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.
