Papers

Early diagnosis of Alzheimer's disease (AD) is critical for enabling timely interventions that may slow disease progression and improve patient outcomes. There is a growing need for AD detection methods that are non-invasive and cost-effective, especially in real-world clinical settings with diverse patient populations and recording conditions. Speech-based screening addresses these needs by using natural speech collected without specialized equipment. Recent advances in large language models (LLMs) have improved speech analysis by providing rich linguistic representations and strong generalization. In this study, we propose LSEAD, a speech-based AD detection framework using pretrained open-source LLMs. Speech recordings are automatically transcribed, and text embeddings are extracted using locally deployed LLMs. Principal component analysis (PCA) is applied to reduce dimensionality before classification. Because the framework relies only on speech transcripts and locally deployed models, it supports privacy-preserving AD risk assessment without external data exchange. We evaluate LSEAD on the ADReSS20 and ADReSSo2021 benchmark datasets. Experimental results show that LLM-based embeddings generalize well across datasets and improve AD classification accuracy by up to 5 percent over existing methods, especially for early-stage detection. These results demonstrate that LSEAD provides a practical, secure, and scalable approach for early AD screening.

The proliferation of AI-generated music in broadcast media raises concerns about transparency and fair compensation, but reliable detection under real broadcast conditions remains unresolved. Existing studies report substantial performance degradation in this domain, yet their evaluations are limited to synthetic broadcast data. To address this gap, we introduce BAMM (Broadcast AI-Music Monitoring), a 40-hour dataset of real-world television recordings containing AI-generated and human-made music. We compare clean-trained and broadcast-trained CNN variants across three progressively more challenging scenarios: Clean Foreground Music (CFM), Synthetic TV Broadcast (STB), and Real TV Broadcast (RTB). Both models achieve near-perfect performance on CFM but degrade substantially under synthetic broadcast conditions. Broadcast-oriented training improves robustness compared with clean training, although performance remains limited. On RTB, evaluated using BAMM, both models degrade further and show substantial score overlap between AI-generated and human-made music. These results expose a critical domain gap and show that current training approaches on CNN-based detectors remain insufficient for reliable AI-generated music detection in broadcast monitoring.

Energy storage and flexible loads both help balance power systems with high shares of variable renewables, but they do so differently. A battery moves electricity from one period to another. A factory or data center instead moves production or computing activity, while meeting demand for its product or service. As batteries become cheaper, it is natural to ask whether demand flexibility is still needed. Many assessments treat that controllable load as already available and focus on enabling or participation costs. For a large load that normally runs near full capacity, however, shifting electricity use can require extra equipment, delay sales, tie up working capital, and disrupt the process. Here, we compare energy storage technologies with industrial and data-center load shifting on a levelized cost basis, measuring the incremental cost of flexible relative to baseline operation. We find that energy storage becomes more expensive per shifted kilowatt-hour as the flexibility period lengthens and assets are cycled less often. Load shifting does not share that rise in capacity cost under a fixed avoided-hour share, but new productive capacity can still be too expensive to justify. Under representative central technology costs, batteries compete well for short-cycle shifting but are more than 80 times more expensive than load shifting of aluminum smelting when providing seasonal flexibility. The answer therefore depends on the timescale, the load, and whether the needed capacity is new or already sunk.

Data centers are among the power grid's fastest-growing loads. Since data center servers are sensitive electronic components, they need to be protected against the grid's voltage disturbances during grid faults. While disconnecting from the grid achieves this, it can further destabilize the power system if many data centers trip at once. To address this emerging concern, voltage ride-through (VRT) grid codes have been proposed to standardize data center behavior. They require a data center to stay connected for a period of time through the disturbance, hold an active power floor, and recover its draw within a deadline upon restoration. However, systematically designing and certifying controllers that satisfy these coupled temporal and operational requirements remains challenging. We propose SolVRT, a system that synthesizes a grid-code-compliant VRT controller for a given data center using formal methods. We develop a specification language that expresses a grid code in Signal Temporal Logic (STL) as the basis for formal reasoning. Our encoding algorithm takes the specification, along with a model of the data center's power topology, and translates the constraints into a controller synthesis problem. This step produces a correct-by-construction controller if a solution can be found, or a proof that no such controller exists. For the latter case, SolVRT provides a diagnostic step: it traces the facility's "conflict frontier," isolates the conflicting clauses that led to non-compliance, and computes the smallest hardware or workload change that would enable compliance. We evaluate SolVRT through closed-loop simulations of a 200 MW data center connected to a 140-bus transmission system. The results demonstrate that SolVRT can synthesize compliant VRT controllers, certify infeasibility when compliance is unattainable, and identify targeted modifications that enable compliance.

AI-generated music is increasingly used at the stem level, with producers integrating synthetic drums, basslines, or vocals alongside human-performed instruments. However, current AI music detection systems are binary, treating tracks as either fully AI or fully human. In this paper, we reformulate AI music detection as a regression problem on a continuous AI energy ratio, alpha in [0, 1]. We propose a methodology that leverages a multi-track music dataset to assemble mixtures of human-performed and AI-reconstructed stems (obtained using a neural audio codec) with known proportions of each content type. Using this approach, we first show that a CNN-based model trained on fully AI-generated or human-performed tracks, which achieves >99% accuracy as a binary detector, when faced with mixed content, yields an output that rises with the AI stems' energy contribution, acting as a noisy and miscalibrated estimator. Our analysis of the influence of different stems shows that detection sensitivity depends on the instrument and reflects its frequency content: drums and guitar carry strong codec-artifact signatures, while vocals and bass are less detectable. Based on these insights, we train a similar CNN-based model for regression of alpha, achieving MAE = 0.076 and R^2 = 0.85 on held-out mixtures from the same pipeline. These results suggest that the regression formulation is an initial promising step towards AI-music detection in realistic music production workflows.

With the broad success of the Transformer architecture, token is becoming a new basic information processing unit. This trend is especially evident in multimodal large language models (MLLMs), where both visual and textual information are represented and processed as tokens. With the rapid deployment of MLLMs, the efficient transmission of tokens has become increasingly important. This paper investigates how to reduce the amount of transmitted data during interactions with MLLMs while preserving their multimodal understanding performance. To address this problem, we propose a token communication framework tailored to MLLMs. In the proposed framework, a neural codec is integrated into the vision tokenizer to control the number of transmitted bits. At the receiver, the decoded latents are processed through two paths. The decoder reconstructs image as a reconstruction prior, while the adapter converts latents into visual tokens and injects them into an intermediate layer of the vision tokenizer. To make the injected tokens suitable for MLLMs, we further design a two-stage visual-language semantic alignment training scheme. The adapter is first warmed up by a distillation loss and then aligned with textual semantics through an alignment loss. An adaptive adapter is also introduced through feature-wise linear modulation, allowing one adapter to support multiple codec rates. Extensive simulations on various MLLM benchmarks show that, under the same amount of transmitted data, the proposed scheme achieves better task performance than other image processing schemes for MLLMs.

Coupled electrical and thermal infrastructures need controllers that respond to market prices and still solve fast enough to run online. This paper presents a unified Economic Model Predictive Control (EMPC) framework for the coordinated operation of integrated thermal and electrical energy networks. Building on cyclic-terminal EMPC, the proposed approach incorporates hybrid thermal-electrical dynamics, network constraints, and time-varying economic signals within a single mixed-integer state-space representation, jointly optimizing combined heat and power units, large-scale heat pumps, thermal energy storage, batteries, and grid interactions under a convex economic stage cost. Computational tractability is ensured by reduced-order models of district heating networks and DC power flow grids. The framework is demonstrated on a campus-scale energy system under time-varying prices and demand profiles. A joint sweep of the prediction horizon against the terminal penalty weight shows that the two act as substitutes rather than as independent tuning knobs. Without terminal anchoring, the closed-loop cost approaches the periodic-reference average-performance bound only once the horizon spans several diurnal cycles. With a sufficiently large terminal weight, the bound is attained essentially tightly at every tested horizon, including the shortest one, so the horizon ceases to be a performance-critical parameter and becomes a purely computational one. The result reproduces on a second, independent price week. Beyond the weight at which the soft terminal constraint activates, closed-loop behavior is insensitive to the weight over a wide multi-decade plateau; below activation, cost and storage tracking both degrade markedly. A residual receding-horizon drift of the cost-neutral thermal-storage state is also documented and interpreted.

Modern AI is no longer a single model but an ecosystem: classical ML predictors, deep and multimodal models, large language models, and agents, each trained and tuned over different data sources and each producing outputs at scale that become inputs to the others. Operating such an ecosystem is fundamentally a data integration problem - the knowledge it depends on is fragmented across dozens of heterogeneous, independently governed sources that must be reconciled and continually maintained. Yet integration alone is not enough. The predictions these systems make are shaped by many interacting factors, and the events, decisions, and variables that drive an outcome are routinely entangled with the ones that merely accompany it; treated as a basis for action, such correlational signals invite confounded decisions. This becomes acute once agents act autonomously: to be trustworthy and reliable, an agent must anticipate the consequences of its actions, not merely extrapolate from what has co-occurred before. Causal reasoning is what closes this gap, distinguishing the drivers of an outcome from its correlates, and enabling prescriptive and counterfactual analysis over the ecosystem's data. We therefore argue that the integrated ecosystem needs an explicit causal layer, and we propose to build it as a shared, persistent, queryable Causal World System (CWS).

Integrated sensing and communications (ISAC) enables simultaneous communication and sensing using shared spectrum and hardware resources in wireless systems. However, securing the sensing functionality against unauthorized receivers remains a fundamental challenge. In this paper, we propose a sub-sampling based sensing-privacy framework for communication-centric (CC)-ISAC systems that jointly exploits sparse arrays and sparse pilot allocations to induce controlled aliasing in the spatial and frequency domains, respectively. By interpreting antenna arrays and pilot subcarriers as spatial and frequency sampling mechanisms, respectively, we show that spatial-frequency undersampling naturally distorts the range-angle multiple-input multiple-output (MIMO) ambiguity function (AF) observed by an unauthorized receiver. To this end, we first derive a closed-form expression for the range-angle MIMO-AF, and subsequently characterize the ghost targets that arise due to spatial and frequency-domain aliasing. Next, we establish a sufficient condition under which these ambiguities jointly translate into positioning ambiguity and show that, for sufficiently large spatial and frequency sub-sampling factors, an unauthorized receiver inevitably positions a target at incorrect ghost positions. Finally, we show that the proposed sub-sampling framework preserves the native legitimate ISAC performance without introducing additional trade-offs. Numerical results verify the analysis and show that sparse arrays and sparse pilots naturally enable sensing and positioning privacy through deception by aliasing.

Integrating renewable energy sources into weak grids presents significant challenges for maintaining grid stability. Several essential services are required to ensure a stable and secure grid, including voltage support, fault current injection, system strength support, frequency control, and inertia support. Synchronous condensers, with their inherent inertia and fault current capabilities, have been traditional solutions for grid stability. Recent advancements in power electronics and control structures have led to the development of various technologies aimed at enhancing power grid stability. Static synchronous compensators (STATCOM) and Enhanced STATCOM, which can provide active and reactive power support, show considerable promise. These technologies offer grid-forming (GFM) capabilities that can significantly improve the stability of weak grids with high renewable penetration. Advancements in STATCOM and E-STATCOM technologies offer enhanced control tunability, controllable damping, and immediate response to grid perturbations. This paper investigates the comparative performance of synchronous condensers versus STATCOM and Enhanced STATCOM with grid-forming capability in providing services and enhancing the stability of weak grids with high renewable penetration.

The development of the low-altitude economy has driven innovation in intelligent antenna systems within ISAC systems. In this paper, we investigate a Rotatable Antenna (RA)-enabled low-altitude integrated sensing and communication (ISAC) system. In practical terms, the RA array can flexibly adjust the three-dimensional (3D) beam direction of each antenna to enhance array directional gain, thereby improving the communication security of legitimate mobile users against potential eavesdropping risks from the unmanned aerial vehicle (UAV). Our objective is to maximize the minimum secrecy rate (SR) by jointly optimizing transmit beamforming matrix, transmit and receive RAs' pointing matrices. To this end, an multi-agent proximal policy optimization with three improvement mechanisms (MAPPO-T) algorithm is proposed to cope with the issue of complex multi-agent collaborative decision-making problem. Simulation results show that the introduction of RAs can effectively improve SR performance compared to the traditional fixed orientation antenna (FOA)-based system. In addition, the proposed MAPPO-T algorithm validate the superiority compared to the standard MAPPO algorithm.

Qualitative transitions in nonlinear dynamical systems (e.g., loss of stability, onset of oscillations, emergence of multistability) delimit operating regimes and can arise as implicit constraints in robust analysis and design under parametric uncertainty. When parameters are inferred from data, admissible values are naturally represented as uncertainty sets, motivating certified tests for the presence or absence of regime-transition candidates. We propose a validated interval workflow that encodes saddle-node and Hopf candidate conditions as square augmented algebraic systems and applies the Krawczyk operator to certify, over a prescribed state-parameter box, either (i) existence and local uniqueness of a candidate solution or (ii) certified absence. Numerical experiments on uncertain synthetic gene-network ODE models yield locally certified saddle-node candidate enclosures on a two-parameter slice for a bistable circuit and certified Hopf candidate enclosures for a three-state oscillator using a Routh-Hurwitz specialization. The resulting certificates are intended to support regime-aware analysis and design under bounded uncertainty by complementing non-validated, pointwise baselines (e.g., Newton method solves at discrete parameter values) and sampling-based workflows with rigorous presence/absence guarantees on user-specified parameter slices.

This paper presents a novel trajectory optimization method for nonlinear l1-optimal control problems in which the control horizon is treated as a free variable, allowing both the control duration and the l1-norm of the control input to be evaluated. Unlike traditional approaches that minimize the l1-norm of the input under a fixed control horizon, the proposed method treats both the control input and control horizon as design variables, enabling joint optimization of energy and temporal efficiency. The method extends a Newton-based algorithm to accommodate this objective, leveraging gradient information with respect to both variables. This formulation enables one to find high-fidelity optimal trajectories without pre-specifying the control horizon. The effectiveness and robustness of the method are demonstrated through numerical simulations, in which the proposed algorithm recovers the analytical solution to the spacecraft Hohmann transfer, and determines an optimal control horizon for an Earth-Moon transfer.

Equilibrium points define operating conditions for nonlinear dynamical and control systems. Their existence, multiplicity, and stability under parametric uncertainty determine feasible operating regimes and the validity of robustness claims. With parameters constrained to a bounded set, one can (i) compute equilibria at sampled parameter values, (ii) trace equilibria along a prescribed path in parameter space, or (iii) identify states in a given operating domain that are equilibria for at least one admissible parameter realization. We compare standard pointwise workflows-direct simulation, numerical continuation, residual minimization, and a multistart Newton-Raphson method-with validated interval-analysis-based workflows. The latter (a) provide formal certificates of exclusion, existence, and uniqueness of equilibria for fixed parameters and, under parametric inclusion conditions, uniformly over entire parameter boxes, and (b) construct rigorous outer enclosures in state space that provably contain all equilibria associated with the full admissible parameter set. Biomolecular circuit models governed by nonlinear ODEs serve as a representative application domain. We benchmark three canonical architectures across four levels of parameter uncertainty, including a genetic toggle switch near a symmetry-breaking bifurcation. Sampling- and slice-based approaches can miss or underrepresent multistability, whereas interval-based outer enclosures yield mathematically rigorous bounds on the equilibrium set induced by parametric uncertainty.

If an automated vehicle is no longer capable of handling the dynamic driving tasks, regulations require that a minimal risk manouvre is initiated aimed at achieving minimum risk condition. Details on measures and actions to resolve this standstill position are missing. This article provides clues for closing this gap and addresses various states in which the human intervention operator must understand the current situation and decide whether it is more acceptable to maintain the stay, e.g. on road-shoulder, or to move the vehicle back into traffic flow or 'limp it' to another position, depending on the outcome of a health check. Our investigations, based on a list of nearly 30 reasons and a subsequent development of 'scenario trees' prove the complexity of the situation and the heavy difficulties faced by the operator to handle in a correct manner. We also discuss how the technical automated driving system can be enhanced so that it stronger supports the operator or allows to even dismiss human intervention. This deems technically feasible but limits became obvious, so that assistance from out side appears indispensable at least for the ramp-up phase of respective automated vehicle. The non-trivial problem of transitioning the 'stranded' vehicle needs to be addressed by further regulation for which this paper may provide useful input.

We investigate the correlation between interleaver length and turbulence for a deployed terrestrial free-space optical link spanning 4.6 km. Using 24 hours of measurements, we show an indicative distribution of interleaver length for capacities up to 350 Gb/s/pol and outage probabilities down to $10^{-4}$.

Developing nonlinear models that are both expressive and computationally efficient remains a challenge in machine learning and nonlinear system identification. Tensor network kernel machines (TNKM) address this challenge by combining nonlinear feature representations with compact low-rank tensor-network parameterizations. However, practical and extensible software frameworks for developing TNKM models remain limited. In this work, we introduce "tnkm", an open-source Python library for constructing and training TNKM models using JAX. The library provides a unified interface for combining different feature maps, tensor-network architectures, and optimization strategies, including alternating least squares and gradient-based methods. We demonstrate the capabilities of "tnkm" on nonlinear benchmark problems, showing that the implemented models achieve competitive prediction accuracy while retaining compact parameterizations and efficient training. The proposed framework facilitates reproducible development and application of tensor-network-based learning methods.

This paper studies linear discrete-time systems affected by independent and identically distributed (i.i.d.) multiplicative uncertainties and additive noise. It establishes the main links between covariance recursions, the spectral properties of associated Kronecker-based matrices, and mean-square stability, and exploits these links to derive tractable conditions for controller synthesis. We first derive a deterministic covariance recursion within the tube-based Stochastic Model Predictive Control (SMPC) framework using a Kronecker product based matrix augmentation. For linear stochastic systems with multiplicative uncertainty and without additive noise, we show that the full-space matrix representation arising from the covariance recursion has the same spectral radius as its symmetric-space counterpart. Combined with the existing symmetric-space characterization, this establishes that Schur stability of the full-space augmented matrix is equivalent to mean-square stability. For state-feedback design, we propose new sufficient Linear Matrix Inequality (LMI) conditions that are numerically more tractable owing to their reduced size compared with the conventional necessary and sufficient conditions. Numerical tests illustrate the usefulness of the covariance characterization for recursively estimating the covariance without relying on sampling-based methods. We also assess the computational burden of the proposed LMI conditions and their conservatism relative to the necessary and sufficient ones.

Horizon detection in images of ice-covered waters is a challenging problem for maritime navigation due to low contrast between water and sky, cluttered ice structures, and varying illumination conditions. This paper presents a comparative evaluation of six horizon detection algorithms, including four classical computer vision methods and two hybrid approaches combining deep learning with classical line detection. A new bespoke IceHorizon dataset consisting of 30 ship-based and 8 drone-based videos is used to evaluate detection accuracy, horizon coverage, and computational performance. The results show that hybrid methods achieve the highest accuracy and most reliable horizon estimates. In contrast, purely classical methods exhibit reduced robustness, particularly in visually ambiguous scenes. Performance on ship-based imagery was consistently higher than on drone-based imagery, indicating a strong dependency on acquisition characteristics. The created dataset and codes used in this study are made publicly available to support further research on this topic. The code is available at https://github.com/allythe/HorizonDetection. The dataset is available at https://doi.org/10.5281/zenodo.20411867

Although remarkable capabilities have been demonstrated by Large Language Models (LLMs) across scientific domains, feedback controller design remains underexplored. Existing benchmarks focus mainly on linear single-Degree-of-Freedom (DoF) systems and large API-hosted models, leaving performance on complex controller-design tasks and feasibility for edge deployment unclear. To address these limitations, we introduce the Complex Dynamics-to-Control Benchmark for Large Language Models (CoDyControlBench), comprising 132 system configurations across five evaluation dimensions: number of DoF, system type, coupling level, damping regime, and controller type. Six state-of-the-art LLMs were evaluated over three independent runs, including three commercial models (GPT, Gemini, and Claude) and three open-source models (GLM, DeepSeek, and Qwen). GPT achieved the highest design success rate at 94.8%, whereas Qwen showed the lowest rate at 50.0%. Across the benchmark dimensions, DoF and controller type exhibited the largest model-averaged variations in design success, with success-rate ranges of 36.3% and 17.6%, respectively, exceeding those associated with system type, coupling level, and damping regime. Comparison of GPT and Qwen showed that their performance gap arose mainly from the control-design knowledge, particularly gain selection and the use of transient-limiting mechanisms. For edge deployment, a specialized 1.5B-parameter model was developed through reasoning distillation. The reasoning-distilled model outperformed the answer-distilled and base model on CoDyControlBench, maintained stable performance across 1-6 DoFs, and achieved successful traget tracking in all three physical trials on a pneumatic-artificial-muscle-driven robotic arm. These results establish a benchmark baseline and highlight the potential of lightweight, edge-deployable controller-design models.

Bidirectional propellers are often treated as signed thrust sources, but their thrust is a signed-quadratic function of rotor speed.Thus, thrust reversal necessarily occurs through zero rotor speed, where the ability of a bounded motor torque to change thrust collapses.This work formalizes this obstruction by studying exact thrust-trajectory reproducibility under bounded motor inputs with prescribed smoothness.We derive a normalized thrust-coordinate model with vanishing input gain at zero thrust, and prove necessary and sufficient reproducibility conditions in terms of the zero-crossing order of the desired thrust.Generic reversals, in which thrust crosses zero with nonzero slope, require unbounded motor input; the resulting conditions provide direct design rules for shaping thrust reversals that avoid singular motor commands.We also derive the corresponding current and voltage regularity requirements for a DC motor driving a bidirectional propeller.Experiments on a motor-propeller setup validate the predicted reversal-order effects, showing localized current/voltage peaks and thrust-tracking degradation for linear reversals, but not for higher-order reversals.These results expose an intrinsic actuator-level limitation that must be considered in force, acceleration, and interaction-control references for aerial robots.

Dense multi-shell diffusion MRI provides rich q-space information but requires long acquisition times. We propose a spatial masked-set framework for sparse multi-shell diffusion MRI signal synthesis. The model treats observed measurements as an unordered set, uses a local $3 \times 3 \times 3$ neighborhood for spatial context, and predicts radial-order-6 SHORE coefficients for the center voxel. The coefficients can then be decoded analytically to synthesize signals at arbitrary q-space locations. Training combines shell-wise gradient dropping, dense signal supervision, and rotation-consistent SHORE targets so that sparse input signals remain aligned with their coefficient supervision under augmentation. We evaluate on held-out HCP100 white-matter voxels by retaining limited subsets of measured diffusion-weighted signals from the reference acquisition. The proposed method achieves lower signal NMSE than both analytical q-space models and a state-of-the-art continuous dMRI signal synthesis model designed for arbitrary input and output q-space sampling. In the $b=1000$ setting with 10 input gradients, it achieves $2.70\%$ NMSE, a $22.4\%$ relative reduction over this continuous model. Fractional anisotropy on reconstructed $b=1000$ signals provides a complementary tensor-derived endpoint, with analytical models remaining competitive for FA despite higher dense-signal NMSE across the evaluated q-space. The implementation is available on \href{https://github.com/xmindflow/SHOREPred}{https://github.com/xmindflow/SHOREPred}.

This article applies dynamic integral quadratic constraints (IQCs) to the analysis and synthesis of accelerated stochastic gradient algorithms. We consider composite objective functions whose gradient can be approximated via mini-batch sampling and we model the resulting stochastic gradient oracle as a feedback nonlinearity in the spirit of Lur'e systems from robust control. Our first main contribution is a family of IQCs that characterize the second-order statistics of mini-batch gradients, extending the classical Zames–Falb multipliers. Our second contribution is a semidefinite-program for certifying exponential convergence rates of stochastic gradient algorithms, and a complementary condition for bounding the asymptotic variance caused by non-vanishing gradient noise. Our third contribution is a convex synthesis procedure that identifies mini-batch gradient algorithms with the smallest certifiable convergence rate.

Recent audio generation systems have progressed from single-modality synthesis to generating complex acoustic scenes containing speech, music, and sound effects. Therefore, evaluating these models requires assessing multiple interacting capabilities, including semantic fidelity, speaker consistency, and temporal control, yet existing benchmarks focus on isolated domains or coarse-grained descriptions. To address this gap, we introduce the Multi-control Mixed Audio Generation (MMAG) benchmark. MMAG contains approximately 4,000 manually verified audio clips with rich annotations covering speech content, speaker identity, music attributes, sound events, and temporal relationships, together with dedicated subsets for voice cloning and timestamp-conditioned generation. We further propose a systematic evaluation protocol that measures acoustic fidelity, speech quality, semantic alignment, and temporal accuracy. Benchmarking representative agentic orchestrators, unified audio-visual generation models, and native mixed-audio generators reveals substantial performance trade-offs across these capabilities, with no existing model performing consistently well. Our results highlight the remaining challenges of controllable mixed audio generation and establish MMAG as a comprehensive benchmark for future research.

In this paper, the Doppler frequency shift (DFS) is exploited as the only sensing parameter for low-cost indoor mobile device tracking. The existing trajectory tracking methods via DFS of Wi-Fi systems often require the knowledge of the starting position or additional information, like angle-of-arrival (AoA) and time-of-flight (ToF), to recover the trajectory of a moving target. This paper proposes the DoDTrack, a novel Difference-of-Doppler (DoD)-based tracking system, to track an active mobile device using a single receiver with distributed antennas. By comparing the signals received at the distributed receive antennas, which share the oscillator, the DoDs among the antennas can be detected robustly. Then, the reconstruction of the trajectory without prior knowledge of the trajectory starting position can be formulated as a minimum mean square error (MMSE) problem, which can be solved via alternating optimization. Particularly, the starting position and the trajectory shape are updated alternately in the proposed algorithm. In performance validation, we implemented the proposed DoDTrack design on a USRP-X310 platform, and assessed its estimation accuracy with various trajectory shapes in an indoor environment. Experimental results demonstrate that DoDTrack achieves a median tracking error of 0.34 m within a 6 m $\times$ 6 m sensing area, offering a high-precision and low-cost solution for active device tracking.

This paper presents a comprehensive performance comparison of several coded sequences for distributed acoustic sensing systems through numerical simulations. Correlation analysis reveals that perfect autocorrelation sequences provide accurate channel estimation. Phase noise sensitivity varies significantly with frequency diversity, with high-diversity sequences maintaining lower estimation errors for phase and intensity despite increased laser linewidths. For a coded distributed acoustic sensing system with \qty{10}{\hertz} laser linewidth, high-diversity sequences achieve $2.7\times10^{-3}$ rad phase error compared to $5.3\times10^{-3}$ rad for low-diversity codes for a \qty{1.5}{\kilo\meter} fibre length. The findings establish performance trade-offs between correlation quality, phase noise resilience, and practical implementation constraints for next-generation fibre-optic distributed acoustic sensing systems.

This work proposes a new adaptive longitudinal platooning strategy in the framework of concurrent learning. Adaptive refers to vehicles facing uncertainty in powertrain parameters via on-line estimation; concurrent learning refers to using both current and past data in the estimation. The proposed platooning strategy advances existing ones since convergence to the true powertrain parameters is guaranteed without imposing persistence of excitation on the vehicle behavior: it suffices the presence of a single non-zero data sample. Meanwhile, the concurrent learning proof we give advances existing ones since it takes into account an extra unknown gain in the error dynamics.

In longitudinal platooning, some key sources of uncertainty are the powertrain time constants of the vehicles. Because such time constants appear in the input matrix of the platooning dynamics, their correct estimation is either impractical with methods requiring persistence of excitation, or impossible with methods requiring the input matrix to be known. This work proposes a novel adaptive longitudinal platooning method with correct estimation of the powertrain time constants. To achieve correct estimation, the composite adaptive control framework and its stability analysis are suitably modified to handle the time constant uncertainty in the design of the adaptive law. The result is a platooning protocol that guarantees convergence of the estimated time constants to their true values without the need for persistence of excitation: it is sufficient the derivative of the acceleration to be nonzero over a possibly short transient, an extremely relaxed excitation condition. Comparisons with state-of-the-art platooning solutions reveal advantages such as no required measurements of acceleration derivative nor collection of past data. The robustness and practicality of the proposed design is also verified with CarSim-based platooning experiments.

Background: Closed-loop neuromodulation integrating electroencephalography (EEG) and transcranial electrical stimulation (tES) has strong potential for neuroscience research and clinical applications. However, existing platforms often rely on benchtop instrumentation or FPGA-centered architectures, limiting portability and increasing system complexity. Methods: We developed a compact wearable bidirectional platform integrating 8-channel EEG acquisition and 2-channel tES within a single microcontroller unit (MCU). An ADS1299-based front-end supports up to 8 kHz sampling per channel for simultaneous neural and stimulation signal recording. The stimulation module uses direct digital synthesis (DDS) technique to generate programmable tDCS, tACS, and tTIS waveforms, with optimized firmware design enabling real-time operation on the microcontroller. Results: Experimental evaluation demonstrated high signal fidelity, with EEG correlation coefficients reaching $99\%$ under controlled conditions and $93.5\%$ on a gelatine head phantom. Stimulation performance showed current errors below $1\%$ across tDCS, tACS, and tTIS modes. The platform also reliably recorded concurrent EEG–tES signals without saturation during high-amplitude and high-frequency stimulation. Conclusion: These findings demonstrate that an MCU-centered architecture can effectively support simultaneous EEG sensing and multimodal tES delivery in a compact wearable form factor, while maintaining accurate stimulation and robust signal acquisition. Significance: This work provides a practical foundation for portable closed-loop neuromodulation systems with integrated host software, expanding access to personalized stimulation paradigms, and future point-of-care neurotechnology applications.

In this paper, we propose a tracking-assisted robust secure transmission framework for cell-free integrated sensing and communication (ISAC) networks that exploits distributed multistatic sensing to recursively track a mobile eavesdropper and quantify the associated position uncertainty. Since robust beamforming requires communication channel uncertainty rather than position uncertainty, directly incorporating tracking information into secure transmission is nontrivial. This challenge becomes more pronounced in cell-free ISAC, where the common position uncertainty propagates differently to the channels of geographically distributed access points (APs), resulting in coupled AP-specific channel uncertainties. To address this challenge, we fuse multistatic sensing measurements from distributed APs and sensing receivers using an extended Kalman filter (EKF) and propose a Jacobian-based anisotropic ellipsoidal uncertainty model that maps the EKF position-error covariance to coupled AP-specific channel uncertainties. Based on the proposed model, we formulate a robust sum secrecy-rate maximization problem under per-AP transmit-power and sensing mean-square error constraints and develop an efficient alternating optimization algorithm for the joint design of communication beamforming and sensing signals. Simulation results demonstrate a fundamental trade-off between tracking accuracy and secrecy performance, the benefits of distributed multistatic sensing and cooperative transmission in cell-free ISAC, and the effectiveness of the proposed robust design in improving secrecy reliability under mobility-induced channel uncertainty.