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RFID technology is one of four basic technologies in the Internet of Things. In order to realize its intensive and large-scale uses, the key point is to increase the identification speed of tags. The conventional algorithm only uses integral Q which decreases the efficiency dramatically. In order to decrease the convergence time and rise the slot efficiency, this article proposes a novel anti-collision...
Nonnegative matrix factorization (NMF) is a powerful technique for dimensionality reduction. Conventional NMF algorithms usually keep the matrices W and H nonnegative while iterating. However, to get the NMF of a matrix, it's unnecessary to force the temporary solutions in iterations nonnegative. In this paper, we propose a two-staged approach for NMF. At the relaxation stage, the nonnegative constraint...
This paper proposes a novel distance matrix reconstruction (DMR) algorithm from incomplete and inaccurate distance information for nodes localization in wireless sensor networks. By using DMR algorithm, we address an improvement for self-organizing isometric embedding (SIEMAP) based localization in which DMR is used instead of the original distance matrix. The core of DMR is finding an Euclidean distance...
A method is proposed for publishing relational data into extensional markup language (XML). First, the characteristics of relational schemas represented by Entity-Relation (E-R) diagrams and XML document type definitions (DTDs) are analyzed. Secondly, the corresponding mapping rules are proposed. At last an algorithm based on edge tables is presented, which has two key points: one is that edge table...
The new locally preserving projections algorithm is proposed in this paper which is based on Bayesian criteria and adapted improved iterative self-organize data analysis. The experiment shows that the new algorithm can put forward the optimum number of dimensions and be more available than principle component analysis. That is because it takes into account the relation the number of between dimensions...
We develop an algorithm RSFA to perform nonlinear blind source separation with temporal constraints. The algorithm is based on slow feature analysis using random Fourier features for shift invariant kernels, followed by a selection procedure to obtain the sought-after signals. This method not only obtains remarkable results in a short computing time, but also excellently handles situations where there...
The two fundamental problems in machine learning (ML) are statistical analysis and algorithm design. The former tells us the principles of the mathematical models that we establish from the observation data. The latter defines the conditions on which implementation of data models and data sets rely. A newly discovered challenge to ML is the Rashomon effect, which means that data are possibly generated...
Group communication significantly influences the performance of data parallel applications. Nevertheless, the important factor that influences the efficiency of group communication is often neglected: a larger communication idle time may occur when there is node contention and difference among message lengths during one particular communication step. Group communication scheduling has attracted more...
Many important scientific kernels compute solutions using finite difference techniques, and the most time consuming part of them is the iterative method, such as Gauss-Seidel or SOR. To improve performance, iterative method can exploit parallelism, intra-iteration data reuse, and inter-iteration data reuse. This paper describes a new parallel Gauss-Seidel method using iteration space alternate tiling...
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