Papers

Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers. It is imperative for AIGC service providers (ASPs) to strategically schedule AIGC workloads to reduce data center energy costs while guaranteeing high-quality content generation. However, the distinctive characteristics of AIGC services pose critical challenges, including model heterogeneity across ASPs, implicit service quality evaluation, and complex inference process control. To tackle these challenges, we propose a joint energy management and coordinated AIGC workload scheduling framework, which introduces an explicit mathematical characterization of service quality to promote both job transfer among ASPs and fine-grained inference process configuration. Moreover, various energy resources within data centers are jointly considered to enhance power usage flexibility. Subsequently, a system utility maximization problem is formulated to balance AIGC service revenue with operational penalties and costs. Nevertheless, the strong coupling among job scheduling decisions induces severe reward sparsity, which limits the effectiveness of existing deep reinforcement learning (DRL) algorithms. To address this issue, we develop a diffusion model-aided reward shaping approach to synthesize complementary reward signals through a multi-step denoising process. This approach is seamlessly integrated with DRL to enable efficient learning of scheduling policies under sparse environmental feedback. Experiments based on real-world models and datasets demonstrate that our scheme effectively accommodates electricity price fluctuations and AIGC model heterogeneity, while achieving superior learning convergence and system utility compared with benchmark methods.

Most modern bridge-diffusion methods achieve finite-time transport by specifying an interpolation, Schrodinger-bridge, or stochastic-control objective and then learning the associated score or drift field with a neural network. In contrast, we identify a restricted but sufficiently broad analytically solvable class in which, for a deterministic source and a Gaussian-mixture target, the score and all intermediate marginals are explicit and protocol objectives of the type used in this paper can be differentiated without inner stochastic simulation loops. We recast the classical linear–quadratic–Gaussian stochastic-control structure as a transport problem of the Path Integral Diffusion type. Linear dynamics, Gaussian noise, and quadratic running costs reduce the bridge calculation to a matrix Riccati cascade, while the terminal state cost is replaced by a prescribed Gaussian-Mixture terminal probability density. Linear Quadratic – Gaussian Mixture – Path Integral Diffusion (LQ-GM-PID) thereby turns bridge diffusion from terminal target matching alone into an analytically controlled laboratory for path shaping. We demonstrate this on a 2D corridor task, a 2D multi-entrance task, and a high-dimensional study reaching d=32 and M=16 terminal modes in separate scaling sweeps. We position LQ-GM-PID as an analytically solvable reference model in which score approximations, path-shaping objectives, and protocol-learning procedures can be tested against explicit quantities.

3D Gaussian splatting (3DGS) is a state-of-the-art representation for real-time photorealistic novel-view synthesis, yet a single high-fidelity scene typically occupies hundreds of megabytes to several gigabytes, exceeding the budgets of mobile, immersive, and volumetric video platforms. Existing 3DGS compression methods (e.g., HAC++, FlexGaussian, LP-3DGS) treat pruning, quantization, and entropy coding as separate stages and rely on hand-tuned heuristics (opacity thresholds, fixed bit-widths, SH truncation), limiting cross-scene generalization and preventing users from specifying a target rate or quality budget. We propose GETA-3DGS, to our knowledge the first end-to-end automatic joint structured pruning and quantization framework for 3DGS. Building on GETA for joint pruning-quantization of deep networks, we contribute: (i) a 3DGS-aware quantization-aware dependency graph (QADG) treating each Gaussian primitive as a group with five attribute sub-nodes and degree-aware SH sub-nodes; (ii) a render-aware saliency fusing transmittance-weighted contribution, screen-space gradient, and pixel coverage into a Gaussian-level importance score; and (iii) a heterogeneous per-attribute mixed-precision scheme co-optimized with structural sparsity under a projected partial saliency-guided (PPSG) descent guarantee. On Mip-NeRF 360, Tanks and Temples, and Deep Blending, GETA-3DGS operates directly on raw Gaussian primitives rather than a post-hoc anchor representation, delivering ~5x storage reduction over Vanilla 3DGS with no per-scene thresholds. Bit-width policy is the dominant rate-distortion lever: a uniform 6-bit cap costs up to -6.74 dB on view-dependent scenes versus our heterogeneous allocation, matching an information-theoretic reverse-water-filling analysis we develop. GETA-3DGS is complementary to existing codecs: entropy coding (HAC++, CompGS) is downstream, so the two can be composed.

The 7.125-7.4 GHz band is attractive for next generation Terrestrial Network (TN) deployments owing to the large bandwidths available and favorable propagation characteristics. Furthermore, recent U.S. Presidential actions have cleared the usage of this band for 6G by stipulating relocation of federal incumbents that share this band. However, this deployment can only be successful if we can also guarantee coexistence of these networks with existing incumbents operating in adjacent bands. This paper presents a comprehensive analysis of the Radio Frequency Interference (RFI) caused by the proposed TNs in the 7.125-7.4 GHz band at passive Earth Exploration Satellite Service (EESS) sensors that operate in the adjacent 6.725-7.125 GHz band. Using TN base stations (BSs) equipped with filtennas (filtering antennas) as well as transmit precoders for RFI mitigation, we carry out an RFI analysis that accounts for increasing BS deployments in the contiguous U.S. over a 10 year period from 2030 to 2040. We also characterize the size of the guard bands needed to protect the EESS sensors for different BS deployment densities. With appropriate filtenna and precoder design, our results reveal that a 100 Mbps increase in the rate requirements of the TN users results in an RFI increase of roughly 2.45 dB at the EESS sensors. For a 25 MHz Guard Band, simulations show that in 2030, there is no significant RFI for user rates upto 500 Mbps. However, the same systems in 2040 would cause RFI that is around 4 dB above the ITU mandated threshold for passive EESS sensors. This would need to be countered by (a) increasing Guard Band width to 35 MHz, or (b) by reducing the user data rate requirements to 300 Mbps.

Morphing aerial vehicles offer enhanced maneuverability and fuel efficiency compared to fixed-wing configurations. However, the trade-off between performance gains and control cost in dynamic, unsteady maneuvers remains under-explored. This paper addresses this by integrating a trajectory optimization framework with a mid-fidelity aeroservoelastic model, coupling nonlinear multi-body structural dynamics with an unsteady vortex lattice method. A physics-based control cost model captures the energy required to overcome instantaneous aerodynamic hinge moments. Applied to an aircraft with flexible, high-aspect-ratio wings and morphing winglets, the framework evaluates trim, maneuver performance, and lateral obstacle avoidance. Results show morphing wings significantly expand the flight envelope by decoupling lift and pitch requirements. In dynamic maneuvers, morphing yields distinct trade-offs: a pull-up maneuver increased altitude gain by 28.95% at a higher control cost, while a banked turn improved lateral displacement by 8.62% while reducing control cost by 13.40%. Notably, in obstacle avoidance, morphing reduced total control cost by 65.65%. This efficiency stems from exploiting aero-mechanical coupling via trajectory optimization to identify coordinated control strategies that offload aerodynamic loads. These findings underscore wing morphing's potential for achieving extreme maneuvers with superior energy efficiency.

This paper presents a dual quaternion framework for 6-DOF visual target tracking that addresses key limitations of perspective-n-point (P$n$P) solvers: sensitivity to noise and outliers, and inability to propagate estimates through measurement dropouts. A nonlinear observability analysis is performed using a Lie algebraic approach, deriving sufficient conditions for local observability under two sensing modalities: relative position vector and unit vector measurements. For the unit vector case, the classical collinear feature point degeneracy of the perspective-three-point problem is recovered through rank analysis of the observability codistribution matrix, providing a control-theoretic interpretation of a previously geometric result. A dual quaternion Lie group unscented Kalman filter is then developed, directly modeling relative dynamics without assumptions about cooperative measurements or slowly-varying motion. Simulations demonstrate improved pose estimation accuracy and robustness to occlusions compared to an off-the-shelf P$n$P solver. Results are broadly applicable to visual-inertial navigation, simultaneous localization and mapping, and P$n$P solver development.

Autonomous spacecraft docking requires control policies that simultaneously ensure collision avoidance and target reachability under coupled, high-dimensional translational-rotational dynamics. Hamilton-Jacobi (HJ) reachability provides formal reach-avoid guarantees, but classical solvers are limited to low-dimensional systems. Learning-based approaches have begun to scale HJ analysis, yet they struggle in reach-avoid settings, especially where goal and failure sets are tightly coupled, as in docking. We propose a learning-based Backward Reach-Avoid Tube (BRAT) framework that addresses this challenge by tightly integrating HJ structure with MPC-based supervision. In the offline phase, we train a neural approximation of the HJ value function using PDE-based losses augmented with curriculum-driven MPC supervision, which provides informative value targets and stabilizes training in regions where purely PDE-based methods fail. In the online phase, the learned value function is deployed through two real-time controllers: (i) a value gradient-driven controller, and (ii) a value-function-augmented terminal MPC that explicitly enforces reachability at the horizon. We evaluate the proposed method on a 6D planar docking problem against grid-based ground truth and then scale to the full 13D system. Across both settings, our approach outperforms existing methods in success rate and computational efficiency.

Building on recent advances in representation learning for wireless channels, this work investigates the cost-benefit trade-offs of high-dimensional channel embeddings in practical systems. We benchmark multiple wireless representations: high-dimensional learned embeddings from a wireless foundation model, compact autoencoder-based representations with significantly lower dimensionality, and raw data baselines, evaluating their performance across diverse downstream tasks. We then systematically analyze data efficiency, noise robustness, and computational complexity, explicitly characterizing the resource overhead associated with high-dimensional embeddings. Beyond standard tasks such as line-of-sight/non-line-of-sight (LoS/NLoS) classification and beam selection, we introduce power allocation as a new downstream task. Our results reveal clear trade-offs: while high-dimensional embeddings can perform well in few-shot regimes for certain tasks, they incur substantial latency and parameter overhead. In contrast, compressed latent representations learned by autoencoders demonstrate improved noise robustness and more stable performance across tasks, while significantly reducing computational and transmission costs.

Nanoparticle metrology has long been constrained by the assumption that, in mixed and unprocessed fluids, particle size, morphology, composition, and species-specific abundance cannot be resolved simultaneously from a single label-free measurement. Here, we revisit this long-standing limitation by showing that complex forward speckle-holographic fields define an information-rich optical space for multidimensional particle signatures. We report deep speckle holography, a physics-informed generative framework that profiles particle identity, size, morphology, and species-resolved abundance from a single non-contact optical measurement. Across purified suspensions, mixed particle populations, environmental waters, human urine, and other unprocessed native fluids, the method enables direct nanoparticle inference without purification, labeling, or destructive preprocessing, delivering concurrent multidimensional readouts in 0.9 s over a dynamic range spanning 10 orders of magnitude. Deep speckle holography establishes a route toward direct label-free nanoparticle phenotyping in real-world fluids, moving nanoscale measurement beyond isolated-particle characterization toward multidimensional inference in complex mixtures, and expanding the scope of questions nanoscale measurement can address, from real-time tracking of nanoparticle transformations in living and environmental systems to non-invasive quality control of nanomedicine formulations, and beyond.

Reinforcement learning (RL) has become the de facto method for achieving locomotion on humanoid robots in practice, yet stability analysis of the corresponding control policies is lacking. Recent work has attempted to merge control theoretic ideas with reinforcement learning through control guided learning. A notable example of this is the use of a control Lyapunov function (CLF) to synthesize the reinforcement learning rewards, a technique known as CLF-RL, which has shown practical success. This paper investigates the stability properties of optimal controllers using CLF-RL with the goal of bridging experimentally observed stability with theoretical guarantees. The RL problem is viewed as an optimal control problem and exponential stability is proven in both continuous and discrete time using both core CLF reward terms and the additional terms used in practice. The theoretical bounds are numerically verified on systems such as the double integrator and cart-pole. Finally, the CLF guided rewards are implemented for a walking humanoid robot to generate stable periodic orbits.

This paper develops a molecular integrated sensing and communication (ISAC) framework that exploits the same molecular observations for physical-parameter sensing and data detection. As a representative instantiation, we consider a microfluidic molecular communication (MC) channel and study transmitter–receiver (TX–RX) distance sensing, where the distance affects the propagation delay, transient response, and inter-symbol interference structure. A distance-parameterized Markov state–space model is established to obtain distance-dependent channel impulse responses and a block observation model for on-off keying signaling. Based on this model, we design a pilot-assisted low-complexity receiver that combines distance initialization, decision-feedback equalization (DFE), and iterative joint refinement. Numerical results show accurate distance sensing and improved bit error ratio (BER), demonstrating the mutual benefit between sensing and communication and highlighting microfluidic MC as a representative platform for molecular ISAC.

A pinching antenna system (PASS) assisted cognitive radio (CR) system is proposed. A secondary system sum rate maximization problem is formulated by jointly considering the base station (BS) power budget, the pinching antenna (PA) deployment constraints, and the interference tolerance requirements of primary users. To address the resulting non-convex problem, a tractable reformulation based on the weighted minimum mean-square error (WMMSE) approach is adopted, followed by the development of an alternating optimization (AO) algorithm. Within this framework, the auxiliary variables are updated in closed form, enabling an efficient transformation of the digital beamforming subproblem to a convex form, while the PA deployment is refined through a tailored element-wise optimization strategy. Numerical results validate the effectiveness of the proposed design and show consistent performance gains compared with conventional benchmark schemes.

We study a decentralized linear quadratic Gaussian control problem, in which a leader and a follower must steer a linear system to a target state. The target state is known only to the leader, and no explicit communication channel exists between the agents. To address the challenge posed by this asymmetric information structure, we propose an integrated communication and control (ICoCo) framework in which the control plant itself serves as a communication channel: the leader encodes the target state into its control input through an additive communication term, and the follower decodes it from the resulting state trajectory. We design an implicit coordination scheme based on joint source-channel coding ideas, and prove that the follower's estimation error decreases monotonically to zero, enabling the two agents to coordinate increasingly well and ultimately steer the system to the target state. We then formulate the design of the communication power as an optimal control problem to minimize the overall control cost. In the fully actuated leader case, we derive necessary optimality conditions and in the under-actuated case, we solve the problem numerically. Numerical results show that the proposed scheme effectively coordinates the two agents and achieves a control cost close to that of the explicit-communication lower bound.

Frame synchronization is the act of accurately detecting frames in an incoming transmission and extracting their payload. It is especially important in environments such as wireless channels where signals are significantly distorted. Digital correlation is the simplest form of frame synchronization where XNOR gates and summations are used to perform correlation and detect binary sync words attached to each frame. In this paper we demonstrate how digital correlation with long syncwords in the context of a standard modem (modulator/demodulator) chain solves the problem of frame synchronization under significant signal distortions in a practically implementable manner with minimal extra resources. The scheme consists of two parts, the modem chain and the frame synchronization architecture. The modem chain is built inside GNU radio software tool and the architecture inside VIVADO. Data transmission and reception in the Rayleigh NLOS channel is performed by the modem and frame synchronization by the architecture with data produced by GNU radio via a verilog testbench. Results indicate high synchronization accuracy with distortion levels much higher than what is reported by other methods.

Diversity and multiplexing are the two fundamental gains of multiple-input and multiple-output (MIMO) communications, enabling systems to simultaneously achieve increased reliability and higher data rates. The intricate interplay between these two metrics is captured by the celebrated diversity-multiplexing tradeoff (DMT). With the rapid evolution of wireless technologies, low-latency integrated sensing and communication (ISAC) has emerged as a key enabler for 6G applications, including extended reality (XR) and massive digital twins. Consequently, understanding the DMT within MIMO ISAC systems becomes critical. In this paper, we investigate the communication DMT in a mono-static MIMO ISAC system under Rayleigh fading, specifically when the transmitter is constrained to emit sensing-optimal waveforms. By unveiling the geometric properties of generalized Stiefel manifolds and employing large-deviation analysis, we characterize the asymptotic outage probability of this typical ISAC channel. This formulation yields an elegant converse bound on the sensing-constrained DMT. Ultimately, our work provides an answer to a pivotal unanswered question in ISAC system design: How much MIMO gain is fundamentally sacrificed in communication to integrate optimal sensing capabilities?

Spatio-temporal (3D+t) generative modelling of cardiac shape and motion is crucial for understanding heart structure and function at population scale. Existing generative models for cardiac shape synthesis either adopt volumetric shape representations that lack anatomical correspondence across different time points and subjects, or rely on VAE-based frameworks that suffer from a trade-off between reconstruction fidelity and generative diversity. In this work, we propose Cardiac Mesh Flow, a novel generative flow model for 3D+t cardiac four-chamber mesh generation with anatomical correspondence, temporal coherence, and periodic consistency. Leveraging the flow matching technique, Cardiac Mesh Flow performs efficient one-step generation of multi-scale free-form deformation fields, which warp a template mesh to generate cardiac four-chamber meshes across a cardiac cycle. Furthermore, Cardiac Mesh Flow enables controllable generation conditioned on cardiac chamber volumes, allowing precise control of the synthetic heart. Experimental results demonstrate that Cardiac Mesh Flow achieves high fidelity and diversity on both unconditional and conditional generation, compared to state-of-the-art 3D+t cardiac mesh generation methods.

This paper proposes a novel informativity-based data-driven synthesis method for a sub-optimal linear quadratic (LQ) regulator for linear input-delay systems from noisy input-state data. Exploiting the augmented state structure of input-delay systems with a known delay length, we derive a linear matrix inequality (LMI) condition for the data-driven synthesis of the augmented state-feedback controller that achieves a prescribed LQ performance level for every plant model consistent with the data. The proposed LMI condition enables efficient controller synthesis via convex optimization. Numerical simulations demonstrate the effectiveness of the proposed method. The trade-off between the achievable LQ performance and the uncertainty in the data is also clarified through a numerical example.

The electromagnetic inverse scattering problem (ISP), due to its inherent strong nonlinearity and severe ill-posedness, has long been a core challenge in microwave imaging. In recent years, physics-informed neural networks (PINNs) have provided a novel paradigm for solving ISPs by embedding Maxwell's equations into the deep learning optimization process. However, conventional PINN methods rely solely on independent data-equation and state-equation residuals to construct the consistency loss, which easily causes them to fall into local minima and suffer from low computational efficiency when facing high-contrast targets or multi-frequency observation data. To transcend the traditional data-physics consistency framework, this paper proposes a novel cross-correlated physics-informed neural network (CC-PINN). The core innovations of this work include: (1) constructing a Fourier feature MLP network architecture based on weight normalization, which possesses excellent capability for solving inverse scattering problems; (2) introducing a cross-correlated residual term that directly couples the reconstructed dielectric parameters and the predicted internal total field to the external observation field, breaking the decoupling between the contrast source and the permittivity optimization in traditional PINNs and significantly enhancing the robustness of PINNs for ISP; (3) introducing a zero-padding-based 2D-FFT acceleration algorithm, which reduces the computational complexity of the forward Green's function integration. Experimental results on synthetic and measured data demonstrate that CC-PINN can reconstruct high-contrast dielectric targets with high fidelity, and its convergence robustness far exceeds that of PINN algorithms using classical cost functions, regardless of whether simultaneous multi-frequency processing or frequency-hopping strategies are employed.

This paper studies a symbiotic system in which a reconfigurable intelligent surface (RIS) assists a radar transmitter while conveying information to a reader via backscattering. The RIS is partitioned into subarrays that redirect the radar signal toward the angular sector under inspection and superimpose a slow-time modulation using orthogonal phase codes, thereby implementing MIMO radar functionalities. Communication is achieved by encoding information in the selection of an unordered subset of orthogonal codewords, without altering the RIS transmit beampattern. At the reader, the proposed index modulation scheme enables low-complexity detection without requiring channel state information. Numerical results demonstrate the effectiveness of the proposed backscatter communication approach.

Power oversubscription is increasingly central to datacenter operation as power density grows, making it necessary to dynamically allocate limited power budgets across devices based on real-time demand. Existing approaches typically assume flat power domains, whereas in practice power distribution is hierarchical and allocation decisions must additionally respect tenant-level contractual constraints. We present nvPAX, a constrained-optimization policy that computes feasible power allocations at every control step via a three-phase hybrid QP/LP procedure. Phase I allocates power with minimum deviation from each device's power request, while respecting job priorities. Phase II fairly distributes excess power among active devices. Phase III fairly distributes any remaining power to idle devices. The rationale behind the three phases is to allow power oversubscription while maximizing datacenter utilization. On a trace-driven large-scale simulation using GPU power telemetry from a production datacenter, nvPAX runs with a mean wall-clock time of 264.69 ms per allocation interval and achieves a mean satisfaction ratio of 98.92%, outperforming static equal-share allocation and providing robustness beyond greedy proportional allocation in the presence of non-uniform hierarchical bottlenecks.

To provide multimode sensory feedback and motion control, bidirectional bionic interfaces for advanced prosthetic systems require continuous and secure energy delivery to implantable electronics and integration in the sensing WBAN (Wireless Body Area Network) of the patient. However, powering such interfaces is still an open issue. Wireless Power Transfer (WPT) avoids implanted batteries and transcutaneous connections, but its design is constrained by stringent requirements on electromagnetic safety, implant size, voltage compliance, and coexistence with sensitive bio-signal acquisition and stimulation circuitry. This paper presents the design and testing of a low-frequency (127 kHz) inductive WPT link for an implantable bidirectional bionic interface. The system includes an autoresonant driving control to maintain operation at resonance under varying coupling and load conditions of the cyber-physical prosthesis. Starting from the requirements of the bionic interface, the wireless body-area sensing system is designed by selecting the working frequency, drawing the electrical schemes, and checking its safety and regulatory compliance. Preliminary WPT prototypes can provide up to ~140 mA and ~20 V, achieving a maximum power transfer efficiency higher than 40% and satisfying the project requirements up to a 2 cm implantation depth.

In this paper, an atomic hybrid sparse/diffuse (aHSD) channel model in the frequency domain is proposed. Based on a structural analysis of the resolvable paths and diffuse scattering statistics, the Hybrid Atomic-Least-Squares (HALS) algorithm is designed to estimate sparse/diffuse components with a combined atomic and $\ell_2$ regularization. A theoretical analysis of the Lagrange dual problem is conducted, and the conditions required for primal and dual solutions are provided, supporting an off-the-grid delay-time estimator. The Cramér–Rao Bound (CRB) analysis in this paper focuses on the estimation of the channel parameters, resulting in a bound on the aggregate channel. Lower and upper bounds for the CRB on parameters are derived as functions of the minimum separations between frequency parameters. Numerical results via simulations on synthetic and real data validate the efficacy of the HALS estimation strategy and show the improved predictive ability of the CRB analysis for the performance of HALS versus previously considered bounds.

Efficient radar resource allocation is a fundamental yet computationally challenging problem, as optimal solutions typically require iterative optimization with high complexity. Motivated by the need for real-time scheduling, robust generalization, and low data dependency, this paper proposes a novel paradigm that leverages large language model (LLM)-guided evolutionary search (AlphaEvolve) to autonomously discover a closed-form power allocation solution for multi-target tracking. The approach encodes high-dimensional radar states into physically inspired features, then evolves a compact and interpretable scoring function, which is transformed to feasible power allocations via a deterministic constraint-satisfying transformation. Extensive experiments demonstrate that the discovered closed-form solution achieves near-optimal tracking accuracy (average relative performance loss of only $1.51\%$), reliable generalization across diverse scenarios and target counts, and over three orders of magnitude speedup compared to conventional iterative solvers. These results highlight the potential of LLM-guided symbolic search to revolutionize not only radar resource management but also broader classes of engineering optimization problems.

Autonomous navigation and obstacle avoidance remain a core challenge of modern Unmanned Aerial Vehicles (UAVs). While traditional control methods struggle with the complexity and variability of the environment, reinforcement learning (RL) enables UAVs to learn adaptive behaviors through interaction with the environment. Existing research with RL prioritizes the mission success at the expense of mission time and safety of UAVs. This study integrates Potential Based Reward Shaping (PBRS) with Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF) to simultaneously optimize mission time and ensure formal safety guarantees. An RL model is trained in a generalized simple environment, then used in complex scenarios incorporating a CLF-CBF-QP filter without further training. Experimental results in simulated environments demonstrate a significant reduction in mission time and outstanding performance in complex environment.

An evolution of Wireless Communications towards 5G and beyond provides improved user experience in terms of quality of services. Understanding and estimating Channel information plays crucial role in providing better user experience. Traditional methods of channel estimation involves periodically sending pilots (known signals), estimating channel and send back estimated channel information to the BS which increases computational complexity and communication complexity. Hence, we focus on data driven approach for channel estimation. In this work, we explore a channel estimation mechanism at 7GHz frequency band for a given user location. This work involves data generation using Ray tracing mechanism and Machine learning model training that contains feature variables such as transmitter location, user location and target variable as channel coefficient . We explored Support Vector Regression, K-nearest neighbor (KNN), Random Forest, XGBoost and MLP. We found via simulations that XG Boost and proposed MLP performs better than Support Vector Regression, KNN and Random forest regression.

Regeneration of the nervous system after injury remains an important therapeutic objective, especially in the central nervous system (CNS), in which regeneration is restricted by both neuronal limitations as well as adverse extracellular environments. Conversely, the peripheral nervous system (PNS) displays enhanced regenerative capability in the presence of supportive Schwann cells (SC) and pro-growth stimuli. While the structure and molecular mechanisms are thoroughly understood, functional biomarkers that can non-invasively monitor regeneration in real time are limited. In this review, we discuss the promise of electroencephalography (EEG) as well as electromyography (EMG) as real-time, non-invasive biomarkers to monitor damage to nerves and regeneration in both CNS and PNS contexts. First, we contrast biological and electrophysiological indicators of CNS/PNS injury, showing how EEG signs, including oscillatory power, connectivity, and evoked potential changes, reflect dysfunction due to injury as well as neuroplastic reorganization. Also, EMG provides direct insight into muscle activation and peripheral output, providing useful EEG complementation in neuromuscular pathway integrity and reactivation. In CNS injuries (e.g., stroke, spinal cord injury (SCI)), EEG typically shows global slowing, disrupted interhemispheric coherence, and partial recovery of higher frequencies. For PNS injuries, EEG can capture cortical remapping and return of somatosensory evoked responses with re-establishment of the peripheries' connectivity. EMG, in turn, enables monitoring of reinnervation and restoration of functional motor output. This review presents a dual-system perspective, positioning EEG and EMG not only as diagnostic tools but also as functional biomarkers of neural regeneration, thereby bridging electrophysiology, plasticity, and clinical recovery.

Multimodal large language models (MLLMs) achieve strong performance on vision- and audio-language tasks, yet can generate responses that conflict with the given visual or auditory inputs, a problem known as multimodal hallucinations. Prior work suggests that this occurs when models rely more on textual cues and learned language patterns than on evidence from the perceptual input. To obtain a more direct account of this imbalance, we apply Layer-wise Relevance Propagation (LRP), which attributes predictions to individual input tokens, and use the resulting relevance scores to analyze and mitigate hallucinations. First, we examine whether this imbalance leads to multimodal hallucinations. We find that hallucinations often arise when the model relies less on perceptual inputs, and that changing this reliance affects its predictions. We further leverage LRP and propose a training-free framework that shifts relevance toward perceptual tokens by optimizing key-value representations during decoding, without modifying model parameters or requiring training data. We call this method Learning Inference-time Modality Enhancement (LIME). Despite using no spatial or temporal supervision, LIME concentrates relevance on query-relevant regions. We evaluate LIME across multiple multimodal benchmarks in both vision and audio domains, demonstrating consistent reductions in hallucinations and enhanced grounding while preserving generation quality.

Compressed sensing magnetic resonance imaging (CS-MRI) heavily relies on the low mutual coherence between the measurement matrix and the sparsity basis. However, under highly accelerated Cartesian undersampling, the severe structural coherence between Fourier measurements and spatial bases, discrete cosine transform (DCT) for example, fundamentally violates this requirement, causing classical sparse recovery algorithms to stagnate. To mitigate this fundamental bottleneck, we propose a synergistic dual-space projection framework, denoted as $\mathbf{PAQ}$. Instead of merely designing heuristic sampling masks, our method directly reshapes the equivalent dictionary. Specifically, we introduce a diagonal-dominant random rotator $\mathbf{Q}$ in the feature space to probabilistically disrupt structural alignment, and an active orthogonalization projector $\mathbf{P}$ in the measurement space to deterministically whiten the residual correlations. We theoretically demonstrate that this dual-space mechanism bounds the mutual coherence with an exponentially decaying tail via sub-exponential distribution properties. Experimental validations on clinical MRI datasets under 20% Cartesian sampling demonstrate that plugging the $\mathbf{PAQ}$ preconditioners into the standard ISTA solver significantly suppresses aliasing artifacts and yields consistent peak signal-to-noise ratio (PSNR) improvements.

Platooning of connected and automated vehicles provides significant benefits in terms of energy efficiency, traffic throughput, and, most critically, safety. These safety benefits depend on string stability, which dictates how disturbances propagate along a vehicle string. Although longitudinal string stability has been extensively examined, lateral string stability, which governs the propagation of path-tracking errors that can lead to unsafe deviations from the desired path, remains underexplored. Its importance is growing as autonomous vehicles increasingly depend on onboard sensing and map-free navigation, where sensor occlusions and tight formations amplify safety risks. This paper presents a framework for lateral string stability that focuses directly on safety-critical, path-relative tracking errors and enables consistent comparison across vehicles that follow the same planned path. The key element of the framework is an arc-length (Eulerian) viewpoint, a departure from traditional analyses, that clarifies how tracking errors at a given point on the path propagate from one vehicle to the next. Building on this foundation, we propose the definition of L2 lateral string stability along with two control strategies: a feedback-feedforward strategy that relies solely on onboard sensing, and a novel learn-from-predecessor strategy that makes use of vehicle-to-vehicle communication. Both strategies are analyzed for lateral string stability with respect to two error measures: tracking error vector and lateral (cross-track) error. Our results show that onboard sensing alone cannot guarantee attenuation of path-tracking errors, imposing a fundamental safety limitation, while V2V communication enables true error attenuation. The analysis further identifies structural controller requirements, showing that nonzero feedback on specific measurements is essential for guaranteeing stability.

This paper presents a comprehensive control-theoretic analysis of water distribution network (WDN) hydraulics. Starting from a general nonlinear differential algebraic equation (DAE) model of WDNs with arbitrary topology and network components (valves and pumps), we investigate three main questions. First, we study local well-posedness of the network dynamics and characterize the loss of differentiability introduced by pump and valve switching. Second, we introduce regularization methods that smooth flow and pressure trajectories under changing controls. Third, we establish error bounds for DAE linearization, local stability, and finite-horizon controllability, and quantify how network-induced parametric uncertainty impacts these properties. We demonstrate that the developed smoothed DAE models produce trajectories closely matching EPANET, a widely used WDN simulator, for various benchmark networks. The case studies also show that the WDN DAE exposes energy dissipation through a weighted Laplacian, ranks pipes by operating point sensitivity, and reveals that aggressive demand variation changes stability and controllability margins without eliminating local stability or pump authority. The developed theoretical foundations enable network analysis, mitigation strategies, and system design.