Robust Fault Estimation Based on a Learning Observer for Linear Continuous-Time Systems with State Time-Varying Delay

Symmetry · 2026-03-11

This study addresses the problem of robust actuator fault estimation for a class of critical linear continuous-time systems subject to state time-varying delays, external disturbances, and actuator faults. A learning observer is proposed to achieve the challenging task of simultaneously estimating both the system states and actuator faults, irrespective of whether the faults are constant or time-varying. A key theoretical contribution is the derivation of a less conservative delay-dependent condition for the existence of the proposed learning observer, which is expressed in terms of linear matrix inequalities (LMIs). The H∞ performance index is employed to attenuate the effects of disturbances to a prescribed level. The efficacy of the proposed strategy is rigorously validated through three illustrative examples, including quantitative performance metrics and a comparative analysis with existing methods.

Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using Hölder Divergence and Mutual Information-Enhanced Knowledge Transfer

IEEE/CAA Journal of Automatica Sinica · 2026-04-01

Multimodal MRI (magnetic resonance imaging) provides critical complementary information for accurate brain tumor segmentation. However, conventional methods struggle when certain modalities are missing due to issues like image quality, protocol inconsistencies, patient allergies, or financial constraints. To address this, we propose a robust single-modality parallel processing framework that achieves high segmentation accuracy even with incomplete modalities. Leveraging Hölder divergence and mutual information, our model maintains modality-specific features while dynamically adjusting network parameters based on available inputs. By using these divergence and information-based loss functions, the framework effectively quantifies discrepancies between predictions and ground-truth labels, resulting in consistently accurate segmentation. Extensive evaluations on the BraTS 2018 and BraTS 2020 datasets demonstrate superior performance over existing methods in handling missing modalities, with ablation studies validating each component's contribution to the framework.

Impact of Insufficient CP on Sensing Performance in OFDM-ISAC Systems

ArXiv.org · 2025-05-02

Orthogonal frequency-division multiplexing (OFDM) is widely considered a leading waveform candidate for integrated sensing and communication (ISAC) in 6G networks. However, the cyclic prefix (CP) used to mitigate multipath effects in communication systems also limits the maximum sensing range. Target echoes arriving beyond the CP length cause inter-symbol interference (ISI) and inter-carrier interference (ICI), which degrade the mainlobe level and raise sidelobe levels in the range-Doppler map (RDM). This paper presents a unified analytical framework to characterize the ISI and ICI caused by an insufficient CP length in multi-target scenarios. For the first time, we derive closed-form expressions for the second-order moments of the RDM under both matched filtering (MF) and reciprocal filtering (RF) processing with insufficient CP length. These expressions quantify the effects of CP length, symbol constellation, and inter-target interference (ITI) on the mainlobe and sidelobe levels. Based on these results, we further derive explicit formulas for the peak sidelobe level ratio (PSLR) and integrated sidelobe level ratio (ISLR) of the RDM, revealing a fundamental trade-off between noise amplification in RF and ITI in MF. Numerical results validate our theoretical derivations and illustrate the critical impact of insufficient CP length on sensing performance in OFDM-ISAC systems.

Spatiotemporal Consistency: A Universal Defense Against Attacks on Autonomous Systems

IEEE Security & Privacy · 2025-11-01

This article examines spatiotemporal consistency-based defenses, showcasing detectable spatiotemporal and contextual anomalies. We explore the real-world applications, current limitations, and future research directions of these spatiotemporal consistency-based defenses, emphasizing their transformative potential for securing autonomous systems across diverse domains.

Efficient Video to Audio Mapper with Visual Scene Detection

2025-10-22

Video-to-audio (V2A) generation aims to produce corresponding audio given silent video inputs. This task is particularly challenging due to the cross-modality and sequential nature of the audio-visual features involved. Recent works have made significant progress in bridging the domain gap between video and audio, generating audio that is semantically aligned with the video content. However, a critical limitation of these approaches is their inability to effectively recognize and handle multiple scenes within a video, often leading to suboptimal audio generation in such cases. In this paper, we first reimplement a state-of-the-art V2A model with a slightly modified light-weight architecture, outperforming the baseline. We then propose an improved V2A model that incorporates a scene detector to address the challenge of switching between multiple visual scenes. Results on VGGSound show that our model can recognize and handle multiple scenes within a video and achieve superior performance against the baseline for both fidelity and relevance. The demo samples and codes are available at https://1mageyi.github.io/V2A-SceneDetector.demo/.

CP-FREEZER: Latency Attacks against Vehicular Cooperative Perception

ArXiv.org · 2025-08-01

Cooperative perception (CP) enhances situational awareness of connected and autonomous vehicles by exchanging and combining messages from multiple agents. While prior work has explored adversarial integrity attacks that degrade perceptual accuracy, little is known about CP's robustness against attacks on timeliness (or availability), a safety-critical requirement for autonomous driving. In this paper, we present CP-FREEZER, the first latency attack that maximizes the computation delay of CP algorithms by injecting adversarial perturbation via V2V messages. Our attack resolves several unique challenges, including the non-differentiability of point cloud preprocessing, asynchronous knowledge of the victim's input due to transmission delays, and uses a novel loss function that effectively maximizes the execution time of the CP pipeline. Extensive experiments show that CP-FREEZER increases end-to-end CP latency by over $90\times$, pushing per-frame processing time beyond 3 seconds with a 100% success rate on our real-world vehicle testbed. Our findings reveal a critical threat to the availability of CP systems, highlighting the urgent need for robust defenses.

Investigating Physical Latency Attacks Against Camera-Based Perception

2025-05-12

Camera-based perception is a central component to the visual perception of autonomous systems. Recent works have investigated latency attacks against perception pipelines, which can lead to a Denial-of-Service against the autonomous system. Unfortunately, these attacks lack real-world applicability, either relying on digital perturbations or requiring large, unscalable, and highly visible patches that cover up the victim's view. In this paper, we propose Detstorm, a novel physically realizable latency attack against camera-based perception. Detstorm uses projector perturbations to cause delays in perception by creating a large number of adversarial objects. These objects are optimized on four objectives to evade filtering by multiple Non-Maximum Suppression (NMS) approaches. To maximize the number of created objects in a dynamic physical environment, Detstorm takes a unique greedy approach, segmenting the environment into “zones” containing distinct object classes and maximizing the number of created objects per zone. Detstorm adapts to changes in the environment in real time, recombining perturbation patterns via our zone stitching process into a contiguous, physically projectable image. Evaluations in both simulated and real-world experiments show that Detstorm causes a 506% increase in detected objects on average, delaying perception results by up to 8.1 seconds, and capable of causing physical consequences on real-world autonomous driving systems.

Semantic Segmentation for Vision and Intelligence

SSRN Electronic Journal · 2025-01-01

Empowering Robotic Systems with Integrated Sensing and Communications in the 6G Era

2025-02-11

Robots are expected to influence various sectors, including healthcare and manufacturing. Robotic systems, however, demand unique technical capabilities, such as high sensing accuracy, ultra-low transmission latency, and cooperative decisionmaking. Indeed, recent advances in integrated sensing and communication (ISAC), combined with 6G technology, hold significant potential to address these specific requirements. Despite its immense potential, integrating 6G with robotics under ISAC presents several challenges. This work explores how ISAC and 6G can transform robotics, examining the specific technical demands of these systems and demonstrating how these can be effectively addressed. A collaborative setup has also been presented where ISAC helps robots work together more efficiently, demonstrating its task efficiency. Furthermore, this work discusses challenges, ongoing efforts to standardize these technologies, and future opportunities for research.

Cordial: Cross-row Failure Prediction Method Based on Bank-level Error Locality for HBMs

2025-06-23

High Bandwidth Memory (HBM) is a promising solution for overcoming memory bottlenecks in high-performance computing, but it remains susceptible to memory errors. Our empirical study on high-performance computing platforms that serve large AI model training with over 80,000 HBMs, reveals that HBM errors have a high burst rate. As a result, conventional failure prediction methods that heavily rely on historical error data are ineffective. Through investigating the locality of uncorrectable errors (UCEs) of HBMs, we found that failure patterns at the bank level primarily exhibit aggregation tendencies, suggesting that errors in neighboring rows are often related. Based on these insights, we propose Cordial, a cross-row failure prediction method based on bank-level error locality. Cordial adopts a hierarchical approach: it first utilizes bank-level error information to predict the bank-level failure pattern. Our approach classifies bank-level failure patterns into three categories: double-row clustering, single-row clustering (these two are aggregation patterns), and scattered patterns. Then, it leverages spatial locality to guide cross-row predictions for aggregation patterns. Evaluation results show our method improves the F1-score by up to 90.7% and enhances the isolation coverage rate by 47.1%, demonstrating its practical applicability.