovec, Frederik Baymler Mathiesen, Thom Badings, Luca Laurenti, Alessandro Abate