The Experts below are selected from a list of 12330 Experts worldwide ranked by ideXlab platform
J K Tugnait - One of the best experts on this subject based on the ideXlab platform.
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blind detection of asynchronous cdma signals in multipath channels using code constrained inverse Filter Criterion
2001Co-Authors: J K TugnaitAbstract:A code-constrained inverse Filter Criterion based approach is presented for blind detection of asynchronous short-code direct sequence code division multiple access (DS-CDMA) signals in multipath channels. Only the spreading code of the desired user is assumed to be known; its transmission delay may be unknown. We focus on maximization of the normalized fourth cumulant of inverse Filtered (equalized) data with respect to (w.r.t.) the equalizer coefficients subject to the equalizer lying in a subspace associated with the desired user's code sequence. Constrained maximization leads to extraction of the desired user's signal, whereas unconstrained maximization leads to the extraction of any one of the active users. Illustrative simulation examples are provided.
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further results on blind asynchronous cdma receivers using code constrained inverse Filter Criterion
2001Co-Authors: J K TugnaitAbstract:A code-constrained inverse Filter Criterion (CC-IFC) based approach was presented Tugnait and Li (see Proc. IEEE 2000 ICASSP, p.V-246-64, Istanbul, Turkey, June 2000) for blind-detection of asynchronous short-code DS-CDMA (direct sequence code division multiple access) signals in multipath channels. Only the spreading code of the desired user is assumed to be known; its transmission delay may be unknown. The equalizer was determined by maximizing the magnitude of the normalized fourth cumulant of inverse Filtered (equalized) data with respect to the equalizer coefficients subject to the fact that the equalizer lies in a subspace associated with the desired user's code sequence. In this paper we analyze the identifiability properties of the approach of Tugnait and Li. Global maxima and some of the local maxima of the cost function are investigated. These aspects were not discussed by Tugnait and Li. More extensive simulation comparisons with existing approaches are also provided.
Feda Seblany - One of the best experts on this subject based on the ideXlab platform.
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Filter Criterion for granular soils based on the constriction size distribution
2018Co-Authors: Feda SeblanyAbstract:The granular discontinuities in hydraulic structures or in their foundation constitute a major source of instabilities causing erosion phenomena, process by which finer soil particles are transported through the voids between coarser particles, under seepage flow. In the long term, the microstructure of the soil will change and the excessive migration become prejudicial to the stability of the structures and may also induce their failure. The safety of earth structures is mainly dependent on the reliability of their Filter performance, i.e. the ability of the Filter placed inside the structure during construction or outside during repair, to retain fine particles. Indeed, the void space of a granular Filter is divided into larger volumes, called pores, connected together by throats or constrictions. Recent researches showed that the distribution of throats (Constriction Size Distribution or CSD) between pores plays a key role to understand the filtration properties of a granular soil. This research is devoted to investigate the constriction sizes and their impact on the mechanisms of filtration in granular spherical materials. To achieve this objective, two approaches were followed in this work: numerical and analytical approaches. In the case of spherical materials, the Discrete Element Method (DEM) can help to compute the CSD using the Delaunay tessellation method. However, a more realistic CSD can be obtained by merging adjacent Delaunay cells based on the concept of the overlap of their maximal inscribed void spheres. Following this consideration and by extending the previously developed analytical models of CSD, a revised model is proposed to quickly obtain the CSD. The DEM data generated are then used to explore the potential of transport of fine particles through a Filter of a given thickness by means of numerical filtration tests. A correlation has been found between the CSD and the possibility of migration of fine grains. Accordingly, an analytical formula has been proposed to calculate the controlling constriction size of a Filter material. This characteristic size, which takes into account the particle size distribution (PSD) and the density of the material, has been used to reformulate a constriction-based Criterion in a more physical manner. The proposed Filter design Criterion is verified based on experimental data from past studies and a good agreement has been found.
Emrah Hancer - One of the best experts on this subject based on the ideXlab platform.
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a multi objective differential evolution feature selection approach with a combined Filter Criterion
2018Co-Authors: Emrah HancerAbstract:This paper proposes an improved Filter evaluation Criterion which uses the components of standard mutual information and fuzzy mutual information criteria by combining them in a simple and practical way. Then, a new Filter approach is developed by integrating this Criterion in multi-objective DE framework in order to enhance the performance in classification tasks. To verify the effectiveness of the developed Filter approach, it is examined with single objective and multi-objective DE approaches based on both the standard mutual information and the fuzzy mutual information on a variety of benchmark datasets. The results indicate that the multi-objective DE Filter approach based on the proposed Filter Criterion is able to achieve better classification accuracy and smaller feature subsets than other approaches based on existing criteria.
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a differential evolution based feature selection approach using an improved Filter Criterion
2017Co-Authors: Emrah Hancer, Bing Xue, Mengjie ZhangAbstract:In Filter feature selection, mutual information approaches have recently gained a high popularity among researchers. In these approaches, mutual information is commonly used to measure two components: the mutual relevance between each feature and the class labels, and the mutual redundancy between each pair of features. Despite their popularity, it has been pointed in the literature that such feature selection approaches may not fairly estimate the redundancy in high dimensional problems. To alleviate this problem, this paper proposes a new Criterion, which uses the concepts of ReliefF instead of the mutual redundancy. Using the proposed Criterion, a new differential evolution based Filter feature selection approach is developed. The performance comparisons and analysis are conducted by comparing it with the most well-known mutual information feature selection (MIFS) Criterion based on maximum-relevance and minimum-redundancy on the differential evolution framework. The results show that performing feature selection using the proposed Criterion can generally achieve better classification performance and smaller feature subset size.
David J Williams - One of the best experts on this subject based on the ideXlab platform.
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a new simple model for the determination of the pore constriction size distribution
2012Co-Authors: Alexander Scheuermann, David J WilliamsAbstract:Suffusion is a process in which particles are removed from pores and transported through the pore constrictions of a solid skeleton. The quantitative analysis of the suffusion process at the particle scale is difficult, because it is not necessarily clear which part of the particles forms the solid skeleton, and which part can be removed. Furthermore, there is the enormous number and various shapes of particles. Therefore, existing computational programs based on DEM often take days to create and to compute a pore model for this problem. This paper presents a new approach based on simple sphere packing with three acceptable assumptions, namely the occurrence of only spherical particles, the assumption of a minimum number of contacts with neighbouring particles, and the substitution of the fraction of small particles of a soil with a non-spherical body. By introduction of the second assumption the calculation speed is significantly improved. Furthermore, three filling types are introduced in the model, which are random, descending and layer-wise. The new approach allows the investigation of the relationship between porosity and probability of suffusion. The contribution will introduce a further understanding on primary fabrics size and point out difficulties in application of Filter Criterion for suffusion assessment according to the German guideline [MSD, 2005].
Mengjie Zhang - One of the best experts on this subject based on the ideXlab platform.
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a differential evolution based feature selection approach using an improved Filter Criterion
2017Co-Authors: Emrah Hancer, Bing Xue, Mengjie ZhangAbstract:In Filter feature selection, mutual information approaches have recently gained a high popularity among researchers. In these approaches, mutual information is commonly used to measure two components: the mutual relevance between each feature and the class labels, and the mutual redundancy between each pair of features. Despite their popularity, it has been pointed in the literature that such feature selection approaches may not fairly estimate the redundancy in high dimensional problems. To alleviate this problem, this paper proposes a new Criterion, which uses the concepts of ReliefF instead of the mutual redundancy. Using the proposed Criterion, a new differential evolution based Filter feature selection approach is developed. The performance comparisons and analysis are conducted by comparing it with the most well-known mutual information feature selection (MIFS) Criterion based on maximum-relevance and minimum-redundancy on the differential evolution framework. The results show that performing feature selection using the proposed Criterion can generally achieve better classification performance and smaller feature subset size.