Superconducting transition of a two-dimensional Josephson junction array in weak magnetic fields

The superconducting transition of a two-dimensional (2D) Josephson junction array exposed to weak magnetic fields has been studied experimentally. Resistance measurements reveal a superconducting-resistive phase boundary in serious disagreement with the theoretical and numerical expectations. Critical scaling analyses of the $IV$ characteristics indicate contrary to the expectations that the superconducting-to-resistive transition in weak magnetic fields is associated with a melting transition of magnetic-field-induced vortices directly from a pinned-solid phase to a liquid phase. The expected depinning transition of vortices from a pinned-solid phase to an intermediate floating-solid phase was not observed. We discuss effects of the disorder-induced random pinning potential on phase transitions of vortices in a 2D Josephson junction array.Comment: 9 pages, 7 figures (EPS+JPG format), RevTeX

Low-power hybrid structure of digital matched filters for direct sequence spread spectrum systems

ABSTRACT 1 This paper presents a low-power structure of digital matched filters (DMFs), which is proposed for direct sequence spread spectrum systems. Traditionally, low-power approaches for DMFs are based on either the transposedform structure or the direct-form one. A new hybrid structure that employs the direct-form structure for local addition and the transposed-form structure for global addition is used to take advantages of both structures. For a 128-tap DMF, the proposed DMF that processes 32 addends a cycle consumes 46 % less power at the expense of 6 % area overhead as compared to the state-of-the-art low-power DM

Theoretical investigations on microwave Fano resonances in 3D-printable hollow dielectric resonators

High-index dielectric structures have recently been studied intensively for Mie resonances at optical frequencies. These dielectric structures can enable extreme light manipulation, similar to that which has been achieved with plasmonic nanostructures. In the microwave region, dielectric resonators and metamaterials can be fabricated directly using 3D printing, which is advantageous for fabricating structurally complicated 3D geometries. It is therefore especially suitable for the fabrication of subwavelength structures. Here we report theoretical investigations on microwave Fano resonances in 3D-printable dielectric materials and structures. In particular, we propose and analyse 3D-printable, hollow, dielectric resonators with relatively low refractive indices, which exhibit sharp Fano resonances. We can control the interaction between bright and dark modes in a coupled dielectric particle pair by adjusting the inner-hole size, and thus we can increase the radiative Q factors further. We also find that Fano resonances in these hollow dielectric resonators are very sensitive to an index change in the surrounding medium, which could be useful for long-distance environmental sensing. New possibilities and opportunities are opening up with the rapid development of 3D-printing technologies. Our findings and the detailed investigations reported here can provide useful guidelines for future photonic devices based on 3D-printable materials and structures

Experimental validation of simulating natural circulation of liquid metal using water

Liquid metal-cooled reactors use various passive safety systems driven by natural circulation. Investigating these safety systems experimentally is more advantageous by using a simulant. Although numerous experimental approaches have been applied to natural circulation-driven passive safety systems using simulants, there has been no clear validation of the similarity law. To validate the similarity law experimentally, SINCRO-V experiment was conducted using Wood's metal and water for simulant of the Wood's metal. A pair of SINCRO-V facilities with length-scale ratio of 14.1:1 for identical Bo' was investigated, which was the main similarity parameter in temperature field simulation. In the experimental range of 0.2-1.0% of decay heat, the temperature distribution characteristics of the small water facility were very similar to that of the large Wood's metal facility. The temperature of the Wood's metal predicted by the water experiment showed good agreement with the actual Wood's metal temperature. Despite some error factors like discordance of Gr' and property change along the temperature, the water experiment predicted the Wood's metal temperature with an error of 27%. The validity of the similarity law was confirmed by the SINCRO-V experiments. (c) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC

Effects of nanofluids containing graphene/graphene-oxide nanosheets on critical heat flux

The superb thermal conduction property of graphene establishes graphene as an excellent material for thermal management. In this paper, we selected graphene/graphene oxide nanosheets as the additives in nanofluids. The authors interestingly found that the highly enhanced critical heat flux (CHF) in the nanofluids containing graphene/graphene-oxide nanosheets (GON) cannot be explained by both the improved surface wettability and the capillarity of the nanoparticles deposition layer. Here we highlights that the GON nanofluid can be exploited to maximize the CHF the most efficiently by building up a characteristically ordered porous surface structure due to its own self-assembly characteristic resulting in a geometrically changed critical instability wavelength.open363

ScissionLite: accelerating distributed deep learning with lightweight data compression for IIoT

Funding: This work was supported in part by the Electronics and Telecommunications Research Institute through the Korean government under Grant 23zs1300 (Research on High Performance Computing Technology to overcome limitations of AI processing) and in part by the Korea Institute for Advancement of Technology (KIAT) through the Korea Government (MOTIE) under Grant P0017011 (HRD Program for Industrial Innovation). Paper no. TII-23-4829.Industrial Internet of Things (IIoT) applications can greatly benefit from leveraging edge computing. For instance, applications relying on deep neural network (DNN) models can be sliced and distributed across IIoT devices and the network edge to reduce inference latency. However, low network performance between IIoT devices and the edge often becomes a bottleneck. In this study, we propose ScissionLite, a holistic framework designed to accelerate distributed DNN inference using lightweight data compression. Our compression method features a novel lightweight down/upsampling network tailored for performance-limited IIoT devices, which is inserted at the slicing point of a DNN model to reduce outbound network traffic without causing a significant drop in accuracy. In addition, we have developed a benchmarking tool to accurately identify the optimal slicing point of the DNN for the best inference latency. ScissionLite improves inference latency by up to 15.7× with minimal accuracy degradation.Peer reviewe