Fast convergence caching replacement algorithm based on dynamic classification for content-centric networks

One of the key research fields of content-centric networking （CCN） is to develop more efficient cache replacement policies to improve the hit ratio of CCN in-network caching. However, most of existing cache strategies designed mainly based on the time or frequency of content access, can not properly deal with the problem of the dynamicity of content popularity in the network. In this paper, we propose a fast convergence caching replacement algorithm based on dynamic classification method for CCN, named as FCDC. It develops a dynamic classification method to reduce the time complexity of cache inquiry, which achieves a higher caching hit rate in comparison to random classification method under dynamic change of content popularity. Meanwhile, in order to relieve the influence brought about by dynamic content popularity, it designs a weighting function to speed up cache hit rate convergence in the CCN router. Experimental results show that the proposed scheme outperforms the replacement policies related to least recently used （LRU） and recent usage frequency （RUF） in cache hit rate and resiliency when content popularity in the network varies.

A variant of raw observation approach for BDS/GNSS precise point positioning with fast integer ambiguity resolution

The Precise Point Positioning(PPP)technique uses a single Global Navigation Satellite System(GNSS)receiver to collect carrier-phase and code observations and perform centimeter-accuracy positioning together with the precise satellite orbit and clock corrections provided.According to the observations used,there are basically two approaches,namely,the ionosphere-free combination approach and the raw observation approach.The former eliminates the ionosphere effects in the observation domain,while the latter estimates the ionosphere effects using uncombined and undifferenced observations,i.e.,so-called raw observations.These traditional techniques do not fix carrier-phase ambiguities to integers,if the additional corrections of satellite hardware biases are not provided to the users.To derive the corrections of hardware biases in network side,the ionosphere-free combination operation is often used to obtain the ionosphere-free ambiguities from the L1 and L2 ones produced even with the raw observation approach in earlier studies.This contribution introduces a variant of the raw observation approach that does not use any ionosphere-free(or narrow-lane)combination operator to derive satellite hardware bias and compute PPP ambiguity float and fixed solution.The reparameterization and the manipulation of design matrix coefficients are described.A computational procedure is developed to derive the satellite hardware biases on WL and L1 directly.The PPP ambiguity-fixed solutions are obtained also directly with WL/L1 integer ambiguity resolutions.The proposed method is applied to process the data of a GNSS network covering a large part of China.We produce the satellite biases of BeiDou,GPS and Galileo.The results demonstrate that both accuracy and convergence are significantly improved with integer ambiguity resolution.The BeiDou contributions on accuracy and convergence are also assessed.It is disclosed for the first time that BeiDou only ambiguity-fixed solutions achieve the similar accuracy with that of GPS/Galileo combined,at least in China's Mainland.The numerical analysis demonstrates that the best solutions are achieved by GPS/Galileo/BeiDou solutions.The accuracy in horizontal components is better than 6 mm,and in the height component better than 20 mm(one sigma).The mean convergence time for reliable ambiguity-fixing is about 1.37 min with 0.12 min standard deviation among stations without using ionosphere corrections and the third frequency measurements.The contribution of BDS is numerically highlighted.

General Synthetic Iterative Scheme for Unsteady Rarefied Gas Flows

In rarefied gas flows,the spatial grid size could vary by several orders of magnitude in a single flow configuration(e.g.,inside the Knudsen layer it is at the order of mean free path of gas molecules,while in the bulk region it is at a much larger hydrodynamic scale).Therefore,efficient implicit numerical method is urgently needed for time-dependent problems.However,the integro-differential nature of gas kinetic equations poses a grand challenge,as the gain part of the collision operator is non-invertible.Hence an iterative solver is required in each time step,which usually takes a lot of iterations in the(near)continuum flow regime where the Knudsen number is small;worse still,the solution does not asymptotically preserve the fluid dynamic limit when the spatial cell size is not refined enough.Based on the general synthetic iteration scheme for steady-state solution of the Boltzmann equation,we propose two numerical schemes to push the multiscale simulation of unsteady rarefied gas flows to a new boundary,that is,the numerical solution not only converges within dozens of iterations in each time step,but also asymptotically preserves the Navier-Stokes-Fourier limit in the continuum flow regime,when the spatial grid is coarse,and the time step is large(e.g.,in simulating the extreme slow decay of two-dimensional Taylor vortex,the time step is even at the order of vortex decay time).The properties of fast convergence and asymptotic preserving of the proposed schemes are not only rigorously proven by the Fourier stability analysis for simplified gas kinetic models,but also demonstrated by several numerical examples for the gas kinetic models and the Boltzmann equation.

Augmented Lagrangian Methods for Convex Matrix Optimization Problems

In this paper,we provide some gentle introductions to the recent advance in augmented Lagrangian methods for solving large-scale convex matrix optimization problems(cMOP).Specifically,we reviewed two types of sufficient conditions for ensuring the quadratic growth conditions of a class of constrained convex matrix optimization problems regularized by nonsmooth spectral functions.Under a mild quadratic growth condition on the dual of cMOP,we further discussed the R-superlinear convergence of the Karush-Kuhn-Tucker(KKT)residuals of the sequence generated by the augmented Lagrangian methods(ALM)for solving convex matrix optimization problems.Implementation details of the ALM for solving core convex matrix optimization problems are also provided.

A nonparametric variable step-size subband adaptive filtering algorithm for acoustic echo cancellation

Acoustic echo cancellation is often applied in communication and video call system to reduce unnecessary echoes generated between speakers and microphones.In these systems,the speech input signal of the adaptive filter is often colored and unstable,which decays the convergence rate of the adaptive filter if the NLMS algorithm is used.In this paper,an improved nonparametric variable step-size subband(NPVSS-NSAF)algorithm is proposed to address the problem.The variable step-size is derived by minimizing the sum of the square Euclidean norm of the difference between the optimal weight vectors to be updated and the past estimated weight vectors.Then the parameters are eliminated by using the power of subband signal noise equal to the power of subband posteriori error.The performance of the proposed algorithm is simulated in the aspects of misalignment and return loss enhancement.Experiment results show a fast convergence rate and low misalignment of the proposed algorithm in system identification.