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EN
In microbiology, computer methods are applied in the analysis and recognition of laboratory-acquired microscopic images concerning, for example, bacterial cells or other microorganisms. Proper recognition of the species and genera of bacteria is a key stage in the microbiological diagnostics process, because it allows a quick start of the appropriate therapy. The original method proposed in the paper concerns the automatic recognition of selected species and genera of bacteria presented in digital images. The classification was made on the basis of the analysis of the physical characteristics of bacterial cells using the product of classifier confidence weights. The end result of the classification process is the classification list, sorted in descending order according to the weights of the classifiers. In addition to the correct classification, a list of other possible results of the analysis is obtained. The method thus allows not only the classification, but also an analysis of the confidence level of the selection made. The proposed method can be used to recognize not only bacterial cells, but also other microorganisms, for example, fungi that exhibit similar morphological characteristics. In addition, the use of the method does not require the application of specialized computer equipment, which widens the scope of applications regardless of the laboratory IT infrastructure, not only in microbiological diagnostics, but also in other diagnostic laboratories.
EN
Epilepsy is a widely spread neurological disorder caused due to the abnormal excessive neural activity which can be diagnosed by inspecting the electroencephalography (EEG) signals visually. The manual inspection of EEG signals is subjected to human error and is a tedious process. Further, an accurate diagnosis of generalized and focal epileptic seizures from normal EEG signals is vital for the supervision of pertinent treatment, life advancement of the subjects, and reduction in cost for the subjects. Hence the development of automatic detection of generalized and focal epileptic seizures from normal EEG signals is important. An approach based on tunable-Q wavelet transform (TQWT), entropies, Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) is proposed in this work for detection of epileptic seizures and its types. Two EEG databases namely, Karunya Institute of Technology and Sciences (KITS) EEG database and Temple University Hospital (TUH) database consisting of normal, generalized and focal EEG signals is used in this work to analyze the performance of the proposed approach. Initially, the EEG signals are decomposed into sub-bands using TQWT and the non-linear features like log energy entropy, Shannon entropy and Stein's unbiased risk estimate (SURE) entropy is computed from each sub-band. The informative features from the computed feature vectors are selected using PSO and fed into ANN for the classification of EEG signals. The proposed algorithm for KITS database achieved a maximum accuracy of 100% for four experimental cases namely, (i) normal-focal, (ii) normal-generalised, (iii) normal-focal + generalised and (iv) normal-focal-generalised. The TUH database achieved an accuracy of 95.1%, 97.4%, 96.2% and 88.8% for the four experimental cases. The proposed approach is promising and able to discriminate the epileptic seizure types with satisfactory classification performance.
EN
The article considers the problem of classification based on the given examples of classes. As a feature vector, a complete characteristic of object is assumed. The peculiarity of the problem being solved is that the number of examples of the class may be less than the dimension of the feature vector, and also most of the coordinates of the feature vector can be correlated. As a consequence, the feature covariance matrix calculated for the cluster of examples may be singular or ill-conditioned. This disenable a direct use of metrics based on this covariance matrix. The article presents a regularization method involving the additional use of statistical properties of the environment.
PL
W artykule rozpatrywany jest problem klasyfikacji na podstawie wskazanych przykładów klas. Jako wektor cech przyjmuje się kompletną charakterystykę obiektów. Osobliwość rozwiązywanego zadania wynika z tego, że liczba przykładów klasy może być mniejsza od wymiaru wektora cech, a także wektor cech może zawierać współrzędne skorelowane. W konsekwencji macierz kowariancji cech obliczana dla klastra przykładów może być osobliwa albo źle uwarunkowana. Uniemożliwia to bezpośrednie stosowanie metryk bazujących na tej macierzy kowariancji. W artykule została przedstawiona metoda regularyzacji polegająca na dodatkowym wykorzystaniu statystycznych właściwości środowiska.
4
Content available remote Methods of picture segmentation in recognition digital satellite images
EN
In the article for the recognition of digital satellite images, the method of segmentation of views by thresholding was chosen. Two algorithms were used: Laplasian of Gaussian and Canny. The Laplasian of Gaussian algorithm with Gauss low-pass filter smoothes the edges and Laplace's high-pass filter sharpens the image. Based on the calculations made, clear boundaries between individual areas were obtained. The presented application in the MATLAB environment effectively detects forest areas and lakes in the satellite images.
PL
W artykule do rozpoznawania cyfrowych zdjęć satelitarnych wybrano metodę segmentacji zobrazowań przez progowanie. Zastosowano dwa algorytmy: Laplasian of Gaussian i Canny’ego. Algorytm Laplasian of Gaussian z filtrem dolnoprzepustowym Gaussa wygładza krawędzie a filtr górnoprzepustowy Laplace’a wyostrza obraz. Na podstawie przeprowadzonych obliczeń otrzymano wyraźne granice między poszczególnymi obszarami. Przedstawiona aplikacja w środowisku MATLAB skutecznie wykrywa obszary leśne i jeziora na zdjęciach satelitarnych.
EN
This paper presents a computer-based method for recognizing digital images of bacterial cells. It covers automatic recognition of twenty genera and species of bacteria chosen by the author whose original contribution to the work consisted in the decision to conduct the process of recognizing bacteria using the simultaneous analysis of the following physical features of bacterial cells: color, size, shape, number of clusters, cluster shape, as well as density and distribution of the cells. The proposed method may be also used to recognize the microorganisms other than bacteria. In addition, it does not require the use of any specialized equipment. The lack of demand for high infrastructural standards and complementarity with the hardware and software widens the scope of the method’s application in diagnostics, including microbiological diagnostics. The proposed method may be used to identify new genera and species of bacteria, but also other microorganisms that exhibit similar morphological characteristics.
EN
The work discusses the construction of a measurement system for determining the relationship between EMG signals and hand grip movements. The relationship is necessary for the synthesis of control of the hand bioprosthesis. The measurement system is based on commercial Myo armband with EMG signals sensors and sensory glove with bend and pressure sensors. There are presented possibilites, advantages and disadvantages of such approach.
EN
A variety of algorithms allows gesture recognition in video sequences. Alleviating the need for interpreters is of interest to hearing impaired people, since it allows a great degree of self-sufficiency in communicating their intent to the non-sign language speakers without the need for interpreters. State-of-the-art in currently used algorithms in this domain is capable of either real-time recognition of sign language in low resolution videos or non-real-time recognition in high-resolution videos. This paper proposes a novel approach to real-time recognition of fingerspelling alphabet letters of American Sign Language (ASL) in ultra-high-resolution (UHD) video sequences. The proposed approach is based on adaptive Laplacian of Gaussian (LoG) filtering with local extrema detection using Features from Accelerated Segment Test (FAST) algorithm classified by a Convolutional Neural Network (CNN). The recognition rate of our algorithm was verified on real-life data.
8
Content available Discrete Fourier transform and permutations
EN
It is well known that the magnitudes of the coefficients of the discrete Fourier transform (DFT) are invariant under certain operations on the input data. In this paper, the effects of rearranging the elements of an input data on its DFT are studied. In the one-dimensional case, the effects of permuting the elements of a finite sequence of length N on its Discrete Fourier transform (DFT) coefficients are investigated. The permutations that leave the unordered collection of Fourier coefficients and their magnitudes invariant are completely characterized. Conditions under which two different permutations give the same DFT coefficient magnitudes are given. The characterizations are based on the automorphism group of the additive group ZN of integers modulo N and the group of translations of ZN. As an application of the results presented, a generalization of the theorem characterizing all permutations that commute with the discrete Fourier transform is given. Numerical examples illustrate the obtained results. Possible generalizations and open problems are discussed. In higher dimensions, results on the effects of certain geometric transformations of an input data array on its DFT are given and illustrated with an example.
EN
The focus of the present research endeavour is to propose a single channel Electromyogram (EMG) signal driven continuous terrain identification method utilizing a simple classifier. An iterative feature selection algorithm has also been proposed to provide effective information to the classifiers. The proposed method has been validated on EMG signal of fifteen subjects and ten subjects for three and five daily life terrains respectively. Feature selection algorithm has significantly improved the identification accuracy (ANOVA, p-value < 0.05) as compared to principal component analysis (PCA) technique. The average identification accuracies obtained by Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Neural Network (NN) classifiers are 96.83 ± 0.28%, 97.45 ± 0.32% and 97.61 ± 0.22% respectively. Subject wise performance (five subjects) of individually trained classifiers shows no significant degradation and difference in performance among the subjects even for the untrained data (ANOVA, p-value > 0.05). The study has been extended to dual muscle approach for terrain identification. However, the proposed algorithm has shown similar performance even with the single muscle approach (ANOVA, p-value > 0.05). The outcome of the proposed continuous terrain identifi-cation method shows a pronounced potential in efficient lower limb prosthesis control.
10
EN
Fibers are raw materials used for manufacturing yarns and fabrics, and their properties are closely related to the performances of their derivatives. It is indispensable to implement fiber identification in analyzing textile raw materials. In this paper, seven common fibers, including cotton, tencel, wool, cashmere, polyethylene terephthalate (PET), polylactic acid (PLA), and polypropylene (PP), were prepared. After analyzing the merits and demerits of the current methods used to identify fibers, near-infrared (NIR) spectroscopy was used owing to its significant superiorities, the foremost of which is it can capture the tiny information differences in chemical compositions and morphological features to display the characteristic spectral curve of each fiber. First, the fibers’ spectra were collected, and then, the relationships between the vibrations of characteristic chemical groups and the corresponding wavelengths were researched to organize a spectral information library that would be beneficial to achieve quick identification and classification. Finally, to achieve intelligent detection, pattern recognition approaches, including principal component analysis (PCA) (used to extract information of interest), soft independent modeling of class analogy (SIMCA), and linear discrimination analysis (LDA) (defined using two classifiers), assisted in accomplishing fiber identification. The experimental results – obtained by combining PCA and SIMCA – displayed that five of seven target fibers, namely, cotton, tencel, PP, PLA, and PET, were distributed with 100% recognition rate and 100% rejection rate, but wool and cashmere fibers yielded confusing results and led to relatively low recognition rate because of the high proportion of similarities between these two fibers. Therefore, the six spectral bands of interest unique to wool and cashmere fibers were selected, and the absorbance intensities were imported into the classifier LDA, where wool and cashmere were group-distributed in two different regions with 100% recognition rate. Consequently, the seven target fibers were accurately and quickly distinguished by the NIR method to guide the fiber identification of textile materials.
EN
Unconventional oil and gas reservoirs from the lower Palaeozoic basin at the western slope of the East European Craton were taken into account in this study. The aim was to supply and improve standard well logs interpretation based on machine learning methods, especially ANNs. ANNs were used on standard well logging data, e.g. P-wave velocity, density, resistivity, neutron porosity, radioactivity and photoelectric factor. During the calculations, information about lithology or stratigraphy was not taken into account. We apply different methods of classification: cluster analysis, support vector machine and artificial neural network—Kohonen algorithm. We compare the results and analyse obtained electrofacies. Machine learning method–support vector machine SVM was used for classification. For the same data set, SVM algorithm application results were compared to the results of the Kohonen algorithm. The results were very similar. We obtained very good agreement of results. Kohonen algorithm (ANN) was used for pattern recognition and identification of electrofacies. Kohonen algorithm was also used for geological interpretation of well logs data. As a result of Kohonen algorithm application, groups corresponding to the gas-bearing intervals were found. Analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in gas-saturated beds is present. It is concluded that ANN appeared to be a useful and quick tool for preliminary classification of members and gas-saturated identification.
12
Content available remote Micro-ontology building – the main variants of the oto method
EN
This article describes the main properties of an iterative method of simple knowledge structure creation. The method is based on an inductive learning scheme. The knowledge structure is built automatically and takes the form of a simplified ontology. Knowledge transformation plays a key role in the process of creating the knowledge structure. In order to regular describe many kinds of these transformations the article provides the relevant theoretical background. The task of finding the proper ontology (knowledge structure) is extremely complex. This paper highlights the necessity to investigate efficient search methods; additionally, the work draws attention to the advantages that arise from building the knowledge structure at the minimal possible size. The paper points to possible areas of the method application, especially in connection with problems of the automatic understanding of images and websites.
PL
Artykuł przedstawia podstawowe własności iteracyjnego procesu (nazywanego w pracy OTO) tworzenia struktury wiedzy. Budowana automatycznie struktura przyjmuje formę ontologii. Artykuł prezentuje podstawy teoretyczne opisywanego procesu. Kluczową rolę odgrywa w nim zestaw specyficznych algorytmów transformacji wiedzy. Opisywany proces jest ekstremalnie złożony obliczeniowo. Artykuł podkreśla konieczność opracowania bardziej efektywnych algorytmów numerycznych, uwypuklając jednocześnie korzyści z budowy ontologii w minimalnej, możliwej formie (mikro-ontologia). Proces budowy wiedzy przybliżono z pomocą odpowiednio dobranego przykładu. W pracy wskazano na możliwe obszary zastosowań metody, w szczególności dotyczące automatycznego rozumienia obrazów oraz rozumienia stron WWW.
13
PL
Rozpatrywany jest problem wykrywania anomalii na podstawie zarejestrowanych obserwacji zachowania systemu. Problem jest sformułowany jako zadanie rozpoznawania wzorców zachowania normalnego i zachowania nietypowego. Obydwa wzorce są określane przez wskazanie odpowiednich przykładów. Osobliwość rozwiązywanego zadania wynika z faktu, że zwykle liczebność przykładów jest dużo mniejsza od wymiaru wektora obserwacji. W artykule zostały przedstawione dwie metody detekcji anomalii bazujące na wyznaczaniu rzutów obserwacji na podprzestrzenie wzorców. Wyróżnikiem pierwszej metody jest wykorzystywanie odległości wektora obserwacji od podprzestrzeni wzorców. Druga metoda polega na przeniesieniu zadania rozpoznawania wzorców do podprzestrzeni wzorców.
EN
The paper considers the issue of anomalies detection based on registered observations of a system behavior. The problem is formulated as recognition of normal and anomalous behavior patterns. Both types of patterns are identified by indication of appropriate examples. A peculiarity of this task is that usually the number of examples is far lower than the dimension of vectors describing the observations. Two methods to solve this task have been presented in the paper, based on projecting the observations on the subspace of examples. The first method is based on a distance of the observation vector from the subspace of examples. The second method is based on transferring the pattern recognition problem to the subspace of examples.
PL
W artykule przedstawiono informacje dotyczące systemu umożliwiającego rozpoznawanie ruchu palców na podstawie dwóch sygnałów elektromiograficznych (EMG). W chwili obecnej system pozwala rozróżnić czy wykonany był ruch palcem wskazującym, środkowym, serdecznym lub małym. W dalszej części artykułu prezentowane są wyniki działania systemu oraz możliwe kierunki rozwoju.
EN
This paper discusses the system that allows to recognition of fingers movement based on a electromyogram (EMG). At the moment it can distinguish between the movement of pinky finger, ring finger, middle finger and index finger. The article presents the results of research on the effectiveness of the system as well as further development possibilities.
EN
Nowadays, most techniques for evaluating rough metal surfaces are based on tactile or confocal measurement procedures. However, these technologies have disadvantages in respect to measuring speeds, resistance to vibration, impact and dust. In this paper we present a novel surface measurement approach, which uses the scattering light technology. Our approach enhances the state-of-the-art scattering light-based surface measurement methodology in both the detector setup and evaluation of the raw intensity values acquired by the scattered light device. The main goal in optimizing the measurement setup is to capture scattering parameters for rough surfaces in a range greater than 10 μm based on an enlarged detector array. Regarding the evaluation, we propose a pattern recognition approach which maps the reflection intensity I back to material structures and the tenpoint mean roughness Rz , the golden standard in tactile roughness characterization. Based on this approach, we are able to classify rough surface deviations like stripes using a simple but robust thresholding. In order to demonstrate the generality of our approach, we evaluate our approach using two rather different materials, i.e. brushed stainless steel and anodized aluminium.
16
EN
This work extends the dynamic programming approach to calculation of an elastic metric between two curves to finding paths in pairs of graph drawings that are closest under this metric. The new algorithm effectively solves this problem when all paths between two given nodes in one of these graphs have the same length. It is then applied to the problem of pattern recognition constrained by a superpixel segmentation. Segmentations of test images, obtained without statistical modeling given two shape endpoints, have good accuracy.
17
Content available The solutions similarity of the similar conflicts
EN
The work deals with the examination of solutions similarity of similar conflicts, presented in the form of multiperson cooperative games. There is examined the similarity of the two most well-known in the literature concepts of the cooperative game solutions: Shapley solution and nucleolus (Schmeidler solution [9]). The work presents an idea of using a solution of the pattern conflict most similar to the considered conflict as a its solution.
PL
Artykuł dotyczy badania podobieństwa konfliktów podobnych, przedstawianych w postaci wieloosobowych gier kooperacyjnych. Zbadano podobieństwo dwóch najbardziej znanych w literaturze koncepcji rozwiązań wieloosobowych gier kooperacyjnych: rozwiązania Shapleya oraz nucleolusa (rozwiązania Schmeidlera) [9]. Przedstawiono ideę wykorzystania rozwiązania najbardziej podobnego konfliktu wzorcowego do rozwiązania konfliktu badanego.
EN
In 1972, V. Keilis-Borok and I. Gelfand introduced the phenomenological approach based on the morphostructural zoning and pattern recognition for identification of earthquake-prone areas. This methodology identifies seismogenic nodes capable of generating strong earthquakes on the basis of geological, morphological, and geophysical data, which do not contain information on past seismicity. In the period 1972–2018, totally, 26 worldwide seismic regions have been studied and maps showing the recognized earthquake-prone areas in each region have been published. After that, 11 of these regions were hit by earthquakes of the relevant sizes. The goal of this work is to analyze the correlation of the post-publication events with seismogenic nodes defined in these 11 regions. The test was performed using the NEIC earthquake catalog because it uniformly defines the location and magnitudes of earthquakes over the globe. The ArcMap facilities were exploited to plot the post-publication events on the maps showing the recognized seismogenic nodes. We found that about 86% of such events fall in the recognized seismogenic nodes. The performed test proved the sufficient validity of the methodology for identifying areas capable of strong earthquakes and confirms the idea on nucleating strong earthquakes at the nodes.
EN
Selective Laser Melting (SLM) is an additive manufacturing process, in which the research has been increasing over the past few years to meet customer-specific requirements. Different parameters from the process and the machine components have been monitored in order to obtain vital information such as productivity of the machine and quality of the manufactured workpiece. The monitoring of parameters related to energy is also realized, but the utilisation of such data is usually performed for determining basic information, for instance, from energy consumption. By applying machine learning algorithms on these data, it is possible to identify not only the steps of the manufacturing process, but also its behaviour patterns. Along with these algorithms, evidences regarding the conditions of components and anomalies can be detected in the acquired data. The results can be used to point out the process errors and component faults and can be adopted to analyse the energy efficiency of the SLM process by comparing energy consumption of one single layer during the manufacturing of different components. Moreover, the state of the manufacturing process and the machine can be determined automatically and applied to predict failures in order to launch appropriate counter measures.
EN
Currently, multiple areas are restricted, and it is necessary to know PIN codes or a proper passwords. However, it is reasonable to use biometrics in order to verify users. This kind of systems are widely known and implemented in our daily life. In this article, the method and an exemplary system to verify users on the basis of palmprint biometrics is proposed. The paper includes the concept and of the device with a full description of all physical elements and algorithms implemented in the system. Finally, it also contains the accuracy results obtained from multiple experiments. The results show that this kind of user verification may be successful and should be developed. In the article, there are also some possible extensions and real-life implementations enumerated.
PL
W dzisiejszych czasach wiele miejsc pozostaje zabezpieczonych przed niepowołanym dostępem. Aby się tam dostać, należy podać numer PIN lub odpowiednie hasło. Rozsądnym wydaje się jednak wykorzystanie biometrii do weryfikowania użytkowników. Ten sposób sprawdzania tożsamości osób jest już dość szeroko stosowany w codziennym życiu. W artykule została zaprezentowana metoda oraz modelowy system, które wykorzystują dane biometryczne w postaci odcisków wewnętrznych części dłoni do weryfikacji użytkowników. Artykuł zawiera opis koncepcji działania urządzenia, wszystkich elementów systemu oraz zaimplementowanych algorytmów. Zawiera także wyniki skuteczności działania urządzenia otrzymane w wyniku przeprowadzonych eksperymentów. Otrzymane wyniki pokazują, że taki sposób weryfikacji użytkowników może być skuteczny i powinien być rozwijany. Omówiono również możliwe rozszerzenia przedstawionego systemu oraz jego możliwe zastosowania w życiu codziennym.
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