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We demonstrate a data mining approach to discover and develop new organic nonlinear optical crystals that produce intense pulses of terahertz radiation. We mine the Cambridge Structural Database for non-centrosymmetric materials and use this structural data in tandem with density functional theory calculations to predict new materials that efficiently generate terahertz radiation. This enables us to (in a relatively short time) discover, synthesize, and grow large, high-quality crystals of four promising materials and characterize them for intense terahertz generation. In a direct comparison to the current state-of-the-art organic terahertz generation crystals, these new materials excel. The discovery and characterization of these novel terahertz generators validates the approach of combining data mining with density functional theory calculations to predict properties of high-performance organic materials, potentially for a host of exciting applications., Comment: 16 pages, 5 figures

The paper reports on our latest developments on fabrication of fine scale 1–3 piezocomposites for application as high frequency ultrasonic transducers using soft mold process. In previous work we showed that operating frequencies can be increased to frequencies of at least 40 MHz by modifying piezoceramic pillar size and layout within 1–3 piezocomposites. Especially circular pillars with hexagonal arrangement turned out to be beneficial to reduce influence of spurious modes. The current investigation focuses on the improvement of piezoelectric properties of 20 MHz ultrasonic transducers to enhance ultrasonic performance. During experimental verification process parameters like PZT powder as well as sintering and poling conditions were systematically varied to optimize dielectric and electromechanical properties, in particular thickness mode coupling factor k t . The results show that sintering temperature and polarization field strength are the most crucial parameters to influence coupling factor. By modifying fabrication process coupling factor could be increased to values of up to k t = 0.6. Best results were achieved by using calcined and pre-sintered PZT powder in combination with relatively low sintering temperatures ϑ s = 1060–1110 °C and high electric fields during poling $E$ pol ≥ 9 V /µm.

T HE recent discovery of ultrafast spintronic THz emitters has attracted a lot of attention due to their high power, broadband emission as well as easy fabrication [1] , [2] . They are based on nanometer-thin bilayers of a magnetic (M) and a nonmagnetic (NM) metal layer and rely on the inverse spin Hall effect. The THz emission properties can be tuned by using different M or NM materials, tailoring the magnetic properties [3] , or building functional layer stacks [4] , [5] . Here, we present a spin valve THz emitter, which allows for switching of the THz emission amplitude by a low magnetic field of a few mT.

Circuit implementations of neuronal networks should also consider dynamic axon models, since this introduces an additional dynamic aspect due to the transmission delays depending on the axon length. In this work, we derive an electrical circuit for self-organized axon growth based on which we design a neuronal network for learning and classifying gait patterns. We do so by utilizing a wave digital model of the axon model with growth concept, from which we can deduce the corresponding electrical circuit. Here, the axon growth is based on Jaumann structures with memristors. Emulation results show that after the successful training of the network, it can indeed recognize the correct gait patterns. In contrast to typical neuronal networks, this training is not based on synaptic weight changes but on the self-organized axon growth and hence delay-selection. Due to the additional degree of freedom, this can allow for a richer dynamic behavior of modeled neuronal networks.

The topic of synchronization has attracted a lot of interest especially because it is related to learning processes. A popular learning process is given by the Hebbian learning, which in the context of oscillatory neural networks can be interpreted as the increasing coupling strength of oscillators with small phase difference and the decreasing coupling strength of oscillators with large phase difference. A well-studied model to investigate coupled oscillators is the Kuramoto model. The goal of this work is to synthesize an electrical circuit which represents the Kuramoto model with Hebbian learning. For this purpose, memristors are used whose model is derived in this work depending on the Hebbian learning rule. A wave digital model is developed to verify the functionality by mimicking spatial correlations causing an optical illusion.

Models of neuronal networks should account for axon growth in addition to dynamical synapses, because this results in a self-organized topology formation introducing an additional dynamic aspect due to the varying axonal delay. In order to bridge the gap of a missing theoretic circuit realization of axon growth, we utilize the wave digital concept to derive an axon model based on a memristive Jaumann structure. As a proof of concept, we make use of this axon growth by designing a self-sustained pattern generator mimicking the gait patterns of a dog. Emulation results show that the resulting circuit acts according to "the whole is more than the sum of its parts" as it can combine the learned gait patterns to a new one. This can be seen as a simple form of intelligence.

The availability of efficient and robust quantum light sources in large numbers is one of the key building blocks for quantum photonic technology. Recently, bright and stable single-photon emitters have been discovered in the 2D material hexagonal boron nitride (hBN), which efficiently emit single photons even at room-temperature [1] . The controlled positioning of nanocrystals hosting light emitters is an important technique for the bottom-up fabrication of functional nanostructures as well as for fundamental investigations on the particles themselves.

The LoRaWAN communication protocol has gained significant adoption within commercial IoT systems through support for low power, long range transmission. LoRaWAN’s growing usage in cities and civilian infrastructure has prompted investigation into its potential usage within military systems for supporting urban operations, involving expanded research on LoRa signal coverage in the presence of urban infrastructure. Through the use of a supporting Commercial-off-the-Shelf (COTS) IoT architecture, a set of experiments were conducted in New York City aimed at testing LoRa signal coverage in both indoor and outdoor settings. Performance was compared for different transmission configurations on the North American Industrial, Scientific, and Medical (ISM) 915 MHz band. This work is seen as a key step to better understand the suitability and applicability of civilian IoT communication protocols to augment and enhance tactical C2 systems for urban operations.

The recent discovery of spintronic THz emitters based on nanometer-thin bilayers of a ferromagnetic (FM) and a nonmagnetic metal (NM) layer has attracted a lot of attention due to their high power, gapless and broadband emission, and easy fabrication [1], [2]. The emission properties can be tuned easily e.g. by varying the FM or NM materials, the magnetic properties or the electrical conductivity. However, the microscopic mechanism governing the THz emission is presently not fully understood, which limits their design and optimization.

Central low-level sensor data fusion approaches are getting more popular in advanced driver assistant systems. They allow for the resolution of ambiguities in the retrieval of environmental information on the basis of a large, raw data pool. Hereby, one emerging challenge is the unification of sensor data of different formats and sensor types. A popular intermediate layer of data is given by spatial occupancy grids. The conversion of a discrete list of radar detections, which is a commonly utilized measurement format, is problematic due to the sparse spatial resolution. This work addresses this conversion by interpolating the data spatially using generic sensor model knowledge. Traditional approaches derive occupancy evidence in the vicinity of a detection. In addition, we analyze spatial and kinematic properties derived from Doppler measurements, compute likelihoods that multiple detections are caused by the same object and deduce the space between them accordingly. The incorporation of sensor parameters allows full- and short-range radars to be used generically. In addition, we outline the deduction of free space evidence. The elaborated models and algorithms are evaluated on realworld datasets and discussed w.r.t. their applicability in a subsequent Dempster-Shafer-based sensor data fusion approach.

The latest generation of geostationary satellites (Himawari 8/9, GOES-16/17, FY-4, GK-2A) carries sensors that closely mimic the spatial and spectral characteristics of widely used polar-orbiting, global monitoring sensors such as MODIS and VIIRS. When combined, data from various currently operating/planned geostationary platforms provide a geo-ring of hyper-temporal (5-10 minutes), multispectral observations at spatial resolutions as high as 500 m. These high frequency observations offer exciting new possibilities for monitoring our planet, including better retrievals of geophysical variables by overcoming cloud cover, enabling studies of diurnally varying phenomena in the atmosphere, land, and the oceans, and support operational decision-making in agriculture, hydrology and disaster management. The NASA Earth Exchange (NEX) team, in collaboration with scientists from JAXA, KARI, NOAA and other international institutions, created the GeoNEX (www.nasa.gov/geonex) pipeline to integrate data from all available geostationary platforms and produce and distribute spatially, temporally, and radiometrically consistent data for the earth science community. We envision various institutions adapting the Geo component (e.g., GeoNOAA, GeoKARI, GeoChiba, GeoJAXA, GeoCMA) and customizing the pipeline and downstream products to serve the local/regional research and applied science communities.

We extended the MODIS 17 algorithm to hourly Gross Primary Product (GPP) estimation for Australia in 2018. AHI 1-km data are available at NASA Earth Exchange (NEX) facility. The high-frequent observation of Advanced Himawari Imager (AHI) enabled us to calculate hourly GPP, which is corresponding to ground observation of carbon flux measured by TERN OzFlux. We interpolated the weather observation data to 1-km grid over Australia ingesting AHI reflectance and radiometric temperature data (NEX-Gridded Hourly Meteorology (NEX-GHM)). Then, we applied MODIS 17 algorithm to each grid with optimized climate regulation functions. The estimation of climate and GPP were well correlated with OzFlux ground observation data.

One major challenge in extended object tracking is the association of a point measurement to its true origin on a target object. The origins of measurements are often spatially distributed over the full extent of the target. The association of measurements to the possible origins within the targets’ extent is difficult, especially for low-resolution sensors which provide only a few measurements per object. We address this using a soft association of a point measurement to its origin candidates on the target. Therefore, association probabilities to different possible origins are calculated for each measurement. These probabilities are weighted according to their probability in the filtering step. We also extend this filter to continuous and not just discrete association possibilities. This allows us to associate point measurements to lines.This paper outlines the derivation of the filter and gives three exemplary applications. A simulation compares the performance of this approach with other filter techniques for tracking a moving line. The transfer of the filter to a moving circle is discussed. Additionally, we discuss its usage for a Doppler-radar-based detection association which exploits the radial speed information. We discuss the advantages and the drawbacks of this approach and give recommendations for the optimization of computation time.

Neuroplasticity is not only related to the coupling strength between neurons but also to the topology formation due to synapse and axon elimination. This leads to the emergence of optimal communication structures in terms of energy efficiency and minimal delay. The goal of this work is to synthesize a bio-inspired electrical circuit mimicking the topology formation and self-healing ability of neuron populations, such that optimal communication structures are found. This is done by exploiting memristors, where a retention characteristic accounts for the topology formation due to synapse and axon elimination. Optimal communication structures can then be found by finding the minimum spanning tree of the resulting memristive circuit. Simulation results show that the circuit is not only able find the optimal communication structures, but also mimics the self-healing ability of neuron populations in case an important interconnection between neurons is lost due to accidents or diseases. We understand our work as a concept to explain aspects of neuroplasticity at a high abstraction level.

The random hypersurface model is well-suited to describe extended target contours. Its applicability is limited only by the mild assumption that the target contour has to be star convex. Gaussian processes provide a sound way to estimate the contour functions, and the ability to model the contour uncertainty in a detailed way at different contour points. However, the association of measurements to target contour points is not optimal in current implementations using Gaussian Processes and the random hypersurface model. In this work, we provide an improved approach compared to the standard approach. The standard approach projects measurements radially onto the predicted contour. Our approach provides expected measurements matching the physical reality of the measurement process more closely. In addition, we perform the association of the whole batch of measurements to the expected contour measurements at once. Compared to a sequential association of individual measurements, this leads to a better association decision.