Papers

Traditional optical system design for imaging applications has long been guided by image quality metrics such as geometrical RMS spot size, wavefront error, and modulation transfer function (MTF). Although these metrics effectively characterize direct imaging performance, they do not necessarily indicate optimal behavior when image restoration is subsequently applied. In modern imaging pipelines, however, optical systems are increasingly combined with physics-based or learning-based restoration methods, making the information transfer properties of the optical system critically important. In this work, we propose a recovery-oriented optical design approach that explicitly links optical system optimization with image restoration performance through an estimation-theoretic framework based on Fisher information. A new design criterion is adopted to ensure that the information encoded by the optical system can be reliably recovered during image restoration. To validate the proposed approach, two dual separated aspheric lenses are designed under identical configurations using conventional RMS-based optimization and the proposed recovery-oriented optimization, respectively. Although the RMS-optimized lens exhibits superior direct imaging quality, it underperforms after image restoration compared to the recovery-oriented design. These results demonstrate that recovery-oriented optical design provides a physically interpretable and effective extension of traditional optical design criteria for imaging systems operating within modern computational imaging pipelines.

Large language models are increasingly deployed without formal guarantees against toxic text generation. Existing control barrier function (CBF) based safety filters can keep the generation trajectory within a toxicity-free region, but do not enforce a positive distance from its boundary, allowing formally safe trajectories to approach the toxicity threshold arbitrarily closely and leaving little buffer against modelling errors or disturbances. We address this limitation with a Barrier Lyapunov Function (BLF) safety filter that enforces a prescribed clearance margin throughout generation while retaining the continuous-time transformer dynamics, including feedforward layers. The resulting safe input set is a half-space defined by a constraint affine in the control input, and is non-empty under certified surjectivity and classifier-nondegeneracy conditions. For margin-compatible initial conditions we prove that the clearance margin is never violated and that the prescribed clearance is restored exponentially, yielding an explicit clearance floor, and show that, as the margin vanishes, the admissible trajectories satisfy the exponential second-order CBF condition. We further derive an explicitly computable sufficient disturbance budget under which the clearance guarantee degrades gracefully. Simulations on a small transformer illustrate the resulting improvement in safety clearance.

This paper presents a robust control framework for safe navigation of a unicycle robot to a goal among multiple static obstacles under an unknown, time-varying disturbance. The controller solves a single real-time quadratic program (QP) that combines a goal-reaching term with one safety term per obstacle. The goal-reaching term is a cross-track control Lyapunov function built from body-frame tracking errors rather than a bearing angle, which keeps its Lie derivatives bounded everywhere, including the goal. Safety is encoded using robust control barrier functions, one per obstacle, each tightened by a closed-form, state-dependent disturbance bound. When stabilization and safety conflict, a slack variable relaxes only the convergence requirement, so that the safety constraints remain strictly enforced. Whenever the QP is feasible, the robust barrier constraints guarantee forward invariance of every obstacle's safe set under all admissible disturbances. Complementing this safety guarantee, a Lyapunov analysis yields an explicit ultimate bound on the distance to the goal. A learning-augmented variant replaces the fixed objective with a reinforcement-learning reference, which accelerates convergence when multiple constraints are simultaneously active. We further derive the obstacle separation below which the gap between two obstacles becomes too narrow to pass through under the robust constraints. For such gaps, a learned virtual goal for the Lyapunov function steers the robot around the obstacles without compromising safety. Simulations, a Monte Carlo study, and a comparison against a sampled-data robust baseline support the theoretical results.

We study the recovery of a three-dimensional volume in single-particle cryo-electron microscopy (cryo-EM) from noisy tomographic projections acquired at unknown viewing rotations, drawn independently and uniformly from $\mathrm{SO}(3)$. We show that, in a spherical-shell representation satisfying an explicit shell-separation condition, the first three moments generically determine the underlying real volume up to its intrinsic global $\mathrm{O}(3)$ ambiguity. The proof is constructive and yields a frequency-marching procedure that successively recovers the volume. Consequently, consistent recovery in the high-noise regime has sample complexity $n=\omega(\sigma^6)$, where $n$ is the number of observations and $\sigma^2$ is the noise variance. Numerical experiments validate the constructive recovery scheme under finite sampling and additive noise, and indicate that direct third-moment optimization can succeed beyond the structural assumptions required by the constructive proof.

Nuclear magnetic resonance (NMR) magnetic tracking offers a promising alternative to magnetic resonance imaging (MRI)-based localization for medical robotic guidance. However, scalar field measurements and field-aligned marker moments constrain position and orientation tracking, requiring careful configuration design. We present a unified information-based framework that uses local covariance analysis to evaluate receiver geometry, marker separation, and external source configuration in on/off-board NMR tracking. Numerical evaluation shows that planar-array precision depends jointly on receiver count and geometry, with triangular lattice arrays achieving the lowest best-case position uncertainty among the evaluated layouts for 9–20 receivers. Two spatially separated, field-aligned markers enable device-axis tracking, with predicted angular uncertainty below $2^\circ$ at the evaluated workspace center for suitable marker separations. We further establish point-dipole source–receiver duality, unifying the analysis of on/off-board NMR tracking. These

Direct Link Interference (DLI) remains a major bottleneck in Orthogonal Frequency Division Multiplexing (OFDM)-based Ambient Backscatter Communication (AmBC) systems. This letter proposes Shuf-Scatter, a scheme that applies a deterministic permutation to the reflected OFDM waveform to create a distinct correlation-domain signature. By exploiting the quasi-orthogonality between the reshuffled backscatter signal and the direct-link component, the proposed receiver effectively mitigates DLI and enables reliable detection without requiring dedicated spectral resources, or instantaneous Backscatter-Link (BL) Channel State Information (CSI). Analytical and simulation results demonstrate that Shuf-Scatter achieves superior Bit Error Rate (BER) performance under strong DLI while preserving the integrity of the legacy OFDM transmission.

This study evaluates the AutoSTEM workshop, an intervention for secondary education that combines a serious game with PID-controlled hardware. Seven workshops were conducted across Sweden and Italy. The six Swedish workshops adopted an explicitly SDT-informed facilitation framework, while the Italian workshop followed the existing educational robotics format without a formally defined SDT facilitation protocol. Self-reports, an objective knowledge quiz, and qualitative responses were analysed using multiple site-adjusted statistical approaches. Technical-specific self-efficacy increased substantially in both conditions, with large effect sizes, while objective knowledge showed only modest, non-significant gains. No significant site-adjusted differences were found between SDT and control conditions for the primary outcomes. Perceived engagement was higher in the control condition, although this finding should be interpreted cautiously. The objective knowledge composite remained positive but non-significant across sensitivity analyses. Female participants reported smaller PID self-efficacy gains than males. Qualitative findings supported evidence of PID learning while identifying technical and pacing barriers. Overall, the results suggest that the technology-rich intervention supports competence development, regardless of SDT-informed facilitation.

Can an integrated optical readout improve the useful operating range of a miniature microphone? We address this question with an analytical model of a diaphragm carrying the sensing arm of a Mach-Zehnder interferometer (MZI). The model links pressure-induced bending to waveguide elongation and then to photocurrent, allowing mechanical sensitivity and optical detection limits to be assessed separately. Optical shot noise and back-cavity thermomechanical noise set the pressure resolution, while a 10% total harmonic distortion criterion defines acoustic overload. Two operating regimes are examined: a compact MEMS microphone with 1 mW optical input and a measurement microphone with 100 mW optical input. For the compact case, a 1 mm diameter, 1 micrometer thick, stress-free silicon diaphragm and 100% modulation efficiency give a predicted signal-to-noise ratio of 78 dB and acoustic overload pressure of 124 dB. The higher-power case lowers the modeled noise floor and increases dynamic range relative to the selected measurement microphone benchmark. These estimates rely on favorable assumptions and do not establish a substantial overall advantage in conventional microphone applications. Remote optical interrogation offers a separate motivation for harsh-environment sensing, but the temperature tolerance of the proposed sensing head remains unverified. The analysis identifies the design tradeoffs and measurements needed to test the predicted performance.

Electrocardiogram (ECG) models need supervision that targets evolving weaknesses and remains faithful to waveform evidence. We introduce ECG-RSI, a recursive self-improvement framework combining verified waveform synthesis with feedback-guided intervention search. Signal remeasurement verifies waveform edits; construction errors guide generator repair, while learner errors motivate interventions compared on a separate selection set. Across seven benchmarks, three rounds raise Qwen3.5-35B-A3B's mean question-answering accuracy from 34.57% to 56.04% and mean classification F1 from 24.27 to 34.03. Across the retained trajectory, the preferred supervision shifts from direct reinforcement to confusion-focused contrasts. Continued feedback correction yields diminishing external gains. These results motivate separating error diagnosis from intervention selection and checking whether gains on the feedback set transfer to independent benchmarks.

Building a co-registered SAR-optical corpus for self-supervised Earth Observation is above all an acquisition problem, yet the main multi-modal datasets are collected opportunistically, with no per-stratum quota control and no mechanism against the geographic dropout that non-uniform cloud cover leaves behind. We present Deficit-Driven Stratified Acquisition (DDSA), an algorithm that treats building a co-registered Sentinel-1/Sentinel-2 corpus as a constrained maximum-coverage problem over a hemisphere-corrected (class x season x region) stratification, enforces strict temporal pairing and a multi-stage quality gate, and closes the loop during acquisition with a human-in-the-loop deficit-repair stage. Measured on its own product, the algorithm yields three results a single-pass acquisition cannot report. Deficit-driven ordering makes a partial run a covering one: it holds every one of the 192 strata populated from the first few percent of the budget, where an abundance-first order leaves most strata empty until the end. Whether a failed attempt can be recovered depends on the temporal persistence of its cause, not the kind of defect: geometric failures such as nodata are among the most repairable (eta = 41.4%) while cloud is among the least (eta = 18.8%), because a cloudy location is cloudy on most dates of its season. The repair loop supplies 39.5% of the released pairs at no measurable quality cost, with 89.8% surviving manual review. The quality guarantee has a price: 2,928,370 acquisition attempts produced 740,746 released pairs. We release the corpus, STRATA-S12, with STAC/PDS4-aligned FAIR sidecars, and a pre-registered linear-probe check confirms it is fit for self-supervised pre-training. A metadata-level coverage comparison against SSL4EO-S12 and SEN12MS situates the corpus over a shared frame; systematic cross-corpus and cross-method comparison is left to follow-up work.

The growing demand for edge computing, content delivery, and AI services is driving the interconnection of edge data centers (DCs) with distribution networks. Before interconnection, utilities evaluate how much DC capacity can be accommodated without violating feeder current and voltage limits. Fixed peak load representations can overlook service dependent demand patterns, cooling related facility demand, and delay tolerant computing tasks. This paper develops a hosting capacity (HC) assessment model for edge DCs with shiftable workload scheduling. Three representative types are considered: mobile edge computing (MEC), content delivery network (CDN), and AI DCs. Facility demand is modeled by combining fixed base demand, service driven demand, prescribed cooling-overhead coefficient, and a shiftable workload component. Workload execution is optimized under arrival causality, maximum delay, daily energy balance constraints, and the resulting active and reactive loads are embedded in a distribution power flow model. Compared with the baseline without load shifting, the maximum bus level HC gain on the IEEE 33-bus system reaches 4.46% for MEC DCs and 9.89% for AI DCs, while CDN case shows negligible gain under the assumed cache management windows.

Low-altitude platforms are evolving from connected sensing nodes into autonomous agents capable of bounded task interpretation, skill invocation, and local decision making. Increasing the intelligence of individual unmanned aerial vehicles (UAVs), however, does not resolve the organization problem that arises when mission workflows depend on capabilities distributed across multiple platforms and provider feasibility changes with network, resource, and mobility conditions. We propose low-altitude agentic networking as a task-level networking paradigm and develop a reference architecture that separates workflow logic from executor identity. Within a validated domain-specific workflow space, each ready operation is translated into a capability requirement and dynamically bound to an agent that remains feasible under current conditions. Task contracts and structured evidence govern workflow execution, while routine provider and link changes are handled through workflow-preserving local adaptation before high-level repair is invoked. A trace-calibrated discrete-event case study identifies a bounded operating region: conventional organization remains effective for simple workflows under stable conditions, whereas agentic networking becomes beneficial when workflow dependencies are tightly coupled, capability feasibility changes during execution, and viable local recovery paths remain available.

We propose FedLoRD, a personalized federated learning (PFL) method based on the low-rank structure of the differences between the global and personalized model parameters. PFL is a branch of federated learning that aims to learn models tailored to the local data of each client under biased local data distributions. In its local training, each client learns a personalized model by regularizing the difference between its model parameters and the global ones sent from the server. The regularization function needs to be designed with priors on the model parameter difference; however, the existing PFL methods rely on empirical choices (e.g., $\ell_1$- and $\ell_2$-norms), which do not take into account the actual characteristics of the difference. The resulting mismatch of priors can lead to suboptimal personalized models. To address this issue, we propose a PFL method whose regularizer is built on an observed characteristic of the model parameter differences: they are approximately low-rank at every layer. In our method, we first formulate a local training problem with nuclear norm regularization on the layer-wise differences. We further rewrite this problem via the factorized representation of the nuclear norm so that it can be efficiently solved by a plain stochastic gradient descent method. Image classification experiments on three benchmark datasets demonstrate that FedLoRD outperforms several existing PFL methods.

High-dimensional microscopy captures features across space, time, and spectrum, but complex backgrounds obscure signals. We present Background2Background (B2B), a self-supervised background removal framework leveraging the backgrounds larger correlation relative to the signal. Local pixel shuffling disrupts signal correlations while preserving background structure, transforming signals into effective noise for Noise2Noise-based background removal. Across fluorescence, stimulated Raman scattering and mid-infrared photothermal imaging, B2B achieves over 40-fold background suppression and reveals hidden signatures.

This paper leverages control barrier functions (CBFs) to systematically account for constraints of voltage source converters (VSCs) and photovoltaic (PV) resources in grid-forming (GFM) control. The proposed control combines (unconstrained) dual-port GFM control with control barrier functions (CBFs) to ensure reliable and safe operation of a GFM PV system under grid faults, sudden changes in irradiance, and load perturbations. First, CBFs are used to encode physical and operational limits of the PV system, namely current and voltage limits at its ac and dc terminals. Then, CBFs are used to restrict the dynamics of dual-port GFM control to within the VSC and PV limits during both transient and steady states. To this end, a cost function is defined that encodes trade-offs between forming the ac voltage phase angle/frequency and voltage magnitude under constraints. Then, real-time optimization is used to make the least modification, as measured by the cost function, to the (unconstrained) dual-port GFM control state to remain within the safe set encoded by the CBFs. Finally, the performance of the developed constrained GFM control is evaluated through EMT simulations in MATLAB/Simulink.

We develop analytical sufficient conditions for certifying the local asymptotic stability of nonlinear autonomous systems based on Lyapunov functions derived from their finite-order Taylor polynomial approximations. This approach translates the stability analysis problem from the original nonlinear dynamics to a more computationally tractable polynomial domain. The derived conditions allow for the explicit calculation of a guaranteed inner approximation of the region of attraction ($\mathrm{ROA}$). We show how sum-of-squares (SOS) programming and grid search can be used to systematically search for polynomial Lyapunov functions that yield a large certified threshold radius and extract a certified invariant set inner approximation of the $\mathrm{ROA}$ of the nonpolynomial original system. The framework is then applied to a control tuning problem: finding reduced-effort Linear Quadratic Regulator (LQR) controllers that ensure that a given initial state of interest belongs to the certified $\mathrm{ROA}$ of the nonpolynomial closed loop system. The effectiveness of the overall methodology is demonstrated using a pendulum with nonlinear damping and a nonlinear control system.

A passive extremely large-scale reconfigurable intelligent surface (XL-RIS) offers a position-sensitive aperture for near-field sensing, but its element-wise signals are not directly observed. Recovering this virtual RIS observation (VOR) from fewer base-station measurements requires prior information. This paper investigates when calibrated propagation geometry or paired training data provides the more effective reconstruction prior. For a common sensing operator and target signal, we develop off-grid near-field sparse parametric reconstruction (OG-NF-SPR) and compare it with compact and DenseNet-based deep reconstructors. OG-NF-SPR uses reflected-path projection for position initialization, followed by Bayesian estimation of all path gains and continuous position refinement. A causal extension incorporates temporal correlation without trajectory labels. Controlled simulations use near-field UAV tracking as an illustration and separately examine noise, position-distribution shift, calibration error, and propagation mismatch. Under matched propagation at 20 dB, OG-NF-SPR achieves approximately 67% lower reconstruction NMSE than the two nominal deep networks. At low SNR, statistical averaging is more effective than selecting a single geometric explanation. Under sufficiently strong repeatable mismatch, a same-size network trained on the mismatch family outperforms the nominal physical estimator, whereas nominal training does not provide this benefit. Temporal regularization also reduces position RMSE from 0.653 m to 0.225 m on a shared trajectory. The results relate reconstruction performance to measurement quality, calibration accuracy, and training coverage, providing guidance on when geometry can replace supervised labels and when additional data are useful.

The shift towards 6G wireless networks calls for advanced waveforms to support emerging applications such as ultra-reliable and massive machine-type communications. Among the proposed candidates, OTFS, ODDM, OTSM, and OCDM share the common feature of exploiting the delay domain for data transmission. While these waveforms offer significant advantages in high-mobility and sensing scenarios, their integration into multi-user uplink systems reveals a critical challenge: timing misalignment (TM). Unlike OFDM, where residual timing offsets translate into phase rotations, delay-based waveforms experience actual shifts of resources, whose impact we investigate in this work. We show that the aforementioned waveforms can be unified under a time-invariant setup through a common input–output relation, which enables a unified analysis of TM in uplink resource allocation. The results reveal a fundamental trade-off between interference mitigation and guard overhead in the presence of TM. We further benchmark the observed behavior against OFDM under Doppler spread as a reference impairment and highlight the analogy between TM in delay-based allocation and Doppler spread in frequency-based allocation. The results show that delay-based waveforms can maintain a net performance advantage over OFDM in the considered setup. Beyond this unified Doppler-free analysis, we evaluate the joint impact of Doppler spread and TM on each waveform individually. We also investigate BER performance and show how coding can mitigate the resulting interference. This study therefore establishes a problem framework that motivates the development of future compensation and scheduling strategies for practical multi-user operation of delay-based waveforms.

In this paper, we investigate the problem of mitigating non-recurrent congestion in urban transportation networks through predictive and adaptive routing for connected and automated vehicles (CAVs). We propose a collision-aware predictive rerouting framework that integrates microscopic SUMO simulation, DCRNN-based spatiotemporal traffic-state forecasting, collision-window lookahead, and multi-objective route scoring. The routing controller uses predicted congestion, live emission information, route-length penalties, and incident-window constraints to decide when CAVs deviate from their current routes. The completed simulation campaign across three urban network scenarios, five CAV penetration levels, three random seeds, and multiple benchmark rerouting strategies shows that the proposed full framework does not dominate all baselines in aggregate travel time, distance, or emissions. Instead, its principal advantage is operational stability: it maintains comparable network-level efficiency while substantially reducing rerouting frequency relative to active rerouting baselines. These results demonstrate both the promise and the tradeoffs of embedding graph-based prediction and collision awareness into real-time CAV rerouting, and they motivate further work on forecast calibration, uncertainty-aware prediction, and system-level fairness between CAV and non-CAV traffic.

As power systems shift toward high shares of wind and solar, long-duration storage becomes pivotal for balancing multi-day deficits and surpluses of uncertain supply and demand. Current dispatch strategies for long-duration storage either assign a fixed value to stored energy or extend the modeled horizon using a single forecast. Stochastic system dispatch with a multi-day horizon for long-duration storage remains unexplored, and existing stochastic dispatch only considers day-ahead uncertainty or Markovian uncertainty beyond one week of perfect foresight. Therefore, we benchmark stochastic multi-day dispatch against deterministic alternatives in a rolling-horizon setup. To emulate probabilistic multi-day forecasts, we combine diffusion with a semi-Markov model of weather regimes and a logistic growth model for meteorological forecast errors. At high renewable shares, stochastic operation reduces thermal backup generation and consistently eliminates loss of load. The benefits decrease with smaller shares of renewables, less long-duration storage, or higher forecast skill. Overall, our results suggest that systems with high shares of variable renewables can substantially benefit from probabilistic multi-day forecasts and stochastic dispatch.

This paper addresses the control of autonomous underwater vehicles (AUVs) operating in dynamic environments, where wave-induced forces, modelling errors, and actuator limitations make reliable control challenging. Active disturbance rejection control (ADRC), together with an extended state observer (ESO), estimates lumped uncertainties online, simplifying control design using nominal models. However, existing stability analyses typically assume bounded control inputs and derivatives, which are difficult to guarantee under unknown disturbances and measurement noise. To bridge this theory–practice gap, this paper introduces an ADRC framework with active input saturation and post-saturation low-pass filtering. The saturation constrains the control magnitude, while the filter regularizes the saturated signal to ensure bounded input-rate variations. By explicitly enforcing these conditions, the proposed system guarantees semi-global practical asymptotic stability. High-fidelity simulations and experiments on an underwater robotic platform demonstrate robust control performance under severe environmental disturbances and actuator constraints. A supplementary video is available at https://youtu.be/AEFlZ69Yg00.

With the incessant demand for green and sustainable communications, energy efficiency (EE) has become a cornerstone of sixth-generation (6G) network design, especially for cell-free millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, which can achieve significant throughput gains by leveraging large-scale antenna arrays. Adaptive radio frequency (RF) chain activation (ARFA) is a key technique for reducing power consumption without severely degrading the achievable rate, thereby enhancing the total EE of 6G MIMO systems. In this paper, we propose a minimum-cost maximum-flow (MCMF)-based ARFA scheme to reduce unnecessary power consumption and maximize the total EE of uplink mmWave cell-free massive MIMO systems. Simulation results indicate that the proposed approach can significantly improve the total EE while maintaining a relatively high achievable rate and acceptable computational complexity.

As the complexity of space missions increases, autonomous spacecraft guidance must accommodate transitions between mission phases with distinct dynamics, objectives, and constraints. Rendezvous and proximity operations (RPO) are particularly challenging because they span multiple operating regimes, from orbital-scale matching and far-range approach to close-range maneuvering and terminal docking. Existing reinforcement-learning approaches generally address only a subset of this sequence or rely on manually defined switching conditions between controllers. This study presents a hierarchical reinforcement-learning framework for autonomous, three-degree-of-freedom RPO that spans the complete trajectory from orbit matching to docking. Five specialized policies are trained for orbit matching, far-range approach, mid-range approach, close-range approach, and docking. A learned high-level manager policy then selects among these phase-specific controllers based on the evolving spacecraft state. The hierarchical approach is evaluated over the full end-to-end rendezvous sequence and compared with a fixed sequential-invocation heuristic to assess trajectory performance and transition behavior. Training and inference metrics are also analyzed for the individual phase policies and the complete hierarchical system.

We present a perceptual audio coder in which the entropy-coding model and the bit allocation co-adapt across blocks without model side information. Mantissas are coded by a hybrid of bit packing and arithmetic coding with per-band probability models learned online. Encoder and decoder accumulate identical histograms from decoded blocks and advance the same model state. The model enters bit allocation directly: per-band rate–distortion frontiers combine measured perceptual distortion with entropy-coded rate estimates under the current model, and a dynamic program attains the exact minimum of total objective perceptual distortion for each block's budget under the model's rate estimates, with reservoir carry-over. Under a strict per-block rate, entropy-coding-aware bit allocation lowers this distortion (band noise-to-mask ratio in linear scale) by 15-24% relative to the same solver without it, and co-adaptation removes a further 3-4% (up to 15% on oboe) that a backward-adaptive coder alone cannot realize in one pass; a MUSHRA test at 80 kbps per channel confirms the ranking.

We develop a Fisher-information framework for optimizing single-photon detector gating in periodic experiments, in contrast to previous more empirical or heuristic gating choices. After each photon detection, such a detector is insensitive for a dead time, so that an early background detection can obscure a later signal. Gating preserves availability for informative photons by selectively deactivating the detector. In our high-pile-up simulation of distance-attenuated lidar, our practical data-independent gating design reduces the worst-case asymptotic range variance by more than half relative to the free-running acquisition (which does not use any gating). More generally, we characterize achievable detector-active frequencies as a convex polytope and construct an adaptive rule that asymptotically attains an optimal "oracle" design without prior knowledge of the physical parameters. We also derive practical criteria that quantify the potential gains of gating and/or photon attenuation over unattenuated free-running acquisition.

Cortisol is an essential hormone secreted by the adrenal glands. Abnormally low level of cortisol is referred to as adrenal insufficiency. This paper focuses on devising treatment strategies for patients suffering from adrenal insufficiency using a model-based approach. Two conditions of adrenal insufficiency are analysed. They are hypothalamic-pituitary (HP) and adrenal gland disorders. The hypothalamus, pituitary, and adrenal glands constitute the hypothalamus-pituitary-adrenal (HPA) axis, which regulates stress and maintains physiological homeostasis through cortisol secretion. A comparative analysis of the existing mathematical models of the HPA axis is conducted. Based on this analysis, a novel model of the HPA axis is proposed that incorporates endogenous circadian rhythm. This proposed model is used to design effective cortisol replacement strategies. In addition, hydrocortisone replacement therapies are proposed and validated with clinical data from the literature. The results presented in this paper are applicable to patient-specific treatment strategies for individuals with adrenal insufficiency.

This thesis presents the direct electromechanical actuation of the shift drum in a non-synchronized motorcycle transmission. A DC motor drives the shift drum through a self-locking worm gear, so the entire foot shift mechanism is eliminated. As a reference, 84 manual gear changes of a large-displacement series-production motorcycle were measured on a chassis dynamometer; the mean shift time is 80 ms for upshifts and 92 ms for downshifts. The system design covers the complete chain from the mechanics to the software. The actuator motor is sized from inertia, friction and gear ratio. A dedicated shift controller is developed with supply filtering, signal conditioning for the position sensor and the incremental encoder, an H-bridge motor driver and a custom circuit board based on a SAMD21 microcontroller. The embedded firmware implements position control of the shift drum with a proportional controller, without inner speed or current loops. On a gearbox test rig, a gear change is completed in less than 80 ms without overshoot in a single measurement, below the mean of the manual reference shifts. The analysis reveals a trade-off in the choice of the worm gear ratio: at a ratio of 30, about 93% of the motor torque is needed to accelerate the motor shaft and the worm. A ratio of about 8 halves the required torque, but the worm gear then loses its self-locking. The measured shift times are published as an open dataset.

Liveness requires a dynamical system to repeatedly accomplish a desired objective over time. In this work, we consider a particular form of liveness known as recurrence, which requires the closed-loop trajectory to regularly revisit a prescribed set, with no more than a specified amount of time between visits. Inspired by the control barrier function (CBF) safety-filter paradigm, we develop a controller-filtering framework that makes adjustments to a nominal controller only when necessary to guarantee recurrence. Unlike conventional CBF methods, which enforce set forward invariance, our framework allows the trajectory to leave the set while guaranteeing its timely return. To achieve this, our framework combines two components: a backup reachability CBF that preserves the ability to return to the prescribed set within the allowable time, and a timing-based mechanism that invokes a reaching controller only when the nominal behavior would otherwise fail to meet the return-time requirement. The resulting framework guarantees recurrence while preserving the nominal behavior when possible, enabling control objectives that require temporary excursions from the set, such as traversal between disjoint regions. Our method is successfully utilized in a drone racing example with a recurrence constraint on beacon visibility.

Electric arc furnaces inject stochastic reactive-power swings that cause voltage flicker at the point of common coupling. This paper compares two STATCOM inner controllers for an 11~kV, 50~Hz furnace feeder: a split-frequency DQ-PI design and a Lyapunov-based nonlinear design whose integral state is retained in the storage function, giving global asymptotic stability of the nominal unsaturated current-error subsystem. Gains for both controllers were tuned by a frozen metaheuristic campaign and then evaluated over 600~s with an IEC~61000-4-15 digital flickermeter. A post-campaign audit showed that the stochastic arc realization used for the original comparison was periodic, so the two selected gain sets were frozen without retuning or reselection and evaluated on ten independent stochastic realizations that had been specified and archived before the STO-ENS validation campaign, together with a paired uncompensated case for each realization. Both controllers reduce the mean short-term flicker severity by about 62% relative to the STO-ENS uncompensated baseline, and no statistically detectable difference in mean flicker severity is found between them. They separate instead in realization sensitivity: the Lyapunov standard deviation across arcs is roughly one third that of DQ-PI. Controller ordering inferred from a single stochastic trajectory is therefore not a reliable measure of stochastic robustness, and ensemble validation is necessary.

Deep unrolling methods for inverse problems augment traditional reconstruction algorithms with learned priors to improve their performance. These methods have been developed under the implicit assumption that the geometry of the measurement operator does not change between training and deployment; when this is no longer the case, the quality of obtained reconstructions can degrade significantly. In this paper, we argue that this is not an inherent limitation of the deep unrolling paradigm and can be mitigated by a careful choice of the underlying iterative algorithm. We propose a deep unrolling framework for the phase retrieval problem that augments the alternating projections method with a learned denoising step. Our method obtains high-quality reconstructions without the need for retraining in settings where conventional model-based alternatives struggle, such as when the dynamic range of the measurement operator changes significantly at test time.