Kompresi Citra Medis dengan DWT dan Variable Length Code

Paper ini merancang sebuah skema kompresi citra medis menggunakan DWT dengan mother wavelet Coiflet dan Symlet. Proses thresholding dan kuantisasi menjadi kunci terjadinya lossy compression di skema ini, dan data outputnya akan dikodekan dengan pengkodean Huffman atau Arithmetic. Terdapat empat kombinasi codec berbeda yakni: Coiflet-Huffman, Coiflet-Arithmetic, Symlet-Huffman yang masing-masing akan dianalisa kinerja kompresinya berdasarkan PSNR dan rasio kompresi. Pengujian kompresi menggunakan 3 citra medis grayscale berdimensi 160x160 piksel. Hasil pengujian menunjukan codec yang mampu menghasilkan PSNR dan rate paling optimal adalah codec Symlet-Arithmetic dengan nilai threshold yang dianjurkan yakni kurang dari 12. Pemberian nilai threshold diatas 12 akan menyebabkan PSNR citra rekonstruksi berada dibawah standar nilai minimum PSNR citra digital sebesar 30 dB.

The Utilization of Signal Analysis by Using Short Time Fourier Transform

Signal Analysis is a part of geophysics work. It is important in analyse the character of signal or waveform in geophysics. In this paper the earthquake waveform is used as the example. One method to do this is used Short Time Fourier Transform. It adopts the basic concept of Fast Fourier Transform in the short period of time in waveform and at the same moment there is a convolutional process between the waveform and the mother wavelet and then resulting the spectrogram. Finally, the spectrogram will show the power spectrum or the magnitude of the amplitude in each time in the waveform. It relates with the energy of the earthquake. The result including three parameters, they are time, frequency and the spectrogram. It makes easier for the geophysicist to analyse the frequency changing in each time based on the spectrogram colour. Besides that, it can be used to identify the arrival time of P and S wave as the important information in calculate the hypocentre location of the earthquake.

Selection of a mother wavelet as identification pattern for the detection of cracks in shafts

Nowadays, there are many methods to detect and diagnose defects in mechanical components during operation. The newest methods that can be found in the literature are based on intelligent classification systems and evaluation of patterns to obtain a diagnosis; however, there is not any standard method to assess features. Wavelet packet transform allows to obtain interesting patterns for evaluating the condition of rotating elements. To perform this calculation, it is necessary to select a series of parameters that affect the resulting pattern. These parameters are the decomposition level and the mother wavelet function. A detailed methodology for the selection of the mother wavelet is proposed, which is the aim of this work, to obtain the most suitable patterns in the diagnostic task. This proposed methodology is applied to data obtained from a rotating shaft with a crack located at the change of section. These signals were measured at low rotation frequency (below the critical rotation frequency) and without eccentricity, where detection becomes more complex.

Penggunaan Jaringan Syaraf Tiruan dan Wavelet Pada Citra EKG 12 Lead

Jantung sangat penting dalam sistem organ tubuh manusia. Apabila terjadi kesalahan pada fungsi jantung akibatnya sangat fatal. Oleh karenanya sangatlah penting menjaga kondisi jantung agar tetap sehat. Penelitian ini mencoba menawarkan untuk meneliti terkait kelainan jantung dengan menggunakan citra Electrocardigram (EKG) 12 lead. Data EKG yang digunakan berupa citra. Tujuan penelitian ini untuk memperoleh model yang tepat dalam mengidentifikasi kelainan jantung dengan menggunakan wavelet. Tahapan penelitian terdiri dari pre-processing, ekstraksi ciri dan klasifikasi. Tahap pre-processing menggunakan metode segmentasi (merubah data citra dari grayscale ke biner), morfologi (metode dilasi dan metode erosi) dan transformasi ke sinyal. Tahap ektraksi ciri menggunakan metode dekomposisi transformasi wavelet dengan tingkatan tiga level, dimana mother wavelet yang digunakan berupa symlet orde 4 (Sym4). Tahap klasifikasi menggunakan jaringan syaraf tiruan dengan metode backpropagation. Adapun metode validasi dan evaluasi menggunakan k-fold cross validation dan confusion matrix. Penggunaan metode k-fold cross validation, dimana k=5 dengan pembagian data training 80% dan testing 20%. Hasil yang diperoleh dari keseluruhan sistem dimana tingkat akurasi sebesar 92,94%, sensitifitas sebesar 90% dan spesifisitas sebesar 94,55%.

Source Localization of EEG Brainwaves Activities via Mother Wavelets Families for SWT Decomposition

A Brain-Computer Interface (BCI) is a system used to communicate with an external world through the brain activity. The brain activity is measured by electroencephalography (EEG) signal and then processed by a BCI system. EEG source reconstruction could be a way to improve the accuracy of EEG classification in EEG based brain-computer interface (BCI). The source localization of the human brain activities can be an important resource for the recognition of the cognitive state, medical disorders, and a better understanding of the brain in general. In this study, we have compared 51 mother wavelets taken from 7 different wavelet families, which are applied to a Stationary Wavelet Transform (SWT) decomposition of an EEG signal. This process includes Haar, Symlets, Daubechies, Coiflets, Discrete Meyer, Biorthogonal, and reverse Biorthogonal wavelet families in extracting five different brainwave subbands for source localization. For this process, we used the Independent Component Analysis (ICA) for feature extraction followed by the Boundary Element Model (BEM) and the Equivalent Current Dipole (ECD) for the forward and inverse problem solutions. The evaluation results in investigating the optimal mother wavelet for source localization eventually identified the sym20 mother wavelet as the best choice followed by bior6.8 and coif5.

Evaluation of spatial–temporal characteristics of precipitation using discrete maximal overlap wavelet transform and spatial clustering tools

In the present study, classical and proposed methods were used to investigate the monthly precipitation characteristics of 30 stations in the southeastern United States during 1968–2018. Maximal overlap discrete wavelet transform (MODWT) as preprocessing method and K-means clustering method were used. First, the monthly precipitation time series of stations were decomposed into several subseries using MODWT and considering db as the mother wavelet. Then, the energy values of theses subseries were calculated and used as inputs in K-means and radial basis functions (RBF) methods. The optimum number of clusters obtained for the considered stations in both classical and proposed methods was five clusters. In order to use the data as the input of the RBF method, the data correlation was evaluated by variogram. Based on the results of clustering and in accordance with the latitude and longitude variations of the stations, it was found that with increasing the energy of the clusters, the amount of precipitation in the stations decreased and vice versa. The silhouette coefficient of clustering for the classical method obtained was 0.3 and for the proposed method it was 0.8, which indicates better clustering of the selected area using the proposed method.