A new type of superconductive true random number generator (TRNG) based on a negative-inductance superconducting quantum interference device (nSQUID) is proposed. The entropy harnessed to generate random numbers comes...A new type of superconductive true random number generator (TRNG) based on a negative-inductance superconducting quantum interference device (nSQUID) is proposed. The entropy harnessed to generate random numbers comes from the phenomenon of symmetry breaking in the nSQUID. The experimental circuit is fabricated by the Nb-based lift-off process. Low-temperature tests of the circuit verify the basic function of the proposed TRNG. The frequency characteristics of the TRNG have been analyzed by simulation. The generation rate of random numbers is expected to achieve hundreds of megahertz to tens of gigahertz.展开更多
Fingerprint matching is adopted by a large family of indoor localization schemes,where collecting fingerprints is inevitable but all consuming.While the increasingly popular crowdsourcing based approach provides an op...Fingerprint matching is adopted by a large family of indoor localization schemes,where collecting fingerprints is inevitable but all consuming.While the increasingly popular crowdsourcing based approach provides an opportunity to relieve the burden of fingerprints collecting,a number of formidable challenges for such an approach have yet been studied.For instance,querying in a large fingerprints database for matching process takes a lot of time and calculation;fingerprints collected by crowdsourcing lacks of robustness because of heterogeneous devices problem.Those are important challenges which impede practical deployment of the fingerprint matching indoor localization system.In this study,targeting on effectively utilizing and mining large amount fingerprint data,enhancing the robustness of fingerprints under heterogeneous devices' collection and realizing the real time localization response,we propose a crowdsourcing based fingerprints collecting mechanism for indoor localization systems.With the proposed approach,massive raw fingerprints will be divided into small clusters while diverse devices' uploaded fingerprints will be merged for overcoming device heterogeneity,both of which will contribute to reduce response time.We also build a mobile cloud testbed to verify the proposed scheme.Comprehensive real world experiment results indicate that the scheme can provide comparable localization accuracy.展开更多
基金Supported by the State Key Program for Basic Research of China under Grant No 2011CBA00304the National Natural Science Foundation of China under Grant No 60836001the Tsinghua University Initiative Scientific Research Program under Grant No 20131089314
文摘A new type of superconductive true random number generator (TRNG) based on a negative-inductance superconducting quantum interference device (nSQUID) is proposed. The entropy harnessed to generate random numbers comes from the phenomenon of symmetry breaking in the nSQUID. The experimental circuit is fabricated by the Nb-based lift-off process. Low-temperature tests of the circuit verify the basic function of the proposed TRNG. The frequency characteristics of the TRNG have been analyzed by simulation. The generation rate of random numbers is expected to achieve hundreds of megahertz to tens of gigahertz.
基金the National Science and Technology Major Project of China(No.2013ZX03001007-004)the Shanghai Basic Research Key Project(No.11DZ1500206)
文摘Fingerprint matching is adopted by a large family of indoor localization schemes,where collecting fingerprints is inevitable but all consuming.While the increasingly popular crowdsourcing based approach provides an opportunity to relieve the burden of fingerprints collecting,a number of formidable challenges for such an approach have yet been studied.For instance,querying in a large fingerprints database for matching process takes a lot of time and calculation;fingerprints collected by crowdsourcing lacks of robustness because of heterogeneous devices problem.Those are important challenges which impede practical deployment of the fingerprint matching indoor localization system.In this study,targeting on effectively utilizing and mining large amount fingerprint data,enhancing the robustness of fingerprints under heterogeneous devices' collection and realizing the real time localization response,we propose a crowdsourcing based fingerprints collecting mechanism for indoor localization systems.With the proposed approach,massive raw fingerprints will be divided into small clusters while diverse devices' uploaded fingerprints will be merged for overcoming device heterogeneity,both of which will contribute to reduce response time.We also build a mobile cloud testbed to verify the proposed scheme.Comprehensive real world experiment results indicate that the scheme can provide comparable localization accuracy.
