Papers

High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms. While aerial robotics simulators have evolved to support specialized needs such as fidelity, differentiability, and swarms independently, a unified platform that can synthesize data across all these domains is missing. In this work, we propose Crazyflow, a simulator designed to push the limits of aerial-robotics algorithm development, from model-based to data-driven methods, gradient-based to sampling-based approaches, and single-agent to multi-agent systems. Compared to existing state-of-the-art drone simulators, it achieves speeds more than an order of magnitude faster for a single drone and can simulate thousands of swarms of 4000 drones each. Real-world experiments show Crazyflow supports both analytical-gradient-based policy learning, achieving sub-centimeter trajectory tracking accuracy without domain randomization, and sampling-based obstacle avoidance at speeds exceeding half a billion steps per second. Breaking the traditional train-then-deploy paradigm, we show that its unprecedented speed even enables in-flight reinforcement learning; we demonstrate this by throwing a physical drone into the air and training a recovery policy from scratch in 0.38 seconds, successfully stabilizing the drone. Crazyflow supports multiple levels of simulation abstraction, is directly compatible with all open-source Crazyflie models, and enables rapid reconfiguration across custom drone platforms and applications by providing a light-weight system identification pipeline. By pushing accuracy, speed, and differentiability simultaneously, Crazyflow serves as an open-source resource for synthetic data generation, with emerging capabilities for large-scale parallelization for online, in-execution learning and optimization, opening the door to novel algorithm development.

Multi-pitch estimation (MPE) typically predicts which pitches are active in a mixture, but not which instrument or source produced them. This paper investigates a lightweight slot-attention framework for multi-instrument MPE (MI-MPE), where a mixture CQT is mapped to an unordered set of source-like pitch maps. The model uses permutation-invariant Hungarian matching to avoid fixed output semantics and treats the number of slots as an upper bound on the number of active sources. We further study two modular extensions: a self-supervised timbre encoder that provides training-time targets for slot-level timbre embeddings, and a polyphony branch that regularizes the pitch density of mixture- and slot-level predictions. Experiments show that Hungarian matching substantially improves instrument family decomposition on URMP. Stem-level prediction remains more challenging: timbre and polyphony supervision improve selected configurations, but do not consistently resolve source assignment. The results suggest that slot-based architectures are a promising direction for source-aware MPE, while highlighting the need to couple auxiliary musical cues to slot identity more carefully.

Adversarial feature extraction and blocking jamming threaten tactical CPM links. This paper presents a unitary spreading-based Transmission Security (TRANSEC) enhancement to obscure physical-layer signatures and improve anti-jamming (AJ) resilience. The enhancement can be used to augment existing techniques. The enhancement preserves the constant-envelope (0 dB PAPR) nature of CPM, ensuring compatibility with high-efficiency tactical amplifiers. Analysis of symbol distributions, spectra, and cyclostationary features demonstrates that the technique masks inherent signatures, preventing modulation classification. We leverage convex optimization to recover symbols under blocking jamming, reducing uncoded BER from 6.25% to 0.04%. Finally, we characterize the engineering trade-offs between security, bandwidth, and BER.

The next generation of 6G networks aims to utilize ultra-wideband spectrum and massive antenna arrays to serve multiple users with both control and data channels at low latency and high efficiency. However, phased arrays at mmWave and mid-bands are fundamentally constrained to a single beam or suffer sharp beamforming loss when split across directions, limiting simultaneous control-data support. In FlexLink, we introduce and prototype a novel delay-phased array architecture that overcomes this limitation by redistributing energy jointly across frequency and space, enabling multiple narrow beams without sacrificing per-beam gain or requiring additional power. We design and prototype FlexLink on a custom 4-7 GHz hardware testbed, demonstrating for the first time that control and data beams can be decoupled in practice, achieving nearly double spectral efficiency compared to conventional phased arrays.

Radio frequency spectrum awareness requires the ability to detect, localize, and characterize emitters in dense and contested wireless environments. In this work, we propose a task-oriented distributed compression framework for joint multi-emitter localization and characterization using spatially distributed receivers. Each receiver observes a short window of complex IQ samples, converts the observation to a time–frequency representation, and encodes it into a compact latent vector. A central fusion decoder combines the receiver latents to estimate an unordered set of active emitters, including their locations, center-frequency offsets, occupied bandwidths, and waveform families. A permutation-invariant training objective is used to handle the arbitrary ordering of emitters and predictions. Experiments on synthetic multi-emitter scenes with spectral overlap show that even extremely compact receiver-side representations can preserve useful information for emitter counting and waveform-family estimation. However, accurate localization and spectral-parameter regression require larger latent dimensions. Increasing the receiver latent dimension from $d_{\mathrm{rx}}=1$ to $d_{\mathrm{rx}}=16$ provides the largest improvement, while further increasing to $d_{\mathrm{rx}}=64$ gives smaller gains. These results demonstrate the potential of learned task-oriented compression for communication-efficient distributed spectrum awareness.

Accurate modeling of leaf spectral reflectance from physiological and biochemical traits is essential for advancing remote sensing applications in plant science and precision agriculture. Widely used radiative transfer models, such as PROSPECT-PRO, rely on generalized trait-reflectance relationships developed from a wide range of species, which may not fully capture the spectral behavior of specific crops like grapevines. In this study, we developed a trait-to-spectra prediction model using a multi-head attention neural network trained on a grapevine-specific dataset that includes 16 leaf traits measured across multiple varieties, growth stages, and years. The model was evaluated using stratified 5-fold cross-validation and achieved an average coefficient of determination (R^2) of 0.84 and normalized root mean squared error (NRMSE) of 1.52 percent, demonstrating high accuracy and generalizability. When compared to PROSPECT-PRO in forward mode, the neural network exhibited lower mean absolute error (MAE), especially in the near-infrared (NIR) and shortwave-infrared (SWIR) regions. These results emphasize the importance of species-specific modeling approaches and show that integrating biochemical and structural traits into data-driven architectures can significantly improve spectral prediction. The proposed model provides a robust framework for generating accurate leaf-level reflectance data, with potential applications in canopy trait retrieval, vineyard monitoring, and remote sensing-driven crop management.

Radio-over-fiber centralizes radio access networks by using a low-loss optical fiber link between the remote radio head and the central unit. Analog radio-over-fiber (A-RoF) transmits RF signals modulated directly onto an optical carrier, avoiding digitization and digital signal processing at the remote radio head. In this way, A-RoF shifts power-hungry processing from the antenna to the baseband unit. This paper outlines a mathematical framework to analyze the effect of fiber nonlinearity in an uplink wireless system supported by A-RoF. We model an input/output relationship that incorporates the wireless channel, thermal noise, and impairments encountered in the optical fiber channel: chromatic dispersion, electrical-to-optical conversion loss, amplification noise, and fiber nonlinear interference. We compare A-RoF with DSP-assisted A-RoF and digital radio receivers. Our results show that A-RoF achieves higher energy efficiency as compared to digital receivers with 8- and 16-bit analog-to-digital converters and DSP-assisted A-RoF. We further characterize the trade-offs among transmit power, nonlinear interference, and spectral efficiency, demonstrating that nonlinear effects fundamentally limit achievable rates. These results identify the linear operating regions where A-RoF is most effective for uplink wireless communication.

With the increasing demand for edge computing and AI-driven workloads, integrating small and medium-sized edge data centers into distribution networks has become increasingly important. This paper investigates the hosting capacity of distribution networks for data center integration and identifies the key physical mechanisms that limit the maximum allowable data center load. The baseline analysis shows that data center hosting capacity varies significantly across candidate buses due to network topology and electrical distance. Three dominant limiting mechanisms are identified: current-constrained locations, voltage-constrained locations, and mixed-constrained locations where both current loading and voltage deviation jointly affect hosting capacity. To increase the hosting capacity, this study evaluates multiple flexible resources, including battery energy storage systems (BESS), dispatchable distributed generators (DDG), and static synchronous compensators (STATCOM). Numerical results demonstrate that these resources provide complementary benefits through active power support, sustained local generation, and reactive power compensation, effectively expanding data center hosting capacity in distribution systems.

A fixed-wing UAV must hold airspeed, altitude, and heading references under wind, gusts, and turbulence, channels coupled so that correcting one can degrade another. Classical autopilots stabilize the airframe well but adapt poorly when a hard crosswind meets an aggressive turn, while reinforcement-learning (RL) policies acting directly on the surfaces concentrate exploration risk at the actuator interface. We place a learned supervisor above an unchanged autopilot rather than inside it: it selects a residual from a finite, bounded action set on the commanded airspeed, altitude, and heading; the modified reference is projected into an admissible command envelope before reaching the autopilot, which stays the only actuator-facing controller. What is new is how the residual is chosen. HJB residual scores candidates with a semi-discrete value-iteration critic in the spirit of the Hamilton-Jacobi-Bellman (HJB) equation, ranks them by a no-op-relative Hamiltonian advantage, and filters them through a control-Lyapunov- and control-barrier-inspired finite-action shield that always keeps a no-op fallback. On a shared 12-state runtime holding the plant, autopilot, and actuator model fixed, so the comparison is at the package level, HJB residual lowers mean RMS path-tracking error to 44.809 m, against 338.617 m for the baseline autopilot and 88.809 m for a tabular-Q residual, an 86.77% reduction over the baseline and 49.54% over Q-learning. The gain concentrates where the baseline fails worst and comes with a measured rise in airspeed error, so no method dominates every metric. We present this autopilot-preserving residual command-supervision design and benchmark with its trade-offs reported intact.

Unmanned aerial vehicles (UAVs) are emerging as a key enabler of next-generation wireless networks, particularly for applications that require ultra-reliable and low-latency communication (URLLC), such as emergency response, industrial automation, and autonomous systems. In these scenarios, maintaining reliable connectivity under strict transmission time constraints is challenging due to dynamic environments, mobility, and limited onboard energy. In particular, communication performance and energy are closely coupled with UAV movement, making trajectory design a critical component of system operation. Most existing approaches rely on offline joint communication and trajectory optimization, where the UAV trajectory and communication parameters are optimized prior to execution based on assumed system information. Although effective under ideal assumptions, such designs cannot adapt to real-time variations in user demand, channel conditions, or environmental disturbances, which are particularly critical in URLLC settings. To address these challenges, this article investigates model predictive control (MPC) as an adaptive framework for UAV-enabled communications. Using a receding-horizon strategy, MPC enables the UAV to continuously update its trajectory based on real-time information, improving reliability and robustness in dynamic environments. Representative application scenarios are discussed to highlight the role of MPC in UAV-enabled URLLC systems. Furthermore, a case study is presented to illustrate key design trade-offs and performance insights under finite blocklength-based URLLC transmission, followed by a discussion on open challenges and future research directions for practical and scalable MPC-enabled UAV communication systems.

This paper proposes a data-driven controller design method for unknown nonlinear systems based on a Koopman bilinear realization. Using Koopman operator theory, the nonlinear system can be represented as a bilinear discrete-time system with a residual error term. The residual error is proportionally bounded by the norm of the lifted state and input, while the system matrices of the bilinear model are unknown. Assuming that bounds on the residual error are available, the unknown system matrices are characterized via a set-membership representation using the collected input-state data pairs of the nonlinear system. A data-driven controller design method is proposed to ensure stability for all bilinear systems within this set-membership description and for all admissible residual errors. More specifically, we design a rational state-feedback controller that stabilizes the bilinear model with residual error and, consequently, the original nonlinear system, by solving a sum-of-squares (SOS) program. The effectiveness of the proposed approach is demonstrated through numerical examples.

Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics. However, inaccuracies of the learned dynamics model are typically exploited by the agent, substantially hampering the capabilities of MBRL methods. We present a framework for dealing with inaccuracies of probabilistic models through targeted handling of uncertainty that effectively mitigates model exploitation. We present recent successes in learning directly on hardware and safe exploration, and discuss future directions for uncertainty-aware MBRL.

Subpacketization remains a major obstacle to the practical deployment of coded caching (CC) in multi-antenna wireless networks. In this paper, we propose a low-complexity multiple-input multiple-output (MIMO) CC scheme that enables flexible delivery rate adaptation while substantially reducing subpacketization requirements. The proposed design employs a virtual broadcast-channel decomposition to align cache-profile placement with flexible MIMO stream elevation which extends the shared-cache model to multi-antenna receivers. This enables flexible selection of feasible user and stream configurations, thereby providing explicit control over the spatial multiplexing gain under linear decodability constraints. Analytical results show that the proposed framework can asymptotically approach the best-known achievable degrees of freedom (DoF) under linear decodability constraints while requiring orders-of-magnitude lower subpacketization than existing schemes. Numerical evaluations further demonstrate that this flexibility yields notable throughput improvements at practical signal-to-noise ratios.

The integration of Artificial Intelligence (AI) and emerging 6G networks introduces new opportunities for scalable coordination in tactical autonomous vehicle systems. This paper proposes a communication-centric hierarchical architecture for Tactical Autonomous Defense Vehicle Networks (TADVNs) that models the integration of edge-assisted Large Language Model (LLM) reasoning with 6G-enabled connectivity and semantic communication. The framework is designed to improve coordination efficiency, reduce communication overhead, and enhance latency resilience under increasing fleet-scale operation. Unlike conventional task-specific AI pipelines that rely on structured feature processing and rule-based coordination, the proposed approach incorporates semantic abstraction and context-aware decision support within a layered edge-cloud communication architecture. We evaluate communication and coordination performance via Monte Carlo simulations across fleet sizes of 5-30 vehicles under contested network conditions. Results indicate that at a 30-vehicle scale, the 6G-LLM configuration achieves 75.2% latency reduction (29.1 ms vs. 117.5 ms), a 68.7 percentage point increase in mission success rate (82.9% vs. 14.2%), and an 88.6% reduction in communication overhead compared to a 5G-based conventional AI baseline. These findings demonstrate measurable benefits in coordination and communication when semantic reasoning is combined with low-latency 6G connectivity.

Accurate segmentation of fetal brain tissues in Magnetic Resonance Imaging (MRI) is critical for early diagnosis of congenital abnormalities and improving prenatal care. However, the task remains difficult because of fetal motion, low tissue contrast, and major anatomical variability throughout gestational ages, particularly in segmenting complex structures such as white matter, gray matter, lateral ventricles, deep gray matter, extra-cerebrospinal fluid, cerebellum, and brainstem. As a solution to these difficulties, this research introduces a novel deep learning model that combines a ResNet-34 encoder with a lightweight decoder leveraging multi-layer perceptron (MLP) modules for adaptive feature refinement. This design specifically enhances the model's ability to preserve anatomical boundaries and mitigate segmentation errors caused by motion artifacts and intensity inhomogeneities. Computational efficiency is achieved by reducing parameter count, employing bilinear upsampling instead of transposed convolutions, and optimizing the decoder for speed without sacrificing accuracy. Trained and validated on the FeTA 2021 dataset using 5-fold cross-validation, the proposed model outperforms baseline architectures such as UNet, UNet++, DeepLabV3, and DeepLabV3+, achieving an average Accuracy of 97.37% with a mean Dice Similarity Coefficient (DSC) of 90.33%, mean Intersection over Union (IoU) of 86.93%, and Precision of 90.83%. Additionally, its fast inference time and reduced computational load make it well-suited for integration into real-time clinical workflows.

Semantic communication has emerged as a promising paradigm for improving transmission efficiency by conveying task-relevant semantics rather than raw data. Although recent studies have achieved notable gains in communication efficiency and average task performance, reliability remains a fundamental bottleneck in dynamic and uncertain environments. In particular, most existing designs are still optimized mainly for average-case behavior, while lower-tail performance under adverse transmission conditions remains insufficiently understood and inadequately protected. In this article, we present a unified perspective on reliable semantic communication beyond average performance. We first review three reliability-oriented design categories: channel-aware adaptation, robustness-oriented codec design, and hybrid automatic repeat request (HARQ)-based retransmission. We show that these approaches address reliability from complementary perspectives, but each still has inherent limitations. Motivated by these observations, we discuss two solution directions: robust adaptive semantic communication under imperfect CSI, and joint source-channel-check coding with adaptive retransmission for sample-level reliability enhancement. Finally, we outline several future research directions, including the joint design of robustness and retransmission, reliability metrics beyond averages, and compatibility with existing digital wireless networks.

Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur during highly dynamic encounters, specifically sudden pedestrian crossings. To address this critical safety vulnerability, we propose DeepIPCv3, a novel multi-modal autonomous navigation framework that synergizes the dense 3D spatial geometry of LiDAR point clouds with the microsecond-level asynchronous event streams of a Dynamic Vision Sensor (DVS). We introduce a Transformer-inspired cross-modal attention mechanism to dynamically correlate these distinct modalities, allowing the network to instantaneously prioritize high-speed dynamic updates without sacrificing structural scene awareness. The fused latent representations are then mapped to safe local waypoints and executable control commands via a hybrid policy network that blends heuristic trajectory tracking with direct neural predictions. Due to the severe physical risks associated with live testing of these sudden crossing scenarios, the framework is rigorously evaluated offline using a custom multi-modal dataset collected across both well-illuminated noon and challenging evening conditions. Extensive comparative and ablation studies demonstrate that DeepIPCv3 achieves state-of-the-art predictive performance. By effectively eliminating exposure failures and motion blur, the proposed LiDAR and DVS fusion yields the lowest trajectory and control command errors, enabling highly reactive, mathematically bounded evasive maneuvers regardless of ambient illumination. To support future research, we will release the codes to our GitHub repo at https://github.com/oskarnatan/DeepIPCv3.

The transition to electric mobility calls for charging infrastructure that is both efficient and socially equitable. This paper examines fairness in electric vehicle (EV) charging station pricing and capacity through a game-theoretic perspective. We model a non-cooperative market in which competing charging service providers set prices and capacities while customers choose stations based on generalized cost, leading to a market equilibrium. We then benchmark this decentralized outcome against an idealized planner solution that jointly optimizes efficiency and equity. To align market outcomes with socially desirable goals, we design targeted incentives that guide operators toward more fair charger placement. Case studies demonstrate that unregulated competition tends to exacerbate disparities in charger access across demographic groups, whereas carefully calibrated incentives can reduce inequities without significant efficiency loss. The framework provides insights for policymakers on reconciling free-market dynamics with the broader societal goals of fairness in electrified mobility systems.

We present a multimodal dataset of 1020 hours of simultaneously recorded scalp electroencephalography (EEG), facial electromyography (EMG), and speech audio from three healthy native Japanese speakers during open-vocabulary overt speech. Recordings were acquired with three EEG systems-an ultra-high-density system (g.Pangolin) and two cap-type systems (g.SCARABEO and eegosports), spanning 62-128 channels-across many sessions over several months. Each session provides time-synchronized EEG, facial EMG, and audio, together with speech-event annotations and transcriptions. Although collected with speech decoding as a primary motivation, the dataset also supports work on multimodal signal processing, artifact modeling, longitudinal and cross-device adaptation, and EEG representation learning. Technical validation included power spectral density and event-related potential analyses across participants, devices, and tasks, which showed the expected 1/f spectral profile, task-related alpha-band attenuation, and time-locked evoked responses. The dataset is released in Brain Imaging Data Structure (BIDS) format via OpenNeuro under a CC0 waiver to support both speech-related and broader EEG research.

Next-generation Internet-of-Things (IoT) is evolving toward a ubiquitous, ultra-low-power, and multi-band heterogeneous networking paradigm that seamlessly integrates terrestrial, non-terrestrial, and ambient devices. This vision places unprecedented demands on conventional radio frequency (RF) receivers, whose fundamental bottlenecks in sensitivity, power consumption, coverage, and multi-band operation are rooted in the RF antenna. To tackle these issues, we show that the quantum properties of Rydberg atomic quantum receivers (RAQRs), including ultra-high sensitivity, broad frequency agility, and diverse reception modalities, provide a physically distinct receiver-side path that replaces the conventional antenna-and-low-noise-amplifier chain. Using LoRa, narrowband IoT, and ambient IoT as case studies, this article shows that RAQRs deliver significant gains in weak-uplink, low-power, and battery-free regimes. A stochastic-geometry analysis in cellular and cell-free architectures then maps these device-level gains onto network coverage, where the RAQR retains roughly a 4 dB half-coverage advantage over the RF receiver in sparse deployments at \(\lambda \sim 10^{-5}~{\mathrm m}^{-2}\), with the gain eroded as device density grows. The open challenges are presented to stand between current RAQR prototypes and deployable IoT infrastructure.

Real-scene indoor millimeter-wave simulation requires efficient modeling of radio frequency (RF)-computable geometry and electromagnetic material properties. To address the low efficiency of manual scene modeling, the limited RF adaptability of visually reconstructed meshes, and the lack of material binding in 28 GHz ray-tracing simulation, RFDT-Channel is developed as an RF digital twin scene construction workflow based on red-green-blue (RGB) images and light detection and ranging (LiDAR) point clouds. Indoor videos and point clouds are collected by a Jetson Orin platform with LiDAR and GMSL cameras. An initial triangular mesh is generated through COLMAP, 3D Gaussian Splatting, and SuGaR. The LiDAR point cloud then provides geometric and scale references for RF-oriented regularization in Blender, including alignment, wall solidification, door/window opening construction, and topology repair. OpenScene semantic segmentation maps major indoor structures to concrete, glass, wood, and metal materials, and Sionna RT performs 28 GHz ray tracing. Under a fixed transmitter-receiver deployment, the generated channel impulse response (CIR), channel frequency response (CFR), and Radio Map results show that material binding mainly changes weak reflection, transmission, and scattering paths, reducing the number of effective paths from about 742 to about 52 while keeping the dominant path amplitude nearly unchanged.

Standard positional encodings for transformers - sinusoidal and rotary (RoPE) - treat every position as equally local: they encode where a token is, but not how far its positional influence should extend. We propose that the Morlet wavelet, which simultaneously minimises uncertainty in position and frequency, is the natural basis for positional encoding, and introduce Morlet Positional Encoding (MoPE): each embedding dimension learns its own frequency and locality bandwidth from data. The main theoretical result is a unification: sinusoidal PE and the RoPE correlation kernel both emerge as limiting cases of MoPE when locality is switched off (sigma_i -> infinity). The phase of MoPE recovers the RoPE rotation angle exactly; the amplitude adds a learned Gaussian locality kernel that standard encodings lack. Empirically, MoPE combined with Energy-Gated Attention achieves +0.119 improvement over standard attention on TinyShakespeare, outperforming either component alone. Analysis of the learned parameters reveals that all 128 frequency-bandwidth pairs converge to the wavelet admissibility boundary - an empirical observation consistent with a companion result on energy gating, suggesting a reproducible property of character-level language signals that warrants further investigation.

Recently, under the presumption of a noise-free input, the augmented complex least lncosh (ACLlncosh) method was introduced for a power system frequency estimate and showed robust performance when impulsive noise polluted the output signal. However, in practical terms, noise often contaminates input signals, which drastically reduces the efficiency of the ACLlncosh method. To enhance robustness against noisy input-output while maintaining resilience to impulsive noise in the output signal, this paper proposed an online censoring-based widely linear total least lncosh (OC-WL-TLlnC) method. This method improves performance under both balanced and unbalanced settings by filtering out less valuable data via online censoring, hence reducing the computing burden. Furthermore, a variable parameter approach is incorporated to accelerate convergence and improve steady-state accuracy, thereby ensuring adaptability to dynamic power system conditions. The proposed methods significantly enhance frequency estimate performance by addressing the constraints of current techniques and offering a computationally efficient, noise-resilient solution for real-time power system monitoring.

Accurate prediction of fruit sugar content is essential for quality control and market valuation in agriculture. Conventional measurement techniques rely on destructive, time-consuming processes (e.g., juicing and refractometry) or direct contact instruments, which hinder high-throughput operations. This paper introduces SweetFruit, a mobile two-stage system that leverages low-cost sensors to estimate fruit sugar content without contact. In Stage 1, we implement a lightweight 3D deep learning model (SF-PointNet) that uses point clouds from a Time-of-Flight (ToF) depth camera to classify fruit as high or low sugar. In Stage 2, a regression network (SF-Net) predicts the fruit's Brix value using measurements from a compact 18-channel near-infrared (NIR) spectrometer. The system uses simple off-the-shelf sensors (AS7265x NIR and Arducam ToF) with efficient processing pipelines for real-time execution on embedded platforms. Experiments on green 'Granny Smith' apples and strawberries demonstrate the system's effectiveness. Stage 1 achieves over 90% classification accuracy, enabling rapid prescreening, while Stage 2 delivers precise sugar estimates, with a root mean square error (RMSE) of 0.57 Brix, reducing error by 22% compared to using NIR sensing alone. SweetFruit offers a scalable, field-ready solution for rapid fruit quality screening, showcasing the benefits of task-specific multimodal sensing in mobile agricultural applications.

While Large Language Models (LLMs) offer a promising path toward intent-driven network management by translating natural language human intents into machine-readable configurations, they often suffer from hallucinations and structural inconsistencies in multi-step and complex tasks. To address these challenges, this paper proposes a retrieval-augmented and task decomposition-based multi-agent LLM framework for Beyond 5G network auto-configuration. The framework employs a semantic retrieval-augmented generation pipeline to ensure that its outputs are aligned with technical standards and vendor-specific manuals. Furthermore, it introduces a modular architecture for configuration generation, closed-loop configuration verification, and network deployment, in which complex tasks are decomposed into smaller sub-tasks handled by specialized agents. In this architecture, hallucinated configuration parameters are identified by the configuration verifier agent and corrected through low computational segment-level regeneration. The performance evaluation experiments with the OpenAirInterface emulator demonstrate that the proposed task decomposition-based configuration and verification approach improves the average success rate by 22.7% over monolithic methods, achieving 94.4% success in network configuration.

This paper investigates near-field propagation in optical reconfigurable intelligent surface (ORIS)-assisted free-space optical (FSO) communication systems. Unlike conventional far-field scenarios, near-field propagation involves complex diffraction effects that hinder tractable closed-form analysis. To address this issue, a numerical framework for evaluating the optical field distribution of ORIS-assisted FSO links is proposed. Specifically, two numerical approaches are considered: direct Riemann-sum evaluation and a fast Fourier transform (FFT)-based method. Although the Riemann sum approach provides accurate field estimation, it incurs extremely high computational complexity due to the fine spatial discretization of the ORIS surface required at optical wavelengths. To improve computational efficiency, the optical-field calculation is reformulated as a convolution in the spatial-frequency domain, enabling efficient FFT-based propagation analysis. Simulation results demonstrate that the proposed FFT-based method achieves accuracy comparable to that of the Riemann-sum approach while significantly reducing computational complexity.

Data-driven control of nonlinear systems with rigorous guarantees is a challenging control problem. Integral quadratic constraints (IQCs) provide a powerful framework for modeling nonlinearities. This paper presents a data-driven min-max model predictive control (MPC) synthesis method for unknown systems subject to (nonlinear) uncertainties using the IQC framework. The unknown system matrices are characterized by a set-membership representation using the input-state data and the knowledge of the IQCs. We derive two semidefinite programs (SDPs) that minimize an upper bound on the worst-case cost over all possible system dynamics and uncertainties. By iteratively solving these SDPs, the proposed state-feedback control law is obtained. We further prove that the resulting closed-loop system is exponentially stable and satisfies the input and state constraints. A numerical example demonstrates the validity of the proposed method.

Sub-terahertz (Sub-THz) wireless communications and their potential applications continue to attract significant attention and foster debate on the usage of their unused frequency bands to relieve existing spectrum congestion. However, for these next-generation networks, experimental research is limited by the lack of flexible, real-time testbeds. This study presents a real-time, multi-radio-frequency(RF) channel, cascaded software-defined radio (SDR)-based Orthogonal Frequency-Division Multiplexing (OFDM) transmission platform achieving an aggregate sampling rate of 2 x 3.84 GSPS and approximately 1.1 GHz of instantaneous bandwidth, targeting sub-THz and THz SDR testbeds. The digital transmitter architecture, including the OFDM signal processing chain and instrumentation workflow, is described in detail. A comparative case study between a conventional sub-6 GHz implementation and a 180 GHz configuration is conducted, evaluating phase noise, spectral occupancy, received average and peak power. The direct impact of sub-THz bandpass filtering, necessitated by harmonic-mixer-based upconversion, is also experimentally analyzed. Measurement results show conversion and filtering losses of up to 24.1 dB, while the system exhibits stationary phase noise levels on the order of -60 dBc/Hz, demonstrating the feasibility and limitations of real-time wideband OFDM transmission at 180 GHz. Beyond immediate current capabilities, the platform builds a foundation for the scalable integration of multiple transmitters and receivers, which is essential for the implementation, conformance, and testing of emerging sub-THz communication systems.

Multi-modal sensing is an important enabler for future environment-aware wireless systems, since a single sensing modality is generally insufficient to provide accurate metric geometry, material awareness, and semantic interpretability in complex environments. This paper presents a measurement-based multi-modal THz sensing and vision framework for indoor environment reconstruction. A three-dimensional monostatic THz channel sounding system operating at 290-310 GHz is integrated with an omnidirectional fisheye camera to acquire radio-frequency and visual observations from a common sensing viewpoint. From the measured THz data, a signal processing pipeline extracts multipath components and infers geometryand material-consistent structural primitives through trajectory tracking-assisted parameter estimation, graph-based structure discovery, planar reconstruction, and reflection-loss analysis. In parallel, AI-based visual perception modules extract object-level semantic masks and depth priors from panoramic images. To associate these heterogeneous representations, an agentic-AI-based task-driven THz-agent module is developed to select appropriate integration tools according to the attributes of the modality-specific outputs. Through angular alignment and consistency analysis, THz-derived metric geometry and material information are associated with vision-derived semantic regions and depth priors, enabling geometry-consistent and semantically interpretable environment reconstruction directly from measurements. Experimental validation in the indoor L-shaped hallway demonstrates that the proposed framework reconstructs dominant structural elements with centimeter-level accuracy while identifying semantic categories and material attributes of representative indoor objects.

Neuromorphic computing relies on low-power, high-reliability hardware, yet the integrity of input/output pads (IOPADs) remains an underexplored factor affecting system performance. This chapter examines the role of IOPAD integrity in neuromorphic VLSI design and connects algorithmic development with practical hardware implementation. While much attention has been given to spiking neural networks (SNNs) and ultra-low-power core logic, the electrical and functional robustness of the I/O interface is equally critical for ensuring signal fidelity and minimizing energy consumption. We review the structure and function of IOPADs, outline their influence on power, performance, and reliability, and discuss design trade-offs involving pad libraries, pad ring architectures, and bonding strategies. The chapter also introduces the fundamentals of SNNs and summarizes the digital hardware design flow from behavioral description to physical layout. Physical implementation considerations are highlighted using the SkyWater 130 nm CMOS process as a practical platform for neuromorphic prototyping. Real-world examples illustrate how early-stage I/O planning can prevent redesign, reduce yield loss, and improve overall system efficiency. This work emphasizes that IOPAD integrity is a key enabler of scalable, energy-efficient neuromorphic systems.