Papers

Tightly integrated low earth orbit (LEO) satellite communications and terrestrial integrated sensing and communication (ISAC) are expected to be key novel aspects of the 6G era. Spectrum sharing between satellite and terrestrial cellular networks may, however, cause severe interference. This paper introduces a cooperation framework for integrated coexistence between satellite and terrestrial networks where the terrestrial network also deploys multistatic ISAC. Unlike prior works that assume ideal channel state information (CSI) acquisition, the proposed approach develops a practical structure consisting of pre-optimization and refinement stages that leverages the predictability of satellite CSI. In addition, a co-design of terrestrial beamforming and satellite power allocation utilizing a weighted minimum mean-squared error algorithm is proposed, and a target-radar association method designed for multistatic ISAC is presented. Simulation results show that the proposed approach significantly enhances the performance of these integrated networks. Furthermore, it is confirmed that the overall performance approaches the interference-free benchmark as the number of spot beams and radar receivers increases, demonstrating the feasibility of spectral coexistence between the two networks.

Complex spatial connectivity patterns, such as interictal suppression and ictal propagation, complicate accurate drug-resistant epilepsy (DRE) seizure detection using stereotactic electroencephalography (SEEG) and traditional machine learning methods. Two critical challenges remain:(1)a low signal-to-noise ratio in functional connectivity estimates, making it difficult to learn seizure-related interactions; and (2)expert labels for spatial pathological connectivity patterns are difficult to obtain, meanwhile lacking the patterns' representation to improve seizure detection. To address these issues, we propose a novel node-graph dual contrastive learning framework, Seizure-NGCLNet, to learn SEEG interictal suppression and ictal propagation patterns for detecting DRE seizures with high precision. First, an adaptive graph augmentation strategy guided by centrality metrics is developed to generate seizure-related brain networks. Second, a dual-contrastive learning approach is integrated, combining global graph-level contrast with local node-graph contrast, to encode both spatial structural and semantic epileptogenic features. Third, the pretrained embeddings are fine-tuned via a top-k localized graph attention network to perform the final classification. Extensive experiments on a large-scale public SEEG dataset from 33 DRE patients demonstrate that Seizure-NGCLNet achieves state-of-the-art performance, with an average accuracy of 95.93%, sensitivity of 96.25%, and specificity of 94.12%. Visualizations confirm that the learned embeddings clearly separate ictal from interictal states, reflecting suppression and propagation patterns that correspond to the clinical mechanisms. These results highlight Seizure-NGCLNet's ability to learn interpretable spatial pathological patterns, enhancing both seizure detection and seizure onset zone localization.

The independent control of multiple magnetic microrobots under a shared global signal presents critical challenges in biomedical applications such as targeted drug delivery and microsurgeries. Most existing systems only allow all agents to move synchronously, limiting their use in applications that require differentiated actuation. This research aims to design a controller capable of regulating the radial distance between micro-agents using only the angle \psi of a global magnetic field as the actuation parameter, demonstrating potential for practical applications. The proposed cascade control approach enables faster and more precise adjustment of the inter-agent distance than a proportional controller, while maintaining smooth transitions and avoiding abrupt changes in the orientation of the magnetic field, making it suitable for real-world implementation. A bibliographic review was conducted to develop the physical model, considering magnetic dipole-dipole interactions and velocities in viscous media. A PID controller was implemented to regulate the radial distance, followed by a PD controller in cascade to smooth changes in field orientation. These controllers were simulated in MATLAB, showing that the PID controller reduced convergence time to the desired radius by about 40%. When adding the second controller, the combined PID+PD scheme achieved smooth angular trajectories within similar timeframes, with fluctuations of only \pm 5^\circ. These results validate the feasibility of controlling the radial distance of two microrobots using a shared magnetic field in a fast and precise manner, without abrupt variations in the control angle. However, the model is limited to a 2D environment and two agents, suggesting future research to extend the controller to 3D systems and multiple agents.

The Multi-Traveling Salesman Problem (MTSP) is a commonly used mathematical model for multi-agent task allocation. However, as the number of agents and task targets increases, existing optimization-based methods often incur prohibitive computational costs, posing significant challenges to large-scale coordination in unmanned systems. To address this issue, this paper proposes a K-means-inspired task allocation framework that reformulates the MTSP as a spatially constrained classification process. By leveraging spatial coherence, the proposed method enables fast estimation of path costs and efficient task grouping, thereby fundamentally reducing overall computational complexity. Extensive simulation results demonstrate that the framework can maintain high solution quality even in extremely large-scale scenarios-for instance, in tasks involving 1000 agents and 5000 targets. The findings indicate that this "cluster-then-route" decomposition strategy offers an efficient and reliable solution for large-scale multi-agent task allocation.

This paper presents a closed-loop framework for conflict-free routing and scheduling of multi-aircraft in Terminal Manoeuvring Areas (TMA), aimed at reducing congestion and enhancing landing efficiency. Leveraging data-driven arrival inputs (either historical or predicted), we formulate a mixed-integer optimization model for real-time control, incorporating an extended TMA network spanning a 50-nautical-mile radius around Changi Airport. The model enforces safety separation, speed adjustments, and holding time constraints while maximizing runway throughput. A rolling-horizon Model Predictive Control (MPC) strategy enables closed-loop integration with a traffic simulator, dynamically updating commands based on real-time system states and predictions. Computational efficiency is validated across diverse traffic scenarios, demonstrating a 7-fold reduction in computation time during peak congestion compared to onetime optimization, using Singapore ADS-B dataset. Monte Carlo simulations under travel time disturbances further confirm the framework's robustness. Results highlight the approach's operational resilience and computational scalability, offering actionable decision support for Air Traffic Controller Officers (ATCOs) through real-time optimization and adaptive replanning.

The paper addresses a continuous-time continuous-space chance-constrained stochastic optimal control (SOC) problem where the probability of failure to satisfy given state constraints is explicitly bounded. We leverage the notion of exit time from continuous-time stochastic calculus to formulate a chance-constrained SOC problem. Without any conservative approximation, the chance constraint is transformed into an expectation of an indicator function which can be incorporated into the cost function by considering a dual formulation. We then express the dual function in terms of the solution to a Hamilton-Jacobi-Bellman partial differential equation parameterized by the dual variable. Under a certain assumption on the system dynamics and cost function, it is shown that a strong duality holds between the primal chance-constrained problem and its dual. The Path integral approach is utilized to numerically solve the dual problem via gradient ascent using open-loop samples of system trajectories. We present simulation studies on chance-constrained motion planning for spatial navigation of mobile robots and the solution of the path integral approach is compared with that of the finite difference method.

This paper proposes a cyber-resilient secure control framework for autonomous vehicles (AVs) subject to false data injection (FDI) threats as actuator attacks. The framework integrates data-driven modeling, event-triggered communication, and fractional-order sliding mode control (FSMC) to enhance the resilience against adversarial interventions. A dynamic model decomposition (DMD)-based methodology is employed to extract the lateral dynamics from real-world data, eliminating the reliance on conventional mechanistic modeling. To optimize communication efficiency, an event-triggered transmission scheme is designed to reduce the redundant transmissions while ensuring system stability. Furthermore, an extended state observer (ESO) is developed for real-time estimation and mitigation of actuator attack effects. Theoretical stability analysis, conducted using Lyapunov methods and linear matrix inequality (LMI) formulations, guarantees exponential error convergence. Extensive simulations validate the proposed event-triggered secure control framework, demonstrating substantial improvements in attack mitigation, communication efficiency, and lateral tracking performance. The results show that the framework effectively counteracts actuator attacks while optimizing communication-resource utilization, making it highly suitable for safety-critical AV applications.

An advanced emotion classification model was developed using a CNN-Transformer architecture for emotion recognition from EEG brain wave signals, effectively distinguishing among three emotional states, positive, neutral and negative. The model achieved a testing accuracy of 91%, outperforming traditional models such as SVM, DNN, and Logistic Regression. Training was conducted on a custom dataset created by merging data from SEED, SEED-FRA, and SEED-GER repositories, comprising 1,455 samples with EEG recordings labeled according to emotional states. The combined dataset represents one of the largest and most culturally diverse collections available. Additionally, the model allows for the reduction of the requirements of the EEG apparatus, by leveraging only 5 electrodes of the 62. This reduction demonstrates the feasibility of deploying a more affordable consumer-grade EEG headset, thereby enabling accessible, at-home use, while also requiring less computational power. This advancement sets the groundwork for future exploration into mood changes induced by media content consumption, an area that remains underresearched. Integration into medical, wellness, and home-health platforms could enable continuous, passive emotional monitoring, particularly beneficial in clinical or caregiving settings where traditional behavioral cues, such as facial expressions or vocal tone, are diminished, restricted, or difficult to interpret, thus potentially transforming mental health diagnostics and interventions...

This paper addresses the offset-free tracking problem for nonlinear systems described by a class of recurrent neural networks (RNNs). To compensate for constant disturbances and guarantee offset-free tracking in the presence of model-plant mismatches, we propose a novel reformulation of the RNN model in velocity form. Conditions based on linear matrix inequalities are then derived for the design of a nonlinear state observer and a nonlinear state-feedback controller, ensuring global or regional closed-loop stability of the origin of the velocity form dynamics. Moreover, to handle input and output constraints, a theoretically sound offset-free nonlinear model predictive control algorithm is developed. The algorithm exploits the velocity form model as the prediction model and the static controller as an auxiliary law for the definition of the terminal ingredients. Simulations on a pH-neutralisation process benchmark demonstrate the effectiveness of the proposed approach.

Online conformal prediction (OCP) wraps around any pre-trained predictor to produce prediction sets with coverage guarantees that hold irrespective of temporal dependencies or distribution shifts. However, standard OCP faces two key limitations: it operates in the output space using simple nonconformity (NC) scores, and it treats all historical observations uniformly when estimating quantiles. This paper introduces attention-based feature OCP (AFOCP), which addresses both limitations through two key innovations. First, AFOCP operates in the feature space of pre-trained neural networks, leveraging learned representations to construct more compact prediction sets by concentrating on task-relevant information while suppressing nuisance variation. Second, AFOCP incorporates an attention mechanism that adaptively weights historical observations based on their relevance to the current test point, effectively handling non-stationarity and distribution shifts. We provide theoretical guarantees showing that AFOCP maintains long-term coverage while provably achieving smaller prediction intervals than standard OCP under mild regularity conditions. Extensive experiments on synthetic and real-world time series datasets demonstrate that AFOCP consistently reduces the size of prediction intervals by as much as $88\%$ as compared to OCP, while maintaining target coverage levels, validating the benefits of both feature-space calibration and attention-based adaptive weighting.

This paper describes a new approach for using changepoint detection (CPD) to estimate the starting and stopping times of a forced oscillation (FO) in measured power system data. As with a previous application of CPD to this problem, the pruned exact linear time (PELT) algorithm is used. However, instead of allowing PELT to automatically tune its penalty parameter, a method of manually providing it is presented that dramatically reduces computation time without sacrificing accuracy. Additionally, the new algorithm requires fewer input parameters and provides a formal, data-driven approach to setting the minimum FO segment length to consider as troublesome for an electromechanical mode meter. A low-order ARMAX representation of the minniWECC model is used to test the approach, where a 98% reduction in computation time is enjoyed with high estimation accuracy.

The Segment Anything Model 2 (SAM2) demonstrates remarkable universal segmentation capabilities on natural images. However, its performance on ultrasound images is significantly degraded due to domain disparities. This limitation raises two critical challenges: how to efficiently adapt SAM2 to ultrasound imaging while maintaining parameter efficiency, and how to deploy the adapted model effectively in resource-constrained clinical environments. To address these issues, we propose UniUltra for universal ultrasound segmentation. Specifically, we first introduce a novel context-edge hybrid adapter (CH-Adapter) that enhances fine-grained perception across diverse ultrasound imaging modalities while achieving parameter-efficient fine-tuning. To further improve clinical applicability, we develop a deep-supervised knowledge distillation (DSKD) technique that transfers knowledge from the large image encoder of the fine-tuned SAM2 to a super lightweight encoder, substantially reducing computational requirements without compromising performance. Extensive experiments demonstrate that UniUltra outperforms state-of-the-arts with superior generalization capabilities. Notably, our framework achieves competitive performance using only 8.91% of SAM2's parameters during fine-tuning, and the final compressed model reduces the parameter count by 94.08% compared to the original SAM2, making it highly suitable for practical clinical deployment. The source code is available at https://github.com/xq141839/UniUltra.

This paper addresses the challenges in short-time Fourier transform (STFT) domain subband adaptive filtering, in particular, subband system identification. Previous studies in this area have primarily focused on setups with subband filtering at a downsampled rate, implemented using the weighted overlap-add (WOLA) filter bank, popular in audio and speech-processing for its reduced complexity. However, this traditional approach imposes constraints on the subband filters when transformed to their full-rate representation. This paper makes three key contributions. First, it introduces a generalized WOLA filter bank that repositions subband filters before the downsampling operation, eliminating the constraints on subband filters inherent in the conventional WOLA filter bank. Second, it investigates the mean square error (MSE) performance of the generalized WOLA filter bank for full-band system identification, establishing analytical ties between the order of subband filters, the full-band system impulse response length, the decimation factor, and the prototype filters. Third, to address the increased computational complexity of the generalized WOLA, the paper proposes a low-complexity implementation termed per-tone weighted overlap-add (PT-WOLA), which maintains computational complexity on par with conventional WOLA. Analytical and empirical evidence demonstrates that the proposed generalized WOLA filter bank significantly enhances the performance of subband system identification.

In recent years, the Transformer architecture has achieved outstanding performance across a wide range of tasks and modalities. Token is the unified input and output representation in Transformer-based models, which has become a fundamental information unit. In this work, we consider the problem of token communication, studying how to transmit tokens efficiently and reliably. Point cloud, a prevailing three-dimensional format which exhibits a more complex spatial structure compared to image or video, is chosen to be the information source. We utilize the set abstraction method to obtain point tokens. Subsequently, to get a more informative and transmission-friendly representation based on tokens, we propose a joint semantic-channel and modulation (JSCCM) scheme for the token encoder, mapping point tokens to standard digital constellation points (modulated tokens). Specifically, the JSCCM consists of two parallel Point Transformer-based encoders and a differential modulator which combines the Gumel-softmax and soft quantization methods. Besides, the rate allocator and channel adapter are developed, facilitating adaptive generation of high-quality modulated tokens conditioned on both semantic information and channel conditions. Extensive simulations demonstrate that the proposed method outperforms both joint semantic-channel coding and traditional separate coding, achieving over 1dB gain in reconstruction and more than 6x compression ratio in modulated symbols.

The rapid growth of large data center loads and inverter-based generation is increasing the stress on transmission networks, while expanding grid capacity at the required pace remains challenging. Power flow controllers (PFCs) that adjust effective line reactances to redistribute flows are often viewed as an interim solution to improve transmission network utilization. Traditional flexibility metrics and analysis approaches for PFCs focus on a limited number of operating points and contingencies. Towards gaining system-wide insights, this paper introduces a framework to quantify network flow controllability- the extent to which line flows can be reshaped through reactance adjustments. We derive analytical results demonstrating that installing PFCs on all lines enables complete controllability of feasible flow patterns. Building on these, we conduct empirical studies on the IEEE 39-bus system to examine how controllability varies with the number of PFCs and their reactance adjustment range. These analyses employ a mixed-integer linear program to optimize the siting and sizing of PFCs. Finally, we validate findings under AC power flow physics using an optimization routine that steers flows toward desired setpoints.

Data-driven methods for electrocardiogram (ECG) interpretation are rapidly progressing. Large datasets have enabled advances in artificial intelligence (AI) based ECG analysis, yet limitations in annotation quality, size, and scope remain major challenges. Here we present CODE-II, a large-scale real-world dataset of 2,735,269 12-lead ECGs from 2,093,807 adult patients collected by the Telehealth Network of Minas Gerais (TNMG), Brazil. Each exam was annotated using standardized diagnostic criteria and reviewed by cardiologists. A defining feature of CODE-II is a set of 66 clinically meaningful diagnostic classes, developed with cardiologist input and routinely used in telehealth practice. We additionally provide an open available subset: CODE-II-open, a public subset of 15,000 patients, and the CODE-II-test, a non-overlapping set of 8,475 exams reviewed by multiple cardiologists for blinded evaluation. A neural network pre-trained on CODE-II achieved superior transfer performance on external benchmarks (PTB-XL and CPSC 2018) and outperformed alternatives trained on larger datasets.

Asymptotic stability in economic receding horizon control can be obtained under a strict dissipativity assumption, related to positive-definiteness of a so-called rotated cost, and through the use of suitable terminal cost and constraints. In the linear-quadratic case a common assumption is that the rotated cost is positive definite. The positive semi-definite case has received surprisingly little attention, and the connection to the standard dissipativity assumption has not been investigated. In this paper, we fill this gap by connecting existing results in economic model predictive control with the stability results for the semi-definite case, the properties of the constrained generalized discrete algebraic Riccati equation, and of two optimal control problems. Moreover, we extend recent results relating exponential stability to the choice of terminal cost in the absence of terminal constraints.

We develop a GPU-accelerated dynamic programming (DP) method for valuing, operating, and bidding energy storage under multistage stochastic electricity prices. Motivated by computational limitations in existing models, we formulate DP backward induction entirely in tensor-based algebraic operations that map naturally onto massively parallel GPU hardware. Our method accommodates general, potentially non-concave payoff structures, by combining a discretized DP formulation with a convexification procedure that produces market-feasible, monotonic price-quantity bid curves. Numerical experiments using ISO-NE real-time prices demonstrate up to a 100x speedup by the proposed GPU-based DP method relative to CPU computation, and an 8,000x speedup compared to a commercial MILP solver, while retaining sub-0.3% optimality gaps compared to exact benchmarks.

Security-Constrained DC Optimal Power Flow (SC DCOPF) is an important tool for transmission system operators, enabling economically efficient and physically secure dispatch decisions. Although CPU-based commercial solvers (e.g., Gurobi) can efficiently solve SC-DCOPF problems with a reasonable number of security constraints, their performance degrades rapidly as both system size and the number of contingencies grow into thousands. In this paper, we design a computational graph representation of the SC-DCOPF-based market-clearing problem, inspired by the third ARPA-E Grid Optimization Competition. Using a tool from the neural network verification community known as Interval Bound Propagation (IBP), we quickly compute bounds on the optimal objective across the full set of N-1 contingencies. Our results demonstrate that IBP can compute certified bounds with mean optimal solution gaps below 3.98% on small cases, and it can efficiently scale up to 8,316 bus systems with thousands of contingencies.

A Cartan-geometric, jet bundle formulation of curvature-aware variance bounds in parametric statistical estimation is developed. Building on our earlier extrinsic Hilbert space approach to the Cramér-Rao and Bhattacharyya-type inequalities, we show that the curvature corrections induced by the square root embedding of a statistical model admit a canonical intrinsic interpretation via jet geometry and Cartan's prolongation theory. For a scalar-parameter family with square root map $s_\theta=\sqrt{f(\cdot;\theta)}\in L^2(\mu)$, we regard $s_\theta$ as a section of the statistical bundle $E=\Theta\times L^2(\mu)$ and study its finite-order prolongations. We point out that the classical algebraic efficiency condition–that the estimator residual $(T-\theta)s_\theta$ lies in the span of derivatives of $s_\theta$ up to order $m$–is equivalent to the existence of a linear ordinary differential equation (ODE) of order $m$ satisfied by the square root map. Geometrically, this means the prolonged section lies in an ODE-defined submanifold of the jet bundle and is an integral curve of the restricted Cartan vector field. The obstruction to such finite-order integrability is identified with the vertical component of the canonical Ehresmann connection on the jet tower, which coincides with the curvature correction term in variance bounds. This establishes a direct correspondence between algebraic projection conditions in $L^2(\mu)$ and intrinsic holonomy properties of statistical sections, yielding a unified geometric interpretation of higher-order information inequalities.

Excitable neuromorphic circuits are physical models of event behaviors: their continuous-time trajectories consist of sequences of discrete events. This paper explores the possibility of extracting a discrete-event model out of the physical continuous-time model. We discuss the potential of this methodology for analysis and design of neuromorphic control systems.

Event-based Sensing (EBS) hardware is quickly proliferating while finding foothold in many commercial, industrial, and defense applications. At present, there are a handful of technologically mature systems which produce data streams with diverse output formats. In the near future it is anticipated there will be vendors who offer new sensor hardware which could also yield unique data schema that are not aligned to past efforts. Thus, due to the relative nascent nature of the technology and its potential for widespread use in a variety of applications, it is an opportune time to define a standard for this class of sensors' output data. The intent of this document is to identify and provide a standard for the collected EBS streaming data. The main objective of the standard is to be sensor agnostic, incorporate some of the current sensor configurations and modalities, and account for the developing configurations and modalities. The intent is also to leave enough place holders and space in the standard for future variations that may develop as EBS technology matures.

In this paper, we propose an efficient transmission scheme for autonomous cooperative Orthogonal Frequency Division Multiplexing (OFDM) based multi-hop Unmanned Aerial Vehicle (UAV) relay networks. These systems often suffer from destructive interference at the destination node due to uncoordinated transmissions of common packets by cooperating UAVs. To address this issue, we introduce the concept of virtual transmit channels at each UAV, implemented using truncated all-pass filters (APFs). This approach ensures that all subcarriers benefit from comparable transmit powers, guaranteeing excellent performance when a single UAV is transmitting. In scenarios where multiple UAVs cooperate without coordination, the inherent randomness of the generated virtual channels facilitates cooperative diversity, effectively mitigating destructive interference. We further integrate this method with the distributed randomized space-time block coding (STBC) scheme to enhance transmission reliability. Additionally, we propose efficient algorithms for estimating the composite channels that combine both the true propagation channels and the virtual channels. Simulation results demonstrate that our proposed scheme significantly outperforms the classical phase dithering scheme across various scenarios.

Catching fast-moving objects serves as a benchmark for robotic agility, posing significant coordination challenges for cooperative manipulator systems holding a catcher, particularly due to inherent closed-chain constraints. This paper presents a nonlinear model predictive control (MPC)-based motion planner that bridges high-level interception planning with real-time joint space control, enabling dynamic object interception for systems comprising two cooperating arms. We introduce an Adaptive- Terminal (AT) MPC formulation featuring cost shaping, which contrasts with a simpler Primitive-Terminal (PT) approach relying heavily on terminal penalties for rapid convergence. The proposed AT formulation is shown to effectively mitigate issues related to actuator power limit violations frequently encountered with the PT strategy, yielding trajectories and significantly reduced control effort. Experimental results on a robotic platform with two cooperative arms, demonstrating excellent real time performance, with an average planner cycle computation time of approximately 19 ms-less than half the 40 ms system sampling time. These results indicate that the AT formulation achieves significantly improved motion quality and robustness with minimal computational overhead compared to the PT baseline, making it well-suited for dynamic, cooperative interception tasks.

We consider uncoordinated random uplink non-orthogonal multiple access (NOMA) systems using a set of predetermined power levels. We propose to optimize the probabilities of selection of power levels in order to minimize performance metrics as block error probability (BLEP) or bit error probability (BEP). When the multiuser detection algorithm at the BS treats at most two colliding users' packets, our optimization problem is a quadratic programming problem. For more colliding users' packets, we solve the problem iteratively. Our solution is original because it applies to any multiuser detection algorithm and any set of power levels.

Accurate assessment of burn severity at injury onset remains a major clinical challenge due to the lack of objective methods for detecting subsurface tissue damage. This limitation is critical in battlefield and mass-casualty settings, where rapid and reliable evaluation of burn depth is essential for triage and surgical decision-making. We present a multimodal optical imaging framework that establishes the foundation for a compact, low-size, weight, and power (low-SWaP) field-deployable device for quantitative burn assessment. The system integrates broadband hyperspectral imaging (VSWIR, 400 – 2100 nm) with laser speckle contrast imaging to jointly evaluate biochemical composition and microvascular perfusion. Using short-wave infrared (SWIR, >1000 nm) wavelengths, we developed and validated novel deep-tissue parameters linked to water, lipid, and collagen absorption features that enhance burn-tissue separability and burn severity classification. We implemented and validated unsupervised learning methods for spectral feature extraction, band down-selection, and clustering against histology, establishing a foundation for a rugged, data-driven device for early quantitative burn evaluation in austere environments.

Cavitation intensity recognition (CIR) is a critical technology for detecting and evaluating cavitation phenomena in hydraulic machinery, with significant implications for operational safety, performance optimization, and maintenance cost reduction in complex industrial systems. Despite substantial research progress, a comprehensive review that systematically traces the development trajectory and provides explicit guidance for future research is still lacking. To bridge this gap, this paper presents a thorough review and analysis of hundreds of publications on intelligent CIR across various types of mechanical equipment from 2002 to 2025, summarizing its technological evolution and offering insights for future development. The early stages are dominated by traditional machine learning approaches that relied on manually engineered features under the guidance of domain expert knowledge. The advent of deep learning has driven the development of end-to-end models capable of automatically extracting features from multi-source signals, thereby significantly improving recognition performance and robustness. Recently, physical informed diagnostic models have been proposed to embed domain knowledge into deep learning models, which can enhance interpretability and cross-condition generalization. In the future, transfer learning, multi-modal fusion, lightweight network architectures, and the deployment of industrial agents are expected to propel CIR technology into a new stage, addressing challenges in multi-source data acquisition, standardized evaluation, and industrial implementation. The paper aims to systematically outline the evolution of CIR technology and highlight the emerging trend of integrating deep learning with physical knowledge. This provides a significant reference for researchers and practitioners in the field of intelligent cavitation diagnosis in complex industrial systems.

In this letter, we propose an efficient mix source separation algorithm for collision resolution in radio frequency identification (RFID) systems equipped with an antenna array at the reader. We first introduce an approach that exploits the zero constant modulus (ZCM) criterion to separate colliding tags through gradient descent, without using pilot symbols. We show that the ZCM characteristic, considered alone, in the design of the objective function can lead to significant ambiguities in the determination of the beamformers used in the recovery of tag messages. To address this limitation, we propose a more sophisticated approach, relying on a hybrid objective function, incorporating a new ambiguity-raising criterion in addition to the ZCM criterion.

Standard linear H-infinity/H2 robust control and analysis tools operate on uncertain parameters assumed to vary independently within prescribed bounds. This paper extends their capabilities in the presence of nonlinear constraints coupling these parameters and restricting the parametric space. Based on the theory of upper-C1 functions, it is shown that the sequential quadratic programming (SQP) algorithm can be slightly adapted to address the search for worst-case H-infinity norm, a nonsmooth constrained optimization problem, and the search for worst-case stability under some assumptions. Specifically, we prove that for such upper-C1 functions, any subgradient provides a descent direction and satisfies Karush-Kuhn-Tucker (KKT) conditions at a local minimum, and that any accumulation point generated by SQP is a KKT point. This worst-case search then enables robust controller synthesis using a standard active configurations approach. Through an application to the robust control of a satellite, the proposed approach is shown to provide a scalable framework for robustness analysis and robust controller synthesis.

We study the contraction of Hodgkin-Huxley model and its role in the reliability of spike timings. Without input, the model is contractive in the region of physiological interest. With impulsive synaptic inputs, contraction is retained provided that the input events are sparse enough. Contraction is lost when the input firing rate is too high. Spike timings are shown to be reliable in the contracting regime.