MODIFICATION OF 5083 ALUMINUM ALLOY WITH GRAPHENE VIA FRICTION STIR PROCESSING

Graphene-modified layer is obtained on 5083 aluminum alloy sheet material via friction stir processing. A special groove is cut in the aluminum plate and filled with graphene. The processing is carried out using an innovative technology with an appropriate tool. The temperature at a chosen point in the heat affected zone is measured in real-time by a remote-control system. Test specimens were prepared from the processed plates and metallographic analysis was carried out. The microhardness of the modified layer is measured perpendicular to the direction of processing and in depth. An increase in microhardness relative to that of the base material is found.

A software system for pathological voice acoustic analysis

International audienceA software system for pathological voice analysis using only the resources of a personal computer with a sound card is proposed. The system is written on the basis of specific methods and algorithms for pathological voice analysis and allows evaluation of: 1) Pitch period (To); 2) Degree of unvoiceness; 3) Pitch perturbation and amplitude perturbation quotients; 4) Dissimilarity of surfaces of the pitch pulses; 5) Ratio aperiodic/periodic components in cepstra; 6) Ratio {energy in the cepstral pitch pulse}-to-{total cepstral energy}; 7) Harmonics-to-noise ratio; 8) Degree of hoarseness; 9) Ratio low-to-high frequency energies; 10) Glottal Closing Quotient. The voices of 400 persons were analyzed - 100 (50 females/50 males) normal speakers and 300 (100 females/200 males) patients. The statistical analysis shows very significant changes in PPQ, DH, DPP, DUV, APR, HNR and PECM, and significant changes in APQ and CQ

PC-Based System for Robust Speaker Recognition

A PC-based system for robust speaker recognition is proposed. It includes three one level recognition methods and a two level classifier. New procedures for voice analysis are proposed: a) Robust periodicity/ aperiodicity separation by neural networks; b) Robust pitch period detection; c) Analysis of the temporal, spectral and cepstral speech characteristics. Several pattern recognition methods are implemented, because they allow analysis of different static and dynamic characteristics of the speech parameters:1) Prototype distribution maps (PDM). The PDM is used because: a) weight vectors of PDM\u27s neurons try to imitate the probability density function - pdf (whatever complex the form of the pdf is) and less significant PDM\u27s neurons are eliminated by filtering.2) AR-vector models (ARVM). The ARVM are used because they model the evolution of speech parameters.3) The covariance approach combined with the arithmetic-harmonic sphericity measure, because this method performs effective speaker recognition over noisy signals.4) Two level classifier, incorporating the discriminant capabilities and classification power of the multilayer perceptron (MLP) with the pdf\u27s estimating, statistical modeling and compressing power of the PDM. The first level consists of several PDMs and the second - of MLP networks.The experiments show that the proposed system is an efficient and useful tool for speaker recognition over clean and noisy signals

Robust hybrid pitch detector for pathologic voice analysis

International audienceA hybrid speech period (To) detector characterizided by parallel analyses of three speech signals in temporal spectral and cepstral domains and preprocessing for periodic/aperiodic (unvoiced) separation (PAS) is proposed. The preprocessing is realized by analysis in these three domains and PAS by multi layer Perceptron neural network.Two phonations of the wowel "a" of 40 speakers and 62 patients were analyzed. For the proposed detector errors were significantly minimized

Comparing community structure identification

We compare recent approaches to community structure identification in terms of sensitivity and computational cost. The recently proposed modularity measure is revisited and the performance of the methods as applied to ad hoc networks with known community structure, is compared. We find that the most accurate methods tend to be more computationally expensive, and that both aspects need to be considered when choosing a method for practical purposes. The work is intended as an introduction as well as a proposal for a standard benchmark test of community detection methods.Comment: 10 pages, 3 figures, 1 table. v2: condensed, updated version as appears in JSTA

The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning

The Minkowski weighted K-means (MWK-means) is a recently developed clustering algorithm capable of computing feature weights. The cluster-specific weights in MWK-means follow the intuitive idea that a feature with low variance should have a greater weight than a feature with high variance. The final clustering found by this algorithm depends on the selection of the Minkowski distance exponent. This paper explores the possibility of using the central Minkowski partition in the ensemble of all Minkowski partitions for selecting an optimal value of the Minkowski exponent. The central Minkowski partition appears to be also a good consensus partition. Furthermore, we discovered some striking correlation results between the Minkowski profile, defined as a mapping of the Minkowski exponent values into the average similarity values of the optimal Minkowski partitions, and the Adjusted Rand Index vectors resulting from the comparison of the obtained partitions to the ground truth. Our findings were confirmed by a series of computational experiments involving synthetic Gaussian clusters and real-world data

Unsupervised morphological segmentation of tissue compartments in histopathological images

Algorithmic segmentation of histologically relevant regions of tissues in digitized histopathological images is a critical step towards computer-assisted diagnosis and analysis. For example, automatic identification of epithelial and stromal tissues in images is important for spatial localisation and guidance in the analysis and characterisation of tumour micro-environment. Current segmentation approaches are based on supervised methods, which require extensive training data from high quality, manually annotated images. This is often difficult and costly to obtain. This paper presents an alternative data-independent framework based on unsupervised segmentation of oropharyngeal cancer tissue micro-arrays (TMAs). An automated segmentation algorithm based on mathematical morphology is first applied to light microscopy images stained with haematoxylin and eosin. This partitions the image into multiple binary ‘virtual-cells’, each enclosing a potential ‘nucleus’ (dark basins in the haematoxylin absorbance image). Colour and morphology measurements obtained from these virtual-cells as well as their enclosed nuclei are input into an advanced unsupervised learning model for the identification of epithelium and stromal tissues. Here we exploit two Consensus Clustering (CC) algorithms for the unsupervised recognition of tissue compartments, that consider the consensual opinion of a group of individual clustering algorithms. Unlike most unsupervised segmentation analyses, which depend on a single clustering method, the CC learning models allow for more robust and stable detection of tissue regions. The proposed framework performance has been evaluated on fifty-five hand-annotated tissue images of oropharyngeal tissues. Qualitative and quantitative results of the proposed segmentation algorithm compare favourably with eight popular tissue segmentation strategies. Furthermore, the unsupervised results obtained here outperform those obtained with individual clustering algorithms

PC-Based System for Robust Speaker Recognition

A PC-based system for robust speaker recognition is proposed. It includes three one level recognition methods and a two level classifier. New procedures for voice analysis are proposed: a) Robust periodicity/ aperiodicity separation by neural networks; b) Robust pitch period detection; c) Analysis of the temporal, spectral and cepstral speech characteristics. Several pattern recognition methods are implemented, because they allow analysis of different static and dynamic characteristics of the speech parameters:1) Prototype distribution maps (PDM). The PDM is used because: a) weight vectors of PDM\u27s neurons try to imitate the probability density function - pdf (whatever complex the form of the pdf is) and less significant PDM\u27s neurons are eliminated by filtering.2) AR-vector models (ARVM). The ARVM are used because they model the evolution of speech parameters.3) The covariance approach combined with the arithmetic-harmonic sphericity measure, because this method performs effective speaker recognition over noisy signals.4) Two level classifier, incorporating the discriminant capabilities and classification power of the multilayer perceptron (MLP) with the pdf\u27s estimating, statistical modeling and compressing power of the PDM. The first level consists of several PDMs and the second - of MLP networks.The experiments show that the proposed system is an efficient and useful tool for speaker recognition over clean and noisy signals