Nanosecond pulsed bioelectric medicine (ns-BEMed)—the use of ultrashort (nanosecond-scale), high-intensity electric pulses (EPs) to modulate biological systems—has advanced rapidly over the past two decades. Serving also as a tutorial, this review summarizes recent progress in the field with emphasis on fundamental mechanisms and emerging biomedical applications. Recent advances in nanosecond pulsed power technology have not only accelerated the development of nanosecond pulsed electric field (nsPEF)-based therapeutic approaches for a wide range of medical applications, including cardiac ablation and tumor treatment, but have also enabled the development of nonequilibrium atmospheric-pressure plasma sources, particularly nanosecond pulsed atmospheric-pressure plasma jets (ns-APPJs), whose unique properties offer promising pathways for safe and effective medical interventions. The review critically examines nsPEF-induced biological mechanisms, followed by a discussion of reactive plasma species responsible for the plasma-mediated bioeffects. Finally, current and emerging medicinal applications of nsPEF and ns-APPJs are surveyed.
Recent advancements in millimeter-wave (mmWave) radar technology have made it possible for radars to generate image-like observations, a capability that was previously challenging to achieve. These radar-generated images can be 4-D (azimuth and elevation angles + range + Doppler) if the sensor offers sufficiently fine spatial resolution. Angular resolution is the system’s ability to distinguish between objects that are in close angular proximity, while range discrimination is achieved through the large bandwidth supported by mmWave frequencies. By employing a substantial number of antenna elements, mmWave radars can perform beamforming in both transmit and receive directions, reducing interference and significantly improving spatial resolution. This allows the sensor to capture detailed information about an object’s distance, velocity, and 2-D angular position. Such capabilities are critical for applications like autonomous driving (AD), in-car monitoring, industrial automation, medical imaging, and security surveillance. This article explores emerging 4-D multiple-input–multiple-output (MIMO) imaging radar technology, highlighting its potential for cost-efficient, high-resolution image-like radar observations. It examines available sensors, their design methodologies, sensing robustness, and gaps in the current application of MIMO radar. In addition, the realization of massive MIMO radars through sparse and virtual array configurations is explored in this work. This study also considers potential new features and advancements in the field. Key topics include signal processing and array configuration design, both essential for optimizing radar performance. In addition, this article addresses various challenges and showcases ongoing industry efforts driving innovation in 4-D MIMO imaging radar systems.
This article provides an overview of the automated driving (AD) stack developments contributed to the UNICARagil research project. It focuses on the novelties in software design tailored to the redundant and fail-safe architectures developed in the project. We focus on the implementation of four mechatronically independent sensor modules and their respective perception, tracking, and fusion algorithms, as well as on the fully redundant planning architecture incorporating the over-actuated four-wheel 90° steer-by-wire drivetrain. Intelligent cloud applications support the on-board AD stack with collective environment modeling, behavior planning, and continuous learning. UNICARagil presented novel architectures for electrics and electronics (E/E) as well as a service-oriented software architecture (SOA) for fully automated and driverless operation of special-purpose software-defined vehicles (SDVs). We demonstrated the vehicles’ capabilities in driverless operations, without safety drivers onboard, in challenging mixed traffic scenarios. The results pave the way for the design of future vehicle architectures dedicated to AD and have already been widely adopted.
A distributed point function (DPF) is a cryptographic primitive that enables compressed additive sharing of a secret weight-1 vector (equivalently, a point function) across two or more parties. The appealing lightweight structure of DPF constructions has enabled a wide range of applications. These include private information retrieval, anonymous messaging, secure computation with preprocessing, and pseudorandom correlation generators for expanding small correlated seeds into large pseudorandom instances of cryptographic correlations. In this article, we survey definitions, constructions, and applications of DPFs. We also discuss the extension of DPF to function secret sharing (FSS), which generalizes point functions to support richer function classes. Efficient FSS schemes yield a similar generalization for most of the applications of DPFs.
Conventional frame-based cameras face significant challenges in detecting objects under high-speed motion blur or in low-light environments. Neuromorphic cameras provide asynchronous visual streams with high temporal resolution and a wide dynamic range, offering a promising solution for object detection under challenging conditions. Despite the development of numerous models and the emergence of various applications in neuromorphic object detection, there is still a lack of deep understanding and standardized benchmarks to assess progress and address key challenges. In this paper, we provide a comprehensive survey and benchmark of existing neuromorphic object detection algorithms. Specifically, we first present a problem description, review the available datasets, and revisit the evaluation metrics. We then explore existing neuromorphic object detection approaches from various perspectives, including event representation, temporal modeling, multimodal fusion, asynchronous processing, low-latency processing, and energy-efficient computing. Furthermore, we evaluate a wide range of representative neuromorphic object detection models and offer detailed analyses of the comparative results. Finally, we discuss unresolved issues in neuromorphic object detection and propose potential future research directions. We hope this survey and benchmark will be a valuable resource for researchers and provide guidance for future advancements in neuromorphic object detection.
Summary form only: Abstracts of articles presented in this issue of the publication.
Summary form only: Abstracts of articles presented in this issue of the publication.
Precise clock synchronization underpins deterministic operation in wireless systems spanning industrial automation, vehicular networks, distributed extended reality (XR), smart infrastructure, and wide-area precision agriculture. Wireless links introduce variable propagation delays, channel asymmetry, interference, clock drift, and scalability constraints that make sub-microsecond alignment difficult. This article provides a comprehensive survey and tutorial on wireless clock synchronization. We introduce a five-dimension taxonomy covering: system architecture, synchronization mechanism, correction strategy, delay and uncertainty modeling, and resource and deployment class, and apply it to eight canonical protocol families and to synchronization as realized across IEEE 802.15.4, ZigBee, Bluetooth low energy (BLE), LoRa, Wi-Fi, Ultrawide band (UWB), and 4G/5G/6G systems. We examine solutions across five application domains: industrial automation and Industrial Internet of Things (IIoT), vehicular V2X, distributed XR and metaverse, infrastructure monitoring, and wide-area Internet of Things (IoT) and precision agriculture; alongside tools, testbeds, and datasets supporting evaluation. Open challenges addressed include scalability and mobility, ultralow-jitter determinism, robust clock parameter estimation under non-Gaussian delay distributions, distributed and consensus-based synchronization for infrastructure-free networks, secure and resilient synchronization against wireless-specific threats, and cross-domain convergence encompassing time-sensitive networking (TSN)-5G/6G interoperability and joint communication, sensing, and timing as an emerging 6G design paradigm. Together, these contributions provide the first unified cross-technology framework connecting fundamentals, protocol families, application domains, and open research challenges in wireless clock synchronization.
The Advanced Land Observing Satellite-4 (ALOS-4) “Daichi-4” is a follow-on mission of ALOS-2 “Daichi-2.” ALOS-4 was launched on July 1, 2024, by the H3 launch vehicle. It carries a state-of-the-art L-band synthetic aperture radar (SAR) named Phased Array type L-band SAR-3 (PALSAR-3) to meet higher performance requirements than those of ALOS-2. The observation swath achieves 200 km, which is four times wider than that of PALSAR-2 onboard ALOS-2, by employing the digital beamforming (DBF) technique while maintaining high resolution. ALOS-4 also carries Space-based Automatic Identification System for ships Experiment 3 (SPAISE3), a successor to SPAISE2 onboard ALOS-2. SPAISE3 features an eight-element antenna and AIS signal processing based on a ground-based DBF system as one method to mitigate AIS signal collisions in ship-crowded areas. After launch, ALOS-4 completed its critical phase and initial checkout phase and is currently undergoing initial calibration and validation. This article describes the current status of ALOS-4 and its future utilization.
Remote sensing (RS) plays a critical role in Earth observation (EO), providing indispensable data for a broad range of downstream applications. Propelled by advances in sensor technology and machine learning, RS field is evolving from task-specific analyses to more generalized modeling frameworks. This article examines the emergence of large RS models (LRSMs), tracing their progress from conventional deep learning (DL) approaches to sophisticated multimodal foundation models with the potential for Earth-scale intelligence (ESI). We outline four pivotal developments shaping this trajectory: 1) from DL to large models: transitioning from specialized DL methods to general-purpose pretrained frameworks, with emphasis on innovative paradigms such as self-supervised contrastive learning (CL) and masked image modeling (MIM) to establish powerful, versatile representations; 2) from vision to multimodality, by combining RS imagery with natural language, LRSMs have expanded from visual analysis to support interactive, user-driven interpretation; 3) from data-driven to physics-informed, incorporating physical mechanisms and principles into LRSMs has led to improved robustness, interpretability, and physically consistent outcomes, significantly enhancing their performance in tasks such as Earth system parameter inversion and spatiotemporal forecasting; and 4) from closed-set to open-world evaluation, evaluation methodologies have evolved from traditional closed-set datasets toward comprehensive, hierarchical, and open-world benchmarks. This shift ensures that assessments reflect real-world complexities more accurately, promoting the development of LRSMs with greater scalability and adaptability. By systematically reviewing these developments and identifying the ongoing challenges and research frontiers, this article aims to guide future innovations, accelerating the practical deployment and societal impact of LRSMs.
Clouds and aerosols are fundamental regulators of Earth's radiation budget and climate system, influencing both solar and terrestrial radiation through scattering, absorption, and emission processes. Accurate characterization of their physical and radiative properties from space requires a rigorous understanding of particle single-scattering, gaseous absorption, and radiative transfer in the atmosphere, as well as reliable inversion methods. This review synthesizes the physical foundations and algorithmic implementations of satellite-based passive optical remote sensing of clouds and aerosols, spanning the ultraviolet to thermal infrared (IR) spectral range. Beginning with electromagnetic scattering theory and state-of-the-art methods for computing single-scattering by nonspherical particles and computationally efficient methods for accounting for atmospheric absorption, we discuss the radiative transfer framework underpinning cloud and aerosol retrievals. In particular, the connection between single-scattering and multiple-scattering is rigorously formulated. We then summarize operational and research-grade retrieval techniques, including cloud masking and thermodynamic phase determination, CO2 slicing for cloud-top pressure, the Nakajima-King shortwave bi-spectral and IR split-window approaches for cloud optical thickness and effective particle size, inversion algorithms for determining aerosol properties from multispectral and/or multiangle radiometric and polarimetric measurements, and active-passive remote sensing synergy. Examples of the global cloud and aerosol climatologies are illustrated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-Angle Imaging SpectroRadiometer (MISR). Furthermore, the unique capabilities of active remote sensing techniques using spaceborne lidar observations are briefly discussed in the context of investigating ice clouds composed of randomly and horizontally oriented ice crystals, which remain a significant challenge for conventional passive remote sensing techniques. By connecting physical theory to practical retrievals, this review highlights both the maturity of current methodologies and the remaining challenges in reducing uncertainties in particle morphology, vertical structure, absorption, and aerosol-cloud interactions. Finally, the impact of artificial intelligence (AI) on atmospheric remote sensing is briefly addressed.
The European Space Agency’s (ESA) BIOMASS mission is a pioneering Earth observation satellite mission launched on April 29, 2025. Utilizing a P-band synthetic aperture radar (SAR), the objective of BIOMASS is to deliver estimates of above-ground forest biomass, forest height (FH), and forest disturbance (FD), with unprecedented accuracy. The mission’s primary scientific goal is to quantify the distribution and changes in forest biomass, thereby reducing uncertainties in carbon flux estimates and informing climate models. The satellite’s advanced instrumentation and innovative approach allow it to penetrate dense forest canopies, capturing data even in challenging environments. The mission will operate in two distinct phases: the tomographic phase and the interferometric phase, which will support polarimetric interferometric SAR (Pol-InSAR) and tomographic SAR (TomoSAR) processing. Additionally, BIOMASS will provide valuable observational data for ice sheets, deserts, the ionosphere, below canopy topography, and other domains.
Reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) is emerging as a key enabler for sixth-generation (6G) wireless systems, unifying communication, sensing, and control within a reconfigurable electromagnetic (EM) environment. This article presents a comprehensive review that integrates theoretical foundations, signal processing methodologies, and experimental implementations of RIS-ISAC. It first summarizes core principles of channel modeling, waveform design, and sensing parameter estimation across near-field, broadband, and multihop propagation regimes. Building on these foundations, the article surveys advances in hardware architectures, testbeds, and prototype demonstrations spanning sub-6 GHz to millimeter-wave and terahertz bands. Cross-cutting insights are drawn on system tradeoffs, performance bounds, and learning-aided adaptation. Finally, unified benchmarking metrics and open challenges are identified, covering synchronization, scalability, and standardization, to guide the evolution of RIS-ISAC from conceptual frameworks to practical 6G network deployments.
The rapid advancement in electric vehicle (EV) technology stimulated the development of fast chargers, with innovative solutions in power electronics (PEs), communication, protection, and control. This literature review explores the evolving landscape of fast EV charging infrastructures, and focuses on their topologies, advanced PEs solutions, and associated challenges. In addition, this article explores the multifaceted aspects of fast charging systems. In addition, the article discusses the protection systems, communication systems, and control techniques for facilitating seamless, safe, and reliable interaction between the grid, fast EV chargers (FEVCs), and the EV. Furthermore, the review investigates the interaction between the grid and FEVCs through ancillary services, and highlights their potential to enhance grid stability and reliability. Although a significant research is conducted in the literature and a significant progress is achieved, a notable gap persists between the available academic literature and commercially deployed solutions. Investigating this gap is crucial for accelerating the adoption of FEVC technologies and maximizing their benefits for both consumers and grid operators. Therefore, a reference design is synthesized that combines the recommended elements identified throughout the literature.
This article provides a comprehensive investigation into reconfigurable integrated sensing and communications (RISAC), an emerging paradigm designed to maximize the performance-cost tradeoff by exploiting the inherent spatial sparsity of multiple-input multiple-output (MIMO) arrays. Deviating from conventional static ISAC, RISAC adopts a cognitive "perception-action" cycle, enabling the adaptive reconfiguration of array geometries and beamforming weights in response to dynamic environmental feedback. Central to RISAC is the exploitation of two intertwined layers of degrees of freedom (DoFs): element-space sparsity via sparse MIMO array design and beam-space sparsity via hybrid beamforming (HBF). We further demonstrate that by synergistically co-designing these coupled DoFs, sparse MIMO HBF can rival the performance of fully digital systems when accounting for practical mutual coupling, at a significantly reduced hardware cost. This work analyzed both far-and near-field propagation regimes across various ISAC frameworks, including radar-centric, communication-centric, and joint co-design. In addition to downlink co-design, we also examine uplink coexistence via shared waveform design. Finally, this article outlines promising research directions, positioning RISAC as a critical evolution toward adaptive, hardware-efficient, and dual-functional systems.
For more than a decade, the remote sensing community has called for longwave spectral measurements of the Earth system to facilitate closing the radiation budget. In an effort to address this large gap in Earth-observing capability, NASA has supported the development and implementation of the Polar Radiant Energy in the Far-Infrared (FIR) Experiment (PREFIRE), which realizes two miniaturized thermal IR spectrometers (TIRSs), one on each of two polar orbiting small satellites with different ascending node crossing times. The spectral radiances measured by the TIRS instruments are utilized to extract cloud presence, atmospheric state, surface properties, and the associated top-of-atmosphere (TOA) spectral fluxes at both poles. This article describes the major system and algorithm components and introduces the geophysical products. PREFIRE data will be used to inform and improve ice-sheet and coupled-Earth system models to better predict the future state of the Earth’s poles.
Satellite remote sensing plays a fundamental role in observing oceanic processes by providing large-scale, long-term, and continuous measurements. With the increasing availability of multisource satellite data, challenges such as data gaps, complex environmental conditions, and the limitations of conventional retrieval methods have become more evident. In recent years, artificial intelligence (AI) has emerged as a practical and effective approach to address these issues. This article reviews the development of AI techniques in satellite ocean remote sensing, focusing on three main application areas: parameter retrieval, data reconstruction, and image-based ocean phenomenon detection. For geophysical variable retrieval, AI models such as convolutional neural networks (CNNs) and Transformer architectures have improved the accuracy of ocean waves, sea surface, salinity, wind, and ocean color estimates, especially under extreme or noisy conditions. In the field of data reconstruction, AI methods enable the completion of missing data in both surface and subsurface ocean layers, offering finer spatial-temporal resolution and better consistency than traditional interpolation approaches. For image interpretation, deep learning (DL) models have been applied to detect and segment dynamic ocean features such as mesoscale eddies, internal waves, sea ice, and tropical cyclones (TCs), achieving high efficiency and precision. This article also highlights the integration of AI with physical knowledge, the use of multisource fusion, and the trend toward near real-time (NRT) applications. These developments indicate that AI will play an increasingly important role in future satellite-based ocean observation and environmental monitoring.
