Papers

Orthogonal frequency division multiplexing (OFDM) is a key waveform for integrated sensing and communication (ISAC) systems due to its high spectral efficiency and inherent compatibility with modern wireless standards. However, its fundamental estimation-theoretic sensing performance under random data modulation remains largely unexplored. This paper presents a unified and explicit performance analysis of OFDM-based ISAC systems for multi-target range estimation, focusing on the distinct impacts of the modulation constellation on the sensing performance. We develop a comprehensive estimation-theoretic framework to characterize the range estimation mean-square error (MSE) for both matched filtering (MF) and reciprocal filtering (RF) sensing receiver architectures. Our theoretical analysis reveals that in multi-target and clutter-rich environments, the sensing performance of the MF receiver is fundamentally limited by the fourth-order moment (kurtosis) of the constellation, which determines the data-dependent sidelobe interference level. In contrast, the RF receiver eliminates such interference at the cost of noise enhancement, with its performance governed by the inverse second-order moment of the constellation. Building on these closed-form MSE derivations, we propose a sensing-receiver specific geometric constellation shaping (GCS) framework. By jointly optimizing the constellation geometry based on the minimum Euclidean distance (MED) and receiver-dependent sensing metrics, we enable a flexible trade-off between communication reliability and sensing precision. Our results demonstrate that the proposed constellation shaping provides significant performance gains and facilitates a tailored sensing and communication trade-off across different receiver architectures in practical over-the-air implementations.

Integrated sensing and communications (ISAC) is a key technology for next-generation wireless networks, enabling communication and radar sensing over shared spectral and hardware resources. In practical multi-user multiple-input multiple-output (MU-MIMO) ISAC transmitters, however, mutual coupling (MC) between antenna elements distorts the array steering vector and each communication user (CU) channel, so that the sensing beampattern deviates from the desired one and the communication link to each user degrades. To address this limitation, we propose a robust MC-compensated beamforming design that guarantees both the sensing and communication performance of MU-MIMO ISAC transmitters against the residual MC error. We introduce a residual error on the MC matrix, so that a norm-bounded residual error induces both the sensing beampattern uncertainty and the communication channel uncertainty. The transmit covariance is then optimized against the worst-case of each uncertainty, minimizing the worst-case beampattern matching mean-squared error (MSE) for sensing while guaranteeing the signal-to-interference-plus-noise ratio (SINR) for each CU. Each worst-case constraint is converted into a linear matrix inequality, and the problem becomes a convex semidefinite program (SDP). Numerical results show that the proposed robust design attains both a lower sensing beampattern matching MSE and a higher communication SINR than those of the conventional designs, with an advantage that widens as the residual error grows.

Integrated sensing and communications (ISAC) is a promising feature in 6G networks. It is envisioned to enhance spectral efficiency and provide sensing and communication services that meet the stringent requirements of future applications. However, it also poses new security and privacy concerns by giving malicious attackers access to new information about the network. In this work, we focus on the sensing privacy of a monostatic ISAC system by investigating the capability of a sensing eavesdropper (EVE) with an unknown location, acting as a passive bistatic radar (PBR) to gain access to user location information. We then propose a joint transmit and artificial noise (AN) beamforming optimization problem to degrade EVE's performance. Finally, we propose an iterative algorithm to solve the proposed optimization problem and evaluate its performance.

Target localization in a multistatic radar system, where multiple receivers cooperate to improve target positioning accuracy, has many applications, including cooperative simultaneous localization and mapping (SLAM) and autonomous robot networks. A key challenge in these applications is the uncertainty in the position and orientation (pose) of the radar receivers due to platform mobility. This work investigates the achievable improvements in both target localization and receiver pose estimation by deriving the Cramer-Rao lower bound (CRLB) for a multistatic radar system performing bistatic range and bearing measurements. We propose an alternating weighted least-squares algorithm that jointly optimizes target and receiver parameters. Monte Carlo simulations demonstrate that the algorithm performance approaches the CRLB for low to moderate noise levels.

Auxiliary navigation systems are essential for the robust operation of aerial vehicles, particularly in self-contained frameworks like terrain-referenced navigation. However, challenges such as multimodal likelihoods, highly nonlinear terrain elevations, and unknown prediction biases result in highly multimodal and less predictable posterior distributions, leading to particle filter degeneration. This study addresses the numerical instability and degeneration of the particle filter approach by proposing a sampling strategy tailored to this problem. The approach introduces a Gaussian mixture random forcing mechanism, which nudges particles along terrain slopes and against biases towards the most probable terrain contours. Each mixture is associated with a mode of likelihood, enhancing adaptability to unmodeled terrain features. To further improve effectiveness, auxiliary sampling selectively applies this mixture sampling to probable particles, yielding a less degenerate and evenly weighted particle set. Numerical experiments demonstrate the effectiveness of the proposed method in reducing weight variance, improving effective sample size. In addition, the approach exhibits strong resilience under deteriorating scenarios, such as severe unknown prediction bias and multimodal measurement noise, ensuring long-term reliable particle filtering.

This paper addresses the challenging problem of output feedback stabilization for nonlinear nonminimum phase (NMP) systems in the presence of parametric uncertainties and external disturbances. The proposed framework integrates three distinct observers: a reduced-order observer for reconstructing the unmeasured states of the internal (zero) dynamics, a high-gain observer for estimating output derivatives, and an observer for estimating the aggregated effect of parametric uncertainties and disturbances. Leveraging these estimates, a sliding mode control law is synthesized to ensure global asymptotic stability of the entire system using only output measurements. The control design requires only partial model knowledge, significantly relaxing the restrictive assumptions common in existing literature. Numerical simulations illustrate the effectiveness of the proposed output-feedback strategy and corroborate the theoretical developments.

The increasing penetration of distributed energy resources (DERs) in distribution system necessitates adaptive coordination frameworks. These frameworks must remain optimal during normal operation and resilient under cyber physical disturbances. Existing coordinated control approaches are typically deployed as static architectures with limited ability to adapt when communication degrades, local instability emerges, and operating conditions become spatially heterogeneous. This work addresses the gap by proposing a DER service-independent edge autonomous holonic adaptive coordination framework. In this framework, each DER controller executes its own control actions and transitions among centralized, distributed, decentralized, and local autonomous coordination modes without always relying on static coordination commands from the grid operator. The framework preserves coordination continuity across modes by retaining local controller states while reconfiguring only the coordination topology, information exchange pattern, and fallback action associated with the active DER service. Graceful transitions are enabled through dwell timers, rate limiting, and safety overrides to prevent dynamic instability during mode changes. Volt VAR control is used as a representative distribution automation application to validate the proposed architecture in a cyber-physical Hardware-in-the-loop (HIL) testbed. The proposed approach is evaluated under diverse cyber-physical event-based scenarios, showing region-confined adaptation under localized disturbance, reduced coordination traffic during coordination switching, and node-confined mitigation under cyber attack through edge anomaly detection and neighbor-corroborated impact estimation.

Industry 5.0 (I5.0) repositions manufacturing around a human-centric vision in which cyber-physical systems must adapt to the worker rather than the other way around. Despite significant advances in worker state monitoring technologies, including markerless computer vision, wearable physiological sensors, and machine-learning-based fatigue and cognitive load estimation, a critical integration gap persists: these monitoring capabilities rarely inform real-time operational decisions, and workers almost never participate in the adaptive processes that affect their tasks and workloads. This paper presents a structured survey of the state-of-the-art across three interconnected dimensions: (1) worker state monitoring in industrial settings, encompassing physical fatigue, cognitive load, and ergonomic risk assessment; (2) human factors integration in Operations Management (OM) models, examining how scheduling, task allocation, and production planning incorporate worker-related objectives; and (3) Human-in-the-Loop (HITL) architectures, analyzing the extent to which worker feedback closes the adaptive decision loop. The survey reveals that while each dimension has matured independently, the integration across them remains largely absent. Based on the reviewed literature, no publicly documented and validated system connects real-time sensor-driven worker state estimation with adaptive OM decisions and worker feedback in a unified closed-loop architecture. This gap is identified as the central challenge for realizing the I5.0 vision, and a five-direction research agenda is proposed.

Multimodal bird's-eye-view (BEV) perception combines LiDAR depth accuracy with dense camera semantics, but its high computational cost and imperfect sensing conditions make real-time deployment challenging. Existing methods largely compress individual detectors and overlook three opportunities: structured sparsity within camera and LiDAR inputs, timing misalignment between modalities, and the fact that many detected objects do not affect the planner's immediate action. We present MM-BEV, a real-time multimodal BEV system guided by a simple principle: compute where and when it matters. MM-BEV divides perception into mandatory work for safety-critical objects within braking distance of the ego vehicle and with short time-to-collision (TTC), and optional work for less urgent regions. It prioritizes mandatory work and reduces or sheds optional work under tight compute budgets. MM-BEV integrates four mechanisms: (1) a criticality-ranked temporal ROI selector based on motion-extrapolated detections from prior frames; (2) sparse, ROI-aware feature extraction using shared-shape camera crops at context-adaptive resolution and ROI-aware LiDAR voxelization; (3) a latency-aware coordinator that adapts LiDAR sweeps, image resolution, and keyframes according to scene dynamics and TTC; and (4) an asynchronous scheduler that decouples sensing from inference and skips stale frames. On nuScenes, MM-BEV reduces inference latency by 1.96x and end-to-end latency by 2.93x, with no loss in geometry-critical recall and only a 0.2 percentage-point drop in safety-critical recall. On a Clearpath Husky A300 equipped with an Ouster-128 LiDAR, BEV cameras, and a Jetson AGX Orin, MM-BEV further reduces mean latency by 2.11x, demonstrating its potential for real-world autonomous systems.

Sea surface temperature (SST) is a critical indicator of global climate change, yet satellite-derived SST imagery often suffers from coarse spatial resolution, limiting the ability to capture fine-scale thermal structures such as ocean fronts. To address this, we propose a Dual-Branch State-Displacement Network (DBSD-Net) for SST super-resolution. DBSD-Net adopts a dual-branch architecture: a wavelet frequency branch that explicitly separates low and high-frequency components via discrete wavelet transform for targeted processing, and a VGGUNet branch that extracts multi-scale semantic features from a frozen pre-trained VGG backbone. Within the wavelet branch, we introduce a Structural State Space Module (SSSM) with a Gated Structure Refinement (GSR) unit to efficiently capture long-range dependencies and enhance structural integrity, and a Displacement Gate Module (DGM) that learns a displacement field for geometry-aware modulation of high-frequency details, thereby mitigating spatially varying degradation. Experiments on multiple public SST datasets demonstrate that DBSD-Net outperforms existing state-of-the-art methods.

While recent advances in minimax dual control have led to exact solutions for uncertain general linear time-invariant systems as well as (sub)optimal dual controllers, corresponding results for linear positive systems are still lacking. This paper aims to fill this gap and thereby pave the way toward scalable dual control algorithms. We study the general minimax optimal dual control problem for positive linear systems with unknown dynamics and reformulate it as a standard zero-sum dynamic game. By allowing randomized control inputs, we solve the corresponding Bellman equation exactly for the scalar case with sign uncertainty in the input. This yields an implicit dual control policy that is optimal both in terms of cost and $\ell_1$-gain. The optimal dual policy uses exploration in a specific region of the hyperstate space to conduct optimal probing. Outside this exploration regime, the controller reduces to a deterministic certainty equivalence policy, indicating that sufficient information has been obtained to identify the correct input direction. In addition, these results allow us to analyze fundamental limitations of minimax dual control for positive systems and provide a foundation for more general dual control problems for positive systems for future work.

Reasoning segmentation enables vision-language models (VLMs) to translate mission-relevant language requests into pixel-level visual grounding, offering a natural perception interface for embodied agents. However, existing benchmarks largely focus on generic visual scenes and overlook the domain and resource constraints encountered in flood-response platforms. We present FloodReasonBench, a benchmark for VLM reasoning segmentation for embodied flood response at the edge. At its core, FloodReasonBench introduces FloodResponseSeg, a flood-specific reasoning-segmentation dataset constructed from real-world scenes and response-relevant targets. Beyond task accuracy, the benchmark characterizes reasoning-segmentation pipelines under lightweight visual encoding, hierarchical split inference, and compressed intermediate representations. We observe strong partition-dependent accuracy variation in the generic pre-adaptation setting, while the flood-adapted target-workload design space exhibits a substantially more compact accuracy range across partitions. Evaluation on an NVIDIA Jetson AGX Xavier further exposes the tradeoffs among reasoning-segmentation accuracy, edge-side latency, energy, and communication footprint, enabling quality-constrained selection of edge operating points. Together, these results provide a task- and system-level characterization of reasoning segmentation for resource-constrained embodied flood response at the edge.

Battery energy storage systems (BESS) are increasingly used in distribution networks for voltage regulation and demand response, which increases the volume and complexity of operational telemetry available to grid operators. This paper presents an AI-enabled monitoring framework that connects a large language model (LLM) interface with a structured telemetry database for BESS-integrated distribution system analysis. Operator questions are submitted in natural language and translated into validated SQL queries using predefined database schema information and approved KPI views. Retrieved measurements, including bus voltages, state of charge, active power, and reactive power, are evaluated against engineering constraints for voltage limits, BESS operation, and demand response tracking. The framework is validated using hardware-in-the-loop co-simulation data from a BESS-equipped distribution feeder operating under reactive power-based voltage control and price-driven demand response. Case studies show that the framework generates valid database queries, identifies repeated voltage violations, detects reactive power overshoot, and evaluates active-power tracking performance. The results show that LLM-assisted monitoring can connect structured grid telemetry with automated engineering assessment for BESS operation analysis.

This paper develops Admissibility-Preserving Control (APC), a realization-centered safety-critical control framework for strict-feedback systems subject to asymmetric actuator limits, time-varying output constraints, and actuator-rate limitations. APC denotes the overall control architecture, whereas an Admissibility-Preserving Input Realization (APIR) denotes its constraint-realization module. Therein, the APIR dynamically generates the physical plant input while rendering its prescribed asymmetric actuator set forward invariant. In contrast to algebraic clipping and post-design saturation compensation, the actuator limits are embedded directly in a continuously differentiable dynamic realization with user-selectable regularity and interpretable tuning parameters. The APIR is integrated with recursive backstepping by treating the realized plant input as an additional state. The resulting design does not require an input-to-state stability assumption on the uncontrolled plant. Instead, the nonlinear drift terms are compensated recursively, subject to an explicit compatibility condition between the desired motion, the available control authority, and the APIR interior gain. The framework is further extended to time-varying output-safe tracking through a smooth asymmetric logarithmic barrier coordinate and its associated Lyapunov function and to simultaneous actuator-magnitude and rate constraints through a cascaded APIR. Rigorous Lyapunov and invariance analyses establish regional asymptotic tracking, forward invariance of the compatible admissible sets, and boundedness of all closed-loop signals. Numerical studies illustrate asymmetric actuator utilization, output-safety preservation, and magnitude-rate constraint enforcement.

Control barrier functions (CBFs) provide a systematic framework for enforcing safety constraints in nonlinear control systems. However, their implementation typically relies on accurate system models, which can limit their applicability in the presence of significant modeling uncertainties or unknown dynamics. This paper proposes a model-free framework for the computation of recursive control barrier functions based on the ultra-local model approach that leverages online estimation of the unknown system dynamics to construct CBF constraints. This approach does not require an explicit model of the system dynamics and enhances robustness with respect to disturbances and model mismatch. The resulting control architecture enables the enforcement as well as the anticipation of safety constraints for systems with higher relative degree. The effectiveness of the proposed approach is illustrated on the adaptive cruise control benchmark.

This paper analyzes the boundedness and feasibility properties of Advantage-Ranked Group Relative Policy Optimization (A-GRPO), a ranking-augmented, critic-free policy gradient method employing a Transformer-encoder actor for fixed-horizon control with terminal constraints. When feasibility is evaluated only at the final step, the resulting sparse feedback destabilizes critic-based advantage estimation and weakens standard Lagrangian approaches. A trajectory-level ranking mechanism that augments group-relative policy updates by reweighting advantages according to constraint satisfaction is formalized, and three results are established: (i) a scale-adaptive per-timestep normalization bounds advantage variance at every timestep independently, (ii) the ranked advantage strictly separates feasible from violating trajectories under a verifiable ranking-weight condition, biasing the policy gradient toward constraint satisfaction, and (iii) the adaptive dual variables remain bounded and exhibit a drift-balance property that acts as a feedback mechanism for feasibility. These results are validated on a 3,605-step series-hybrid powertrain energy management task with a terminal state-of-charge constraint, where A-GRPO achieves 75.4% mean sustained feasibility with return within 3.7% of the dynamic programming optimum, outperforming a Proximal Policy Optimization with Lagrangian penalties (PPO-Lag) baseline (27.4% sustained), and ablation experiments confirm that both the ranking and Lagrangian components are necessary for this performance.

To accelerate single image super-resolution (SISR) networks on large images (2K-8K), many recent approaches decompose an image into small patches and dynamically determine an execution path according to its difficulty (referred to as a dynamic network). To quantify the hardness of a patch, they mainly rely on a handcrafted assessment score, e.g., edge, which weakly associates a patch's texture with the computational complexity of a SISR model. To address the problem, we introduce ENAF - a dynamic network for SISR with an adaptive patch fusion. Built on top of a backbone, ENAF incorporates multiple early exits (EEs) to tackle the over-parameterized SISR model. More importantly, ENAF plugs a tiny network that estimates PSNR to associate data texture with a computation cost at an EE. Based on the scores, ENAF effectively assigns image patches to an exit, enhancing the quality-complexity trade-off. Extensive experiments on common datasets with popular SISR backbones demonstrate the effectiveness of ENAF in various settings. The source code is provided in https://github.com/nmduonggg/ENAF

This paper presents an iterative model predictive control algorithm that stabilizes constrained nonlinear systems without evaluating a single plant derivative. By factoring the exact nonlinear dynamics into a pseudo-linear form using state- and control-dependent coefficients (SCDCs), we replace the standard nonconvex optimization with a sequence of constrained linear-quadratic programs. Refreezing the coefficient matrices along the previously predicted trajectory drives the iteration. Near the origin, we prove this sequence contracts to a unique fixed point. We explicitly bound the number of iterations required to reach any stopping tolerance, and we quantify the distance from the fixed point to a true Karush-Kuhn-Tucker point, showing this optimality gap vanishes quadratically as the state approaches the origin. Inflating the discrete algebraic Riccati equation generates terminal ingredients that guarantee recursive feasibility and asymptotic stability, even when the solver terminates early. We adapt the terminal penalty online, proving it remains uniformly bounded, and we secure output feedback through the block-observable canonical form, which extracts the exact system state directly from past inputs and outputs. Retaining the block-banded structure of the subproblem forces the computational cost to scale linearly with the horizon length $\ell$. This $O(\ell)$ complexity matches the iterative linear quadratic regulator (iLQR) but sharply undercuts the $O(\ell^3)$ scaling of dense sequential quadratic programming (SQP). Numerical studies on a saturated quadrotor, a nonholonomic integrator, and a nonminimum-phase plant illustrate the theoretical bounds and map how the algorithm compares with iLQR, SQP, and linear-parameter-varying MPC.

Replicator dynamics (RD) is a fundamental model in learning in games, connecting evolutionary game theory and online learning. This paper studies payoff-based higher-order variants of RD represented as a cascade interconnection between an integrator in parallel with an auxiliary linear time-invariant (LTI) system and the softmax mapping. We investigate learnability of Nash equilibria under this Nash-stationary learning rule. First, we revisit recent results that establish convergence to Nash Equilibrium whenever the auxiliary LTI system is strictly passive and prove a converse passivity result: if the auxiliary LTI system is not passive, then there exists a static strictly contractive game whose interior Nash equilibrium is unstable under the closed-loop learning dynamics. Second, we show that there exists a class of games with isolated interior Nash equilibria that cannot be locally asymptotically stabilized by any payoff-based higher-order RD whose auxiliary LTI system is asymptotically stable and strictly proper. Finally, we show that if Nash stationarity (i.e., all Nash equilibria are stationary points of the learning dynamics) is relaxed, then generalized exponential RD (Ex-RD) can locally asymptotically stabilize a logit equilibrium for any continuously differentiable game. The stabilized equilibrium can be viewed as an entropy-regularized approximate Nash equilibrium.

This paper investigates a fluid active reconfigurable intelligent surface (FARIS)-aided downlink non-orthogonal multiple access (NOMA) system. We formulate a network sum-rate maximization problem that jointly optimizes user clustering, NOMA power allocation, FARIS amplification gains, discrete phase shifts, and fluid element selection under quality-of-service, reflected-power, and hardware constraints. To address the resulting nonconvex mixed-integer problem, we develop a two-stage framework comprising distance-based interleaved clustering for constructing successive-interference-cancellation (SIC)-friendly user groups and per-cluster alternating optimization. The resulting subproblems are handled using geometric programming (GP), fractional programming (FP), majorization-minimization (MM) with mixed-integer phase optimization, and the cross-entropy method (CEM). Numerical results demonstrate rapid convergence, near-optimal performance relative to brute-force search (BFS)-based optimum, and consistently outperforms the benchmarks. These results verify the effectiveness of jointly integrating FARIS and NOMA for high-rate downlink transmission.

Link-budget studies of indoor optical wireless systems frequently assume receiver parameter sets–large photodetector area, large transimpedance, and wide bandwidth simultaneously–that violate basic circuit constraints, and noise budgets that omit dominant amplifier and laser noise. This paper develops a realizability-constrained design-space analysis of a diffused-beam laser-diode (LD) LiFi link anchored to a hardware prototype. The analysis couples the generalized Lambertian channel of a holographic-diffuser source to a receiver model that enforces the transimpedance-amplifier gain-bandwidth/capacitance constraint and carries a complete noise budget: shot, feedback-resistor thermal, input current noise, capacitance-driven voltage-noise gain, and laser relative intensity noise (RIN). Against this budget we evaluate unipolar M-PAM under two FEC tiers (7%-overhead hard-decision at $3.8 \times 10^{-3}$, 20%-overhead soft-decision at $2 \times 10^{-2}$), first-bounce diffuse multipath, and a quantitative extended-source eye-safety assessment. The full model predicts 140 Mb/s net at the prototype's demonstrated 14-m range with 6.7 dB margin (OOK, HD tier), 240 Mb/s at the zero-margin 4-PAM/SD reach boundary of 14.0 m, and 480-558 Mb/s at 5 m–a factor 3.9-6.6 below what the same link yields under a naive textbook budget, quantifying how strongly idealized assumptions inflate LiFi projections. First-bounce analysis shows the downfacing-source/up-facing-receiver geometry confines multipath to a worst-case LOS-to-diffuse ratio of 4.2 dB and delay spreads below 0.13 ns, and the 500-mW source remains a factor $\ge 7.8$ under the Class-1 eye-safety limit. All models are released as an ns-3 module and Python engine backed by automated testing.

Automated five-stage diabetic retinopathy (DR) grading requires more than high accuracy alone. Medical-grade deployment calls for lesion-aware preprocessing, ordinal predictions, calibrated uncertainty, and explainability to support reliable diagnostic systems. We present a unified pipeline that addresses these requirements using a Ben-Graham-green-channel CLAHE feature representation, an EfficientNetV2-L ordinal regressor, and Monte Carlo dropout for uncertainty-driven referral. Grad-CAM provides visual explanations aligned with clinically relevant lesions. The proposed method achieves a QWK of 91.31% on the APTOS-2019 official test split, placing it within the near-perfect agreement band (>80%). At a 20% referral rate, 293 of 366 images are automatically graded with a QWK of 90.40%. More complex cases are referred for specialist assessment, demonstrating a practical trade-off among grading quality, automation, and patient safety in robust, reliable, and deployment-ready medical diagnostic systems.

Zipf's law of abbreviation – the tendency of frequent forms to be short – is one of the best-supported regularities in language, and recent work has moved from demonstrating it to measuring how far lexicons are compressed relative to principled baselines. That programme has so far addressed word lengths in alphabetic and syllabic scripts. We transfer it to a logographic script, taking the stroke as the unit of articulatory cost and the Chinese character as the coded form. Combining a stroke-order database covering all 20,902 characters of the CJK basic block with two independent frequency corpora (258.9M and 193.3M tokens), we find that the mean character type costs 12.71 strokes but the mean character token in running text only 7.22. Using the dually normalised optimality score of Petrini et al. (2026), the simplified inventory reaches Omega = 0.668, with the replication corpus at 0.609 – inside and just below the 62-67% band those authors report for word lengths across 20 languages and 8 scripts, suggesting a compression ceiling largely independent of script type and cost unit. A logographic script also makes absolute coding bounds computable, since strokes come from a closed five-element taxonomy: the exact 5-ary Huffman optimum is 4.34 strokes and the entropy bound 4.28, so the observed system is 1.66x above optimal coding. This gap is not slack but structure. The Kraft sum of 5^(-l_i) is 2.05 on the frequency list and 5.03 on the full inventory, so stroke strings are provably not uniquely decodable in one dimension; characters are disambiguated by the two-dimensional arrangement of strokes, not their sequence, and the forgone compression buys componential transparency. Finally, treating the mid-twentieth-century simplification reform as a controlled compression event, we find it raised optimality from 0.555 to 0.668, with savings concentrated in the 1,000 commonest characters.

Passivity-based control (PBC) is a nonlinear control design framework that has proven adequate for controlling a wide range of systems, especially physical ones. Their main ingredients are physical quantities such as energy and dissipation, making the control design more intuitive and endowing the controllers with a physical interpretation. In contrast to other, mathematically-based nonlinear control approaches, the energy-based viewpoint and physical intuition of PBC often make this strategy more robust and energy efficient. This chapter provides an overview of PBC, revisiting the basic aspects of this powerful nonlinear control framework and the most common PBC approaches.

The passivity-based control (PBC) framework focuses on understanding and modifying the energy storage and dissipation in the system to be controlled. To this end, PBC techniques often proceed in two steps: (i) ensuring that the closed-loop system's energy is minimum at the desired point, and then (ii) forcing the system to dissipate energy until reaching that point. These control methods have proven effective in controlling a wide range of systems, especially physical ones, even when they exhibit highly nonlinear behaviors. This chapter discusses the main aspects of some PBC strategies for nonlinear systems.

The rising number of users and antennas imposes exponentially growing computational loads on future communication systems. Yet conventional processors are facing a bottleneck for their nature of memory-computing separation. In-memory computing (IMC) emerges as a promising solution leveraging its intrinsic high parallelism. This work proposes an IMC architecture that employs resistive random access memory (RRAM) to reduce the computational complexity of the nonlinear Tomlinson-Harashima precoding (THP) to a linear scale. We present a computation-constraint principle for designing RRAM circuits to perform nonlinear matrix operations and construct an LQ decomposition RRAM circuit. Since the conductance of memristor is generally quantized, we perform the bit precision analysis and derive the lower bound of the Signal-to-Interference-plus-Noise Ratio (SINR) and achievable rate. Our analysis indicates that at a high Signal-to-Noise Ratio (SNR) or with a large number of antennas, each 1-bit precision increase yields 6 dB SINR gain and linear rate growth. For practical implementation, we derive the optimal bit precision to sustain SINR performance under varying system configurations. Simulation demonstrates the feasibility and accuracy of the RRAM-based circuit and our theoretical analysis. Our work proves that RRAM-based IMC holds significant potential for high-complexity nonlinear precoding, addressing the escalating computational demands for future communications.

High-altitude platform stations (HAPS) are pivotal in next-generation wireless networks for reducing core network burdens and enabling cost-effective communication. In this article, we propose a spherical stochastic geometry-based analytical framework for the coverage performance evaluation of HAPS networks. Considering the significant influence of directional antenna gain on interference evaluation, we analyze coverage performance under a general channel model that accommodates various beam patterns. Analytical expressions of the uplink and downlink coverage probabilities in cellular and cell-free networks are provided respectively and their accuracy are verified by Monte Carlo simulation. Furthermore, the influences of network-level and physical-level parameters on coverage probability are studied. Finally, several factors that align with the analytical framework of this article are discussed.

The consensus of continuous-time multi-agent systems with unbounded and heterogeneous constant delays is investigated by combining frequency-domain analysis and algebraic graph theory. Several types of signed Laplacians are constructed to characterize consensusability under delays. The core results are established based on the defined delay-embedded signed Laplacian, where a small-delay link creates a cooperative interaction and a possibly unbounded large-delay link creates an antagonistic interaction between the agents. The dividing line between small and large delays is given by $\tau_{ij}=\pi/2\lambda_{\max}(L_0)$, where $\lambda_{\max}(L_0)$ refers to the maximum eigenvalue of the conventional graph Laplacian. It is proved that the consensusability is preserved if the delay-embedded signed Laplacian is positive semi-definite with a simple zero eigenvalue. Moreover, we derived some consensus conditions in terms of the extended effective resistance which measures the overall coupling between two sets of agents. The obtained results provide new insights into the mechanism of delayed consensus from the interplay between the small-delay-induced cooperativeness and large-delay-induced antagonism in the underlying network topology.

Voltage stability margin assessment is essential for the secure operation of renewable-dominated power systems. Conventional continuation-based methods evaluate the margin along predefined load-growth paths with fixed generator partic- ipation, while practical operation allows generators to be redis- patched to alleviate voltage stress and reshape the power-flow trajectory as the system approaches voltage collapse. This paper proposes a path-coupled margin assessment approach that incor- porates corrective generator response into static voltage stability margin assessment. In the proposed approach, the load-growth direction and generator response direction are simultaneously determined at each continuation step, enabling the assessment trajectory to account for generator response while tracing the system toward voltage collapse. The voltage stability margin is then evaluated by the cumulative active load increase along this coupled trajectory. Based on the obtained trajectory and collapse point, a feasible redispatch direction is further derived to improve the margin of the current operating state. The economic cost of voltage stability enhancement is quantified through a marginal stability cost, providing an economic indicator for additional sta- bility support. Case studies on various test systems demonstrate that the proposed framework can effectively capture the impact of corrective generator redispatch on voltage stability assessment, provide effective guidance for margin enhancement, and quantify the cost associated with voltage stability improvement.

Multi-modal object re-identification (Re-ID) aims to facilitate cross-camera object retrieval in complex environments by leveraging complementary information from visual (e.g., RGB, NIR, TIR) and textual modalities. However, existing approaches often lack principled feature disentanglement and coherent multi-modal integration, leading to entangled representations that introduce cross-modal conflicts, obscure discriminative cues, and suffer distribution shift under modality-missing conditions. To tackle these challenges, we propose MODAL, a novel multi-modal object re-identification framework, grounded in coupled sparse coding theory and differential suppression principles. A core component of MODAL is a Multi-modal Feature Sparse Decoupling module, developed in a model-driven deep unrolling manner based on multi-modal coupled sparse coding. It explicitly decomposes multi-modal features into uni-modal specific, bi-modal and tri-modal shared representations, thereby achieving more transparent and effective feature disentanglement. Benefiting from the principled feature disentanglement, MODAL naturally mitigates performance degradation in incomplete-modality scenarios via a Modality-Aware Subspace Activation that selectively activates only the consistently shared subspaces. Moreover, we propose a Text-Image Differential Filtering module that leverages coarse-grained textual semantics to adaptively suppress task-irrelevant responses in the decoupled visual representations, thereby enhancing discriminative information. Extensive experiments on four datasets demonstrate that MODAL achieves state-of-the-art performance with superior transparency.