Papers

Large-scale cyber-physical systems (CPS), such as railway control systems and smart grids, consist of geographically distributed subsystems that are connected via unreliable, asynchronous inter-region networks. Their scale and distribution make them especially vulnerable to faults and attacks. Unfortunately, existing fault-tolerant methods either consume excessive resources or provide only eventual guarantees, making them unsuitable for real-time resource-constrained CPS. We present GeoShield, a resource-efficient solution for defending geo-distributed CPS against Byzantine faults. GeoShield leverages the property that CPS are designed to tolerate brief disruptions and maintain safety, as long as they recover (i.e., resume normal operations or transition to a safe mode) within a bounded amount of time following a fault. Instead of masking faults, it detects them and recovers the system within bounded time, thus guaranteeing safety with much fewer resources. GeoShield introduces protocols for Byzantine fault-resilient network measurement and inter-region omission fault detection that proactively detect malicious message delays, along with recovery mechanisms that guarantee timely recovery while maximizing operational robustness. It is the first bounded-time recovery solution that operates effectively under unreliable networks without relying on trusted hardware. Evaluations using real-world case studies show that it significantly outperforms existing methods in both effectiveness and resource efficiency.

A wireless channel foundation model for pathloss map generation (WiCo-PG) via Synesthesia of Machines (SoM) is developed for the first time. Considering sixth-generation (6G) uncrewed aerial vehicle (UAV)-to-ground (U2G) scenarios, a new multi-modal sensing-communication dataset is constructed for WiCo-PG pre-training, including multiple U2G scenarios, diverse flight altitudes, and diverse frequency bands. Based on the constructed dataset, the proposed WiCo-PG enables cross-modal pathloss map generation by leveraging RGB images from different scenarios and flight altitudes. In WiCo-PG, a novel network architecture designed for cross-modal pathloss map generation based on dual vector quantized generative adversarial networks (VQGANs) and Transformer is proposed. Furthermore, a novel frequency-guided shared-routed mixture of experts (S-R MoE) architecture is designed for cross-modal pathloss map generation. Simulation results demonstrate that the proposed WiCo-PG achieves improved pathloss map generation accuracy through pre-training with a normalized mean squared error (NMSE) of 0.012, outperforming the large language model (LLM)-based scheme, i.e., LLM4PG, and the conventional deep learning-based scheme by more than 6.98 dB. The enhanced generality of the proposed WiCo-PG can further outperform the LLM4PG by at least 1.37 dB using 2.7% samples in few-shot generalization.

Precise modeling of channel multipath is essential for understanding wireless propagation environments and optimizing communication systems. In particular, sixth-generation (6G) artificial intelligence (AI)-native communication systems demand massive and high-quality multipath channel data to enable intelligent model training and performance optimization. In this paper, we propose a wireless channel foundation model (WiCo) for multipath generation (WiCo-MG) via Synesthesia of Machines (SoM). To provide a solid training foundation for WiCo-MG, a new synthetic intelligent sensing-communication dataset for uncrewed aerial vehicle (UAV)-to-ground (U2G) communications is constructed. To overcome the challenges of cross-modal alignment and mapping, a two-stage training framework is proposed. In the first stage, sensing images are embedded into discrete-continuous SoM feature spaces, and multipath maps are embedded into a sensing-initialized discrete SoM space to align the representations. In the second stage, a mixture of shared and routed experts (S-R MoE) Transformer with frequency-aware expert routing learns the mapping from sensing to channel SoM feature spaces, enabling decoupled and adaptive multipath generation. Experimental results demonstrate that WiCo-MG achieves state-of-the-art in-distribution generation performance and superior out-of-distribution generalization, reducing NMSE by more than 2.59 dB over baselines, while exhibiting strong scalability in model and dataset growth and extensibility to new multipath parameters and tasks. Owing to higher accuracy, stronger generalization, and better scalability, WiCo-MG is expected to enable massive and high-fidelity channel data generation for the development of 6G AI-native communication systems.

Power systems remain highly vulnerable to disturbances and cyber-attacks, underscoring the need for resilient and adaptive control strategies. In this work, we investigate a data-driven Federated Learning Control (FLC) framework for transient stability resilience under cyber-physical disturbances. The FLC employs interpretable neural controllers based on the Chebyshev Kolmogorov-Arnold Network (ChebyKAN), trained on a shared centralized control policy and deployed for distributed execution. Simulation results on the IEEE 39-bus New England system show that the proposed FLC consistently achieves faster stabilization than distributed baselines at moderate control levels (10%–60%), highlighting its potential as a scalable, resilient, and interpretable learning-based control solution for modern power grids.

Spatially distributed peripheral nerve recordings can be used to reconstruct motor intention and improve natural control of prosthetics However, many existing clinical solutions rely on percutaneous wires to access peripheral nerves; these sites are prone to infection and motion-induced electrode degradation, preventing chronic use. To address the need for fully wireless neural recording systems, this paper presents DustNet: a spatially-distributed network of ultrasonically-powered neural recording implants capable of supporting up to 8 simultaneously recording nodes over a single ultrasound link. To enable high throughput multi-implant communication, DustNet implements a time-division multiple-access (TDMA) protocol with up to 16-level amplitude modulation of the ultrasound backscatter that achieves up to 4x higher data rates than traditional on-off keying methods. Each neural implant consists of a 0.7x0.7x0.7 mm$^3$ piezoceramic transducer, a 100 nF off-chip capacitor, and an IC mounted on a flexible PCB. The implant IC was fabricated in a 28nm CMOS process and occupies an area of 0.43 mm$^2$. System functionality was verified at 90mm depth in oil, achieving a maximum measured data rate of 200 kb/s at 2 MHz ultrasound carrier frequency, with each implant transmitting uplink data at 50 kb/s and dissipating just 7 $\mu$W; the system is demonstrated to support up to 400 kb/s total data rate over the same link.

Automatic transcription of stuttered speech remains a challenge, even for modern end-to-end (E2E) automatic speech recognition (ASR) frameworks. Dysfluencies and fluency-shaping artifacts are often overlooked, resulting in non-verbatim transcriptions with limited clinical and research value. We propose a parameter-efficient adaptation method to decode dysfluencies and fluency modifications as special tokens within transcriptions, evaluated on simulated (LibriStutter, English) and natural (KSoF, German) stuttered speech datasets. To mitigate ASR performance disparities and bias towards English, we introduce a multi-step fine-tuning strategy with language-adaptive pretraining. Tokenization analysis further highlights the tokenizer's English-centric bias, which poses challenges for improving performance on German data. Our findings demonstrate the effectiveness of lightweight adaptation techniques for dysfluency-aware ASR while exposing key limitations in multilingual E2E systems.

In the pursuit of sustainable manufacturing, ultra-short pulse laser micromachining stands out as a promising solution while also offering high-precision and qualitative laser processing. However, unlocking the full potential of ultra-short pulse lasers requires an optimized monitoring system capable of early detection of defective workpieces, regardless of the preprocessing technique employed. While advances in machine learning can help predict process quality features, the complexity of monitoring data necessitates reducing both model size and data dimensionality to enable real-time analysis. To address these challenges, this paper introduces a machine learning framework designed to enhance surface quality assessment across diverse preprocessing techniques. To facilitate real-time laser processing monitoring, our solution aims to optimize the computational requirements of the machine learning model. Experimental results show that the proposed model not only outperforms the generalizability achieved by previous works across diverse preprocessing techniques but also significantly reduces the computational requirements for training. Through these advancements, we aim to establish the baseline for a more sustainable manufacturing process.

Accurately recognizing human context from smartphone sensor data remains a significant challenge, especially in sedentary settings where activities such as studying, attending lectures, relaxing, and eating exhibit highly similar inertial patterns. Furthermore, social context plays a critical role in understanding user behavior, yet is often overlooked in mobile sensing research. To address these gaps, we introduce LogMe, a mobile sensing application that passively collects smartphone sensor data (accelerometer, gyroscope, magnetometer, and rotation vector) and prompts users for hourly self-reports capturing both sedentary activity and social context. Using this dual-label dataset, we propose DySTAN (Dynamic Cross-Stitch with Task Attention Network), a multi-task learning framework that jointly classifies both context dimensions from shared sensor inputs. It integrates task-specific layers with cross-task attention to model subtle distinctions effectively. DySTAN improves sedentary activity macro F1 scores by 21.8% over a single-task CNN-BiLSTM-GRU (CBG) model and by 8.2% over the strongest multi-task baseline, Sluice Network (SN). These results demonstrate the importance of modeling multiple, co-occurring context dimensions to improve the accuracy and robustness of mobile context recognition.

Electrifying passenger cars will impact future power systems. To understand the challenges and opportunities that arise, it is necessary to reflect "sector coupling" in the modeling space. This paper focuses on a specific modeling approach that includes dozens of individual BEV profiles rather than one aggregated BEV profile. Although including additional BEV profiles increases model complexity and runtime, it avoids losing information in the aggregation process. We investigate how many profiles are needed to ensure the accuracy of the results and the extent to which fewer profiles can be traded for runtime efficiency gains. We also examine whether selecting specific profiles influences optimal results. We demonstrate that including too few profiles may result in distorted optimal solutions. However, beyond a certain threshold, adding more profiles does not significantly enhance the robustness of the results. More generally, for fleets of 5 to 20 million BEVs, we derive a rule of thumb consisting in including enough profiles such that each profile represents 200,000 to 250,000 vehicles, ensuring accurate results without excessive runtime.

Game engines such as Unity and Unreal Engine have become popular tools for creating perceptual and behavioral experiments in complex, interactive environments. They are often used with flat-panel displays, and also with head-mounted displays. Here I describe and test a mathematical model of luminance and color in Unity's High Definition Render Pipeline (HDRP). I show that the HDRP has several non-obvious features, such as nonlinearities applied to material properties and rendered values, that must be taken into account in order to show well-controlled stimuli. I also show how the HDRP can be configured to display gamma-corrected luminance and color, and I provide software to create the specialized files needed for gamma correction.

Spaceborne Light Detection and Ranging (LiDAR) systems, such as NASA's Global Ecosystem Dynamics Investigation (GEDI), provide forest structure for global carbon assessments. However, geolocation uncertainties (typically 5-15 m) propagate systematically through derived products, undermining forest profile estimates, including carbon stock assessments. Existing correction methods face critical limitations: waveform simulation approaches achieve meter-level accuracy but require high-resolution LiDAR data unavailable in most regions, while terrain-based methods employ deterministic grid searches that may overlook optimal solutions in continuous solution spaces. We present SALPA (Spaceborne LiDAR Point Adjustment), a multi-algorithm optimization framework integrating three optimization paradigms with five distance metrics. Operating exclusively with globally available digital elevation models and geoid data, SALPA explores continuous solution spaces through gradient-based, evolutionary, and swarm intelligence approaches. Validation across contrasting sites: topographically complex Nikko, Japan, and flat Landes, France, demonstrates 15-16% improvements over original GEDI positions and 0.5-2% improvements over the state-of-the-art GeoGEDI algorithm. L-BFGS-B with Area-based metrics achieves optimal accuracy-efficiency trade-offs, while population-based algorithms (genetic algorithms, particle swarm optimization) excel in complex terrain. The platform-agnostic framework facilitates straightforward adaptation to emerging spaceborne LiDAR missions, providing a generalizable foundation for universal geolocation correction essential for reliable global forest monitoring and climate policy decisions.

This paper addresses data quality issues in multimodal emotion recognition in conversation (MERC) through systematic quality control and multi-stage transfer learning. We implement a quality control pipeline for MELD and IEMOCAP datasets that validates speaker identity, audio-text alignment, and face detection. We leverage transfer learning from speaker and face recognition, assuming that identity-discriminative embeddings capture not only stable acoustic and Facial traits but also person-specific patterns of emotional expression. We employ RecoMadeEasy(R) engines for extracting 512-dimensional speaker and face embeddings, fine-tune MPNet-v2 for emotion-aware text representations, and adapt these features through emotion-specific MLPs trained on unimodal datasets. MAMBA-based trimodal fusion achieves 64.8% accuracy on MELD and 74.3% on IEMOCAP. These results show that combining identity-based audio and visual embeddings with emotion-tuned text representations on a quality-controlled subset of data yields consistent competitive performance for multimodal emotion recognition in conversation and provides a basis for further improvement on challenging, low-frequency emotion classes.

Reconstruction of 3D erythrocyte or red blood cell (RBC) morphology from partial observations, such as microscope images, is essential for understanding the physiology of RBC aging and the pathology of various RBC disorders. In this study, we propose a multi-fidelity neural network (MFNN) approach to fuse high-fidelity cross-sections of an RBC, with a morphologically similar low-fidelity reference 3D RBC shape to recover its full 3D surface. The MFNN predictor combines a convolutional neural network trained on low-fidelity reference RBC data with a feedforward neural network that captures nonlinear morphological correlations, and augments training with surface area and volume constraints for regularization in the low-fidelity branch. This approach is theoretically grounded by a topological homeomorphism between a sphere and 3D RBC surfaces, with training data generated by dissipative particle dynamics simulations of stomatocyte-discocyte-echinocyte transformation. Benchmarking across diverse RBC shapes observed in normal and aged populations, our results show that the MFNN predictor can reconstruct complex RBC morphologies with over 95% coordinate accuracy when provided with at least two orthogonal cross-sections. It is observed that informative oblique cross-sections intersecting spicule tips of echinocytes improve both local and global feature reconstruction, highlighting the value of feature-aware sampling. Our study further evaluates the influence of sampling strategies, shape dissimilarity, and noise, showing enhanced robustness under physically constrained training. Altogether, these results demonstrate the capability of MFNN to reconstruct the 3D shape of normal and aged RBCs from partial cross-sections as observed in conventional microscope images, which could facilitate the quantitative analysis of RBC morphological parameters in normal and disease-related RBC samples.

The method described in the first part for frequency-selectively measuring the transfer function and the noise power spectral density of the superimposed noise at the output of a disturbed, real system with nonlinearities using windowing was limited to time-invariant systems with stationary and zero-mean processes. Here, we investigate how this measurement method can be extended so that all correlations existing between the input and output signals can also be measured using windowing for a periodically time-varying system disturbed by a cyclostationary noise process. An extended version of the window construction algorithm presented in the first part is introduced, in which some degrees of freedom not used there can be used to appropriately influence the properties of the window sequence depending on the application. – Das im ersten Teil beschriebene Verfahren die Uebertragungsfunktion und das Rauschleistungsdichtespektrum des ueberlagerten Rauschens am Ausgang eines gestoerten, realen Systems mit Nichtlinearitaeten mit Hilfe der Fensterung frequenzselektiv zu vermessen beschraenkte sich auf zeitinvariante Systeme mit stationaeren und mittelwertfreien Prozessen. Hier wird untersucht, wie dieses Messverfahren zu erweitern ist, so dass man damit auch alle Korrelationen, die zwischen Ein- und Ausgangssignal bestehen, bei einem periodisch zeitvarianten System, das von einem zyklostationaeren Rauschprozess gestoert wird, mit einer Fensterung messen kann. Es wird eine erweiterte Variante des im ersten Teil vorgestellten Algorithmus zur Konstruktion des Fensters angegeben, bei der einige dort nicht genutzte Freiheitsgrade dazu verwendet werden koennen, die Eigenschaften der Fensterfolge je nach Applikation geeignet zu beeinflussen.

This technical report investigates the performance of pre-trained audio models on COVID-19 detection tasks using established benchmark datasets. We fine-tuned Audio-MAE and three PANN architectures (CNN6, CNN10, CNN14) on the Coswara and COUGHVID datasets, evaluating both intra-dataset and cross-dataset generalization. We implemented a strict demographic stratification by age and gender to prevent models from exploiting spurious correlations between demographic characteristics and COVID-19 status. Intra-dataset results showed moderate performance, with Audio-MAE achieving the strongest result on Coswara (0.82 AUC, 0.76 F1-score), while all models demonstrated limited performance on Coughvid (AUC 0.58-0.63). Cross-dataset evaluation revealed severe generalization failure across all models (AUC 0.43-0.68), with Audio-MAE showing strong performance degradation (F1-score 0.00-0.08). Our experiments demonstrate that demographic balancing, while reducing apparent model performance, provides more realistic assessment of COVID-19 detection capabilities by eliminating demographic leakage - a confounding factor that inflate performance metrics. Additionally, the limited dataset sizes after balancing (1,219-2,160 samples) proved insufficient for deep learning models that typically require substantially larger training sets. These findings highlight fundamental challenges in developing generalizable audio-based COVID-19 detection systems and underscore the importance of rigorous demographic controls for clinically robust model evaluation.

Load shifting by commercial and industrial power consumers reduces costs and Scope 2 emissions for the consumer and the grid. Incentivizing this behavior requires tools for valuing flexibility amidst the heterogeneity in load characteristics across diverse sectors and the spatiotemporal variation in electricity prices and emissions factors. This work presents a top-down approach to screen and broadly understand the benefits of flexibility based on system uptime, power capacity (PC), energy capacity (EC), and round- trip efficiency (RTE). Depending on the region and season, cost savings from flexibility range from 0 to over 100% and emissions savings are generally bounded between 5-40%. We also find the magnitude and cost of emissions abatement from flexibility is highly variable and, in some cases, up to four orders of magnitude less than regional renewable energy credits or common investing or policy benchmarks like the social cost of carbon. While the value of flexibility is highly dynamic, estimating savings as a function of load characteristics and incentives can inform heuristic design of new systems, siting strategies, comparison of flexibility to other decarbonization options, and new avenues for incentivizing flexibility.

We develop a $\mu$-analysis and synthesis framework for infinite-dimensional systems that leverages the Integral Quadratic Constraints (IQCs) to compute the structured singular value's upper bound. The methodology formulates robust stability and performance conditions jointly as Linear Partial Integral Inequalities within the Partial Integral Equation framework, establishing connections between IQC multipliers and $\mu$-theory. Computational implementation via PIETOOLS enables computational tools that practically applicable to spatially distributed infinite dimensional systems. Illustrations with the help of Partial and Delay Differential Equations validate the effectiveness of the framework, showing a significant reduction in conservatism compared to unstructured methods and providing systematic tools for stability-performance trade-off analysis.

While machine learning is widely used to optimize wireless networks, training a separate model for each task in communication and localization is becoming increasingly unsustainable due to the significant costs associated with training and deployment. Foundation models offer a more scalable alternative by enabling a single model to be adapted across multiple tasks through fine-tuning with limited samples. However, current foundation models mostly rely on large-scale Transformer architectures, resulting in computationally intensive models unsuitable for deployment on typical edge devices. This paper presents a lightweight foundation model based on simple Multi-Layer-Perceptron (MLP) encoders that independently process input patches. Our model supports 4 types of downstream tasks (long-range technology recognition, short-range technology recognition, modulation recognition and line-of-sight-detection) from multiple input types (IQ and CIR) and different sampling rates. We show that, unlike Transformers, which can exhibit performance drops as downstream tasks are added, our MLP model maintains robust generalization performance, achieving over 97% accurate fine-tuning results for previously unseen data classes. These results are achieved despite having only 21K trainable parameters, allowing an inference time of 0.33 ms on common edge devices, making the model suitable for constrained real-time deployments.

There is already a method known from the literature with which it is possible to measure both the transfer function and the noise power spectral density of the superimposed noise at the output of a disturbed, time-invariant, real system with nonlinearities in one measurement. By using a suitable window, the frequency selectivity of the measurement of the power spectral density of the superimposed noise can be noticeably improved without significantly increasing the computational complexity of the method. The unbiasedness and consistency of the measurement method with a suitable window are proven. The measurement of the power spectral density of a zero-mean, stationary process without measuring the transfer function is investigated as a special case for both real-valued and complex-valued signals. It is derived which requirements a suitable window should meet and how it can be calculated with high numerical accuracy. – Es gibt bereits eine aus der Literatur bekannte Methode mit der es moeglich ist in einer Messung sowohl die Uebertragungsfunktion als auch das Rauschleistungsdichtespektrum des ueberlagerten Rauschens am Ausgang eines gestoerten, zeitinvarianten, realen Systems mit Nichtlinearitaeten zu messen. Durch den Einsatz eines geeigneten Fensters kann die Frequenzselektivitaet der Messung des Leistungsdichtespektrums der Stoerung deutlich verbessert werden, ohne den Aufwand der Berechnungen des Verfahrens nennenswert zu erhoehen. Die Erwartungstreue und die Konsistenz des Messverfahren mit Fensterung wird gezeigt. Die Messung des Leistungsdichtespektrums eines mittelwertfreien, stationaeren Prozesses ohne Messung der Uebertragungsfunktion wird als Sonderfall sowohl fuer reellwertige, als auch fuer komplexwertige Signale untersucht. Es wird hergeleitet, welche Forderungen ein geeignetes Fenster erfuellen sollte und wie es sich numerisch hochgenau berechnen laesst.

As perception-based controllers for autonomous systems become increasingly popular in the real world, it is important that we can formally verify their safety and performance despite perceptual uncertainty. Unfortunately, the verification of such systems remains challenging, largely due to the complexity of the controllers, which are often nonlinear, nonconvex, learning-based, and/or black-box. Prior works propose verification algorithms that are based on approximate reachability methods, but they often restrict the class of controllers and systems that can be handled or result in overly conservative analyses. Hamilton-Jacobi (HJ) reachability analysis is a popular formal verification tool for general nonlinear systems that can compute optimal reachable sets under worst-case system uncertainties; however, its application to perception-based systems is currently underexplored. In this work, we propose RoVer-CoRe, a framework for the Robust Verification of Controllers via HJ Reachability. To the best of our knowledge, RoVer-CoRe is the first HJ reachability-based framework for the verification of perception-based systems under perceptual uncertainty. Our key insight is to concatenate the system controller, observation function, and the state estimation modules to obtain an equivalent closed-loop system that is readily compatible with existing reachability frameworks. Within RoVer-CoRe, we propose novel methods for formal safety verification and robust controller design. We demonstrate the efficacy of the framework in case studies involving aircraft taxiing and NN-based rover navigation. Code is available at the link in the footnote.

Trajectory optimization methods provide an efficient and reliable means of computing feasible trajectories in nonconvex solution spaces. However, a well-known limitation of these algorithms is that they are inherently local in nature, and typically converge to a solution in the neighborhood of their initial guess. This paper presents a sequential operator-splitting framework, based on the alternating direction method of multipliers (ADMM), aimed at promoting exploration within the sequential convex programming (SCP) framework. In particular, diverse initial solutions are modeled as agents within the consensus ADMM framework. Driving these agents toward consensus facilitates exploration of the nonconvex optimization landscape. Numerical simulations demonstrate that the proposed method consistently yields equivalent or lower-cost solutions compared to the standard SCP approach, with the same number of or fewer agents.

DC Optimal Power Flow (DCOPF) is widely utilized in power system operations due to its simplicity and computational efficiency. However, its lossless, reactive power-agnostic model often yields dispatches that are infeasible under practical operating scenarios such as the nonlinear AC power flow (ACPF) equations. While theoretical analysis demonstrates that DCOPF solutions are inherently AC-infeasible, their widespread industry adoption suggests substantial practical utility. This paper develops a unified DCOPF-ACPF pipeline to recover AC feasible solutions from DCOPF-based dispatches. The pipeline uses four DCOPF variants and applies AC feasibility recovery using both distributed slack allocation and PV/PQ switching. The main objective is to identify the most effective pipeline for restoring AC feasibility. Evaluation across over 10,000 dispatch scenarios on various test cases demonstrates that the structured ACPF model yields solutions that satisfy both the ACPF equations, and all engineering inequality constraints. In a 13,659 bus case, the mean absolute error and cost differences between DCOPF and ACOPF are reduced by 75% and 93%, respectively, compared to conventional single slack bus methods. Under extreme loading conditions, the pipeline reduces inequality constraint violations by a factor of 3 to 5.

Seismic sensing has emerged as a promising solution for border surveillance and monitoring; the seismic sensors that are often buried underground are small and cannot be noticed easily, making them difficult for intruders to detect, avoid, or vandalize. This significantly enhances their effectiveness compared to highly visible cameras or fences. However, accurately detecting and distinguishing between overlapping activities that are happening simultaneously, such as human intrusions, animal movements, and vehicle rumbling, remains a major challenge due to the complex and noisy nature of seismic signals. Correctly identifying simultaneous activities is critical because failing to separate them can lead to misclassification, missed detections, and an incomplete understanding of the situation, thereby reducing the reliability of surveillance systems. To tackle this problem, we propose HyMAD (Hybrid Multi-Activity Detection), a deep neural architecture based on spatio-temporal feature fusion. The framework integrates spectral features extracted with SincNet and temporal dependencies modeled by a recurrent neural network (RNN). In addition, HyMAD employs self-attention layers to strengthen intra-modal representations and a cross-modal fusion module to achieve robust multi-label classification of seismic events. e evaluate our approach on a dataset constructed from real-world field recordings collected in the context of border surveillance and monitoring, demonstrating its ability to generalize to complex, simultaneous activity scenarios involving humans, animals, and vehicles. Our method achieves competitive performance and offers a modular framework for extending seismic-based activity recognition in real-world security applications.

Numerous diffusion model (DM)-based methods have been proposed for solving inverse imaging problems. Among these, a recent line of work has demonstrated strong performance by formulating sampling as an optimization procedure that enforces measurement consistency, forward diffusion consistency, and both step-wise and backward diffusion consistency. However, these methods have only considered 2D reconstruction tasks and do not directly extend to 3D image reconstruction problems, such as in Computed Tomography (CT). To bridge this gap, we propose NEtwork-Regularized diffusion sampling for 3D CT (NERD) by incorporating an L1 regularization into the optimization objective. This regularizer encourages spatial continuity across adjacent slices, reducing inter-slice artifacts and promoting coherent volumetric reconstructions. Additionally, we introduce two efficient optimization strategies to solve the resulting objective: one based on the Alternating Direction Method of Multipliers (ADMM) and another based on the Primal-Dual Hybrid Gradient (PDHG) method. Experiments on medical 3D CT data demonstrate that our approach achieves either state-of-the-art or highly competitive results.

This paper proposes a novel Kernelized Data-Driven Predictive Control (KDPC) scheme for robust, offset-free tracking of nonlinear systems. Our computationally efficient hybrid approach separates the prediction: (1) kernel ridge regression learns the nonlinear map from past trajectories, and (2) analytical linearization of the kernel map approximates the effect of future inputs. This linearization is key, allowing the controller to be formulated as a standard Quadratic Program (QP) for efficient real-time implementation. Offset-free tracking is inherently achieved by using input increments. We provide theoretical guarantees for recursive feasibility and asymptotic stability. The algorithm is validated on a nonlinear Van der Pol oscillator, where it successfully rejects unmeasured disturbances and eliminates steady-state errors, outperforming a standard model-based controller.

Radio spectrum monitoring in contested environments motivates the need for reliable automatic signal classification technology. Prior work highlights deep learning as a promising approach, but existing models depend on brute-force Doppler augmentation to achieve real-world generalization, which undermines both training efficiency and interpretability. In this paper, we propose a convolutional neural network (CNN) architecture with complex-valued layers that exploits convolutional shift equivariance in the frequency domain. To establish provable frequency bin shift invariance, we use adaptive polyphase sampling (APS) as pooling layers followed by a global average pooling layer at the end of the network. Using a synthetic dataset of common interference signals, experimental results demonstrate that unlike a vanilla CNN, our model maintains consistent classification accuracy with and without random Doppler shifts despite being trained on no Doppler-shifted examples. Overall, our method establishes an invariance-driven framework for signal classification that offers provable robustness against real-world effects.

What limits how fast a Lyapunov function can decay under input bounds? We address this question by showing how the shape of Lyapunov comparison functions governs guaranteed decay for control affine systems. Using a windowed nominal exponential rate together with the endpoint cap induced by actuator limits, we establish a strict ordering: concave comparison functions strictly outperform linear and convex ones, and strict concavity is necessary to improve the best achievable global exponential rate under a fixed endpoint cap. We derive a computable lower bound on the required actuation level for a target nominal rate and show that only concave shaping can reduce this level under the endpoint cap. We then establish a feasibility-preserving acceleration result: whenever a margin exists on a sublevel set, a feasible linear comparison can be replaced by a concave one that preserves feasibility while strictly increasing the guaranteed windowed decay. Finally, we give a tunable rational concave factor with controlled slope that yields a constructive design and integrates with CLF QP, as illustrated by examples.

The evolution of next-generation wireless networks has spurred the vigorous development of the low-altitude economy (LAE). To support this emerging field while remaining compatible with existing network architectures, integrated sensing and communication (ISAC) based on 5G New Radio (NR) signals is regarded as a promising solution. However, merely leveraging standard 5G NR signals, such as the Synchronization Signal Block (SSB), presents fundamental limitations in sensing resolution. To address the issue, this paper proposes a two-stage coarse-to-fine sensing framework that utilizes standard 5G NR initial access signals augmented by a custom-designed sparse pilot structure (SPS) for high-precision unmanned aerial vehicles (UAV) sensing. In Stage I, we first fuse information from the SSB, Type\#0-PDCCH, and system information block 1 (SIB1) to ensure the initial target detection. In Stage II, a refined estimation algorithm is introduced to overcome the resolution limitations of these signals. Inspired by the sparse array theory, this stage employs a novel SPS, which is inserted into resource blocks (RBs) within the CORSET\#0 bandwidth. To accurately extract the off-grid range and velocity parameters from these sparse pilots, we develop a corresponding high-resolution algorithm based on the weighted unwrapped phase (WUP) technique and the RELAX-based iterative method. Finally, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is adopted to prune the redundant detections arising from beam overlap. Comprehensive simulation results demonstrate the superior estimation accuracy and computational efficiency of the proposed framework in comparison to other techniques.

Conventional high-altitude platforms (HAPs) face challenges in achieving continuous all-weather operation due to intermittent photovoltaic power generation, limited energy storage capacity, and high mission loads resulting from functional integration. To address this fundamental issue, we propose a stratospheric energy grid in which wireless power transfer (WPT) interconnections constitute the grid layer, while HAPs operate as dynamically reconfigurable integrated generation-grid-load-storage (IGGLS) nodes that harvest, buffer, consume, and peer-to-peer transfer energy for constellation-level balancing and resilience. In this system, each HAP node can flexibly switch among energy source, load, and storage roles according to its energy status and mission requirements, enabling energy exchange and spatiotemporal optimization within the stratosphere. Through cooperative scheduling, the stratospheric grid not only enables surplus-to-deficit energy support among HAPs but also extends upward to satellites and downward to the terrestrial grid and communication infrastructure, forming a heterogeneous, WPT-mediated interconnection. As an IGGLS ecosystem, it exploits peer-to-peer energy logistics, spatiotemporal smoothing of intermittency, cross-domain backup via the terrestrial grid, and service-aware dispatch, thereby boosting overall energy utilization and operational resilience. The proposed approach is validated through case studies, and we delineate an agenda of feasible research directions.

A novel fully-connected (FC) tri-hybrid beamforming (THB) architecture is proposed for pinching antenna systems (PASS). In contrast to conventional sub-connected (SC) PASS, the proposed FC architecture employs a tunable phase-shifter network to interconnect all radio frequency (RF) chains with all waveguides. This facilitates a THB framework that integrates conventional hybrid analog-digital beamforming with pinching beamforming. A weighted sum-rate (WSR) optimization problem is then formulated to jointly optimize the transmit beamformers and pinching antenna (PA) positions. Two algorithms are developed to address this challenging non-convex problem. 1) Fractional programming (FP)-based algorithm: This algorithm directly maximizes the WSR using an FP-based alternating optimization framework. Particularly, a success-history based adaptive differential evolution (SHADE) method is proposed to optimize PA positions, effectively addressing the intractable multimodal objective function. 2) Zero-forcing (ZF)-based algorithm: To reduce design complexity, zero-forcing is employed for transmit beamforming. The PA positions are subsequently optimized to maximize the WSR via a modified SHADE method. Simulation results validate the effectiveness of the proposed algorithms, revealing that the FC-THB PASS achieves WSR comparable to the SC architecture while delivering superior energy efficiency with fewer RF chains.