The Carpathian Orava Basin is a tectonic structure filled with Neogene and Quaternary deposits superimposed on the collision zone between the ALCAPA and European plates. Tectonic features of the south-eastern margin of the Orava Basin and the adjoining part of the fore-arc Central Carpathian Palaeogene Basin were studied. Field observations of mesoscopic structures, analyses of digital elevation models and geological maps, supplemented with electrical resistivity tomography surveys were performed. Particular attention was paid to joint network analysis. The NE-SW-trending Krowiarki and Hruštinka-Biela Orava sinistral fault zones were recognized as key tectonic features that influenced the Orava Basin development. They constitute the north-eastern part of a larger Mur-Mürz-Žilina fault system that separates the Western Carpathians from the Eastern Alps. The interaction of these sinistral fault zones with the older tectonic structures of the collision zone caused the initiation and further development of the Orava Basin as a strike-slip-related basin. The Krowiarki Fault Zone subdivides areas with a different deformation pattern within the sediments of the Central Carpathian Palaeogene Basin and was active at least from the time of cessation of its sedimentation in the early Miocene. Comparison of structural data with the recent tectonic stress field, earthquake focal mechanisms and GPS measurements allows us to conclude that the Krowiarki Fault Zone shows a stable general pattern of tectonic activity for more than the last 20 myr and is presently still active.
An automated method for crack identification and quantitative description of crack systems in concrete was developed in order to aid a service life assessment of concrete elements in structures. Flat polished specimens for crack analysis were impregnated with epoxy resin containing fluorescent dye. The examination of the crack system was performed in ultraviolet light using a stereomicroscope and an Image Pro Plus image analysis system on specimens cored out of several concrete structures. The laboratory tests were performed on cast specimens to establish correlations between water penetration and chloride diffusion and crack system parameters. The analysis of cracks in concrete cores taken from structures resulted in interesting conclusions based on the crack width distribution and crack localization with respect to steel reinforcement. The method was found very effective to support standard concrete diagnostics methods.
In industrial processes electrical motors are serviced after a specific number of hours, even if there is a need for service. This led to the development of early fault diagnostic methods. Paper presents early fault diagnostic method of synchronous motor. This method uses acoustic signals generated by synchronous motor. Plan of study of acoustic signal of synchronous motor was proposed. Two conditions of synchronous motor were analyzed. Studies were carried out for methods of data processing: Line Spectral Frequencies and K-Nearest Neighbor classifier with Minkowski distance. Condition monitoring is useful to protect electric motors and mining equipment. In the future, these studies can be used in other electrical devices.
This paper proposes a new approach to the processing and analysis of medical images. We introduce the term and methodology of medical data understanding, as a new step in the way of starting from image processing, and followed by analysis and classification (recognition). The general view of the situation of the new technology of machine perception and image understanding in the context of the more well known and classic techniques of image processing, analysis, segmentation and classification is shown below
Purpose: to demonstrate the possibility of finding features reliable for more precise distinguishing between normal and abnormal Pattern Electroretinogram (PERG) recordings, in Continuous Wavelet Transform (CWT) coefficients domain. To determine characteristic features of the PERG and Pattern Visual Evoked Potential (PVEP) waveforms important in the task of precise classification and assessment of these recordings. Material and methods: 60 normal PERG waveforms and 60 PVEPs as well as 47 PERGs and 27 PVEPs obtained in some retinal and optic nerve diseases were studied in the two age groups (<= 50 years, > 50 years). All these signals were recorded in accordance with the guidelines of ISCEV in the Laboratory of Electrophysiology of the Retina and Visual Pathway and Static Perimetry, at the Department and Clinic of Ophthalmology of the Pomeranian Medical University. Continuous Wavelet Transform (CWT) was used for the time-frequency analysis and modelling of the PERG signal. Discriminant analysis and logistic regression were performed in statistical analysis of the PERG and PVEP signals. Obtained mathematical models were optimized using Fisher F(n1; n2) test. For preliminary evaluation of the obtained classification methods and algorithms in clinical practice, 22 PERGs and 55 PVEPs were chosen with respect to especially difficult discrimination problems (borderline recordings). Results: comparison between the method using CWT and standard time-domain based analysis showed that determining the maxima and minima of the PERG waves was achieved with better accuracy. This improvement was especially evident in waveforms with unclear peaks as well as in noisy signals. Predictive, quantitative models for PERGs and PVEPs binary classification were obtained based on characteristic features of the waveform morphology. Simple calculations algorithms for clinical applications were elaborated. They proved effective in distinguishing between normal and abnormal recordings. Conclusions: CWT based method is efficient in more precise assessment of the latencies of the PERG waveforms, improving separation between normal and abnormal waveforms. Filtering of the PERG signal may be optimized based on the results of the CWT analysis. Classification of the PERG and PVEP waveforms based on statistical methods is useful in preliminary interpretation of the recordings as well as in supporting more accurate assessment of clinical data.
Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855~2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications.