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1

Recognition of lip prints using Fuzzy c-Means clustering

Wrobel K., Froelich W.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

67--73

EN

In this paper a new method for lip print recognition is proposed. The proposed approach is based on Fuzzy c-Means clustering of the characteristics features of lip prints. First, the Hough transform is applied for the recognition of the characteristic features within lip prints, then Fuzzy c-Means clustering is performed to cluster those features. The proposed algorithm applies the results of clustering to find an unknown image withing the collected repository of lip prints. Instead of comparing all pairs of individual characteristic features, the proposed algorithm uses the representatives of clusters for the comparison of images. The advantage of using the proposed method is its increased tolerance to the noise in data and thus the increased efficiency of the recognition. The effectiveness of presented method has been verified experimentally using real-world images. The results are satisfactory and suggest the possibility of using the method in forensic identification systems

2

Determination of the optimum locations of distribution centres based on vector quantization

Sawicki A., Wiktorzak A., Malenko N.

Logistyka

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2014

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nr 6

EN

Planning and optimization of distribution centers locations and routes between them and their recipients is one of the fundamental issues in logistics, which influences the operating costs of enterprises. This article describes the problem of how to optimize the selection of locations for the distribution centers. In the real environment, transportation of goods requires moving between several locations, delivering goods to different locations in a place and its surrounding area. In this case, the goods distribution problem can be split into optimizing the route selection between groups of locations and location grouping into individual clusters, in whose focal points a local warehouse and distribution center will be created. The authors of this article propose a novel approach which contains clustering techniques by means of vector quantization methods.

PL

Planowanie i optymalizacja lokalizacji centrów dystrybucyjnych oraz tras między nimi a odbiorcami jest jednym z podstawowych problemów w logistyce, który ma wpływ na koszty operacyjne przedsiębiorstw. W artykule tym opisano w jaki sposób można zoptymalizować wybór lokalizacji centrów dystrybucyjnych. W rzeczywistym środowisku, transport towarów wymaga przemieszczania się pomiędzy wieloma miastami oraz dostarczanie produktów do kilku miejsc w mieście i jego okolicach. W tym przypadku zagadnienie dystrybucji towarów można podzielić na optymalizację wyboru tras pomiędzy grupami lokalizacji i wyznaczenie lokalizacji poszczególnych punktów, w których zostaną utworzone lokalne centra magazynowo-dystrybucyjne. Autorzy artykułu proponują nowe podejście wyznaczania takich centrów w oparciu o metody grupowania za pomocą algorytmów kwantyzacji wektorowej.

3

Blind Estimation of Linear and Nonlinear Sparse Channels

Georgoulakis K.

Journal of Telecommunications and Information Technology

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2013

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nr 1

65-71

EN

This paper presents a Clustering Based Blind Channel Estimator for a special case of sparse channels - the zero pad channels. The proposed algorithm uses an unsupervised clustering technique for the estimation of data clusters. Clusters labelling is performed by a Hidden Markov Model of the observation sequence appropriately modified to exploit channel sparsity. The algorithm achieves a substantial complexity reduction compared to the fully evaluated technique. The proposed algorithm is used in conjunction with a Parallel Trellis Viterbi Algorithm for data detection and simulation results show that the overall scheme exhibits the reduced complexity benefits without performance reduction.