Biblio

Named in-network computing is an emerging technology of Named Data Networking (NDN). Through deploying the named computing services/functions on NDN router, the router can utilize its free resources to provide nearby computation for users while relieving the pressure of cloud and network edge. Benefitted from the characteristic of named addressing, named computing services/functions can be easily discovered and migrated in the network. To implement named in-network computing, integrating the computing services as Virtual Machines (VMs) into the software router is a feasible way, but how to effectively deploy the service VMs to optimize the local processing capability is still a challenge. Focusing on this problem, we first give the design of NDN-enabled software router in this paper, then propose a service earning based named service deployment scheme (SE-NSD). For available service VMs, SE-NSD not only considers their popularities but further evaluates their service earnings (processed data amount per CPU cycle). Through modelling the deployment problem as the knapsack problem, SE-NSD determines the optimal service VMs deployment scheme. The simulation results show that, comparing with the popularity-based deployment scheme, SE-NSD can promote about 30% in-network computing capability while slightly reducing the service invoking RTT of user.

Nanosize multiferroic La-doped YFeO3 powders are harvested via a low-temperature solid-state reaction method. X-ray diffraction (XRD), scanning electron microscopy (SEM) and Raman spectra analysis reveal that with La addition, YFeO3 powders are successfully fabricated at a lower temperature with the size below 60 nm, and a refined structure is obtained. Magnetic hysteresis loop illustrates ferromagnetic behavior of YFeO3 nano particles can be enhanced with La addition. The maximum and remnant magnetization of the powders are about 4.03 and 1.22 emu/g, respectively. It is shown that the optical band gap is around 2.25 eV, proving that La doped YFeO3 nano particles can strongly absorb visible light. Both magnetic and optical properties are greatly enhanced with La addition, proving its potential application in magnetic and optical field.

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to map activities both spatially and temporally.