Papers

This paper investigates how node update rules and admissible state domains affect the minimum number of observation nodes required for global finite-horizon observability in recurrent neural networks with linear-threshold and ReLU update functions. Over a common binary state domain, we construct a class of $K$-linear-threshold ($K$-LT) networks whose initial states can be uniquely reconstructed from the finite output trajectory of a single observation node. We further establish a dynamical equivalence between binary-valued $K$-ReLU networks and $K$-AND Boolean networks, which transfers existing class-level observation-node bounds to binary-valued $K$-ReLU networks. For nonnegative-valued ReLU networks, the observability problem reduces to the classical linear-system setting whenever the relevant pre-activations remain nonnegative. For general real-valued ReLU networks, we prove that global finite-horizon observability requires at least $n/2$ observation nodes when no restriction is imposed on the number of state variables involved in each node update. This lower bound is tight when $n=2K$, for which we construct a $K$-ReLU network observable from exactly $K$ nodes. These results show that both update rules and state domains fundamentally affect extremal observation requirements: temporal evolution can concentrate finite-state information into a single measured trajectory, whereas activation-induced rank loss creates an intrinsic sensor lower bound in continuous-state ReLU networks.

Safety-critical optimization applications, such as real-time power system operation, maintain feasibility at every intermediate step, not merely at convergence. Existing approaches either violate constraints mid-solve (interior-point methods) or enforce feasibility through per-instant quadratic programming subproblems with cubic computational cost and unbounded worst-case execution time. We propose a continuous-time optimization framework for smooth constrained nonlinear problems that preserves feasibility throughout the optimization process without requiring projection operators, quadratic programming subproblems, or other per-iteration optimization routines. The method is built around a reciprocal multiplier manifold, which establishes an explicit relationship between inequality constraints and their associated Lagrange multipliers. By designing a continuous multiplier update law, the manifold is shown to remain forward invariant, while the resulting dynamics are equivalent to continuous-time logarithmic barrier gradient descent. The proposed framework naturally extends to multiple inequality constraints, equality constraints, nonconvex feasible sets, and infeasible initial conditions. The method is further enhanced through an augmented Uzawa flow that eliminates oscillatory transients commonly observed in classical primal-dual saddle-point dynamics. The effectiveness of the proposed approach is applied to the AC Optimal Power Flow problem of IEEE 9-bus and IEEE 57-bus systems. Numerical results show convergence to solutions within 0.4% of the benchmark optimum while maintaining strict feasibility of all constraints. A computational complexity analysis shows that the proposed dynamics reduce the per-step computational cost from cubic to linear complexity. Finally, dynamic tracking studies under time-varying operating conditions demonstrate reliable feasibility preservation.

This paper investigates robust decentralized transmission for cooperative multi-satellite massive multiple-input multiple-output (MIMO) systems under imperfect statistical channel state information (sCSI). In the considered scenario, each satellite has complete access to its local information but receives partial information from other satellites due to limited inter-satellite links (ISLs), with only imperfect sCSI available. To address these challenges, we propose a knowledge distillation (KD) framework that transfers cooperative precoding knowledge from a centralized teacher neural network (NN) to lightweight decentralized student NNs. Specifically, a global-clean teacher, aggregating information from all satellites and accessing accurate sCSI during offline training, transfers its cooperative precoding knowledge to partial-noisy students, relying on complete local information, limited information exchanged by other satellites, and error-corrupted sCSI for local precoding. The teacher NN combines patch-wise self-attention with dual-axis attention to learn inter-user interference and inter-satellite coordination, whereas each student NN adopts a compact per-satellite architecture for efficient onboard inference. The teacher learns a high-quality weighted minimum mean square error precoding policy from global-clean inputs, which is then distilled into the students operating on partial-noisy inputs. To mitigate the resulting teacher-student performance gap, we develop a hybrid KD mechanism with explicit angle- and phase-error calibration. Simulation results demonstrate that the proposed framework significantly enhances the decentralized sum-rate performance and remains robust under diverse configurations.

Online learning-based control is a promising approach to control uncertain systems, where unknown components are identified during operation to improve control performance. However, resource-intensive online learning algorithms introduce non-negligible computational delays, especially when executed on systems with limited local computational resources. To mitigate this, an in-network online learning-based control structure is employed by deploying the learning-based controller on a remote computation node and connecting it via a communication channel. In this paper, control performance guarantee is first established by deriving tracking error bound for the in-network control architecture, while accounting for computational delays. The derived tracking error bound allows for diverse communication and computation strategies under a specific condition, including time-/event-triggered mechanisms. Additionally, the trade-off between communication and computation performances is shown for a given desired control performance. Furthermore, to enhance the efficiency in both communication and computation, an efficient control framework with an asynchronous event-triggered mechanism in both control and online learning is devised under the existence of computational delay. The proposed event-triggered strategy is proven to achieve the same control performance as time-triggered scenario while excluding Zeno behavior. Finally, we derive an explicit expression of the proposed event-trigger condition for exponentially stabilizable systems, and demonstrate its effectiveness through simulations.

The increasing penetration of grid-connected converters has greatly altered the large-signal behavior of power systems. Their angle dynamics, shaped by diverse control algorithms and coupled through complex circuit interactions, pose substantial challenges to large-signal stability evaluation of multi-converter systems. To resolve this issue, the small-gain theorem, which characterizes the dissipation capability of interconnected systems (the small-gain-like property) via the individual dissipation capabilities of subsystems, is introduced to investigate transient angle motions in multi-converter systems. A large-signal model involving a set of interconnected relative angle motions is first developed, and the small-gain-like property is then established for multi-converter systems within certain angle limits, which further enables the construction of Lyapunov functions. Based on this, an ellipsoidal forward-invariant region is identified inside the angle-limit region, which serves as an effective estimate of the large-signal stability region for multi-converter systems. The method is further applied to a paralleled system and a four-converter system, where the large-signal stability boundaries are explicitly computed and subsequently validated through experiments. The proposed small-gain-like based large-signal stability evaluation method enables quantitative stability assessment in multi-converter systems with diverse control algorithms, which may provide a scalable framework for large-signal stability evaluation and parameter design in modern power systems.

Ensuring the safe operation of multi-agent systems (MASs) under uncertain environments is crucial for cooperative robotic, where external disturbances and inaccurate dynamic models can significantly compromise performance and reliability. To address this challenge, calibrated machine learning models, particularly Gaussian process (GP) regression, are extensively employed due to their interpretable performance quantification. As the interconnected communication of MASs facilitates cooperative learning, agents are able to enhance learning performance by exchanging local GP inferences with their neighbors and aggregating the received information via distributed GP strategies. However, variations in computational power and prediction tasks among agents inevitably lead to heterogeneous computational delays and differences in query points, which are often overlooked in existing aggregation methods. To overcome these limitations, this work proposes an asynchronous cooperative learning strategy that explicitly accounts for prediction accuracy, query point variations and delay effects. Additionally, a distributed control law based on an adjoint MAS is developed to ensure the desired control performance. Simulations on unmanned surface vehicles validate the effectiveness of the proposed approach, demonstrating substantial improvements in both learning and control performance compared to the state-of-the-art approaches.

Uncertainty quantification for continuous-time trajectories is a prerequisite in many safety-critical engineering domains. However, a major challenge in data-driven uncertainty quantification is that calibration trajectories are sampled only at discrete, often sparse, and random intervals. Standard conformal prediction methods typically fail to provide guarantees in between sampling times. In this work, we introduce a new technique to obtain valid conformal prediction regions for continuous-time trajectories that are sampled at discrete and possibly random times. To accomplish this goal, we make three contributions: (1) we provide an algorithm that leverages regularity properties of the underlying trajectories to obtain valid prediction regions in between samples, (2) we provide methods that estimate valid bounds on the aforementioned regularity properties from an additional high-frequency calibration dataset, and (3) we introduce and compare several algorithms to deal with random sampling times. Finally, we present experiments where we validate that our methods achieve valid coverage across the entire continuous trajectory.

Teleoperation systems are increasingly used in medical, rehabilitation, and remote manipulation applications, where accurate position/force tracking and stable interaction are essential. In such applications, the remote environment may exhibit viscoelasticity, frictional memory, contact transitions, and other dynamic interaction effects, causing the system response to depend not only on the current state but also on its previous evolution. This history dependence, together with communication delays and uncertain nonlinear dynamics, makes accurate uncertainty compensation particularly challenging. Conventional feedforward neural approximators do not inherently retain temporal information, while fully recurrent architectures may introduce additional computational and online training complexity. To address this limitation, this article introduces the first application of a liquid state machine (LSM) to bilateral teleoperation control. A finite-time adaptive controller is developed using a hybrid position/force auxiliary error system with velocity and force filters, while the LSM is employed to estimate uncertain dynamics by exploiting its intrinsic temporal processing and fading-memory capabilities with a simple adaptation mechanism. Closed-loop stability and finite-time convergence are established through a Lyapunov–Krasovskii framework. Simulations in spring–damper and generalized Maxwell viscoelastic environments demonstrate improved position and force tracking and lower mean execution time compared with an RBFNN-based controller.

As presented in Part I of this series, closely located poles can produce high-amplitude oscillations even under small perturba-tions, which are referred to as stimulated oscillations. As the second installment of this series, this paper develops a method-ology for stimulated oscillation mitigation. Firstly, the logical relationship between system stability and oscillation risk is in-vestigated, clarifying that stability is neither a necessary nor a sufficient condition for oscillation risk. Secondly, the dominant factors affecting the pole and zero position on the complex plane, as well as their effectiveness and limitations are analyzed. For feedback control systems in particular, the influence of feedback paths on the pole-zero distribution of closed-loop systems is ana-lyzed. On this basis, a methodology for stimulated oscillation mitigation is proposed. Combined with the reduced-order trans-fer function of systems with closely separated poles, the order relation and configuration of poles and zeros for the feedback path transfer function, together with the criteria for gain selec-tion, are elaborated. Finally, a parameter tuning scheme for the poles, zeros and gain of the feedback path transfer function is presented. The proposed methodology for stimulated oscillation mitigation based on feedback control is not restricted to specific devices and can serve as a methodological reference for the design of diverse oscillation suppression schemes.

We study the design of linear time-invariant (LTI) encoder-decoder pairs for transmitting the state of a discrete-time LTI vector source over power-constrained parallel Gaussian channels with feedback. Two types of power constraints are considered. Under individual subchannel power constraints, a necessary and sufficient condition for designing an encoder-decoder pair that achieves bounded estimation error covariance (EEC) is established via two coupled majorization inequalities involving the subchannel signal-to-noise ratios and the antistable poles of the source. Under total channel power constraint, we derive the minimum total power required for a feasible encoder-decoder design by exploiting partial-order progamming under majorization order. An analytical optimal power allocation is obtained for the case of equal noise variances, which admits a water-filling interpretation; for general noise case, a sequential water-filling algorithm is developed. Our results reveal that the difficulty of transmitting a discrete-time LTI source via LTI coding is governed not only by its topological entropy, but also by the evenness of the log-magnitudes of its antistable poles. The design methods for feasible encoder-decoder pairs are also provided.

De Cock and De Moor conjectured that two matrices built from the solutions of three coupled Lyapunov equations always share their eigenvalues, an identity linking principal angles between model subspaces with the canonical correlations between the past and future of a stochastic process. The conjecture was recorded as Problem 9.1 of Unsolved Problems in Mathematical Systems and Control Theory. We prove it under the natural nonresonance condition, without stability or diagonalizability, and show that the two matrices are similar. The proof exposes a Cauchy-matrix structure created by the rank-one perturbation and reduces the problem to rational interpolation, and the main theorem has also been verified in the Lean~4 proof assistant. The same structure gives a normal form that depends only on the poles and zeros of the model. Off the stable set the eigenvalues become squared cosines of angles in an indefinite metric. We prove an inertia theorem for them and show that reflecting a pole or a zero across the unit circle acts on the angles by a principal pivot transform. For several inputs and outputs, where the conjecture fails, we identify the subspaces whose angles do give the canonical correlations. We also relate our approach to the independent proof of Gillberg and Löfberg.

This letter reveals a fundamental limitation of passivity-based distributed stability analysis in power systems. Under the standard formulation, passivity certification inherently imposes a relative-degree compatibility constraint that excludes many high-fidelity inverter dynamic models (e.g., those that include electromagnetic transients). Potential extensions of passivity frameworks are discussed to break this limitation.

This survey provides overview of distributed estimation, filtering, and fault detection techniques in the context of cyber-physical systems (CPS). To establish a strong foundation, we first define essential aspects of linear dynamical systems and related graph theoretic concepts, emphasizing observability conditions that are key for local state estimation. After discussing consensus algorithms, this survey highlights single-time and double-time-scale consensus-based estimation and filtering approaches. We provide a detailed comparative analysis of these methodologies, examining their computational requirements, communication overhead, and performance characteristics in resource-constrained environments. We further explore different diffusion-based estimation techniques and observationally redundant designs to enhance resilience and robustness against failures and adversarial attacks. In addition, we investigate distributed fault detection methods that enable local isolation of faults over large-scale CPS. We present both stateless and stateful detection mechanisms, along with threshold-based techniques that balance detection accuracy and false alarm rates. These approaches are essential for maintaining system integrity and preventing cascading failures in critical infrastructure. This survey concludes with an exploration of diverse real-world applications. We examine implementation challenges and algorithm adaptations in smart grid and power networks, social systems, target tracking and localization, and intelligent transportation systems. By bridging theoretical insights with practical applications, this survey and tutorial provides valuable understanding and research directions in the field of distributed algorithm design for CPS, offering both newcomers and experienced researchers a comprehensive resource for addressing current challenges and future opportunities.

Robust Principal Component Analysis (RPCA) is a fundamental technique for extracting low-rank structures from data corrupted by sparse anomalies and noise. While classical RPCA and its stable variants have been widely studied, their computational cost grows prohibitively with data dimensionality. Motivated by emerging trends in quantum machine learning, this paper introduces Quantum-Stable RPCA, the first quantum algorithm for robust low-rank and sparse decomposition under noisy intermediate-scale quantum (NISQ) constraints. Our approach integrates Quantum Singular Value Thresholding (QSVT) for low-rank recovery with Quantum Sparse Approximation (QSA) for anomaly detection, while explicitly modeling gate, decoherence, and measurement errors inherent in quantum hardware. We establish six theoretical results covering recovery guarantees, identifiability, robustness to approximate structure, quantum noise resilience, convergence of alternating minimization, and generalization bounds, thereby extending classical RPCA theory to the quantum regime. Extensive Qiskit-based simulations confirm that Quantum-Stable RPCA delivers significant runtime improvements over classical solvers while maintaining competitive reconstruction accuracy, even under realistic NISQ noise levels. This work provides a blueprint for quantum-enhanced robust learning, bridging machine learning and signal processing paradigms.

The question of what constitutes a system remains fundamental to General Systems Theory. Existing definitions commonly characterize a system in terms of elements, relationships, boundaries, functions, or interactions, but these perspectives do not always provide a unified account of how system structure and system behavior constitute one another. This paper proposes Structure-Behavior Coalescence (SBC) as an interaction-based account of what a system is. From the SBC perspective, a system is not merely a collection of elements or relationships, nor is it adequately characterized by behavior considered independently of structure. Rather, a system is a structured entity whose behavior arises through interactions among its constituent entities, with those interactions simultaneously contributing to both its structural organization and behavioral realization. The paper develops this perspective by distinguishing system structure, interaction, and behavior while treating them as inseparable aspects of a unified system representation. It argues that interactions provide the essential link through which structural relationships become behavioral processes and through which behavioral processes reveal and instantiate system structure. This perspective provides a basis for representing systems in a manner that maintains consistency between what a system is and what a system does. The paper further considers the implications of SBC for General Systems Theory, particularly for system identity, system boundaries, behavioral emergence, and the representation of complex systems. SBC is presented as a general conceptual foundation that complements existing systems theories by placing the coalescence of structure and behavior through interaction at the center of the definition of a system.

This paper examines a foundational assumption in traditional systems theory, namely that structure (the organization of components) and behavior (the evolution of system activity over time) can be treated as separable analytical dimensions. It argues that this separation contributes to persistent difficulties in explaining system identity, particularly in cases involving change, emergence, and boundary specification. To address this issue, the paper introduces Structure-Behavior Coalescence (SBC) as a reframing principle. SBC proposes that structure and behavior should not be understood as independently existing entities that are subsequently related through modeling constructs, but as mutually constitutive aspects of a single systemic process. From this perspective, system identity is understood as arising from the sustained co-determination of structural organization and behavioral dynamics, rather than from their external correspondence or alignment. This reframing provides a unified way of understanding system identity, emergence, and boundary formation within a cybernetically informed systems perspective.

This paper investigates physical-layer security (PLS) over correlated composite generalized-Gamma (GG)/GG fading channels, where both shadowing and small-scale fading follow GG distributions. Using Mellin transforms and Fox-H functions, closed-form expressions are derived for the single-link probability density function (PDF), joint distribution, survival function, and zero-rate secrecy outage probability (SOP)/probability of non-zero secrecy capacity (PNZSC). The general-rate SOP is expressed as an exact double series with one residual onedimensional integral per term. The model includes the Nakagamim/GG and Nakagami-m/Gamma channels as special cases. Numerical results validate the analysis and demonstrate the impact of the fading parameters on secrecy performance.

We present FIREQ (FPGA Instrumentation for Readout and Qubit control), an open-source RFSoC-based framework for the control and readout of superconducting qubits. FIREQ combines a modular AXI-compliant firmware architecture with a PYNQ-based software stack designed to support extensible hardware integration, deterministic experiment timing, and low-overhead execution of repeated calibration and characterization workflows. The firmware implements direct RF synthesis and acquisition, trigger-based sequencing, programmable pulse generation, frequency-multiplexed readout, and memory-efficient acquisition and waveform buffering. The software adopts a client-server architecture with streamed data transfer and dependency-aware configuration updates to reduce host-device and reconfiguration overhead during parameter sweeps. On an AMD Zynq UltraScale+ RFSoC ZCU216, FIREQ generates RF pulses up to 9.3 GHz with a pulse-duration resolution of 107 ps and an event-timing resolution of 1.7 ns. FPGA resource utilization is compared with representative open-source RFSoC control frameworks, showing a low BRAM footprint while retaining full-rate I/Q generation and acquisition. The RF output is characterized in terms of phase noise, noise spectral density, and inter-channel timing skew. End-to-end operation is validated on a superconducting qubit through resonator spectroscopy, Rabi, Ramsey, and relaxation measurements, yielding T1 = 6.94 us and T2* = 13.50 us. FIREQ can therefore be used both as a qubit-control platform and as an experimental environment for evaluating alternative control and readout IP architectures.

Optimal control of queueing networks is typically studied under continuous-time control. In many service settings, however, managers can adjust decisions only at discrete and potentially infrequent review epochs. We study a multiclass queue scheduling problem in which server assignments can be changed only at the beginning of discrete review periods, and examine how performance depends on the review-period length. We analyze a family of associated fluid control problems parameterized by the review-period length and characterize the first- and second-order sensitivity of the value function. For the two-class case, we derive explicit expressions for these derivatives and characterize their signs. We find that for short review periods, the value function need not be monotone. Once the review period is sufficiently large, the value function becomes monotone nondecreasing and may exhibit a convex or linear region before eventually becoming concave. These results provide insights on when the value of more frequent control, or flexibility with respect to server assignments, is higher. We further numerically examine the robustness of these observations for the original stochastic scheduling problem and show that stochasticity smooths the nonmonotonicity at smaller scales, while the qualitative sensitivity patterns remain visible and become more pronounced as the scale of the system increases.

Securing grid interconnection capacity has become a bottleneck for AI data center projects and can take longer than constructing the facilities themselves. This mismatch can delay deployment for years, making early interconnection planning essential. This paper develops ICP-AI, an interconnection capacity planning framework from a data center developer's perspective. The framework minimizes grid import capacity under a prescribed onsite investment budget while jointly sizing photovoltaic (PV) and battery energy storage system (BESS) resources and scheduling deadline constrained workload flexibility. A secondary refinement fixes the minimum grid capacity and selects the minimum-investment PV-BESS portfolio among solutions that achieve that capacity. The framework is evaluated using monthly composite stress profiles across varying temporal assumptions, load shapes, flexible load fractions, and deferral windows. Results show that interconnection capacity reduction depends strongly on the planning environment: at a $100M budget, it is about 6% for the high load factor baseline, exceeds 10% under monthly average solar availability, and reaches 13.3% for a more diurnal load. At a $10M budget, 5% flexible load with a 1 h workload deferral window reduces BESS capacity from 15.30 to 4.87 MWh while increasing capacity reduction from 4.43% to 4.84%. To test sensitivity to temporal compression, the model is also solved over the full 8,760 h chronology, which preserves the main capacity and flexibility trends. Overall, ICP-AI quantifies the interconnection capacity and infrastructure substitution value of workload flexibility, providing an investment-interconnection frontier to support capital allocation and early project planning in constrained grid environments.

This article addresses the problem of designing neural feedback controllers for unknown nonlinear systems. We propose an indirect data-driven framework that uses offline data to identify the system dynamics, upon which a neural feedback controller and a neural Lyapunov function are jointly synthesized. Input constraints are enforced by integrating a hard-saturation structure into the controller architecture. Robust synthesis conditions are derived to account for data perturbations during identification. Formal stability is certified by combining SMT verification with local Lyapunov analysis near the equilibrium. Numerical examples validate the effectiveness of the proposed framework.

This paper studies the class of Bayesian tracking filters for which the probability generating functional of the joint target-measurement process can be derived from the statistical assumptions that define the problem. The class includes filters for labeled and unlabeled targets, as well as hybrid filters in which both labeled and unlabeled targets are present. New results include the probability generating functional for interval filtering of target trajectories for both labeled an unlabeled targets, and a novel method for computing the probability generating function of measurement-to-target assignment probabilities. Probability generating functionals give exact expressions for calculating the importance weights in particle filter implementations. Low computational complexity approximations to the particle weights are derived from the probability generating functionals via the saddle point method.

We propose a novel Stochastic Nonlinear Model Predictive Control (SNMPC) framework for nonlinear systems with additive noise. Building on recent advances in nonlinear uncertainty propagation, we show that the state distribution of the system can be tractably approximated over time by Gaussian mixture distributions, with formal error bounds in Wasserstein distance. This representation yields closed-form expressions for expected costs and chance constraints, which become exact for affine constraints and exact up to a constant for quadratic costs. Consequently, the resulting control problem can be solved efficiently via nonlinear programming, while providing formal open-loop guarantees of correctness and asymptotic optimality. Experiments on a set of benchmarks demonstrate that the proposed approach compares favorably with existing methods in nonlinear settings with multi-modal disturbances, where standard approaches lead to poorly scaled solutions and unsafe or overly conservative control actions.

The uncontrolled equations of motion in the Nechvile frame for the restricted three-body problem have been well-known since at least the 1960s. It would seem that adding an external force to these equations is quite trivial: simply add an external force per mass term to the acceleration equations. Here we show that the last statement is not true. In fact, we show that the additive generic external force must be multiplied by the inverse of $(1 + e \cos\theta)^3$ where $e$ is the relative eccentricity of the primaries and $\theta$ is the true anomaly of the rotating frame located at the barycenter. Furthermore, when this result is combined with the mass flow rate equation, it generates several surprising results due to the mismatch between the resulting quadratic term and the cubic term in the equations of motion. This leads to a corresponding modification of the rocket equation itself. A Birkhoff-theoretic solution to an illustrative cislunar space mission problem shows propellent savings of 80% with the use of the correct cost functional. The popular quadratic cost utilizes more than $2X$ the minimum propellant consumption.

Late gadolinium enhancement (LGE) cardiac magnetic resonance (MR) imaging is the modality of choice to assess myocardial infarction (MI) lesions. Nowadays MI volume quantification is not performed routinely in clinical practice. Numerous deep learning (DL) methods have been developed to automate the segmentation of the myocardium and infarct regions. However, most studies rely on relatively small datasets which typically undergo pre-processing steps to standardize images and focus on a specific phase of myocardial infarction following reperfusion therapy. These limitations have impeded the development of models that are generalizable across diverse conditions and thus suitable for routine clinical use. To advance research and establish benchmarks in generalizable learning for myocardial infarct quantification, this paper presents findings from the Myocardial Segmentation with Automated Infarct Quantification (MYOSAIQ) challenge. The dataset set up for the challenge combines 439 CMR volumes from two multicenter clinical trials, with representative data acquired in acute and chronic phases after acute MI. Data were acquired in 16 centers using MRI scanners from three different vendors. Six teams participated until the end of the challenge, employing various baseline models, data augmentation techniques, and confidence strategies. To enhance the significance of this study, we compare the challengers' results with those of fine-tuned foundation models. Our results indicate that well-designed UNet-based techniques outperform fully automatic foundation models for LGE MR segmentation. While the best methods achieve high-quality and stable delineations of the left ventricle and myocardium under various conditions, they remain improvable in accurately segmenting infarct regions.

Low-resource ASR remains difficult because scarce transcripts provide limited supervised evidence for target-side generation. To address this gap, we propose SAMA-ASR, a lightweight adapter mechanism that augments the decoder with semantic anchors from auxiliary translations and an acoustic anchor from speech; in principle, the mechanism can be applied to similar encoder–decoder multitask speech models. Through cross-modal adaptation, SAMA-ASR conditions decoder states on translation-derived semantic embeddings and a speech embedding, combining utterance-level meaning with speech-grounded evidence before token prediction. At evaluation time, these semantic anchors can be generated automatically by an upstream speech-to-text translator rather than supplied as oracle translations. Experiments on two 30-hour datasets covering the low-resource Sinitic varieties Taiwanese Hokkien and Hakka show that SAMA-ASR improves over acoustic, prior prompt-based, and semantic-only translation-guided baselines and remains effective in practical automatic semantic-anchor settings; translator-capacity analyses show that useful semantic anchors can be produced by a compact ST model.

Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances in computer vision and deep learning have enabled detailed analysis of pollinator behaviour, but monitoring must trade-off detail against spatial coverage and human or technological resources. This paper presents the Automated Guided Robot for Insect and Crop Activity Monitoring (AGRICAM), a purpose-built robotic system designed to meet the requirements of large-scale pollination monitoring in protected cropping systems. AGRICAM operates autonomously on low-cost, easily installed track for movement along crop rows, without disrupting farm operations or insect behaviour. The platform integrates two RGB cameras, microclimate sensors, GPS and RFID modules, motion sensors, and 4G cellular network connectivity for data transmission. A web interface enables remote device configuration and scheduling. The system autonomously captures video and image data of insects' locations and local environmental conditions. These are transferred to the cloud and analysed using computer vision models to quantify pollinator visitation and spatio-temporal activity variation. We deployed the system on a commercial blueberry farm to demonstrate and test its capability. It successfully mapped insect pollination patterns across 80 m long industrial polytunnels over 30 hours. This data enabled spatial analyses of insect activity we used to confirm a uniform pollinator distribution within polytunnels, as desired by the farm management team. The data also highlighted variation of insect activity associated with time of day and microclimate. AGRICAM therefore has been shown to be a scalable, automated crop pollination monitor that can support data-driven decisions to enhance pollination management, thereby improving crop productivity and food security.

This paper presents a novel methodology for the estimation of the rotational speed of induction motors operating under non stationary conditions, using acoustic signals acquired by a low cost microphone. The proposed approach integrates multi rate digital signal processing with advanced time frequency analysis to extract speed dependent harmonic components from motor acoustic emissions, while effectively suppressing electrical interference and electromagnetic noise. Numerical simulations and experimental validations are conducted under both steady state and transient operating conditions, considering a wide variety of speed profiles, including slow ramps and abrupt speed variations. The experimental results demonstrate that the proposed method is capable of accurately tracking rapid and dynamic speed changes caused by load disturbances and mechanical irregularities. The estimated instantaneous speed exhibits a high degree of agreement with reference measurements obtained from a conventional speed sensor. The results confirm that the proposed acoustic based technique provides a reliable, noninvasive, and cost effective solution suitable for speed monitoring of induction motors.

Optic disc (OD) segmentation is essential for diagnosing ophthalmic pathologies from retinal fundus images. However, prevailing deep learning approaches operate as opaque black boxes, lacking the inference-stage mathematical traceability–a critical requirement for algorithmic auditing and failure analysis in clinical workflows. This paper presents a fully algorithmically traceable and trainable segmentation pipeline that jointly combines superpixel decomposition, hybrid brightness-proximity superpixel scoring, morphological regularization, iterative GrabCut refinement, and elliptical shape fitting. The hyperparameter optimization is formulated as an objective function and solved via Bayesian optimization to eliminate manual parameter tuning. A quantitative evaluation on the Drishti-GS dataset demonstrates that our method achieves a Dice coefficient of 0.9536, matching state-of-the-art performance. By maintaining explicit mathematical transparency across all processing stages, our framework offers a deterministic, traceable alternative to black-box architectures for medical review and debugging.

The growing demand for high spectral efficiency in 6G and beyond has driven research into adaptive antenna architectures capable of exploiting the spatial structure of multipath propagation channels. Conventional base station arrays are deployed with fixed element positions and cannot adapt to the instantaneous spatial structure of the propagation channel. In contrast, movable antenna (MA) systems enable dynamic reconfiguration of antenna positions, allowing the array geometry to track the channel characteristics of the current user set. While prior MA studies have demonstrated significant gains under perfect or statistical channel state information (CSI) assumptions, the interplay between imperfect instantaneous CSI, antenna placement optimization, and precoder design has received limited attention. This paper addresses this gap by proposing a practical end-to-end framework encompassing uplink pilot transmission, MMSE channel estimation under a clustered multipath model, and downlink ZF precoding designed from estimated CSI. Antenna positions are optimized via particle swarm optimization under two objectives: sum-rate maximization and max-min fairness. Simulation results show that MA gains are most pronounced under sparse, near-LoS propagation and diminish as channel richness increases. Furthermore, we reveal a fundamental coupling between array geometry and precoder design: a fairness-oriented antenna geometry encodes spatial fairness information that is only recoverable when evaluated with a compatible power allocation strategy. A mismatched precoder can completely mask the geometric advantage, leading to misleading conclusions about the robustness of antenna placement to the choice of optimization objective. These findings provide practically relevant guidance for the design and evaluation of movable antenna systems under realistic operating conditions.