Papers

We extend the Datamodels framework from supervised learning to Model Predictive Path Integral (MPPI) control. Whereas Datamodels estimate sample influence via regression on a fixed dataset, we instead learn to predict influence directly from sample cost features, enabling real-time estimation for newly generated samples without online regression. Our influence predictor is trained offline using influence coefficients computed via the Datamodel framework across diverse MPPI instances, and is then deployed online for efficient sample pruning and adaptive constraint handling. A single learned model simultaneously addresses efficiency and safety: low-influence samples are pruned to reduce computational cost, while monitoring the influence of constraint-violating samples enables adaptive penalty tuning. Experiments on path-tracking with obstacle avoidance demonstrate up to a $5\times$ reduction in the number of samples while maintaining control performance and improving constraint satisfaction.

Movable antenna (MA) technology exhibits great promise for enhancing the sensing capabilities of future sixth-generation (6G) networks due to its capability to alter antenna array geometry. With the growing prevalence of near-field propagation at ultra-high frequencies, this paper focuses on the application of one-dimensional (1D) and two-dimensional (2D) MA arrays for near-field sensing to jointly estimate the angle and distance information about a target. First, for the 1D MA array scenario, to gain insights into MA-enhanced near-field sensing, we investigate two simplified cases with only angle-of-arrival (AoA) or distance estimation, respectively, assuming that the other information is already known. The worst-case Cramer Rao bounds (CRBs) on the mean square errors (MSEs) of the AoA estimation and the distance estimation are derived in these two cases. Then, we jointly optimize the positions of the MAs within the 1D array to minimize these CRBs and derive their closed-form solutions, which yield an identical array geometry to MA-enhanced far-field sensing. For the more challenging joint AoA and distance estimation, since the associated worst-case CRB is a highly complex and non-convex function with respect to the MA positions, a discrete sampling-based approach is proposed to sequentially update the MA positions and obtain an efficient suboptimal solution. Furthermore, we investigate the worst-case CRB minimization problems for a 2D MA array under various conditions and extend our proposed algorithms to solve them efficiently. Numerical results demonstrate that the proposed MA-enhanced near-field sensing scheme dramatically outperforms conventional fixed-position antennas (FPAs). Moreover, the joint angle and distance estimation results in a different array geometry from that in the individual estimation of angle/distance or far-field sensing.

The transition from 5G to 6G requires frequency-dependent, physically consistent radio channel models across the upper-FR1/FR3 transition region, particularly in the under-explored $4$–$8$~GHz region targeted in the current WRC-$27$ studies, where outdoor urban channel measurements and characterizations remain scarce. This paper presents a $4.85$~GHz measurement-anchored study of urban channels and a literature-referenced cross-band analysis. Double-directional measurements were conducted at $4.85$~GHz in urban macrocell (UMa) and urban microcell (UMi) routes in Yokohama, Japan, from which path loss, delay spread (DS), azimuth spread of arrival/departure (ASA/ASD), $K$-factor, and route-dependent spatial-consistency statistics were extracted. To align these results in a broader cross-band context, the measured $4.85$~GHz large-scale parameter (LSP) means were combined with scenario-matched literature anchors to derive log-log trends for DS, ASA, and ASD over an approximately $4$–$28$~GHz range around the $7.125$~GHz upper-FR1/FR3 cross-band boundary. The resulting trends were compared with 3GPP UMa/UMi reference parameterizations over the same interval, and the sensitivity of the UMi DS fit was examined via leave-one-out analysis. Because the cross-band analysis still relies on a single in-house measurement band alongside heterogeneous anchors from different campaigns, it is presented as measurement-informed and indicative rather than as a definitive multi-band model. The paper therefore contributes both a detailed, parameterized $4.85$~GHz urban measurement reference and a bounded literature-referenced view of channel behavior near the upper-FR1/FR3 transition.

Radio maps (RMs) provide a spatially continuous description of wireless propagation, enabling cross-layer optimization and unifying communication and sensing for integrated sensing and communications (ISAC). However, constructing high-fidelity RMs at operational scales is difficult, since physics-based solvers are time-consuming and require precise scene models, while learning methods degrade under incomplete priors and sparse measurements, often smoothing away critical discontinuities. We present RMSup, a physics-informed super-resolution framework that functions with uniform sparse sampling and imperfect environment priors. RMSup extracts Helmholtz equation-informed boundary and singularity prompts from the measurements, fuses them with base-station side information and coarse scene descriptors as conditional inputs, and employs a boundary-aware dual-head network to reconstruct a high-fidelity RM and recover environmental contours jointly. Experimental results show the proposed RMsup achieves state-of-the-art performance both in RM construction and ISAC-related environment sensing.

Short-term voltage stability assessment (STVSA) is critical for secure power system operation. While classical machine learning-based methods have demonstrated strong performance, they still face challenges in robustness under adversarial conditions. This paper proposes QSTAformer-a tailored quantum-enhanced Transformer architecture that embeds parameterized quantum circuits (PQCs) into attention mechanisms-for robust and efficient STVSA. A dedicated adversarial training strategy is developed to defend against both white-box and gray-box attacks. Furthermore, diverse PQC architectures are benchmarked to explore trade-offs between expressiveness, convergence, and efficiency. To the best of our knowledge, this is the first work to systematically investigate the adversarial vulnerability of quantum machine learning-based STVSA. Case studies on the IEEE 39-bus system demonstrate that QSTAformer achieves competitive accuracy, reduced complexity, and stronger robustness, underscoring its potential for secure and scalable STVSA under adversarial conditions.

Quantum machine learning has emerged as a promising approach to improve feature extraction and classification tasks in high-dimensional data domains such as medical imaging. In this work, we present a hybrid Quantum-Classical Convolutional Neural Network (QCNN) architecture designed for the binary classification of the BreastMNIST dataset, a standardized benchmark for distinguishing between benign and malignant breast tumors. Our architecture integrates classical convolutional feature extraction with two distinct quantum circuits: an amplitude-encoding variational quantum circuit (VQC) and an angle-encoding VQC circuit with circular entanglement, both implemented on four qubits. These circuits generate quantum feature embeddings that are fused with classical features to form a joint feature space, which is subsequently processed by a fully connected classifier. To ensure fairness, the hybrid QCNN is parameter-matched against a baseline classical CNN, allowing us to isolate the contribution of quantum layers. Both models are trained under identical conditions using the Adam optimizer and binary cross-entropy loss. Experimental evaluation in five independent runs demonstrates that the hybrid QCNN achieves statistically significant improvements in classification accuracy compared to the classical CNN, as validated by a one-sided Wilcoxon signed rank test (p = 0.03125) and supported by large effect size of Cohen's d = 2.14. Our results indicate that hybrid QCNN architectures can leverage entanglement and quantum feature fusion to enhance medical image classification tasks. This work establishes a statistical validation framework for assessing hybrid quantum models in biomedical applications and highlights pathways for scaling to larger datasets and deployment on near-term quantum hardware.

Effective medical simulators necessitate realistic haptic rendering of biological tissues that exhibit viscoelastic material properties, such as creep and stress relaxation. Fractional-order models provide an effective means of describing intrinsically time-dependent viscoelastic dynamics with few parameters, as they naturally capture memory effects. However, due to the unintuitive, frequency-dependent coupling among the order of the fractional element and other parameters, determining appropriate parameter values for fractional-order models that yield high perceived realism remains a significant challenge. In this study, we propose a systematic means of determining the parameters of fractional-order viscoelastic models that optimizes the perceived realism of haptic rendering across general populations. First, we demonstrate that the parameters of fractional-order models can be effectively optimized through active learning, using qualitative feedback-based human-in-the-loop (HiL) optimization, to ensure consistently high realism ratings for each individual. Second, we propose a rigorous method to combine HiL optimization results into an aggregate perceptual map trained on the entire dataset, and demonstrate how to select population-level optimal parameters from this representation that are broadly perceived as realistic across general populations. Finally, we provide evidence of the effectiveness of the generalized fractional-order viscoelastic model parameters for three viscoelastic materials by characterizing their perceived realism through human-subject experiments. Overall, generalized fractional-order viscoelastic models established through the proposed HiL optimization and aggregation approach possess the potential to significantly improve the sim-to-real transition performance of medical training simulators.

This paper introduces an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery, sensing data gathering, and mobile edge computing (MEC) services. Three tenants provide services to their clients by sharing a common infrastructure of a set of UAVs. The content delivery tenant needs to guarantee that each of its clients (users) receives the required content, the sensing tenant aims to gather an adequate amount of uncorrelated data, and the MEC tenant provides computing service to its clients. An energy consumption minimization framework is considered to meet the tenants' requirements by optimizing the number of deployed UAVs, the deployment location of each UAV, the transmit power of each deployed UAV, the user-UAV association, and the transmission power as well as the computing resources of each UAV. Taking into account the spatial correlation among the sensing users, a subset of these users is activated to gather the required sensing information. A solution approach technique inherited from graph theory is presented, in which the Lagrange approach derives the transmission power and computing resource allocation expressions. Simulation results illustrate that the proposed framework significantly reduces the total energy consumption.

The interplay between large antenna apertures and high carrier frequencies in future wireless systems gives rise to near-field communications, where the curvature of spherical wavefronts renders traditional far-field beamforming models inadequate. This chapter addresses the following fundamental questions on near-field operation: (i) What is the maximum distance where far-field approximations remain effective for path gain prediction and beam design? (ii) What level of position resolution is needed for accurate near-field beam focusing? (iii) How frequently must channel state information be updated to maintain highly directive bweamforming in dynamic scenarios? We develop an analytical framework for assessing near-field beamforming gain degradation due to mismatches between the focusing point and the coordinates of a user. Closed-form expressions for beam correlation, beam sensitivity to user movement, and the direction of fastest beamforming gain degradation are derived. A dynamic polar coordinate grid is also proposed for low complexity and adaptive near-field beam search. Furthermore, we introduce the novel concept of beam coherence time, quantifying the temporal robustness of focused beams and enabling proactive sensing-aided beam tracking strategies. The effect of microstrip losses on the preceding derivations is also analyzed. Finally, extensive simulation results validate the presented theoretical analysis and beam tracking method over randomly generated user trajectories.

Box Decoding is a sort-free tree-search MIMO detector whose complexity is independent of the QAM order, achieved by searching a fixed candidate box around a zero-forcing (ZF) estimate. However, without pruning, the number of visited nodes grows exponentially with the MIMO dimension, limiting scalability. This work proposes two deterministic, low-complexity, sort-free pruning strategies to control node growth. By exploiting the geometric symmetry of the QAM grid and the relative displacement between the ZF estimate and nearby constellation points, the proposed methods eliminate unnecessary metric evaluations while preserving QAM-order independence. The resulting detector achieves substantial complexity reduction with negligible error-rate degradation and enables fully parallel, hardware-efficient implementations for large-scale MIMO and higher-order QAM systems.

This work develops a constraint-aware grid-forming (GFM) control that explicitly accounts for current limits and modulation limits within the GFM oscillator dynamics generating the GFM voltage reference (i.e., phase angle and magnitude). Broadly speaking, the voltage reference generated by the constraint-aware GFM control minimizes the deviation from conventional unconstrained GFM droop control, while respecting current and modulation limits. The resulting GFM control achieves fast current limiting while preserving transient stability, e.g., exhibiting infinite critical clearing time. To develop the control, we first characterize and analyze the set of converter voltages that do not result in constraint violations. Next, an efficient algorithm for projecting voltages onto the feasible set is developed. Subsequently, these results are used to restrict the dynamics of GFM droop control to the set of feasible voltages. Finally, detailed simulation studies and hardware experiments are used to illustrate and validate the response to short-circuit faults and phase jumps.

The synthesis of robust invariant sets for nonlinear systems has traditionally been hindered by the inherent non convexity and a strict reliance on exact analytical models. This paper presents a purely data-driven framework to compute robust polytopic contractive sets for unknown nonlinear systems operating under persistent bounded process noise and state-input constraints. Rather than attempting to identify a single, potentially nominal model, we utilize a finite data set to construct a polytopic consistency set–a rigorous geometric boundary encapsulating all possible system dynamics compatible with the noisy measurements. The core contribution of this work extends an established sufficient condition for {\lambda} contractiveness into the data-driven setting. Crucially, we prove that enforcing this condition strictly over the vertices of the consistency set guarantees robust invariance.

Force feedback gloves in haptic applications remain constrained by limited adaptability, simplified feedback, and fixed architectures that limit force feedback versatility. To address these challenges, we present KinesCeTI, a modular force feedback exoskeleton for the index and thumb, designed as a multipurpose device adaptable to a wide range of hand sizes. The glove incorporates interchangeable thimbles for fingertip or phalanx attachment and a bidirectional tendon transmission that supports both passive and active feedback. It is combined with a modular actuation design, where different feedback systems may be attached. The system was tested with two actuation modules: a compliant ratchet-pawl braking mechanism for passive feedback and a novel one-way clutch for variable active feedback, newly introduced here. The system was evaluated in three user studies with 20 participants each, assessing ergonomics, actuation performance and usability in both real and virtual tasks. Results indicate that the glove adapts to different hand sizes and provides effective feedback with both mechanisms, highlighting its potential as a versatile platform for haptic research.

This paper analyzes the outage performance of downlink NOMA systems with transmit antenna selection (TAS) and multi-antenna users over {\alpha}-{\mu} fading. Maximal-ratio combining (MRC) and equal-gain combining (EGC) are considered, with imperfect successive interference cancellation (ipSIC) explicitly modeled. Exact closed-form outage probability (OP) expressions and asymptotic results are derived, offering insights into diversity and coding gains. Simulations validate the analysis, showing that TAS improves the far user's performance and that MRC outperforms EGC. The results also quantify the loss from ipSIC, and highlight the impact of power allocation on joint OP. The proposed framework serves as a unified tool for evaluating TAS-NOMA systems under generalized fading, providing design insights for B5G/6G networks.

Recent advances in deep learning for wireless communications have renewed interest in channel output feedback codes. In the additive white Gaussian broadcast channel with feedback (AWGN-BC-F), feedback can expand the channel capacity region beyond that of the no-feedback case, but linear analytical codes perform poorly with even small amounts of feedback noise. Deep learning enables the design of nonlinear feedback codes that are more resilient to feedback noise. We extend single-user learned feedback codes for the AWGN channel to the broadcast setting, and compare their performance with existing analytical codes, as well as a newly proposed analytical scheme inspired by the learned schemes. Our results show that, for a fixed code rate, learned codes outperform analytical codes at the same blocklength by using power-efficient nonlinear structures and are more robust to feedback noise. Analytical codes scale more easily to larger blocklengths with perfect feedback and surpass learned codes at higher SNRs.

Brain-Computer Interfaces (BCIs) based on Motor Execution (ME) and Motor Imagery (MI) electroencephalogram (EEG) signals offer a direct pathway for human-machine interaction. However, developing robust decoding models remains challenging due to the complex spatio-temporal dynamics of EEG, its low signal-to-noise ratio, and the limited generalizability of many existing approaches across subjects and paradigms. To address these issues, this paper proposes Graph-guided Clustering Mixture-of-Experts CNN-GRU (GCMCG), a novel unified framework for MI-ME EEG decoding. Our approach integrates a robust preprocessing stage using Independent Component Analysis and Wavelet Transform (ICA-WT) for effective denoising. We further introduce a pre-trainable graph tokenization module that dynamically models electrode relationships via a Graph Attention Network (GAT), followed by unsupervised spectral clustering to decompose signals into interpretable functional brain regions. Each region is processed by a dedicated CNN-GRU expert network, and a gated fusion mechanism with L1 regularization adaptively combines these local features with a global expert. This Mixture-of-Experts (MoE) design enables deep spatio-temporal fusion and enhances representational capacity. A three-stage training strategy incorporating focal loss and progressive sampling is employed to improve cross-subject generalization and handle class imbalance. Evaluated on three public datasets of varying complexity (EEGmmidb-BCI2000, BCI-IV 2a, and M3CV), GCMCG achieves overall accuracies of 86.60%, 98.57%, and 99.61%, respectively, which demonstrates its superior effectiveness and strong generalization capability for practical BCI applications.

The North American commercial electric vehicle (EV) sector is undergoing rapid expansion, with unit sales rising from 21,120 in 2022 to 36,491 in 2023 - a 73% increase, according to the International Energy Agency. However, this accelerating adoption brings emerging technical challenges. One critical concern is the impact of low to extreme winter temperatures (25 degree F to -25 degree F) on EV performance, including reduced energy efficiency and extended charging times. This paper presents a systematic analysis of commercial EV performance degradation under cold weather conditions and its broader implications on grid operations. Monte Carlo simulations, applied using real-world fleet parameters, indicate that approximately 200 MWh of additional daily energy demand may be required in the U.S. alone to offset efficiency losses during severe cold events. The resulting strain on an already stressed winter grid could exacerbate reliability risks. Moreover, increased harmonic distortion associated with cold weather charging behaviors has also been observed, raising concerns about power quality. To address these challenges, this study proposes two practical mitigation strategies: (1) a 'design-integrated safety' battery swapping station model operating in thermally controlled environments to significantly reduce charging downtime, and (2) a hybrid architecture combining roadside fast charging with depot-based deep charging to support continuous fleet utilization without compromising range. Together, these interventions provide a robust foundation for resilient commercial EV integration in cold climates, supporting fleet operators and utilities in managing seasonal performance variability.

Dithering is a technique commonly used to improve the perceptual quality of lossy data compression. In this work, we analytically and experimentally justify the use of dithering for ASR input compression. We formalize an understanding of optimal ASR performance under lossy input compression and leverage this to propose a parametric dithering technique for a low-complexity speech compression pipeline. The method performs well at 1-bit resolution, showing a 25% relative CER improvement, while also demonstrating improvements of 32.4% and 33.5% at 2- and 3-bit resolution, respectively, with our second dither choice yielding a reduced data rate. The proposed codec is adaptable to meet performance targets or stay within entropy constraints.

With more devices competing for limited spectrum, dynamic spectrum sharing is increasingly vulnerable to interference from unauthorized emitters. This motivates fast detection and localization of these emitters using low-cost, distributed sensors that do not require precise time synchronization. This paper presents two convolutional neural network (CNN) approaches for multi-emitter detection and localization from sparsely sampled power maps. The first method performs single-stage prediction of existence probabilities and positions. The alternative two-stage method first estimates an occupancy map as an interpretable intermediate representation and then localizes emitters. A unified training objective combines binary cross entropy with coordinate regression loss and can handle an unknown emitter count. Small footprint networks, on the order of 70\,k parameters, are trained and evaluated on simulated free-space and urban scenes. Experiments demonstrate that both approaches localize multiple emitters from sparse measurements across diverse environments, with the logits based two-stage variant remaining competitive, and in some cases superior, under extreme sensor sparsity. The findings indicate that small CNNs with a unified objective can be deployed for spectrum monitoring and localization.

Vehicle-to-Everything (V2X) cooperation is reshaping traffic safety from an ego-centric sensing problem into a networked intelligence problem involving distributed sensing, cooperative perception, and coordinated decision-making. This survey reviews recent progress in V2X-enabled cooperative safety intelligence through a unified Sensor-Perception-Decision (SPD) framework, which characterizes how distributed observations are exchanged, fused, calibrated, and transformed into safety-ready evidence for risk-aware intervention. Within this framework, V2X networking conditions, multi-modal sensing, cooperative perception, and decision-making are analyzed as coupled components of a safety-intelligence pipeline. Cross-layer communication and networking constraints, including latency, synchronization, bandwidth, packet or feature loss, reliability, trust, and PQoS, are explicitly considered to assess whether shared evidence remains timely, robust, and actionable. Compared with prior V2X safety surveys, this work organizes the literature around a formal SPD safety loop and synthesizes representative methods, datasets, benchmarks, and platforms under communication-aware constraints from 2017 to 2026. It further examines semantic and task-oriented evidence exchange, bandwidth-efficient cooperative perception, ISAC-enabled V2X systems, safety-ready outputs, and evaluation practices under practical conditions such as delayed or lossy messages, pose misalignment, NLOS coverage, and heterogeneous deployment. The survey concludes with a roadmap toward scalable data infrastructure, PQoS-aware evaluation, embodied predictive intelligence, and trustworthy human-in-the-loop cooperation for next-generation V2X safety systems.

Lensless cameras replace bulky optics with thin modulation masks, enabling compact imaging systems. However, existing methods rely on an idealized model that assumes a globally shift-invariant point spread function (PSF) and sufficiently large sensors. In reality, the PSF varies spatially across the field of view (FOV), and finite sensor boundaries truncate modulated light–effects that intensify as sensors shrink, degrading peripheral reconstruction quality and limiting the effective FOV. We address these limitations through a local-to-global hierarchical framework grounded in a locally shift-invariant convolution model that explicitly accounts for PSF variation and sensor truncation. Patch-wise learned deconvolution first adaptively estimates local PSFs and reconstructs regions independently. A hierarchical enhancement network then progressively expands its receptive field–from small patches through intermediate blocks to the full image–integrating fine local details with global contextual information. Experiments on public datasets show that our method achieves superior reconstruction quality over a larger effective FOV with significantly reduced sensor sizes. Under extreme miniaturization–sensors reduced to 8% of the original area–we achieve improvements of 2 dB (PSNR) and 5% (SSIM), with particularly notable gains in structural fidelity. Code is available at https://github.com/KB504-public/l2g_lensless_imaging .

Data-driven modeling of building thermal dynamics is emerging as an increasingly important field of research for large-scale intelligent building control. However, research in data-driven modeling using machine learning (ML) techniques requires massive amounts of thermal building data, which is not easily available. Neither empirical public datasets nor existing data generators meet the needs of ML research in terms of data quality and quantity. Moreover, existing data generation approaches typically require expert knowledge in building simulation. To fill this gap, we present a thermal building data generation framework which we call BuilDa. BuilDa is designed to produce synthetic data of adequate quality and quantity for ML research. The framework does not require profound building simulation knowledge to generate large volumes of data. BuilDa uses a single-zone Modelica model that is exported as a Functional Mock-up Unit (FMU) and simulated in Python. We demonstrate BuilDa by generating data and utilizing it for a transfer learning study involving the fine-tuning of 486 data-driven models.

Speech enhancement (SE) models advance rapidly, yet how input degradation affects their internal representations remains underexplored. We introduce a probing framework to characterize how internal representations in SE models behave under controlled input degradation. We probe three SE models across controlled levels of signal-to-noise ratio (SNR) and reverberation, quantified by $C_{50}$, measuring layer-wise similarity to clean references with Centered Kernel Alignment (CKA) and summarizing each layer by a linear fit against degradation level: the intercept measures robustness, whereas the slope measures sensitivity. All three models are sharply non-uniform across depth, but they organize that non-uniformity differently. MUSE and MP-SENet grow more sensitive with depth, the sharpest transitions falling at MUSE's skip-connection junctions, where encoder information is reintegrated; Demucs inverts the trend. A randomly initialized model shows a near-flat profile, with slopes one to two orders of magnitude smaller, and the profile forms during fine-tuning, indicating that it is induced by the enhancement objective rather than a particular model design. Because CKA saturates at the clean reference, intercept and slope are partly coupled; we derive the identity relating them and report a saturation spread statistic that indicates when their relationship is informative. Together, these results characterize where SE models are most sensitive to degradation. An exploratory analysis of whether residual variation tracks output-level quality, after controlling for SNR, shows that the speaker, rather than the utterance, must be treated as the sampling unit. Code and precomputed analysis artifacts for the main sweeps are publicly available.

This study investigates the fundamental diffuse scattering mechanisms from three typical building wall surfaces, conducting measurements and model parameterization at 28 GHz and two key FR3 frequencies (8 GHz and 12 GHz). A novel three-dimensional (3D) measurement procedure is proposed to capture comprehensive spatial characteristics, and its effectiveness in improving parameterization accuracy was verified using 28 GHz data. For parameterization, we developed a new method utilizing two dimensions of the high-bandwidth power delay profile-received power and delay spread-thereby fully leveraging the rich information provided by such measurements. Furthermore, we introduce the ER-BK hybrid model, which integrates the Beckmann-Kirchhoff (BK) model's high accuracy and cross-frequency adaptability with the Effective Roughness (ER) model's simplicity, applying it to the building surfaces. Our results show that diffuse scattering at 8 GHz and 12 GHz is highly similar, distinct from that at 28 GHz. A comparison revealed that the BK model provides a better fit for our FR3 measurement data. Crucially, we validated the angular generalization of the parameterized BK model using data from a different incident angle than the one used for fitting. The feasibility of the ER-BK hybrid model was also verified through simulation of the parameterized marble surface.

Dynamic obstacle avoidance (DOA) for unmanned aerial vehicles (UAVs) requires fast reaction under limited onboard resources. We introduce the distributionally robust acceleration control barrier function (DR-ACBF) as an efficient collision avoidance method maintaining safety regions. The method constructs a second-order control barrier function as linear half-space constraints on commanded acceleration. Latency, actuator limits, and obstacle accelerations are handled through an effective clearance that considers dynamics and delay. Uncertainty is mitigated using Cantelli tightening with per-obstacle risk. A DR-conditional value at risk (DR-CVaR)based early trigger expands margins near violations to improve DOA. Real-time execution is ensured via constant-time Gauss-Southwell projections. Simulation studies achieve similar avoidance performance at substantially lower computational effort than state-of-the-art baseline approaches. Experiments with Crazyflie drones demonstrate the feasibility of our approach.

A Low-Power Variable Gain (VG) mm-Wave Low Noise Amplifier (LNA) is designed and simulated in a 28-nm CMOS process. The LNA utilizes a simple, yet novel, technique presented in this paper to vary the small-signal output resistance to provide gain control. The amplifier also utilizes forward body biasing to reduce the supply voltage to 0.7 V and enhance power efficiency. A simultaneous noise and input matching (SNIM) technique is used to provide robust input matching and noise performance during gain adjustment. The proposed VG-LNA achieves a peak gain of 21 dB at 40.5 GHz with a noise figure of 2.8 dB and consumes only 4.5 mW. At the highest gain configuration, an input-referred 1-dB compression of -21 dBm and IP3 of -7.8 dBm are achieved, which increase to -14.8 dBm and 1.2 dBm, respectively, at the lowest gain configuration. Regardless of the gain control voltage, the LNA attains a very good FoM as compared to the state-of-the-art.

In a practical multi-antenna receiver, each element of the receive antenna array has a directive antenna pattern, which is still not fully explored and investigated in academia and industry until now. When the emitter is deviated greatly from the normal direction of antenna element or is close to the null-point direction, the sensing energy by array will be seriously attenuated such that the direction-sensing performance is degraded significantly. To address such an issue, a rotatable array system is established with the directive antenna pattern of each element taken into account, where each element has the same antenna pattern. Then, the corresponding the Cramer-Rao lower bound (CRLB) is derived. Finally, a recursive rotation Root-MUSIC (RR-Root-MUSIC) direction-sensing method is proposed and its root-mean-squared-error (RMSE) performance is evaluated by the derived CRLB. Simulation results show that the proposed rotation method converges rapidly with about ten iterations, and make a significant enhancement on the direction-sensing accuracy in terms of RMSE when the target direction departs seriously far away from the normal vector of array. Compared with conventional Root-MUSIC, the sensing performance of the proposed RR-Root-MUSIC method is much closer to the CRLB.

The growing complexity of embedded systems creates tension between rich functionality and strict resource and real-time constraints. Traditional monolithic operating system and hypervisor designs suffer from resource bloat and unpredictable scheduling, making them unsuitable for time-critical workloads where low latency and low jitter are essential. We propose TenonOS, a demand-driven, self-generating, lightweight operating system framework for time-critical embedded systems that rethinks both hypervisor and operating system architectures. TenonOS introduces a LibOS-on-LibOS model that decomposes hypervisor and operating system functionality into fine-grained, reusable micro-libraries. A generative orchestration engine dynamically composes these libraries to synthesize a customized runtime tailored to each application's criticality, timing requirements, and resource profile. TenonOS consists of two core components: Mortise, a minimalist micro-hypervisor, and Tenon, a real-time library operating system. Mortise provides lightweight isolation and removes the usual double-scheduler overhead in virtualized setups, while Tenon provides precise and deterministic task management. By generating only the necessary software stack per workload, TenonOS removes redundant layers, minimizes the trusted computing base, and maximizes responsiveness. Experiments show a 40.28 percent reduction in scheduling latency, an ultra-compact 361 KiB memory footprint, and strong adaptability.

We study on-device time-series analysis for gait detection in Parkinson's disease (PD) from short windows of triaxial acceleration, targeting resource-constrained wearables and edge nodes. We compare magnitude thresholding to three 1D CNNs for time-series analysis: a literature baseline (separable convolutions) and two ultra-light models - one purely separable and one with residual connections. Using the BioStampRC21 dataset, 2 s windows at 30 Hz, and subject-independent leave-one-subject-out (LOSO) validation on 16 PwPD with chest-worn IMUs, our residual separable model (Model 2, 533 params) attains PR-AUC = 94.5%, F1 = 91.2%, MCC = 89.4%, matching or surpassing the baseline (5,552 params; PR-AUC = 93.7%, F1 = 90.5%, MCC = 88.5%) with approximately 10x fewer parameters. The smallest model (Model 1, 305 params) reaches PR-AUC = 94.0%, F1 = 91.0%, MCC = 89.1%. Thresholding obtains high recall (89.0%) but low precision (76.5%), yielding many false positives and high inter-subject variance. Sensor-position analysis (train-on-all) shows chest and thighs are most reliable; forearms degrade precision/recall due to non-gait arm motion; naive fusion of all sites does not outperform the best single site. Both compact CNNs execute within tight memory/latency budgets on STM32-class MCUs (sub-10 ms on low-power boards), enabling on-sensor gating of transmission/storage. Overall, ultra-light separable CNNs provide a superior accuracy-efficiency-generalization trade-off to fixed thresholds for wearable PD gait detection and underscore the value of tailored time-series models for edge deployment.

This paper investigates the problem of consensus-based distributed control of linear time-invariant multi-channel systems subject to unknown inputs. A distributed observer-based control framework is proposed, within which observer nodes and controller nodes collaboratively perform state estimation and control tasks. Consensus refers to a distributed cooperative mechanism by which each observer node compares its state estimate with those of neighboring nodes, and use the resulting discrepancies to update its own state estimate. One key contribution of this work is to show that the distributed observers and the distributed controllers can be designed independently, which parallels the classical separation principle. This separability within the distributed framework is enabled by a discontinuous consensus strategy and two adaptive algorithms developed specifically for handling the unknown inputs. Theoretical analysis and numerical simulation results demonstrate the effectiveness of the proposed framework in achieving state estimation, stabilization, and tracking control objectives.