Wyniki wyszukiwania - BazTech

1

Utilization of ASTER data in lithological and lineament mapping of the southern flank of the Central High Atlas in Morocco

Errami Maryam, Algouti Ahmed, Algouti Abdellah, Farah Abdelouhed, Agli Saloua

Geologos

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2023

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Vol. 29, No. 1

1--20

EN

Geological mapping undoubtedly plays an important role in several studies and remote sensing data are of great significance in geological mapping, particularly in poorly mapped areas situated in inaccessible regions. In the present study, Principal Component Analysis (PCA), Band Rationing (BR) and Minimum Noise Fraction (MNF) algorithms are applied to map lithological units and extract lineaments in the Amezri-Amassine area, by using multispectral ASTER image and global digital elevation model (GDEM) data for the first time. Following preprocessing of ASTER images, advanced image algorithms such as PCA, BR and MNF analyses are applied to the 9ASTER bands. Validation of the resultant maps has relied on matching lithological boundaries and faults in the study area and on the basis of pre-existing geological maps. In addition to the PCA image, a new band-ratio image, 4/6–5/8–4/5, as adopted in the present work, provides high accuracy in discriminating lithological units. The MNF transformation reveals improvement over previous enhancement techniques, in detailing most rock units in the area. Hence, results derived from the enhancement techniques show a good correlation with the existing litho-structural map of the study area. In addition, the present results have allowed to update this map by identifying new lithological units and structural lineaments. Consequently, the methodology followed here has provided satisfactory results and has demonstrated the high potential of multispectral ASTER data for improving lithological discrimination and lineament extraction.

2

Wavelet analysis of the EMG signal to assess muscle fatigue in the lower extremities during symmetric movement on a rowing ergometer

Daniel Natalia, Małachowski Jerzy

Acta of Bioengineering and Biomechanics

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2023

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Vol. 25, no. 2

15--27

EN

The aim of this publication was to propose a method to determine changes in fatigue in selected muscle groups of the lower extremity during dynamic and cyclical motion performed on a rowing ergometer. The study aimed to use the discrete wavelet transform (DWT) to analyze electromyographic signals (EMG) recorded during diagnostic assessment of muscle and peripheral nerve electrical activity (electroneurography) using an electromyography device (EMG). Methods: The analysis involved implementing calculations such as mean frequency (MNF) and median frequency (MDF) using the reconstructed EMG signal through DWT. The study examined the efficacy of DWT analysis in assessing muscle fatigue after physical exertion. Results: The study obtained a negative regression coefficient for DWT analysis in all muscles except for the right gastrocnemius (GAS). The results suggest that DWT analysis can be an effective tool for evaluating muscle fatigue after physical exertion. Conclusions: The use of DWT in the analysis of EMG signals during rowing ergometer exercises has shown promising results in assessing muscle fatigue. However, additional investigations are necessary to confirm and expand these findings. This publication addresses the literature gap on the determination of muscle fatigue considering motion analysis on a rowing ergometer using the discrete wavelet transform. Previous studies have extensively compared and analyzed methods such as the Fourier transform (FFT), short-time Fourier transform (STFT), and wavelet transform (WT) for muscle fatigue analysis. However, no previous work has specifically examined the assessment of muscle fatigue by incorporating DWT analysis with motion analysis on a rowing ergometer.

3

Analiza danych z systemu stacjonarnego odczytu wodomierzy dla średniej wielkości miasta

Koral Wojciech

Instal

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2022

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nr 12

46--52

PL

Systemy zdalnego stacjonarnego odczytu wodomierzy są coraz szerzej wdrażane w polskich systemach wodociągowych, obejmując wszystkie wodomierze odbiorców i umożliwiając odczyt stanu wodomierzy co godzinę. Dzięki takiemu rozwiązaniu możliwe jest wyznaczenie rzeczywistego zużycia wody przez odbiorców, wyznaczenie minimalnego, autoryzowanego zużycia dla stref DMA i całego miasta oraz wyznaczenie rzeczywistych strat wody. Jednak liczba pomiarów (w analizowanym systemie to ponad 66 milionów odczytów rocznie), problemy z łącznością spowodowane trudnymi warunkami zabudowy (głębokie, zalane studnie wodomierzowe) oraz błędne/brakujące odczyty mogą powodować poważne problemy/błędy w bilansie wody. Z tego powodu kluczowym elementem poprawności wyciąganych wniosków jest krytyczna analiza otrzymywanych pomiarów. W artykule przedstawiono wybrane przykłady problemów w eksploatacji i analizę danych otrzymanych ze stacjonarnego systemu odczytu wodomierzy (SSOW), wdrożonego dla prawie 7600 wodomierzy w mieście 46-tysięcznym.

EN

Smart water-meter networks are more and more often implemented in Polish water supply systems. They include all water meters of recipients and their indications are saved every hour. This solution makes it possible to calculate real water consumption, estimate minimal authorised water usage for DMA and the whole town and calculate real water balance and water loss. However, the number of records (above 66 of millions yearly), communication problems and wrong/missing water meter indications can lead to serious mistakes in water balance. Therefore critical approach to records is the key element of water balance. The paper shows selected possibilities and problems of maintenance of Advanced Metering Infrastructure (AMI system, about 7600 water meters) in a town with 46 000 inhabitants.

4

A combined field and automatic approach for lithological discrimination in semi-arid regions, the case of geological maps of bir later region and its vicinity, Nementcha mounts, Algeria

Chibani Abdelmouhcene, Hadji Riheb, Younes Hamed

Geomatics, Landmanagement and Landscape

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2022

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no. 4

7--26

EN

The Sahara’s Nememcha mountains chain suffers from a significant lack of large-scale geological information. In the Bir Later region with complex morpho-structural settings and arid climate conditions; geological maps have not been yet completed by competent authorities. However, this region harbours Algeria’s largest phosphate mine; with its reserves estimated at more than one billion tons of ore grading 20% phosphorus pentoxide. Geomatic-based techniques of Multisource Remote Sensing data allow the classification and identification of the lithologic features. The adopted method quarries the spectral signal, the alteration processes, and the thickness of the rocky banks. For this task, we apply Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), directional filters, and unsupervised classification (K-Means data) techniques to calibrate and correct Landsat 8 OLI and Sentinel-2A multispectral images. A petrographic study with field and laboratory work was carried out in order to confirm the machine description of the different facies. The results showed that the proposed lithology classification scheme can achieve accurate classification of all lithologic types, in the Cenozoic, Mesozoic, and Holocene deposits of the study area. The lithological map obtained from the GIS-RS-Processing is highly correlated with our field survey. Therefore, multispectral image data (Landsat 8 OLI and Sentinel-2A) coupled with an advanced image enhancement technique and field surveys are recommended as a rapid and cost-effective tool for lithologic discrimination and mapping. The experimental results fully demonstrated the advantages of the reliance on laboratory tests in the sensed lithology validation in an arid area.

5

Crop classification with neural networks using airborne hyperspectral imagery

Olesiuk D., Bachmann M., Habermeyer M., Heldens W., Zagajewski B.

Roczniki Geomatyki

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2009

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T. 7, z. 2

107-112

EN

Mainly due to size of input data, the artificial neural networks (ANNs) methods for remote sensing image classification can be expensive to use, in terms of computer resources and expert analyst time (Mahesh, Mather, 2006). In the case of hyperspectral data, neural networks training process may take weeks of time, in order to determine the number of input nodes in network structure needed by hundreds of image bands. In addition, not every neural networks package, such as the Stuttgart Neural Network Simulator (SNNS) used in this study, works with binary data, which makes dimensionality data reduction methods necessary to develop an effective classification scheme based on an ASCII text file. Despite these reservations, ANNs offer a wide field of research and investigation in crop and land cover classification, because they are a non-parametric method in the sense that they make no assumptions about the statistical distribution of the classes to be identified. As additional benefit, they can accept non-numeric inputs as well as ratio and interval-scale data. Moreover, the SNNS software provides the user a unique opportunity to design the input layers in a network structure, such as sub pattern window, which makes it possible to include texture information as additional data in the classification process (Zell et al., 1995). This method is especially useful in discrimination of non-homogeneous classes (Zagajewski, Olesiuk, 2008), and has been applied in this study. The objective of this work was to compare the results of crop classifications based on two data sets derived from hyperspectral HyMap imagery: (1) after MNF transformation, (2) vegetation and soil indices. The minimum noise fraction (MNF) transformation is used to segregate noise in the data, to determine the inherent dimensionality of the image data, and to reduce the computational requirements for subsequent processing (Boardman, Kruse, 1994). Essentially, it is two cascaded transformations. The first transformation, based on an estimated noise covariance matrix, de-correlates and rescales the noise in the data. This first step results in transformed data in which the noise has unit variance and no band-to-band correlations. The second step is a standard Principal Components transformation of the noise-whitened data. MNF bands are in a descending order of eigen values with almost no noise in the bands where the eigen values are near unity and below unity indicating signal-to-noise ratio (S/N) decreasing with decreasing order of MNF bands. The second data set contains hyperspectral indices which were selected to estimate pigment, nitrogen, cellulose and water content in vegetation, and clay and iron content in soil. The study area is located in the Demmin region in north Germany (Figure 1). This is a previously mapped agricultural area, where the main land cover/ land use types are represented by agriculture and grassland farming, with intermixed forestry and urban areas. This area is used as an agricultural and multi-disciplinary test site, and is included in the Committeee on Earth Observation Satellites (CEOS) catalogue for calibration and validation sites.

PL

Celem opracowania jest porównanie wyników klasyfikacji upraw uzyskanych ze zdjęć hiperspektralnych HyMap. Teren badań znajduje się w rolniczym regionie Demmin w północnych Niemczech. Do klasyfikacji wykorzystano dwa zestawy danych: 1) obrazy po transformacji Minimum Noise Fraction (MNF) oraz 2) mapy wskaźników roślinnych i glebowych. Transformacja MNF polega na redukcji wymiarów przestrzeni spektralnej (kompresji danych) i składa się z dwóch kaskadowych transformacji. Pierwszy etap polega na dekorelacji szumu, a drugi to standardowa transformacja PCA przeprowadzona na danych po oddzieleniu szumu. W rezultacie powstają nowe kanały, które uszeregowane są od największej do najmniejszej wariancji, przez co do dalszych prac mogą być wykorzystane najbardziej przydatne informacje. Drugi zestaw danych zawiera utworzone na podstawie obrazu hiperspektralnego wskaźniki roślinne i glebowe. Definiują one zawartość pigmentów, azotu, celulozy oraz wody w roślinność, a także iłu i żelaza w glebie. Klasyfikacja przeprowadzona została z wykorzystaniem sztucznych sieci neuronowych. Wykorzystano do tego celu oprogramowanie Stuttgart Neural Network Simulator (SNNS). Zastosowano sieć wielowarstwową, jednokierunkową, uczoną z użyciem metody wstecznej propagacji błędów (back- propagation errors). Klasyfikacje obu zestawów danych wykonano z zastosowaniem dwóch typów struktury neuronów w warstwie wejściowej. Pierwszy typ to struktura standardowa, gdzie liczba neuronów wejściowych odpowiada liczbie wykorzystywanych kanałów obrazowych. Druga struktura zaprojektowana została poprzez zdefiniowanie okna maski w postaci macierzy 3x3 piksele, dzięki czemu do procesu klasyfikacji włączona została informacja o teksturze badanego obiektu. Najlepszą dokładność całkowitą klasyfikacji wynoszącą 92,5% oszacowano dla zestawu zawierającego kanały wynikowe transformacji MNF i przeprowadzonej z wykorzystaniem struktury sieci odpowiadającej masce 3x3 piksele. Dla zestawu danych składającego się ze wskaźników roślinnych i glebowych dokładność klasyfikacji wyniosła około 80% w obydwu zastosowanych strukturach sieci.