Papers

Cancer detection using AI methods and image databases faces several challenges, including limited and imbalanced image datasets, long algorithm training times, and high-complexity feature generation. In particular, many cancer image datasets contain very few malignant examples compared to benign ones, limiting the amount of minority-class data available for training. Although classical AI and especially deep learning methods have had some success in providing cancer diagnostics, Quantum Machine Learning (QML) methods may offer advantages through high-dimensional feature representations, generative data augmentation, and the exploitation of feature correlations through quantum properties such as entanglement. In this paper, we propose using a generative QML method on the compressed breast ultrasound image dataset (BUSI) to generate synthetic images and hence augmented datasets for classifier training. In particular, we train a convolutional neural network with real, synthetic, and a combination of real and synthetic images. We test the algorithm on real images and provide comparative results for all three training cases. Our results show that the classifier trained entirely on synthetic images achieved an AUC-ROC of 0.802 (80.2%), compared with 0.760 (76.0%) for the real-data baseline.

In integrated sensing and communication (ISAC) systems, strict sensing requirements can substantially constrain the transmit power available for communication. A hybrid reconfigurable intelligent surface (HRIS) with passive reflection and active amplification capabilities mounted on an unmanned aerial vehicle (UAV) can enhance coverage and strengthen communication links in such power-limited regimes. Meanwhile, rate-splitting multiple access (RSMA) provides an effective transmission strategy for managing inter-user interference, particularly in dense multiuser wireless scenarios. Motivated by this, we analyze the performance of a UAV-mounted HRIS-assisted monostatic ISAC system employing RSMA scheme. We consider the imperfections in the channel state information and successive interference cancellation in the RSMA, which are inevitable in practical system implementations. We then derive highly accurate expressions for outage probability, ergodic rate, and throughput to evaluate communication performance over Nakagami-$m$ fading channels. These expressions reveal key insights into the diversity order and coding gain of the system in the high signal-to-noise ratio regime. To characterize the sensing performance, we derive expressions for the probability of detection, the probability of false alarm, and the Cramér-Rao lower bound. Numerical results validate the theoretical findings and highlight the performance gains of the proposed system over conventional multiple-access schemes and RIS-based systems.

This paper proposes a channel-knowledge-map (CKM)-based predictive beamforming framework for integrated sensing, communication, and powering (ISCAP) of a mobile energy-constrained node in non-line-of-sight (NLoS) multipath environments. The CKM provides location-dependent channel information for both predictive beamforming and EKF-based tracking, while resolved path parameters estimated from the sensing echoes are used as observations. Based on the predicted node location, the transmit covariance and power-splitting ratio are jointly optimized to maximize wireless power-transfer efficiency subject to communication, sensing, transmit-power, and tracking-quality constraints. A finite-battery model and an adaptive inter-frame energy-management policy are further introduced to balance decoding energy consumption and battery preservation. The resulting non-convex fractional problem is solved using Dinkelbach's method, alternating optimization, and semidefinite relaxation. Numerical results show that the proposed framework maintains reliable tracking, satisfies the prescribed position-uncertainty requirement, achieves near-oracle wireless power-transfer performance after the initial tracking transient, and preserves more battery energy while meeting the required communication quality of service.

The paper addresses the synthesis of a cost-optimal hardware architecture of the automated process control system (APCS) for continuous processes. The problem is formalized as the search for a minimum-cost rooted tree of devices subject to constraints on the number of I/O channels, the types and number of communication interfaces, the information capacity of processors, the regulation time and the reliability. The signals are grouped by control rooms. The problem is NP-hard, and a hybrid method is proposed that combines a deterministic hierarchy construction with a modified ant colony optimization (ACO) for device-type selection, enhanced by an adaptive heuristic. On ten instances the method is compared with a genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA) and CP-SAT over 30 runs each. By the best found cost the hybrid ACO outperforms GA, PSO and SA on all instances with more than 100 IOs (by 14-38 %) and runs 9-14 times faster. By the median cost, however, the deterministic-greedy baselines are more reliable (GA attains the best Friedman rank) because the cost of the ACO runs is highly variable. All differences are confirmed by the Friedman and Wilcoxon tests. The method targets the early-design search for the cheapest feasible architecture and synthesizes one within minutes at industrial scale (A up to 1086).

Oscillation monitors have root-mean-square (RMS) energy detectors at their core. These detect oscillation in a predefined frequency band by comparing the signal power in that band with a threshold (usually set empirically). A key design decision is the choice of frequency bands. Traditionally, for monitoring the stability of wide-area electromechanical modes, finer bands were set below 1 Hz, while higher frequencies were covered by wider bands. However, this approach can yield poor detection in modern power systems with fast and largely unknown local dynamics. This letter proposes an algorithm to set frequency bands from the ambient noise spectrum to maximize the average detection performance.

Diffusion-based policies have recently emerged as a powerful paradigm for offline reinforcement learning, but their reliance on multi-step denoising introduces substantial computational overhead and requires backpropagation through time during training, limiting scalability and practical deployment. In this work, we propose Instant Flow Q-Learning (InsQL), a single-step generative policy learning framework that retains the expressive capacity of diffusion models while eliminating iterative sampling and BPTT. InsQL builds on the flow-matching perspective of diffusion policies, but replaces marginal velocity parameterization with average-velocity modeling and introduces a principled compositional mechanism, yielding a theoretically grounded objective that learns average velocity consistently across scales. This formulation provides a direct noise-to-action mapping and enables single-step action generation. InsQL further unifies compositional average-velocity modeling with behavior-regularized actor–critic optimization in a single framework, requiring only a single policy network and avoiding distillation, multi-stage procedures, and Jacobian computations during training. At inference time, actions are generated in a single forward pass and can be refined via value-guided candidate selection to improve performance. Extensive experiments on standard offline RL benchmarks and real-world robotic manipulation tasks show that InsQL matches or surpasses state-of-the-art diffusion-based methods while substantially reducing both training and inference time. Overall, these results establish compositional average-velocity modeling as an efficient and scalable alternative to multi-step generative policies for offline RL.

Imminent blast furnace relinings create a consequential investment window for U.S. primary steel decarbonization. Using plant-level capacity planning and stochastic operations modeling, we quantify the costs, infrastructure, and fiscal requirements to transition legacy blast furnace sites to direct reduction under supply-side incentives. Under baseline assumptions, post-subsidy premiums above average blast furnace costs, including relining, are 272 to 427 dollars per ton of steel for near-zero-emission hydrogen-based direct reduction and 115 to 181 dollars for natural-gas-based production. Representative facilities producing two million tons of steel annually at legacy locations require approximately 4 to 6.5 and 0.7 to 1.1 GW of renewable generation and battery storage, respectively. This translates to 65 and 12 GW across the unrelined fleet for hydrogen- and natural gas-based pathways, respectively. These findings connect replacement timing to costs, infrastructure, and public support, motivating a sequenced transition through clean-powered natural-gas-based direct reduction to avert blast furnace relining, reduce emissions by 57 percent relative to blast furnaces, and preserve options for deeper decarbonization through hydrogen substitution or carbon capture.

The increasing adoption of peer to peer energy trading has enabled prosumers to actively participate in decentralized energy markets. However, the growing dependence of P2P energy markets on communication infrastructures and distributed coordination mechanisms introduces an expanded cyberattack surface, enabling sophisticated adversaries to manipulate market interactions while remaining concealed from conventional detection mechanisms. This paper first designs an optimal stealthy attack that maximizes economic disruption of the MicroGrids, while preserving the apparent convergence characteristics of the ADMM based market-clearing process. To detect such attacks, a Mahalanobis Boosting detector is developed using market layer features to effectively capture inter feature dependencies and distinguish compromised updates from normal market operation. An adaptive rho based mitigation strategy is then introduced to limit the propagation of compromised information and restore robust market operation once an attack is flagged. The effectiveness of the proposed framework is demonstrated through comprehensive case studies, achieving an attack detection accuracy of 98.49% while the proposed mitigation strategy reduces the operating cost by approximately 18.43% compared to the unmitigated attack case. The proposed approach provides a practical pathway toward trustworthy and cyber-secure distributed P2P energy trading systems.

Indoor human perception is the foundation for smart homes and healthcare. However, RGB cameras raise privacy concerns in private indoor environments, while low-resolution thermal cameras provide limited spatial and semantic information. Large-scale indoor thermal datasets and real-world deployments remain limited. Here, we introduce Indoor RGB-T, a large-scale paired RGB–thermal dataset for indoor human perception. The dataset covers 20 indoor rooms and contains 90,576 80x62 thermal frames, 87,016 temporally matched RGB–thermal pairs, and 115,557 annotated human bounding boxes. We establish benchmarks for robust thermal occupant-state detection with 0.75 mAP and occupancy estimation with an accuracy of 0.847 in unseen rooms. We further evaluate RGB-to-thermal and thermal-to-RGB translation, achieving a temperature MAE of 0.654 C and an R2 of 0.923. Finally, we deploy the thermal perception model on a commercial sleep-monitoring system in the real world. Field testing with 100 participants and deployment of approximately 1,000 devices further demonstrate its practical feasibility. We identify practical challenges that motivate future research directions, including environmental thermal interference, sensing range and multi-person occlusion. The code, trained models and partial dataset are available at https://github.com/kailaisun/RGBT and https://huggingface.co/skl24/RGBT.

Speech representation learning requires a discrete modeling interface while preserving high waveform fidelity. Existing approaches rarely achieve both: discrete tokens ease modeling but lose fine-grained acoustic information, while continuous latents preserve fidelity but require specialized generative objectives. We introduce SemiCodec, a novel Semi-discrete neural speech Codec with synchronized discrete tokens and continuous latents. Direct joint optimization may create a reconstruction shortcut through the continuous pathway, leaving the discrete tokens insufficiently grounded. To address this issue, we propose a Preserve-then-Ground training paradigm that first establishes a high-fidelity synthesis space and then grounds SSL-guided discrete tokens within it. Experiments show that SemiCodec outperforms fully discrete codecs in reconstruction quality and intelligibility while remaining competitive with continuous representations in acoustic fidelity and speaker preservation. Audio samples are available at https://pennyw1-se.github.io/SemiCodec-Demo-Page/

Recent alignment-free Text-to-Speech (TTS) models achieve high synthesis quality by removing explicit alignment modules and simplifying training. However, they often struggle in cross-lingual scenarios due to differences in character durations across languages, leading to duration mismatch and degraded speech quality. Existing approaches typically address this issue using explicit duration predictors, increasing model complexity. Systematic evaluation of TTS performance for Indian languages in cross-lingual settings remains under-explored due to the lack of standardized evaluation benchmarks. In this work, we construct a cross-lingual evaluation benchmark covering English and nine Indian languages and propose a language-aware inference-time duration estimation strategy for alignment-free TTS that incorporates language-specific duration-per character statistics without retraining or architectural modifications. We further develop heuristic formulations that refine duration estimation based on language-dependent duration variations. Experiments on bilingual and multilingual A-DMA models demonstrate improved speech naturalness, intelligibility, and speaker similarity while reducing duration mismatch.

Multipath and non-line-of-sight (NLOS) propagation degrade global navigation satellite system (GNSS) positioning accuracy in urban environments. Although fisheye sky segmentation identifies obstructions along satellite directions, binary visibility does not fully characterize observation quality. This study proposes a point-supervised visual framework that predicts GNSS observation faults from fisheye images using supervision derived from GNSS observations. During offline labeling, a vision-assisted differential GNSS procedure selects multiple reference satellites based on sky visibility and elevation constraints. Double-difference pseudorange residuals automatically generate fault labels and sample importance weights at satellite projection locations. A residual-weighted loss is computed only at valid labeled points, enabling learning from sparse supervision without manual pixelwise fault annotations. The trained network generates dense fault probability maps, which are sampled at satellite projection locations to weight observations in weighted least squares positioning, reducing the influence of potentially faulty observations. The framework is evaluated using U-Net, DeepLabv3+, DINOv3, and SAM3 on urban vehicle data collected on a different day from the training data. All four models reduce horizontal root mean square error (RMSE) by 29.51% to 36.07% compared with SAM binary sky segmentation, with U-Net achieving the lowest RMSE of 0.78 m. The framework also provides positioning solutions during some intervals when the segmentation baseline fails. These results demonstrate the framework's generalization ability and show that GNSS-derived point supervision enables fisheye images to provide observation quality information beyond binary sky visibility, supporting GNSS fault detection.

State-of-the-art beat tracking is based on neural networks that model beats and downbeats as individual events, often followed by a separate Dynamic Bayesian Network (DBN) enforcing musical consistency. We propose a novel architecture that combines deep learning with differentiable theory-based modules: Specifically, we design a system that first predicts a tatum grid, then groups tatum events to beats and downbeats, explicitly modelling multiple options for the current tempo and time signature in a bar-pointer-like fashion. Our approach improves continuity metrics compared to existing DBN-free systems and is more versatile than the de-facto standard DBN, but still falls behind the best DBN-based methods on average. Given the many design choices and hyperparameters involved, we assume we have not yet explored the full potential of this idea and publish our code and invite others to explore this further.

The increasing penetration of renewable energy sources in microgrids demands accurate day-ahead forecasting to support optimal battery energy storage system (BESS) scheduling. Yet the conventional predict-then-optimize paradigm trains forecasters on statistical accuracy metrics that are agnostic to the asymmetric and time-varying cost impacts of forecast errors. This work proposes a decision-focused learning (DFL) framework that trains gated recurrent unit forecasters for both load demand and photovoltaic generation directly on the downstream operational cost of a two-stage energy management system, where day-ahead BESS commitments are fixed prior to real-time balancing. Gradients are backpropagated from the real-time operational cost through both optimization stages to the forecaster weights via implicit differentiation, enabling the forecasters to internalize the penalty structure of the scheduling problem. The framework is validated on the MG2Lab experimental microgrid testbed at Politecnico di Milano across two operational scenarios of increasing grid constraint and eight forecasting configurations comprising single-source and combined-forecaster settings. Results illustrate that the DFL-trained pipeline reduces operational cost by 3.78% under normal grid conditions and by 30.60% under grid-constrained conditions relative to the accuracy- optimized baseline, while nearly eliminating load shedding events. Notably, the DFL forecasters exhibit higher statistical errors than their accuracy-trained counterparts yet achieve consistently lower operational costs, confirming that forecast accuracy and decision quality do not share a monotonic relationship in constrained microgrid scheduling.

Virtual Reality (VR) requires strict Quality-of-Experience (QoE) guarantees, making radio resource management harder. Static slice allocations are wasteful, while dynamic approaches often rely on heuristic penalty functions lacking an empirical basis. We introduce a QoE-driven network-slicing framework for Open RAN in which resource allocation is guided by empirically derived multi-user VR traffic statistics. Closed-form n-fold convolutions model the aggregate data rate and inter-arrival time of n concurrent users, validated empirically for up to four. The fitted data-rate cumulative distribution function is checked against the measured throughput to give a percentile-based satisfaction indicator; the inter-arrival law sets the slice targets. A near-real-time RIC xApp closes a loop over the E2SM-KPM and E2SM-RC interfaces to apply slice-level Physical Resource Block (PRB) quotas. On an over-the-air testbed it reclaims up to 41% of the cell's committed PRBs from a greedy neighbouring slice while never reducing the protected VR slice. An analytical study then estimates the PRB demand and per-user QoE across user counts and channel qualities, giving the admission boundary beyond which the target QoE is unattainable. A controlled comparison further shows that a fixed-rate synthetic load overstates the VR slice's PRB footprint by 68%, showing the necessity of data-driven slice dimensioning.

User-generated audio collections frequently contain textual metadata such as titles, tags, and descriptions. Effectively leveraging these complementary fields requires accounting for both field het erogeneity and varying semantic relevance relative to the acoustic content. We propose FAM-Gate, a two-stage framework for metadata-enriched audio classification. In the first stage, field-aware metadata-view attention dynamically aggregates multiple metadata views using audio-guided weighting, explicit field-identity embeddings, a shared identity-initialized projection, and mean-anchored regularized aggregation. In the second stage, a prior-constrained vector gate performs bounded, dimension-wise fusion of audio and metadata representations centered around an acoustic prior. Ex periments on BSD10K, BSD35K, and FSD50K show consistent improvements in both hierarchical single-label and multi-label classification. Compared with matched random-view scalar-fusion base lines, FAM-Gate improves the hierarchical F-score by 2.53 and 5.72 percentage points on BSD10K and BSD35K, respectively, while increasing macro mAP and macro F1 by 5.07 and 10.25 percentage points on FSD50K.

Pose Graph Optimization (PGO) is a classical and widely applied problem in state estimation for robotics, playing a fundamental role in localization, mapping, and navigation. Yet, it is nonconvex in nature and may have many local minimizers, thus difficult to analyze. In this paper, we investigate the optimization landscape and establish a sufficient condition of global optimality for PGO. The ultimate goal of this paper is to theoretically confirm that the optimization landscape is benign and attractive around the global minimizer, if noise is smaller than an explicit and a priori bound. To reveal such an insight, this paper develops three main technical results. First, we identify a proper basin of attraction (BOA) containing global minimizer, and characterize its geometric property by showing that its radius is controlled by noise in an explicit way. Then, by exploring the first order optimality conditions of PGO, we prove that a local minimizer is globally optimal if a noise-controlled criterion is satisfied. Finally, by combining above two results, we prove that one and only one local minimizer exists in the BOA and this minimizer is globally optimal, when the noise is smaller than an explicit bound that can be verified a priori. Numerical simulations validate the proposed theory under various random cases, showing that the obtained conditions provide an effective criterion for certifying the global optimality of PGO.

In frequency-division duplex (FDD) systems, the uplink (UL) and downlink (DL) channels are not fully reciprocal. Therefore, DL channel state information (CSI) estimated by each user equipment (UE) must be fed back to the base station (BS). However, because the channel varies between CSI acquisition and utilization, the feedback CSI may become outdated before it is used at the BS. To address this challenge, this paper proposes an UL-assisted parametric CSI feedback and prediction scheme for multi-user FDD systems with time-varying channel parameters. By exploiting the partial reciprocity between the UL and DL channel parameters, the BS first estimates the UL channel parameters from the sounding reference signals (SRSs) transmitted by the UEs. The BS then broadcasts DL pilots, allowing each UE to obtain observations containing information about its current DL channel. Instead of explicitly estimating the DL channel from these observations, each UE directly encodes them into feedback symbols and transmits the symbols to the BS. The BS decoder combines the feedback symbols with the partially reciprocal UL parameters to predict the future DL channel parameters. Finally, the corresponding DL channel matrix is synthesized from the predicted parameters using an explicit physical channel model. Simulation results on Sionna ray tracing (RT) dataset show that the proposed scheme outperforms the considered baselines in terms of channel prediction performance over different signal-to-noise ratios and prediction lengths.

In integrated sensing and communication (ISAC) enabled six-generation (6G) networks, unmanned aerial vehicle (UAV) swarms increasingly rely on consensus-based cooperative sensing: each node iteratively exchanges and fuses decisions with its neighbors to approach a global picture of the environment, supporting the high-reliability, low-latency inference demanded by mission-critical tasks. Yet this very mechanism of conformity becomes the lever of Byzantine attacks. By exploiting the update rule, malicious nodes manufacture a false consensus that lures honest nodes into drifting away from their true observations round after round, so that a normal node's actual error probability evolves dynamically and rises far above its intrinsic sensing error. Conventional fusion models, which assume node errors are fixed and independent, are therefore severely mismatched. This article argues for a different defensive perspective: instead of hunting for bad nodes, we seek to understand how good nodes turn bad, characterizing the inducement dynamics with evolutionary game theory and handing this law to the fusion center so that it can dynamically correct the true error level of normal nodes. Simulations show that the approach sustains near-perfect situational inference across a wide range of attack intensities and network sizes, while naturally supporting low latency decisions. We further discuss several open challenges on the path toward secure ISAC-6G swarm sensing.

Direction-of-arrival (DOA) estimation can degrade markedly in impulsive noise, where methods relying on Gaussian or second-order statistical assumptions are particularly susceptible to large-amplitude outliers. This paper develops an adaptive DOA estimator that employs Gaussian kernel weighting to attenuate the influence of large prediction errors while adjusting the kernel bandwidth online according to a Kullback–Leibler (KL) divergence criterion. A sample-based approximation of the KL criterion yields a recursive bandwidth update that adapts the degree of outlier suppression to the observed error statistics rather than prescribing a fixed bandwidth a priori. The spatial-filter coefficients and kernel bandwidth are updated recursively, and the DOAs are recovered from the resulting spatial spectrum. Mean and mean-square analyses are developed under standard adaptive-filtering approximations to characterize the coefficient convergence and steady-state behavior. Simulation results under Gaussian-mixture impulsive noise demonstrate stable coefficient adaptation and improved DOA estimation accuracy over representative competing methods in several challenging operating regimes, with the most pronounced gains occurring at low-to-moderate signal-to-noise ratios. The results also indicate reduced sensitivity to the initial kernel bandwidth through online bandwidth adaptation.

Horizon optimization in finite-horizon LQR treats the horizon $T$ as a decision variable and adds a penalty $wT$ to the cost. Recent composition-based methods evaluate all candidate horizons in $O(Nn^3)$, instead of the $O(N^2n^3)$ of one Riccati recursion per horizon, but they track the inverse cost-to-go $P^{-1}$ and therefore require every stage and terminal cost to be positive definite, whereas LQR requires only positive semidefiniteness. This excludes costs that leave some states unpenalized. In this work, we track instead the shifted inverse $\widehat{P}_k=(P_k+cI)^{-1}$, the inverse coordinate after a symplectic shear of the state-costate variables. Its domain contains every positive semidefinite Riccati matrix, and $P_k=\widehat{P}_k^{-1}-cI$ recovers the Riccati solution. We show that the shift can be absorbed into the stage coefficients of the inverse-coordinate composition, so the $O(Nn^3)$ search carries over, exact in exact arithmetic, subject to invertibility conditions. These hold for all small $c$ when the dynamics contract the unpenalized directions, fail for at most finitely many $c$ outside a degenerate case, and are certified per horizon at run time. In our experiments, diagonal loading $Q_k+\varepsilon I$ either left cost errors several orders of magnitude above those of the shifted chart or, for small fixed $\varepsilon$, selected the wrong horizon. The shifted chart with a fixed default $c$ selected the correct horizon on all tested instances, reporting long horizons as uncertified in one stress case.

Due to the high cost of hardware experiments, robot policies are predominantly evaluated by comparing their empirical performance on a limited number of hardware trials. However, finite-sample statistical uncertainty can lead to erroneous conclusions, especially when ranking multiple policies using a fixed batch of evaluation data. We propose AnyRank: a sequential method for multi-policy ranking that (i) adaptively allocates trials to hard-to-separate policies, (ii) provides (partial) rankings at any stopping time while bounding the probability of an incorrect ranking, and (iii) optionally exploits prior information to prioritize hardware evaluations more efficiently. This prior information could be informed from expert intuition, a small set of held-out hardware evaluations, or from policy rollouts in a simulator or an action-conditioned video world model. Notably, the prior can be imperfect; while a strong prior can provide efficiency gains on hardware, any inaccuracies in prior policy rankings do not invalidate the statistical assurances of AnyRank. We achieve the aforementioned properties through novel theoretical contributions that combine sequential testing with multiple-hypothesis graphical testing approaches. Experiments in simulation and real-world policy evaluation benchmarks show that AnyRank requires up to 50% fewer hardware evaluations compared to existing statistically rigorous approaches, while guarding against the incorrect rankings produced by heuristic methods used by state-of-the-art benchmarks.

Camera-based beam selection can reduce the need for exhaustive channel measurement in highly mobile millimeter-wave UAV links, but the complementary value of visual and position-derived representations and their behavior under modality degradation require further investigation. This paper develops a staged multimodal framework for current-beam prediction using real-world UAV measurements. The proposed 32-beam classifier combines an ImageNet-pretrained EfficientNet-B3 visual encoder with two deterministic position-derived representations: an explicit Geometric-Kinematic vector and a nonlinear synthetic feature basis constructed from position. The image and synthetic representations are fused first, followed by late fusion with the geometry representation. Experiments on DeepSense 6G Scenario 23 show that Image-Synthetic fusion achieves 86.92% Top-1, 99.03% Top-3, and 99.56% Top-5 accuracy, improving Top-1 performance by 2.64 percentage points over image-only inference and outperforming the 60.71% Top-1 accuracy of synthetic-only inference. Adding the Geometric-Kinematic branch yields the highest measured performance, reaching 87.14% Top-1, 99.12% Top-3, and 99.60% Top-5 accuracy. Controlled input interventions further demonstrate complementary behavior under visual degradation and strong sensitivity to severe synthetic-input corruption. Position-derived nonlinear features provide complementary information to visual context and improve current-beam prediction in the evaluated real-world UAV setting.

Moving extended-target sensing and tracking are studied in a beamformed PRS/OFDM ISAC framework. The target is modeled as a rigid cluster of scatterers whose Tx–target–Rx echoes coherently combine in a MIMO scattering channel. Delay, azimuth/elevation, and Doppler are estimated using PRS matched filtering with delay centroiding, single-cluster 2D-MUSIC, and FFT-based Doppler estimation, then used for independent localization and EKF-based tracking. The framework is evaluated in monostatic and bistatic modes under target-directed, receiver-directed, and omnidirectional beamforming. Simulation results show that EKF tracking reduces the aggregate 3D RMSE from 1.61 m to 1.38 m relative to independent localization. Target-directed beamforming substantially improves tracking accuracy, achieving 1.38 m RMSE compared with 5.91 m and 8.38 m for omnidirectional and receiver-directed beamforming, respectively. The results highlight the importance of target-aware beam management, delay-angle-Doppler fusion, and sensing geometry for mobile-target tracking in ISAC networks.

Unmanned aerial vehicle-enabled synthetic aperture radar (SAR) offers significant potential for providing communication services to ground users while sensing the surrounding environment in low-altitude integrated sensing and communication (ISAC) systems. However, its practical deployment is challenged by known non-ULM trajectories and unknown position errors, which violate conventional SAR assumptions and cause severe image defocusing. We address these non-ideal impacts by formulating the ISAC SAR imaging as an exact linear inverse problem to accurately model arbitrary trajectories with multiple-input multiple-output orthogonal frequency division multiplexing waveforms. Based on this formulation, we develop a diffusion posterior sampling (DPS)-based super-resolution SAR framework, where a pre-trained diffusion model provides a data-driven image prior and the exact physical model enforces measurement consistency during posterior inference. The inference process is further accelerated for practical implementation. For unknown position errors, we develop a joint iterative DPS and motion compensation (MoCo) optimization algorithm with discrete cosine transform reparameterization and coarse-to-fine spectral scheduling. Simulations demonstrate that our framework achieves high-accuracy reconstruction across diverse trajectories and remarkable super-resolution, maintaining greater than 21.3 dB peak signal-to-noise ratio (PSNR) even under half physical resolution truncation. Under unknown motion errors, the joint DPS and MoCo algorithm achieves 23.65 dB PSNR with a residual position-error estimation error of 0.19 cm.

Holographic multiple-input multiple-output arrays provide high spatial resolution and flexible wavefront control, but curved or conformal deployments jointly introduce curvature, mutual coupling (MC), and direction-dependent visibility. This paper studies a two-dimensional (2D) holographic curvaturereconfigurable aperture (HoloCuRA). A geometry-based model characterizes element positions, steering, and spatial correlation, while an impedance-based coupling matrix and a visibility vector capture electromagnetic interactions and shadowing. Relative directivity and spectral efficiency are evaluated versus curvature. Results show that MC mainly reshapes local directivity fluctuations, whereas visibility dominates the global pattern and spectral-efficiency variation under both isotropic scattering and CDL-A channels.

We propose an encrypted collision avoidance maneuver (CAM) design protocol using homomorphic encryption (HE) that enables operators to outsource CAM design to an untrusted server without revealing their private covariance. To this end, we reformulate the constrained CAM optimization as a sampling based estimation problem via a variational approach, so that the private covariance dependent terms can be efficiently evaluated under HE. The protocol requires only a single round of communication, and experiments on six real-world conjunction data messages demonstrate improved feasibility and reduced fuel consumption compared with CAMs designed using public covariance.

Accurate emission-rate quantification of methane point sources is essential for inventory verification and leak detection. UAV-borne sensing provides high-resolution, near-source measurements, but existing quantification methods mostly follow a predefined trajectory and rely on a single UAV. Building on a source receptor matrix (SRM) estimator and empirical observability Gramian (EMGR)-based path planning, this paper proposes a spatial-temporal hybrid path planning strategy for multi-UAV methane emission rate quantification. Measurements are time-averaged, and the forward Gaussian plume model is calibrated against a filament-based plume simulator (MOABS) to reduce model mismatch. Cooperating UAVs first spread out spatially to locate the source quickly, then switch to sequentially retracing a leader trajectory with a time lag to reduce quantification variance. Simulations show that the spatial stage localizes the source markedly faster than a single-UAV baseline, the temporal stage yields the lowest variance but converges slowly, and the proposed hybrid strategy converges substantially faster than the purely temporal strategy while matching its steady-state accuracy, with a narrower error band than the purely spatial strategy. A window-length study further shows that longer time-averaging windows exhibit diminishing returns, justifying the balance between spatial coverage and temporal averaging.

This article offers an overview of recent developments in deep learning from a control vantage point. It reviews several classes of models including feedforward and residual neural networks, transformers, large-language models, and selective state space models while explaining how control theory plays an important role in understanding their training, properties, or architecture design. The article explains how residual neural networks can be interpreted as the discretization of a continuous-time control system thereby enabling the use of control theoretic techniques for its analysis. To illustrate the power of this bridge between control and deep learning, two technical results are discussed: 1) the ability of a residual neural network to approximate arbitrary continuous functions is a non-standard controllability problem that can be solved using geometric nonlinear control and 2) transformers, the key architectural component of most large language-models, can be seen the discretization of a consensus algorithm and thus large language-models exhibit consensus. Another point of contact between control theory and deep learning is the connection between backpropagation, the algorithm used to train neural networks, and optimal control. The article addresses how the older connection between these problems, established in the 80's using discrete-time models can be reconciled with the more recent approach using continuous-time models. Several open problems and directions for future research are discussed, including selective state-space models, a recent class of models inspired by the models used in control.

We study learned one-step predictive control of a synthetic population of 64 leaky integrate-and-fire neurons whose light sensitivity is scaled by a hidden factor s, using two prospective confirmations on plants simulated only after the analysis was frozen. In the first (50 plants, 11 conditions), removing the 200-ms input-output history substantially worsened a parameter-count-matched neural predictor at low and moderate gain (s = 0.6, 0.8; RMSE rose by 2.47 Hz at s = 0.6, 97.5% CI 2.43-2.51). In secondary comparisons, recursively adapted ARX had the lowest mean error of the deployable controllers in 10 of 11 conditions, tuned PI with anti-windup matched or beat the network for s >= 0.7, and training for 10,000 rather than 900 epochs lowered prediction error but worsened control. Development work suggested the network's learned action sensitivity did not track the hidden gain. A second protocol (50 new plants, 9 sensitivities, 2 noise levels) confirmed three pre-specified predictions at the primary noise level. Re-estimating a single multiplicative input gain recovered 87% (95% CI 86-88%) of the benefit of full adaptive ARX. Supplying that online estimate to the network as an extra input reduced high-gain tracking error by 1.84 Hz [1.81, 1.88]. Longer training worsened the plain network by 1.09 Hz but improved the gain-input network by 1.07 Hz, a differential of 2.16 Hz [2.13, 2.20]. Under high process noise (sigma = 2.0) the effects shrank to about 0.04 Hz or less and gain-only adaptation recovered only 24% of the adaptive-ARX benefit. In this synthetic plant, at the primary noise level, one-parameter online gain estimation accounts for most of the gap between a history-based network and full adaptive ARX, and for its prediction-control mismatch.