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1

Combined rotor faults diagnosis in induction motors using MVSA and Intra-Mode Variational Modal Decomposition (IM-VMD)

Ait Mellal Ismail, Lahbabi Salma, Dahi Khalid

Diagnostyka

|

2026

|

Vol. 27, No. 2

art. no. 2026205

EN

In modern industry, constant production and operation without stoppages is viewed as being vital in ensuring competitiveness and efficiency. To maintain continuity in production, industries make use of advanced diagnostic measures by using of different performance measures such as reliability and risk assessment. In addition, within the context of Industry 4.0, there are also smart devices and Internet of Things (IoT) that can be used to enhance monitoring and optimize production processes intelligently. In such a context, this study seeks to examine the problem of detecting combined faults in three-phase induction machines via vibration signals. The purpose of this research is to diagnose fault signatures as early as possible even in the presence of multiple interactions between the faults. To achieve this objective, an intra-mode variational mode decomposition (IM-VMD) approach that employs an embedded source separation strategy was applied for decomposing vibration signals into multiple intrinsic modes. Using this framework, it became possible to isolate those vibration components associated with faults and increase interpretability of signals in terms of time and frequency domains. The outcomes demonstrated an effective identification of fault signatures; the extracted vibration frequency components match the theoretical frequencies of the defects. More precisely, the coefficient of correlation between extracted frequency components and theoretically calculated ones equals 0.95 to 1. This finding suggests that the proposed algorithm provides reliable results that can be applied in practice for detecting combined faults in three-phase induction machines. It is also possible to highlight that by utilizing the introduced method, it becomes possible to detect fault signatures at the earliest stage of their appearing.

2

Wykorzystanie urządzeń mobilnych do akwizycji i przetwarzania sygnałów biomedycznych w czasie rzeczywistym

Harasimczuk Adrian, Sieczkowski Krzysztof

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

|

2025

|

nr 1

21--27

PL

W artykule opisano projekt aplikacji mobilnej, dedykowanej dla urządzenia typu smartphone pracującego pod kontrolą systemu operacyjnego Android, przeznaczonej do akwizycji sygnałów biomedycznych. Wybranym sygnałem biomedycznym stosowanym w niniejszej pracy był sygnał PPG (ang. Photoplethysmogram), pozyskany z dedykowanego układu. Dane zbierane są przy pomocy modułu ESP 32 z podłączonym sensorem typu MA X30102. Następnie przesyłane są w czasie rzeczywistym do urządzenia mobilnego za pośrednictwem sieci Bluetooth Low Energy. Do przetwarzania danych biomedycznych wykorzystano procesy filtrowania sygnału, detekcji wartości szczytowych, wyliczania pulsu i saturacji. Użytkownik ma możliwość obserwacji wyników w czasie rzeczywistym na dedykowanych wykresach. Wykonane badania testowe potwierdzają poprawność działania aplikacji oraz zawierają porównanie efektów pracy z pomiarami wykonanymi za pomocą urządzeń komercyjnych typu pulsoksymetr i smartwatch.

EN

This paper describes the design of a mobile application for a smartphone device running the Android operating system with the intent of acquiring biomedical signals. The selected biomedical signal used in this work was the Photoplethysmogram (PPG ) signal, acquired via a dedicated Analog Front-End (AFE) chip. The data is collected using an ESP 32 module with a MA X30102 type AFE sensor connected. It is then transmitted in real time to a mobile device via Bluetooth Low Energy. Signal filtering, peak detection, as well as pulse and saturation calculation processes were used to process the biomedical data. The user can observe the results in real time on dedicated graphs. Conducted test studies confirm that the application functions correctly and include a comparison of the results with measurements made with commercial devices such as pulse oximeter and smartwatch.

3

Research on target positioning and signal processing based on the four-quadrant detector

Shen Wen, Fang Shaoxi, Gao Xu, Cui Xuejian, Feng Shuanglong

Opto-Electronics Review

|

2025

|

Vol. 33, No. 2

art. no. e154200

EN

In a target location system, the spot size and spot position of the laser light, which are received by a four-quadrant detector, are closely related to the accuracy of the target location. To acquire a pA-level current, according to the characteristics of the signals generated by the four-quadrant detector and the related signal processing circuits, this paper designs signal processing circuits with amplification multiples of 140 dB and 160 dB. Meanwhile, a T‑shaped compensation network is added to the circuit to solve the problem of bandwidth and gain not being increased simultaneously. Theoretical calculations and numerical simulations are carried out to verify this circuit gain, phase margin, and other parameters. Simulation results show that the bandwidth of the signal processing circuit with an amplification of 140 dB is 199.256 kHz, the bandwidth of the signal processing circuit with an amplification of 160 dB is 100 kHz, and the test bandwidth is 96 kHz in 140 dB, which provides a strong support for the calculation of the spot position of the four-quadrant detector.

4

Group alternating integration for distributed acoustic sensing with improved signal-to-noise ratio

Angelsky Oleg, Strynadko Myroslav, Zenkova Claudia, Zheng Jun

Opto-Electronics Review

|

2025

|

Vol. 33, No. 4

art. no. e155878

EN

Distributed acoustic sensing (DAS) based on Rayleigh backscattering is actively used for perimeter monitoring of critical infrastructure. However, traditional signal processing methods often face challenges in detecting weak or short-term events in noisy conditions. In this paper, we propose an improved signal accumulation method based on group averaging with alternating sign integration. The proposed method provides the efficient noise suppression and improves the detection of local mechanical disturbances along the fibre. Comparative simulation study between the classical and the proposed approach demonstrates a significant improvement in signal visibility in the presence of additive noise. Potential implementations of multi-layered and mesh-integrated DAS configurations are also discussed to further enhance signal-to-noise ratio (SNR). The obtained results can serve as a basis for the development of modern security systems for critical facilities.

5

Detection and elimination of unwanted spikes (artifacts) in rotational seismographic signals using artificial intelligence

Sakowicz Bartosz, Kamiński Marek, Marciniak Paweł, Jankowski Mariusz, Jaroszewicz Leszek. R.

Opto-Electronics Review

|

2025

|

Vol. 33, No. 4

art. no. e157333

EN

This paper presents methods for detecting and eliminating artifacts in signals recorded by the FOS6 rotational seismograph based on the Sagnac effect. A combination of classical threshold-based techniques and artificial intelligence (AI) algorithms was employed, aimed not only at detecting artifacts but also at improving the overall quality of the recorded data. Particular emphasis was placed on the deliberate use of AI – not as a direct filtering tool, but as a means of identifying regions of the signal that can be effectively smoothed or removed while preserving waveform integrity. The threshold-based algorithm mainly functioned as a source of training data for the AI models, enabling effective learning and testing of the approaches developed. Training data were obtained from the earlier FOS5 device, and verification was performed using recordings from both FOS5 and FOS6, enabling evaluation of the proposed methods under real-world conditions. To suppress artifacts, a simple linear interpolation method was proposed that preserves signal continuity and morphology while minimising distortion. The results show that this combined approach significantly increases the usability of the measurement system, enabling a more reliable analysis of seismic events and reducing the number of false alarms.

6

Ultrasonic testing data denoising and image enhancement method combined with fuzzy algorithm

Song Wei, Xu Huazhen, Qiu Jianlin

Diagnostyka

|

2025

|

Vol. 26, No. 4

art. no. 2025405

EN

This paper proposes an ultrasound data denoising and image enhancement method combining fuzzy logic and genetic algorithm optimization to improve imaging quality and diagnostic accuracy. Ultrasound imaging, widely used in medical and industrial testing, is often degraded by speckle, thermal and quantization noise, reducing clarity and contrast. To address this, a fuzzy system was designed including fuzzification interface, rule base, inference engine, and defuzzification output, with Gaussian membership functions adaptively tuned through genetic algorithms. Experiments were conducted on the Duke Abdominal Ultrasound Dataset and the BUS Breast Ultrasound Dataset, and the results were evaluated using signal-to-noise ratio (SNR), peak signalto-noise ratio (PSNR), and structural similarity index (SSIM). Compared with mean filter, median filter, wavelet transform, adaptive Wiener filter, and convolutional neural networks, the proposed method achieved consistent improvements. For instance, on abdominal ultrasound images, the fuzzy algorithm increased SNR by approximately 5.5% and PSNR by 6.4% compared with CNN, while SSIM improved by 2.3%. On breast ultrasound data, the method yielded a 5.6% higher SNR, a 5.5% higher PSNR, and a 2.3% higher SSIM than CNN. These results show integrating fuzzy logic and genetic optimization offers an effective, efficient, generalizable ultrasound image enhancement strategy, with potential clinical and industrial use.

7

Electrocardiogram signal processing: integrating filter fusion techniques

Atanasov Veselin E., Iliev Iliyan Y.

Diagnostyka

|

2025

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Vol. 26, No. 4

art. no. 2025402

EN

The accurate detection and analysis of the P-QRS-T complex in electrocardiogram (ECG) signals are crucial for diagnosing and managing cardiac diseases. This paper presents a practical approach to ECG signal processing by integrating multiple filter fusion techniques to enhance detection accuracy. Recognizing the specific challenges at sea, including motion-induced noise and electromagnetic interference, the study examines the performance of lowpass, high-pass, and Chebyshev Type II filters in improving ECG signal quality. Using a dataset generated by a Dynamic Model for Synthetic ECG Signal Generation, the analysis evaluates the effectiveness of these filters under various noise conditions, such as baseline drift, electrode contact noise, and muscle noise. The proposed method combines filtered outputs from multiple channels: P and T waves are extracted using low-pass and high-pass filters, while the QRS complex is identified through the Chebyshev Type II filter. Results indicate improved detection accuracy, with performance varying based on the type of noise present. While not introducing a novel algorithm, the study demonstrates that the fusion of established filtering techniques offers a fast and reliable solution suitable for maritime health monitoring systems. However, these results are derived from simulated signals under controlled experimental settings and therefore reflect proof-of-concept performance rather than clinical validation.

8

Application of low-cost dynamometer based on full octagonal ring for turning process monitoring

Susanto Agus, Setiyana Budi, Ismail Rifky, Wicaksana Ramadhana Eka

Archive of Mechanical Engineering

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2025

|

LXXII, nr 1

153--175

EN

This article discusses a low-cost dynamometer based on a novel full octagonal ringshaped transducer applied to measure the cutting forces and analyze them with the use of signal processing tools, in order determine their compatibility and the potential to monitoring machining conditions. The results showed that the performance of the developed dynamometer was in good agreement with cutting forces simulation. The cutting forces were about 30 N in stable cutting and 80 N in unstable cutting. In stable cutting, the spectrum showed the spindle frequency 𝑓𝑠 of 5.33 Hz, corresponding to 320 rpm of spindle speed, which was followed by its harmonic frequencies of 10.66, 16 and 21.33 Hz. Revealing of all characteristic frequencies during turning processes through the frequency spectrum showed the evidence that the developed dynamometer indeed functioned well measuring cutting forces in machining. Apart of the spindle frequency of 7.5 Hz and its harmonics, there also appeared the chatter frequency 𝑓𝑐 of 62 Hz in unstable cutting condition. By using the combination of signal processing tolls of the ensemble empirical mode decomposition with the shorttime Fourier transform (EEMD-STFT), the chatter frequency was clearly captured. The combination of cutting forces measured by the developed dynamometer with EEMD-STFT makes it easier for the operator to reveal the machining conditions within a relatively short time.

9

An optimized EMD method based on cross correlation and root mean square statistical analysis and its application to bearing faults detection

Bousseloub Yahia, Benamira Nadir, Benmassoud Ahmed, Belhamra Ali, Medjani Farida

Archive of Mechanical Engineering

|

2025

|

LXXII, nr 1

105--130

EN

The early detection of bearing faults is critical for ensuring the reliability and performance of electromechanical systems. Vibration signals provide valuable insights into fault characteristics. However, effectively extracting fault-related features remains challenging due to issues like over-decomposition and mode mixing in traditional signal processing methods such as Empirical Mode Decomposition (EMD). To address these challenges, this paper proposes an optimized Empirical Mode Decomposition (OEMD) technique enhanced by cross-correlation (CC) and root mean square (RMS) statistical analysis. The proposed method introduces three novel correlation-based stopping criteria to ensure the independence of Intrinsic Mode Functions (IMFs) in the decomposition result. Furthermore, an RMS-based selection strategy is implemented to identify optimal IMFs that retain fault-related information. The proposed approach is validated using real-world vibration signals from two datasets: an experimental bearing vibration dataset and a public dataset from the Case Western Reserve University (CWRU). The results highlight the feasibility and effectiveness of the method in accurately and automatically selecting the optimal Intrinsic Mode Functions (IMFs) containing high-amplitude peaks at defect characteristic frequencies. These findings demonstrate the robustness of the proposed method in both fault detection and identification.

10

Improve the safety of air transport, especially in militarized terrain, by use of side looking airborne radar and space time adaptive processing

Ślesicki Błażej, Ślesicka Anna, Kawalec Adam

Zeszyty Naukowe. Transport / Politechnika Śląska

|

2024

|

z. 123

335--346

EN

The paper explores the potential to enhance aviation safety, particularly in militarized regions, by outfitting aircraft with Side Looking Airborne Radar (SLAR) and employing space-time adaptive processing (STAP) algorithms. The research objective revolves around implementing a model of side-looking airborne radar and the corresponding STAP algorithms. This technology enables the detection of slow-moving targets amidst strong interference, encompassing both passive (clutter) and active (jammer) elements. Slow-moving targets relative to the aircraft's speed include tanks, combat vehicles, command vehicles, artillery, and logistical assets of enemy forces. The theoretical framework of space-time adaptive processing is presented, elucidating the sequential steps of the classical Sample Matrix Inversion Space-Time Adaptive Processing (SMI STAP) algorithm. The paper underscores the significance of characteristic parameters delineating a linear STAP processor. The proposed solution facilitates the detection of enemy combat measures and enhances aviation safety. It outlines a radar model installed beneath the aircraft's fuselage and elucidates algorithms for space-time adaptive processing of radar signals. The simulations conducted within the article were executed using the MATLAB environment. The simulation results indeed suggest that the proposed solution holds promise for deployment in equipping aircraft of one's own military and those engaged in operations within conflict zones. This paper stands as one of the few contributions in the literature addressing the augmentation of aircraft safety through radar and space-time adaptive processing.

11

A new result on description of the spectrum of a sampled signal

Borys A.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2024

|

Vol. 18 No. 2

393--397

EN

It is explained, in introduction of this paper, why the description of the output signal at an A/D converter in the form that is presented in such respected textbooks as: a one written by Prandoni and Vetterli, and another one by van de Plassche is appropriate and correct. Unlike all others, especially those using in it the so-called comb of Dirac deltas. The latter ones do not lead to getting a correct formula for the spectrum of the output waveform of an A/D converter, or they yield no formula at all. Using the description of the A/D output signal in form of a step function (as in the textbooks mentioned above), a new, correct formula for calculating the spectrum of the sampled signal is derived in this paper. It is a revised version of the formula currently used in the literature, that is of the so-called Discrete-Time Fourier Transform (DTFT), and it is a product of this DTFT and a certain correction factor. Finally, some literature items are referred to in which the designers of integrated circuits (containing A/D converters) point out discrepancies that arise in designs when the multiplying factor mentioned above is not taken into account.

12

Klasyfikacja sygnałów EEG w interfejsach mózg-komputer z wykorzystaniem sieci neuronowych

Galas Krzysztof

Studia i Materiały Informatyki Stosowanej

|

2024

|

T. 16, nr 3

18--23

PL

Celem pracy jest zaprezentowanie metod klasyfikacji sygnałów EEG w interfejsach mózg-komputer (BCI) z wykorzystaniem sieci neuronowych. Dzięki ich zdolności do modelowania złożonych zależności w danych, możliwe jest skuteczniejsze rozpoznawanie wzorców aktywności mózgowej, co przyczynia się do poprawy dokładności i szybkości działania systemów BCI. W pracy omówiono architektury sieci neuronowych wykorzystywane do analizy sygnałów EEG, takie jak sieci konwolucyjne (CNN) czy rekurencyjne (RNN). Badania pokazują, że te metody mają ogromny potencjał w zastosowaniach takich jak sterowanie urządzeniami wspomagającymi, komunikacja oraz rozrywka.

EN

The aim of this paper is to present methods for classifying EEG signals in brain-computer interfaces (BCIs) using neural networks. Thanks to their ability to model complex relationships in the data, it is possible to recognise patterns of brain activity more effectively, which contributes to improving the accuracy and speed of BCI systems. This paper discusses neural network architectures used to analyse EEG signals, such as convolutional networks (CNNs) or recurrent networks (RNNs). The research shows that these methods have immense potentialin applications such as assistive device control, communication,and entertainment.

13

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.

14

Electronic system and signal processing in torque-meter interface of switched reluctance motor drive laboratory test bench

Fabiański Bogdan, Pajchrowski Tomasz, Zawirski Krzysztof

Przegląd Elektrotechniczny

|

2024

|

R. 100, nr 11

59--66

EN

The article aims to present – from a functional point of view – the key solutions of an automated laboratory stand for testing a switched reluctance motor drive. Particular emphasis was placed on one of the proprietary modules of this stand: the dedicated interface of the FUTEK TRS705 torque meter. In this respect, the physical layer and the most important algorithms are discussed. The concept of building a research test bench that will be able to collect information about the basic relationships of a given specimen in an automated and – at the same time – in precise manner is important since fundamental phenomena: generation of electromagnetic torque and an electromotive force are characterized by significant nonlinearities that must be taken into account in the motor models. Hence the issues presented in the article (precise, open torque meter interface) can be considered and useful in a much broader (generic) context, constituting a contribution to solutions in electric drive. The presented methods and system solutions were verified experimentally along with the final presentation of the results of the station operation.

PL

Artykuł ma na celu przedstawienie -- z funkcjonalnego punktu widzenia -- kluczowych rozwiązań zautomatyzowanego stanowiska laboratoryjnego do badania silnika reluktancyjnego przełączalnego. Szczególny nacisk położono na jeden z autorskich modułów tego stanowiska tj. dedykowany interfejs momentomierza przelotowego marki FUTEK TRS705. W tym zakresie omówiono warstwę fizyczną, jak i najważniejsze algorytmy zaimplementowane w programie systemu wbudowanego. Koncepcja opisywanej budowy stanowiska badawczego, które w sposób zautomatyzowany i zarazem precyzyjny będzie mogło zebrać informacje o podstawowych relacjach danego egzemplarza silnika jest bardzo istotna z perspektywy rozwoju algorytmów sterowania -- w szczególności tych wykorzystujących model referencyjny. Fundamentalne zjawiska: generowania momentu elektromagnetycznego oraz wytwarzania siły elektromotorycznej charakteryzują się w silniku SRM istotnymi nieliniowościami, które powinny być uwzględnione w jego modelach obwodowych. Stąd, przedstawione w artykule zagadnienia mogą być rozpatrywane i użyteczne w znacznie szerszym kontekście. Co istotne, przedstawione metody i rozwiązania układowe zweryfikowano eksperymentalnie wraz z końcowym przedstawieniem rezultatów działania stanowiska.

15

Enhanced diagnosis and monitoring of broken rotor bar faults in induction motors using a combined CEEMDAN-MLP approach

Cherif Bilal Djamal Eddine, Rahali Hilal, Seninete Sara

Metrology and Measurement Systems

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2024

|

Vol. 31, nr 4

673--690

EN

This paper presents a novel approach for diagnosing and monitoring Broken Rotor Bar (BRB) faults in induction motors through vibration signal analysis. The method integrates advanced signal processing techniques such as the Hilbert Huang Transform (HHT) with machine learning methods, specifically Multilayer Perceptron (MLP). The study initiates with an HHT application to identify fault-related harmonics, achieved through complete Empirical Ensemble Mode Decomposition with Adaptive Noise (CEEMDAN) of the vibration signal (Vx), producing intrinsic mode functions (IMFs). A statistical analysis, employing correlation coefficients (CC), facilitates the selection of relevant IMFs indicative of BRB faults. IMFs with CC values equal to or greater than 0.2, notably IMF1, IMF2, IMF3, and IMF4, appear informative. Following IMF selection, signal reconstruction ensues by incorporating these useful IMFs. After rebuilding the signal, we use global thresholding based on a statistical analysis that includes Root Mean Square (RMS) and Energy Coefficient (EC) calculations. The Signal Reconstruction Denoising (SRD) meets the criteria for selection. Spectral envelope analysis of SRD is then employed for BRB fault detection. The subsequent phase employs a Multi-Layer Perceptron (MLP) for BRB localization. Features utilized for training the MLP model include EC and various frequency components (fvb-, fvb+, 2fvb-, 2fvb+, 4fvb-, 4fvb+, 6fvb-, 6fvb+, 8fvb-, and 8fvb+). Results from MLP demonstrate exceptional performance, achieving a classification rate of 99.99%. The proposed CEEMDAN-MLP method exhibits robust efficiency, validated by experimental results, and offers promising prospects for BRB fault diagnosis and monitoring in induction motors.

16

Preliminary study: mobile phone as a phonocardiographic signal recorder

Łuczyński Michał

International Journal of Electronics and Telecommunications

|

2024

|

Vol. 70, No. 3

621-626

EN

The aim of this paper is to analyze the possibility of using a mobile phone with a voice recorder function as a phonocardiographic signal recorder. Test measurements were carried out by placing the phone at various points on the chest. For one selected point, measurements were carried out for a group of 120 people, using different models of mobile phones. Data on weight, height and age were collected through a survey. Participants of the study were also asked about diagnosed heart defects and potential problems related to the measurement. Signal quality was assessed using quality parameters. It was checked how the selected methods of signal pre-processing (editing of recordings, filtering) affect the values of quality parameters. The obtained recordings were subjected to automatic signal classification. The result of this work is an extended analysis of the use of mobile phones as electronic stethoscopes and an analysis of the usefulness of signals obtained using this measurement method. The results of these studies are important for the field of medical diagnostics, especially in situations where access to traditional stethoscopes is limited. If mobile phones prove to be effective recorders of phonocardiographic signals, it will open new possibilities in the field of remote heart monitoring and telemedicine. However, it should be noted that further research, including validation and comparison of results obtained with mobile phones with those obtained with traditional stethoscopes, is needed before this technology is introduced into clinical practice.

17

MFCC-Based Sound Classification of Honey Bees

Libal Urszula, Biernacki Pawel

International Journal of Electronics and Telecommunications

|

2024

|

Vol. 70, No. 4

849-853

EN

Smart beekeeping is a rapidly developing field. Automated detection and classification of honey bees opens many new opportunities for studies on their behavior. In this paper, we focus on distinguishing between two classes of bees: female workers and male drones. The classification is performed on mel-frequency cepstral coefficients obtained for audio recordings of their flights in a close proximity to an entrance to a beehive. We compare the classification accuracy for several classifiers. We investigate how partitioning of the frequency spectrum influences the classification results. The study involves series of experiments performed for different cepstral representations in the form of 5, 10, 15, 20 and 40 mel-frequency cepstral coefficients.

18

Deep learning in the classification and recognition of cardiac activity patterns

Jeleń Łukasz, Ciskowski Piotr, Kluwak Konrad

International Journal of Electronics and Telecommunications

|

2024

|

Vol. 70, No. 1

79--85

EN

Electrocardiography is an examination performed frequently in patients experiencing symptoms of heart disease. Upon a detailed analysis, it has shown potential to detect and identify various activities. In this article, we present a deep learning approach that can be used to analyze ECG signals. Our research shows promising results in recognizing activity and disease patterns with nearly 90% accuracy. In this paper, we present the early results of our analysis, indicating the potential of using deep learning algorithms in the analysis of both onedimensional and two–dimensional data. The methodology we present can be utilized for ECG data classification and can be extended to wearable devices. Conclusions of our study pave the way for exploring live data analysis through wearable devices in order to not only predict specific cardiac conditions, but also a possibility of using them in alternative and augmented communication frameworks.

19

Wavelet analysis of ultrasonic lamb wave displacements in three-layer adhesive plates: continuous wavelet transform (CWT) versus the semi-analytical finite element method (SAFEM)

Yacoubi Abdelali, Jabiri Ayoub, Azkour Mustapha, Mandry Rachid, Allami Mhammed El

Engineering Transactions

|

2024

|

Vol. 72, iss. 2

203--224

EN

In this paper, we aim to identify the most appropriate mother wavelet for analyzing the displacements of ultrasonic guided waves in tri-layered adhesive plates.We determine the group velocities of a given mode using various mother wavelets. The precision of each mother wavelet is evaluated by comparing the values of the group velocities with those found by the semianalytical finite element method (SAFEM). The most appropriate mother wavelet function can then be used to study tri-layered adhesive plates with defects.

20

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis

Al-Haddad Luttfi A., Giernacki Wojciech, Shandookh Ahmed A., Jaber Alaa Abdulhady, Puchalski Radosław

Eksploatacja i Niezawodność

|

2024

|

Vol. 26, no. 1

art. no. 176318

EN

In the modern technological advancements, Unmanned Aerial Vehicles (UAVs) have emerged across diverse applications. As UAVs evolve, fault diagnosis witnessed great advancements, with signal processing methodologies taking center stage. This paper presents an assessment of vibration-based signal processing techniques, focusing on Kalman filtering (KF) and Discrete Wavelet Transform (DWT) multiresolution analysis. Experimental evaluation of healthy and faulty states in a quadcopter, using an accelerometer, are presented. The determination of the 1024 Hz sampling frequency is facilitated through finite element analysis of 20 mode shapes. KF exhibits commendable performance, successfully segregating faulty and healthy peaks within an acceptable range. While thesix-level multi-decomposition unveils good explanations for fluctuations eluding KF. Ultimately, both KF and DWT showcase high-performance capabilities in fault diagnosis. However, DWT shows superior assessment precision, uncovering intricate details and facilitating a holistic understanding of fault-related characteristics.