Native lattice strain induced structural earthquake in sodium layered oxide cathodes (batteries)


Abstract "High-voltage operation is essential for the energy and power densities of battery cathode materials, but its stabilization remains a universal challenge. To date, the degradation origin has been mostly attributed to cycling-initiated structural deformation while the effect of native crystallographic defects induced during the sophisticated synthesis process has been significantly ove... » read more

SolidPAC is an interactive battery-on-demand energy density estimator for solid-state batteries


Summary "Solid-state batteries hold the promise to be highly impactful next-generation technologies for high-energy and -power-density rechargeable battery applications. It is crucial to identify the metrics that an emerging battery technology should fulfill to achieve parity with conventional Li-ion batteries, primarily in terms of energy density. However, limited approaches exist today to as... » read more

Bell state analyzer for spectrally distinct photons


Abstract "We demonstrate a Bell state analyzer that operates directly on frequency mismatch. Based on electro-optic modulators and Fourier-transform pulse shapers, our quantum frequency processor design implements interleaved Hadamard gates in discrete frequency modes. Experimental tests on entangled-photon inputs reveal fidelities of ∼98% for discriminating between the |Ψ+⟩ and |Ψ−⟩... » read more

Experimental photonic quantum memristor


Abstract "Memristive devices are a class of physical systems with history-dependent dynamics characterized by signature hysteresis loops in their input–output relations. In the past few decades, memristive devices have attracted enormous interest in electronics. This is because memristive dynamics is very pervasive in nanoscale devices, and has potentially groundbreaking applications ranging... » read more

Wavelength Multiplexed Ultralow-Power Photonic Edge Computing


Abstract "Advances in deep neural networks (DNNs) are transforming science and technology. However, the increasing computational demands of the most powerful DNNs limit deployment on low-power devices, such as smartphones and sensors -- and this trend is accelerated by the simultaneous move towards Internet-of-Things (IoT) devices. Numerous efforts are underway to lower power consumption, but ... » read more

5G: The Telecommunications Horizon and Homeland Security


Summary "Produced in conjunction with the Department of Homeland Security (DHS) HQ Emerging Technologies Policy team, this horizon scanning report provides information and insight into the future of fifth generation (5G) and sixth generation (6G) networking technologies and the associated impacts to the homeland security enterprise. The measures taken by DHS to manage and secure its networks w... » read more

Improving Emergency Response in the Era of ADAS Vehicles in the Smart City


Abstract: "Management of emergency vehicles can be fostered within a Smart City, i.e. an urban environment in which many IoT devices are orchestrated by a distributed intelligence able to suggest to road users the best course of action in different traffic situations. By extending MATSim (Multi-Agent Transport Simulation Software), we design and test appropriate mitigation strategies when tr... » read more

OverlapNet: Loop Closing for LiDAR-based SLAM


Abstract: "Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our approach utilizes a deep neural network exploiting different cues generated from LiDAR data for finding loop closures. It estimates an image overlap gene... » read more

Improving Machine Learning-Based Modeling of Semiconductor Devices by Data Self-Augmentation


Abstract: "In the electronics industry, introducing Machine Learning (ML)-based techniques can enhance Technology Computer-Aided Design (TCAD) methods. However, the performance of ML models is highly dependent on their training datasets. Particularly in the semiconductor industry, given the fact that the fabrication process of semiconductor devices is complicated and expensive, it is of grea... » read more

The resurrection of tellurium as an elemental two-dimensional semiconductor


Abstract "The graphene boom has triggered a widespread search for novel elemental van der Waals materials thanks to their simplicity for theoretical modeling and easy access for material growth. Group VI element tellurium is an unintentionally p-type doped narrow bandgap semiconductor featuring a one-dimensional chiral atomic structure which holds great promise for next-generation electronic, ... » read more

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