NEC

About: NEC is a company organization based out in Tokyo, Japan. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 33269 authors who have published 57670 publications receiving 835952 citations. The organization is also known as: NEC Corporation & NEC Electronics Corporation.

Flux-driven Josephson parametric amplifier

Abstract: We have developed a Josephson parametric amplifier comprising a superconducting coplanar-waveguide resonator terminated by a dc superconducting quantum interference device (SQUID). An external field (the pump, ∼20 GHz) modulates the flux threading of the dc SQUID and, therefore, the resonant frequency of the cavity field (the signal, ∼10 GHz), which leads to the parametric signal amplification. We operated the amplifier at different band centers and observed amplification (17 dB at maximum) and deamplification depending on the relative phase between the pump and the signal. The noise temperature is estimated to be less than 0.87 K.

Electrical control of the ferromagnetic phase transition in cobalt at room temperature

Abstract: Electrical control of magnetic properties is crucial for device applications in the field of spintronics. Although the magnetic coercivity or anisotropy has been successfully controlled electrically in metals as well as in semiconductors, the electrical control of Curie temperature has been realized only in semiconductors at low temperature. Here, we demonstrate the room-temperature electrical control of the ferromagnetic phase transition in cobalt, one of the most representative transition-metal ferromagnets. Solid-state field effect devices consisting of a ultrathin cobalt film covered by a dielectric layer and a gate electrode were fabricated. We prove that the Curie temperature of cobalt can be changed by up to 12 K by applying a gate electric field of about ±2 MV cm(-1). The two-dimensionality of the cobalt film may be relevant to our observations. The demonstrated electric field effect in the ferromagnetic metal at room temperature is a significant step towards realizing future low-power magnetic applications.

SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

Abstract: This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos. The proposed SegFlow has two branches where useful information of object segmentation and optical flow is propagated bidirectionally in a unified framework. The segmentation branch is based on a fully convolutional network, which has been proved effective in image segmentation task, and the optical flow branch takes advantage of the FlowNet model. The unified framework is trained iteratively offline to learn a generic notion, and fine-tuned online for specific objects. Extensive experiments on both the video object segmentation and optical flow datasets demonstrate that introducing optical flow improves the performance of segmentation and vice versa, against the state-of-the-art algorithms.