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We present an algorithm that computes exactly (optimally) the S-sparse (1≤S<D) maximum-L1-norm-projection principal component of a real-valued data matrix X ∈ ℝD×N that contains N samples of dimension D. For fixed sample support N, the optimal L1-sparse algorithm has linear complexity in data dimension, O(D). For fixed dimension D (thus, fixed sparsity S), the optimal L1-sparse algorithm has polynomial...
Fetal electrocardiogram (f-ECG) elicitation is crucial for cardiac health monitoring of fetus and neonates. This work presents five non-invasive f-ECG elicitation techniques using Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Sign-error Least Mean Square (SELMS), Affine Projection Algorithm (AP), and Recursive Least Square (RLS). The performance of the algorithms is compared on the...
Electricity market reconstituting relegates in power generation technologies and correspondence on the diminution of global greenhouse gas emission have paved the way for an increase in the use of generation. Holocene exploitation in the electric power and difficulty originate from construction and maintenance of large power plants have embossed a gravid deal of interest in distributed power generation...
Evaluating influential nodes is one of the fundamental problems in large scale networks having wide range of applications. The centrality metric, in particular betweenness centrality plays a significant role in ranking influential nodes. Existing exact algorithms for evaluating betweenness centrality metric consider the entire network and hence incur high computational cost. In this paper, we reduce...
Skyline is widely used in multi-objective decisionmaking, data visualization and other fields. With the rapid increasing of data volume, skyline of big data has also attracted more and more attention. However, skyline of big data has its own shortcomings. When the dimension increases, skyline results will be numerous, and we would like to select k points from the result sets. In this paper, we propose...
Prime factor fast algorithms are computationally efficient for various discrete transforms. However, they generally need an index mapping process to convert one-dimensional input sequence into a two-dimensional array, which results in a substantially computational overhead and an irregular computational structure. This letter attempts to minimize the computation overhead by a simple and general mapping...
Hubs are the data instances appearing frequently on the nearest neighbours lists. As the hubs of a high-dimensional dataset are close to the centres of clusters or sub-clusters, some of them are selected as the centres of clusters by hub based clustering algorithms. In the process of hub selection, these algorithms rank data instances in terms of their global hubness scores computed upon their nearest...
The uncertainties in design variables are unavoidable in the optimal design of electromagnetic devices, and there is an imperative demand to find a robust design, which is insensitive to the uncertainties and remains within the feasible region of constraints even perturbed by the uncertainties. In this paper, a gradient-based worst case optimization (G-WCO) algorithm is proposed in a limited uncertainty...
Association rule mining is a well researched method for discovering interesting relations between variables in large databases. The first phase of association rule mining is the discovery of frequent itemsets which is a critical step and plays important role in many data mining tasks. The existing algorithms for finding frequent itemsets suffer from many problems related to memory and computational...
K-dominant skyline query has been proposed as an important operator for multi-criteria decision making, data mining and so on, this technology can reduce the large result sets of skyline query in high dimensional space. In this paper, a new concept was firstly proposed: k-dominant Skyline cube, which consists of all the k-dominant skylines. Although existing algorithms can compute every k-dominant...
Looping operations impose a significant bottleneck to achieving better computational efficiency for embedded applications. To confront this problem in embedded computation either in the form of programmable processors or FSMD (Finite-State Machine with Datapath) architectures, the use of customized loop controllers has been suggested. In this paper, a thorough examination of zero-cycle overhead loop...
This paper studies the extension of the Modularity measure for categorical data clustering. It first shows the relational data presentation and establishes the relationship between the extended Modularity and the Relational Analysis criterion. Two extensions are presented in this work: the early integration and the intermediate integration approaches. The proposed Modularity measure introduces an...
A novel image compression scheme that takes advantages of side-match vector quantization (SMVQ) and search-order-coding (SOC) algorithm is proposed in this article. In the proposed scheme, the image to be compressed is firstly encoded into an index table by applying the traditional SMVQ compression technique. Then, the index table of image is further compressed based on the ordinary SOC algorithm...
In this paper, two effective techniques are proposed to improve the computation efficiency of the sub-code constrained algorithm for the determination of free distance for Turbo codes. Firstly, it is known that, the determination of the minimum output Hamming weight of the recursive systematic convolutional (RSC) code under some input constraints is the crucial problem in the constrained sub-code...
Adaptive bit allocation for orthogonal frequency division multiplexing (OFDM)-based systems are critical for avoiding performance degradation due to additional bit allocations when channel conditions are bad. In this paper, we present two novel bit-loading algorithms for OFDM-based systems with target bit rate and fixed energy constraints. The proposed algorithms converge to the same bit allocation...
A fast search procedure to reduce the search complexity required to locate the codevectors during the encoding process in multistage tree-structured vector quantization (MTVQ) is proposed. Quantization of line spectral frequency (LSF) parameters at different rates is used to provide experimental results, which are compared to the multipath sequential search or M-L search (MSS) and the multipath sequential...
Reverse skyline queries have been proved very useful in business location, environmental monitoring and some other applications. In this paper, we consider reverse skyline queries processing on data stream, which provides continuous, high-speed data elements. Specifically, we consider the latest objects in the sliding window. The challenge is that it is difficult to maintain a multidimensional index...
Considering the deficiency of direct-encoding raster data in computation efficiency and storage capacity, in this paper, a new run-length encoding data structure is proposed, which is suitable for algebraic operations and could optimize the algebraic operations that are based direct-encoding raster data. In this paper, the implementation of ldquointersectionrdquo operation based on this data structure...
Aiming at the low computation efficiency and storage insufficiency of direct-encoded raster data, a new data structure on the basis of run-length encoding has been proposed to optimize the algebraic operations that are based on direct encoded raster data. In this paper, employing this new data structure, the realization of ldquointersectionrdquo is introduced; moreover, all kinds of algebraic operations...
In a real world, it is often in a group setting that sensitive information has to be stored in databases of a server. Although personal information does not need to be stored in a server, the secret information shared by group members is likely to be stored there. The shared sensitive information requires more security and privacy protection. To our best knowledge, there is no paper which deals with...
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