Papers

The IEEE 802.11bn amendment defines significant modifications to the standard by establishing Ultra High Reliability (UHR) targets in Wireless Local Area Networks (WLANs). This is expected to deliver substantial enhancements over previous standards, including modes of operation that increase throughput, reduce the 95th percentile of the latency distribution, and decrease MAC Protocol Data Unit (MPDU) loss (all by at least 25%) compared to Extremely High Throughput (EHT) operations defined in the 802.11be amendment. A fundamental innovation for achieving these ambitious goals is the introduction of Multi-Access Point Coordination (MAPC), an unprecedented feature whereby APs will be able to coordinate among themselves to enhance spectrum utilization and advance towards reliability. This paper provides a comprehensive overview and analysis of this key framework. We begin by reviewing existing AP coordination solutions that precede the 802.11bn standard, which serve as a foundation for understanding the transition to the current framework. We then describe the technical 802.11bn MAPC framework as defined by the task group. A detailed overview of each candidate MAPC feature is provided, contextualized with the relevant state-of-the-art. Furthermore, we introduce Kom8ndor, an open-source Wi-Fi 8 simulation tool, to evaluate these candidate MAPC features and showcase their potential to achieve UHR goals. Finally, we outline the future of MAPC beyond 802.11bn, exploring promising directions such as coordination schemes beyond 802.11bn (e.g., Joint Transmission (JT)) and new ideas.

Aerial wildfire suppression requires decisions about when, where, and how to deploy limited aircraft. We present an intervention-design framework built on a frozen hybrid convolutional neural network and cellular automaton (CNN-CA) simulator trained jointly on six historical wildfires. First, we jointly optimize binary drop execution and continuous location and orientation, with aircraft-specific footprints, wind drift, and availability, turnaround, and grounded-day constraints. Second, we model water as an immediate transfer of burning probability to the unburned state and retardant as a persistent reduction in the fuel contribution to spread. Third, we remove drops by a rollout-verified backward elimination while limiting degradation in selected fire-performance metrics. Fourth, we evaluate fixed schedules under daily state sampling and a separate spatially correlated probability-field sensitivity test. Fifth, we compare the optimizer with random, tactical, greedy, and derivative-free planners under shared fleet, drop-budget, and simulator-evaluation allowances, and measure computational scaling. A 2020 Bear Fire case study considers two objectives: total fire-affected area and protected-region exposure. The two-stage total-area schedule reduces deterministic terminal extent by 89.5% relative to the simulator baseline with 1,111 drops. The nominal schedule is sensitive to small pose and effectiveness perturbations. The tactical heuristic is more economical at small evaluation allowances; the gradient planner achieves better objectives with more computation.

This paper presents a distribution-agnostic robust trajectory-optimization framework based on chance-constrained reinforcement learning. The uncertainty is represented here through initial conditions and process noise, with the only requirement being that it can be sampled. A deterministic nominal trajectory is first computed offline, and reinforcement learning is then used only to robustify that baseline through a structured affine closed-loop correction law comprising a feedforward control adjustment and time-varying feedback gains. Probabilistic feasibility is enforced empirically through rollout-based upper-tail quantiles, while terminal dispersion is regulated through covariance-feasibility penalties. The framework is assessed on two materially different trajectory design problems. The flagship case study is a three-dimensional multi-impulse Earth-Mars transfer, where the learned policy is benchmarked against a recent robust trajectory-optimization reference under Gaussian uncertainty and then evaluated under bounded uniform uncertainty and under process disturbances not seen during training. The second case study is a stochastic atmospheric pinpoint rocket landing problem, used to assess portability to a short-horizon continuous-thrust setting with drag, mass depletion, and glide-slope constraints. The results show that the proposed framework can remain competitive in upper-tail fuel cost while preserving probabilistic feasibility, and that the same robustification scaffold can be carried across heterogeneous spacecraft trajectory planning problems without redesign of its core stochastic-control structure.

Dexterous robotic hands are usually formulated as high dimensional active control systems governed by degrees of freedom, actuation, and algorithms. Human hand dexterity, however, is partly encoded in the physical architecture of bones, ligaments, tendons, aponeuroses, and intrinsic muscles. This work describes that contribution as two linked forms of structural intelligence: structural prior generation, in which wrist to finger tenodesis, FDS/FDP routing, and the dorsal extensor hood transform low dimensional posture inputs into default grasp configurations and PIP to DIP coordination; and muscle mediated modulation, in which extrinsic muscles, lumbricals, and interossei regulate MCP posture, distal stability, fingertip force paths, and contact states around that default state. Based on this framework, MCR-Bionic Hand is developed as a 1:1 musculoskeletal biomimetic hand integrating a two row eight bone wrist, cross wrist tendons, anatomical flexor routing, volar plate and collateral ligament constraints, the dorsal extensor hood, and intrinsic muscle pathways within one body. Functional demonstrations and geometric mechanical models show that wrist posture induces multi joint pre shaping, the extensor hood maps PIP posture to a coupled DIP response, and intrinsic plus pathways modulate distal stability and fingertip action direction after grasp formation. Contact rich tasks, including coin rotation, pen transfer, dorsal coin flipping, and cube manipulation, show that MCR-Bionic links low dimensional state generation with fine post contact modulation. These results suggest that anatomical biomimetics is valuable not for visual similarity, but for identifying human hand structures that perform part of control.

In this paper, we investigate a secure integrated sensing and communication (ISAC) system in which multiple communication users (CUs) coexist with multiple untrusted sensing users (SUs) that may eavesdrop on the confidential information intended for the CUs. To promote security fairness among users, we formulate a max-min secrecy rate optimization problem subject to a transmit power budget and sensing quality requirements characterized by beampattern matching error constraints. The resulting design problem is highly non-convex due to the secrecy rate expressions and non-convex sensing constraints. To address these challenges, we first reformulate the problem using semidefinite relaxation (SDR). Based on the reformulated problem, we develop a branch-and-bound (BB) framework combined with convex relaxations to obtain the globally optimal solution within a prescribed accuracy. To further reduce computational complexity, we propose a low-complexity algorithm based on successive convex approximation (SCA), which iteratively solves a sequence of convex subproblems and converges to a local solution. Numerical results demonstrate that the proposed BB algorithm achieves the global optimum and provides a benchmark for performance evaluation. Moreover, the proposed SCA-based algorithm attains near-optimal secrecy performance with significantly lower computational complexity, making it attractive for practical ISAC deployments.

Koopman linearization opens many possibilities for control synthesis and analysis of nonlinear systems. Whether or not any given nonlinear control system admits a finite-dimensional Koopman representation remains a crucial question to address. A related problem is to categorize the class of all Koopman linearizable nonlinear control systems. In this work, we present differential geometric conditions on the drift and control vector fields of a control-affine nonlinear system, that must be necessarily satisfied for Koopman linear transformation to exist. The same conditions are also shown to be sufficient for (a slightly weaker notion of) Koopman linearizability on control-invariant manifolds. Further, these conditions, together with an additional condition, become necessary and sufficient for Koopman linearizability to a controllable linear system. Our examples illustrate the ease of checking these conditions, and also shed light on how Koopman linearizing transformation may not exist for a control-affine system even though one can linearize the autonomous part of the system via Koopman lifting.

Turn-taking in multi-party spoken conversations remains a fundamental challenge for voice-based agents, particularly under dynamic floor competition and varying user expectations. We propose ModeratorLM, a role-playing voice agent that conditions turn-taking behavior on an explicitly assigned role in multi-party settings. The system is built on a speech large language model operating in chunk-wise streaming manner. We further introduce a reasoning-augmented variant that incorporates chain-of-thought reasoning over conversational context and the assigned role. We construct RolePlayConv, a large-scale synthetic dataset of spoken multi-party conversations with diverse assistant roles. Experiments on real-world meeting data and RolePlayConv show improved turn-taking precision by over 40% and recall by more than 70%, while substantially reducing false-positive interruptions compared to non-role-conditioned baselines.

The frequency bands between 7 and 24 GHz, also known as upper midband or Frequency Range (FR) 3, are being considered as an enabler of 6th Generation (6G) mobile networks. This portion of the spectrum exhibits different propagation characteristics compared to frequencies above 24 GHz, while also offering the potential to provide larger bandwidth allocations for mobile systems than those available in the sub-6 GHz range. 6G technology and spectrum policy, however, will need to guarantee coexistence with the incumbents that already use these frequency bands, which include a variety of services, from radiolocation to satellite-based communications, remote sensing, and radioastronomy. In this paper, we consider the challenge of coexistence between 6G terrestrial systems and satellite incumbents in different portions of the FR3 bands. Using a large-scale 3D model of a terrestrial deployment in the city of Boston and an open-source ray tracing solution, we evaluate the level of Radio Frequency Interference (RFI) that tens of terrestrial Next Generation Node Bs (gNBs) generate toward satellites at different elevation angles. Our model, based on realistic obstruction, clutter, diffraction, and reflections, shows that sidelobes and Non-Line-of-Sight (NLoS) paths can significantly contribute to RFI. Besides directionality, the spatial distribution of gNBs also plays a key role in defining the RFI levels, suggesting that a careful design and operation of terrestrial deployments can create coexistence opportunities.

Safe rehabilitation is an interaction-dynamics problem: the controller must regulate a prescribed motion while absorbing involuntary spasm, voluntary effort, actuator compliance, and model mismatch as disturbances. This paper instantiates the predictive interaction-dynamics framework of the base pHRI formulation on a SEA knee joint. SEA feedforward reduces the gravity-compensated knee to the same scalar double integrator as the base framework, while a dynamic-residual measurement from spring deflection supplies an interaction-disturbance observation. A steady-state target converts the estimated disturbance into a cancelling input, and a finite-horizon quadratic program regulates deviations from that target under range-of-motion, torque, and velocity constraints. The evaluation matches stiffness and damping across controllers so gains cannot be attributed to higher impedance. Under a motion-opposing $15\unit{Nm}$ step, classical impedance and MPC without estimation produce about $500\unit{mrad}$ steady-state error, whereas Kalman-augmented interaction MPC reduces this to $1.17\unit{mrad}$ at 100~Hz and $0.70\unit{mrad}$ at 500~Hz; the 500~Hz peak is $7.27\unit{mrad}$. In 30 randomized trials, the 95th-percentile peak is $21.57\unit{mrad}$. Bounded Assist-as-Needed scheduling, a corrective-channel energy tank, constrained OSQP stress cases, direct MuJoCo execution, and a posture-clamped MyoSuite knee slice are implemented. The framework holds on a single-mass, closed-inner-loop SEA approximation; an explicit two-mass plant with a finite-bandwidth, pole-placed inner torque loop (Section~VIII) confirms this for nominal tracking but shows delivered torque can overshoot the commanded bound by 21.7% near saturation. Scope excludes clinical intent recognition, full-system passivity, safety certification, hardware trials, and multi-joint validation.

The posture of the vocal folds produced by laryngeal muscle activation plays a central role in determining the dynamics of voice production. Abnormal vocal fold configurations are frequently associated with inefficient phonation and a variety of voice disorders. Although diverse glottal closure patterns have been observed clinically, the biomechanical mechanisms governing their dynamic behavior and resulting phonatory characteristics remain incompletely understood. Moreover, existing numerical models that incorporate the effects of the intrinsic musculature on posturing and glottal conformation are computationally expensive, which limits their suitability for large-scale parametric investigations. In this work, we introduce a computationally inexpensive vocal fold (VF) model wherein the body and cover VF layers are treated as a composite beam and a coupled membrane, respectively. Intrinsic laryngeal muscle activation, in addition to positioning the arytenoid cartilages and cricothyroid joint, introduces moments at the boundaries of the structure that influence glottal conformation. The model produces phonatory characteristics that are qualitatively consistent with those reported in high-fidelity finite-element models and clinical studies, thereby supporting its predictive capability while offering substantial computational advantage. The proposed framework provides biomechanical insights into the influence of incomplete glottal closure on phonation dynamics and may serve as a computationally tractable tool for investigating mechanisms underlying certain voice disorders.

We present a communication-aware task decomposition framework for multi-agent systems with collaborative relative configuration objectives specified in Signal Temporal Logic (STL), allowing for dynamic task reallocation under time-varying communication networks. Building on our prior work, the framework supports the direct use of existing feedback controllers for reactive task satisfaction. We address two key challenges: disjunctive STL specifications and time-varying communication networks. Disjunctive specifications are handled through a graph transition system that captures the alternative task sequences induced by logical OR operators. To address time-varying connectivity, we introduce a redistribution mechanism that transfers tasks from disconnected agents to connected ones as the network evolves while preserving decentralized execution. Simulations and experiments on a swarm of Crazyflie drones demonstrate scalability in the number of agents, communication connectivity, and specification complexity.

This paper proposes a novel adaptive control framework that embeds nonlinear opinion dynamics within the dynamical sensorimotor layers of an automated vehicle governed by second-order nonholonomic bicycle kinematics. The framework enables an ego vehicle to perform adaptive decision-making and achieve safe motion control under interaction uncertainty with non-cooperative neighboring agents. We consider a representative case study in which an ego vehicle autonomously attempts to merge into a lane occupied by human-driven or automated vehicles whose intentions are unknown. Within the proposed framework, the ego vehicle adaptively selects and executes merging versus non-merging behaviors in response to changing environmental conditions. Formal safety guarantees, as well as equilibrium and stability analyses of the closed-loop system, are provided. Numerical simulations further demonstrate the effectiveness of the proposed approach.

While low-latency interaction is critical for spoken dialogue, cascaded architectures are often bottlenecked by reactive turn-completion detection. We propose Endpoint Anticipation, shifting from reactive detection to proactive forecasting of end-of-turn signals. Our speech-based model anticipates endpoints upto 2.56 seconds in advance, enabling speculative execution of LLM and TTS pipelines on partial context. We introduce metrics to quantify the trade-off between realized latency reduction and computational redundancy. Evaluation across conversational and task-oriented datasets shows our model consistently outperforms competitive VAP-based baselines. Integration with the Unmute framework demonstrates a 505 ms average latency reduction with a 28.4% increase in speculative computation, effectively masking sequential bottlenecks to enable complex reasoning in real-time speech-to-speech interaction.

We consider stochastic model predictive control (MPC) for constrained linear systems subject to multiplicative binary input uncertainty, motivated by applications such as networked control with packet losses and intermittent actuation. A common approach in this setting replaces the stochastic dynamics with their expectation, yielding tractable formulations that admit standard terminal ingredients and stability guarantees in expectation. We show that such formulations can exhibit structural properties that differ fundamentally from those of deterministic MPC and may be misleading as indicators of realized closed-loop behaviour. In particular, the expected value function is not necessarily monotonic in the prediction horizon, and value function-based inner approximations of the region of attraction may deteriorate as the horizon increases. Furthermore, we establish a probabilistic comparison with certainty-equivalent (optimistic) MPC, showing that the latter can ensure a strictly positive probability of recursive feasibility in situations where stochastic MPC certifies feasibility but fails with probability one. These results highlight inherent limitations of expectation-based stochastic MPC for systems with multiplicative binary uncertainty and motivate a re-examination of how stochasticity is incorporated into constrained predictive control design for such systems.

Low-resolution data converters can significantly reduce the power consumption and silicon area of all-digital massive multi-user (MU) multiple-input multiple-output (MIMO) basestations. However, the existing literature almost exclusively focuses on idealistic quantization models, neglecting the inherent non-idealities present in real-world analog-to-digital converter (ADC) implementations. To overcome this limitation, we propose two affine models, one based on Bussgang's decomposition and one that maximizes the signal-to-distortion ratio (SDR), both accounting for the most prominent non-idealities in successive approximation register (SAR) ADCs. Subsequently, we utilize these models to devise low-complexity methods that mitigate SAR-ADC non-idealities in massive MU-MIMO wireless systems.

Affine frequency division multiplexing~(AFDM) has emerged as a compelling waveform candidate for future wireless networks, owing to its strong resilience to doubly selective channels and its ability to enable the seamless integration of communication and sensing functionalities. Against this context, this article provides a systematic study of AFDM from a standardization perspective. We first introduce the principles of AFDM and discuss the major considerations involved in waveform standardization. We then examine the backwards compatibility of AFDM with 4G/5G multi-numerology frameworks and their anticipated evolution, frequency-modulated continuous-wave (FMCW) radar waveforms, and long-range (LoRa) modulation, demonstrating that AFDM can be incorporated into legacy processing chains with limited modification. Key standardization-critical capabilities are further discussed, including multiple-antenna and multi-user support, and peak-to-average power ratio (PAPR). Finally, we investigate the potential of AFDM in several emerging scenarios, including non-terrestrial networks~(NTN), integrated sensing and communications (ISAC), vehicle-to-everything (V2X), and underwater acoustic (UWA) communications, whereby severe delay-Doppler dispersion places stringent demands on waveform robustness. Through these explorations, it is shown that that AFDM represents a timely and compelling technology for future wireless networks.

Practical implementations of phased arrays suffer from per-antenna gain, phase, and delay mismatches, which can significantly worsen the maximum sidelobe level (SLL) of beampatterns. The existing literature either analyzes specific structured mismatch patterns or derives per-angle marginal statistics under random mismatches, which fail to characterize global beampattern metrics such as the maximum SLL. To address this limitation, we propose a frequency-domain framework in which the beampattern is described by a tapering-window-dependent base function evaluated along a deformation determined by the array architecture and signal bandwidth. This formulation enables a spectral analysis of mismatches, revealing that element-wise errors generate weighted replicas of the ideal beampattern whose amplitudes are given by the discrete Fourier transform of the mismatch sequence. Building on this insight, we derive an approximation of the maximum SLL distribution under random gain and phase mismatches. The resulting expressions enable yield-oriented design and rapid design-space exploration without relying on computationally intensive Monte-Carlo simulations.

Line-commutated converter high-voltage direct current (LCC-HVDC) has proven to be a reliable technology for bulk power transmission over long distances. However, the growing penetration of converter interfaced generation (CIG) is resulting in weaker AC grids, rendering the operation of LCC-HVDC systems vulnerable and posing a serious challenge to their stability. Grid-forming (GFM) controlled voltage source converter (VSC) have been shown to provide stabilizing impact in weak grid conditions. However, the impact of GFM controlled VSCs (GFM-VSC) on stability of LCC-HVDC in weak grid conditions has not been studied in depth in the literature. In this paper, a simplified model of LCC-HVDC is proposed and validated. Then a small-signal state-space model of a system consisting of aforementioned LCC-HVDC, a GFM-VSC and an infinite grid is developed to study the interactions between different components. The small-signal stability analysis shows the stabilizing effect of the GFM-VSC on the stability of the LCC-HVDC link in weak grid condition. Furthermore, the study on the sizing of the GFM power converter reveals that even a modest share of the capacity of the GFM power converter relative to the total nominal apparent power (sum of nominal power of LCC-HVDC and the nominal apparent power of GFM-VSC) is sufficient to ensure the stability of the system, in the test system analyzed in this study. This work just focuses in small-signal stability, but it is important to highlight that other stability phenomena should also be taken into account when selecting the final size of the GFM-VSC.

Self-supervised foundation models have shown strong promise in medical imaging. However, existing MRI foundation-model studies have primarily emphasized segmentation and dense prediction tasks, while systematic investigation of self-supervised foundation models for MRI-based disease detection remains limited. In this work, we investigate two major self-supervised pretraining paradigms for MRI-based disease detection: reconstruction-based learning via Masked Autoencoders (MAE) and predictive representation learning via Joint Embedding Predictive Architectures (JEPA). We study the role of auxiliary objectives by introducing a novel spectral-domain reconstruction loss for MAE to enhance sensitivity to fine-grained anatomical structure, and by integrating variance–covariance regularization (VCR) within our JEPA framework to encourage decorrelated latent representations. Our models are pretrained on heterogeneous single-contrast MRI volumes in a contrast-agnostic setting, without modality concatenation. Across five downstream disease detection tasks, our results highlight the importance of self-supervised objective design for medical foundation model pretraining, demonstrating that the downstream benefit of each objective is determined by its relevance to the task's structure. Specifically, spectral regularization yields the largest improvements when the downstream discriminative signal is characterized by strong high-frequency anatomical structures, while covariance regularization is most beneficial when discriminative information spans multiple decorrelated feature dimensions. MAE with spectral-domain supervision consistently achieves superior downstream performance for MRI-based disease detection. These findings suggest that self-supervised objectives in medical imaging encode specific biases, and their downstream benefit is fundamentally conditioned on the task's structure.

The transition to heavy-duty battery electric vehicles requires an efficient and cost-effective deployment of the charging infrastructure, particularly when multiple operators share resources. This paper presents a multi-phase optimization framework for the joint planning of charging stations in a shared network, using high-resolution empirical truck trajectory data from two freight companies with distinct operational characteristics. The model is formulated to minimize the total number of charging stations while ensuring that the predefined electrification targets are met over successive expansion stages. The analysis captures heterogeneity in fleet usage, with one company operating a spatially concentrated network with shorter and more consistent routes, and the other exhibiting more dispersed operations with longer and more variable driving patterns. The results show that early-stage infrastructure deployment primarily supports fleets with concentrated operations, while later expansion phases are essential to accommodate long-haul and geographically dispersed transport demand. Furthermore, shared infrastructure not only enables reductions in redundant investments, but also introduces dependencies where certain fleets rely heavily on the full network to sustain electrified operations. In general, the findings highlight the importance of coordinated and data-driven infrastructure planning, and demonstrate that fleet-specific characteristics strongly influence both infrastructure requirements and electrification outcomes. The proposed framework provides practical insights on how collaborative and phased deployment strategies can enhance the scalability and efficiency of freight transport electrification.

As energy prices surge for the second time in recent years driven by the ongoing crisis in the Middle East, the European Union's continuing reliance on fossil energy imports is becoming increasingly apparent. However, despite offering an intriguing prospect of improved energy resilience, the ramp-up of local green hydrogen production lags far behind the officially stated ambitions set after the 2022 energy crisis. A prominent reason for the widening implementation gap between announced and realised production projects is overly strict rules on renewable power sourcing, prompting Member states' ministries and the European Commission to propose advancing a planned rules review from 2028 to 2026. To contribute to a successful review and rule adjustments, we address an important gap in understanding the effects of power purchase rules on green hydrogen production. By taking the perspective of European electrolyser operators, we show how the criterion of additionality and its interaction with required temporal correlation can jeopardise the fulfilment of green hydrogen offtake agreements and affect green hydrogen production costs across different European bidding zones. Applying different design paradigms to a green hydrogen production system reveals that electrolyser operator measures, such as PPA and storage upsizing, can help to mitigate the business risks posed by the additionality criterion but come with increased costs. Alternatively, relaxed temporal correlation and increased offtake flexibility both increase production system robustness and reduce production costs simultaneously. Whereby relaxing temporal correlation rules does not result in exceeded emission intensity thresholds, underlining the potential of extended transitional rules to support the ramp-up of European green hydrogen production.

We describe faust2clap, a framework establishing the first officially maintained compilation pathway from Faust DSP specifications to the CLAP format. The system operates in two different modes. A static mode employs ahead-of-time compilation to yield native binaries of optimal efficiency, while a dynamic mode uses runtime interpretation to permit DSP code modification without interrupting the host application. This latter capability addresses a persistent friction in audio software development, namely the cumulative overhead of the edit, compile, and reload cycle. We detail the algorithmic machinery underlying both modes, focusing specifically on the problem of parameter identity. To preserve both parameter values and their bindings to host automation across structural DSP mutations, we introduce an address-based identity matching algorithm and a stable slot allocation scheme. The implementation, comprising approximately 2,400 lines of C++ architecture and Python tooling code, has been integrated into the main Faust distribution.

Pixel-bin image sensors are becoming the default choice for smartphone cameras due to their resolution vs light-gathering trade-off. However, their larger inter-color separation compared to the Bayer color filter array (CFA) makes them challenging to demosaic. Furthermore, existing deep learning-based demosaicing methods are CFA-specific, requiring multiple individual models that take up precious onboard resources and demand larger development and maintenance efforts. In this work, we propose a modular unified architecture for demosaicing various pixel-bin sensors that provides higher image quality while being extensible and lightweight. Additionally, to enable plug-and-play operation, we introduce a learning-free CFA-identification module to detect the CFA type of raw data accurately.

Chopper stabilized amplifiers are popularly used for realizing amplifiers with low offset and for rejecting flicker noise. One of the main limitations of these amplifiers is the low Input Impedance (Zin) produced by the switch capacitor input network. Zin here is resistive due to the switch capacitor action and is inversely proportional to the product of Chopping frequency (Fch) and Input Capacitance (Ci). Since Fch should be greater than the flicker noise corner frequency, this results in a low Zin. When interfacing sensors with high Sensor Output Impedance (Zo), chopper stabilized amplifiers load the sensors resulting in reduced sensitivity. This paper presents a novel input impedance boosting technique - Differential capacitor flipping technique for chopper based Capacitively Coupled Instrumentation Amplifier (CCIA), which prevents discharge and recharge of Ci's in every cycle by reconfiguring the capacitor positions while preserving the chopping operation. This ideally results in a purely capacitive Zin which is independent of Fch. The proposed architecture is used to demonstrate Electrocardiogram (ECG) signal acquisition with dry electrodes that have Zo in the order of a few Mega Ohms. This circuit implemented in TSMC 65 nm CMOS technology node features Zin of 21 GOhms at DC. The circuit has a power consumption of 2.6E(-6)W (2.8E(-6)W including clock generation circuits), with 7.2E(-6)Vrms (1 Hz-150 Hz) of total integrated input referred noise. ~

Underactuated spacecraft faces controllability limitations and heightened sensitivity to environmental disturbances, complicating attitude maneuvering and stabilization. Due to the lack of control authority along the underactuated axis, conventional controllers cannot directly stabilize all attitude components and therefore require reference planning strategies. Furthermore, MPC approaches remain sensitive to inertia uncertainty and unmodeled dynamic couplings, resulting in degraded tracking performance under mismatch. To address these issues, we consider a hierarchical architecture integrating three layers: (i) a nonlinear model predictive controller (NMPC) for constraint and underactuation-aware maneuver planning and nominal closed-loop stability under actuator limits; (ii) a physics-informed neural network (PINN) trained offline on simulation data to estimate residual disturbance torques, with loss terms that enforce consistency with rigid-body rotational dynamics; (iii) a Lyapunov-based supervisory safety mechanism that evaluates the learned correction online and bounds or suppresses its influence to preserve the stability properties of the baseline controller. The architecture is evaluated in a high-fidelity simulation environment modelling reaction wheel dynamics, actuator saturation, and environmental disturbances. Experimental studies show statistically significant reductions in steady-state attitude error relative to standalone NMPC while maintaining robust behavior under uncertainty. The supervisory layer ensures graceful degradation to purely model-based control when the learning-based augmentation is unreliable.

Variational autoencoder-based neural video coding has demonstrated impressive rate-distortion performance. However, its adoption in real-world applications remains hindered by challenges, such as prohibitively high computational complexity and limited cross-platform interoperability. These issues are often overlooked, as most neural video codecs rely on floating-point arithmetic to fully explore their rate-distortion potential. Practical deployment, however, requires integer-based implementations. Converting floating-point implementations into integer-based networks is non-trivial, since it involves quantizing inter-dependent coding components, whose sensitivity to precision may vary across codec designs. This paper introduces a Jointly-Optimized Mixed-Precision (JOMP) framework, in which both quantization parameters and bit widths are treated as learnable variables during training. This enables different codec modules to operate at varying precision levels, thereby jointly optimizing the rate-distortion-complexity trade-off. To the best of our knowledge, JOMP is the first mixed-precision quantization framework for neural video codecs. Its effectiveness is validated through a systematic investigation of quantization across different coding frameworks and temporal buffering strategies. Our study marks the first attempt to a unified understanding of the combined effects of modern coding frameworks and temporal buffering strategies, with the aim of informing future development of neural video codecs from a practicality perspective. In addition, we develop a complete integerization pipeline to achieve deterministic decoding. Overall, when applied to our best-performing model, JOMP enables end-to-end mixed-precision learning for integer neural video codecs, achieving rate-distortion performance comparable to that of the state-of-the-art DCVC-FM while reducing bit operations by 87.6%.

Training neural networks (NNs) for speech enhancement (SE) in distant speech-capturing scenarios requires paired distorted and clean reference speech signals. While such data are often generated through simulation, the mismatch between simulated and real recordings significantly limits SE accuracy. To address this issue, we propose Close-to-Distant microphone Projection (C2D projection), a method that generates paired data from real recordings captured by close and distant microphones. C2D projection estimates an optimal projection matrix that transforms close-microphone inputs into clean reference signals aligned with distant-microphone recordings, while simultaneously performing denoising. We show this projection can be effectively realized using a variant of the Parametric Multichannel Wiener Filter (PMWF). Experimental results demonstrate that an NN trained with C2D-projected data outperforms the state-of-the-art Guided Source Separation (GSS) on the challenging CHiME6 dinner party ASR task under oracle diarization, when using the enhanced output from GSS as an auxiliary input to the NN.

Multi-talker speech recognition is often addressed by combining automatic speech recognition (ASR) and speaker diarization in a pipeline system. Recently, LLM-based approaches have shown promise by jointly modeling semantic and speaker information, but they typically require large-scale multi-talker corpora that are costly to annotate. In this paper, we investigate how to efficiently train an LLM-based system with limited real-recorded data while maintaining high accuracy in speaker attribution. We propose several strategies: (1) a dual-encoder architecture to extract semantic and speaker features, (2) a feature interleaving format to merge these features as the inputs to the LLM, (3) a length-aware speaker ID loss to enhance diarization capability, and (4) an adaptive threshold strategy for ASR loss computation to mitigate hallucinations caused by speech overlaps. These strategies balance training between ASR and diarization tasks. Our system outperforms open-source baseline approaches, achieving relative improvements of 18% on the AliMeeting corpus and 24% on the Aishell4 corpus.

6G is expected to bring unprecedented advancements in the capabilities of vehicular networks. However, the advent of 6G will also introduce changes in the operation of vehicular communication infrastructures such as roadside units (RSUs), including the incorporation of autonomous intent-based network paradigm and integrated sensing and communication (ISAC) capabilities. While ISAC enables sensing and communication within a single 6G network node, intent-based network design paradigm ensures that network nodes such as RSUs, act as autonomous cognitive agents to fulfill the objectives of their respective communication service providers. This paradigm shift necessitates the development of V2I communication strategies that learns and adapts to the sensing-assisted communication and the autonomous decision-making strategies of RSUs. We model the RSU as a constrained utility maximizer, where the utility function characterizes the RSU intent, and formulate an inverse learning (IL) problem to infer the underlying utility function from observed ISAC RSU actions, for example the adaptive beamwidth allocation in response to the kinematic states of vehicles within a vehicular micro-cloud (VMC). The main contributions of this paper are: (i) ATIL, a nonparametric method based on Afriat theorem for fixed utility learning; (ii) FICNNIL, a parametric approach using fully input-concave neural networks, for structured fixed utility learning; and (iii) PICNNIL, a parametric approach based on partially input-concave neural networks, for inverse learning of state-dependent utilities. (iv) Federated inverse learning algorithms FedFICNNIL and FedPICNNIL for fixed and state dependent utility, respectively. We demonstrate the proposed IL-based framework for two V2I communication applications in VMCs, namely predictive scheduling for cooperative data downloading and dynamic cluster-head selection.

This paper studies feedback Nash equilibria (FNE) in scalar discounted linear quadratic (LQ) games with $N$ players. By explicitly incorporating the discount factor, we show that finite-cost equilibria may fail to stabilize the original system, motivating a distinction between FNE and stable FNE together with a sufficient stability condition. Based on a parametric characterization of the policies, we propose numerical methods for computing all equilibria. Particular attention is devoted to the symmetric game, where a closed-form expression of the symmetric FNE and conditions for the existence of up to $M\leq2^N-2$ equilibria are derived. Numerical experiments illustrate how equilibrium multiplicity depends on the game configuration and highlight the emergence of finite-cost non-stabilizing equilibria.