Wyniki wyszukiwania - BazTech

1

Two-stage wavelet compression framework for audio signal processing

Poovely Jaimy James, Dolly Raveena Judie

Przegląd Elektrotechniczny

|

2025

|

R. 101, nr 12

265--277

EN

Efficient audio compression is critical for storage and transmission in modern multimedia applications. This paper presents a novel multi-stage audio compression framework that integrates Haar Wavelet Transform, Free Lossless Audio Codec (FLAC) encoding, and Lempel–Ziv–Welch (LZW) entropy coding to achieve high compression efficiency while preserving audio quality. The Haar Wavelet Transform enables effective signal decorrelation, followed by FLAC for lossless compression, and finally LZW to further enhance entropy reduction. Experimental evaluations over a 7-s, 44.1-kHz mono WAV file show that the HWFL-LZW framework achieves a compression ratio (CR, defined as compressed size / original size) of 1.80×, with Signal-to- Noise Ratio (SNR) of 39.0 dB and Peak SNR (PSNR) of 92.1 dB at the chosen hard-thresholds. Because wavelet thresholding is applied (τ > 0), the pipeline operates in a near-lossless regime; when τ = 0, the method becomes strictly lossless and SNR/PSNR tend to ∞, as expected. A prototype FPGA analysis indicates feasibility for real-time deployment; expanded, multi-file testing and statistical analysis are provided.

PL

Skuteczna kompresja dźwięków (audio) ma kluczowe znaczenie dla przechowywania i transmisji w nowoczesnych zastosowaniach multimedialnych. Niniejsza praca przedstawia nowoczesne ramy wielostopniowej kompresji audio, integrującej Transformację Falkową Haara (Haar Wavelet Transform), kodowanie FLAC i entropowe kodowanie Lempel-Ziv-Welch (LZW) w celu uzyskania wysokiej efektywności kompresji przy zachowaniu jakości dźwięku. HWT umożliwia skuteczną dekorelację dźwiękową, następnie kompresję FLAC bez strat, oraz finalnie, LZW dla dalszego wzmocnienia redukcji entropii. Ocena doświadczalna przeprowadzona na pliku powyżej 7 s 44.1 kHz mono WAV pokazuje, że zastosowanie techniki HWFL-LZW pozwala uzyskać współczynnik kompresji (CR), compression ratio, definiowany jako stosunek wielkości skompresowanej do wielkości oryginalnej, wynoszący 1.80x , przy stosunku Signal-to-Noise (SNR) równym 39.0 dB oraz szczytowym SNR (PSNR) 92.1 dB przy wybranych wartościach progowych (hard thresholds). Ponieważ stosowane są progowe wartości falkowe, linia pracuje w reżimie prawie bez strat; kiedy τ = 0, metoda nie powoduje strat jakości a SNR/PSNR wykazuje dążenie do ∞, jak tego oczekiwano. Analiza prototypu FPGA wskazują na możliwość wdrożenia w czasie rzeczywistym; przedstawiono badania wielo-plikowe oraz analizę statystyczną.

2

Porównanie jakości audio wybranej metody znakowania wodnego dla sygnału mowy, muzyki klasycznej i popularnej

Jekateryńczuk Gabriel, Piotrowski Zbigniew

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

|

2024

|

nr 4

333--336

PL

W artykule przedstawiono szczegółową analizę oraz porównanie metody znakowania wodnego sygnałów audio za pomocą metody cyfrowego przetwarzania sygnałów bez przetwarzania sygnałów sieciami neuronowymi. Skupiono się na zastosowaniu dyskretnej transformaty falkowej – DWT oraz dyskretnej transformaty kosinusowej - DCT. Głównym celem badań jest ocena i porównanie miar jakości znaku wodnego osadzonego w różnych rodzajach treści audio, takich jak sygnał mowy, muzyka popularna i muzyka klasyczna.

EN

The article presents a detailed analysis and comparison of watermarking method for audio signals using digital signal processing without signal processing by neural networks. The focus is on the application of discrete wavelet transform - DWT and discrete cosine transform - DCT. The main goal of the research is to evaluate and compare the quality measures of the watermark embedded in various types of audio content, such as speech signal, popular music, and classical music.

3

Probabilistic and explainable modeling of Phase-Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis

Castillo-Barnes Diego, Gallego-Molina Nicolás J., Formoso Marco A., Ortiz Andrés, Figueiredo Patrícia, Luque Juan L.

Biocybernetics and Biomedical Engineering

|

2024

|

Vol. 44, no. 4

814--823

EN

This work explores the intricate neural dynamics associated with dyslexia through the lens of Cross-Frequency Coupling (CFC) analysis applied to electroencephalography (EEG) signals evaluated from 48 seven-year-old Spanish readers from the LEEDUCA research platform. The analysis focuses on CFS (Cross-Frequency phase Synchronization) maps, capturing the interaction between different frequency bands during low-level auditory processing stimuli. Then, making use of Gaussian Mixture Models (GMMs), CFS activations are quantified and classified, offering a compressed representation of EEG activation maps. The study unveils promising results specially at the Theta-Gamma coupling (Area Under the Curve = 0.821), demonstrating the method’s sensitivity to dyslexia-related neural patterns and highlighting potential applications in the early identification of dyslexic individuals.

4

Wykrywanie zdarzeń awaryjnych w silnikach indukcyjnych na podstawie analizy danych z rejestratorów zakłóceń

Sajewicz Dariusz, Łaguna Wojciech, Chmielak Waldemar

Przegląd Elektrotechniczny

|

2023

|

R. 99, nr 1

207--210

PL

Celem artykułu jest wykazanie skuteczności nowo opracowanych metod detekcji uszkodzeń opartych na analizie danych z rejestratorów zakłóceń. W trakcie prac badawczych wyekstrahowano najbardziej istotne cechy sygnałów prądów w dziedzinie częstotliwości. Pozyskane cechy stanowiły podstawę budowy probabilistycznego klasyfikatora zdarzeń awaryjnych. Detekcja uszkodzeń dotyczy wykrywania: pękniętych prętów wirnika i stopnia jego degradacji oraz awarii łożysk na wale silnika. Przeprowadzone badania potwierdzają wysoką skuteczność wykrywania uszkodzeń we wszystkich rozpatrywanych obszarach.

EN

The aim of this paper is to demonstrate the effectiveness of developed fault detection methods based on the analysis of data from fault recorders. During the research work, the most significant features of current signals in the frequency domain were extracted. The extracted features provided the base for building a probabilistic classifier of fault incidents. The fault detection concerned the detection of cracked rotor cages and the degree of its degradation as well as the failure of bearings on the motor shaft. The conducted research confirms the high efficiency of detection faults in all areas concerned.

5

Correlation between linear and non-linear vibroartrographic parameters

Kręcisz Krzysztof, Bączkowicz Dawid, Łysiak Adam, Szmajda Mirosław, Kawala-Sterniuk Aleksandra

Przegląd Elektrotechniczny

|

2023

|

R. 99, nr 7

180--185

EN

In this paper the authors compared linear and non-linear features of the vibroarthrogram (VAG) in knee joint context. There were 220 healthy participants in the study, divided into five age-related groups (third, fourth, fifth, sixth and seventh decade of life). Four linear features (i.e. variance of the mean squares, amplitude, and two frequency parameters) and two non-linear features (i.e. multi-scale entropy and recurrence rate) were correlated with each other. Correlations within linear and non-linear groups proved to be positive, while inter-group correlations turn out to be negative. Also, in the context of age differentiation, recurrence rate feature proved to be the most informative one.

PL

W niniejszej pracy autorzy porównali liniowe i nieliniowe cechy wibroartrogramu (VAG) w kontekście stawu kolanowego. W badaniuwzięło udział 220 zdrowych uczestników, podzielonych na pięć grup wiekowych (trzecia, czwarta, piąta, szósta i siódma dekadażycia). Cztery cechy˙ liniowe (tj. wariancja średnich kwadratów, amplituda i dwa parametry częstotliwości) oraz dwie cechy nieliniowe (tj. wieloskalowa entropia i częstość nawrotów) były ze sobą skorelowane. Korelacje w obrębie grup liniowych i nieliniowych okazały się dodatnie, natomiast korelacje międzygrupowe okazały się ujemne. Również w kontekście zróżnicowania wieku cecha częstości nawrotów okazała się najbardziej informatywna.

6

Automatic detection of Alzheimer's disease based on artificial intelligence

Benba Achraf, Abdelilah Kerchaoui

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2023

|

T. 13, nr 1

18--21

EN

Alzheimer's disease is a neurodegenerative disease that progressively destroys neurons through the formation of platelets that prevent communication between neurons. The study carried out in this project aims to find a precise and relevant diagnostic solution based on artificial intelligence and which helps in the early detection of Alzheimer's disease in order to stop its progression. The study went through a process of processing MRI images followed by training of three deep learning algorithms (VGG-19, Xception and DenseNet121) and finally by a step of testing and predicting the results. The results of the accuracy metric obtained for the three algorithms were respectively 98%, 95%, 91%.

PL

Choroba Alzheimera jest chorobą neurodegeneracyjną, która stopniowo niszczy neurony poprzez tworzenie płytek krwi, które uniemożliwiają komunikację między neuronami. Badania prowadzone w ramach tego projektu mają na celu znalezienie precyzyjnego i trafnego rozwiązania diagnostycznego opartego na sztucznej inteligencji, które pomoże we wczesnym wykryciu choroby Alzheimera w celu zatrzymania jej postępu. Badanie przeszło przez proces przetwarzania obrazów MRI, po którym następowało szkolenie trzech algorytmów głębokiego uczenia (VGG-19, Xception i DenseNet121), a na koniec etap testowania i przewidywania wyników. Wyniki metryki dokładności otrzymane dla trzech algorytmów wyniosły odpowiednio 98%, 95%, 91%.

7

Efficient Covid-19 disease diagnosis based on cough signal processing and supervised machine learning

Bensid Khaled, Lati Abdelhai, Benlamoudi Azeddine, Ghouar Brahim Elkhalil, Senoussi Mohammed Larbi

Diagnostyka

|

2023

|

Vol. 24, No. 1

art. no. 2023103

EN

The spread of the coronavirus has claimed the lives of millions worldwide, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of the coronavirus to limit its spread. In this work, we propose an automated system to detect COVID-19 based on the cough as one of the most important infection indicators. Several studies have shown that coughing accounts for 65% of the total symptoms of infection. The proposed system is mainly based on three main steps: first, cough signal detection and segmentation; second, cough signal extraction; and third, three techniques of supervised machine learning-based classification: Support Vector Machine (SVM), KNearest Neighbours (KNN), and Decision Tree (DT). Our proposed system showed high performance through good accuracy values, where the best accuracy for classifying female coughs was 99.6% using KNN and 88% for males using SVM.

8

End-to end decision support system for sleep apnea detection and Apnea-Hypopnea Index calculation using hybrid feature vector and Machine learning

Arslan Recep Sinan, Ulutas Hasan, Köksal Ahmet Sertol, Bakir Mehmet, Çiftçi Bülent

Biocybernetics and Biomedical Engineering

|

2023

|

Vol. 43, no. 4

684--699

EN

Sleep apnea is a disease that occurs due to the decrease in oxygen saturation in the blood and directly affects people’s lives. Detection of sleep apnea is crucial for assessing sleep quality. It is also an important parameter in the diagnosis of various other diseases (diabetes, chronic kidney disease, depression, and cardiological diseases). Recent studies show that detection of sleep apnea can be done via signal processing, especially EEG and ECG signals. However, the detection accuracy needs to be improved. In this paper, a ML model is used for the detection of sleep apnea using 19 static sensor data and 2 dynamic data (Sleep score and Arousal). The sensor data is recorded as a discrete signal and the sleep process is divided into 4.8 M segments. In this work, 19 different sensor data sets were recorded with polysomnography (PSG). These data sets have been used to perform sleep scoring. Then, arousal status marking is done. Model training was carried out with the feature vector consisting of 21 data obtained. Tests were performed with eight different machine learning techniques on a unique dataset consisting of 113 patients. After all, it was automatically determined whether people were diseased (a kind of apnea) or healthy. The proposed model had an average accuracy of 97.27%, while the recall, precision, and f-score values were 99.18%, 95.32%, and 97.20%, respectively. After all, the model that less feature engineering, less complex classification model, higher dataset usage, and higher classification performance has been revealed.

9

An EEG mobile device as a game controller

Dawid Aleksander, Buchwald Paweł

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

|

2021

|

Vol. 25, No 4

397--407

EN

In this work the real-time control of computer games was explored by a single elec-trode mobile electroencephalography (EEG) device with a Bluetooth interface. The amplitudevariation in the two frequency bands of 4-12 Hz and 60-200 Hz was selected as the real-timecontrol parameter. The frequency-domain of a raw EEG signal was calculated using the discreteFourier transform. The time-dependent signal samples equal to 512, 1024, and 2048 time pointsin size were used in our research. The well-known classic Pong game was used to try out ourcontroller. The developed software handles communication with the device and real-time gamerendering. The .NET Framework with the C# programming language was used as a develop-ment tool. 50 gameplay trials were made for each controller setup. The obtained results arepromising for the possible use of the device in real-time communication with computer devicesfor people with hand disabilities.

10

A new measurement system to determine stiffness distribution in machine tool workspace

Pawełko Piotr, Jastrzębski Daniel, Parus Arkadiusz, Jastrzębska Joanna

Archives of Civil and Mechanical Engineering

|

2021

|

Vol. 21, no. 2

157--168

EN

This paper presents an innovative system determining machine tool quasi-static stiffness in machining space, so-called Stiffness Workspace System (SWS). The system allows for the assessment of the accuracy of a machine which has become a vital aspect over past years for machine tool manufacturers and users. Since machine tools static stiffness is one of the main criteria using to evaluate the machines' quality, it is crucial to highlight the relevance of experimental and analytical stiffness determination methods. Therefore, the proposed method is applied to estimate the spatial variation of static stiffness in the machine tool workspace. This paper describes the SWS system-its design, working principle, mounting conditions and signal processing. The major advantage of the system is the capability to apply forces of controlled magnitude and orientation as well as simultaneously measure the resulting displacements. The obtained results give possibility to estimate and evaluate static stiffness coefficients depending on the position and direction under loaded conditions. The results confirm the validity of the analyses of spatial stiffness distribution in the machine workspace.

11

Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels

Tuncer Turker, Dogan Sengul, Acharya Udyavara Rajendra

Biocybernetics and Biomedical Engineering

|

2020

|

Vol. 40, no. 1

211--220

EN

In this study, a novel method to automatically detect Parkinson's disease (PD) using vowels is proposed. A combination of minimum average maximum (MAMa) tree and singular value decomposition (SVD) are used to extract the salient features from the voice signals. A novel feature signal is constructed from 3 levels of MAMa tree in the preprocessing phase. The SVD operator is applied to the constructed signal for feature extraction. Then 50 most distinctive features are selected using relief feature selection technique. Finally, k nearest neighborhood (KNN) with 10-fold cross validation is used for the classification. We have achieved the highest classification accuracy rate of 92.46% using vowels with KNN classifier. The dataset used consists of 3 vowels for each person. To obtain individual results, post processing step is performed and best result of 96.83% is obtained with KNN classifier. The proposed method is ready to be tested with huge database and can aid the neurologists in the diagnosis of PD using vowels.

12

A new numerical approach for DNA representation using modified Gabor wavelet transform for the identification of protein coding regions

Raman Kumar M., Vaegae Naveen Kumar

Biocybernetics and Biomedical Engineering

|

2020

|

Vol. 40, no. 2

836--848

EN

The fundamental step in genomic signal processing applications is to assign mathematical descriptor to nucleotides {A, T, G, C} of DNA molecule for discrete representation. The discrete representation should replicate biological information of gene when analyzed with digital signal processing tools. In this aspect, a novel binary representation of DNA sequence by combining structural and chemical information of original DNA sequence has been proposed for the identification of protein coding regions of eukaryotes. The identification model comprises two stages, mainly, numerical encoding in first stage, and analysis of biological behavior through digital signal processing algorithms in second stage. In the first stage, a new numerical encoding method based on Walsh codes of order-4 is proposed to obtain 1-D binary discrete sequence. In the second stage, the modified Gabor wavelet transform (MGWT) is employed on the discretized DNA sequence for spectrum analysis. The optimal gene numerical encoding and multiresolution approach of MGWT has readily identified the structures of coding regions of unknown gene sequences. The proposed model is validated by analyzing prediction efficiency in terms of statistical metrics such as sensitivity, specificity, accuracy on both sequence and data base level. Furthermore, the results are compared by plotting receiver operating curves (ROC) for all classification thresholds for the state-of-art encoding methods. Area under curve (AUC) value of 0.86 at sequence level and 0.84 at database level is achieved. Performance metrics indicate that the proposed encoding method exhibits relatively better performance than other numerical encoding methods.

13

A comparative analysis of signal processing and classification methods for different applications based on EEG signals

Khosla Ashima, Khandnor Padmavati, Chand Trilok

Biocybernetics and Biomedical Engineering

|

2020

|

Vol. 40, no. 2

649--690

EN

Electroencephalogram (EEG) measures the neuronal activities in the form of electric currents that are generated due to the synchronized activity by a group of specialized pyramidal cells inside the brain. The study presents a brief comparison of various functional neuroimaging techniques, revealing the excellent neuroimaging capabilities of EEG signals such as high temporal resolution, inexpensiveness, portability, and non-invasiveness as compared to the other techniques such as positron emission tomography, magnetoencephalogram, func-tional magnetic resonance imaging, and transcranial magnetic stimulation. Different types of frequency bands associated with the brain signals are also being summarized. The main purpose of this literature survey is to cover the maximum possible applications of EEG signals based on computer-aided technologies, ranging from the diagnosis of various neurological disorders such as epilepsy, major depressive disorder, alcohol use disorder, and dementia to the monitoring of other applications such as motor imagery, identity authentication, emotion recognition, sleep stage classification, eye state detection, and drowsiness monitoring. After reviewing them, the comparative analysis of the publicly available EEG datasets and other local data acquisition methods, preprocessing techniques, feature extraction methods, and the result analysis through the classification models and statistical tests has been presented. Then the research gaps and future directions in the present studies have been summarized with the aim to inspire the readers to explore more opportunities on the current topic. Finally, the survey has been completed with the brief description about the studies exploring the fusion of brain signals from multiple modalities.

14

The enhanced method of a spectrum’s window estimation

Kulyk Yaroslav, Knysh Bohdan, Ivanov Yuryy, Poplavskyy Anatolii, Kotyra Andrzej, Denissova Nataliya, Ziyatbekova Gulzat

Przegląd Elektrotechniczny

|

2019

|

R. 95, nr 4

151--157

EN

In this paper is proposed the advanced method of the spectrum’s window estimation, which, in opposition to currently existing methods, uses the mean multiwindow estimation of the logarithm’ signal spectrum, which cause the more strict signal’s spectrum estimation and increasing the efficiency of the trash-hold methods of the signals estimation. Analyzing its efficiency is provided by the means of the numerical experiments and by the implementation of modeling the nets can program.

PL

W artykule przedstawiono zaawansowaną metodę estymacji okna widmowego, która wykorzystuje średnią wielookienkową estymację widma sygnału logarytmicznego. Analizowanie efektywności metody odbywa się za pomocą eksperymentów numerycznych oraz poprzez wdrożenie modelowania sieci z możliwością programowania.

15

Use of incremental decomposition and spectrogram in vibroacoustic signal analysis in knee joint disease examination

Dołęgowski M., Szmajda M., Bączkowicz D.

Przegląd Elektrotechniczny

|

2018

|

R. 94, nr 7

162--166

EN

This paper presents two methods that enable indication of chosen dysfunction of knee joint: different stages of chondromalacia and osteoarthritis. The incremental decomposition of voltage in time and spectrogram were used. Both methods enable detection and identification of particular stages of knee joint dysfunction.

PL

W artykule zaprezentowano dwie metody umożliwiające wykrywanie wybranych dysfunkcji stawu kolanowego: różnych stadiów chondromalacji oraz choroby zwyrodniowej. Zastosowano metody badania rozkładów przyrostów przebiegów oraz spektrogram. Obydwie metody umożliwiają detekcję oraz identyfikację poszczególnych schorzeń.

16

Przetwarzanie i analiza sygnału elektrycznego z mięśni przy użyciu komputerowego stanowiska pomiarowego

Krawiecki Z., Hulewicz A., Cichocki-Kaiser M.

Poznan University of Technology Academic Journals. Electrical Engineering

|

2016

|

No. 88

35--45

PL

Autorzy artykułu opisują przetwarzanie i analizę zmierzonego sygnału elektrycznego z mięśni w odniesieniu do budowy funkcjonalnej komputerowego stanowiska pomiarowego. Komputerowe stanowisko pomiarowe zostało wykonane z części sprzętowej i programowej jako wirtualny przyrząd pomiarowy. Część sprzętowa realizuje zadania przetwarzania sygnału takie jak: kondycjonowanie, filtrowanie, konwersja na postać cyfrową i wizualizacja danych. Część programowa to: formatowanie danych, analiza sygnału, filtracja cyfrowa, przygotowanie wyników pomiarów do wizualizacji i archiwizacji oraz sterowanie aparaturą. W pracy szczególną uwagę zwrócono na blok analizy sygnału elektromiograficznego i realizowane funkcje związane z usuwaniem artefaktów, wyznaczaniem wartości bezwzględnej oraz wartości średniej i skutecznej napięcia sygnału.

EN

The authors of the article describe the processing and the analysis of the measured electrical signal from the muscles in relation to the functional construction of the computer measurement system. The computer measurement system was made of hardware and software as a virtual instrument. The hardware performs signal processing tasks such as: conditioning, filtering, conversion to digital form and data visualization. The software includes: formatting data, the signal analysis, digital filtration, preparing the measurement results to visualization and archiving and controlling the equipment. In this article, the particular attention has been paid to the block of the electromyographic signal analysis and the implemented functions related to the removal of the artifacts, the determination of absolute value and the effective value of the voltage signal.

17

Early stage of chronic kidney disease by using statistical evaluation of the previous measurement results

Akben S. B.

Biocybernetics and Biomedical Engineering

|

2016

|

Vol. 36, no. 4

626--631

EN

Chronic kidney disease (CKD) that causes the progressive losses in kidney functions is one of the major public health problems. Expert medical doctors can diagnose the CKD through symptoms, blood and urine tests in its early stages. However, the diagnosis of CKD might be difficult for expert medical doctors in case of the questionable measurement result. Therefore a new mathematical method that would be helpful to the expert medical doctors is required. It can be said that there is no studies related with automatic diagnosis of CKD in the literature. This study aims to remedy this shortcoming in the literature. In this study, for each of test and symptom values, averages of measurement results were calculated separately for healthy subjects and patients. Then the measured values were marked as ‘‘0’’ or ‘‘1’’ (healthy or patient) according to closeness to average values. Finally, the classification was performed by averaging the values marked for each subject. The success rate of the proposed method is between 96% and 98% according to the age ranges. In conclusion section of the study, how to implement the proposed method in real life is offered.

18

Problematyka przetwarzania parametrów uzyskanych z pokładowych rejestratorów eksploatacyjnych lotu

Bartosiewicz J., Stelmach A.

Prace Naukowe Politechniki Warszawskiej. Transport

|

2014

|

z. 103

5--13

PL

Artykuł przedstawia zagadnienia związane z pozyskiwaniem parametrów lotu oraz przygotowaniem ich do późniejszego wykorzystania w modelowaniu i komputerowej identyfikacji. Przedstawiono zarys historyczny oraz zastosowanie pokładowych rejestratorów, ich budowę a także sklasyfikowano typy danych oraz rodzaje parametrów lotu. Wskazano na problemy, jakie mogą zaistnieć w procesie powstawania i przesyłania sygnału oraz w jaki sposób dokonuje się wstepnego przetwarzania zapisanych danych.

EN

The article presents the issues related to grathering flight data inside Flight Data Recorder and preparing them for further processing. transmitted signals, coming from numerous sensors and detectors on board can be deformed or disturbed. It is important to recognize invalid signals and transform them into real, adequate shape. the article contains construction of Flight Date Recorder, history of development and data types description as well.

19

Advanced Processing Techniques and Classification of Full-waveform Airborne Laser Scanning Data

Słota M.

Geomatics and Environmental Engineering

|

2014

|

Vol. 8, no. 2

85--95

EN

This article presents an overview of advanced processing techniques of full-waveform airborne laser scanning data. The well known processing methods, such as signal decomposition or correlation techniques, could not be sufficient for the processing of strongly deformed or complex reflected echo data. The first part of this paper describes the advanced processing techniques. The radiometric calibration procedure and advanced waveform decomposition methods, as well as algorithms for the detection of weak and overlapping echoes are presented. The second part focuses on the possibility of point cloud classification improvement based on full-waveform data. The usefulness of particular full-waveform parameters in the classification process is described.

PL

W artykule zamieszczono przegląd zaawansowanych technik przetwarzania pełnych profili energii zarejestrowanych przez systemy lidarowe. Popularne metody przetwarzania danych, takie jak dekompozycja sygnału czy metody korelacyjne, mogą się nie sprawdzić w sytuacjach, gdy odbity sygnał laserowy jest silnie zdeformowany lub gdy odległość pomiędzy dwoma echami jest mniejsza niż długość emitowanego impulsu. W pierwszej części publikacji opisano zaawansowane techniki przetwarzania zarejestrowanej, odbitej energii laserowej. Scharakteryzowano metodę kalibracji radiometrycznej sygnału, opisano zaawansowane techniki dekompozycji falek oraz metody detekcji słabych i nachodzących na siebie odbić. Część druga poświęcona została klasyfikacji chmury punktów ze szczególnym uwzględnieniem dodatkowych parametrów, wyznaczanych na podstawie profili energii. Opisana została przydatność poszczególnych parametrów w klasyfikacji.

20

Signal segmentation for operational regimes detection of heavy duty mining mobile machines - a statistical approach

Wyłomańska A., Zimroz R.

Diagnostyka

|

2014

|

Vol. 15, No. 2

33--42

EN

In the paper an automatic procedure for diagnostic signals segmentation is proposed. The purpose of the procedures is to detect/identify part of the signal, that is related to stationary operation regime of machine. Detection and parameterization of such events might help to improve efficiency of machine usage, for example to minimize number of segments and their duration for machine operation under idle mode or overload mode. It is proposed to use a procedures that are based on statistical analysis to estimate the critical point of the division in a structural change in a time series. Two measures have been proposed for critical points detection: the first one is based on testing of empirical moment of order two for time subseries with length k, second one is related to analysis of second order moment moving along the signal. These techniques have been validated using simulations and then applied to real data acquired from on board monitoring system developed for mobile mining machines (loaders are considered here). Results of application are discussed in the paper.

PL

W artykule przedstawiono procedury segmentacji sygnałów drganiowych. Cel opracowanych procedur jest związany z detekcją/identyfikacją fragmentów sygnału (segmentów) które związane są z różnymi trybami pracy maszyny jak na przykład bieg jałowy czy przeciążenie. Detekcja i parametryzacja tych segmentów pozwoli poprawić efektywność pracy tj. minimalizować czas pracy na biegu jałowym czy zapobiegać przyspieszonym procesom degradacji wynikającym z nieprawidłowego użytkowania maszyny. Zaproponowano dwie procedury bazujące na statystykach, które estymują punkt dzielący szereg czasowy na podprocesy o znaczących różnicach statystycznych. Pierwsza ze statystyk bazuje na badaniu zachowania empirycznego momentu rzędu 2 danej próby w oknie o długości k, druga jest analizą kroczącego drugiego momentu z próby. W pracy przedstawiono wyniki walidacji metody na sygnałach symulacyjnych oraz wyniki zastosowania procedur do sygnałów zarejestrowanych na górniczych maszynach samojezdnych pracujących w kopalni podziemnej.