Treatment of Noise in Experimental Transport Measurements Plots With Discrete Wavelet Transforms

2009 ◽  
Author(s):  
Rube´n Panta Pazos
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Transport Theory ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Thresholding Algorithm ◽  
High Pass

In this work it is applied the wavelet transform method [2] in order to reduce diverse type of noises of experimental measurement plots in transport theory. First, suppose that a problem is governed by the transport equation for neutral particles, and an unknown perturbation occurs. In this case, the perturbation can be associated to the source, or even to the flux inside the domain X. How is the behavior of the perturbed flux in relation to the flux without the perturbation? For that, we employ the wavelet transform method in order to compress the angular flux considered as a 1D, or n-th dimensional signal ψ. The compression of this signal can be performed up to some a convenient order (that depends of the length of the signal). Now, the transport signal is decomposed as [9, 11]: ψ=〈am|dm|dm−1|dm−2|⋯|d2|d1〉 where ak represents the sub signal of k-th level generated by the low-pass filter associated to the discrete wavelet transform (DWT) chosen, and dk the sub signal of k-th level generated by the high-pass filter associated to the same DWT. It is applied basically the Haar, Daub4 and Coiflet wavelets transforms. Indeed, the sub signal am cumulates the energy, for this work of order 96% of the original signal ψ. A thresholding algorithm provides treatment for the noise, with significant reduction in the compressed signal. Then, it is established a comparison with a base of data in order to identify the perturbed signal. After the identification, it is recomposed the signal applying the inverse DWT. Many assumptions can be established: the rate signal-to-noise is properly high, the base of data must contain so many perturbed signals all with the same level of compression. The problem considered is for perturbations in the signal. For measurements the problem is similar, but in this case the unknown perturbations are generated by the apparatus of measurements, problems in experimental techniques, or simply by random noises. With the same above assumptions, the DWT is applied. For the identification, it is used a method evolving statistical and metric techniques. It is given some results obtained with an algebraic computer system.

scholarly journals Design Engineering Instrumental Carotid Pulse System in Analitical Heart’s Dinamyc With Continuous Wavelet Transform Method

2016 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Eko Agus Suprayitno ◽  
Indah Sulistyowati ◽  
Izza Anshory ◽  
Jamaaluddin Jamaaluddin
Keyword(s):  
Wavelet Transform ◽  
Notch Filter ◽  
Design Engineering ◽  
Continuous Wavelet ◽  
Pulse System ◽  
Transform Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Quality Factor Q

Klasifikasi sinyal Carotid Pulse sangat penting untuk mengetahui proses terjadinya tekanan darah dalam mengalirkan darah melewati katup-katup jantung menuju ruang-ruang jantung dan paru-paru serta mendukung analisa suara jantung yang ada untuk mendeteksi murmur, mengklasifikasikan suara jantung normal dan tidak normal, serta untuk menjelaskan dinamika jantung, oleh sebab itu pada penelitian ini dilakukan beberapa pekerjaan pendahuluan pada pembuatan instrumentasi pendeteksian sinyal tekanan darah (Carotid Pulse).Pada penelitian ini dihasilkan informas Low Pass Filter Analog Orde 4 dengan frekuensi cutoff 100 Hz penguatan -3 dB terjadi di area frekuensi 120 Hz. Hasil uji Notch Filter dihasilkan tegangan output terkecil di frekuensi 50 Hz dengan besar Vout (0.14 ± 0.00) volt dengan Quality factor (Q) sebesar 6.28. Analisa sinyal Carotid Pulse dengan DFT memberikan informasi bahwa pada tekanan darah, area frekuensinya di antara 1 Hz sampai 20 Hz. Sinyal Carotid Pulse untuk siklus pertama Frekuensi tertingginya (6.81 ± 0.41) Hz dan merupakan gelombang P yang menggambarkan kenaikan Carotid Pulse secara tiba-tiba dari pemompaan darah pada ventrikel kiri menuju aorta hinggga mencapai puncak. Analisa dengan menggunakan CWT menghasilkan data keterangan waktu yang menunjukkan waktu terjadinya frekuensi tertinggi sinyal Carotid Pulse. Penelitian ini selanjutnya akan dikembangkan pada Multimodal Cardiac Analysis untuk mendapatkan informasi anatomi dan fisiologi jantung yang lebih kompleks.

A semi-lumped microstrip UWB bandpass filter

2009 ◽  
Vol 1 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Darine Kaddour ◽  
Jean-Daniel Arnould ◽  
Philippe Ferrari
Keyword(s):  
Group Delay ◽  
Bandpass Filter ◽  
Filter Design ◽  
Sensitivity Study ◽  
Critical Parameters ◽  
Center Frequency ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
High Pass

In this paper, a miniaturized bandpass filter for ultra-wide-band applications is proposed. It is based on the embedding of high-pass structures in a low-pass filter. A semi-lumped technology combining surface-mounted capacitors and transmission lines has been used. The filter design rules have been carried out. Furthermore, two filters having a 3-dB fractional bandwidth of 142 and 150%, centered at 0.77 and 1 GHz, respectively, have been realized for a proof of concept. Measured characteristics, in good agreement with simulations, show attractive properties of return loss (|S11| <−18 dB), insertion loss (<0.3 dB), and a maximum group delay and group delay variation of 2 and 1.3 ns, respectively. A distributed filter based on the same low-pass/high-pass approach has been also realized and measured for comparison. The size reduction reaches 85% for the semi-lumped filter, and its selectivity is improved with a shape factor of 1.3:1 instead of 1.5:1. The semi-lumped filter's drawback is related to a smaller rejection bandwidth compared to the distributed one. To improve the high-frequency stopband, an original technique for spurious responses suppression based on capacitively loaded stubs has been proposed. Even if the performances do not reach that obtained for the distributed approach, with this technique spurious responses are pushed until eight times the center frequency. A sensitivity study vs. critical parameters has also been carried out, showing the robustness of the design.

scholarly journals Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids

ELKHA ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 54
Author(s):  
Eska Rizqi Naufal ◽  
Gigih Priyandoko ◽  
Fachrudin Hunaini
Keyword(s):  
Induction Motor ◽  
Extraction Method ◽  
Data Extraction ◽  
Working Environment ◽  
Discrete Wavelet ◽  
Clustering Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Group Data ◽  
Low Pass

The 3 phase induction motor is a reliable and strong motor also has cheap price. However induction motor are also vulnerable, from the result of survey conducted by Electric Power Research Institute (EPRI), there are 41% cases of damage occur in the bearing caused by working environment condition, bearing age, and several other factors. Bearing fault is not easily to identified, with applying the data extraction method using the Discrete Wavelet Transform (DWT) and the K-Medoids clustering method will facilitate the identification process. The extraction method will pass the data in the form of current signals into the digital filter (Low Pass Filter and High Pass Filter) to be mapped into the region of frequency and time simultaneously, and clustering method will group data based on certain characteristics. Based on the clustering tests that have been done on the 3 phase induction motor current signal data with 3 bearing conditions, the Discrete Wavelet Transformation with mother wavelet bior1.1 decomposition level 2 and K-Medoids produce an accuracy rate of 86.8%.

MULTIVARIATE SPECTRAL ANALYSIS USING HILBERT WAVELET PAIRS

2004 ◽  
Vol 02 (04) ◽  
pp. 567-587 ◽  
Author(s):  
BRANDON WHITCHER ◽  
PETER F. CRAIGMILE
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multivariate Time Series ◽  
Discrete Wavelet ◽  
Pass Filter ◽  
Wavelet Filters ◽  
The Hilbert Transform ◽  
High Pass

We investigate the use of Hilbert wavelet pairs (HWPs) in the non-decimated discrete wavelet transform for the time-varying spectral analysis of multivariate time series. HWPs consist of two high-pass and two low-pass compactly supported filters, such that one high-pass filter is the Hilbert transform (approximately) of the other. Thus, common quantities in the spectral analysis of time series (e.g., power spectrum, coherence, phase) may be estimated in both time and frequency. Compact support of the wavelet filters ensures that the frequency axis will be partitioned dyadically as with the usual discrete wavelet transform. The proposed methodology is used to analyze a bivariate time series of zonal (u) and meridional (v) winds over Truk Island.

Plasma models and rescaling methods

1984 ◽  
Vol 31 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Philippe Lotte ◽  
M. R. Feix
Keyword(s):  
High Temperature ◽  
Electrostatic Interactions ◽  
Practical Reasons ◽  
Electrostatic Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Numerical Studies ◽  
Low Pass ◽  
Vlasov Limit ◽  
High Pass

The plasma models used in analytical or numerical studies exhibit, mainly for practical reasons, symmetry reducing the dimensions of the space. It is not obligatory that the restrictions on the dynamics and the chosen form of the electrostatic interactions should correspond to the same symmetry and this introduces the concept of a d (dynamic), d' (electrostatic) model with d' ≥ d. We show that the crucial parameter is d' – d. For d' – d = 0, the model exhibits both at low and at high temperature the same characteristic plasma frequency. At high temperature we recover the Vlasov limit and the plasma acts as a high-pass filter. For d' – d = 1, the two frequencies (purely collective at high temperature and ‘crystal’ frequency at very low temperature) are different, while in its high temperature (Vlasov) limit the plasma acts as a low-pass filter. Finally if d' – d ≥ 2 there is no longer a Vlasov limit for all values of temperature and density. These properties are obtained through a systematic use of the homothety transformations and a search for the corresponding invariants.

scholarly journals Portable Electrocardiograph Dengan Sadapan Pada Telapak Tangan Dan Kaki

2020 ◽  
Vol 13 (2) ◽  
pp. 107-114
Author(s):  
Muhammad Syifaul Linnas ◽  
Sumber Sumber ◽  
Prastawa Assalim Tetraputra
Keyword(s):  
Personal Computer ◽  
Notch Filter ◽  
Pass Filter ◽  
Low Pass Filter ◽  
High Pass Filter ◽  
Low Pass ◽  
High Pass

    Electrocardiograph (ECG) secara rutin dilakukan oleh operator terampil yang terbiasa dengan penempatan masing-masing elektroda pada pasien. Posisi elektroda yang salah dapat menyebabkan kesalahan kritis dalam diagnosis dan perawatan penyakit jantung. Tujuan dari penelitian ini adalah mendesain sebuah Portable Electrocardiograph dengan Sadapan Pada Telapak Tangan dan Kaki. Kontribusi dari penelitian ini adalah  memudahkan orang awam dalam bidang kesehatan dalam hal penggunaan Electrocardiograph. Penelitian ini juga bertujuan  mendesain Electrocardiograph yang cukup terjangkau bagi puskesmas/pusat-pusat pelayanan medis di daerah. Agar desain ini dapat memudahkan dalam metode penggunaan alat, maka dibuatlah alat Electrocardiograph (ECG) dengan elektroda pad yang akan diletakkan pada telapak tangan dan telapak kaki yang telah di berikan tanda pada elektroda tersebut. Electrocardiograph (ECG) menggunakan desain High Pass Filter (Pasif 20dB ditambah Non Inverting Amplifier, Low Pass Filter (LPF) 40dB, dan notch filter yang akan ditampilkan pada layar monitor Personal Computer (PC). Dari hasil sadapan beberapa responden, terlihat hasil tampilan yang menyerupai/mendekati sinyal Electrocardiograph (ECG) sebenarnya. Kekurangan dari modul ini jika tidak adanya grounding yang baik dari power suply maka akan terjadi noise pada hasil sinyal yang akan ditampilkan. Hasil penelitian ini dapat di implementasikan pada Electrocardiograph (ECG) konvensional untuk meningkatkan kemudahan dalam hal penggunaan alat.    

scholarly journals Klasifikasi Kanker Paru Paru menggunakan Naïve Bayes dengan Variasi Filter dan Ekstraksi Ciri GLCM

2021 ◽  
Vol 11 (2) ◽  
pp. 256
Author(s):  
Mohtar Yunianto ◽  
Soeparmi Soeparmi ◽  
Cari Cari ◽  
Fuad Anwar ◽  
Delta Nur Septianingsih ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Median Filter ◽  
Naïve Bayes ◽  
Gray Level ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Otsu Thresholding ◽  
Low Pass ◽  
Occurrence Matrix ◽  
High Pass

<p class="AbstractText">Telah berhasil dilakukan klasifikasi kanker paru-paru dari 120 data citra CT Scan. Pada penelitian, proses preposisi dimulai dengan variasi filtering yaitu low pass filter, median filter, dan high pass filter. Segmentasi yang digunakan yaitu Otsu Thresholding yang kemudian teksturnya akan diekstraksi menggunakan fitur Gray Level Co-occurrence Matrix (GLCM) dengan variasi arah sudut. Hasil dari ekstraksi GLCM dijadikan database yang akan menjadi dataset untuk pengklasifikasian citra menggunakan klasifikasi naïve bayes. Hasil dari penelitian dengan 12 buah variasi diperoleh hasil variasi terbaik adalah median filter dengan arah sudut GLCM 0° menunjukkan tingkat akurasi yang paling tinggi sebesar 88,33 %.</p>

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