Wavelet Based Signal Processing

This paper presents an alternative method to classical Fourier transform approaches used in data analysis. In this manner, application of Continuous Wavelet Transform (CWT) was introduced for vibration data which is collected by an accelerometer replaced on an induction motor. Hence, related results were indicated as properties of the vibration signal and additional information was extracted from the raw data using the time-scale analysis. This extracted information was represented in high frequency region of the original data which is defined between 2 and 4 kHz.

vi A Deus, pela vida, por me dar forças nas horas mais difíceis, guiar meu caminho, e por tudo que sou. Aos meus pais, Edson e Eloisa Cardoso, pelo amor, exemplo de caráter e empenho na minha educação. Às mulheres da minha vida: Joelle e Vitória, pelo amor, carinho, paciência, incentivo e por todos os momentos que passamos juntos. vii AGRADECIMENTOS _______________________________________________ Aos fundos de apoio através do acordo de cooperação acadêmica -PROCAD-CAPES com a parceria entre a UFPA (PPGEE "Programa de Pós-Graduação em Engenharia Elétrica") e a UFCG (COPELE "Coordenadoria de Pós-Graduação em Engenharia Elétrica"), sem a qual este trabalho não seria possível. Ao estimado professor Raimundo Nazareno Cunha Alves, pela oportunidade oferecida de cursar o mestrado, por acreditar no meu potencial e por sua amizade. Aos professores orientadores Ivan Sebastião de Souza e Silva e Manoel da Silva Filho, que estiveram presentes ao longo deste trabalho contribuindo com valiosas orientações e sugestões de melhorias. Ao professor Raimundo Carlos Silvério Freire, pelo apoio como tutor em muitas disciplinas ministradas em Campina Grande, pela orientação, correção deste trabalho e por sua amizade. Aos professores Benedito Luciano, Hiran de Melo e Gutenberg, pelo aprendizado nas disciplinas cursadas em Campina Grande, além da amizade feita. Aos amigos e colegas dos Laboratórios de Automação e Acionamento Eletrônico (LAAE) da UFPA e de Instrumentação e Metrologia Científica (LIMC) da UFCG: Denis, Alan, Miguel, Alécio, Lucilene, Francisco, Will, professores Guilherme e Petrônio, Ricardo, Ari, e muitos outros que não citei, mas deixo meus agradecimentos. Aos colegas do Laboratório de Eletrofisiologia Clínica, no Centro de Ciências Biológicas da UFPA, em especial ao Givago e Bruno, pelas colaborações na realização dos testes com o amplificador. A todos vocês meus sinceros agradecimentos. viii SUMÁRIO 1 INTRODUÇÃO 1 6 RESULTADOS EXPERIMENTAIS COM O CONDICIONADOR DE BIOPOTENCIAIS CORTICAIS 6.1 Potencial Cortical Provocado Visual (PCPV)

The proposed contribution describes a method for the detection and classification of harmonic transients, as they occur in electrical networks. The method uses, as bases, a special class of wavelet-packets, i. e. smooth biorthogonal sine and cosine-packets in order to find the best ''Shannon-basis". The algorithm is based on the minimization of Shannon's function for the decomposed wanted signal over the biorthogonal analyzing functions. The proposed method is validated by using a real application, signals from rail systems. One shows the fastness and the robustness of the algorithm and thus its real time applicability. In particular, one detects and classifies inrush currents. Der vorliegende Beitrag beschreibt ein Verfahren zur Erkennung und Klassifizierung von harmonischen Transienten, wie sie unter anderem in elektrischen Netzen auftreten. Das Verfahren verwendet als Basis eine spezielle Klasse von Wavelet-Paketen, nämlich glatte lokalisierte biorthogonale Sinus-und Kosinus-Pakete, um für ein klassifizierendes Signal die beste ,,Shannon-Basis" zu finden. Dieser Signalzerlegung liegt die Minimierung der Shannon'schen Entropiefunktion zugrunde, sie hängt also adaptiv vom zu analysierenden Signal ab. Die Praxistauglichkeit des sehr schnellen echtzeitfähigen Verfahrens wird anhand eines praktischen Beispiels aus der Bahntechnik illustriert, bei dem für die Schutzabschaltung zwischenbetriebstypische Spannungsänderungen aufgrund der Einfahrt in einen neutralen Netzabschnitt und sicherheitskritische Spannungsänderungen aufgrund interner Fehler unterschieden werden müssen.

This paper presents MEM|8, a novel cognitive architecture that simulates human-like consciousness through wave-based memory encoding and multi-modal sensor integration. The system represents memories as interference patterns in a dynamic wave grid, enabling natural temporal decay, emotional modulation, and cross-sensory binding. Key innovations include a unified .m8 file format integrating Marqant compression (70-90%) with quantum-semantic encoding, achieving 99% total compression. The architecture features four hierarchical reactive memory layers (0-10ms hardware reflexes to >200ms conscious deliberation) and SIMDoptimized operations (AVX2/AVX-512). Experimental results demonstrate memory access speeds up to 973× faster than traditional vector databases, with just 32 bytes per memory pattern. The system achieves 91.8% accuracy in semantic fusion across modalities and maintains collective emotional intelligence with psychological safety guarantees. Critical safety mechanisms include the Custodian memory guard system that prevents cognitive overload, repetition poisoning, and therapeutic memory reintroduction protocols. This represents a significant advance toward safe and beneficial general artificial intelligence with genuine autonomous decision-making capabilities.

ElectroCardiogram(ECG) is used to measure and diagnose electrical activity of heart. R peak detection from ECG signal is our main concern. It is the basic mark for the identification of different arrhythmias. In this paper, R wave extraction is performed by using Wavelet Transform. The wavelet transform has risen over late years as an effective time-frequency analysis and it is efficiently analyze complex non stationary signals. In this research, R wave is extracted accurately then heart beat is analyzed by the detection of RR intervals. R wave extraction is performed and implemented in the most familiar multipurpose tool, MATLAB .In this research,99.9% accurate R peak is detected by this type of approach. By accurate detection of R peak, cardiac diseases can easily be identified such as Sinus tachycardia, Sinus bradycardia, Supraventricular tachycardia (SVT), Atrial fibrillation (AF), Ventricular tachycardia and Heart block.

This paper presents a comprehensive methodology for diagnosing a range of common mechanical and hydraulic faults in centrifugal pumps utilizing audio Fast Fourier Transform (FFT) data. It details the unique frequency signatures, harmonic patterns, and amplitude characteristics associated with faults such as cavitation, rubbing, impeller imbalance, various bearing defects (BPFO, BPFI, BSF, FTF), mechanical looseness, resonance, bent shaft, hydraulic anomalies (turbulence, recirculation), and lubrication issues. The focus is on providing a practical guide for technicians to confidently differentiate these faults through audible sound analysis, contrasting audio FFT features with those from traditional accelerometer-based vibration analysis. Strategies for interpreting complex spectra arising from co-occurring faults and the influence of operating conditions on audio signatures are discussed, supported by real-world spectral patterns and case study insights. The aim is to equip practitioners with the knowledge to effectively implement audio-based condition monitoring for enhanced pump reliability.

Deus, a Virgem Maria e toda a sua intercessão. Aos meus Pais e minha Namorada, pessoas especiais que sempre fizeram diferença. RESUMO KREPSKI, Gustavo G. Aquisição de sinais eletromiográficos com aplicação em Fourier para controle de dispositivos biomecânicos. 2018. 76 páginas. Engenharia Elétrica -UNICESUMAR, Maringá -Pr, 2018. O acionamento de dispositivos biomecânicos é proveniente da aquisição de sinais eletromiográficos. Esses dispositivos biomecânicos são comum mente conhecidos como próteses, podendo ser de membros superiores, que é o caso empregado neste trabalho. As próteses controladas pelo acionamento desses sinais possuem um custo muito elevado atualmente no mercado, com isso impossibilitanto o acesso para todos aqueles que necessitam, aqueles que não possuem os seus membros superiores, acabam se delimitando a certas atividades. Esse trabalho apresenta um estudo a respeito da aquisição dos sinais eletromiográficos, e assim proporcionar uma abordagem com um custo mais acessivel, pois o circuito aqui construido se da em sua forma mais simples, contendo um amplificador de instrumentação, filtro passa-baixa, filtro passa-alta, retificador de precisão e um detector de envelope, com este circuito então foi elaborada uma placa de circuito impresso para melhor desempenho, também é apresentado um estudo referente a esses sinais, através de modelagens matemáticas isso será necessário para conhecermos mais esse sinal, como ele funciona, os niveis de interação e comunicação que podemos ter com ele, e com isso passar a um melhoramento no dimensionamento do circuito de aquisição destes sinais. Devido a esse estudo podemos nos deparar com a interação dos niveis de frequência que as fibras músculares apresentam e aplicador a análise por Fourier que vem trazer as componentes senoidais e cossenoidais do sinal estudado.

Reverberasi (multiple echo) adalah sinyal noise akustik yang muncul dalam suatu ruang tertutup akibat adanya superposisi dari suatu pantulan multiple dan difraksi dari dinding dan objek-objek yang ada di dalam ruangan tersebut. Sinyal yang tereverberasi merupakan masalah kompleks yang sulit dicari solusinya, karena sinyal suara asli berkorelasi dengan efek reverberasi.

Many studies have focused on the nonlinear analysis of electroencephalography mainly for the characterization of epileptic brain states. The spatial and temporal dynamics of the epileptogenic process is still not clear completely especially the most challenging aspects of epileptology which is the anticipation of the seizure. Despite all the efforts we still don't know how and when and why the seizure occurs. However actual studies bring strong evidence that the interictal-ictal state transition is not an abrupt phenomena. Findings also indicate that it is possible to detect a preseizure phase. We will study the patients admitted to the epilepsy monitoring unit for the purpose of recording their seizures. These patients have their EEG signal recorded 24 hours a day for several days until they have enough number of seizures to determine eligibility for seizure surgery. Thus, preictal, ictal, and post ictal electroencephalography recordings are available on such patients for analysis. We propose to use wavelet analysis in order to investigate a case study of the electroencephalography signal and determine the localization of the seizure and its characteristics.

Many studies have focused on the nonlinear analysis of electroencephalography mainly for the characterization of epileptic brain states. The spatial and temporal dynamics of the epileptogenic process is still not clear completely especially the most challenging aspects of epileptology which is the anticipation of the seizure. Despite all the efforts we still don't know how and when and why the seizure occurs. However actual studies bring strong evidence that the interictal-ictal state transition is not an abrupt phenomena. Findings also indicate that it is possible to detect a preseizure phase. We will study the patients admitted to the epilepsy monitoring unit for the purpose of recording their seizures. These patients have their EEG signal recorded 24 hours a day for several days until they have enough number of seizures to determine eligibility for seizure surgery. Thus, preictal, ictal, and post ictal electroencephalography recordings are available on such patients for analysis. We propose to use wavelet analysis in order to investigate a case study of the electroencephalography signal and determine the localization of the seizure and its characteristics.

Electrocardiogram (ECG) signals are used for prognosis of anatomic heart functions in clinical routine. During recording, ECG is contaminated with artifacts owing to specific reasons, which makes accurate delineation and interpretation of ECG more difficult. The commonly noticed artifacts include; baseline wanders, power line interference and muscle tremors. This paper aims at elucidation for the algorithms for optimal selection of wavelet function and decomposition level for removal of these artifacts in ECG, while preserving the diagnostic information. The features of wavelet transforms viz. multi-resolution, information in time and frequency, and thresholding have been employed for the implementation of algorithms in MATLAB. The performance of wavelet functions at various decomposition levels has been evaluated using percentage root mean square difference (PRD) criterion. Simulated results indicate that optimal value of wavelet function is db7 at 8th decomposition level for removal of artificially added baseline wander with PRD of 0.1636%, coif2 at 5th decomposition level for removal of artificially added power line interference with PRD of 0.3980% and db7 at 8th decomposition level for removal of artificially added muscle tremor with PRD of 0.8959% in selected records of European ST-T database (EDB). The proposed method shows better performance than existing methods in terms of lower PRD. Moreover it also addresses to the issues of conventional methods.

Inspiration of this project is from the need to find an efficient method for analysis of ECG signal which needs to be simple, have good accuracy and have less computational time. As the main cause of death around the globe is being led by heart related diseases, hence a recent study shows that in the working age group of around 24-65 years, the death percentage of 25 is only because of the various heart related diseases. Hence ECG is the most important signal for consideration and observation to prevent these issues. ECG is a bio medical signal which can be detected by using electrodes placed in the particular locations of human body. In my research work there are two stages for the efficient analysis of ECG signal. Primary stage is the enhancement of ECG signal, that is removal of noise. It is done by extracting the required cardiac components by rejecting the background noises mainly Baseline Wander noise which is a low frequency noise and generally lies below 0.5 Hz. This is done...

Dalam proses speech recognition, speech syntesis dan speech enhancement, signal suara yang diinputkan tidak dapat langsung dikenali atau diindentifikasi sebagai gelombang signal voice atau unvoice. Proses analisis ucapan dalam menetapkan voice dan unvoice biasanya dilakukan dengan ekstrasi dari speech signal atau signal suara. Dalam penelitian ini, kami melakukan dan membandingkan 3 akurasi metode yakni: penentuan manual dengan software Adobe Audition dibandingkan pada tool matlab dengan metode separation of voice and unvoice using non overlapping block, zero-crossing rate and energy of a speech signal, dan juga membandingkan dengan metode peneliti yaitu penggabungan teknik overlapping blocks, zero crossing rate dan short time energy dalam menentukan voice dan unvoice untuk memisahkan bagian unvoice dan voice ucapan dari sinyal suara. Dengan ada perbedaan pada overlapping block dan non-overlapping block. Kami mengevaluasi hasil dari semua metode tersebut bahwa teknik yang digunakan peneliti dengan overlapping blocks, zero crossing rate dan short time energy terbukti lebih efektif dalam pemisahan voice dan unvoice.

Dalam proses speech recognition, speech syntesis dan speech enhancement, signal suara yang diinputkan tidak dapat langsung dikenali atau diindentifikasi sebagai gelombang signal voice atau unvoice. Proses analisis ucapan dalam menetapkan voice dan unvoice biasanya dilakukan dengan ekstrasi dari speech signal atau signal suara. Dalam penelitian ini, kami melakukan dan membandingkan 3 akurasi metode yakni: penentuan manual dengan software Adobe Audition dibandingkan pada tool matlab dengan metode separation of voice and unvoice using non overlapping block, zero-crossing rate and energy of a speech signal, dan juga membandingkan dengan metode peneliti yaitu penggabungan teknik overlapping blocks, zero crossing rate dan short time energy dalam menentukan voice dan unvoice untuk memisahkan bagian unvoice dan voice ucapan dari sinyal suara. Dengan ada perbedaan pada overlapping block dan non-overlapping block. Kami mengevaluasi hasil dari semua metode tersebut bahwa teknik yang digunakan ...

Wavelets Daubechies have been widely applied to signal processing, such as automatic speech recognition system. Wavelet Daubechies, which is one of the wavelet families distinguished by its order, defined as N. The magnitude of the order N value has an influence on the wavelet decomposition where with the greater N value there is an increase in the smoothness of multiresolution analysis results. However, not all order Daubechies wavelet can give the same good recognition results so that its application still such as trial and error. Therefore, it is necessary to determine the order of the Daubechies wavelet base function on the Indonesian voice signal through its similarity level. The method can be used to determine the similarity level between speech signal and wavelet Daubechies function N order by calculating its crosscorrelation coefficient. The result shows that there is inconcistency of the best wavelet daubechies basis function for Indonesian vowels a,i,u,e,è,o, and ò. Which ...

Electrocardiography (ECG) is a medical test used to measure the heart's electrical conduction system. Many cardiac abnormalities can be detected through ECG analysis. Various computerized techniques have been applied to assist physicians in an accurate diagnosis, among them Artificial neural networks (ANNs), Genetic algorithms (GAs) and their combinations (GANNs). A cardiac arrhythmia computerized diagnostics is a good example of such an application. Many different ANN approaches to arrhythmia classification appear in the literature, but we couldn't find any work related to GANN for this problem. In this paper we are closing this gap by presenting classification system of cardiac arrhythmia using GANN classifier. ANN is the base of the system, while GAs are used to evolve ANN architecture and weights. In addition, another GA is used to perform a feature selection task. The system is trained and tested for online UCI Machine Learning data set for cardiac arrhythmia. The classification performance of the system is evaluated by means of classification accuracy. The proposed classifier gives best classification results of 90.23%, 100%, 94.98%, 99.46%, 97.88% and 86.5% on classifying ischemic changes, old anterior myocardial infarction, old inferior myocardial infarction, sinus tachycardia, sinus bradycardia and right bundle branch block respectively. These results are competitive with the state of the art results in the field; that proves the effectiveness of our application. In addition the tool is enough generic to be used in solving of a wide range of problems. Also we have investigated effectiveness of GA as a training method. Exhaustive experiments demonstrate that classification accuracy of GA-trained classifiers is inversely proportional to the number of classification cases and depends on the content and size of the feature set for the classifier that is built by help of it.

This paper describes an investigation on techniques for analyses of lung sounds using wavelet decomposition. The proposed methodology, called filtering by selective spectral analysis, reduces the amplitude of the heart sounds and the sounds generated by the airflow in the respiratory system, increasing the audibility of the adventitious sounds in digitally recorded lung sounds. The lung sounds digitally recorded have its spectra segmented using wavelet 4th level of decomposition. So, a spectral analysis is computed on the approximation signal, looking for the detection of this signal's main spectral components. This spectral components are used to guide the construction of a digital filter, which is applied to the approximation signal with the goal of eliminating all the low-amplitude spectral components. After the filtering procedure the lung sound signal is recomposed, by wavelet recomposition technique, using the filtered approximation signal. The resulting signals show a reduction in the heart sound and in the normal airflow signals in lung sounds. Otherwise, the adventitious sounds magnitude remain unchanged when their spectral components are located outside the approximation 4 spectral band. When the adventitious sound's spectral components are located into the approximation's band it shows a little reduction in its magnitude level, when compared to the reduction of heart and airflow signals.

ABSTRACT
The Electroencephalogram (EEG) allows a specialized professional the help needed to perform a diagnostic or therapeutic techniques. In this work the artifact from an eyelid blink (blink of the eyes) was studied in these EEG signals. These blinks are usually ignored by medical experts, but it is possible that they have important details to the diagnostic. Using MATLAB software, a way to detect these blinks was made using a trained Artificial Neural Network to detect patterns. For the filtering of the blink was used a selective technique allowing to remove only the artifact without interfering in the important signals mixed to it, the Savitzky Golay (SG) digital filter, very used for this purpose, having been tested using a simulated blink and the Cross-correlation technique. At the end, an algorithm was developed to scan the signal of an EEG to detect the blinks, to filter them and to replace them in the original signal.
Key-words: Artificial Neural Network. Savitzky Golay Filter. Electroencephalogram.

There is an increase in cardio logical patients all over the world due to change in modern life style. It forces the medical researchers to search for smart devices that can diagnosis and predict the onset of cardiac problem before it is too late. This motivates the authors to predict Arrhythmia that can help both the patients and the medical practitioners for better healthcare services. The proposed method uses the frequency domain information which can represent the ECG signals of Arrhythmia patients better. Features representing the MIT-BIH Arrhythmia are extracted using the efficient Short Time Fourier Transform and the Wavelet transform. A comparison of these features is made with that of normal human being using Neural Network based classifier. Wavelet based features has shown an improvement of Accuracy over that of STFT features in classifying Arrhythmia as our results reveal. A Mean Square Error (MSE) of with wavelet transform has validated our results.

ElectroCardiogram(ECG) is used to measure and diagnose electrical activity of heart. R peak detection from ECG signal is our main concern. It is the basic mark for the identification of different arrhythmias. In this paper, R wave extraction is performed by using Wavelet Transform. The wavelet transform has risen over late years as an effective time-frequency analysis and it is efficiently analyze complex non stationary signals. In this research, R wave is extracted accurately then heart beat is analyzed by the detection of RR intervals. R wave extraction is performed and implemented in the most familiar multipurpose tool, MATLAB .In this research,99.9% accurate R peak is detected by this type of approach. By accurate detection of R peak, cardiac diseases can easily be identified such as Sinus tachycardia, Sinus bradycardia, Supraventricular tachycardia (SVT), Atrial fibrillation (AF), Ventricular tachycardia and Heart block.

Metode Wavelet merupakan salah satu metode yang unggul untuk menganalisis serta mengekstraksi ciri isyarat suara tutur. Dalam proses ekstraksi ciri menggunakan metode Wavelet, terdapat beberapa faktor yang dapat berpengaruh dalam mendapatkan ciri yang bersifat diskriminan, diantaranya: pemilihan mother wavelet, pemilihan sub-band, dan pemilihan level dekomposisi. Beberapa contoh mother Wavelet yang sering digunakan diantaranya: Daubechies, Coiflet, Meyer, Haar, Symlet, dan Biortogonal. Pada penelitian ini dilakukan perbandingan berbagai macam mother Wavelet untuk mendapatkan ciri yang efektif untuk mengklasifikasi isyarat tutur, yang diharapkan melalui proses perbandingan berbagai mother Wavelet akan didapatkan mother Wavelet yang terbaik dan paling cocok untuk mengolah dan mengekstraksi isyarat tutur. Tap filter yang akan digunakan pada masing-masing Mother Wavelet pada rentang 1 hingga 10. Hasil penelitian perbandingan mother Wavelet untuk mengklasifikasi isyarat tutur menunjukkan bahwa koefisien filter Mother Wavelet Daubechies 2 menghasilkan akurasi klasifikasi yang paling dibandingkan Mother Wavelet lainnya (Haar, Coiflet2, Meyer, Symlet, dan Biortogonal) untuk mengekstraksi dan mengklasifikasi suku kata konsonan hambat bahasa Indonesia ditunjukkan oleh hasil akurasi 90

Due to its simplicity and low cost, analyzing an electrocardiogram (ECG) is the most common technique for detecting cardiac arrhythmia. The massive amount of ECG data collected every day, in home and hospital, may preclude data review by human operators/technicians. Therefore, several methods are proposed for either fully automatic arrhythmia detection or event selection for further verification by human experts. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification. There are number of challenges in detection of arrhythmias in heart beat dataset. Although many researchers have suggested various approaches to resolve them, still there are requirements for invention and improvements.

Abstrak Penelitian ini adalah untuk mengembangkan algoritma TDOA (Time Difference of Arrival) untuk penentuan jarak roket berdasar pada korelasi waktu dan korelasi frekuensi. Algoritma TDOA ini dikembangkan karena penentuan jarak roket selama ini masih mengandalkan teknologi satelit (menggunakan GPS). Ketergantungan pada satelit sangat rentan dengan aspek keamanan karena teknologinya masih dikuasai negara lain. Algoritma ini dikembangkan agar pengukuran jarak roket dapat memanfaatkan teknologi radio UHF. Radio transponder diletakkan di sisi roket dan perangkat radar sekunder ada di sisi stantsiun pengamat. Ada satu masalah yang harus diatasi terlebih dahulu yaitu akurasi penggunaan algoritma TDOA karena timbulnya distorsi sinyal yang diterima pada radar sekunder. Algoritma TDOA digunakan untuk menghitung jarak roket. Simulasi yang telah dilakukan untuk sinyal bersuara dan sinyal dengan gangguan acak. Simulasi dilakukan dua kali, pertama tanpa menggunakan filter dan yang kedua disimu...

This paper presented the development of an automatic speech recognition (ASR) system based on Indonesian syllable using HMM classifier. The recognition rate for an ASR based on Indonesian syllable is still low. This problem might be occurs because the extraction process was applicated directly to the whole syllables. Therefore, in this research the feature extraction process is implemented to each of its constituent phonemes. MFCC and WPT were used as the feature extraction method. Feature of MFCC is obtained by applying frame of 512 sample for each phoneme. In Mel Frequency Warping process using 40 units of triangular filter banks. As for WPT feature extraction process, wavelet daubechies db3 and db7 were used with 5th level decomposition. Feature that extracted then randomly selected and established as a syllable's feature. The recognition accuracy using training data showed 100% accuracy for WPT feature, and 75% for MFCC feature. While using the external testing data the result showed the best accuracy are 100% for WPT db7, 83.33% for WPT db3 and 50% for MFCC. Whole best recognition results were obtained at the point of intersection for the consonants are of 1024 samples.

Penelitian ini akan mengkaji salah satu pembahasan dalam Ilmu Psikolinguistik yakni gangguan berbahasa pada jenis gangguan berbicara berupa serak-serak basah. Berdasarkan hal tersebut maka penelitian ini bertujuan untuk mengetahui faktor-faktor yang menyebabkan gangguan bahasa berupa serak-serak basah. Data pada analisis ini diperoleh dengan teknik wawancara pada seorang pria dewasa yang dianggap mengalami gangguan berbahsa berupa serak-serak basah.. Metode penelitian yang digunakan adalah metode penelitian deskriptif kualitatif. Metode tersebut diharapkan bisa mendeskripsikan secara sistematis, aktual dan akurat terhadap data yang diperolah. Penelitian ini merupakan penelitian analisis pada data hasil wawancara yang dilakukan pada orang atau narasumber  yang dianggap mengalami gangguan berbahsa tersebut. Instrument penelitian yang digunakan berupa pertanyaan-pertanyaan yang dapat memberikan gambaran tentang penyebab gangguan berbahasa berupa serak-serak basah. Sehingga pada akhirnya diharapkan penelitian ini dapat menjelaskan penyebab gangguan berbahasa berupa serak-serak basah.

View the article online for updates and enhancements. You may also like Size estimation of spall-like fault on rolling element bearing using an integrated approach incorporating total variation regularization and singular spectrum analysis Dhaval V Patel, Vipul M Bhojawala and Kaushik M Patel-Experimental study on enhancement of defect detection and defect size estimation in deep groove ball bearing Priyank Patel and V N Patel-Robust condition monitoring and fault diagnosis of rolling element bearings using improved EEMD and statistical features Fan Jiang, Zhencai Zhu, Wei Li et al.

In this letter we present a wavelet with very low sidelobes whose spectral occupancy in the frequency domain is controlled by a parameter that can assume any positive real value. The associated scaling function is derived from the Gaussian waveform. Due to its good spectral characteristics, this function is well suited as an elementary shaping pulse for digital modulation.

Orientador: Prof. Dr. Paulo Henrique SiqueiraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa : Curitiba, 20/04/2021Inclui referências: p. 90-94Área de concentração: Programação MatemáticaResumo: As condições meteorológicas afetam diretamente diversos setores das atividades humanas, tornando a previsão da ocorrência de hidrometeoros um importante recurso. Uma das ferramentas de sensoriamento remoto que fornecem informações sobre as condições meteorológicas são os radares meteorológicos, porém somente os radares de dupla polarização fazem a classificação de hidrometeoros, de modo que devem ser usadas técnicas alternativas para fazer essa classificação nas áreas monitoradas por radares de polarização simples. O objetivo deste trabalho é aplicar técnicas de redes neurais artificiais para fazer a classificação de hidrometeoros a partir de dados de radares de polarização simples utilizando c...

ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiological parameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of pattern recognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes of predefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generated waveform gives almost all information about activity of the heart. The ECG signal ...

In this paper, among different methods of signal processing, wavelet transform is chosen due to its advantages over other methods. To show wavelet transform capabilities, first an HVDC system which has noise on its output current is simulated. In the first step noise is removed by applying discrete wavelet transform. In the next steps, harmonic problem is resolved thorough appropriate passive filters. In this paper, we suppose that not only low order harmonics exist in the output current, but also high order ones. The results indicate that we can obtain a good passive filter design for harmonic reduction by decomposing a signal into its harmonic components via applying discrete wavelet transform.

The progress of power electronics application technology, harmonic pollution in the electrical power system network is increasing seriously, having a serious impact on the energy supply network and equipment. In this discussion, the author studied harmonic characteristics, analysis of harmonic detection methods using wavelet algorithms with Matlab. The results of the simulation show that wavelet transforms can compose harmonic components that are settled in the system according to their needs.

The article examines the technical condition of rotating parts using acoustic diagnostics. The measured object was a mechanical transmission of a field vehicle. Recently this method has been developing very quickly and is expected to be used not only for the signal analysis itself, but also for the failure occurrence prediction which is our aim in the future. In our article we observe the technical condition of a four-speed transmission and analyse the acoustic signal expressed by the root mean square of a noise level in decibels.

We consider non-parametric Bayesian estimation of the drift coefficient of a one-dimensional stochastic differential equation from discrete-time observations on the solution of this equation. Under suitable regularity conditions that are weaker than those previosly suggested in the literature, we establish posterior consistency in this context. Furthermore, we show that posterior consistency extends to the multidimensional setting as well, which, to the best of our knowledge, is a new result in this setting.

The major problems experienced in reciprocating air compressor are excessive noise in operation and vibration of the compressor. In this paper a reciprocating air compressor is studied and it is monitored to find the mechanical or other faults present in it by performing noise and vibration test. Here vibration signature analysis is done along with measurement of noise harshness level of a reciprocating compressor. After the test the faulty part of the compressor was replaced and same test was performed again. Microscopic analysis of parts of bearings was also done to study and analyze the cause of defects. Here significant improvement is achieved in NVH.

This work shows the development of a wireless EEG signal conditioning system where they are processed by digital signal processing using LabVIEW software for the specific application of driving a cooler that controls the height of a Styrofoam ball that It is inside a clear plastic tube according to the type of alpha and beta signal that corresponds, respectively, to the state of relaxation and concentration of an individual. Wireless communication of information from biomedical hardware makes data acquisition more portable to the person who will make use of such a device. A total gain of 11326.36 [V / V] EEG signal conditioning board was obtained for signals applied at a frequency of 20 Hz and wireless data communication was successfully achieved via two NRF24L01 radio frequency modules . The prototype for the application of this wireless EEG signal conditioning system perfectly controlled the application, in other words, the height of the Styrofoam ball. And the system was validate...

Wavelets Daubechies have been widely applied to signal processing, such as automatic speech recognition system. Wavelet Daubechies, which is one of the wavelet families distinguished by its order, defined as N. The magnitude of the order N value has an influence on the wavelet decomposition where with the greater N value there is an increase in the smoothness of multiresolution analysis results. However, not all order Daubechies wavelet can give the same good recognition results so that its application still such as trial and error. Therefore, it is necessary to determine the order of the Daubechies wavelet base function on the Indonesian voice signal through its similarity level. The method can be used to determine the similarity level between speech signal and wavelet Daubechies function N order by calculating its crosscorrelation coefficient. The result shows that there is inconcistency of the best wavelet daubechies basis function for Indonesian vowels a,i,u,e,è,o, and ò. Which ...

This paper discusses the enhancement of flight stability of using an inspection drone to scan the condition of buildings on low and high altitude. Due to aerial perturbations and wakes, the drone starts to shake and may be damaged. One of the mechanical optimization methods it so add a built-in stabilizing mechanism. However, the performance of this supporting device becomes critical on certain flying heights, thus to avoid losing the drone. The paper is divided in two parts: the description of the gravitropism-like stabilizer and the diagnostic of its status using wavelet transformation and neural network classification.

The defective bearing in a rotating machine may affect its performance and hence reduce its efficiency. So the monitoring of bearing health and its fault diagnosis is essential. A vibration signature is one of the measuring parameters for fault detection. However, this vibration signature may get corrupted with noise. As a result this noise must be removed from the actual vibration signature before its analysis to detect and diagnose the fault. ANC (adaptive noise control)-based filtering techniques are used for this noise removal and hence to improve the SNR (signal-to-noise ratio). In our study an experimental setup is developed and then the proposed work is executed in three stages. In the first stage the vibration signatures are acquired and then ANC is implemented to remove the background noise. In the second stage the time (statistical) and the frequency analysis of the filtered vibration signals are done to detect the fault. In the third stage the statistical parameters of the vibration signatures are used for the classification of the fault present in the bearing using random forest and J48 classifiers.

This paper studies the detection of twisted blade in a multi stages rotor system. Experimental study was undertaken to simulate twisted blade conditions in a three stages rotor system. The feasibility of vibration analysis as the technique to detect twisted blade based on the rotor operating frequency and its blade passing frequency was investigated in this study. Experimental results show that twisted blade can be easily detected by looking into the pattern of the vibration spectrum and its individual peaks.

Metode Wavelet merupakan salah satu metode yang unggul untuk menganalisis serta mengekstraksi ciri isyarat suara tutur. Dalam proses ekstraksi ciri menggunakan metode Wavelet, terdapat beberapa faktor yang dapat berpengaruh dalam mendapatkan ciri yang bersifat diskriminan, diantaranya: pemilihan mother wavelet, pemilihan sub-band, dan pemilihan level dekomposisi. Beberapa contoh mother Wavelet yang sering digunakan diantaranya: Daubechies, Coiflet, Meyer, Haar, Symlet, dan Biortogonal. Pada penelitian ini dilakukan perbandingan berbagai macam mother Wavelet untuk mendapatkan ciri yang efektif untuk mengklasifikasi isyarat tutur, yang diharapkan melalui proses perbandingan berbagai mother Wavelet akan didapatkan mother Wavelet yang terbaik dan paling cocok untuk mengolah dan mengekstraksi isyarat tutur. Tap filter yang akan digunakan pada masing-masing Mother Wavelet pada rentang 1 hingga 10. Hasil penelitian perbandingan mother Wavelet untuk mengklasifikasi isyarat tutur menunjukkan bahwa koefisien filter Mother Wavelet Daubechies 2 menghasilkan akurasi klasifikasi yang paling dibandingkan Mother Wavelet lainnya (Haar, Coiflet2, Meyer, Symlet, dan Biortogonal) untuk mengekstraksi dan mengklasifikasi suku kata konsonan hambat bahasa Indonesia ditunjukkan oleh hasil akurasi 90,6% (WPT+Daub2), 66,7% (WPT+Haar), 76,7% (WPT+Coif2), 62,3% (WPT+Meyer), 64,2% (WPT+Symlet), dan 61,7% (WPT+Biortogonal).

Wavelet techniques possess many desirable properties, some of which are useful in economics and finance, but many of which are not. In this paper we use wavelets to reveal the different cycles that are hidden by the data. So, we consider some of the advantages and drawbacks of this time series multiresolution analysis technique for the purpose of obtaining simultaneous time and frequency information in determining the periodicity and the modelling of the water consumption in Santa Cruz de Tenerife (Canary Islands, Spain), energy and electricity consumption in Spain.

Wavelets are known to be closely related to atomic orbital. A new approach of 2D, 3D and multidimensional wavelet system is proposed from a paralell with anti-symmetric systems of several isolated particles. The theory of fermionic states is used to generate new n-dimensions wavelets, n ≥ 2, by the determinant of Slater. As pioneering paper in exchanging formalism between particle wave-functions and wavelets, it opens some perspectives for further adaptations derived from the physics of particles in the wavelet analysis scope.

Many continuous wavelets are defined in the frequency domain and do not have analytical expressions in the time domain. Meyer wavelet is ordinarily defined in this way. In this note, we derive new straightforward analytical expressions for both the wavelet and scale function for the Meyer basis. The validity of these expressions is corroborated by numerical computations, yielding no approximation error. Keyterms-Meyer wavelets, multiresolution analysis, close expressions for Meyer wavelet.