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.
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.
This article focuses on the investigation and analysis of vibrations transmitted to cargo during off-road transportation, with particular emphasis on the impact of vehicle and road surface interactions. The primary objective of the research is to quantify and characterize the amplitudes of vibrations generated by the interaction between heavy-duty truck tires and rough terrain, and their subsequent transmission to cargo containers. To achieve this, a virtual model of a tactical transportation truck was created using TruckSim software. Two characteristic off-road tracks were simulated, based on driving conditions typically experienced by heavy-duty vehicles in demanding logistical scenarios. The experimental validation of the virtual model was conducted using a heavy-duty truck outfitted with a 20 ft (6096 mm) cargo container. The results of the work include recorded acceleration data, suspension behavior, and the maximum driving speed at which the vehicle remained stable on both tracks. Moreover, the work is a direct response to the needs of the automotive industry and the military.
Artykuł zawiera analizę porównawczą statystycznych i niestatystycznych metod estymacji macierz kowariancji zakłóceń w przestrzenno-czasowym adaptacyjnym przetwarzaniu (ang. Space-Time Adaptive Processing STAP) sygnału radarowego dla modelu radaru MIMO (ang. Multiple Input Multiple Output). Zaprezentowano istotę, przebieg algorytmu STAP oraz właściwości najnowszych typów metod estymacji macierzy kowariancji zakłóceń. Autorzy przytoczyli w artykule również swój wkład w rozwój tej technologii.
EN
The article presents a comparative analysis of statistical and non-statistical methods of estimating the clutter covariance matrix in Space-Time Adaptive Processing by using Multiple Input Multiple Output radar geometry. In addition, the properties and stages of the STAP algorithm are presented, as well as the main features of the latest methods for estimating the clutter covariance matrix. Moreover, the authors referenced their contributions to the development of this technology in the article.
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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.
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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.
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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.
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Introduction: Tight glucose monitoring is crucial for diabetic patients by using a Continuous Glucose Monitor (CGM). The existing CGMs measure the Blood Glucose Concentration (BGC) from the interstitial fluid. These technologies are quite expensive, and most of them are invasive. Previous studies have demonstrated that hypoglycemia and hyperglycemia episodes affect the electrophysiology of the heart. However, they did not determine a cohort relationship between BGC and ECG parameters. Material and method: In this work, we propose a new method for determining the BGC using surface ECG signals. Recurrent Convolutional Neural Networks (RCNN) were applied to segment the ECG signals. Then, the extracted features were employed to determine the BGC using two mathematical equations. This method has been tested on 04 patients over multiple days from the D1namo dataset, using surface ECG signals instead of intracardiac signal. Results: We were able to segment the ECG signals with an accuracy of 94% using the RCNN algorithm. According to the results, the proposed method was able to estimate the BGC with a Mean Absolute Error (MAE) of 0.0539, and a Mean Squared Error (MSE) of 0.1604. In addition, the linear relationship between BGC and ECG features has been confirmed in this paper. Conclusion: In this paper, we propose the potential use of ECG features to determine the BGC. Additionally, we confirmed the linear relationship between BGC and ECG features. That fact will open new perspectives for further research, namely physiological models. Furthermore, the findings point to the possible application of ECG wearable devices for non-invasive continuous blood glucose monitoring via machine learning.
This paper presents a novel measurement method and briefly discusses the basic properties of direction of arrival (DoA) measurement in a multiple-input multiple-output (MIMO) radar system by using orthogonality with time-division multiplexing (TDM), where only one transmitting antenna element is active in each time slot. This paper presents the mathematical model of the TDM-MIMO radar operating at 10 GHz, transmitting a string of pulses, the method of transmitting and receiving the signal, and the method of measuring the angle of arrival of the signal based on the use of the Capon algorithm and its modifications. Finally, the correctness of the theory, algorithm and method of measuring the direction of arrival of the signal is verified by experimental simulation. The work discussed in this paper is of great significance to practically demonstrate the capabilities of the TDM MIMO radar sensor in practical implementations like reconnaissance and electronic warfare systems.
In mining, super-large machines such as rope excavators are used to perform the main mining operations. A rope excavator is equipped with motors that drive mechanisms. Motors are easily damaged as a result of harsh mining conditions. Bearings are important parts in a motor; bearing failure accounts for approximately half of all motor failures. Failure reduces work efficiency and increases maintenance costs. In practice, reactive, preventive, and predictive maintenance are used to minimize failures. Predictive maintenance can prevent failures and is more effective than other maintenance. For effective predictive maintenance, a good diagnosis is required to accurately determine motor-bearing health. In this study, vibration-based diagnosis and a one-dimensional convolutional neural network (1-D CNN) were used to evaluate bearing deterioration levels. The system allows for early diagnosis of bearing failures. Normal and failure-bearing vibrations were measured. Spectral and wavelet analyses were performed to determine the normal and failure vibration features. The measured signals were used to generate new data to represent bearing deterioration in increments of 10%. A reliable diagnosis system was proposed. The proposed system could determine bearing health deterioration at eleven levels with considerable accuracy. Moreover, a new data mixing method was applied.
Often, operators of machines, including unmanned ground vehicles (UGVs) or working machines, are forced to work in unfavorable conditions, such as high tem‐ peratures, continuously for a long period of time. This has a huge impact on their concentration, which usu‐ ally determines the success of many tasks entrusted to them. Electroencephalography (EEG) allows the study of the electrical activity of the brain. It allows the determination, for example, of whether the operator is able to focus on the realization of his tasks. The main goal of this article was to develop an algorithm for determining the state of brain activity by analyzing the EEG signal. For this purpose, methods of EEG sig‐ nal acquisition and processing were described, including EEG equipment and types and location of electrodes. Particular attention was paid to EEG signal acquisition, EEG signal artifacts, and disturbances, and elements of the adult’s correct EEG recording were described in detail. In order to develop the algorithm mentioned, basic types of brain waves were discussed, and exem‐ plary states of brain activity were recorded. The influ‐ ence of technical aspects on the recording of EEG sig‐ nals was also emphasized. Additionally, a block diagram was created which is the basis for the operation of the said algorithm. The LabVIEW environment was used to implement the created algorithm. The results of the research showing the operation of the developed EEG signal analyzer were also presented. Based on the results of the study, the EEG analyzer was able to accurately determine the condition of the examined person and could be used to study the concentration of machine operators.
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%.
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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%.
This research article presents a comparative analysis of vibration assessments in lecture halls to investigate their influence on people using contact (accelerometers) and non-contact (laser vibrometers) measurement techniques. The study aims to verify the accuracy and reliability of vibration analysis in relation to two approaches and determined physical parameters, i.e. acceleration amplitudes and vibration velocities. The intriguing fact was that none of the building users reported any perceived discomfort from vibrations, despite the determined parameters of the signal measured using a laser vibrometer indicating exceedance of permissible vibration amplitudes in several frequency bands. The conducted comparative analysis leads to the conclusion that the location of the laser head tripod on the vibrating floor introduces significant vibration amplification, which in turn may lead to an incorrect assessment of the impact of vibrations on people in buildings. The studies described in the article were carried out in accordance with the procedure contained in the Polish national standard PN-B-02171. The obtained results and the resulting conclusions are an important contribution to a better understanding of the advantages and limitations resulting from the use of non-contact measurements.
The ongoing armed conflicts in the world today demonstrate the huge role of electronic reconnaissance, which is becoming one of the primary sources of data on the enemy and the area of operations. One of the tools of radioelectronic warfare, which includes electronic reconnaissance, are radiolocation stations, which play a key role in detecting, tracking and identifying enemy aircraft and in directing armaments to combat these threats. The enemy’s own radiolocation stations are also targeted. The missiles used to do this are called anti-radar missiles (ARM). One way to defend against them is to modify the electromagnetic field around the protected radar. The purpose of this article was to present a model of coherent interference of an anti-radiation missile in a radar trap system The authors applied an analysis of the available literature and carried out computer simulations in the MATLAB environment. In conclusion, the important role played by radar decoys on today’s battlefield was pointed out.
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Toczące się obecnie na świecie konflikty zbrojne dowodzą ogromnej roli rozpoznania elektronicznego, które staje się jednym z podstawowych źródeł danych na temat przeciwnika i obszaru działań. Jednym z narzędzi walki radioelektronicznej, w której skład wchodzi rozpoznanie elektroniczne, są stacje radiolokacyjne, pełniące kluczową rolę w wykrywaniu, śledzeniu i identyfikacji przeciwników latających oraz w kierowaniu uzbrojeniem w celu zwalczania tych zagrożeń. Własne stacje radiolokacyjne są również celem przeciwnika. Pociski, które do tego służą, nazywa się pociskami przeciwradiolokacyjnymi. Jednym ze sposobów obrony jest modyfikacja pola elektromagnetycznego w otoczeniu chronionego radaru. Celem artykułu było przedstawienie modelu koherentnego zakłócenia pocisku przeciwradiolokacyjnego w systemie pułapek radiolokacyjnych radaru. Autorzy zastosowali analizę dostępnej literatury oraz przeprowadzili symulacje komputerowe w środowisku Matlab. Podsumowując, wskazano na istotną rolę, jaką pełnią na dzisiejszym polu walki pułapki radiolokacyjne.
In order to design a stable and reliable voice communication system, it is essential to know how many resources are necessary for conveying quality content. These parameters may include objective quality of service (QoS) metrics, such as: available bandwidth, bit error rate (BER), delay, latency as well as subjective quality of experience (QoE) related to user expectations. QoE is expressed as clarity of speech and the ability to interpret voice commands with adequate mean opinion score (MOS) grades. This paper describes a quality evaluation study of a two-way speech transmission system via bandwidth over power line – power line communication (BPL-PLC) technology in an operating underground mine. We investigate how different features of the available wired medium can affect end-user quality. The results of the described study include: two types of coupling (capacitive and inductive), two transmission modes (mode 1 and 11), and four language sets of speech samples (American English, British English, German, and Polish) encoded at three different bit rates (8, 16, and 24 kbps). Our findings can aid both researchers working on low-bit rate coding and compression, signal processing and speech perception, as well as professionals active in the mining and oil industry.
This work presents an analysis of vibration signals for bearing defects using a proposed approach that includes several methods of signal processing. The goal of the approach is to efficiently divide the signal into two distinct components: a meticulously organized segment that contains relatively straightforward information, and an inherently disorganized segment that contains a wealth of intricately complex data. The separation of the two component is achieved by utilizing the weighted entropy index (WEI) and the SVMD algorithm. Information about the defects was extracted from the envelope spectrum of the ordered and disordered parts of the vibration signal. Upon applying the proposed approach to the bearing fault signals available in the Paderborn university database, a high amplitude peak can be observed in the outer ring fault frequency (45.9 Hz). Likewise, for the signals available in XJTU-SY, a peak is observed at the fault frequency (108.6 Hz).
W artykule przedstawiono rozwiązania konstrukcyjne stosowane w nowoczesnych, elektronicznych licznikach energii elektrycznej. Opisana została budowa podstawowych układów elektronicznych, ze szczególnym uwzględnieniem obwodów pomiarowych wpływających na dokładność oraz możliwości pomiarowe liczników. Zaprezentowane zostały również najważniejsze wymagania norm dotyczące niezawodności oraz bezpieczeństwa urządzeń.
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This article presents information about electricity meters construction. Measurement circuits of meters and informations about different circuits solutions accuracy has been presented. The most important requirements of standards regarding the safety and accuracy of electricity meters were also described.
Structural active noise control (ANC) is one of few solutions applicable when global noise reduction is required: control of a whole device casing allows to lower the acoustic energy emitted by this device. Unfortunately, structural ANC usually requires a large number of sensors and actuators, making the control system multichannel with large dimensionality. This in turn presents a huge computational power demands. There are several ways to lower this demand, the partial updates being one of them. The goal of this paper is to show applicability of the leaky partial update LMS algorithms in structural ANC of a washing machine casing. The transfer functions of the numerous device paths were identified using a real washing machine in the ANC laboratory. The identified transfer functions allowed to create a simulation system, where different algorithms could be easily tested. The results of the simulations confirm effectiveness of the proposed solution.
The development of the Internet of things and automatisation in everyday life also influences our houses. There are more and more devices on the market which can be controlled remotely. One kind of such control involves the use of voice signals. This method tends to use microphone arrays and dedicated algorithms to enhance the speech signal and recognize the words in it. In this project, a small 5-microphone array was developed. To enhance the quality of the signal, dedicated software was written. It consists of several modules, including the direction of arrival estimation, denoising, and differentiation between adults and children. The results showed that the custom algorithm can increase the signal to noise ratio by up to 6 dB.
Correct posture is a key element in the proper functioning of the entire body. Both defects and postural disorders lead to overload syndromes and degenerative changes in the musculoskeletal system. Different body positions correlate with respiratory parameters, which form the basis in modifying loudness and accentuation when speaking or singing Body posture can affect the quality of the voice signal and its fatigue. As movement and duration intensify, vocal effort increases. What is still open, however, is the problem of speech signal evaluation, especially in order to obtain assessments useful in the context of supporting medical diagnosis, optimizing therapy and monitoring rehabilitation. Meanwhile, such evaluations are what we need in medicine, rehabilitation and sports. This paper presents excerpts from a study of the effects of changes in posture and fatigue in healthy subjects, and those with phonation disorders, on changes in the acoustic parameters of the speech signal.
The objective of this paper is to show from another perspective that the definition of the spectrum of a sampled signal, which is used at present by researchers and engineers, is nothing else than an arbitrary choice for what is possibly not uniquely definable. To this end and for illustration, the Shannon’s proof of reconstruction formula is used. As we know, an auxiliary mathematical entity is constructed in this proof by performing periodization of the spectrum of an analog, bandlimited, energy signal. Admittedly, this entity is not called there a spectrum of the sampled signal - there is simply no need for this in the proof – but as such it is used in signal processing. And, it is not clear why just this auxiliary mathematical object has been chosen in signal processing to play a role of a definition of the spectrum of a sampled signal. We show here what are the interpretation inconsistences associated with the above choice. Finally, we propose another, simpler and more useful definition of the spectrum of a sampled signal, for the cases where it can be needed.
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